Show all abstracts
View Session
- Front Matter: Volume 10605
- LIDAR Imaging Detection
- Target Recognition
Front Matter: Volume 10605
Front Matter: Volume 10605
Show abstract
This PDF file contains the front matter associated with SPIE Proceedings Volume 10605 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.
LIDAR Imaging Detection
Super-resolving polarization rotation angle measurement using parity detection at shot noise limit
Show abstract
The polarization of the light is an excellent information carrier, but polarization information of coherent state in the quantum measurement of the past has not been clearly expressed. We refer to some ideas of quantum computation and quantum information, considering the polarization mode of the electromagnetic field to describe polarization information of a coherent state. And on this basis we put forward a polarization rotation angle measurement device based on a Mach-Zehnder interferometer and two polarizers. We also consider the intensity detection, parity detection and Z detection as detection strategies. The results show that this device can realize the super-resolution and shot noise limit with parity detection and Z detection. By simulation analysis, we finally find parity detection is the best method for our scheme, and we also discuss the effects of some parameters on sensitivity and resolution with parity detection.
Anti-dynamic-crosstalk method for single photon LIDAR detection
Show abstract
With increasing number of vehicles equipped with light detection and ranging (LIDAR), crosstalk is identified as a critical and urgent issue in the range detection for active collision avoidance. Chaotic pulse position modulation (CPPM) applied in the transmitting pulse train has been shown to prevent crosstalk as well as range ambiguity. However, static and unified strategy on discrimination threshold and the number of accumulated pulse is not valid against crosstalk with varying number of sources and varying intensity of each source. This paper presents an adaptive algorithm to distinguish the target echo from crosstalk with dynamic and unknown level of intensity in the context of intelligent vehicles. New strategy is given based on receiver operating characteristics (ROC) curves that consider the detection requirements of the probability of detection and false alarm for the scenario with varying crosstalk. In the adaptive algorithm, the detected results are compared by the new strategy with both the number of accumulated pulses and the threshold being raised step by step, so that the target echo can be exactly identified from crosstalk with the dynamic and unknown level of intensity. The validity of the algorithm has been verified through the experiments with a single photon detector and the time correlated single photo counting (TCSPC) technique, demonstrating a marked drop in required shots for identifying the target compared with static and unified strategy
Intensity and average orbital angular momentum of partially coherent flat-topped vortex beam in slant atmospheric turbulence
Show abstract
On the basis of the extended Huygens-Fresnel principle and the cross-spectral density function (CSDF), the intensity and average orbital angular momentum (OAM) of the partially coherent flat-topped vortex beams in the slant atmospheric turbulence are presented. The effects of the order, topological charge, waist radius, and propagation distance of the beam on the intensity and average OAM are discussed. Results obtained show that the intensity of the partially coherent flat-topped vortex beam is changed due to the variations of the propagation distance, waist radius, topological charge and beam order, the average OAM is constant during the beam propagation in the atmospheric turbulence and related only to the waist radius and beam order. Results obtained by this paper may serve as theory bases for future applications in the atmospheric optical communication.
The study of a high-precision and wide-range echo-laser simulator
Show abstract
A novel realization method of a high-precision and wide-range echo-laser simulator is presented according to the detection requirement for ranging performance measurement of some fire control equipment.The simulator is designed to achieve both high resolution precision and wide dynamic range, the method of time delay combined with counter method and time to amplitude conversion method is adopted, the first stage time delay adopts the counter method which use FPGA as the core device to enlarge the simulation range, the second stage delay adopts time to amplitude conversion method which use a ramp delay circuit as the core device to improve the resolution precision of the simulation. The method can realize echo laser simulation of 0.5m high-precision with 50m-2km wide range, the detailed design of each component of the laser echo simulator is also given in this paper. The experimental results show that the echo simulation accuracy of the simulator is better than 0.5m, meeting the detection requirement of laser ranger performance test.
Detection probability limitation of a pulsed Gm-APD laser ranging system with turbulence effects
Show abstract
When evaluating the detection probability of a pulsed Gm-APD laser ranging system with a propagation path close to the ground, the detection probability limitation caused by the atmospheric turbulence cannot be ignored. Based on modulated Poisson model, the detection probability limitation due to the turbulence effects is investigated, and a cumulative pulses detection method is proposed to restrain the turbulence effects. The results show that the influence of the turbulence effects is equivalent to adding a new noise source to the echo intensity, the detection probability results in a worse situation with stronger turbulence effects, when the turbulence degree is 1.5 and the echo intensity is 10, the target detection probability decrease by 0.17, and the false alarm probability increases by 0.03. By utilizing the cumulative pulses detection technique, the target detection probability and false alarm probability improve by 0.3 and 0.07, respectively.
Analysis of continuous and pulsed laser ranging systems based on electro-optical modulation
Show abstract
To meet the requirements of a variety of applications, range precision is an important specification for three-dimensional laser radar systems. The proposed laser ranging systems adopt electro-optical modulation and only measures the energy of a laser pulse to obtain range, so that it can reduce many errors in comparison to conventional systems. In this paper, the principle of continuous and pulsed laser ranging systems are introduced, through simulation analysis the target of 20 m, the continuous laser ranging system can achieve a range precision of several centimeters, and the pulsed laser ranging system can achieve a rang precision of several millimeters. And by analyze their advantages and disadvantages, it can be concluded that pulsed laser ranging accuracy is higher than the continuous laser ranging systems. The pulsed laser ranging system is expected to be an alternative method for three-dimensional laser radar system requiring high range precision in many applications.
Computational three-dimensional imaging method of compressive LIDAR system with gain modulation
Yan-mei Zhang,
Yu-long An
Show abstract
The distance resolution of a 3D LIDAR imaging is largely decided by the pulse duration of the laser source and rise time of the detector. Considering that breaking these limits generates low-cost systems, we present a computational method of 3D imaging by a compressive LIDAR system. Based on the theory of compressive sensing, reflective pulses are obtained by single-pixel detector and intensity maps are reconstructed by TVAL3 algorithm. Moreover, the distance information of each pixel can be calculated from the reconstructed intensity maps with gain modulation technology. The simulations are accomplished to validate the effectiveness of our method. Convincing computational results shows that our method is capable to achieve 3D imaging with less budget.
Research of cartographer laser SLAM algorithm
Show abstract
As the indoor is a relatively closed and small space, total station, GPS, close-range photogrammetry technology is difficult to achieve fast and accurate indoor three-dimensional space reconstruction task. LIDAR SLAM technology does not rely on the external environment a priori knowledge, only use their own portable lidar, IMU, odometer and other sensors to establish an independent environment map, a good solution to this problem. This paper analyzes the Google Cartographer laser SLAM algorithm from the point cloud matching and closed loop detection. Finally, the algorithm is presented in the 3D visualization tool RViz from the data acquisition and processing to create the environment map, complete the SLAM technology and realize the process of indoor threedimensional space reconstruction
Investigation of 100 mJ all solid state end-pumped 1064 nm Q-switched laser
Show abstract
High energy 1064 nm Q-switched laser output is obtained by LD vertical array end pumping Nd:YAG. Cylindrical lens are used for beam shaping of LD array for different divergence angle of fast and slow axis. Based on the theoretical simulation of fundamental mode radius using ABCD transfer matrix, the resonant cavity is optimized and curvature radius of cavity mirrors is determined. The intracavity power density is calculated according to the output laser pulse energy and transmittance of output coupling mirror is optimized under the condition that optical device is not damaged. 1064 nm laser with a maximum output of 110 mJ is generated under LD pump energy of 600 mJ, corresponding to optical conversion efficiency of 18.3%. The laser pulse width is 11 ns and divergence angle is 1.2 mrad. For saturation phenomenon of Q-switched laser output, LD temperature is adjusted to make wavelength deviate from absorption peak of Nd:YAG crystal. The parasitic oscillation, which affects the enhancement of Q-switched laser energy, can be effectively suppressed by reducing gain of pump end of laser medium, which provides an effective technical means for obtaining high energy end-pumped Q-switched laser.
Research on point cloud matching of lidar based on odometer
Show abstract
Multisensor information fusion of mobile devices is the basis of the research of mobile location. Odometer and laser point cloud are the main methods to determine the pose in existing positioning techniques. But the initial pose uncertainty, complex iterative process and longer time consuming problems will cause the positioning accuracy greatly reduced, especially after a long distance movement. Therefore, a general odometer-assisted method is proposed for the wheeled mobile platform based on laser point cloud matching. In the approximate motion hypothesis of wheeled mobile platform based on the path curve, rough pose estimation based on the characteristics of the odometer first mobile platform, and this attitude for the initial attitude laser point cloud matching, with the attitude as point cloud matching iteration begins. Experimental results show that the odometer in pose positioning way, effectively reduce the accumulative error of point cloud matching, improves the accuracy of pose determination; also increased the time spent in iteration, improves the work efficiency of the device. In the assumption that the wheeled mobile platform movement path approximates the arc, according to the work characteristic of odometer. Firstly, a rough pose estimate of the mobile platform is presented, and the initial pose of the laser point cloud is taken as the initial value of the matching iteration of the point cloud. Experimental results show that the odometer in pose positioning effectively reduce the accumulative error of point cloud matching and improves the accuracy of pose determination. At the same time, it also reduce the iteration time cost and improve the work efficiency of the device.
Ranging accuracy improvement of time-correlated signal-photon counting lidar
Show abstract
Lidar based on Geiger-mode Avalanche Photodiode Detector (Gm-APD), also called Gm-APD Lidar for short, has the advantages of the ultra-high sensitivity and ranging accuracy, and therefore it is widely used in the weak signal detection over a long distance. Time-Correlated Single Photon Counting (TCSPC) is a more commonly used signal processing method of Gm-APD Lidar. However, after each avalanche response, Gm-APD needs a certain time to quench avalanche current, which is called the dead time. In the dead time, Gm-APD can't response any signal. This will result in the uneven response by Gm-APD, and the response probability of the front of the echo pulse signal is higher than that of the back of the echo pulse signal. The peak of photon counting results will deviate from the real peak of the echo signal, and this deviation will become larger with the increase of the echo pulse width. In many application environments (for example, underwater, battlefield smoke, fog and dust, etc.), the broadening effect of the echo pulse signal is obvious, and this will seriously impact the ranging accuracy of Gm-APD Lidar. In this paper, an improved method uses the multi-gate detection to response the complete waveform of the echo pulse signal, and thus improves the ranging accuracy of GmAPD due to obtaining more accurate echo pulse peak.
The analysis of application of telescopic system in pulse-dilation framing tube
Show abstract
The inertial confinement fusion experiment requires framing tube to have picseconds temporal and micrometer spatial resolution. Although the temporal resolution is promoted to ~10 ps by pulse-dilation technology, the spatial resolution is descended by the single magnetic lens. In order to promote spatial performance, the imaging system of tube is design using telescopic imaging technology, the spatial resolution is simulated by the imaging distribution and the Rayleigh Criterion, and the influence of telescopic system on spatial resolution is analyzed. The research results show that telescopic system is can promote the spatial performance of tube, which is made up of two magnetic lenses. The smaller the focal length of telescopic system is, the better the spatial resolution is. The conclusion can provide a theoretical support for designing framing tube with the large object plane.
The effects of laser beam incident angle and intensity distribution on Fabry-Perot etalon spectrum
Show abstract
Fabry-Perot(F-P) etalon has important applications in laser detection, lidar and laser communication systems. In practical applications, the spectrum of the F-P etalon is affected by various factors, such as incident angle, divergence angle, spectral width, intensity distribution of the incident beam, absorption loss, surface defects of the plate and so on. The effects of the incident angle and the beam intensity distribution on F-P etalon spectrum are mainly analyzed. For the first time, taking into account both the beam incident angle and divergence angle, the precise analytical expression of the F-P etalon transmission spectrum is derived. For the Gaussian light intensity distribution, the precise analytical expression of the F-P etalon transmission spectrum is derived. The simulation analysis is carried out and the results are as follows. When the beam divergence angle is not zero, the incident angle increases, on the one hand, the center of the etalon spectrum is moved to the high frequency, and the frequency shift is linear with the square of the incident angle. The slope decreases with the increase of the divergence angle. On the other hand, resulting in peak reduction, spectral line broadening, and with the divergence angle increases, the more obvious the phenomenon. Considering the distribution of Gaussian light intensity, the spectrum of the etalon will be improved with the increase concentration of beam energy. On the one hand, the peak value is increased, the spectral line is narrowed and with the incidence angle increases, the degree of improvement is more obvious. On the one hand, the center of the spectrum moves toward the low frequency, but the larger the incident angle, the smaller the movement amount. The error of frequency discrimination or frequency locking by using the F-P etalon spectrum increases rapidly with the increase of the beam incident angle and beam divergence angle, and the Gaussian light intensity distribution beam can effectively reduce the measurement error.
Optimization of single photon detection model based on GM-APD
Show abstract
One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.
Multiple targets detection method in detection of UWB through-wall radar
Show abstract
In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.
Barycenter location of planar or shelly target in laser reflective tomography imaging
Show abstract
Laser reflective tomography(LRT) imaging is a effective technique in high-resolution imaging of remote target. Since the mass distribution information of target is contained in echo, the barycenter of target could be located from echoes in different angle. We proposed a universal method to locate the distance barycenter of 2D planar target or shelly target applied LRT. Simulation results show the barycenter could be located with relevant uncertainty of 0.0226.
Design of pulsed laser diode drive power for ZY3(02) laser altimeter
Show abstract
Solid laser pumped by semiconductor laser has the large value in the area of space laser technology, because of the advantages of high efficiency, small volume and long life. As the indispensable component of laser, laser power is also very important. Combined with ZY3(02) laser altimeter project, a high voltage(0～300V), high current(0～80A), long pulse width(0～230us) and high precision temperature semiconductor laser power is developed. IGBT is applied in the driving circuit as the switch to provide a current pulse for LD. The heating or cooling capacity of TEC is controlled by PID compensation circuit quickly adjusts the duty cycle of the UC1637 PWM signal, to realize the high accuracy controlling of LD working temperature. The tests in the external ambient temperature of 5°C, 20°C, 30°C show that the LD current pulse is stable and the stability of LD working temperature up to ±0.1°C around the set point temperature, which ensure the highly stable operation of DPL.
Application of backpack Lidar to geological cross-section measurement
Show abstract
As the traditional geological cross section measurement, the artificial traverse method was recently substituted by using point coordinates data. However, it is still the crux of the matter that how to acquire the high-precision point coordinates data quickly and economically. Thereby, the backpack Lidar is presented on the premise of the principle of using point coordinates in this issue. Undoubtedly, Lidar technique, one of booming and international active remote sensing techniques, is a powerful tool in obtaining precise topographic information, high-precision 3-D coordinates and building a real 3-D model. With field practice and date processing indoors, it is essentially accomplished that geological sections maps could be generated simply, accurately and automatically in the support of relevant software such as ArcGIS and LiDAR360.
