Image segmentation is the key step for image processing to image analysis. In order to deal with the difficulties in automatic selection and the vast computation of multi- threshold image segmentation，a multi-threshold segmentation algorithm based on Ant Colony is proposed. Compared with traditional multi-threshold segmentation algorithm, the proposed algorithm has better segmentation result and decreases the computation greatly. The experimental results demonstrate that the algorithm given in this paper can cope with multil-threshold image segmentation efficiently and rapidly.

Performance of original color-based MeanShift tracking algorithm decreases drastically under variant illumination environment. To enhance the robustness of the tracking ability under variant illumination environment, an adaptive model MeanShift tracking scheme is proposed in this paper. The statistically approximate LBP texture information is adaptively integrated into the model description to increase the descriptive ability of the model under different illuminating condition. The weighted coefficient of the color information and texture information adjust according to the discriminative ability of the character. Besides, H(Hue) element Gaussian model is introduced for more precisely decription as well as reducing the computational cost of the original histogram-based color model. Experiments on video sequences show the proposed model scheme and advance MeanShift tracking algorithm give effective and robust results in variant illumination condition.

Due to most of the existing condition monitoring systems have a rather complicated structure and the high cost makes even big companies can only afford on a few key equipments, a developing scheme of low-cost micro condition monitoring system based on LabVIEW and SQL Server is proposed in this paper. The low-cost micro condition monitoring system can realize the effective monitoring to general machinery by full taking the advantages of LabVIEW and SQL Server respectively. The system supplements the existing condition monitoring systems to some extent. It affords good applicability and expanding ability, which make it suitable for the equipment management of enterprises for general equipment condition monitoring and health maintenance.

Inverse synthetic aperture radar (ISAR) can image the moving target, especially the target in the air, so it is important in the air defence and missile defence system. Time-frequency Transform was applied to ISAR imaging process widely. Several time frequency transforms were introduced. Noise jamming methods were analysed, and when these noise jamming were added to the echo of the ISAR receiver, the image can become blur even can‟t to be identify. But the effect is different to the different time frequency analysis. The results of simulation experiment show the Performance Comparison of the method.

As the traditional workflow system in privilege management has the following weaknesses: low privilege management efficiency, overburdened for administrator, lack of trust authority etc. A secure workflow model based on PKI/PMI is proposed after studying security requirements of the workflow systems in-depth. This model can achieve static and dynamic authorization after verifying user's ID through PKC and validating user's privilege information by using AC in workflow system. Practice shows that this system can meet the security requirements of WfMS. Moreover, it can not only improve system security, but also ensures integrity, confidentiality, availability and non-repudiation of the data in the system.

Adaptive resource allocation is an important issue in OFDM systems. In this paper, a new bandwidth optimization method to search for all unusable sub-channels is proposed. The new method proposed in this paper can be applied conveniently to different optimization problems (RA or MA) only by changing the initial value of the water-filling level and the formula when update the water-filling level. Simulation results show that the improved algorithm proposed based on the new method also can achieve the optimal solution to that of the well-known optimal algorithm - Greedy algorithm with lower computational complexity.

Cloud computing is one of the most popular topics in the IT industry and is recently being adopted by many companies. It has four development models, as: public cloud, community cloud, hybrid cloud and private cloud. Except others, private cloud can be implemented in a private network, and delivers some benefits of cloud computing without pitfalls. This paper makes a comparison of typical open source platforms through which we can implement a private cloud. After this comparison, we choose Eucalyptus and Wavemaker to do a case study on the private cloud. We also do some performance estimation of cloud platform services and development of prototype software as cloud services.

In recent years, the rapid development of Internet technology and the advent of information age, people are increasing the strong demand for the information products and the market for information technology. Particularly, the network security requirements have become more sophisticated. This paper analyzes the wireless network in the data security vulnerabilities. And a list of wireless networks in the framework is the serious defects with the related problems. It has proposed the virtual private network technology and wireless network security defense structure; and it also given the wireless networks and related network intrusion detection model for the detection strategies.

To cope with a large amount of data in current sensed environments, decision aid tools should provide their understanding of situations in a time-efficient manner, so there is an increasing need for real-time network security situation awareness and threat assessment. In this study, the state transition model of vulnerability in the network based on semi-Markov process is proposed at first. Once events are triggered by an attacker’s action or system response, the current states of the vulnerabilities are known. Then we calculate the transition probabilities of the vulnerability from the current state to security failure state. Furthermore in order to improve accuracy of our algorithms, we adjust the probabilities that they exploit the vulnerability according to the attacker’s skill level. In the light of the preconditions and post-conditions of vulnerabilities in the network, attack graph is built to visualize security situation in real time. Subsequently, we predict attack path, recognize attack intention and estimate the impact through analysis of attack graph. These help administrators to insight into intrusion steps, determine security state and assess threat. Finally testing in a network shows that this method is reasonable and feasible, and can undertake tremendous analysis task to facilitate administrators’ work.

Sinusoidal steady-state field exists widely in power transmission and transformation system and other practical engineering. In the case of two dimensions (2-D), the calculation formula for the maximum of the field vector has been proposed and the study on the endpoint trajectory of field vector has been completed. However, further research on three dimensional (3-D) Sinusoidal steady-state field vector is required to compute the maximum and understand the endpoint trajectory. In this paper, analytical formulas for the maximum and minimum of 3-D sinusoidal steady-state field vector have been deduced based on phasor. It is demonstrated that the endpoint trajectory is an elliptical orbit in 3-D space by coordinate transformation. The availability of the formulas is verified by comparing the numerical method and the formula results. The application of the formulas presented here results in important computation convenience and time savings.

Least squares support vector machine is presented to predict regional electricity consumption in the paper.Least squares support vector machine is a kind of modified support vector machine, the method can use equality constraints for the error instead of inequality constraints which is used in the support vector machine. A certain regional electricity consumption data from 1999 to 2008 are applied to study the regional electricity consumption prediction performance of LSSVM. The least squares support vector machine prediction model of regional electricity consumption is created and the support vector machine model is applied to compare with the least squares support vector machine model.The comparison of relative error between least squares support vector machine prediction model and support vector machine prediction model is given.The experimental result indicates that the proposed model is accurate to predict the electricity consumption.

The mathematical model of the quantum reversible circuits is unitary matrix. Matrix can better reflect the quantum state evolution and further reflect the physical properties of the quantum computation. Matrix transformation based algorithm for 4-qubit reversible logic circuits synthesis is proposed in this paper. The algorithm skillfully uses the matrix representation of the quantum circuit and the circuit of the adjacent matrix to construct 4-qubit circuit with lower cost.

