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10th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2010)

作者： Okarma, Krzysztof Lech, Piotr W Pomeranian Univ Technol Fac Elect Engn Chair Signal Proc & Multimedia Engn PL-71126 Szczecin Poland

ISBN: (纸本)9783642132315

Fast and simplified image processing and analysis methods can be successfully implemented for the robot control algorithms. Statistical methods seem to be very useful for such an approach, mainly because a significant reduction of analysed data is possible. In the paper the use of the fast image analysis based on the Monte Carlo area estimation for the simplified binary representation of the image is analysed and proposed for the mobile robot control. A possible implementation of the proposed method can applied in the line tracking robots and such application has been treated as the basic one for the testing purposes.

关键词： robot vision statistical image analysis line tracking robots

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Towards real time implementation of reconstructive signal processing algorithms using systolic arrays coprocessors

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JOURNAL OF SYSTEMS ARCHITECTURE 2010年第8期56卷 327-339页

作者： Castillo-Atoche, A. Torres-Roman, D. Shkvarko, Y. Univ Autonoma Yucatan Merida Yuc Mexico CINVESTAV Ctr Invest Estudios Avanzados IPN Guadalajara Jalisco Mexico

Reconstructive signal processing algorithms encompass a broad spectrum of computational methods. Fortunately, most of the methods fall into the classes of the matrix algebraic calculations, convolution, or transform type algorithms. These algorithms possess common properties such as regularity, locality and recursiveness. Considering such general class of reconstructive signal processing (SP) techniques, in this paper we propose a new Hardware/Software (HW/SW) co-design paradigm for the implementation of reconstructive SP algorithms via efficient systolic arrays integrated as digital SP coprocessors units. In particular, the selected matrix-matrix and matrix-vector multiplication algorithms are implemented in a systolic computing fashion that meets the real time SP system requirements when employing the developed Hardware/Software Co-Design method oriented at the use of a Xilinx Field Programmable Gate Array (FPGA) XC4VSX35-10ff668. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Systolic arrays Hardware/Software co-design FPGA

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Biomedical signal Analysis 1

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丛书名： The MIT Press Ser.

2010年

作者： Fabian J. Theis Anke Meyer-Bäse

ISBN: (数字)9780262259248

ISBN: (纸本)9780262013284

A comprehensive introduction to innovative methods in the field of biomedical signal analysis, covering both theory and practice. Biomedical signal analysis has become one of the most important visualization and interpretation methods in biology and medicine. Many new and powerful instruments for detecting, storing, transmitting, analyzing, and displaying images have been developed in recent years, allowing scientists and physicians to obtain quantitative measurements to support scientific hypotheses and medical diagnoses. This book offers an overview of a range of proven and new methods, discussing both theoretical and practical aspects of biomedical signal analysis and *** an introduction to the topic and a survey of several processing and imaging techniques, the book describes a broad range of methods, including continuous and discrete Fourier transforms, independent component analysis (ICA), dependent component analysis, neural networks, and fuzzy logic methods. The book then discusses applications of these theoretical tools to practical problems in everyday biosignal processing, considering such subjects as exploratory data analysis and low-frequency connectivity analysis in fMRI, MRI signal processing including lesion detection in breast MRI, dynamic cerebral contrast-enhanced perfusion MRI, skin lesion classification, and microscopic slice image processing and automatic labeling. Biomedical signal Analysis can be used as a text or professional reference. Part I, on methods, forms a self-contained text, with exercises and other learning aids, for upper-level undergraduate or graduate-level students. Researchers or graduate students in systems biology, genomic signal processing, and computer-assisted radiology will find both parts I and ii (on applications) a valuable handbook.

关键词： methods applications contemporary analysis signal biomedical

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Cellular neural Networks: Analysis, Design and Optimization