A 1J LD pumped Nd:YAG pulsed laser system
Show abstract
A 1J LD pumped Nd;YAG pulsed laser was designed. The laser uses an oscillation and two-staged amplification structure, and applies diode bar integrated array as side-pump. The TEC temperature control device combing liquid cooling system is organized to control the temperature of the laser system. This study also analyzed the theoretical threshold of working material, the effect of thermal lens and the basic principle of laser amplification. The results showed that the laser system can achieve 1J, 25Hz pulse laser output, and the laser pulse can be output at two width: 6-7ns and 10ns, respectively, and the original beam angle is 1.2mrad. The laser system is characterized by small size, light weight, as well as good stability, which make it being applied in varied fields such as photovoltaic radar platform and etc
Key technologies of laser point cloud data processing in power line corridor
Show abstract
Airborne LiDAR can quickly obtain the high precision three-dimensional information of the target object. It can be used for 3D visualization of power line and measuring distance between power line and ground object. In recent years, it has been widely used in the power industry which is one of the most successful industries for the application laser technology. This paper introduces common post-processing technique of point cloud data in the power line corridor, including the point cloud generation, point cloud filtering and power line classification, power line reconstruction, power line safety distance inspection, power lines 3D visualization. This paper provide reference for application of airborne LiDAR power line inspection technology.
Method study on acquiring space borne laser elevation control point
Show abstract
Space borne laser altimeter is able to measure the distance between satellite and land surface. Combining with the attitude, orbit and equipment placement information, the elevation of the measured area can be calculated, which can be used as an elevation control point in mapping. By the influence of satellite orbit height and laser spread angle, the footprint of laser on the land surface is in diameter of tens of meters. For the situation of a flat surface, every point has equal elevation and the central point of the footprint can be chosen as an elevation control point. For the situation of undulating surface, each point has different elevation, and there will be much error if the central point of the footprint is chosen as an elevation control point. Now, the point in latter situation is excluded which makes the efficiency of laser altimeter low. In this paper, the situation of undulating surface is considered, and new approaches of using DSM and image recognition are adopted, resulting in acquiring elevation control point in this situation and increasing the efficiency of laser altimeter.
Electro-optical design of a long slit streak tube
Show abstract
A small size and long slit streak tube with high spatial resolution was designed and optimized. Curved photocathode and screen were adopted to increase the photocathode working area and spatial resolution. High physical temporal resolution obtained by using a slit accelerating electrode. Deflection sensitivity of the streak tube was improved by adopting two-folded deflection plates. The simulations indicate that the photocathode effective working area can reach 30mm × 5mm. The static spatial resolution is higher than 40lp/mm and 12lp/mm along scanning and slit directions respectively while the physical temporal resolution is higher than 60ps. The magnification is 0.75 and 0.77 in scanning and slit directions. And also, the deflection sensitivity is as high as 37mm/kV. The external dimension of the streak tube are only ∅74mm×231mm. Thus, it can be applied to laser imaging radar system for large field of view and high range precision detection.
Opto-mechanical design and gravity-deformation analysis on optical telescope in laser communication system
Show abstract
In space laser communication, optical antennas are one of the main components and the precision of optical antennas is very high. In this paper, it is based on the R-C telescope and it is carried out that the design and simulation of optical lens and supporting truss, according to the parameters of the systems. And a finite element method (FEM) was used to analyze the deformation of the optical lens. Finally, the Zernike polynomial was introduced to fit the primary mirror with a diameter of 250mm. The objective of this study is to determine whether the wave-front aberration of the primary mirror can meet the imaging quality. The results show that the deterioration of the imaging quality caused by the gravity deformation of primary and secondary mirrors. At the same time, the optical deviation of optical antenna increase with the diameter of the pupil.
Differential absorption lidar observation on small-time-scale features of water vapor in the atmospheric boundary layer
Wei Kong,
Jiatang Li,
Hao Liu,
et al.
Show abstract
Observation on small-time-scale features of water vapor density is essential for turbulence, convection and many other fast atmospheric processes study. For the high signal-to-noise signal of elastic signal acquired by differential absorption lidar, it has great potential for all-day water vapor turbulence observation. This paper presents a set of differential absorption lidar at 935nm developed by Shanghai Institute of Technical Physics of the Chinese Academy of Science for water vapor turbulence observation. A case at the midday is presented to demonstrate the daytime observation ability of this system. “Autocovariance method” is used to separate the contribution of water vapor fluctuation from random error. The results show that the relative error is less than 10% at temporal and spatial resolution of 10 seconds and 60 meters in the ABL. This indicate that the system has excellent performance for daytime water vapor turbulence observation.
Research on large-aperture primary mirror supporting way of vehicle-mounted laser communication system
Show abstract
In the satellite to earth laser communication link, large-aperture ground laser communication terminals usually are used in order to realize the requirement of high rate and long distance communication and restrain the power fluctuation by atmospheric scintillation. With the increasing of the laser communication terminal caliber, the primary mirror weight should also be increased, and selfweight, thermal deformation and environment will affect the surface accuracy of the primary mirror surface. A high precision vehicular laser communication telescope unit with an effective aperture of 600mm was considered in this paper. The primary mirror is positioned with center hole, which back is supported by 9 floats and the side is supported by a mercury band. The secondary mirror adopts a spherical adjusting mechanism. Through simulation analysis, the system wave difference is better than λ/20 when the primary mirror is in different dip angle, which meets the requirements of laser communication.
Overview of the Chinese lidar satellite development
Show abstract
The Domestic Spaceborne Lidar as a pivotal method in satellite remote sensing is introduced, including the development status and the key technology. By analysing the Lidar system design among the weighted Chang’e-1, resource satellite, the expectation of Spaceborne Lidar Development is released.
Research on recognition of healthy situation of vegetation leaves based on multispectral LiDAR
Show abstract
The healthy and withered situation of vegetation has important influence on its biological and chemical process. Neither single wavelength LiDAR (light detection and ranging) nor spectral image can capture the spatial and spectral information of vegetation simultaneously. However, the invention of multispectral LiDAR provided the new method for vegetation detection. There have been some researches on vegetation detection based on multispectral LiDAR, but the potential of multispectral LiDAR’s capability of recognition of healthy and withered vegetation leaves is not totally revealed. So, this research, based on multispectral LiDAR, classified the healthy and withered scindapsus leaves with SVM (support vector machine). And then we also compared the classification capability between the vegetation index and spectral reflectance. The results showed that, the multispectral LiDAR can classify the healthy and withered scindapsus leaves effectively: overall classification accuracy is 95.556%. Compared with spectral reflectance, vegetation index could help increase the classification accuracy: the producer accuracy of withered leaves increased from 23.272% to 70.507%.
The development of a high speed underwater optoelectronic imaging module
Show abstract
According to the application of underwater lidar imaging, a high speed optoelectronic imaging module was developed. On the basis of traditional technology of ICCD, the high speed blue-green enhanced multi-alkali cathode was developed, A number of key technologies have been breached for range gating, including low delay and high speed gating, high precision delay control, high speed automatic gain control. A high-precision gated ICCD module with minimum gate width of 3ns, jitter less than 100ps and 100ps minimum delay step was developed. The test conditions of range gated imaging in air were established, the depth of field was less than 2m，the imaging blind area limit is less than 2.5m. The effective detection distance underwater was 50m.
An image registration algorithm for interferometric synthetic aperture lidar
Show abstract
Interferometric synthetic aperture lidar (InSAL) can achieve high precision 3D imaging, while the image registration algorithm for generating critical interference figure is very important for InSAL. AS the difference between Interferometric Synthetic Aperture Radar (InSAR) and InSAL in dealing with the noise, it is hard for registration algorithmto be used directly in InSAL. To solve this problem, this paper proposes a combination registration algorithm, using the correlation function method both in rough registration and fine registration in the data of Doppler away from zero, and using the spectrum registration method in the data of near zero point by Doppler. The registration accuracy can reach 0.1 a pixel. The simulation results show that the accuracy of the proposed algorithm is improved to 1.43% compared with the traditional spectral registration algorithm.
3D range-gated super-resolution imaging based on stereo matching for moving platforms and targets
Show abstract
3D range-gated superresolution imaging is a novel 3D reconstruction technique for target detection and recognition with good real-time performance. However, for moving targets or platforms such as airborne, shipborne, remote operated vehicle and autonomous vehicle, 3D reconstruction has a large error or failure. In order to overcome this drawback, we propose a method of stereo matching for 3D range-gated superresolution reconstruction algorithm. In experiment, the target is a doll of Mario with a height of 38cm at the location of 34m, and we obtain two successive frame images of the Mario. To confirm our method is effective, we transform the original images with translation, rotation, scale and perspective, respectively. The experimental result shows that our method has a good result of 3D reconstruction for moving targets or platforms.
Target Recognition
Research on implementation of an improved finite state machine model in satellite simulated system
Show abstract
Satellite simulated system is a very important sub system of satellite payload ground comprehensive testing system which tests the satellite payload’s order and telemetering before delivering the payload to satellite. For all kinds of satellite simulated system, a new implementation which used an improved Finite State Machine (FSM) model can make the whole system modules clear, reduce the coupling between modules, improve the modules’ reusability, enhance productivity, and make the research and development of the whole system easier. The engineering applications’ experimental results show that the implementation of an improved FSM model can make the satellite simulated system stable and reliable.
Reflector control technology in space laser communication
Show abstract
The optical frequencies band is used as information carrier to realize laser communication between two low-orbit micro-satellites in space which equipped with inter-satellite laser communication terminals, optical switches, space routers and other payload. The laser communication terminal adopts a two-dimensional turntable with a single mirror structure. In this paper, the perturbation model of satellite platform is established in this paper. The relationship between the coupling and coordinate transformation of satellite disturbance is analyzed and the laser pointing vector is deduced. Using the tracking differentiator to speed up the circular grating angle information constitute speed loop feedback, which avoids the problem of error amplification caused by the high frequency of the conventional difference algorithm. Finally, the suppression ability of the satellite platform disturbance and the tracking accuracy of the tracking system are simulated and analyzed. The results show that the tracking accuracy of the whole system is 10μrad in the case of satellite vibration, which provides the basis for the optimization of the performance of the space-borne laser communication control system.
A design of high-precision BLDCM drive with bus voltage protection
Show abstract
In the application of space satellite turntable, the design of balance wheel is very necessary. To solve the acquisition precision of Brushless DC motor speed is low, and the encoder is also more complex, this paper improves the original hall signal measurement methods. Using the logic device to achieve the six frequency multiplication of hall signal, the signal is used as speed feedback to achieve speed closed-loop control and improve the speed stability. At the same time, in order to prevent the E.M.F of BLDC motor to raise the voltage of the bus bar when reversing or braking, and affect the normal operation of other circuit modules, the analog circuit is used to protect the bus bar voltage by the way of energy consumption braking. The experimental results are consistent with the theoretical design, and the rationality and feasibility of the frequency multiplication scheme and bus voltage protection scheme are verified.
A novel rotational invariants target recognition method for rotating motion blurred images
Show abstract
The imaging of the image sensor is blurred due to the rotational motion of the carrier and reducing the target recognition rate greatly. Although the traditional mode that restores the image first and then identifies the target can improve the recognition rate, it takes a long time to recognize. In order to solve this problem, a rotating fuzzy invariants extracted model was constructed that recognizes target directly. The model includes three metric layers. The object description capability of metric algorithms that contain gray value statistical algorithm, improved round projection transformation algorithm and rotation-convolution moment invariants in the three metric layers ranges from low to high, and the metric layer with the lowest description ability among them is as the input which can eliminate non pixel points of target region from degenerate image gradually. Experimental results show that the proposed model can improve the correct target recognition rate of blurred image and optimum allocation between the computational complexity and function of region.
Localization of subastral point based on matching between salient regions
Show abstract
To determine the position of the subastral point in the base map and reduce the computational burden, an approach was developed based on scale invariant feature transform (SIFT) and match between salient regions. The salient regions of the base map can be determined by manual or algorithm in advance. And also, the salient regions of the descent image can be obtained by saliency computation. The extraction of SIFT feature was only performed on the salient regions of the base map and the descent image. These feature points were used to match between the two images. The method of maximum likelihood estimation sample consensus (MLESAC) was employed to eliminate the wrong matches. Then the correct matching points were used to determine the transform matrix between the base map and the descent image. The position of the probe can be predefined in the descent image. Through the transform matrix, the position of the subastral point can be determined in the base map. The experimental results demonstrate that the proposed approach can determine the position of the subastral point simply by matching in the salient regions rather than traversing the entire image to search for the matching points so as to reduce the cost of comparing all regions in two images.
On-line bolt-loosening detection method of key components of running trains using binocular vision
Show abstract
Bolt loosening, as one of hidden faults, affects the running quality of trains and even causes serious safety accidents. However, the developed fault detection approaches based on two-dimensional images cannot detect bolt-loosening due to lack of depth information. Therefore, we propose a novel online bolt-loosening detection method using binocular vision. Firstly, the target detection model based on convolutional neural network (CNN) is used to locate the target regions. And then, stereo matching and three-dimensional reconstruction are performed to detect bolt-loosening faults. The experimental results show that the looseness of multiple bolts can be characterized by the method simultaneously. The measurement repeatability and precision are less than 0.03mm, 0.09mm respectively, and its relative error is controlled within 1.09%.
Trajectory control method of stratospheric airship based on the sliding mode control and prediction in wind field
Show abstract
The stratospheric airship has the characteristics of large inertia, long time delay and large disturbance of wind field , so the trajectory control is very difficult .Build the lateral three degrees of freedom dynamic model which consider the wind interference , the dynamics equation is linearized by the small perturbation theory, propose a trajectory control method Combine with the sliding mode control and prediction, design the trajectory controller , takes the HAA airship as the reference to carry out simulation analysis. Results show that the improved sliding mode control with front-feedback method not only can solve well control problems of airship trajectory in wind field, but also can effectively improve the control accuracy of the traditional sliding mode control method, solved problems that using the traditional sliding mode control to control. It provides a useful reference for dynamic modeling and trajectory control of stratospheric airship.
Research on compressive sensing reconstruction algorithm based on total variation model
Show abstract
Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.