Fitness evaluation is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. But these algorithms may require huge computation power for solving nonlinear programming problems. This paper proposes a novel fitness evaluation approach which employs similarity-base learning embedded in a classical differential evolution (SDE) to evaluate all new individuals. Each individual consists of three elements: parameter vector (v), a fitness value (f), and a reliability value(r). The f is calculated using NFEA, and only when the r is below a threshold is the f calculated using true fitness function. Moreover, applying error compensation system to the proposed algorithm further enhances the performance of the algorithm to make r much closer to true fitness value for each new child. Simulation results over a comprehensive set of benchmark functions show that the convergence rate of the proposed algorithm is much faster than much that of the compared algorithms.

Previous methods on improving translation quality by employing multiple SMT models usually carry out as a second-pass decision procedure on hypotheses from multiple systems using extra features instead of using features in existing models in more depth. In this paper, we propose translation model generalization (TMG), an approach that updates probability feature values for the translation model being used based on the model itself and a set of auxiliary models, aiming to alleviate the over-estimation problem and enhance translation quality in the first-pass decoding phase. We validate our approach for translation models based on auxiliary models built by two different ways. We also introduce novel probability variance features into the log-linear models for further improvements. We conclude our approach can be developed independently and integrated into current SMT pipeline directly. We demonstrate BLEU improvements on the NIST Chinese-to-English MT tasks for single-system decodings.

A novel method to optimize the number of virtual channel buffers is proposed in this paper. More precisely, given the application task graph of a specific application, an ACO (Ant Colony Optimization) algorithm is used during the mapping of tasks to the NoC (Network-on-Chip) such that the number of virtual channel buffers is minimized. The benefit of our method is evaluated by simulation and the simulation results show that our method can achieve 33% reduction in the number of virtual channel buffers compared to the state-of-art method.

To achieve lower energy and higher detection coverage simultaneously in scattering distribution wireless sensor networks (WSNs), a multi-objective optimization function combined with area coverage and node-communication energy is constructed. Based on the multi-objective quantum genetic algorithm (Mo-QGA) proposed by Li Bin and Zhuang-zhen Quan et al, we have obtained optimum solutions close to Pareto front. Experimental results indicate that the Mo-QGA has advantages both on efficiency and coverage, as well as low energy.

In optical burst switching networks, wavelength converters (WCs) of core nodes are used to decrease the burst loss rate. The implementation of the WCs is difficult in the current technology and the cost of WCs is high. So some core nodes may be configured insufficient WCs to reduce the cost in OBS networks. However, many data channel scheduling algorithms do not count the number of WCs and the performance of burst loss rate is not good in the condition of insufficient WCs. To overcome the defect, two novel batch scheduling algorithm with insufficiency of WC are proposed in this paper. The former algorithm improves the WCs’ resource utilization probability to reduce the burst loss rate and the later algorithm saves the WCs’ resource for the incoming bursts to use to improve the burst loss performance. The later algorithm can reduce more burst loss rate with the same number of WCs, compared with the other scheduling algorithms. The simulation results show that the later algorithm is more effective in reducing the burst loss rate with insufficient WCs.

In this paper, we have presented an uncertain database model based on possibility certainty. The idea is to associate every candidate value (or disjunction of such values) representing an ill-known piece of data with a degree expressing the extent to which the candidate value (or disjunction) is certain. We have extended relational algebra in this context and shown that the model constitutes a strong representation system for this set of operators. The only constraints concerns (i) the join which has to be based on an equality condition, (ii) the union, Cartesian product, and join operations which must take independent relations as arguments. An important result is that the data complexity of these operations is the same as in the classical database case.

For How to match supply items with demand items is the most important on technology transaction platform. A matching algorithm based on bipartite graph is proposed in this paper. Firstly, by abstracting characters the suppliers and demanders can be clustered into groups based on bipartite graph joint clustering method. Then, an incidence matrix between items in each group is built which is used to find the optimal matching relation. The simulate experiment results showed that the algorithm can return the item pairs of biggest transaction probability so as to make the Technology transaction platform efficient and profit.

In this paper a new neural network model with weight-function is proposed. In the model, the weight is a function with adjustable parameters, and the sum of these weight functions as the neuron output. And according to BP algorithm, the learning algorithm of feed-forward neural network with weight-function neurons is studied. Simulation results show that, applying the back-propagation algorithm to the new neural network the better convergence rate can be obtained and in some applications the new neural network based on the weight-function neurons is superior to the BP network based on the MP neuron model, so that it has a significant value in further research and application.

At the traditional location problems on networks consider discrete nodal demand. However, accidents may happen at any point in the road. Demand set is the continuum set of all points on a network graph. The issue of general absolute center was studied in this paper. With the assumption that the distance-matrix of the network is available, let | E | be the number of edges of a network. According to the character of the graph of distance function and its graphical nature which reflects the relationship between points and edges, an O(| E |² ⋅lg | E |) algorithm about the location problem of single emergency service was given, so that emergency service emergency response time of the maximum shortest.

Composable modeling method has been a research hotpot in the area of Modeling and Simulation for a long time. In order to increase the reuse and interoperability of BOM based model, this paper put forward a composable modeling method based on BOM, studied on the basic theory of composable modeling method based on BOM, designed a general structure of the coupled model based on BOM, and traversed the structure of atomic and coupled model based on BOM. At last, the paper introduced the process of BOM based composable modeling and made a conclusion on composable modeling method based on BOM. From the prototype we developed and accumulative model stocks, we found this method could increase the reuse and interoperability of models.

A new kind of integrable couplings of soliton hierarchies with self-consistent sources associated with s~l(4) was presented. Based upon this method, a new integrable couplings of Li soliton hierarchy with self-consistent sources was obtained by making use of loop algebra s~l(4). Meanwhile, the Hamiltonian structure of the integrable couplings of Li soliton hierarchy was obtained by making use of variational identity. The method in this study can be applied to other soliton hierarchies with self-consistent sources.

This paper is concerned with an one-dimensional linear beam equation with hinged boundary conditions. This linear beam equation can be transformed into a constant coefficient linear beam equation.

Started from the general wireless channel model, the paper presented an IEEE802.20-protocol-based channel model, based on the analysis of existing MIMO channel models and modeling method. Through simulation and performance analysis, the study obtained both the temporal-domain and frequency-domain characteristics of the model, supporting that the channel model and simulation method presented in this paper can accurately describe the IEEE802.20 channel characteristics.

Audio classification is the basis of content-based audio analysis and retrieval. The conventional classification methods mainly depend on feature extraction of audio clip, which certainly increase the time requirement for classification. An approach for classifying the narrow-band audio stream based on feature extraction of audio frame-level is presented in this paper. The audio signals are divided into speech, instrumental music, song with accompaniment and noise using the Gaussian mixture model (GMM). In order to satisfy the demand of actual environment changing, a universal noise background model (UNBM) for white noise, street noise, factory noise and car interior noise is built. In addition, three feature schemes are considered to optimize feature selection. The experimental results show that the proposed algorithm achieves a high accuracy for audio classification, especially under each noise background we used and keep the classification time less than one second.