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2010年

作者： Martin Hnggi George S Moschytz

ISBN: (纸本)9781441949882

Cellular neural Networks (CNNs) constitute a class of nonlinear, recurrent and locally coupled arrays of identical dynamical cells that operate in parallel. ANALOG chips are being developed for use in applications where sophisticated signal processing at low power consumption is required. signal processing via CNNs only becomes efficient if the network is implemented in analog hardware. In view of the physical limitations that analog implementations entail, robust operation of a CNN chip with respect to parameter variations has to be insured. By far not all mathematically possible CNN tasks can be carried out reliably on an analog chip; some of them are inherently too sensitive. This book defines a robustness measure to quantify the degree of robustness and proposes an exact and direct analytical design method for the synthesis of optimally robust network parameters. The method is based on a design centering technique which is generally applicable where linear constraints have to be satisfied in an optimum way. processing speed is always crucial when discussing signal-processing devices. In the case of the CNN, it is shown that the setting time can be specified in closed analytical expressions, which permits, on the one hand, parameter optimization with respect to speed and, on the other hand, efficient numerical integration of CNNs. Interdependence between robustness and speed issues are also addressed. Another goal pursued is the unification of the theory of continuous-time and discrete-time systems. By means of a delta-operator approach, it is proven that the same network parameters can be used for both of these classes, even if their nonlinear output functions differ. More complex CNN optimization problems that cannot be solved analytically necessitate resorting to numerical methods. Among these, stochastic optimization techniques such as genetic algorithms prove their usefulness, for example in image classification problems. Since the inception of the CNN, the

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Real-time estimation of point-of-interest in video surveillance based on visual fixation

Real-time estimation of point-of-interest in video surveilla...

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International Symposium on signal processing and Its Applications (ISSPA)

作者： Lim Mei Kuan Tang Sze Ling Zulaikha Kadim Ahmed A. Al-Obaidi Norshuhada Samudin Liang Kim Meng Mimos Berhad Kuala Lumpur Malaysia

Analyzing and interpreting the behavior of humans in a sequence of images from a video stream is an active and growing topic in computer vision and its applications. This paper focuses on real-time visual fixation analysis in which the study of human's gaze allows investigation and reasoning of a point-of-interest (POI) in the scene. The system's architecture comprises of 3 major components; (i) head detection, (ii) head direction detection and (iii) POI analysis. We employ the 3D color space (RGB) model as proposed in the installation of “15 Seconds of Fame” to detect skin color pixels for head detection. The head direction specified is based on estimating the orientation in tilt angle and pan angle using neural network. Finally, the POI is determined via the correlation between multiple head directions. Experimental results are given to demonstrate the reliability of our system in estimating POI in a sequence of low-resolution images.

关键词： Iris recognition Real time systems image color analysis Analytical models Fitting Approximation methods Robots

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Nonlinear identification using a B-spline neural network and chaotic immune approaches

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MECHANICAL SYSTEMS AND signal processing 2009年第8期23卷 2418-2434页

作者： Coelho, Leandro dos Santos Pessoa, Marcelo Wicthoff Pontificia Univ Catolica Parana PPGEPS Ind & Syst Engn Grad Program BR-80215901 Curitiba Parana Brazil

one of the important applications of B-spline neural network (BSNN) is to approximate nonlinear functions defined on a compact subset of a Euclidean space in a highly parallel manner. Recently, BSNN, a type of basis function neural network, has received increasing attention and has been applied in the field of nonlinear identification. BSNNs have the potential to "learn" the process model from input-output data or "learn" fault knowledge from past experience. BSNN can be used as function approximators to construct the analytical model for residual generation too. However, BSNN is trained by gradient-based methods that may fall into local minima during the learning procedure. When using feed-forward BSNNs, the quality of approximation depends on the control points (knots) placement of spline functions. This paper describes the application of a modified artificial immune network inspired optimization method - the opt-aiNet - combined with sequences generate by Henon map to provide a stochastic search to adjust the control points of a BSNN. The numerical results presented here indicate that artificial immune network optimization methods are useful for building good BSNN model for the nonlinear identification of two case studies: (i) the benchmark of Box and Jenkins gas furnace, and (ii) an experimental ball-and-tube system. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Nonlinear identification Experimental process Artificial immune network B-spline neural network Chaotic optimization

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Noise-driven image processing based on Array-Enhanced stochastic Resonance with Population Heterogeneity

Noise-driven Image Processing based on Array-Enhanced Stocha...

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International Symposium on Intelligent signal processing and Communication Systems

作者： Utagawa, Akira Asai, Tetsuya Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 060 Japan

ISBN: (纸本)9781424450152

We found a new class of stochastic resonance (SR) in a simple neural network that consists of i) photoreceptors generating nonuniform outputs for common inputs with random offsets, ii) an ensemble of noisy McCulloch-Pitts (MP) neurons each of which has random threshold values in the temporal domain, iii) local connections between the photoreceptors and the MP neurons with variable receptive fields (RFs), iv) the output cells, and v) local connections between the MP neurons and output cells. We calculated correlation values between the inputs and the outputs as a function of the RF size and intensities of the random components in photoreceptors and the MP neurons. We show the existence of "optimal noise intensities" of the MP neurons under the nonidentical photoreceptors and "nonzero optimal RF sizes", which indicated that optimal correlation values of this SR model were determined by two critical parameters;noise intensities (well-known) and RF sizes as a new parameter.