Performance evaluation of sea surface simulation methods for target detection
Show abstract
With the fast development of sea surface target detection by optoelectronic sensors, machine learning has been adopted to improve the detection performance. Many features can be learned from training images by machines automatically. However, field images of sea surface target are not sufficient as training data. 3D scene simulation is a promising method to address this problem. For ocean scene simulation, sea surface height field generation is the key point to achieve high fidelity. In this paper, two spectra-based height field generation methods are evaluated. Comparison between the linear superposition and linear filter method is made quantitatively with a statistical model. 3D ocean scene simulating results show the different features between the methods, which can give reference for synthesizing sea surface target images with different ocean conditions.
Brain MRI tumor image fusion combined with Shearlet and wavelet
Show abstract
In order to extract the effective information in different modalities of the tumor region in brain Magnetic resonance imaging (MRI) images, we propose a brain MRI tumor image fusion method combined with Shearlet and wavelet transform. First, the source images are transformed into Shearlet domain and wavelet domain. Second, the low frequency component of Shearlet domain is fused by Laplace pyramid decomposition. Then the low-frequency fusion image is obtained through inverse Shearlet transform. Third, the high frequency subimages in wavelet domain are fused. Then the high-frequency fusion image is obtained through inverse wavelet transform. Finally, the low-frequency fusion image and high-frequency fusion image are summated to get the final fusion image. Through experiments conducted on 10 brain MRI tumor images, the result shown that the proposed fusion algorithm has the best fusion effect in the evaluation indexes of spatial frequency, edge strength and average gradient. The main spatial frequency of 10 images is 29.22, and the mean edge strength and average gradient is 103.77 and 10.42. Compared with different fusion methods, we find that the proposed method effectively fuses the information of multimodal brain MRI tumor images and improves the clarity of the tumor area well.
Structure design of AlN double-ended tuning fork resonators
Show abstract
The resonant sensors based on aluminum nitride double-ended tuning fork (AlN DETF) have the characteristics of small size, good stability and reliability, fast response. In order to improve the sensitivity and resolution, it is necessary to analyze the influence of the structure parameters of vibrating beam on the sensitivity and signal power of AlN resonator. The multi-physics model of AlN DETF resonator was established to verify effect of single parameter on the sensitivity by pre-stressed eigenfrequency analysis. The relationships between signal power and length, width of vibrating beam were obtained by post-processing data of simulation results when the thickness remained constant. The results show that relative sensitivity and signal power are growing with opposite direction with the width or the length of the beam. Therefore, there is a design tradeoff between signal power and relative sensitivity of AlN resonator according to the process and structure strength. The optimized AlN DETF resonator was simulated, its sensitivity, signal power and Q are 56 Hz/μN, 6.8e-4 nW and 958, respectively.
Structure design of a high-performance aluminum nitride differential resonant accelerometer
Show abstract
A high-performance aluminum nitride (AlN) differential resonant accelerometer is proposed. The inertia force of the proof mass is amplified to improve the sensitivity by two-stage microlever; the cross sensitivity is reduced by I-shape supporting beam; and the differential frequency detection scheme is used to decrease the effect of temperature common mode error. The accelerometer is mainly composed of proof mass, supporting beam, two-stage microlever and resonator, and its structural parameters are optimized by theoretical analysis and finite element simulation. The modal analysis shows that the fundamental frequencies of the two resonators are approximately 373.3 kHz, and the frequency differences from the interferential modes are about 9.4 kHz, which effectively achieves mode isolation. According to the simulation results of sensitivity, the sensitivity, linearity and cross sensitivity of AlN differential resonator accelerometer are 64.6 Hz/g, 0.787% and 0.0033 Hz/g, respectively. The simulation results of thermal stress show that the temperature sensitivity of a single resonator is about 490 Hz/°C, and the temperature sensitivity of output differential frequency is - 0.83 Hz/°C, which demonstrate that the differential frequency detection scheme can reduce the influence of temperature common mode error. All the above simulation results prove that this structural design of the accelerometer is feasible.
Application of industrial robots in automatic disassembly line of waste LCD displays
Show abstract
In the automatic disassembly line of waste LCD displays, LCD displays are disassembled into plastic shells, metal shields, circuit boards, and LCD panels. Two industrial robots are used to cut metal shields and remove circuit boards in this automatic disassembly line. The functions of these two industrial robots, and the solutions to the critical issues of model selection, the interfaces with PLCs and the workflows were described in detail in this paper.
Calibration method for equivalent extinction ratio of polarized pixel in integrated micropolarizer array camera
Show abstract
Equivalent extinction ratio and polarization orientation are two significant parameters representing the performance of a polarized pixel in an integrated micropolarizer array camera. With manufacturing and integrating errors of the micropolarizer array, equivalent extinction ratios are nonuniform and polarization orientations of polarized pixels deviate from their nominal values. Measuring the equivalent extinction ratio and the polarization orientation of each polarized pixel by rotating a polarizer at a tiny step is extremely time-consuming and even inaccurate. Therefore, this paper proposes a calibration method for the equivalent extinction ratio and the polarization orientation of each polarized pixel. Its principle is derived by theorizing the relationship between an orientation of a linearly polarized incident light and its digital output of a polarized pixel. In experiment, this derived principle is applied to an integrated micropolarizer array camera. Experimental result proves that calibrated equivalent extinction ratios generally vary from 4.5 to 10, with a mean of 7.939 and a standard variation of 1.053.
A method of camera calibration in the measurement process with reference mark for approaching observation space target
Show abstract
Binocular stereoscopic vision can be used for space-based space targets near observation. In order to solve the problem that the traditional binocular vision system cannot work normally after interference, an online calibration method of binocular stereo measuring camera with self-reference is proposed. The method uses an auxiliary optical imaging device to insert the image of the standard reference object into the edge of the main optical path and image with the target on the same focal plane, which is equivalent to a standard reference in the binocular imaging optical system; When the position of the system and the imaging device parameters are disturbed, the image of the standard reference will change accordingly in the imaging plane, and the position of the standard reference object does not change. The camera's external parameters can be re-calibrated by the visual relationship of the standard reference object. The experimental results show that the maximum mean square error of the same object can be reduced from the original 72.88mm to 1.65mm when the right camera is deflected by 0.4 degrees and the left camera is high and low with 0.2° rotation. This method can realize the online calibration of binocular stereoscopic vision measurement system, which can effectively improve the anti - jamming ability of the system.
Design of platform for removing screws from LCD display shields
Show abstract
Removing the screws on the sides of a shield is a necessary process in disassembling a computer LCD display. To solve this issue, a platform has been designed for removing the screws on display shields. This platform uses virtual instrument technology with LabVIEW as the development environment to design the mechanical structure with the technologies of motion control, human-computer interaction and target recognition. This platform removes the screws from the sides of the shield of an LCD display mechanically thus to guarantee follow-up separation and recycle.
Research on automated disassembly technology for waste LCD
Show abstract
In the field of Waste LCD disassembling and recycling, there are existing two major problems: 1) disassembling waste LCD mainly depends on manually mechanical crushing; 2) the resource level is not high. In order to deal with the above problems, in this paper, we develop an efficient, safe and automated waste LCD disassembling assembly line technology. This technology can disassembly and classify mainstream LCD into four components, which are liquid crystal display panels, housings and metal shield, PCB assembly. It can also disassembly many kinds of waste LCD. Compared with the traditional cooperation of manual labor and electric tools method, our proposed technology can significantly improve disassembling efficiency and demonstrate good prospects and promotional value.
Design of an S band narrow-band bandpass BAW filter
Show abstract
An S band narrowband bandpass filter BAW with center frequency 2.460 GHz, bandwidth 41MHz, band insertion loss - 1.154 dB, the passband ripple 0.9 dB, the out of band rejection about -42.5dB@2.385 GHz; -45.5dB@2.506 GHz was designed for potential UAV measurement and control applications. According to the design specifications, the design is as follows: each FBAR’s stack was designed in BAW filter by using Mason model. Each FBAR’s shape was designed with the method of apodization electrode. The layout of BAW filter was designed. The acoustic-electromagnetic cosimulation model was built to validate the performance of the designed BAW filter. The presented design procedure is a common one, and there are two characteristics: 1) an A and EM co-simulation method is used for the final BAW filter performance validation in the design stage, thus ensures over-optimistic designs by the bare 1D Mason model are found and rejected in time; 2) An in-house developed auto-layout method is used to get compact BAW filter layout, which simplifies iterative error-and-try work here and output necessary in-plane geometry information to the A and EM cosimulation model.
A novel star extraction method based on modified water flow model
Show abstract
Star extraction is the essential procedure for attitude measurement of star sensor. The great challenge for star extraction is to segment star area exactly from various noise and background. In this paper, a novel star extraction method based on Modified Water Flow Model(MWFM) is proposed. The star image is regarded as a 3D terrain. The morphology is adopted for noise elimination and Tentative Star Area(TSA) selection. Star area can be extracted through adaptive water flowing within TSAs. This method can achieve accurate star extraction with improved efficiency under complex conditions such as loud noise and uneven backgrounds. Several groups of different types of star images are processed using proposed method. Comparisons with existing methods are conducted. Experimental results show that MWFM performs excellently under different imaging conditions. The star extraction rate is better than 95%. The star centroid accuracy is better than 0.075 pixels. The time-consumption is also significantly reduced.
Surface EMG signals based motion intent recognition using multi-layer ELM
Show abstract
The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human’s intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.
Research on autonomous identification of airport targets based on Gabor filtering and Radon transform
Show abstract
Target recognition is a leading key technology in intelligent image processing and application development at present, with the enhancement of computer processing ability, autonomous target recognition algorithm, gradually improve intelligence, and showed good adaptability. Taking the airport target as the research object, analysis the airport layout characteristics, construction of knowledge model, Gabor filter and Radon transform based on the target recognition algorithm of independent design, image processing and feature extraction of the airport, the algorithm was verified, and achieved better recognition results.
Robotic fish tracking method based on suboptimal interval Kalman filter
Show abstract
Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.
A new readout structure with multiplexed output circuits for monolithic uncooled FPA
Show abstract
In this paper, a new readout structure for monolithic uncooled focal plane array is proposed. The proposed readout structure contains several or dozens of readout integrated circuits modules and these modules work independently and output their pixel signals concurrently. For monolithic uncooled focal plane array, each module of readout integrated circuit can output its infrared sensing voltage signals at its maximum output speed, and the whole focal plane array has multiplexed outputs at a time. This kind of readout structure is appropriate for ultra-large-scale monolithic uncooled focal plane array and can be applied to future uncooled focal plane arrays.
An improved multi-targets tracking algorithm based on cubature information particle filter
Show abstract
In the traditional bootstrap particle filter, the state transition density is used as the importance sampling function, which brings some problems such as particle degradation and poor tracking accuracy. In this paper, the posterior probability is used as the importance sampling function and its estimation method is proposed. By means of cubature information filtering and Gating technique, the mean and variance of the importance sampling function are estimated, and the importance sampling function is designed. The improved particle filter method is used to estimate the number of targets and the number of targets in the nonlinear situation. The simulation results show that the proposed algorithm has the advantages of high estimation accuracy and good stability in the nonlinear multi-target tracking scenario.
Influences of artificial biological particles structures on far-infrared extinction performance
Show abstract
With the increasing demands for new biological extinction materials in military and civilian fields, the artificially prepared flocculent biological particles are equivalent to bullet rosette particles. Then the unit particles with different numbers and lengths of branches are built, and the aggregated particles with different structures are built further. Next the structures of biological particles are characterized by parameterization. And the discrete dipole approximation method is used to calculate the extinction efficiency factor for biological particles. The results indicate that the structures and spatial arrangement of unit particles have great impact on the extinction performance of biological particles. The extinction performance of unit particles is positively correlated to the number and length of branches in the far infrared waveband. Furthermore, the extinction performance of aggregated particles is positively correlated to the porosity in the far infrared waveband. The model provides a theoretical basis for the further development and morphology control of biological extinction materials.
A new improved HSV image fusion method
Show abstract
A new image fusion method based on hue-saturation-value (HSV) color space is proposed, the improved HSV fuses multi-spectral (MS) and panchromatic (PAN) images to improve spatial information and preserve spectral characteristics. The main advantage of the new fusion method is a simple and efficient way which can maximize the extraction of the spatial information and eliminate the disturbance of spectral information in PAN image. the difference of the low components information between PAN and the value component is filtered from the PAN image, and then value component are replaced by the new PAN image, the new HSV color space perform reverse transform to obtain a multispectral image with the high spatial resolution.SPOT-5 and QuickBird MS and PAN images were employed to execute the existing HSV and improved HSV fusion methods. Qualitative and quantitative analyses and classification accuracy assessment were conducted to evaluate the performance of the fusion methods. The results demonstrate that the improved HSV is better than traditional HSV methods. The new fusion method can achieve a wide range of balance between high spatial resolution retention and spectral characteristic preservation.
A background extraction technique based on hyper circle with LMS algorithm
Show abstract
How to substract the background clutter accurately has always been an important research for many years because it is a major factor influencing the RCS measurement. A traditional method to substract the clutter is based on a fact that if the dihedral rotating angle (around the radar line of sight (LOS)) is an integer multiple of 180°, the mathematic expectation of the dihedral signal samples equals zero. However, the algorithm proposed in this paper can get higher accuracy, which is the combination of Hyper Circle and LMS algorithm. What’s more, averaging the sum of four channels gets better results which are superior to any single channel results. Numerical simulation demonstrates the excellent performance of the proposed technique.
Intrusion recognition for optic fiber vibration sensor based on the selective attention mechanism
Show abstract
Distributed fiber-optic vibration sensors receive extensive investigation and play a significant role in the sensor panorama. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, an intrusion recognition based on the auditory selective attention mechanism is proposed. Firstly, considering the time-frequency of vibration, the spectrogram is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Based on these maps, the feature matrix is formed after normalization. The system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises. What’s more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.
Research on image complexity evaluation method based on color information
Show abstract
In order to evaluate the complexity of a color image more effectively and find the connection between image complexity and image information, this paper presents a method to compute the complexity of image based on color information.Under the complexity ,the theoretical analysis first divides the complexity from the subjective level, divides into three levels: low complexity, medium complexity and high complexity, and then carries on the image feature extraction, finally establishes the function between the complexity value and the color characteristic model. The experimental results show that this kind of evaluation method can objectively reconstruct the complexity of the image from the image feature research. The experimental results obtained by the method of this paper are in good agreement with the results of human visual perception complexity,Color image complexity has a certain reference value.
