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EDITORIAL SPECIAL ISSUE: PART IV-iiI-ii-I SERIES - FRACTALS-FRACTIONAL AI-BASED ANALYSES AND APPLICATIONS TO COMPLEX SYSTEMS finestre of creativity, complexity and universality…

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第10期31卷 2302005-2302005页

作者： Karaca, Yeliz Baleanu, Dumitru Moonis, Majaz Zhang, Yu-dong Gervasi, Osvaldo Univ Massachusetts Chan Med Sch 55 Lake Ave North Worcester MA 01655 USA Lebanese Amer Univ Beirut 11022 Lebanon Inst Space Sci Bucharest 077125 Romania Univ Leicester Univ Rd Leicester LE1 7RH England Univ Perugia I-06123 Perugia Italy

Complex systems, as interwoven miscellaneous interacting entities that emerge and evolve through self-organization in a myriad of spiraling contexts, exhibit subtleties on global scale besides steering the way to understand complexity which has been under evolutionary processes with unfolding cumulative nature wherein order is viewed as the unifying framework. Indicating the striking feature of non-separability in components, a complex system cannot be understood in terms of the individual isolated constituents' properties per se, it can rather be comprehended as a way to multilevel approach systems behavior with systems whose emergent behavior and pattern transcend the characteristics of ubiquitous units composing the system itself. This observation specifies a change of scientific paradigm, presenting that a reductionist perspective does not by any means imply a constructionist view;and in that vein, complex systems science, associated with multiscale problems, is regarded as ascendancy of emergence over reductionism and level of mechanistic insight evolving into complex system. While evolvability being related to the species and humans owing their existence to their ancestors' capability with regards to adapting, emerging and evolving besides the relation between complexity of models, designs, visualization and optimality, a horizon that can take into account the subtleties making their own means of solutions applicable is to be entailed by complexity. Such views attach their germane importance to the future science of complexity which may probably be best regarded as a minimal history congruent with observable variations, namely the most parallelizable or symmetric process which can turn random inputs into regular outputs. Interestingly enough, chaos and nonlinear systems come into this picture as cousins of complexity which with tons of its components are involved in a hectic interaction with one another in a nonlinear fashion amongst the other related systems

关键词： Applied Mathematics Computational Mathematics Pure Mathematics Mathematical Sciences Discrete Mathematics Mathematical Physics Theoretical Sciences Exact Science Computational methods Computational Intelligence Applied Sciences Computer Science Computational Science Data Science Engineering Electrical and Electronics Engineering Exact Sciences Natural Sciences Social Sciences Nonlinear Dynamics Nonlinear Systems Linear Systems Fractional Calculus AI Control Control Systems Optimization Complexity Chaos Uncertainty Causality Symmetry Oscillations Bifurcation Stability Sensitivity Quantum Medicine Biology Physics Chemistry Mathematical Bioengineering Mathematical Neuroscience Mathematical Biology Artificial Intelligence neural Networks Artificial neural Networks Data-Driven Models Encryption Soft Computing Coding Theory Machine Learning Deep Learning Intelligent Systems Probabilities and stochastic Processes Game Theory Solitons, Fractals, Multifractals Fractional Calculus Fractional Dynamics Fractional Mathematics Fractional Systems Fractional Models Fractals Multifractals Waves Wavelet Entropy Algebraic methods Computational (-Algorithmic) Complexity Fractional Order Differential Equations Fractional Differential Equations Fuzzy Fractional Differential Equations Special Functions Functional Analysis Error Estimations Fuzzy Calculus Differential/Integral Equations, stochastic Differential Equations, Fractal-Fractional Differential Equations Fractional-Order Controllers Fractional-Order Systems Intelligent Fractional Order Systems Variational Principles image and/or signal processing Mathematical Models Fractal AI Fractional AI Fractals-Fractional AI Quantum AI Dynamical systems Chaotic Systems Complex Systems

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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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Modeling visual patterns by integrating descriptive and generative methods

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2003年第1期53卷 5-29页

作者： Guo, CE Zhu, SC Wu, YN Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90024 USA

This paper presents a class of statistical models that integrate two statistical modeling paradigms in the literature: ( I) Descriptive methods, such as Markov random fields and minimax entropy learning (Zhu, S. C., Wu, Y. N., and Mumford, D. 1997. neural Computation, 9( 8)), and (ii) Generative methods, such as principal component analysis, independent component analysis ( Bell, A. J. and Sejnowski, T. J. 1997. Vision Research, 37: 3327-3338), transformed component analysis ( Frey, B. and Jojic, N. 1999. ICCV), wavelet coding ( Mallat, S. and Zhang, Z. 1993. IEEE Trans. on signal processing, 41: 3397-3415;Chen, S., