Parallel Image Processing Technology of Surface Detection System

To improve image processing speed and detection precision of a surface detection system on a strip surface,based on the analysis of the characteristics of image data and image processing in detection system on the strip surface,the design of parallel image processing system and the methods of algorithm implementation have been studied. By using field programmable gate array(FPGA) as hardware platform of implementation and considering the characteristic of detection system on the strip surface,a parallel image processing system implemented by using multi IP kernel is designed. According to different computing tasks and the load balancing capability of parallel processing system,the system could set different calculating numbers of nodes to meet the system's demand and save the hardware cost.

RESEARCH ON SATELLITE IMAGE PROCESSING AND RECOGNITION WITH PARALLEL ALGORITHM

Using the method of mathematical morphology,this paper fulfills filtration,segmentation and extraction of morphological features of the satellite cloud image.It also gives out the relative algorithms,which is realized by parallel C programming based on Transputer networks.It has been successfully used to process the typhoon and the low tornado cloud image.And it will be used in weather forecast.

Parallel Processing Based on Ship Maneuvering in Identification of Interaction Force Coefficients

The parallel processing based on the free running model test was adopted to predict the interaction force coefficients （flow straightening coefficient and wake fraction） of ship maneuvering. And the multipopulation genetic algorithm （MPGA） based on real coding that can contemporarily process the data of free running model and simulation of ship maneuvering was applied to solve the problem. Accordingly the optimal individual was obtained using the method of genetic algorithm. The parallel processing of multiopulation solved the prematurity in the identification for single population, meanwhile, the parallel processing of the data of ship maneuvering （turning motion and zigzag motion） is an attempt to solve the coefficient drift problem. In order to validate the method, the interaction force coefficients were verified by the procedure and these coefficients measured were compared with those ones identified. The maximum error is less than 5%, and the identification is an effective method.

On the Communication-Efficiency of Parallel Systems

This peper defines the communication-efficiency, which is directly related to the cost-efficiency, and Studies the relationship between the communication-efficiency and the processor-efficiency when they are applied to scalability analysis. An example of algorithms is given to analyze some typical architectures.

A Survey of MapReduce Based Parallel Processing Technologies

Along with the increasing Big Data challenges, the MapReduce based systems are extensively welcomed, because of their remarkable simplicity and scalability. However, from the first day MapReduce is proposed, its argument with parallel Dt3MSs never stops, as it over-focuses on the scalability but overlooks the efficiency. Accordingly, extended systems are proposed in order to improve the peDbrmance on the limited scale clusters. In the meantime, traditional RDBMS technologies like structured data model, transaction, SQL, etc. are also getting more attention. This paper reviews such systems, from Google and also the third parties, trying to indicate the directions for the future research.

REALIZATION OF QUICK-LOOK IMAGING FOR SPACEBORNE SAR BASED ON PARALLEL PROCESSING

Large range cell migration is a severe challenge to imaging algorithm for spaceborne SAR. Based on design of Finite Impulse Response (FIR) filter and Range Doppler (RD) algorithm, a realization of quick-look imaging for large range cell migration is proposed. It realized quick-look imaging of 8 times reduced resolution with parallel processing on memory shared 8 CPU SGI server. According to simulation experiment, this quick-look imaging algorithm with parallel processing can image 16384x16384 SAR raw data within 6 seconds. It reaches the requirement of real-time imaging.

Efficient Partially Asynchronous Parallel Simulation on Multicomputer Systems: Research and Practice

This paper presents partially asynchronous parallel simulation of continuous-system (PAPSoCS) and some approaches to the issues of its implementation on a multicomputer system. To guarantee the simulation results correct and speedup the simulation, the scheme for efficient PAPSoCS is proposed and the virtual topology star is constructed to match the path of message passing for solving algorithm-architecture adequation problem. Under the circumstances that messages frequently passed inter-processor are much shorter, typically within several 4 bytes, asynchronous communication mode is employed to reduce the communication ratio. Experiment results show that asynchronous parallel simulation has much higher efficiency than its synchronous counterpart.