Fast infrared sea ship target detection based on improved BING algorithm
Show abstract
The key to the target detection of infrared sea ship is real-time, fast and efficient detection of the real goal. The BING method, which introduces the binarization approximation calculation, can quickly detect target. But for infrared images, the approximate calculation also brings some shortcomings. The approximation of the gradient feature makes the overall gradient of the image amplitude decrease and some of the smaller gradient edge details disappear, so that leading to the weak distinguished ability. Based on the BING algorithm, we propose an improved BING algorithm which can quickly extract the candidate regions of infrared ship images. In the normed gradients(NG) feature, we introduce the Laplace difference operator and use the two-level cascaded SVM to learn them. Experimental results have shown that our method is effective and rapid to extract the region of interest (ROI) of the target ship.
Hand gesture recognition based on convolutional neural networks
Show abstract
Hand gesture has been considered a natural, intuitive and less intrusive way for Human-Computer Interaction (HCI). Although many algorithms for hand gesture recognition have been proposed in literature, robust algorithms have been pursued. A recognize algorithm based on the convolutional neural networks is proposed to recognize ten kinds of hand gestures, which include rotation and turnover samples acquired from different persons. When 6000 hand gesture images were used as training samples, and 1100 as testing samples, a 98% recognition rate was achieved with the convolutional neural networks, which is higher than that with some other frequently-used recognition algorithms.
A novel readout integrated circuit for ferroelectric FPA detector
Show abstract
Uncooled infrared detectors haves some advantages such as low cost，light weight，low power consumption, and superior reliability, compared with cryogenically cooled ones．Ferroelectric uncooled focal plane array(FPA) are being developed for its AC response and its high reliability．As a key part of the ferroelectric assembly，the ROIC determines the performance of the assembly. A top-down design model for uncooled ferroelectric readout integrated circuit(ROIC) has been developed. Based on the optical，thermal and electrical properties of the ferroelectric detector，the RTIA readout integrated circuit is designed. The noise bandwidth of RTIA readout circuit has been developed and analyzed. A novel high gain amplifier, a high pass filter and a low pass filter circuits are designed on the ROIC. In order to improve the ferroelectric FPA package performance and decrease of package cost a temperature sensor is designed on the ROIC chip．At last the novel RTIA ROIC is implemented on 0.6μm 2P3M CMOS silicon techniques. According to the experimental chip test results，the temporal root mean square(RMS)noise voltage is about 1.4mV，the sensitivity of the on chip temperature sensor is 0.6 mV/K from -40°C to 60°C，the linearity performance of the ROIC chip is better than 99%．Based on the 320×240 RTIA ROIC, a 320×240 infrared ferroelectric FPA is fabricated and tested. Test results shows that the 320×240 RTIA ROIC meets the demand of infrared ferroelectric FPA.
Research on facial expression simulation based on depth image
Show abstract
Nowadays, face expression simulation is widely used in film and television special effects, human-computer interaction and many other fields. Facial expression is captured by the device of Kinect camera .The method of AAM algorithm based on statistical information is employed to detect and track faces. The 2D regression algorithm is applied to align the feature points. Among them, facial feature points are detected automatically and 3D cartoon model feature points are signed artificially. The aligned feature points are mapped by keyframe techniques. In order to improve the animation effect, Non-feature points are interpolated based on empirical models. Under the constraint of Bézier curves we finish the mapping and interpolation. Thus the feature points on the cartoon face model can be driven if the facial expression varies. In this way the purpose of cartoon face expression simulation in real-time is came ture. The experiment result shows that the method proposed in this text can accurately simulate the facial expression. Finally, our method is compared with the previous method. Actual data prove that the implementation efficiency is greatly improved by our method.
Three-dimensional particle cloud simulation based on illumination model
Show abstract
The simulation of 3D clouds has been a challenging research question in the field of computer graphics. Aiming at the problem that the existing three-dimensional cloud is not realistic, a three-dimensional particle cloud simulation method based on the illumination model is proposed, which randomly generate the particles according to the principle of the particle system and give the particles the initial color, size and shape. And then add the lighting effects and render them to achieve the three-dimensional cloud simulation. Comparing with the previous three-dimensional cloud modeling method, this method has the advantages of rapid rendering of cloud, because of the effect of adding light, the real feeling more intense.
A trajectory design method via target practice for air-breathing hypersonic vehicle
Show abstract
There are strong coupling interactions between aerodynamics and scramjet, this kind of aircraft also has multiple restrictions, such as the range and difference of dynamic pressure, airflow, and fuel. On the one hand, we need balance the requirement between maneuverability of vehicle and stabilization of scramjet. On the other hand, we need harmonize the change of altitude and the velocity. By describing aircraft’s index system of climbing capability, acceleration capability, the coupling degree in aerospace, this paper further propose a rapid design method which based on target practice. This method aimed for reducing the coupling degree, it depresses the coupling between aircraft and engine in navigation phase, satisfy multiple restriction conditions to leave some control buffer and create good condition for control implementation. According to the simulation, this method could be used for multiple typical fly commissions such as climbing, acceleration or both.
Multispectral image compression algorithm based on spectral clustering and wavelet transform
Show abstract
In this paper, a method based on spectral clustering and the discrete wavelet transform (DWT) is proposed, which is based on the problem of the high degree of space-time redundancy in the current multispectral image compression algorithm. First, the spectral images are grouped by spectral clustering methods, and the clusters of similar heights are grouped together to remove the redundancy of the spectra. Then, wavelet transform and coding of the class representative are performed, and the space redundancy is eliminated, and the difference composition is applied to the Karhunen-Loeve transform (KLT) and wavelet transform. Experimental results show that with JPEG2000 and upon KLT + DWT algorithm, compared with the method has better peak signal-to-noise ratio and compression ratio, and it is suitable for compression of different spectral bands.
Multi-model convolutional extreme learning machine with kernel for RGB-D object recognition
Show abstract
With new depth sensing technology such as Kinect providing high quality synchronized RGB and depth images (RGB-D data), learning rich representations efficiently plays an important role in multi-modal recognition task, which is crucial to achieve high generalization performance. To address this problem, in this paper, we propose an effective multi-modal convolutional extreme learning machine with kernel (MMC-KELM) structure, which combines advantages both the power of CNN and fast training of ELM. In this model, CNN uses multiple alternate convolution layers and stochastic pooling layers to effectively abstract high level features from each modality (RGB and depth) separately without adjusting parameters. And then, the shared layer is developed by combining these features from each modality. Finally, the abstracted features are fed to the extreme learning machine with kernel (KELM), which leads to better generalization performance with faster learning speed. Experimental results on Washington RGB-D Object Dataset show that the proposed multiple modality fusion method achieves state-of-the-art performance with much less complexity.
Research of test fault diagnosis method for micro-satellite PSS
Show abstract
Along with the increase in the number of micro-satellite and the shortening of the product’s lifecycle, negative effects of satellite ground test failure become more and more serious. Real-time and efficient fault diagnosis becomes more and more necessary. PSS plays an important role in the satellite ground test’s safety and reliability as one of the most important subsystems that guarantees the safety of micro-satellite energy. Take test fault diagnosis method of micro-satellite PSS as research object. On the basis of system features of PSS and classic fault diagnosis methods, propose a kind of fault diagnosis method based on the layered and loose coupling way. This article can provide certain reference for fault diagnosis methods research of other subsystems of micro-satellite.
Research on installation quality inspection system of high voltage customer metering device based on image recognition
Bei He,
Fu-li Yang,
Xue-dan Tao,
et al.
Show abstract
With the rapid development of the scale of the power grid, the site construction and the operations environment is more widespread and more complex. The installation work of the high-voltage customer metering device is heavy, which is not standardized. In addition, managers supervise the site construction progress only through the person in charge of each work phrase. It is inefficient and difficult to control the multi-team and multi-unit cross work. Therefore, it is necessary to establish a scientific system to detect the quality of installation and management practices to standardize installation work of the metering device. Based on the research of image recognition and target detection system, this paper presents a high-voltage customer metering device installation quality inspection system based on digital image processing, image feature extraction and SVM classification decision. The experimental results show that the proposed scheme is feasible. And it can be used to accurately extract the metering components in the image, which can be also accurately and quickly classified. Our method is of great significance for the implementation and monitoring of the power system in installation and specification
Change detection from remote sensing images based on fractional integral and improved FLICM
Show abstract
This paper presents a new change detection method based on fractional integral and improved FLICM clustering. Firstly, the log-ratio operator is applied to obtain the difference image from two registered and corrected remote sensing images; and then, the fractional integral operator is introduced to de-nosing and preserve the edge and texture information of the difference image; Finally, the improved FLICM is carried out to get the change areas, which fully considering the pixel neighborhood information and the spatial distance information of the objective function. Experimental results show that the proposed algorithm has strong ability to suppress noise, and can obtain good detection results.
The small low SNR target tracking using sparse representation information
Show abstract
Tracking small targets, such as missile warheads, from a remote distance is a difficult task since the targets are “points” which are similar to sensor’s noise points. As a result, traditional tracking algorithms only use the information contained in point measurement, such as the position information and intensity information, as characteristics to identify targets from noise points. But in fact, as a result of the diffusion of photon, any small target is not a point in the focal plane array and it occupies an area which is larger than one sensor cell. So, if we can take the geometry characteristic into account as a new dimension of information, it will be of helpful in distinguishing targets from noise points. In this paper, we use a novel method named sparse representation (SR) to depict the geometry information of target intensity and define it as the SR information of target. Modeling the intensity spread and solving its SR coefficients, the SR information is represented by establishing its likelihood function. Further, the SR information likelihood is incorporated in the conventional Probability Hypothesis Density (PHD) filter algorithm with point measurement. To illustrate the different performances of algorithm with or without the SR information, the detection capability and estimation error have been compared through simulation. Results demonstrate the proposed method has higher estimation accuracy and probability of detecting target than the conventional algorithm without the SR information.
A kind of graded sub-pixel motion estimation algorithm combining time-domain characteristics with frequency-domain phase correlation
Show abstract
The mode of navigation based on scene match can assist UAV to achieve autonomous navigation and other missions. However, aerial multi-frame images of the UAV in the complex flight environment easily be affected by the jitter, noise and exposure, which will lead to image blur, deformation and other issues, and result in the decline of detection rate of the interested regional target. Aiming at this problem, we proposed a kind of Graded sub-pixel motion estimation algorithm combining time-domain characteristics with frequency-domain phase correlation. Experimental results prove the validity and accuracy of the proposed algorithm.
A novel algorithm for electronic image stabilization based on improved optical flow
Show abstract
Aiming at the problem of the jitter of video sequence recorded by the strapdown seeker in the terminal guidance process, a novel algorithm for electronic image stabilization based on improved optical flow is proposed. The algorithm uses Shi-Tomasi corner detection method to extract the image corner, and estimates the global motion parameters of jittery video sequence based on improved Pyramid LK optical flow which is designed. Then the Kalman smoothing global motion vector is adopted, which effectively makes compensation on the current image motion and retains the active movement while filtering random jitter parameters. Finally, stable image sequence output is achieved. The simulation test and the embedded platform for the actual test results indicate that the proposed algorithm has a good image stabilization effect on the translation, rotation and scaling motion of the random jittery video sequence recorded by the strapdown seeker, and possesses good robustness and real-time performance.
Target tracking algorithm based on Kalman filter and optimization MeanShift
Show abstract
Background change ,shape change and target covering will all cause target tracking failure. Real-time and accuracy in target tracking is the problem that must be considered. This paper first presents the Mean Shift algorithm, then the Mean Shift algorithm iterative weight is modified with main information more prominent, secondary information suppressed, avoiding the tedious root, improving the real-time and effectiveness of target tracking：The target template updating algorithm is present to solve change of background and target shape change. Then a Kalman filter in the horizontal position and the vertical position is established to solve the problem of target tracking completely covered. Simulation results show that target tracking algorithm on the condition of target template update has higher tracking accuracy , higher real-time property and at the same time is robust than the traditional Mean Shift tracking algorithm .
Improved gap filling method based on singular spectrum analysis and its application in space environment
Show abstract
Data missing is a common phenomenon in the space environment measurements, which impacts or even blocks the following model-building procedures, predictions and posterior analysis. To fill these data gaps, an improved filling method based on iterative singular spectrum analysis is proposed. It first extracts a distribution array of the gaps and then fills the gaps with all known data. The distribution array is utilized to generate the test sets for cross validation. The embedding window length and principal components are determined by the discrete particle swarm optimization algorithm in a noncontinuous fashion. The effectiveness and adaptability of the filling method are proved by some tests done on solar wind data and geomagnetic indices from different solar activity years.
Image segmentation algorithm based on improved PCNN
Show abstract
A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.
The extraction for echo signal of pulse laser ladar
Show abstract
Aiming at the low precision of small pulse laser ladar in practical application, the wavelet threshold de-noising algorithm is adopted to extract the echo signal. According to the echo signal equation and the ladar noise characteristics, a noisy echo model is established. In order to extract the echo signal, the wavelet threshold de-noising algorithm is used. Select the best wavelet basis and decomposition scale. According to the simulation results, the actual echo signal is processed, and the noise is effectively improved. The detection accuracy improves effectively.
Target recognition based on convolutional neural network
Show abstract
One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.
Crack image segmentation based on improved DBC method
Show abstract
With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.
A new airborne laser rangefinder dynamic target simulator for non-stationary environment
Show abstract
For the non-stationary environment simulation in laser range finder product testing, a new dynamic target simulation system is studied. First of all, the three-pulsed laser ranging principle, laser target signal composition and mathematical representation are introduced. Then, the actual nonstationary working environment of laser range finder is analyzed, and points out that the real sunshine background light clutter and target shielding effect in laser echo become the main influencing factors. After that, the dynamic laser target signal simulation method is given. Eventlly, the implementation of automatic test system based on arbitrary waveform generator is described. Practical application shows that the new echo signal automatic test system can simulate the real laser ranging environment of laser range finder, and is suitable for performance test of products.
Spatio-temporal distribution of total suspended sediment concentration derived from MODIS data in the Yellow and East China Seas from 2001 to 2013
Show abstract
The monthly mean suspended sediment concentration of the upper layer in the Yellow and East China Seas was derived from the retrieval of the monthly binned MODIS Level 3 remote sensing data from 2001 to 2013. The seasonal variation and spatial distribution of the suspended sediment concentration in the study area were investigated. The results showed that the suspended sediment distribution presents apparent spatial characteristics and seasonal variations, which are mainly affected by the resuspension and transportation of the suspended sediment in the Yellow and East China Seas. The concentration of suspended sediment in the study area is high inshore and low offshore. The suspended sediment covers amuch wider area in winter than in summer, and for the same site the concentration is generally higher in winter. Winds, waves, currents and bottom sediment feature and distribution in the study area are important influencing factors for the distribution pattern.