The transformer working at the rated state will partically be saturated, and its mangetic current will be distorted accompanying with various of harmonic, increasing reactive power demand and some other affilicated phenomenon, which will threaten the safe operation of power grid. This paper establishes a transformer saturation circuit model of DCbias under duality principle basing on J-A theory which can reflect the hysteresis characteristics of iron core, and develops an software can assess the effects of transformer DC-bias using hybrid programming technology of C#.net and MATLAB with the microsoft.net platform. This software is able to simulate the mangnetizing current of different structures and assess the Saturation Level of transformers and the influnces of affilicated phenomenon accroding to the parameter of transformers and the DC equivalent voltage. It provides an effective method to assess the influnces of transformers caused by magnetic storm disaster and the earthing current of the HVDC project.

In the strong tracking Kalman filter algorithm with multiple suboptimal fading factors, the optimum filter tracking performance cannot been achieved when the forgetting factor in estimation formula of state error covariance matrix takes an inappropriate value. In this paper, an estimation method of error variance matrix on the basis of fuzzy forgetting factor was proposed. Using the fuzzy logic controller to monitor fuzzy similarity coefficient and state estimation variance, this method regulates fuzzy forgetting factor according to fuzzy rules, and then adjusts suboptimal multiple fading factors to improve the tracking precision of the filter in the strong tracking Kalman filter algorithm. The simulation result proves the effectiveness of the algorithm.

Anycast use the same address pool with unicast, but its routing table is difficult to converge for anycast servers’ dispersive location, and this leads to low efficiency of packet forwarding. This paper proposes an algorithm which can decrease the anycast routing table entries count by converge them based on interfaces. Analyses and experiment shows this algorithm performance is improved in packet forwarding. And this algorithm is routing loop-free as discussed in this paper. But it has limitation in network topology that must be applied in point-to-point network but not point-to-multipoint.

It is easy to be detected by X2 and RS steganalysis with high accuracy that using LSB algorithm to hide information in digital image. We started by selecting information embedded location and modifying the information embedded method, combined with sub-affine transformation and matrix coding method, improved the LSB algorithm and a new LSB algorithm was proposed. Experimental results show that the improved one can resist the X2 and RS steganalysis effectively.

In this paper, simulates the nano-TiO_2 spraying device in all terrain vehicle when the vehicle sprayed the solution spilled material trajectory based on MATLAB simulation software, and calculates the spray droplet distribution has a better trajectory when the vehicle’ speed at 2.0m / s, there will be some guidance to correct nozzle and branches of the distribution device. Meanwhile a reasonable solution can be used both to avoid waste of resources.

In this paper, an analysis of the sources of errors in conventional algorithm of synchronous sampling is presented. An alternate synchronous sampling optimization algorithm is proposed, in which the sampling intervals are dynamically adjusted within each signal cycle. A rule for the remainder and rounding is identified to accelerate the execution of the algorithm. Results from experiments show that this approach is effective in reducing the synchronous errors, increasing the precision of measurements, speeding up the execution, and enhancing the real-time capability of the sampling.

This paper is focused on the problem of frame loss and truncation on multi-channel universal asynchronous receiver transmitter (UART) embedded in Integrated Navigation Systems, and it contains attitude heading reference system (AHRS) and global positioning system (GPS). An advanced design based on FPGA and ARM processor is discussed in this paper, in which FPGA would be used to coordinate with each logic modules, expand UART for GPS and AHRS, resolve navigation information, and save specify data to SD card, which can reduce the delay in data receiving and resolving, while ARM is applied in the area of parameters estimation and navigation algorithms. The experiment results show that this navigation system can use UART to receive, resolve data frames and save data while ARM execute parameter estimation and navigation algorithms in real time. This integrated navigation can effectively avoid the phenomenon of data frame loss or truncation in UART receiving, and can improve the navigation precision.

In view on this change of filter performance by the deviation of circuit component parameter values from its design values, the concept of robust optimization design for the passive filter circuit is presented. The function, that is to minimize the ripples and maximal variations of system performance is chosen as the objective function. The optimization strategy by combining random direction searching method with compound optimum was adopted for solving this nonlinear programming problem with two-level optimization. This theory is used on an 800MHz transmitter bandpass filter circuit. By comparing with original design and conventional optimization, passband performance of the robust optimized circuit is more flat and its fluctuation is more small when component parameters change within their rated tolerance. So filter performance of the circuit is improved, and the method mentioned in this paper is effective and superior.

This work presents an ICA-based UHF RFID multi-tag hybrid data blind separation algorithm. After analysis, we find that UHF RFID multi-tag hybrid data is consistent with the requirements of ICA algorithm. Simulated experimental results show that excellent results can be obtained by using ICA techniques in blind separating of tags data. For evaluating the separation performance objectively, a new indicator- the Similarity of Sources and Results (SSR) is defined. The anti- noise performance of this algorithm is analyzed quantitatively too. A good theoretical and experimental basis for applying blind separation technology to UHF RFID tags anti-collision algorithm has been established in this paper.

In the paper, the serial communication in the queue system is discussed. Firstly the data send and receive is research and the routine diagram of send and receive is drawn up; secondly the data frame from main computer to end point is introduced; finally the realization of data process and data integrality is discussed. In all the paper, the realization process of the serial communication in the queue is described.

This paper presents a symbol-by-symbol decoding algorithm of 3GPP MBMS Raptor. We redefine the initial matrix of 3GPP MBMS Raptor, and add some ancillary information to help make up for destruction of linear relationship in matrix caused by advanced Gauss elimination in 3GPP MBMS Raptor. So we can realize a correct decoding by symbolby- symbol, while 3GPP can not. The proposed algorithm is adapted to an erasure channel with large symbols, low code rate, big time delay or high error probability , and it can greatly improve decoding efficiency.

Efficient utilization of energy is a core area of research in wireless sensor networks. Data compression methods to reduce the number of bits to be transmitted by the communication module will significantly reduce the energy requirement and increase the lifetime of the sensor node. Based on the lifting scheme 2-point discrete cosine transform (DCT), this paper proposed a new reversible recursive algorithm named Difference-Median-Difference (DMD) transform for lossless data compression in sensor node. The DMD transform can significantly reduce the spatio-temporal correlations among sensor data and can smoothly run in resource limited sensor nodes. Through an entropy encoder, the results of DMD transform can be compressed more compactly based on their statistical characteristics to achieve compression. Compared with the typical lossless algorithms, the proposed algorithm indicated better compression ratios than others for non-slowly-varying data, despite a less computational effort.