A new data acquisition and processing system for profiling sonar

A multi-beam chirp sonar based on IP connections and DSP processing nodes was proposed and designed to provide an expandable system with high-speed processing and mass-storage of real-time signals for multi-beam profiling sonar.The system was designed for seabed petroleum pipeline detection and orientation,and can receive echo signals and process the data in real time,refreshing the display 10 times per second.Every node of the chirp sonar connects with data processing nodes through TCP/IP. Merely by adding nodes,the system’s processing ability can be increased proportionately without changing the software.System debugging and experimental testing proved the system to be practical and stable.This design provides a new method for high speed active sonar.

A Decentralized Parallel One-Pass Fixed-Interval Deconvolution Algorithm for Multisensor Systems with Multiplicative Noises

A decentralized parallel one-pass deconvolution algorithm for multisensor systems with multiplicative noises is proposed. Comparing with the conventional deconvolution algorithm, it avoids the computational overload and the high storage requirement. The algorithm is optimal in the sense of linear minimum-variance. The simulation results illustrate the validity of the proposed algorithm.

Using Graphics Processing Units to Parallelize the FDK Algorithm for Tomographic Image Reconstruction

The paper presents the implementation of a parallel version of FDK （Felkamp, David e Kress） algorithm using graphics processing units. Discussion was briefly some elements the computed tomographic scan and FDK algorithm; and some ideas about GPUs （Graphics Processing Units） and its use in general purpose computing were presented. The paper shows a computational implementation of FDK algorithm and the process of parallelization of this implementation. Compare the parallel version of the algorithm with the sequential version, used speedup as a performance metric. To evaluate the performance of parallel version, two GPUs, GeForce 9400GT （16 cores） a low capacity GPU and Quadro 2000 （192 cores） a medium capacity GPU was reached speedup of 3.37.

Fourier hologram method using Gerchberg-Saxton algorithm for parallel femtosecond laser processing

In order to improve femtosecond laser throughput,a parallel processing system consisting of liquid crystal on silicon(LCOS)device as spatial light modulator is put forward.A method is described for displaying Fourier hologram on LCOS,and a high uniformity of several diffraction peaks in the computer reconstruction is achieved.Application of this method to the parallel femtosecond laser processing is also demonstrated,and two intersecting rings and three tangent rings are fabricated respectively by one time in the photoresist.

Parallel Processing Design for LTE PUSCH Demodulation and Decoding Based on Multi-Core Processor

The Long Term Evolution (LTE) system imposes high requirements for dispatching delay.Moreover,very large air interface rate of LTE requires good processing capability for the devices processing the baseband signals.Consequently,the single-core processor cannot meet the requirements of LTE system.This paper analyzes how to use multi-core processors to achieve parallel processing of uplink demodulation and decoding in LTE systems and designs an approach to parallel processing.The test results prove that this approach works quite well.

Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes.Because of its operation,the application of this classification may be limited to problems with a certain number of instances,particularly,when run time is a consideration.However,the classification of large amounts of data has become a fundamental task in many real-world applications.It is logical to scale the k-Nearest Neighbor method to large scale datasets.This paper proposes a new k-Nearest Neighbor classification method(KNN-CCL)which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts.The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters.The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets.Finally,sets of experiments are conducted on the UCI datasets.The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them.They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results.Artificial neural network(ANN)offers optimal solutions in classifying and clustering the various reels of data,and the results obtained purely depend on identifying a problem.In this research work,the design of optimized applications is presented in an organized manner.In addition,this research work examines theoretical approaches to achieving optimized results using ANN.It mainly focuses on designing rules.The optimizing design approach of neural networks analyzes the internal process of the neural networks.Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters.The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues.The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors.The proposed ANN offered optimal results in real-world application problems,and the results were obtained using MATLAB.