Uyghur face recognition method combining 2DDCT with POEM
Show abstract
In this paper, in light of the reduced recognition rate and poor robustness of Uyghur face under illumination and partial occlusion, a Uyghur face recognition method combining Two Dimension Discrete Cosine Transform (2DDCT) with Patterns Oriented Edge Magnitudes (POEM) was proposed. Firstly, the Uyghur face images were divided into 88 block matrix, and the Uyghur face images after block processing were converted into frequency-domain status using 2DDCT; secondly, the Uyghur face images were compressed to exclude non-sensitive medium frequency parts and non-high frequency parts, so it can reduce the feature dimensions necessary for the Uyghur face images, and further reduce the amount of computation; thirdly, the corresponding POEM histograms of the Uyghur face images were obtained by calculating the feature quantity of POEM; fourthly, the POEM histograms were cascaded together as the texture histogram of the center feature point to obtain the texture features of the Uyghur face feature points; finally, classification of the training samples was carried out using deep learning algorithm. The simulation experiment results showed that the proposed algorithm further improved the recognition rate of the self-built Uyghur face database, and greatly improved the computing speed of the self-built Uyghur face database, and had strong robustness.
Feature extraction based on LBP in bar code detection
Show abstract
In order to compare the effects of traditional LBP operators and various improved Local Binary Patterns (LBP) operators’ ability on feature extraction and classification. In this paper, the LBP feature is extracted by slidin g window method. The integrity of the texture information extracted by different LBP operators and the com putational complexity and detection effect are compared. The effects of sliding window parameters on the de tection effect is studied respectively.
High power solid state laser with corner cube retro-reflectors of mutual-injection confinement
Show abstract
For compact and lightweight LASERs, producing stable output laser beams in adverse environments，such as high-vibration and high-temperature shock is a global problem. A solid-state high-power pulsed LASER is developed in this study. This LASER adopts tightly set six-channel LD pumped configuration and a single-aperture output scheme employs mutual-injection and confinement technic. By applying the method "confining oscillation and amplification", LASER parameters are: 100 mm × 100 mm × 300 mm in block dimension, 1.06μm in wavelength, 10Hz, 9.66J/pulse@0.5ms, beam quality 5mm × 2mrad, energy instability < 2%, and another beam: 3.1J/pulse@8.39ns, beam quality 5 mm × 2.5 mrad. The LASER is compact, lightweight, highly reliable, and with high pulse energy and excellent beam quality, thus allowing long-pulse and short-pulse operations. The developed approach can be potentially used for future applications.
Coarse-to-fine deep neural network for fast pedestrian detection
Show abstract
Pedestrian detection belongs to a category of object detection is a key issue in the field of video surveillance and automatic driving. Although recent object detection methods, such as Fast/Faster RCNN, have achieved excellent performance, it is difficult to meet real-time requirements and limits the application in real scenarios. A coarse-to-fine deep neural network for fast pedestrian detection is proposed in this paper. Two-stage approach is presented to realize fine trade-off between accuracy and speed. In the coarse stage, we train a fast deep convolution neural network to generate most pedestrian candidates at the cost of a number of false positives. The detector can cover the majority of scales, sizes, and occlusions of pedestrians. After that, a classification network is introduced to refine the pedestrian candidates generated from the previous stage. Refining through classification network, most of false detections will be excluded easily and the final pedestrian predictions with bounding box and confidence score are produced. Competitive results have been achieved on INRIA dataset in terms of accuracy, especially the method can achieve real-time detection that is faster than the previous leading methods. The effectiveness of coarse-to-fine approach to detect pedestrians is verified, and the accuracy and stability are also improved.
The method of micro-motion cycle feature extraction based on confidence coefficient evaluation criteria
Show abstract
In radar target recognition, the micro motion characteristics of target is one of the characteristics that researchers pay attention to at home and abroad, in which the characteristics of target precession cycle is one of the important characteristics of target movement characteristics. Periodic feature extraction methods have been studied for years, the complex shape of the target and the scattering center stack lead to random fluctuations of the RCS. These random fluctuations also exist certain periodicity, which has a great influence on the target recognition result. In order to solve the problem, this paper proposes a extraction method of micro-motion cycle feature based on confidence coefficient evaluation criteria.
Water quality real-time monitoring system via biological detection based on video analysis
Show abstract
With the development of society, water pollution has become the most serious problem in China. Therefore, real-time water quality monitoring is an important part of human activities and water pollution prevention. In this paper, the behavior of zebrafish was monitored by computer vision. Firstly, the moving target was extracted by the method of saliency detection, and tracked by fitting the ellipse model. Then the motion parameters were extracted by optical flow method, and the data were monitored in real time by means of Hinkley warning and threshold warning. We achieved classification warning through a number of dimensions by comprehensive toxicity index. The experimental results show that the system can achieve more accurate real-time monitoring.
Distance information extraction method of moving target based on single photon
Show abstract
Single photon ranging has high sensitivity and can effectively improve the measurement range. In order to improve the target detection efficiency of commonly, we use the method of multiple accumulation to improve SNR. For moving target echo signal, each has a certain offset. The echo signal accumulation broadening greatly increases the difficulty of signal extraction. The traditional method is using multiple filters. But for single photon signals, the filtering method is not effective. This paper studies the single photon echo ranging model. The mathematical model of the echo signal of moving target is established. Accumulation of moving target with velocity estimation overcomes the accumulation of the problem. Compared with the traditional filtering method, it has more superior performance. And the detection probability of the method is calculated by Matlab simulation, verifying its good performance.
Target detection method by airborne and spaceborne images fusion based on past images
Show abstract
To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.
Projector calibration algorithm in omnidirectional structured light
Show abstract
This paper aims to study the projector calibration algorithm in omnidirectional structured light (OSL). The traditional projector calibration method can not directly be used in omnidirectional system, because the projector is perpendicular to the omnidirectional camera in our experiment. Therefor, we design a complete algorithm for the calibration of omnidirectional structured light. Firstly, a calibration plane is applied. And a checkerboard calibration board are placed on that and the checkerboard pattern projected from the projector onto that. Secondly, the equation of the calibration plane are computed based on the extrinsic parameters of the calibration board. Thirdly, the corners of the projected pattern are detected in the image captured by omnidirectional camera. Lastly, 3D projected points for each projected corner are obtained based on the ray-plane intersection. We designed a complete set of OSL calibration toolbox based on the proposed methods in Matlab. The proposed method and toolbox in Matlab have been shown to be accurate and easyto-use in projector calibration.
Single image super resolution algorithm based on edge interpolation in NSCT domain
Show abstract
In order to preserve the texture and edge information and to improve the space resolution of single frame, a superresolution algorithm based on Contourlet (NSCT) is proposed. The original low resolution image is transformed by NSCT, and the directional sub-band coefficients of the transform domain are obtained. According to the scale factor, the high frequency sub-band coefficients are amplified by the interpolation method based on the edge direction to the desired resolution. For high frequency sub-band coefficients with noise and weak targets, Bayesian shrinkage is used to calculate the threshold value. The coefficients below the threshold are determined by the correlation among the sub-bands of the same scale to determine whether it is noise and de-noising. The anisotropic diffusion filter is used to effectively enhance the weak target in the low contrast region of the target and background. Finally, the high-frequency sub-band is amplified by the bilinear interpolation method to the desired resolution, and then combined with the high-frequency subband coefficients after de-noising and small target enhancement, the NSCT inverse transform is used to obtain the desired resolution image. In order to verify the effectiveness of the proposed algorithm, the proposed algorithm and several common image reconstruction methods are used to test the synthetic image, motion blurred image and hyperspectral image, the experimental results show that compared with the traditional single resolution algorithm, the proposed algorithm can obtain smooth edges and good texture features, and the reconstructed image structure is well preserved and the noise is suppressed to some extent.
Evolution-based outlier removal for geometric model fitting
Show abstract
In this paper, we propose a novel method, called Evolution-based Outlier Removal (EOR) method, to remove outliers for robust geometric model fitting. We first select some data points and guide them to evolve towards the inliers. And then, we statistically analyze the evolutional results and distinguish inliers from outliers. Our main contribution in this paper is that, we develop a fitness function to improve the “quality” of selected point sets, which is then used to remove outliers. Experiments on real images illustrate the superiority of the proposed method over several state-of-the-art outlier removal methods.
Investigation of combining output a uniform flat-top beam of six solid state lasers by DOE
Show abstract
A pair of special diffractive optical element (DOE) is used in the combining of six Nd:YAG lasers to obtain flat-top output beam. The function of this pair of DOE in the system has been discussed. The property of output beam distribution has been analyzed and simulated. Both the measured far field and near field intensity distributions match the simulated results. Combining output energy of 15.3J is obtained, and the transfer efficiency of the DOEs is about 81%.
Application of high precision temperature control technology in infrared testing
Show abstract
In allusion to the demand of infrared system test, the principle of Infrared target simulator and the function of the temperature control are presented. The key technology of High precision temperature control is discussed, which include temperature gathering, PID control and power drive. The design scheme of temperature gathering is put forward. In order to reduce the measure error, discontinuously current and four-wire connection for the platinum thermal resistance are adopted. A 24-bits AD chip is used to improve the acquisition precision. Fuzzy PID controller is designed because of the large time constant and continuous disturbance of the environment temperature, which result in little overshoot, rapid response, high steady-state accuracy. Double power operational amplifiers are used to drive the TEC. Experiments show that the key performances such as temperature control precision and response speed meet the requirements.
Influence of fundamental mode fill factor on disk laser output power and laser beam quality
Show abstract
An three-dimensional numerical model based on finite element method and Fox-Li method with angular spectrum diffraction theoy is developed to calculate the output power and power density distribution of Yb:YAG disk laser. We invest the influence of fundamental mode fill factor(the ratio of fundamental mode size and pump spot size) on the output power and laser beam quality. Due to aspherical aberration and soft aperture effect in laser disk, high beam quality can be achieve with relative lower efficiency. The highest output power of fundamental laser mode is influenced by the fundamental mode fill factor. Besides we find that optimal mode fill factor increase with pump spot size.
AOD furnace splash soft-sensor in the smelting process based on improved BP neural network
Show abstract
In view of argon oxygen refining low carbon ferrochrome production process, in the splash of smelting process as the research object, based on splash mechanism analysis in the smelting process , using multi-sensor information fusion and BP neural network modeling techniques is proposed in this paper, using the vibration signal, the audio signal and the flame image signal in the furnace as the characteristic signal of splash, the vibration signal, the audio signal and the flame image signal in the furnace integration and modeling, and reconstruct splash signal, realize the splash soft measurement in the smelting process, the simulation results show that the method can accurately forecast splash type in the smelting process, provide a new method of measurement for forecast splash in the smelting process, provide more accurate information to control splash.
Research on optimization method of deep neural network
Show abstract
Image recognition technology has been widely applied and played an important role in various fields nowadays. Because of multi-layer structure of deep network can use a more concise way to express complex functions, deep neural network (DNN) will be applied to the image recognition to improve the accuracy of image classification. Analysis the existing problems of deep neural network. Then put forward new approaches to solve the gradient vanishing and over-fitting problems. The experimental results which verified on the MNIST, show that our proposed approaches can improve the classification accuracy greatly and accelerate the convergence speed. Compared to support vector machine (SVM), the optimized model of the neural network is not only effective, but also converged quickly.
Structure guided GANs
Show abstract
Generative adversarial networks (GANs) has achieved success in many fields. However, there are some samples generated by many GAN-based works, whose structure is ambiguous. In this work, we propose Structure Guided GANs that introduce structural similar into GANs to overcome the problem. In order to achieve our goal, we introduce an encoder and a decoder into a generator to design a new generator and take real samples as part of the input of a generator. And we modify the loss function of the generator accordingly. By comparison with WGAN, experimental results show that our proposed method overcomes largely sample structure ambiguous and can generate higher quality samples.
An online ID identification system for liquefied-gas cylinder plant
Show abstract
An automatic ID identification system for gas cylinders’ online production was developed based on the production conditions and requirements of the Technical Committee for Standardization of Gas Cylinders. A cylinder ID image acquisition system was designed to improve the image contrast of ID regions on gas cylinders against the background. Then the ID digits region was located by the CNN template matching algorithm. Following that, an adaptive threshold method based on the analysis of local average grey value and standard deviation was proposed to overcome defects of non-uniform background in the segmentation results. To improve the single digit identification accuracy, two BP neural networks were trained respectively for the identification of all digits and the easily confusable digits. If the single digit was classified as one of confusable digits by the former BP neural network, it was further tested by the later one, and the later result was taken as the final identification result of this single digit. At last, the majority voting was adopted to decide the final identification result for the 6-digit cylinder ID. The developed system was installed on a production line of a liquefied-petroleum-gas cylinder plant and worked in parallel with the existing weighing step on the line. Through the field test, the correct identification rate for single ID digit was 94.73%, and none of the tested 2000 cylinder ID was misclassified through the majority voting.
The optional selection of micro-motion feature based on Support Vector Machine
Show abstract
Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).
Spectral clustering for water body spectral types analysis
Show abstract
In order to study the spectral types of water body in the whole country, the key issue of reservoir research is to obtain and to analyze the information of water body in the reservoir quantitatively and accurately. A new type of weight matrix is constructed by utilizing the spectral features and spatial features of the spectra from GF-1 remote sensing images comprehensively. Then an improved spectral clustering algorithm is proposed based on this weight matrix to cluster representative reservoirs in China. According to the internal clustering validity index which called Davies-Bouldin(DB) index, the best clustering number 7 is obtained. Compared with two clustering algorithms, the spectral clustering algorithm based only on spectral features and the K-means algorithm based on spectral features and spatial features, simulation results demonstrate that the proposed spectral clustering algorithm based on spectral features and spatial features has a higher clustering accuracy, which can better reflect the spatial clustering characteristics of representative reservoirs in various provinces in China - similar spectral properties and adjacent geographical locations.
Multi-focus elemental images fusion via NSCT in integral imaging
Show abstract
In synthetic aperture integral imaging system (SAII), a camera array, replacing lenslet array, is employed to obtain highresolution elemental images with multiple perspective. However, the SAII system still suffer from the limitation of depth of field (DoF). In this paper, we present a multi-focus elemental image fusion method by using NSCT to solve limitation of DoF problem. In proposed method, the depth estimation are achieved to register elemental images. Then, a fusion method based on non sub-sampled contourlet transform (NSCT) is employed to obtain full-focus elemental images. Finally, the resolution enhanced 3D images are reconstructed by using the full-focus elemental images. To show the feasibility of the proposed method, the preliminary experiments are carried out.
