This PDF file contains the front matter associated with SPIE Proceedings Volume 10395, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

We expect commercial high resolution imaging systems, which are able to provide data with 25cm ground sample distance (GSD) or better in the near future. For selling the data, it is necessary to re-sample it to 30cm. The situation is similar when swinging out the satellite perpendicular to his ight direction. The GSD is then variable with the angle to Nadir direction. In this paper a method is proposed that the resolution adjusts adaptively according to the requirements.

An algorithm to generate ronchigrams of parabolic concave mirrors is proposed. Unlike the conventional direct ray-tracing method, which produces scattered pixels, the proposed algorithm returns regularly sampled images. Thus, the proposed algorithm is fully compatible with further fringe processing tasks such as phase demodulation and wavefront analysis. The theoretical principles of our proposal are explained in detail, and the programming code is provided. Several computer experiments highlight the performance and advantages of our proposal.

Digital cameras are used in a large variety of scientific and industrial applications. For most applications, the acquired data should represent the real light intensity per pixel as accurately as possible. However, digital cameras are subject to physical, electronic and optical effects that lead to errors and noise in the raw image. Temperature- dependent dark current, read noise, optical vignetting or different sensitivities of individual pixels are examples of such effects. The purpose of radiometric calibration is to improve the quality of the resulting images by reducing the influence of the various types of errors on the measured data and thus improving the quality of the overall application. In this context, we present a specialized neural network architecture for radiometric calibration of digital cameras. Neural networks are used to learn a temperature- and exposure-dependent mapping from observed gray-scale values to true light intensities for each pixel. In contrast to classical at-fielding, neural networks have the potential to model nonlinear mappings which allows for accurately capturing the temperature dependence of the dark current and for modeling cameras with nonlinear sensitivities. Both scenarios are highly relevant in industrial applications. The experimental comparison of our network approach to classical at-fielding shows a consistently higher reconstruction quality, also for linear cameras. In addition, the calibration is faster than previous machine learning approaches based on Gaussian processes.

On-orbit Modulation Transfer Function (MTF) is an important indicator to evaluate the performance of the optical remote sensors in a satellite. There are many methods to estimate MTF, such as pinhole method, slit method and so on. Among them, knife-edge method is quite efficient, easy-to-use and recommended in ISO12233 standard for the wholefrequency MTF curve acquisition. However, the accuracy of the algorithm is affected by Edge Spread Function (ESF) fitting accuracy significantly, which limits the range of application. So in this paper, an optimized knife-edge method using Powell algorithm is proposed to improve the ESF fitting precision. Fermi function model is the most popular ESF fitting model, yet it is vulnerable to the initial values of the parameters. Considering the characteristics of simple and fast convergence, Powell algorithm is applied to fit the accurate parameters adaptively with the insensitivity to the initial parameters. Numerical simulation results reveal the accuracy and robustness of the optimized algorithm under different SNR, edge direction and leaning angles conditions. Experimental results using images of the camera in ZY-3 satellite show that this method is more accurate than the standard knife-edge method of ISO12233 in MTF estimation.

A holographic display method based on complex wavefront modulation using single spatial light modulator is proposed. The holographic display is achieved from complex wavefront encoded by double phase hologram. The modulated beam by single phase-only spatial light modulator passes through a 4f optical system to synthesize the expected complex modulated wavefront on the output plane, with a low-pass filter in the Fourier plane. The performance of holographic display is also improved by complex wavefront modulation, compared with the holographic display based on phase-only wavefront modulation. The proposed encoding and display technique is theoretically demonstrated, as well as validated in numerical simulations.