关键词： Neurons

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Noise-driven image processing based on array-enhanced stochastic resonance with population heterogeneity

Noise-driven image processing based on array-enhanced stocha...

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IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Akira Utagawa Tetsuya Asai Graduate School of Information Science and Technology Hokkaido University Japan

We found a new class of stochastic resonance (SR) in a simple neural network that consists of i) photoreceptors generating nonuniform outputs for common inputs with random offsets, ii) an ensemble of noisy McCulloch-Pitts (MP) neurons each of which has random threshold values in the temporal domain, iii) local connections between the photoreceptors and the MP neurons with variable receptive fields (RFs), iv) the output cells, and v) local connections between the MP neurons and output cells. We calculated correlation values between the inputs and the outputs as a function of the RF size and intensities of the random components in photoreceptors and the MP neurons. We show the existence of ¿optimal noise intensities¿ of the MP neurons under the nonidentical photoreceptors and ¿nonzero optimal RF sizes¿, which indicated that optimal correlation values of this SR model were determined by two critical parameters; noise intensities (well-known) and RF sizes as a new parameter.

关键词： image processing stochastic resonance Strontium Neurons Photoreceptors Optical noise Radio frequency neural networks Optical network units Circuit noise

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Texture analysis based on a family of stochastic models

Texture analysis based on a family of stochastic models

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IEEE International Conference on signal and image processing Applications (ICSIPA)

作者： K. Seetharaman Department of Computer Science and Engineering Annamalai University Tamil Nadu India

A statistical approach, based on a family of Full Range Autoregressive models, is proposed for texture analysis. Bayesian approach is adopted to estimate the model parameters. Using the parameters, autocorrelation (AC) coefficient is computed. Based on the AC, two texture descriptors are proposed: (i) texnum, the local descriptor and (ii) texspectrum, the global descriptor. Decimal numbers are computed and that are proposed to represent textures present in a small image region. These numbers uniquely represent the texture primitives. The textured image under analysis is represented globally by observing the frequency of occurrences of the texnums called texspectrum. The textures are identified and are distinguished from the untextured regions with edges.

关键词： stochastic processes image texture analysis Pixel image analysis signal analysis Bayesian methods Autocorrelation image segmentation Markov random fields signal processing

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Configurable ultrasonic flaw classification of oil pipelines

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NONDESTRUCTIVE TESTING AND EVALUATION 2008年第1期23卷 43-55页

作者： Ravanbod, H. Jalali, A. Sharif Univ Technol Elect Res Ctr Tehran Iran Sharif Univ Technol Dept Comp Engn Tehran Iran

To make a decision on the nature of a flaw within a pipeline, it is necessary to detect suspicious regions in ultrasonic inspection images reliably and to, derive geometrical parameters such as shape, position and orientation from each measured signal and finally to collate these parameters intelligently by associating each measured signal with a possible flaw type. These two papers present an innovative method of configurable flaw classification and volume estimation in oil pipelines. This method includes: (1) An adaptive thresholding selection for estimation of the area and volume of the flaws, (2) Dynamic detection of interesting points as feature points at different levels of images by wavelet transform, (3) A guided searching strategy for the best matching from the coarse level to a fine level with configurable classes of flaws, (4) A neural network error reducer using the area and volume of flaws and (5) A statistical error analyser to verify system outputs by distinct inputs. In part I, at first, the ultrasonic scan system acquires ultrasonic flaw signals. Then, the preprocessing module performs tasks such as filtering and smoothing. In addition, adaptive thresholding selection based on compactness measures of fuzzy S-functions is performed and the area and volume of flaws are estimated through a number of real images illustrating the performance of system. Comparing the proposed fuzzy edge detection method with the other well-known methods demonstrate the compatibility of approach in nondestructive ultrasonic inspection imaging. Flops calculation demonstrates that the proposed fuzzy estimator could be integrated into a real time flaw detection system. In part ii, feature extractors such as wavelet transform, volume of the flaw, flaw classification and statistical error analysis are proposed, through acquired and preprocessed image.

关键词： nondestructive testing ultrasonics signal processing fuzzy logic edge detection compactness measurement

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