Adaptive compressed sensing of multi-view videos based on the sparsity estimation
Show abstract
The conventional compressive sensing for videos based on the non-adaptive linear projections, and the measurement times is usually set empirically. As a result, the quality of videos reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was described. Then an estimation method for the sparsity of multi-view videos was proposed based on the two dimensional discrete wavelet transform (2D DWT). With an energy threshold given beforehand, the DWT coefficients were processed with both energy normalization and sorting by descending order, and the sparsity of the multi-view video can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of video frame effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparsity estimated with the energy threshold provided, the proposed method can ensure the reconstruction quality of multi-view videos.
Research on multi-source image fusion technology in haze environment
Show abstract
In the haze environment, the visible image collected by a single sensor can express the details of the shape, color and texture of the target very well, but because of the haze, the sharpness is low and some of the target subjects are lost; Because of the expression of thermal radiation and strong penetration ability, infrared image collected by a single sensor can clearly express the target subject, but it will lose detail information. Therefore, the multi-source image fusion method is proposed to exploit their respective advantages. Firstly, the improved Dark Channel Prior algorithm is used to preprocess the visible haze image. Secondly, the improved SURF algorithm is used to register the infrared image and the haze-free visible image. Finally, the weighted fusion algorithm based on information complementary is used to fuse the image. Experiments show that the proposed method can improve the clarity of the visible target and highlight the occluded infrared target for target recognition.
Low illumination color image enhancement based on improved Retinex
Show abstract
Low illumination color image usually has the characteristics of low brightness, low contrast, detail blur and high salt and pepper noise, which greatly affected the later image recognition and information extraction. Therefore, in view of the degradation of night images, the improved algorithm of traditional Retinex. The specific approach is: First, the original RGB low illumination map is converted to the YUV color space (Y represents brightness, UV represents color), and the Y component is estimated by using the sampling acceleration guidance filter to estimate the background light; Then, the reflection component is calculated by the classical Retinex formula and the brightness enhancement ratio between original and enhanced is calculated. Finally, the color space conversion from YUV to RGB and the feedback enhancement of the UV color component are carried out.
Distance-based over-segmentation for single-frame RGB-D images
Show abstract
Over-segmentation, known as super-pixels, is a widely used preprocessing step in segmentation algorithms. Oversegmentation algorithm segments an image into regions of perceptually similar pixels, but performs badly based on only color image in the indoor environments. Fortunately, RGB-D images can improve the performances on the images of indoor scene. In order to segment RGB-D images into super-pixels effectively, we propose a novel algorithm, DBOS (Distance-Based Over-Segmentation), which realizes full coverage of super-pixels on the image. DBOS fills the holes in depth images to fully utilize the depth information, and applies SLIC-like frameworks for fast running. Additionally, depth features such as plane projection distance are extracted to compute distance which is the core of SLIC-like frameworks. Experiments on RGB-D images of NYU Depth V2 dataset demonstrate that DBOS outperforms state-ofthe-art methods in quality while maintaining speeds comparable to them.
Dissipation function and adaptive gradient reconstruction based smoke detection in video
Show abstract
A method for smoke detection in video is proposed. The camera monitoring the scene is assumed to be stationary. With the atmospheric scattering model, dissipation function is reflected transmissivity between the background objects in the scene and the camera. Dark channel prior and fast bilateral filter are used for estimating dissipation function which is only the function of the depth of field. Based on dissipation function, visual background extractor (ViBe) can be used for detecting smoke as a result of smoke’s motion characteristics as well as detecting other moving targets. Since smoke has semi-transparent parts, the things which are covered by these parts can be recovered by poisson equation adaptively. The similarity between the recovered parts and the original background parts in the same position is calculated by Normalized Cross Correlation (NCC) and the original background’s value is selected from the frame which is nearest to the current frame. The parts with high similarity are considered as smoke parts.
Monitoring Ulva prolifera in the Yellow Sea and East China Sea derived from multi-source remote sensing images
Show abstract
Ulva prolifera is a new type of marine ecological disaster in China. Since 2007, macroalgal blooms of Ulva prolifera occurred every summer in the Yellow Sea and East China Sea, causing significant environmental damage and economic loss especially for coastal areas. Because of its distribution range covering tens of thousands of square kilometers during its outbreak, it’s obviously difficult to monitor using ship, and therefore the author of this article attempted to use remote sensing technology to monitor its temporal and spatial distribution.In the present study, CCD of China’s Environmental Satellite and EOS-MODIS were employed to detect distribution of Ulva prolifera in the Yellow Sea and East China Sea in 2014.The remote sensing monitoring results show that early enteromorpha prolifera appeared not only in the sea adjacent to Yan Cheng, but also in the sea offshore from the Yangtze River estuary. Under the influence of favorable conditions, it drifted from south to north with the area gradually increased. During the period , its maximum area of the distribution reched to 3.5 square kilometers.
Detecting of foreign object debris on airfield pavement using convolution neural network
Show abstract
It is of great practical significance to detect foreign object debris (FOD) timely and accurately on the airfield pavement, because the FOD is a fatal threaten for runway safety in airport. In this paper, a new FOD detection framework based on Single Shot MultiBox Detector (SSD) is proposed. Two strategies include making the detection network lighter and using dilated convolution, which are proposed to better solve the FOD detection problem. The advantages mainly include: (i) the network structure becomes lighter to speed up detection task and enhance detection accuracy; (ii) dilated convolution is applied in network structure to handle smaller FOD. Thus, we get a faster and more accurate detection system.
Crowd counting via region based multi-channel convolution neural network
Show abstract
This paper proposed a novel region based multi-channel convolution neural network architecture for crowd counting. In order to effectively solve the perspective distortion in crowd datasets with a great diversity of scales, this work combines the main channel and three branch channels. These channels extract both the global and region features. And the results are used to estimate density map. Moreover, kernels with ladder-shaped sizes are designed across all the branch channels, which generate adaptive region features. Also, branch channels use relatively deep and shallow network to achieve more accurate detector. By using these strategies, the proposed architecture achieves state-of-the-art performance on ShanghaiTech datasets and competitive performance on UCF_CC_50 datasets.
Generation of vector beams with polarization rotation metasurfaces
Show abstract
We theoretically demonstrate high efficiency broadband vector beams generation with polarization rotation metasurfaces composed of L-shaped silver antenna array, silica spacer, and silver ground plane. 0° to 90° arbitrary optical rotation with high degree of linear polarization (DoLP) over a broadband can be achieved readily by adjusting arm length of the L-shaped antenna. And through turning the L-shaped antennas upside down, the 0° to 90° optical rotation can be turned into 0° to -90°. Reflected phase can be shift by π after a 90° rotation of the L-shaped or Γ-shaped antennas, while optical rotation angle remains the same. Then six discrete units are designed to realize 0° to 360° polarization rotation with a step of 60°. With the combination of these units, we proposed metamaterial structures for highly efficient generation of radially polarized and azimuthally polarized vector beams.
Testing of null correctors by tilted computer-generated holograms with maximum likelihood algorithm
Show abstract
Aspheric mirrors are often tested by interferometer with two different ways ensuring the correctness of the testing result. As the two most common methods, null correctors and CGHs are often used in actual testing at the same time. Considering the accuracy of CGH is higher than null lens, it can be also used to calibrate the accuracy of null lens. However, the central section of null correctors can’t be tested by traditional CGHs. In the article, tilted CGHs are proposed to test null correctors. In addition, we provided an experimental demonstration by testing a null corrector with tilted CGH applying the maximum likelihood (ML) algorithm. The result demonstrates the feasibility of the testing of null correctors by tilted CGHs with ML algorithm.
Analysis on detection accuracy of binocular photoelectric instrument optical axis parallelism digital calibration instrument
Show abstract
Low-light level night vision device and thermal infrared imaging binocular photoelectric instrument are used widely. The maladjustment of binocular instrument ocular axises parallelism will cause the observer the symptom such as dizziness, nausea, when use for a long time. Binocular photoelectric equipment digital calibration instrument is developed for detecting ocular axises parallelism. And the quantitative value of optical axis deviation can be quantitatively measured. As a testing instrument, the precision must be much higher than the standard of test instrument. Analyzes the factors that influence the accuracy of detection. Factors exist in each testing process link which affect the precision of the detecting instrument. They can be divided into two categories, one category is factors which directly affect the position of reticle image, the other category is factors which affect the calculation the center of reticle image. And the Synthesize error is calculated out. And further distribute the errors reasonably to ensure the accuracy of calibration instruments.
Geometric shapes inversion method of space targets by ISAR image segmentation
Show abstract
The geometric shape of target is an effective characteristic in the process of space targets recognition. This paper proposed a method of shape inversion of space target based on components segmentation from ISAR image. The Radon transformation, Hough transformation, K-means clustering, triangulation will be introduced into ISAR image processing. Firstly, we use Radon transformation and edge detection to extract space target’s main body spindle and solar panel spindle from ISAR image. Then the targets’ main body, solar panel, rectangular and circular antenna are segmented from ISAR image based on image detection theory. Finally, the sizes of every structural component are computed. The effectiveness of this method is verified using typical targets’ simulation data.
Tissues segmentation based on multi spectral medical images
Show abstract
Each band image contains the most obvious tissue feature according to the optical characteristics of different tissues in different specific bands for multispectral medical images. In this paper, the tissues were segmented by their spectral information at each multispectral medical images. Four Local Binary Patter descriptors were constructed to extract blood vessels based on the gray difference between the blood vessels and their neighbors. The segmented tissue in each band image was merged to a clear image.
Fast iterative censoring CFAR algorithm for ship detection from SAR images
Show abstract
Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.
Review of intelligent bionic vision navigation
Show abstract
With the popularization of intelligent equipment such as UAV (Unmanned Aerial Vehicle) and UV (Unmanned Vehicle), their demands for autonomy, independence and intelligence of navigation gradually increase, and traditional navigation methods can’t meet this demand. In order to make a thorough study, a review of intelligent bionic vision navigation methods is made on its background, research status and related fields. Through the analysis and summarization of the above information, the development trend of intelligent bionic vision navigation is pointed out, and its advantages and disadvantages are discussed.
High-resolution extraction of particle size via Fourier Ptychography
Show abstract
This paper proposes a method which can extract the particle size information with a resolution beyond λ/NA. This is achieved by applying Fourier Ptychographic (FP) ideas to the present problem. In a typical FP imaging platform, a 2D LED array is used as light sources for angle-varied illuminations, a series of low-resolution images was taken by a full sequential scan of the array of LEDs. Here, we demonstrate the particle size information is extracted by turning on each single LED on a circle. The simulated results show that the proposed method can reduce the total number of images, without loss of reliability in the results.
High precision surface measurement with a trans-scale optical measurement method
Show abstract
Increasing requirements on the complexity and accuracy of dimensional metrology demand the application of aerospace turbine blade, cutting tools, and so on. The multi-scale data of holistic geometrical is needed. In order to obtain all meaningful details of the surface at various required scales, a novel trans-scale optical measurement method mixing macro and micro measurement technology is proposed. The optical scanning system is composed of a variable-focus structured light sensor fitted with zoom lens camera and a focus variation microscopy sensor. The structured light sensor is used to acquire the form of the object. The focus variation microscopy sensor is used to acquire the waviness or roughness of the object. The macro structured light sensor can flexibly zoom in or out to measure a 3D object profile in sections according to the approximate surface profile and the view of the micro measurement system. It originally connects the macro and micro scale at view, resolution, precision. The different scale measurement data are registered and fusion with multi-dimensional images which includes 2D image, 2.5D range image, and 3D point cloud image. Experimental measurement results show that macro holistic geometry profile and micro surface texture can be acquired with the developed method in a single frame system.
Deep residual networks of residual networks for image super-resolution
Show abstract
Single image super-resolution (SISR), which aims at obtaining a high-resolution image from a single low-resolution image, is a classical problem in computer vision. In this paper, we address this problem based on a deep learning method with residual learning in an end-to-end manner. We propose a novel residual-network architecture, Residual networks of Residual networks (RoR), to promote the learning capability of residual networks for SISR. In residual network, the signal can be directly propagated from one unit to any other units in both forward and backward passes when using identity mapping as the skip connections. Based on it, we add level-wise connections upon original residual networks, to dig the optimization ability of residual networks. Our experiments demonstrate the effectiveness and versatility of RoR, it can get a faster convergence speed and gain higher resolution accuracy from considerably increased depth.
High speed 3D two-photon fluorescence microscopy by femtosecond laser pulses
Show abstract
A high speed two-photon fluorescence microscopy system based on 2D galvanometer scanning is developed, and imaging of Rhodamine B samples and labeled Caski cells is performed. Coherent Micra-5 femtosecond laser is used as the light source, which has 82 MHz frequency, 45 fs pulse width and 400 mW average power. Galvo mirrors and prism pairs are applied in order to obtain higher imaging speed and better imaging resolution respectively. To prove the ability of system, two-photon imaging of Rhodamine B samples and Caski cells labeled by Rhodamine-dyed phalloidin are performed. The results show that the system imaging speed is greatly improved from previous work. The acquisition time reaches the order of 1frame/s, which makes up imprecision of mechanical translation platform and reduces photobleaching and structure damage. By measuring FWHM of lined region of image, lateral resolution is confirmed to be 1.05μm; the vertical resolution of the system can reach 3μm, which is restricted by Z-axis step motor. What’s more, images of different frames are reconstructed to perform three-dimensional imaging. The ability of the phalloidin’s specific binding to the cell is also verified by observing obtained two-photon image.
Environment exploration and SLAM experiment research based on ROS
Show abstract
Robots need to get the information of surrounding environment by means of map learning. SLAM or navigation based on mobile robots is developing rapidly. ROS (Robot Operating System) is widely used in the field of robots because of the convenient code reuse and open source. Numerous excellent algorithms of SLAM or navigation are ported to ROS package. hector_slam is one of them that can set up occupancy grid maps on-line fast with low computation resources requiring. Its characters above make the embedded handheld mapping system possible. Similarly, hector_navigation also does well in the navigation field. It can finish path planning and environment exploration by itself using only an environmental sensor. Combining hector_navigation with hector_slam can realize low cost environment exploration, path planning and slam at the same time
Measurement of atmospheric NO2 profile using three-wavelength dual-differential absorption lidar
Show abstract
Lidar instruments are efficient detectors of air pollutants such as nitrogen dioxide (NO2). However, the measurement errors are not negligible due to the influence of the aerosol in the atmosphere. We present a novel lidar for measuring tropospheric NO2 vertical profiles. For improving the received powers, the emitter unit consists of two pulsed pump laser – dye laser combination, and use three wavelengths of 448.10nm, 447.20nm and 446.60 nm corresponding to the strong, medium and weak absorption of NO2 respectively. The effects of aerosol on tropospheric NO2 measurements by three - wavelength (448.10 -447.20 -446.60 nm) dual differential absorption lidar (dual-DIAL) and conventional two - wave length (448.10- 446.60nm) differential absorption lidar (DIAL) are theoretical analyzed, and their system err are computer simulated. Experimental results show that the three - wavelength dual - DIAL method is more effective to reduce the effects of aerosol than the two - wavelength DIAL method, and its system error is no more than 4% without correcting the aerosol effect.