Recently we demonstrated a novel and simplified model enabling to calculate the voltage dependent retardance provided by parallel aligned liquid crystal devices (PA-LCoS) for a very wide range of incidence angles and any wavelength in the visible. To our knowledge it represents the most simplified approach still showing predictive capability. Deeper insight into the physics behind the simplified model is necessary to understand if the parameters in the model are physically meaningful. Since the PA-LCoS is a black-box where we do not have information about the physical parameters of the device, we cannot perform this kind of analysis using the experimental retardance measurements. In this work we develop realistic simulations for the non-linear tilt of the liquid crystal director across the thickness of the liquid crystal layer in the PA devices. We consider these profiles to have a sine-like shape, which is a good approximation for typical ranges of applied voltage in commercial PA-LCoS microdisplays. For these simulations we develop a rigorous method based on the split-field finite difference time domain (SF-FDTD) technique which provides realistic retardance values. These values are used as the experimental measurements to which the simplified model is fitted. From this analysis we learn that the simplified model is very robust, providing unambiguous solutions when fitting its parameters. We also learn that two of the parameters in the model are physically meaningful, proving a useful reverse-engineering approach, with predictive capability, to probe into internal characteristics of the PA-LCoS device.

The problem of particle field diagnosing using holography can be met in many areas. But single frame hologram can only catch one moment of the fast event, which can’t reveal the change process of an unrepeatable fast event. For events in different time-scale, different solution should be used. We did this work to record a laser induced particle field in the time-scale of tens of micron seconds. A laser of pulse sequence mode is applied to provide 10 pulses, the energy and time interval of whom is 150mJ and 1μs. Four pockels cells are employed to pick up the last four pulses for holographic recording, the other pulses are controlled to pre-expose the photopolymer based recording material, which can enhance photosensitivity of the photopolymer during the moment of holographic recording. The angular multiplexing technique and volume holography is accepted to avoid shifting the photopolymer between each shot. Another Q-switch YAG laser (pulse energy 100mJ, pulse width 10ns) is applied to produce the fast event. As a result, we successfully caught the motion process of the laser induced particle field. The time interval of each frame is 1μs, the angular range of the four references is 14°, and the diffraction efficiency of each hologram is less than 2%. After a basic analysis, this optical system could catch more holograms through a compact design.

Structured light projection is one of the most useful methods for accurate three-dimensional scanning. Video projectors are typically used as the illumination source. However, because video projectors are not designed for structured light systems, some considerations such as gamma calibration must be taken into account. In this work, we present a simple method for gamma calibration of video projectors. First, the experimental fringe patterns are normalized. Then, the samples of the fringe patterns are sorted in ascending order. The sample sorting leads to a simple three-parameter sine curve that is fitted using the Gauss-Newton algorithm. The novelty of this method is that the sorting process removes the effect of the unknown phase. Thus, the resulting gamma calibration algorithm is significantly simplified. The feasibility of the proposed method is illustrated in a three-dimensional scanning experiment.

Electronic portal imaging devices (EPIDs) are x-ray detector systems conventionally used for medical imaging applications in cancer radiotherapy. Our group has developed a novel prototype EPID with the unique capability of performing both imaging and dose measurements. Our prototype utilizes an array of plastic scintillating fibers in place of the standard copper and gadolinium dioxysulfide phosphor components¹.

While our prototype EPID exhibits a detective quantum efficiency that exceeds that of commercial products, there is further scope for improvement. In particular, there is scope to improve optical coupling between the scintillating fiber array and the underlying photodetector where currently an air gap exists. Here, we investigate the effect of a layer of polystyrene nanofibers placed at the end interface of the scintillator array on light extraction efficiency using finite element modelling. We demonstrate that the total light extraction, which depends on the polarization of the incident light, can be enhanced by up to 14%.

This enhancement stems from two effects: Bragg diffraction arising from the periodic arrangement of the fibers and Whispering Gallery Modes (WGMs) formed at each fiber’s cross-section due to Mie resonances. We show that the nanofibers increase optical transmittance above the critical angle. Moreover, we demonstrate that the light extraction efficiency strongly depends on the polarization of the incident light (s- and p-polarizations), as well as the diameter and periodicity of the nanofibers.