This paper describes SMS4 algorithm by using dynamic partial self-reconfiguration. The design is implemented on Xilinx VirtexII-Pro XC2VP30 FPGA devices. The partial self-reconfiguration encryption/decryption module data throughput is up to 50Mb/s, key expansion and encryption/decryption modules use 1606 and 1570 slices respectively, and the resource utilization ratio of the key expansion by using partial self-reconfiguration technology is less 32.03% and slices are less 757 than the non-reconfiguration technology. SMS4 implementation gets a good balance between high performance and low complexity in area. The theoretical and practical research of dynamic partial self-reconfiguration has a broad space for development and application prospect.

Bluetooth is one of the wireless communication technology, which is developing rapidly in recent years. As a result of low cost and short distance, Bluetooth can set up a special connection for portable electronic devices and stationary electronic equipment communication environment. The paper studies a data acquisition system based on Bluetooth. The system can collect the angle of motor rotation and send it to the Receiver through the Bluetooth. The results show that the system can be run normal.^[1]

Data communication plays a very important role in the hardware in the loop simulation system. The system architecture of periscope semi-physical simulation system is proposed at first. Then the data communication method based on FINS between PLC and PC is introduced, the user’s interaction of scene is achieved by PLC. The communication based on TCP between 2D chart console and scene simulation system is also introduced. The 6-DOF motion model and the scene simulation system is connected by TCP, and a DR method is introduced in solving the data amount problem. The test shows that the simulation system has no error package and no missing in a simulation circle. And can meet the requirements of training, also shows good performance in reliability and real-time.

In a periodic spectrum sensing framework where each frame consists of a sensing duration and a data transmitting duration, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame. Analysis and simulation results reveal that the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.

In this paper, we discuss the problem of sum rate improvement for coded MIMO interference system, and propose joint beam design and user selection over interference channel. Firstly, we have formulated non-binary LDPC coded MIMO interference networks model. Then, the least square beam design for MIMO interference system is derived, and the low complexity user selection is presented. Simulation results confirm that the sum rate can be improved by the joint user selection and beam design comparing with single interference aligning beamformer.

In cognitive radio (CR) networks, Learning Engine has considerable significance on dynamic spectrum access (DSA) and implementation of cognitive function. In this paper, a cross-layer learning engine design scheme is proposed by jointly considering physical-layer dynamic channel selection, modulation and coding scheme, data-link layer frame length in CR networks, with the purpose to maximize system throughput and simultaneously meet heterogeneous Quality of Service (QoS) requirements. The wireless fading channel is modeled as a continuous state space Markov decision process (MDP) and the licensed network activity is abstracted as a finite-state one. We introduce Q-learning algorithm to realize the function of learning from state space and adapt wireless environment. And meanwhile a large scale Qfunction approximator based on support vector machine (SVM) is employed to effectively reduce storage requirement and decrease the operation complexity. A cross-layer learning engine communication platform is realized by using Matlab simulator. the simulation results demonstrate that while lacking system prior knowledge, the learning engine can effectively achieve configuration function by system cross-layer learning approach, and furthermore, it can converge to the best—i.e., realize reconfiguration function in CR networks while meeting users’ QoS.

This paper presents a development of the algorithm for combination of block-merge algorithm and octree algorithm. The memory insufficiency problem, which is caused by heaps of temporary variables when researching pore structure by octree algorithm, has been effectively solved. The pore structure information of cellular metallic material with large area has been extracted, which provides an important method for the study on the relationship between the structure of porous metal material and function. By applying this method to store more data with less memory, an effective and accurate result is achieved. This work would represent a significant advance for research of pore structure using improved octree algorithm.

Wireless sensor network is a field of information technology in recent years. In order to obtain accurate and timely information and to ensure the robustness of sensor network, sensor nodes must monitor to some extent. There is overlap, so the sensor node and the data collected is some redundancy [6]. To avoid this problem, wireless sensor network in the process of data collection need to adopt data fusion (data aggregation or data fusion) technology. This paper introduces wireless sensor network data fusion concepts and roles, and it analyzes the data fusion in wireless sensor networks the major technical challenges. It describes the data fusion that is currently the main model, and finally, we give the wireless sensor networks main fusion. We hope that the contents of this article in the wireless sensor network for the majority of researchers in data integration to provide a reference.

A Differential Evolution Clustering algorithm with weighted validity function is presented in this paper, five validity functions are selected to form the fitness function with weights, and in selection of Differential Evolution, individuals not being selected are put into secondary population. During evolution, individuals in secondary population replace those in main population if their fitness values are less than those in main population. We have carried out experiments on 3 datasets from UCI machine learning repository and compared validity results to those from K-Means and classical Differential Evolution, experimental results show that our approach can improve clustering performance.

With the development of international wireless communication standards, there is an increase in computational requirement for baseband signal processors. Time-to-market pressure makes it impossible to completely redesign new processors for the evolving standards. Due to its high flexibility and low power, software defined radio (SDR) digital signal processors have been proposed as promising technology to replace traditional ASIC and FPGA fashions. In addition, there are large numbers of parallel data processed in computation-intensive functions, which fosters the development of single instruction multiple data (SIMD) architecture in SDR platform. So a new way must be found to prototype the SDR processors efficiently. In this paper we present a bit-and-cycle accurate model of programmable SIMD SDR processors in a machine description language LISA. LISA is a language for instruction set architecture which can gain rapid model at architectural level. In order to evaluate the availability of our proposed processor, three common baseband functions, FFT, FIR digital filter and matrix multiplication have been mapped on the SDR platform. Analytical results showed that the SDR processor achieved the maximum of 47.1% performance boost relative to the opponent processor.

Attribute reduction is an important issue in rough set theory. Many efficient algorithms have been proposed, however, few of them can process huge data sets quickly. In this paper, combining the Trie tree, the algorithms for computing positive region of decision table are proposed. After that, a new algorithm for attribute reduction based on Trie tree is developed, which can be used to process the attribute reduction of large data sets quickly. Experiment results show its high efficiency.

It is popular to access the cloud storage by mobile devices. However, this application suffer data security risk, especial the data leakage and privacy violate problem. This risk exists not only in cloud storage system, but also in mobile client platform. To reduce the security risk, this paper proposed a new security solution. It makes full use of the searchable encryption and trusted computing technology. Given the performance limit of the mobile devices, it proposes the trusted proxy based protection architecture. The design basic idea, deploy model and key flows are detailed. The analysis from the security and performance shows the advantage.