Face sketch recognition based on edge enhancement via deep learning
Show abstract
In this paper,we address the face sketch recognition problem. Firstly, we utilize the eigenface algorithm to convert a sketch image into a synthesized sketch face image. Subsequently, considering the low-level vision problem in synthesized face sketch image .Super resolution reconstruction algorithm based on CNN(convolutional neural network) is employed to improve the visual effect. To be specific, we uses a lightweight super-resolution structure to learn a residual mapping instead of directly mapping the feature maps from the low-level space to high-level patch representations, which making the networks are easier to optimize and have lower computational complexity. Finally, we adopt LDA(Linear Discriminant Analysis) algorithm to realize face sketch recognition on synthesized face image before super resolution and after respectively. Extensive experiments on the face sketch database(CUFS) from CUHK demonstrate that the recognition rate of SVM(Support Vector Machine) algorithm improves from 65% to 69% and the recognition rate of LDA(Linear Discriminant Analysis) algorithm improves from 69% to 75%.What'more,the synthesized face image after super resolution can not only better describer image details such as hair ,nose and mouth etc, but also improve the recognition accuracy effectively.
3D face analysis by using Mesh-LBP feature
Show abstract
Objective: Face Recognition is one of the widely application of image processing. Corresponding two-dimensional limitations, such as the pose and illumination changes, to a certain extent restricted its accurate rate and further development. How to overcome the pose and illumination changes and the effects of self-occlusion is the research hotspot and difficulty, also attracting more and more domestic and foreign experts and scholars to study it. 3D face recognition fusing shape and texture descriptors has become a very promising research direction. Method: Our paper presents a 3D point cloud based on mesh local binary pattern grid (Mesh-LBP), then feature extraction for 3D face recognition by fusing shape and texture descriptors. 3D Mesh-LBP not only retains the integrity of the 3D geometry, is also reduces the need for recognition process of normalization steps, because the triangle Mesh-LBP descriptor is calculated on 3D grid. On the other hand, in view of multi-modal consistency in face recognition advantage, construction of LBP can fusing shape and texture information on Triangular Mesh. In this paper, some of the operators used to extract Mesh-LBP, Such as the normal vectors of the triangle each face and vertex, the gaussian curvature, the mean curvature, laplace operator and so on. Conclusion: First, Kinect devices obtain 3D point cloud face, after the pretreatment and normalization, then transform it into triangular grid, grid local binary pattern feature extraction from face key significant parts of face. For each local face, calculate its Mesh-LBP feature with Gaussian curvature, mean curvature laplace operator and so on. Experiments on the our research database, change the method is robust and high recognition accuracy.
Multi-sensor image fusion algorithm based on multi-objective particle swarm optimization algorithm
Show abstract
On the basis of DT-CWT (Dual-Tree Complex Wavelet Transform，DT-CWT) theory, an approach based on MOPSO (Multi-objective Particle Swarm Optimization Algorithm) was proposed to objectively choose the fused weights of low frequency sub-bands. High and low frequency sub-bands were produced by DT-CWT. Absolute value of coefficients was adopted as fusion rule to fuse high frequency sub-bands. Fusion weights in low frequency sub-bands were used as particles in MOPSO. Spatial Frequency and Average Gradient were adopted as two kinds of fitness functions in MOPSO. The experimental result shows that the proposed approach performances better than Average Fusion and fusion methods based on local variance and local energy respectively in brightness, clarity and quantitative evaluation which includes Entropy, Spatial Frequency, Average Gradient and QAB/F.
Pedestrian detection in infrared image using HOG and Autoencoder
Show abstract
In order to guarantee the safety of driving at night, vehicle-mounted night vision system was used to detect pedestrian in front of cars and send alarm to prevent the potential dangerous. To decrease the false positive rate (FPR) and increase the true positive rate (TPR), a pedestrian detection method based on HOG and Autoencoder (HOG+Autoencoder) was presented. Firstly, the HOG features of input images were computed and encoded by Autoencoder. Then the encoded features were classified by Softmax. In the process of training, Autoencoder was trained unsupervised. Softmax was trained with supervision. Autoencoder and Softmax were stacked into a model and fine-tuned by labeled images. Experiment was conducted to compare the detection performance between HOG and HOG+Autoencoder, using images collected by vehicle-mounted infrared camera. There were 80000 images for training set and 20000 for the testing set, with a rate of 1:3 between positive and negative images. The result shows that when TPR is 95%, FPR of HOG+Autoencoder is 0.4%, while the FPR of HOG is 5% with the same TPR.
Convolutional neural network for road extraction
Junping Li,
Yazhou Ding,
Fajie Feng,
et al.
Show abstract
In this paper, the convolution neural network with large block input and small block output was used to extract road. To reflect the complex road characteristics in the study area, a deep convolution neural network VGG19 was conducted for road extraction. Based on the analysis of the characteristics of different sizes of input block, output block and the extraction effect, the votes of deep convolutional neural networks was used as the final road prediction. The study image was from GF-2 panchromatic and multi-spectral fusion in Yinchuan. The precision of road extraction was 91%. The experiments showed that model averaging can improve the accuracy to some extent. At the same time, this paper gave some advice about the choice of input block size and output block size.
Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data
Show abstract
Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.
Image defog algorithm based on open close filter and gradient domain recursive bilateral filter
Show abstract
To solve the problems of fuzzy details, color distortion, low brightness of the image obtained by the dark channel prior defog algorithm, an image defog algorithm based on open close filter and gradient domain recursive bilateral filter, referred to as OCRBF, was put forward. The algorithm named OCRBF firstly makes use of weighted quad tree to obtain more accurate the global atmospheric value, then exploits multiple-structure element morphological open and close filter towards the minimum channel map to obtain a rough scattering map by dark channel prior, makes use of variogram to correct the transmittance map,and uses gradient domain recursive bilateral filter for the smooth operation, finally gets recovery images by image degradation model, and makes contrast adjustment to get bright, clear and no fog image. A large number of experimental results show that the proposed defog method in this paper can be good to remove the fog , recover color and definition of the fog image containing close range image, image perspective, the image including the bright areas very well, compared with other image defog algorithms,obtain more clear and natural fog free images with details of higher visibility, what’s more, the relationship between the time complexity of SIDA algorithm and the number of image pixels is a linear correlation.
X-ray counting imaging based on spherical collimation
Show abstract
Aiming at the difficulty in X-ray focusing, small field of view and the low sensitivity for the X-ray imaging, a spherically collimated X-ray counting imaging method was proposed based on the concept of single-pixel camera. The spatial X-ray star maps that are sparse in the airspace were measured under the function of binary sparse observation matrix, and reconstructed rapidly by the use of TVAL3 algorithm. Finally, a series of simulations were designed to evaluate the performance of the reconstruction in Peak Signal to Noise Ratio (PSNR), Bhattacharyya Coefficient and Pearson Correlation Coefficient (PCC). The results demonstrate that the PSNR and PCC of the reconstructed image are respectively 27.1992 and 0.94273 for the sparse ratio 0.05.
Navigation system based on machine vision of multiple reference markers
Show abstract
The position and attitude measurement of space object is a key problem in the field of real-time navigation, modern control and motion tracking. As a non-contact position and attitude estimation method, machine vision position and attitude estimation has the advantages of simple structure and convenient measurement. This paper presents a vision positioning system and method based on multiple reference markers. The camera moving along the object continuously collects images containing reference markers from the camera's field of view．The spatial position information of reference mark is determined in advance, and the position and direction of moving target are calculated according to location and attitude algorithm. The main contribution of this paper: first, a plurality of reference markers is arranged in the range of moving objects so as to enlarge the range of visual positioning; second, when more than one reference marker appears in the field of view, it is possible to improve the positioning accuracy by selecting the marker of the larger contour area or the marker of the distance closer to the imaging plane principal point; third, we use the decoder to transform the reference marker into digital number. This method can improve the robustness of the system.
Automatic detection method of sea surface target based on morphological processing
Yan Chen,
Shuhua Wang,
Guangping Wang,
et al.
Show abstract
Aiming at the typical features of IR image under complex sea-sky background such as low contrast and fuzzy image edge, an automatic detection method of sea surface target based on morphological processing is proposed in this paper. At first the preprocesses such as denoising and contrast enhancement are carried out, then the Hough transform is employed to detect the sea-sky-line, and the target potential area is determined, then edge detection, dilation and hole filling are used to obtain the whole connected region of the target. Finally, the automatic detection of the target is achieved by extracting the contour of the connected region. The experimental results show that the method can effectively detect the sea surface target in complex sea-sky background, and it can lay a good foundation for subsequent geometric features extraction and recognition.
A multichannel and wide suitablity digital control device for liquid-crystal microlens controlled electrically
Show abstract
In order to overcome the difficulty in imaging detection of high-speed moving targets under complex environments, and to get more comprehensive image information of the target, there is a urgent need to develop new high-performance optical imaging components. Compared to traditional lenses which have fixed shapes and immutable focal length, liquid-crystal microlens (LCMs) can not only adjust the focal length without changing the external shape, but also realize many practical functions such as swinging focus, spectral selection, depth of field adjustment, etc. The physical properties of spatial electric fields constructed between electrode plates of the LCMs are directly related to the light-field adjusting performances of LCMs, such as the polarity of electric field, the frequency and amplitude of applied voltage signal. In other words, the optical behaviors of LCMs will be affected remarkably by the parameters of driving voltage signal mentioned above. To implement these important functions flexibly and effectively, the driving voltage signal must be powerful and flexible. It had better to have multiple channels to control the direction of swinging focus, with relatively wide variance range to spread spectrum selection range, and with high precision to ensure accurately controlling LCMs. In addition, special waveforms may be required to support special functions of LCMs. Therefore a digital control device, which meet the requirements mentioned above, is designed, and then LCMs with it can realize imaging detection of targets in complex environment.
Learning binary code via PCA of angle projection for image retrieval
Show abstract
With benefits of low storage costs and high query speeds, binary code representation methods are widely researched for efficiently retrieving large-scale data. In image hashing method, learning hashing function to embed highdimensions feature to Hamming space is a key step for accuracy retrieval. Principal component analysis (PCA) technical is widely used in compact hashing methods, and most these hashing methods adopt PCA projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit by thresholding. The variances of different projected dimensions are different, and with real-valued projection produced more quantization error. To avoid the real-valued projection with large quantization error, in this paper we proposed to use Cosine similarity projection for each dimensions, the angle projection can keep the original structure and more compact with the Cosine-valued. We used our method combined the ITQ hashing algorithm, and the extensive experiments on the public CIFAR-10 and Caltech-256 datasets validate the effectiveness of the proposed method.
Remote sensing image ship target detection method based on visual attention model
Show abstract
The traditional methods of detecting ship targets in remote sensing images mostly use sliding window to search the whole image comprehensively. However, the target usually occupies only a small fraction of the image. This method has high computational complexity for large format visible image data. The bottom-up selective attention mechanism can selectively allocate computing resources according to visual stimuli, thus improving the computational efficiency and reducing the difficulty of analysis. Considering of that, a method of ship target detection in remote sensing images based on visual attention model was proposed in this paper. The experimental results show that the proposed method can reduce the computational complexity while improving the detection accuracy, and improve the detection efficiency of ship targets in remote sensing images.
Present situation and trend of precision guidance technology and its intelligence
Show abstract
This paper first introduces the basic concepts of precision guidance technology and artificial intelligence technology. Then gives a brief introduction of intelligent precision guidance technology, and with the help of development of intelligent weapon based on deep learning project in foreign: LRASM missile project, TRACE project, and BLADE project, this paper gives an overview of the current foreign precision guidance technology. Finally, the future development trend of intelligent precision guidance technology is summarized, mainly concentrated in the multi objectives, intelligent classification, weak target detection and recognition, intelligent between complex environment intelligent jamming and multi-source, multi missile cooperative fighting and other aspects.
Quality assessment method based on the image edge for monoclonal-picking instrument
Show abstract
The image quality assessment is the basis of autofocus for monoclonal-picking instrument. For the random distribution of the monoclonal colonies, the following situation may happen, edge blurring or edge overlapping. In practice, due to the complicated morphological characteristics of colonies, to a certain degree, camera takes image with feeble features is unavoidable, so the image quality assessment based on the image edge for monoclonal-picking instrument is proposed in this paper. The experimental results show that when dealing with the image of fine colony or image with feeble features, this method is consistent with fast autofocus demand, its accuracy is higher and consumes less storage.
Image denoising method based on FPGA in digital video transmission
Show abstract
In the image acquisition and transmission link, due to the acquisition of equipment and methods, the image would suffer some different degree of interference ,and the interference will reduce the quality of image which would influence the subsequent processing. Therefore, the image filtering and image enhancement are particularly important.The traditional image denoising algorithm smoothes the image while removing the noise, so that the details of the image are lost. In order to improve image quality and save image detail, this paper proposes an improved filtering algorithm based on edge detection, Gaussian filter and median filter. This method can not only reduce the noise effectively, but also the image details are saved relatively well, and the FPGA implementation scheme of this filter algorithm is also given in this paper.
A new registration evaluation index of multi-mode images
Show abstract
In the field of image night vision, the registration accuracy of multi-model images might have a significant effect on the final fusion image quality. Due to the lack of the image registration objective evaluation method which conforms to subjective feeling, we researched on the objective evaluation index of multi-sensor image registration based on boundary definition, target saliency and color consistency. Through the introduction of weber's law, we built the human eye perception model. Combined with the local band contrast model, we proposed the boundary definition index of gray fusion image. At the same time, we used the frequency-tuned salient region detection model to calculate the target saliency index of the color fusion image. We calculated the color difference between the fused image and reference color image, and then proposed a color consistency index of image registration. At last, we proposed a new registration evaluation method for multi-model images which has superior performance. The experimental results showed that the new index has higher consistency with the subjective feeling and this index could effectively assess the multi-model images registration accuracy problem in the night vision field.
Study on extraction method of smoke contour by improved vibe algorithm and watershed algorithm
Show abstract
A Vibe improved algorithm based on HIS color space and a watershed algorithm are used to obtain the smoke contours. The Vibe algorithm is used to extract smoke anomalies and smoke inaccuracies. In this paper, the classical Vibe algorithm is described, and the existing problems in the smoke detection are analyzed. A Vibe algorithm is proposed to change the gray color space and the HIS color space by using the color feature information and Vibe. The influence of light and shadow on the smoke extraction noise is reduced by using the difference of pixel value based on Euclidean distance. The problem of imprecision of smoke caused by Vibe algorithm is analyzed by using the watershed algorithm. Finally, the proposed method is evaluated by Barnich experiment standard. The experimental results show that the proposed method can extract the smoke profile more accurately and has good robustness.