Image restoration consists in retrieving an original image by processing captured images of a scene which are degraded by noise, blurring or optical scattering. Commonly restoration algorithms utilize a single monocular image of the observed scene by assuming a known degradation model. In this approach, valuable information of the three dimensional scene is discarded. This work presents a locally-adaptive algorithm for image restoration by employing stereo vision. The proposed algorithm utilizes information of a three-dimensional scene as well as local image statistics to improve the quality of a single restored image by processing pairs of stereo images. Computer simulations results obtained with the proposed algorithm are analyzed and discussed in terms of objective metrics by processing stereo images degraded by optical scattering.

As the U.S. Navy, Army, and Special Operations Forces progress towards fielding more advanced uniforms with multi-colored and highly detailed camouflage patterning, additional test methodologies are necessary in evaluating color in these types of camouflage textiles. The apparent color is the combination of all visible wavelengths (380-760 nm) of light reflected from large (≥1m² ) fabric sample sizes for a given standoff distance (10-25ft). Camouflage patterns lose resolution with increasing standoff distance, and eventually all colors within the pattern appear monotone (the “apparent color” of the pattern). This paper presents an apparent color prediction model that can be used for evaluation of camouflage fabrics.

Image restoration is a classic problem in image processing. Image degradations can occur due to several reasons, for instance, imperfections of imaging systems, quantization errors, atmospheric turbulence, relative motion between camera or objects, among others. Motion blur is a typical degradation in dynamic imaging systems. In this work, we present a method to estimate the parameters of linear motion blur degradation from a captured blurred image. The proposed method is based on analyzing the frequency spectrum of a captured image in order to firstly estimate the degradation parameters, and then, to restore the image with a linear filter. The performance of the proposed method is evaluated by processing synthetic and real-life images. The obtained results are characterized in terms of accuracy of image restoration given by an objective criterion.

Phase shifting techniques are often limited in digital photoelasticity by the quantity of acquisitions they require, and the process to perform them. This work simplifies such process by developing only a part of the acquisitions, and the rest are generated computationally. Our proposal was validated for a six-acquisition method by generating synthetic images from the analytical model of a disk under diametric compression. The results show that although our method uses less acquisitions, it is capable to recover the stress field with similar performance than conventional methods. This proposal could be useful for evaluating dynamic cases because the reduction of the exposure time expended during the acquisition stage.

A reliable approach for object segmentation based on template-matching filters is proposed. The system employs an adaptive strategy for the generation of space-variant filters which take into account several versions of the target and local statistical properties of the input scene. Moreover, the proposed method considers the geometric modifications of the target while is moving through a video sequence. The detection accuracy of the matched filter brings the location of the target of interest. The estimated location coordinates are used to compute the support area covered by the target using watershed segmentation technique. In each frame, the filter adapts according the geometrical changes of the target in order to estimate its current support region. Experimental tests carried out in a video sequence show that the proposed system yields a very good performance for accuracy detection, and object segmentation efficiency in real-life scenes.

The information density in hyperspectral data is not uniform across the spectral and spatial dimensions, and the overall information sparsity is often high. While these non-uniformities underpin the sought-after image contrast, high sparsity generates unnecessarily long acquisition and data processing times. Conventional reduction techniques like those based on principal components analysis (PCA) sacrifice the contributions of minority pixel populations while retaining those representing a greater portion of the overall variability. The effect is that some regions in the reconstructed images achieve a higher degree of recovery than other locations, making it difficult to assess the meaning or relevance of the minority pixels, even when this information would reveal important sample defects or spectral inhomogeneities. In the work presented here, we introduce a novel user-unassisted data reduction and image segmentation method called reduction of spectral images (ROSI). The aim of ROSI is to achieve a threshold information density in the spectral dimension for all image pixels. The result effectively segments the image in a manner that provides rapid image contrast that is comparable to traditionally classified images, but does so without a priori information. In addition, ROSI results are suitable for subsequent data analysis and enable ROSI to be performed alone or as a preprocessing data reduction step. A full description of ROSI is presented along with results from both model and real hyperspectral data, and its performance is compared quantitatively to conventional class of data reduction methods.