Parallel Iterative FEM Solver with Initial Guess for Frequency Domain Electromagnetic Analysis

The finite element method is a key player in computational electromag-netics for designing RF(Radio Frequency)components such as waveguides.The frequency-domain analysis is fundamental to identify the characteristics of the components.For the conventional frequency-domain electromagnetic analysis using FEM(Finite Element Method),the system matrix is complex-numbered as well as indefinite.The iterative solvers can be faster than the direct solver when the solver convergence is guaranteed and done in a few steps.However,such complex-numbered and indefinite systems are hard to exploit the merit of the iterative solver.It is also hard to benefit from matrix factorization techniques due to varying system matrix parts according to frequency.Overall,it is hard to adopt conventional iterative solvers even though the system matrix is sparse.A new parallel iterative FEM solver for frequency domain analysis is implemented for inhomogeneous waveguide structures in this paper.In this implementation,the previous solution of the iterative solver of Matlab(Matrix Laboratory)employ-ing the preconditioner is used for the initial guess for the next step’s solution process.The overlapped parallel stage using Matlab’s Parallel Computing Toolbox is also proposed to alleviate the cold starting,which ruins the convergence of early steps in each parallel stage.Numerical experiments based on waveguide structures have demonstrated the accuracy and efficiency of the proposed scheme.

Improved Strategies for Parallel Medical Image Processing Applications

In order to meet the demands of high efficient and real-time computer assisted diagnosis as well as screening in medical area, to improve the efficacy of parallel medical image processing is of great importance. This article proposes improved strategies for parallel medical image processing applications,which is categorized into two genera. For each genus individual strategy is devised, including the theoretic algorithm for minimizing the exertion time. Experiment using mammograms not only justifies the validity of the theoretic analysis, with reasonable difference between the theoretic and measured value, but also shows that when adopting the improved strategies, efficacy of medical image parallel processing is improved greatly.

APPLICATION OF MVP IN REAL TIME IMAGE PROCESSING

MVP is a digital signal processor, which is of MIMD structure and fit for multimedia application. MVP has several processors in it, and its operation is characteristic of parallelism and pipeline; therefore, real-time signal processing can be done on it. This paper presents the image processing system based on MVP, explains the principles of parallel task assignment and hardware pipeline design, and gives out the example of target tracking and edge detection.

Parallel Algorithm Case Study Using Virtual Machine

This paper takes the Sobel operator as example to study parallel sequential algorithm onto a memory-sharing multiprocessor by using a virtual machine. Several different parallel algorithms using function decomposition and/or data decomposition methods are compared and their performances are analyzed in terms of processor utilization, data traffic, shared memory access, and synchronization overhead. The analysis is validated through a simulation experiment on the virtual machine of 64 parallel processors. Conclusions are presented at the end of this paper.

PARALLEL REGION PRESERVING MULTISECTION METHOD FOR SOLVING GENERALIZED EIGENPROBLEM

The parallel multisection method for solving algebraic eigenproblem has been presented in recent years with the development of the parallel computers, but all the research work is limited in standard eigenproblems of symmetric tridiagonal matrix. The multisection method for solving the generalized eigenproblem applied significantly in many science and engineering domains has not been studied. The parallel region preserving multisection method (PRM for short) for solving generalized eigenproblems of large sparse and real symmetric matrix is presented in this paper. This method not only retains the advantages of the conventional determinant search method (DS for short), but also overcomes its disadvantages such as leaking roots and disconvergence. We have tested the method on the YH 1 vector computer, and compared it with the parallel region preserving determinant search method the parallel region preserving bisection method (PRB for short). The numerical results show that PRM has a higher speed up, for instance, it attains the speed up of 7.7 when the scale of the problem is 2 114 and the eigenpair found is 3, and PRM is superior to PRB when the scale of the problem is large.