Design of real-time communication system for image recognition based colony picking instrument
Show abstract
In order to aachieve autommated observatiion and pickinng of monocloonal colonies, an overall dessign and realizzation of real-time commmunication system based on High-throoughput monooclonal auto-piicking instrumment is propossed. The real-time commmunication system is commposed of PCC-PLC commuunication systtem and Centrral Control CComputer (CCC)-PLC communicatioon system. Bassed on RS232 synchronous serial communnication methood to develop a set of dedicated shoort-range commmunication prootocol betweenn the PC and PPLC. Furthermmore, the systemm uses SQL SSERVER database to rrealize the dataa interaction between PC andd CCC. Moreoover, the commmunication of CCC and PC, adopted Socket Ethernnet communicaation based on TCP/IP protoccol. TCP full-dduplex data cannnel to ensure real-time data eexchange as well as immprove system reliability andd security. We tested the commmunication syystem using sppecially develooped test software, thee test results show that the sysstem can realizze the communnication in an eefficient, safe aand stable way between PLC, PC andd CCC, keep thhe real-time conntrol to PLC annd colony inforrmation collecttion.
TWT transmitter fault prediction based on ANFIS
Show abstract
Fault prediction is an important component of health management, and plays an important role in the reliability guarantee of complex electronic equipments. Transmitter is a unit with high failure rate. The cathode performance of TWT is a common fault of transmitter. In this dissertation, a model based on a set of key parameters of TWT is proposed. By choosing proper parameters and applying adaptive neural network training model, this method, combined with analytic hierarchy process (AHP), has a certain reference value for the overall health judgment of TWT transmitters.
Non-linear phase noise processing method in thin film measurement with the frequency domain white light microscopic interferometry
Show abstract
Based on the frequency domain white light microscopic interferometry, this paper provides a non-linear phase noise reduction method to effectively increase the accuracy in the measurement of the thin film thickness, with the Linnik type system structure. This paper firstly outlines the system structure and the basic principles for the measurement of the thin film thickness, and explains the major non-linear phases component in the interference spectrum signal and their sources in detail, including those from the thin film itself and the non-linear phase noise from the effective thickness of beam splitter prism and the mismatch between the two objectives for the system. To mitigate such effect of noise, this paper corrects the effect of the non-linear phase noise on the measurement resulting from effective thickness based on the wavelength correction theory, and proposes the method for extracting the non-linear phase noise from the mismatch between two objectives. Finally, the extraction of non-linear phase noise is conducted by the experiment based on the above method. And the standard thin film verification test for the thickness measurement demonstrates that both the wavelength correction theory and the extraction method of non-linear phase noise can effectively increase the accuracy of the measurement.
Optical second harmonic generation in thin-films of MoS2
Show abstract
Our experiment shows that monolayer molybdenum disulfide (ML MoS2) has big second-order non-linear susceptibility, which allows strong second harmonic generation. As the SHG has relations with the structure of crystal, we can identify the crystal’s orientation by detecting the angle dependency of SHG. We rotate the MoS2 and find the intensity of SHG has a period of 60° , which follows the symmetry of structure. At last, to detect the integrality of crystal, we perform two SHG mappings.
An intelligent identification algorithm for the monoclonal picking instrument
Show abstract
The traditional colony selection is mainly operated by manual mode, which takes on low efficiency and strong subjectivity. Therefore, it is important to develop an automatic monoclonal-picking instrument. The critical stage of the automatic monoclonal-picking and intelligent optimal selection is intelligent identification algorithm. An auto-screening algorithm based on Support Vector Machine (SVM) is proposed in this paper, which uses the supervised learning method, which combined with the colony morphological characteristics to classify the colony accurately. Furthermore, through the basic morphological features of the colony, system can figure out a series of morphological parameters step by step. Through the establishment of maximal margin classifier, and based on the analysis of the growth trend of the colony, the selection of the monoclonal colony was carried out. The experimental results showed that the auto-screening algorithm could screen out the regular colony from the other, which meets the requirement of various parameters.
Induced subgraph searching for geometric model fitting
Show abstract
In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the “qualified” subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.
Survey of computer vision technology for UVA navigation
Show abstract
Navigation based on computer version technology, which has the characteristics of strong independence, high precision and is not susceptible to electrical interference, has attracted more and more attention in the filed of UAV navigation research. Early navigation project based on computer version technology mainly applied to autonomous ground robot. In recent years, the visual navigation system is widely applied to unmanned machine, deep space detector and underwater robot. That further stimulate the research of integrated navigation algorithm based on computer version technology. In China, with many types of UAV development and two lunar exploration, the three phase of the project started, there has been significant progress in the study of visual navigation. The paper expounds the development of navigation based on computer version technology in the filed of UAV navigation research and draw a conclusion that visual navigation is mainly applied to three aspects as follows.(1) Acquisition of UAV navigation parameters. The parameters, including UAV attitude, position and velocity information could be got according to the relationship between the images from sensors and carrier’s attitude, the relationship between instant matching images and the reference images and the relationship between carrier’s velocity and characteristics of sequential images.(2) Autonomous obstacle avoidance. There are many ways to achieve obstacle avoidance in UAV navigation. The methods based on computer version technology ,including feature matching, template matching, image frames and so on, are mainly introduced. (3) The target tracking, positioning. Using the obtained images, UAV position is calculated by using optical flow method, MeanShift algorithm, CamShift algorithm, Kalman filtering and particle filter algotithm. The paper expounds three kinds of mainstream visual system. (1) High speed visual system. It uses parallel structure, with which image detection and processing are carried out at high speed. The system is applied to rapid response system. (2) The visual system of distributed network. There are several discrete image data acquisition sensor in different locations, which transmit image data to the node processor to increase the sampling rate. (3) The visual system combined with observer. The system combines image sensors with the external observers to make up for lack of visual equipment. To some degree, these systems overcome lacks of the early visual system, including low frequency, low processing efficiency and strong noise. In the end, the difficulties of navigation based on computer version technology in practical application are briefly discussed. (1) Due to the huge workload of image operation , the real-time performance of the system is poor. (2) Due to the large environmental impact , the anti-interference ability of the system is poor.(3) Due to the ability to work in a particular environment, the system has poor adaptability.
Optical measuring system for the geometrical parameters of Rockwell and Vickers diamond hardness indenters
Show abstract
Hardness testing is widely used for characterizing the mechanical properties of materials. However, the measured hardness values in hardness measurements are strongly influenced by the geometrical parameters of diamond hardness indenters. In the most severe case, the geometrical error of the diamond indenter, Rockwell hardness measurements in particular, leads to be about 50% hardness measurement uncertainty. It has been generally recognized for many years that the geometry of diamond indenters must be calibrated or verified before use to correct the hardness value for each indenter and improve the hardness measurement uncertainty. The contact-based calibration methods and the contactless based optical measuring methods are two typical ways to calibrate the geometrical form of an indenter at present. The contact-based calibration methods characterized by large measurement range of tens of mm with nanometer resolution, has a time-consuming measurement process, the contactless based optical measuring methods have become a general trend. In this work, an optical measuring system, which employs the combination of an interferometric microscope and a profile projection technique, is presented to measure and calibrate the geometrical parameters of Rockwell and Vickers diamond hardness indenters in National Institute of Metrology of China. Initial experiments demonstrated that the angle and axis angle measurement of indenter are achieved with accuracy of 0.1°, the straightness deviation of Rockwell indenters is less than 2μm, the radius measurement uncertainty of the tip of Rockwell indenters is better than 5μm.
An improved self-correct algorithm for pavement texture
Show abstract
Pavement texture has great influence in terms of road safety. Until recently, laser distance measuring technique that can measure pavement texture depth has become available. Compared with the volumetric patch technique which are now widely used, the laser distance measuring is a relatively new technology. This method has certain applications in the world. Through a large number of experiments, the researchers found that the accuracy of many instruments has not been high enough to fulfill the requirement. Local anomaly is the main factor of the accuracy in the distance measurement. This paper presents an improved self-correct algorithm for texture depth. The objective is to analyze the improved self-correct algorithm used in vehicle bearing road laser texture-meter for pavement texture depth evaluation carried out under ordinary testing conditions, referring to the Chinese standards in pavement texture depth. All pavement texture measurements were performed on four selected road pavements with different texture depth. The novel approach obtained a complete and consistent three-dimensional model representation from road surface scans, using three-dimensional line-scan technology. The four selected road pavements measured with 100 vehicle bearing road laser texture-meters respectively. The improved self-correct algorithm was applied to a vehicle bearing road laser texture-meter. The improved self-correct algorithm reduced the indication error of the general algorithm. The manufacturers can adjust the parameters according to the result, so that it can improve the reliability of the instruments.
Design of an adaptive regulator for an automated microscope stage
Show abstract
Although traditional optical microscopes have simple structures, it plays an important role in the fields of scientific research and laboratories. Microscope Stage is an important part of the microscope and the traditional stage is adjusted manually by operator, which makes the stage not arrive at the desired position accurately and smoothly due to human errors. In order to alleviate this problem, an adaptive regulator for an automated microscope stage is proposed to control the stage movement online to reach the operator desired position in this paper. First, the base controller is designed to stabilize the closed loop system, then the base controller is parameterized with Q parameters in a set of all stabilizing controller and the design of the regulator is formulated within a Youla-Kucera parameterized set of stabilizing controllers and an adaptive algorithm is designed to search online the optimal Q parameters in the controller in order to make the stage move to the desired position of operator that can achieve regulation against the unknown exogenous signals and provide the desired movement trajectory. Finally, the simulation model is built and the performance of the designed controller is verified through simulation. The results illustrate that the capability of the designed regulator for an automated microscope stage to make the stage move to the desired position smoothly and precisely and eliminate the external disturb signals. It is proved effectively that this method can be used for position adjustment of automatic microscope stage.
Novel dual-probes atomic force microscope for line width measurements
Show abstract
Dual-probe Atomic Force Microscope (AFM) can effectively eliminate the influence of the probe size on measurement of the line width, and realize true three-dimensional measurement. Novel dual-probe AFM consists of probe system, scanning system, alignment system and displacement measurement system. As displacement measurement system, the interferometers are added to the novel dual-probes AFM. In order to simplify the dual-probe AFM structure, self-sensing tuning fork probe is used. Measurement method has two steps: the first step is to align two probes and obtain the reference point; the second step is to scan two sides of measured line by two probes separately, and calculate the line width value according to the reference point. In the alignment of two probes, the alignment method is improved by using the edge alignment and the feedback scanning alignment.
Moving object detection in video satellite image based on deep learning
Show abstract
Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.
Estimation for aerial detection effectiveness with cooperation efficiency factors of early-warning aircraft in early-warning detection SoS under BSC framework
Show abstract
In the military field, the performance evaluation of early-warning aircraft deployment or construction is always an important problem needing to be explored. As an effective approach of enterprise management and performance evaluation, Balanced Score Card (BSC) attracts more and more attentions and is studied more and more widely all over the world. It can also bring feasible ideas and technical approaches for studying the issue of the performance evaluation of the deployment or construction of early-warning aircraft which is the important component in early-warning detection system of systems (SoS). Therefore, the deep explored researches are carried out based on the previously research works. On the basis of the characteristics of space exploration and aerial detection effectiveness of early-warning detection SoS and the cardinal principle of BSC are analyzed simply, and the performance evaluation framework of the deployment or construction of early-warning aircraft is given, under this framework, aimed at the evaluation issue of aerial detection effectiveness of early-warning detection SoS with the cooperation efficiency factors of the early-warning aircraft and other land based radars, the evaluation indexes are further designed and the relative evaluation model is further established, especially the evaluation radar chart being also drawn to obtain the evaluation results from a direct sight angle. Finally, some practical computer simulations are launched to prove the validity and feasibility of the research thinking and technologic approaches which are proposed in the paper.
High order sum and difference of axial neighborhood algorithm for subpixel edge localization
Show abstract
Affected by the point spread function of optical microscopic imaging system, the edge of microscopic structure and target becomes smooth, at the same time the edge pixel contour distortion is serious because of noise. These factors make positioning precision reduced by using the traditional edge detection algorithm. Thus combining direction information measure and moment invariant theory, the paper puts forward edge detection algorithm of sum and difference of axial neighborhood, and then formulates the high order sum and difference of axial neighborhood to localize sub-pixel edge by using high-order spatial gray moment. Through artificial simulated image the algorithm is test, results show it has stronger antinomies' ability and high positioning accuracy. The algorithm is used in measurement experiment for line width of 1.272μm, the uncertainty is only 0.067μm. This shows that the algorithm reached high accuracy for measurement.
Design method of LED rear fog lamp based on freeform micro-surface reflectors
Show abstract
We propose a practical method for the design of a light-emitting diode (LED) rear fog lamp based on freeform micro-surface reflectors. The lamp consists of nine LEDs and each of them has a freeform micro-surface reflector correspondingly. The micro-surface reflector design includes three steps. An initial freeform reflector is first built based on the light energy maps. The micro-surface reflector is then constructed on the bias of the initial one. Finally, a two-step method is designed to optimize the micro-surface reflector. With the proposed method, a module is designed and LCW DURIS E5 LED source whose emitting surface is 5.7 mm × 3.0 mm is adopted for simulation. A prototype is also assembled and fabricated to verify the real performance. Both the simulation and experimental results demonstrate that the luminous intensity distribution can well fulfill the requirements of ECE No.38 regulation. Furthermore, more than 79% energy can be saved when compared with the rear fog lamps using conventional sources.
Influence of atmospheric turbulence on Lidar performance
Guo-bei Chai,
Xiao Sun,
Jian Yang,
et al.
Show abstract
In the interference analysis of LIDAR system, atmospheric turbulence model is indispensable. To improve the accuracy of atmospheric effects in the LADAR simulator, Exponential Weibull model is adopted to calculate atmospheric turbulence, achieving a physically-based simulation of a LADAR system integrated with quantitative atmospheric turbulence. The feasibility of the proposed method is verified by comparing simulated and field data. To evaluate LIDAR performance in complex environments, the method of analyzing the system performance based on a general simulation framework is proposed. A general and systematic physically reasonable imaging LADAR simulation model combining "laser - target - atmosphere: LADAR imaging” is achieved for assessment of LADAR imaging system. Experimental results show that the turbulence can cause energy dispersion, leading to the detection of false alarm