A visual approach in environment recognition for robot navigation is proposed. This work includes a template matching filtering technique to detect obstacles and feasible paths using a single camera to sense a cluttered environment. In this problem statement, a robot can move from the start to the goal by choosing a single path between multiple possible ways. In order to generate an efficient and safe path for mobile robot navigation, the proposal employs a pseudo-bacterial potential field algorithm to derive optimal potential field functions using evolutionary computation. Simulation results are evaluated in synthetic and real scenes in terms of accuracy of environment recognition and efficiency of path planning computation.

Deep-learning methods are gaining popularity because of their state-of-the-art performance in image classification tasks. In this paper, we explore classification of laser-beam images from the National Ignition Facility (NIF) using a novel deeplearning approach. NIF is the world’s largest, most energetic laser. It has nearly 40,000 optics that precisely guide, reflect, amplify, and focus 192 laser beams onto a fusion target. NIF utilizes four petawatt lasers called the Advanced Radiographic Capability (ARC) to produce backlighting X-ray illumination to capture implosion dynamics of NIF experiments with picosecond temporal resolution. In the current operational configuration, four independent short-pulse ARC beams are created and combined in a split-beam configuration in each of two NIF apertures at the entry of the pre-amplifier. The subaperture beams then propagate through the NIF beampath up to the ARC compressor. Each ARC beamlet is separately compressed with a dedicated set of four gratings and recombined as sub-apertures for transport to the parabola vessel, where the beams are focused using parabolic mirrors and pointed to the target. Small angular errors in the compressor gratings can cause the sub-aperture beams to diverge from one another and prevent accurate alignment through the transport section between the compressor and parabolic mirrors. This is an off-normal condition that must be detected and corrected. The goal of the off-normal check is to determine whether the ARC beamlets are sufficiently overlapped into a merged single spot or diverged into two distinct spots. Thus, the objective of the current work is three-fold: developing a simple algorithm to perform off-normal classification, exploring the use of Convolutional Neural Network (CNN) for the same task, and understanding the inter-relationship of the two approaches. The CNN recognition results are compared with other machine-learning approaches, such as Deep Neural Network (DNN) and Support Vector Machine (SVM). The experimental results show around 96% classification accuracy using CNN; the CNN approach also provides comparable recognition results compared to the present feature-based off-normal detection. The feature-based solution was developed to capture the expertise of a human expert in classifying the images. The misclassified results are further studied to explain the differences and discover any discrepancies or inconsistencies in current classification.

Path planning for autonomous vehicles is a challenging computer vision problem. In this work, we propose an algorithm to generate dynamically a smooth path for trajectory guidance of an autonomous vehicle. For this, we use B-spline curves and the perspective-distorted images obtained from an onboard camera. The theoretical principles of the algorithm are presented in detail. Preliminary results obtained with an experimental prototype are shown.

In this study we present a novel approach for road mark detection and recognition based on the commercial VIAPIX® module. The proposed approach combines two different techniques, an optical one based on correlation and a numerical technique based on the linear SVM (Support Vector Machine) classifier using HOG (Histogram of Gradient) as descriptor. The first step of our proposed approach consists to applying an inverse perspective mapping of the image acquired by the VIAPIX® module. Then, white color segmentation is applied in order to detect all road marks on the road. Next, a classification of the detected objects is performed using the correlation technique. Finally, the linear SVM technique is used for validating the recognized objects.

In this paper will be presented new opportunities for developing innovative solutions for semantic pattern classification and visual cryptography, which will base on cognitive and bio-inspired approaches. Such techniques can be used for evaluation of the meaning of analyzed patterns or encrypted information, and allow to involve such meaning into the classification task or encryption process. It also allows using some crypto-biometric solutions to extend personalized cryptography methodologies based on visual pattern analysis. In particular application of cognitive information systems for semantic analysis of different patterns will be presented, and also a novel application of such systems for visual secret sharing will be described. Visual shares for divided information can be created based on threshold procedure, which may be dependent on personal abilities to recognize some image details visible on divided images.