Load modeling is recognized as a difficult issue in field of power system digital simulation. The reliability of the simulation results depends on the veracity of the load model which will further affect power system planning and aid decision making. In order to increase the accuracy of the load model, the composite loads of power consuming-industries were classified by their industry attributes and the components of them were also analyzed in this paper. Then, the mathematical model of load composition is established on the basic of typical daily load profile and the identification algorithm developed by C language is used to identify the parameters of composite loads by choosing the data collected during the corresponding characteristic time period of the typical day. Based on the model vector machine theory and the parameters identified, the parameters of composite load model of power consuming-industries can be calculated by using the way of least square approximation. And the BP neural network was used to forecast the parameters of composite loads of power consuming-industries. Finally, an example shows the validity of the proposed scheme.

This paper focused on analyzing the algorithm of multi-object vehicles detection and picking up in the vision field when the camera is fixed. From the perspective of given condition and environment, combining the key and difficult points, according to the continual vision sequence recorded by camera, discussing and analyzing the several realization algorithms of the multi-moving vehicles detection, extraction, identification and classification. On this basis we present modification and improvement of the relevant algorithms. Therefore in the limit of given condition, effectively increasing the real-time, object, robustness and adaptability multi-moving vehicles detection and extracting.

Three-tank water represents a typical plant with non-linearity and large time delay. By combining the linearization method for non-linear plant, PID control structure and fuzzy control based on T-S model, the self-adaptive fuzzy PID control of the three-tank water is devised. The strategy aims at improving the control performance of the three-tank water by weighing the membership function to produce PID parameters and making parameters vary steadily with the variation of water level . Simulation results show that the control strategy proposed in this paper is correct and effective.

A pseudo 9-bit 10 MSample/s hybrid Analog to Digital Converter (ADC) is proposed for applying to digital power controller. It features its structure that consists of three 3-bit ADCs: a flash ADC and two delay-line based window ADCs. The first one works in the entire voltage range. And the other two only work in the desired voltage window to improve the resolution. The ADC is designed and simulated in TSMC 0.35-μm mixed signal process. Simulation results show that the expected funtions are achieved.

Harmonic analysis based on Fast Fourier Transform (FFT) is usually used for estimating harmonic distortion degree. However, the estimation precision is seriously affected by spectral leakage caused by non-synchronous sampling. To low the spectrum leakage effect and improve the estimation precision, a novel kind of the software frequency measurement and secondary sampling algorithm for approximately synchronous sampling is proposed. And the principle of the proposed algorithm is profoundly investigated. Furthermore, the algorithm error is comprehensively analyzed. The results of the simulation and the application show that the error of the distortion degree can be less than 1% by increasing signal sampling frequency, and the algorithm is feasible, efficient and effective to inhibit the spectrum leakage in harmonic analysis and ensure high accuracy in harmonic analysis and harmonic distortion degree measurement.

In order to improve the efficiency of wireless mesh network transmission, this paper focused on the network coding technology. Using network coding can significantly increase the wireless mesh network’s throughput, but it will inevitably increase the computational complexity to the network, and the traditional linear network coding algorithm requires the aware of the whole network topology, which is impossible in the ever-changing topology of wireless mesh networks. In this paper, we use a distributed network coding strategy: random network coding, which don’t need to know the whole topology of the network. In order to decrease the computation complexity, this paper suggests an improved strategy for random network coding: Do not code the packets which bring no good to the whole transmission. In this paper, we list several situations which coding is not necessary. Simulation results show that applying these strategies can improve the efficiency of wireless mesh network transmission.

This paper proposes a novel recursive algorithm, towards finding all routing paths in an unidirectional flow- network. The algorithm is recursive, so it provides a flexible approach comparing to iterative methods. Initially, all the paths from source to destinations are identified and represented in a two-dimensional matrix. The recursive algorithm uses the concept of blanking patterns, by identifying definite patterns of combinations of rows and columns in the two-dimensional matrix.

Based on 16bit STEREO AUDIO CODEC and C#， this paper introduces a virtual signal analyzer. It mainly describes the system’s overall structure, hardware design, PC software framework, etc. With reducing costs dramatically, the system also achieves being a signal generator, oscilloscope, recorder, spectrum analyzer, time-frequency analyzer and so on.

In this paper, a novel RSSI-based position algorithm is proposed for Mobile ad hoc network. The proposed range-based position scheme is implemented by a blind node and several beacons. We show how to use parameters-tuning model and position-correction model to effectively compute a good estimation of the position of blind node. The results of our method in CC2431 indicate that the calculated distance is closer to the actual values and the position of the blind node can be computed in a small amount of time and that the quality of the solution is competitive with previous approaches.

For traditional radio astronomy instruments, we usually achieve the wide bandwidth by using the single high speed sampling rate analogy to digital convertor (ADC). But this method can lead some problems for processing, such as radio frequency interference (RFI), data transmission and so on. Take advantage of ADCs’ wide input bandwidth, we can also sample observation signals with wide bandwidth using comparatively low speed sampling rate. In this paper, we introduce the under-sampling technology, and then analyze some applications in radio astronomy. Finally, we introduce a new digital spectrometer based on this technology for solar radio observation whose core ADC is AD9430.

In the study, support vector machine optimized by genetic algorithm is applied to evaluate network safety. As the parameters in the support vector machine have a great influence on its evaluation ability. Genetic algorithm is applied to select the optimal combination of the parameters of support vector machine. The evaluation accuracy of GA-SVC is 100% after the testing experiments. The experimental results indicate that SVM has high evaluation accuracy in the evaluation of network safety.

In the study, RBF neural network optimized by particle swarm optimization algorithm is applied to evaluate network safety. In the RBF neural network, the choice of the three parameters including the center of RBF, the width of RBF and the weight have an important influence on the classification performance of RBF neural network. Particle swarm optimization algorithm is used to select the optimal combination of the parameters of the RBF neural network parameters. The experimental results show that the network evaluation model based on PSO-RBF neural network has better evaluation performance than RBF neural network.

Aims at the requirement of real-time processing in Real-Time Automatic Target Recognition(RTATR) system, this paper presents a hardware acceleration based two-scan connected-component labeling algorithm. Conventional pixel and run based algorithm’s merits are combined, in the first scan, the pixel is processed scan unit while line as label unit, label equivalences are recorded while scanning the image by pixel. Lines with provisional label are outputted as the connected component labeling result. Then the union-find algorithm is used for resolving label equivalences and finds the representative label for each provisional label after the first scan. The labels are replaced in the second scan to complete the connected-component labeling. Experiments on RTATR platform demonstrate that the hardware acceleration implementation of algorithm reaches a higher performance and efficiency and consumes few resources. The implementation of proposed algorithm can meet the demand of real-time processing, and possesses a better practicability.

