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.

Application of TMS320C80 in Image Processing

To study the application of TMS320C80 in image processing, an image processing system was designed based on this device, and the task of real time image processing was well accomplished on the hardware platform. TMS320C80 architecture's high degree of on chip integration and software flexibility will make it widely used in image processing that requires high processing speeds.

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.

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.

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.

The inversion of density structure by graphic processing unit(GPU) and identification of igneous rocks in Xisha area

Organic reefs, the targets of deep-water petro- leum exploration, developed widely in Xisha area. However, there are concealed igneous rocks undersea, to which organic rocks have nearly equal wave impedance. So the igneous rocks have become interference for future explo- ration by having similar seismic reflection characteristics. Yet, the density and magnetism of organic reefs are very different from igneous rocks. It has obvious advantages to identify organic reefs and igneous rocks by gravity and magnetic data. At first, frequency decomposition was applied to the free-air gravity anomaly in Xisha area to obtain the 2D subdivision of the gravity anomaly and magnetic anomaly in the vertical direction. Thus, the dis- tribution of igneous rocks in the horizontal direction can be acquired according to high-frequency field, low-frequency field, and its physical properties. Then, 3D forward model- ing of gravitational field was carried out to establish the density model of this area by reference to physical properties of rocks based on former researches. Furthermore, 3D inversion of gravity anomaly by genetic algorithm method of the graphic processing unit （GPU） parallel processing in Xisha target area was applied, and 3D density structure of this area was obtained. By this way, we can confine the igneous rocks to the certain depth according to the density of the igneous rocks. The frequency decomposition and 3D inversion of gravity anomaly by genetic algorithm method of the GPU parallel processing proved to be a useful method for recognizing igneous rocks to its 3D geological position. So organic reefs and igneous rocks can be identified, which provide a prescient information for further exploration.

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.

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.

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 High Speed Signal Processing Machine -Its Architecture, Language and Compiler

A systolic array architecture computer (FXCQ) has been designed for signal processing. R can handle floating point data at very high speed. It is composed of 16 processing cells and a cache that are connected linearly and form a ring structure. All processing cells are identical and programmable. Each processing cell has the peak performance of 20 million floating-point operations per second (20MFLOPS). The machine therefore has a peak performance of 320 M FLOPS. It is integrated as an attached processor into a host system through VME bus interface. Programs for FXCQ are written in a high-level language -B language, which is supported by a parallel optimizing compiler. This paper describes the architecture of FXCQ, B language and its compiler.

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.

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.

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.

Hybrid Segment Approximate Multiplication for Image Processing Applications

It is critical in terms of approximate computation errors in VLSI multiplier circuits are increasing with technology scaling. The most common method for fast and energy efficient execution of multiplication result is approximation of operands. But this traditional approximate result is not suitable for image processing applications. This paper proposes the two architectures of high accurate hybrid segment approximate multiplier (HSAM) and enhanced HSAM for image compression. Existing static segment method based approximate multiplier is not suitable for certain accurate applications and dynamic segment method based approximate multiplier is not suitable for cost efficient applications. The proposed work combines the advantages of both static segment method and dynamic segment method to drive the efficiency in accuracy and cost. The proposed approximate multipliers HSAM8 × 8 and EHSAM8 × 8 provide 99.85% and 99.999% accuracy respectively for various inputs. The proposed HSAM consumes less energy with small increase of area overhead. The proposed EHSAM consumes less energy without any area overhead. The proposed HSAM and EHSAM is improved the speed by 40% and 85% compared to the existing SSM8 × 8 technique.

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.

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.

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.

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.

Data partitioning based on sampling for power load streams

A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,which is implemented as an extended reservoir-sampling algorithm.A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively.The second step of this method is to partition the fluxes of data streams averagely,which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases：one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector.The experimental results on actual data prove that the method is efficient,practical and suitable for time-varying data streams processing.