In an earlier study [Opt. Express 22, 22349-22368 (2014)], a compression and encryption method that simultaneous compress and encrypt closely resembling images was proposed and validated. This multiple-image optical compression and encryption (MIOCE) method is based on a special fusion of the different target images spectra in the spectral domain. Now for the purpose of assessing the capacity of the MIOCE method, we would like to evaluate and determine the influence of the number of target images. This analysis allows us to evaluate the performance limitation of this method. To achieve this goal, we use a criterion based on the root-mean-square (RMS) [Opt. Lett. 35, 1914-1916 (2010)] and compression ratio to determine the spectral plane area. Then, the different spectral areas are merged in a single spectrum plane. By choosing specific areas, we can compress together 38 images instead of 26 using the classical MIOCE method. The quality of the reconstructed image is evaluated by making use of the mean-square-error criterion (MSE).

Low light level video is an important method of observation under low illumination condition, but the SNR of low light level video is low, the effect of observation is poor, so the noise reduction processing must be carried out. Low light level video noise mainly includes Gauss noise, Poisson noise, impulse noise, fixed pattern noise and dark current noise. In order to remove the noise in low-light-level video effectively, improve the quality of low-light-level video. This paper presents an improved time domain recursive filtering algorithm with three dimensional filtering coefficients. This algorithm makes use of the correlation between the temporal domain of the video sequence. In the video sequences, the proposed algorithm adaptively adjusts the local window filtering coefficients in space and time by motion estimation techniques, for the different pixel points of the same frame of the image, the different weighted coefficients are used. It can reduce the image tail, and ensure the noise reduction effect well. Before the noise reduction, a pretreatment based on boxfilter is used to reduce the complexity of the algorithm and improve the speed of the it. In order to enhance the visual effect of low-light-level video, an image enhancement algorithm based on guided image filter is used to enhance the edge of the video details. The results of experiment show that the hybrid algorithm can remove the noise of the low-light-level video effectively, enhance the edge feature and heighten the visual effects of video.

A self-tuning filter for noise reduction in sinusoidal signals is proposed. Unlike the conventional sine fitting methods, no a priori knowledge of the encoded phase distribution is assumed. For this, an analytical model with three parameters (the average, the amplitude of the sinusoid, and the standard deviation of the noise) is used. The estimated standard deviation is used for adaptively tuning the noise filter. Obtained results show the feasibility of the proposal for fringe pattern normalization.

Recently, visible light communication (VLC) based on light-emitting diodes (LEDs) is considered as a candidate technology for fifth-generation (5G) communications, VLC is free of electromagnetic interference and it can simplify the integration of VLC into heterogeneous wireless networks. Due to the data rates of VLC system limited by the low pumping efficiency, small output power and narrow modulation bandwidth, visible laser light communication (VLLC) system with laser diode (LD) has paid more attention. In addition, orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) is currently attracting attention in optical communications. Due to the non-requirement of cyclic prefix (CP) and time-frequency domain well-localized pulse shapes, it can achieve high spectral efficiency. Moreover, OFDM/OQAM has lower out-of-band power leakage so that it increases the system robustness against inter-carrier interference (ICI) and frequency offset. In this paper, a Discrete Fourier Transform (DFT)-based channel estimation scheme combined with the interference approximation method (IAM) is proposed and experimentally demonstrated for VLLC OFDM/OQAM system. The performance of VLLC OFDM/OQAM system with and without DFT-based channel estimation is investigated. Moreover, the proposed DFT-based channel estimation scheme and the intra-symbol frequency-domain averaging (ISFA)-based method are also compared for the VLLC OFDM/OQAM system. The experimental results show that, the performance of EVM using the DFT-based channel estimation scheme is improved about 3dB compared with the conventional IAM method. In addition, the DFT-based channel estimation scheme can resist the channel noise effectively than that of the ISFA-based method.

We propose a novel method for analyzing images that exploits the natural lines of a human poses to find areas where self-occlusion could be present. Errors caused by self-occlusion cause several modern human pose estimation methods to mis-identify body parts, which reduces the performance of most action recognition algorithms. Our method is motivated by the observation that, in several cases, occlusion can be reasoned using only boundary lines of limbs. An intelligent edge detection algorithm based on the above principle could be used to augment the silhouette with information useful for pose estimation algorithms and push forward progress on occlusion handling for human action recognition. The algorithm described is applicable to computer vision scenarios involving 2D images and (appropriated flattened) 3D images.