We consider the problem of both fast acquisition and efficient image reconstruction from partial Fourier data due to the missing or compressed information. We investigate the possibility of using different chaotic sequences to construct measurement matrices in Fourier data. In particular, we consider sequences generated by Chen chaotic system. We investigate the accuracy of reconstruction based on our proposed accelerated Dykstra-like proximal algorithm when to use different chaotic systems to construct measurement matrices of sparse or nearly sparse signals in frequency domain. We compare the recovery rate of the different chaotic sequences with Gaussian random sequences. We also investigate the recovery rate on the initial values of the chaotic systems. In practice, the relationship between the structurally sampling matrices controlled by the initial values of the chaotic systems and the structure of the sparse signal is a promising problem to enhance the recovery rate and to perform fast and efficient compressed sensing. The performance of the proposed Chen chaotic compressed sensing is analyzed by using numerical simulation with radio interferometric image and magnetic resonance image.

In Cognitive Radio, because secondary users are completely unknown to the primary users, a statistics algorithm about spectrum sensing is constructed in the paper, which overcomes the shortcoming that the characteristics of the primary users signal and channel are completely unknown to secondary users. The statistic, based on generalized likelihood ratio test (GLRT), is only calculated by means of the received signal. The unknown parameters of the channel are also obtained based on maximum likelihood estimates (MLE) by making use of the sampling signal covariance matrix. Theoretical analysis shows that we can compute the detection probability of primary users with the covariance matrix eigenvalue and the simulation proves that the statistics has the characteristics of the simple calculation and practicability.

An improved watershed image segmentation algorithm is proposed to solve the problem of over-segmentation by classical watershed algorithm. The new algorithm combines region growing with classical watershed algorithm. The key to region growing lies in choosing a growing threshold to reach a desired result of image segmentation. An entropy evaluation criterion is constructed to determine the optimal threshold. Considering the entropy evaluation criterion as an objective function, the particle swarm optimization algorithm is employed to search global optimization of the objective function. Experimental results show that this new algorithm can solve the problem of over-segmentation effectively.

The constant modulus algorithm (CMA) has low computational complexity while presenting slow convergence and possible convergence to local minima, the CMA family of algorithms based on affine projection version (AP-CMA) alleviates the speed limitations of the CMA. However, the computational complexity has been a weak point in the implementation of AP-CMA. To reduce the computational complexity of the adaptive filtering algorithm, a new AP-CMA algorithm based on set membership (SM-AP-CMA) is proposed. The new algorithm combines a bounded error specification on the adaptive filter with the concept of data-reusing. Simulations confirmed that the convergence rate of the proposed algorithm is significantly faster; meanwhile, the excess mean square error can be maintained in a relatively low level and a substantial reduction in the number of updates when compared with its conventional counterpart.

In this paper, we aim at the study on multi-mode transceiver based on software defined radio(SDR). Multi-rate signal processing and polyphase filtering technique are both applied in the design and implementation of the transceiver. Simplified FFT butterfly algorithm has been employed in the polyphase filter design as well. Simulation results illustrate that BER performance can be improved by adopting the SDR proposed in this paper. Especially, it has obvious advantages at low SNR. Meanwhile, improved filter design scheme has much more predominant in-band and out-ofband performance.

The reflectivity level of double-bounce rays of a mini rectangular anechoic chamber with a uniform absorber lining has been investigated by using the geometrical optics approach. The influence of the reflectivity level of the double-bounce rays has been analyzed in Hertzian dipole excitation antennas as compared with that of single-reflection specular rays from the side walls including the ceiling and floor. The result shows that the effect of the double-bounce rays on the total reflectivity level is relatively unimportant and can be ignored at excitation frequency range from 500MHz to 8GHz for the chamber.

Estimating arithmetic is a design paradigm for DSP hardware. By allowing structurally incomplete arithmetic circuits to occasionally perform imprecise calculations, higher performance can be achieved in many different electronic systems. By means of approximate compressor design and bottom-up tree topology, this paper presents a novel approach of implementing high-speed, area-efficient and power-aware multipliers. Experimental results are given to show the applicability and effectiveness of our proposed approach.

In the wire-driven parallel suspension system, because manufacturing and assembling deviations exist, the expected control accuracy can not be reached. A mathematical model of wire-driven parallel manipulators is established. Effects of the deviations eliminated can improve the accuracy of the mathematical model. Particle swarm optimization (PSO) is a robust stochastic evolutionary computation technique, which is very easy to understand and implement. Particle swarm optimization is used to calculate model deviations and find values of the deviations. The results obtained by the particle swarm optimization algorithm can update the mathematical model of the wire-driven parallel manipulators and improve the control accuracy of the wire-driven suspension system.

This paper presents a general control iteration algorithm for a class of chaotic systems. The algorithm contains two iterations, which are performance index function iteration and the corresponding control iteration. In order to prove theoretically, two theorems are proposed, which demonstrates the iteration algorithm is convergent. At last, an example is given to show the effectiveness of the proposed algorithm.

The reconstructions of 3D temperature fields by acoustic tomography are simulated studied. Four planes along the axial of the space to be measured are selected as typical planes. Eight acoustic sensors are amounted on each plane’s periphery. Using the reconstruction algorithm based on radial basis function approximation and Tikhonov regularization we proposed, the 2D temperature fields in the four planes are reconstructed, then the 3D temperature field is reconstructed by interpolation of the 2D images. Reconstructions of three temperature field models by using exact and corrupted time-of-flight data demonstrate that the method proposed in this paper has good ability in complex 3D temperature field reconstruction.

The telecommunication application server (AS) is an important part of the value-added service network in the next generation network(NGN) as the support platform. Remote, unified and secure management to a variety of services is implemented after the remote network management of the telecommunications application server. The network management system based on Simple Network Management Protocol (SNMP) is decided to utilized to analyse and study the network managed objects in AS because of the integration of telecommunications networks and computer networks on the network management and convenient, economical and flexible features of SNMP protocol. The network management agent module of AS is designed and an example is illustrated the details of the model. Results indicate the good performance of the network management agent.

Smoking in public indoor spaces has become prohibited in many countries since it not only affects the health of the people around you, but also increases the risk of fire outbreaks. This paper proposes a novel scheme to automatically detect and recognize smoking events by using exsiting surveillance cameras. The main idea of our proposed method is to detect human smoking events by recognizing their actions. In this scheme, the human pose estimation is introduced to analyze human actions from their poses. The human pose estimation method segments head and both hands from human body parts by using a skin color detection method. However, the skin color methods may fail in insufficient light conditions. Therefore, the lighting compensation is applied to help the skin color detection method become more accurate. Due to the human body parts may be covered by shadows, which may cause the human pose estimation to fail, the Kalman filter is applied to track the missed body parts. After that, we evaluate the probability features of hands approaching the head. The support vector machine (SVM) is applied to learn and recognize the smoking events by the probability features. To analysis the performance of proposed method, the datasets established in the survillance camera view under indoor enviroment are tested. The experimental results show the effectiveness of our proposed method with accuracy rate of 83.33%.