Fiber-optic transmission system of analyzed signal is considered to allow signals transmission from optical sources with either impossible or undesirable contact. Diffraction grating spectral device is chosen as investigation system. It should be noted that diffraction grating operates with transmitted light but not reflected. Influence of optical fiber consists in the distortion of wave front incident on the spectral device. Front distortion leads to a broadening of the device spread function in all diffraction orders, and as a consequence, to a deterioration in the device resolution. In this case, the complex spread function is a reaction of the device to the homogeneous plane monochromatic wave which clearly links the input-output of spectral device. Fiber-optic system influence is determined by introducing a fictitious transparency located directly in front of the diffraction grating.

Research of the effect the fiber-optic system has on the spread function of the diffraction grating spectral device is made in two ways. On one hand, mathematical model is proposed to describe the influence of a single-mode optical fiber to a spread function of the diffraction grating spectral device. We performed computer simulations of the analyzed signal transmission from the end of the optical fiber to the photodetector based on the proposed model. The calculations are performed for a single-mode optical fiber with a core diameter of 8 microns. On the other hand, experimental laboratory set up of the diffraction grating spectral device with a fiber optic transmission system is created. Theoretical calculations are compared with the experimental results.

The acousto-optic modulator as an element of the radio and optical signal processing system is considered. The transparency function of the acousto-optic modulator is established and a procedure for its linearization is proposed. A new generalized superposition principle for the transparency function of a pair of acousto-optic modulators, which located close to each other, is introduced. In the framework of computer simulation, the diffraction divergence of acoustic waves in the non-dissipative and dissipative mediums of acousto-optic interaction is investigated. The criterion of difference of diffracted field from rectangle consists in the increase of plane figure radius of inertia, which describing a one-dimensional diffraction acoustic field, from the radius of inertia of rectangle, which describes a non-diffracted field.

During space reconnaissance applications, edge detection from remote sensing imagery plays an important role in the target recognition processing. However, traditional edge detection methods usually only utilize the high-frequency information in one image. Since low-frequency elements may be aliasing with high-frequency parts, the edges extracted may be unconnected under complex topography, different objects and imaging conditions. This paper proposes a novel image edge detection method based on Non-Subsampled Contourlet Transform (NSCT) to keep the object boundary continuously. It transforms the image into Contourlet domain in both high-frequency and low-frequency sub-bands respectively. Depending on the feature of flexible directivity reservation of an image during NSCT, the further edge extraction consists of 3 steps: firstly, the elements of the high-frequency coefficient matrix in Contourlet domain are filtered with high values left using adaptive thresholds. Then the low-frequency edge information is extracted via Canny operator from the low-frequency sub-band information. Finally, to achieve a more consistent edge image, the low-frequency edge image is achieved according to the low-frequency matrix and adopted to compensate the high-frequency image with the isolated noise points eliminated as well. The numerical simulation and practical test results show the higher effectiveness and robustness of the proposed algorithm when comparing with the classical edge detectors, such as Sobel operator, Canny operator, Log operator and Prewitt operator, etc.