This paper research on tire wear problem of heavy vehicles with Double Front Axle Steering Mechanism from the flexible effect of Steering Mechanism, and proposes a structural optimization method which use both traditional static structural theory and dynamic structure theory – Equivalent Static Load (ESL) method to optimize key parts. The good simulated and test results show this method has high engineering practice and reference value for tire wear problem of Double Front Axle Steering Mechanism design.

Batch crystallization is one of the most important chemical separation unit operations. Due to the complex mechanism and dynamic nature of this process the mathematical model research is a challenging task. In this paper, the authors present research achievement on batch crystallization modeling, simulation, optimization and parameter estimation. Within the proposed control strategy, a dynamic optimization is first preformed with the objective to obtain the optimal cooling temperature policy of a batch crystallizer, maximizing the total volume of seeded crystals. Next, owing to the complex and highly nonlinear behavior of the batch crystallizer, the nonlinear control strategy based on a generic model control (GMC) algorithm is implemented to track the resulting optimal temperature profile.

When high level architecture is used to build complex systems in support of modeling and simulation, the problems are low level of interoperability and poor reusage. Based on the concept of service-oriented and distributed technologies, a service bus of network-centric simulation is proposed. Definite the concept of community of service and service bus, establish the architecture of simulation service bus and its implementation framework, analyze three key technologies of its implementation process. The primary research and practice show that: the proposed simulation service bus can support to run community simulation tasks based on Internet and provide a reference for building a service-oriented environment of network centric simulation.

Some of the current challenges of computer auditing are the obstacles to retrieving, converting and translating data from different database schema. During the last few years, there are many data exchange standards under continuous development such as Extensible Business Reporting Language (XBRL). These XML document standards can be used for data exchange among companies, financial institutions, and audit firms. However, for many companies, it is still expensive and time-consuming to translate and provide XML messages with commercial application packages, because it is complicated and laborious to search and transform data from thousands of tables in the ERP databases. How to transfer transaction documents for supporting continuous auditing or real time auditing between audit firms and their client companies is a important topic. In this paper, a collaborative computer auditing system under SOA-based conceptual model is proposed. By utilizing the widely used XML document standards and existing data transformation applications developed by different companies and software venders, we can wrap these application as commercial web services that will be easy implemented under the forthcoming application environments: service-oriented architecture (SOA). Under the SOA environments, the multiagency mechanism will help the maturity and popularity of data assurance service over the Internet. By the wrapping of data transformation components with heterogeneous databases or platforms, it will create new component markets composed by many software vendors and assurance service companies to provide data assurance services for audit firms, regulators or third parties.

According to taper thread’s tool-setting problem of petroleum drill rod’s repairing, puts forward a method that uses zero-position signal control the pilot lamp outside, then setting-tool by hands, making the old-thread’s repairing job simple, easy and has good result.

Wireless Sensor Networks, an emerging technology, have been used in various fields for monitoring and tracking. Because of its application, the problem of localization is extremely important. Although a lot of algorithms have been presented recently, how to localization is a challenge. This paper describes how to classify localization algorithms and discusses the principles and characteristics of some localization approaches of range-free, such as DV-Hop, APIT, etc. The directions of how to improve DV-Hop algorithm is introduced as well.

As an important part of the Internet of Things, WSN also has routing strategies which plays a critical role as well as it does in Ad hoc. After the introduction of some distinct traits of WSN, a detailed analysis is performed for a typical ondemand routing protocol in Ad hoc AODV, which then is transplanted into WSN. Furthermore, a transition method of adaptive clustering routing is proposed, based on which, a new adaptive hierarchical routing transition protocol (BCRP) is put forward. Finally, simulations and comparisons of BCRP are performed which lead to a conclusion that BCRP has been greatly improved concerning adaptability and extensibility.

Cylinder air tightness is an important indicator to the comprehensive performance of internal combustion engine. It can be got the low-frequency and high-frequency signals of the starting voltage waveform using multi-scale analysis method and by binary discrete wavelet transform with the Mallat algorithm. The experiment results show that the working conditions of diesel engine starting process can be shown from the low-frequency signals, and the main frequency distribution can be recognised from the high frequency partial. This algorithm can effectively identify signal characteristics, and provide a reliable basis for signal feature recognition.

Aiming at geometric rules of trajectory generation, the oblique projection approach is proposed to generating trajectories along arbitrary direction to improve reach-ability and provide more choices for 5-axis machining. First of all, a middle plane is constructed with the normal direction at the center point and passing through the center point of the encapsulating box. Secondly, a set of parallel and equidistant lines in the middle plane is constructed as the source object of oblique projection, and the inclination angles of the line set are uniformed as any value in the range of 0 to 2π. Thirdly, oblique projections are employed to produce projection trajectories on the machined surface. Finally, a user function APathGenerator V1.0 is developed in UG NX. The algorithm is robust and provides a feasible way to control direction angle and trajectory gap for the 5-axis machining of any smooth surfaces whether concave, convex or mixed.

This paper introduces the methods of three-dimensional modeling, assembling and simulating design of an automobile based on 3D engine, Pro/Engineer and 3DSMax. Research is also carried out on the order and the route of virtual assembling as well the corresponding processes.

The Metal Oxide Varistors (MOV), of which the application features have been extensively researched, are widely used in the follow-up lightning protection in the low voltage power distribution system. However, to facilitate the design process, the simulation is always carried out to have preliminary study on the model selection of MOV, and therefore the MOV modeling becomes important. In this paper, based on the PSPICE, two characteristics of MOV, i.e., the V-A characteristic in the low current region and the response characteristic under fast impulses, are considered, for a circuit model to be translated into and further a MOV model under total current conditions given. What’s more, the 8/20μs impulse current generator and the square wave circuit with a rising edge of 2ns are utilized to have actual measurement, to a conclusion that the simulation results verify the feasibility of the model. This is of a certain application value to engineering design.

To solve the problems among traditional methods which are reliability and robustness of the system, we propose a novel target recognition system based on multi-sensor fusion in this article. The system presented uses the sensors data of infrared, visible light and sound to recognize moving objects in field environment. The design consists a set of algorithms and the simulation system. Tested with field data, it shows that the recognition system has high recognition rates and low time delay compared with the traditional system. The system is highly suitable for real-time implementation and is demonstrated through a series of experiments.

To solve the difficult problem that exists in the location of single-phase ground fault for coal mine underground distribution network, a fault location method using RBF network optimized by improved PSO algorithm based on the mapping relationship between wavelet packet transform modulus maxima of specific frequency bands transient state zero sequence current in the fault line and fault point position is presented. The simulation analysis results in the cases of different transition resistances and fault distances show that the RBF network optimized by improved PSO algorithm can obtain accurate and reliable fault location results, and the fault location perfor- mance is better than traditional RBF network.

With the rapid development of the national economy and the improvement of people's living standards, people are making higher demands on the living environment. And the estate management content, management efficiency and service quality have been higher required. This paper in-depth analyzes about the intelligent community of the structure and composition. According to the users' requirements and related specifications, it achieves the district management systems, which includes Basic Information Management: the management level of housing, household information management, administrator-level management, password management, etc. Service Management: standard property costs, property charges collecting, the history of arrears and other property expenses. Security Management: household gas, water, electricity and security and other security management, security management district and other public places. Systems Management: backup database, restore database, log management. This article also carries out on the Intelligent Community System analysis, proposes an architecture which is based on B / S technology system. And it has achieved a global network device management with friendly, easy to use, unified human - machine interface.

In this paper, the hardware architecture of the servo system composed by self-made inverter and permanent magnet synchronous motor is introduced as well as its measurement and control system software features. Basing on the Modbus-RTU protocols which contain the CRC data verification between IPC and the inverter, we have built a communication system. The measurement and control software of servo system are realized, which consists of self-made inverter and PMSM. Compared with the servo system which consists of Schindler’s ATV31 inverter and SIMO’s asynchronous motor, the results are given. The experiment parameters include harmonic content which is contained by phase current and voltage, and also the motor speed. The results show that the measurement and control system are stable and reliable. Both the harmonic content of the Inverter output and the motor speed meet the requirements.

In this paper, the driver’s hardware structure based on TMS320F28335 is introduced, the control software flow of host computer based on CAN bus is designed, the rule of CAN communication protocol is fulfilled and accordingly the hybrid programming is realized in the background of low speed and large sinusoid operation. This system can realize the CAN communication setting, download the PID parameters to DSP, operate at constant rotate speed and at given large sinusoid rotate speed. Meanwhile the dynamical monitoring and alarm are implemented. Finally the real-time display and storage of measured current, voltage and rotate speed are completed well.

Along with the popularity of computer and rapid development of information technology, how to increase the accuracy of the agricultural diagnosis becomes a difficult problem of popularizing the agricultural expert system. Analyzing existing research, baseing on the knowledge acquisition technology of rough set theory, towards great sample data, we put forward a intelligent diagnosis model. Extract rough set decision table from the samples property, use decision table to categorize the inference relation, acquire property rules related to inference diagnosis, through the means of rough set knowledge reasoning algorithm to realize intelligent diagnosis. Finally, we validate this diagnosis model by experiments. Introduce the rough set theory to provide the agricultural expert system of great sample data a effective diagnosis model.

In recent years, mesh-pull peer-to-peer streaming systems are popular. However, the service providers of these systems have to pay high expense for the high consumption of servers’ bandwidth. Therefore, the research on providing better video quality with limited supply of bandwidth is very meaningful. However, there are few works focusing on this topic. In this paper, we analyzed the optimal throughput scheduling problem, and proposed a near optimal scheduling algorithm BBS and an effective heuristic MRBF, they eliminate or reduce scheduling waste significantly and get high throughput. The results of extensive simulations shows: our solution can provide better streaming quality or sustain higher streaming rate with limited supply of bandwidth than existing scheduling methods.

How to expand the rough set model in incomplete information systems play a crucial role in the development of the rough set theory. In this paper, by considering the perferenc-ordered domains of the attribtues, the expanded dominance relation and the limited dominance relation are employed to construct multigranulation rough sets, respectively. Different from the traditional dominance relation based rough sets, these multigranulation rough sets use a family of the dominance relations instead of a single one for the target approximation. Not only the basic properties about these dominance-based multigranulation rough sets are proved, but also the relationships among several rough sets are discussed.

This paper presents a novel Time Division Multiplex Access (TDMA) based Media Access Control (MAC) protocol of wireless Ad Hoc network. To achieve collision free transmission, time slots in a MAC frame are cataloged into three types, that is access slot, control slot and traffic slot. Nodes in the network access to the network in the access slot, and an exclusive control is allocated subsequently. Data packets are transmission by dynamic schedule the traffic slots. Throughput and transmission delay are also analyzed by simulation experiment. The proposed protocol is capable of providing collision free transmission and achieves high throughput.

Staff line detection plays a key role in OMR technology, and is the precon-ditions of subsequent segmentation 1& recognition of music sheets. For the phenomena of horizontal inclination & curvature of staff lines and vertical inclination of image, which often occur in music scores, an improved approach based on subsection projection is put forward to realize the detection of original staff lines and revision in an effect to implement staff line detection more successfully. Experimental results show the presented algorithm can detect and revise staff lines fast and effectively.

This paper was presented at the conference indicated above but was previously presented and published in an earlier SPIE proceedings volume. The paper has been withdrawn from Proc. SPIE 8784 by the publisher. The citation for the earlier paper is: Peipei Gu, Zhendong Niu, Xuting Chen, and Wei Chen, "A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning," Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83343S (May 1, 2012); DOI: http://dx.doi.org/10.1117/12.966822.

The application of AES algorithm in the digital cinema system avoids video data to be illegal theft or malicious tampering, and solves its security problems. At the same time, in order to meet the requirements of the real-time, scene and transparent encryption of high-speed data streams of audio and video in the information security field, through the in-depth analysis of AES algorithm principle, based on the hardware platform of TMS320DM6446, with the software framework structure of DaVinci, this paper proposes the specific realization methods of AES algorithm in digital video system and its optimization solutions. The test results show digital movies encrypted by AES128 can not play normally, which ensures the security of digital movies. Through the comparison of the performance of AES128 algorithm before optimization and after, the correctness and validity of improved algorithm is verified.

Decision tree has most widely used for classification. However the main influence of decision tree classification performance is attribute selection problem. The paper considers a number of different attribute selection measures and experimentally examines their behavior in classification. The results show that the choice of measure doesn’t affect the classification accuracy, but the size of the tree is influenced significantly. The main effect of the new attribute selection measures which base on normal gain and distance is that they generate smaller trees than traditional attribute selection measures.

The variable structure multiple-model (VSMM) algorithm to passive radar maneuvering target tracking problem is considered. A new VSMM design, expected mode augmentation based on likely model set (LMS-EMA) algorithm is presented. The LMS-EMA algorithm adaptively determines the fixed grid model set using likely model set (LMS) algorithm, and generates the expected mode based on this set. Then, the union of fixed grid model set and expected model is used to perform multiple-model estimation. The performance of the LMS-EMA algorithm is evaluated via simulation of a highly maneuvering target tracking problem.