The improving of the technology related to the Spatial Light Modulators (SLM), which can be used to modulate the wavefront of a light beam in many different applications in Optics and Photonics, has widespread their use in many new ways. In particular, the continue miniaturization of the pixel size let them be used as a master for Diffractive Optical Elements(DOE) recording applications. One of these displays isthe parallel-addressed liquid crystal on silicon (PA-LCoS) microdisplay, which offers easily the possibility of phase-only modulation without coupled amplitude modulation, but can be use also as an amplitude master just rotating the angles of two polarizers. Together with the DOEs, the optic recording material is also one of the crucial componentsin the system. Photoresist has been used classically for this purpose. Recently some works provide results of the incorporation of photopolymers, initially used for holographic recording, to fabricate DOEs. Among photopolymers, polyvinil alcohol/acrylamide (PVA/AA) materials have been studied firstly due to the accurate control of their optical properties and the ease of fabrication. Nevertheless, this kind of photopolymer presents a high level of toxicity due mainly to the monomer, acrylamide. In this sense, we made efforts to search alternative “green” photopolymers, one of these is called “Biophotopol”. This material presents good optical properties; although, it has two principal drawbacks: its refractive index modulation is lower than the PVA/AA one and the dye used presents very low absorption at 532 nm. In order to solve these problems for recording spherical diffractive lenses, in the present work we have explored different possibilities. On the first place, we have modified the fabrication technique of the solid layer to achieve thicker samples, on the second place, we have introduced a biocompatible crosslinker monomer. These two actions provide us a higher value of the phase modulation capability. On the third place, we have modified the dye to record DOE’s with the wavelength of 532 nm and obtain a direct comparison with the results obtained with PVA/AA materials.

This report is devoted to simulation of speckle field dynamics during coherent light scattering by the cement surface in the process of setting and hardening. Cement particles are represented by the spheres, which sizes and reflection indexes are changing during the hydration process. The study of intensity fluctuations of scattered coherent radiation – it is a technique, that is quite suitable for the analysis not only fast, but also slow processes of mineral binders hydration and polycrystalline structures creation in the process of hardening. The results of simulation are in good agreement with the experimental results.

A compact light detection and ranging (LiDAR) is a system that provides aerosols profile measurements by identifying the aerosol scattering ratio as function of the altitude. The aerosol scattering ratios are used to obtain multiple aerosol intensive ratio parameters known as backscatter color ratio, depolarization ratio, and lidar ratio. The aerosol ratio parameters are known to vary with aerosol type, size, and shape. In this paper, we employed lidar measurements to detect the potential source of the aerosol in the neighborhood of the campus of Old Dominion University. The lidar is employed to collect measurements at several locations in the area of study. Then, the lidar ratio and the color ratio are retrieved from collected measurements. To find the source of aerosol in the measurements, a tracking algorithm is implemented and employed to track the concentration of that pollution in the data. The results show that the source of soot pollution in the area of study is Hampton Blvd, a major street, in the area of the campus where the diesel trucks travel between the ports in the city of Norfolk.

Phase demodulation is an essential image processing stage required by digital fringe projection profilometers. Currently, several approaches for phase demodulation have been proposed. In this work, a set of phase demodulation methods useful for digital fringe projection profilometry is presented. This survey covers fringe pattern normalization, extraction of wrapped phase, and phase unwrapping. Experimental results obtained with a laboratory fringe projection system are presented.

Liquid-crystal on Silicon (LCoS) microdisplays are one of the competing technologies to implement wavelength selective switches (WSS) for optical telecommunications. Last generation LCoS, with more than 4 megapixels, have decreased pixel size to values smaller than 4 microns, what increases interpixel cross-talk effects such as fringing-field. We proceed with an experimental evaluation of a 3.74 micron pixel size parallel-aligned LCoS (PA-LCoS) device. At 1550 nm, for the first time we use time-average Stokes polarimetry to measure the retardance and its flicker magnitude as a function of voltage. We also verify the effect of the antireflection coating when we try to characterize the PA-LCoS out of the designed interval for the AR coating. Some preliminary results for the performance for binary gratings are also given, where the decrease of modulation range with the increase in spatial frequency is shown, together with some residual polarization effects.

In this paper will be presented classification of new cognitive information systems dedicated to cryptographic data splitting and sharing processes. Cognitive processes of semantic data analysis and interpretation, will be used to describe new classes of intelligent information and vision systems. In addition, cryptographic data splitting algorithms and cryptographic threshold schemes will be used to improve processes of secure and efficient information management with application of such cognitive systems. The utility of the proposed cognitive sharing procedures and distributed data sharing algorithms will be also presented. A few possible application of cognitive approaches for visual information management and encryption will be also described.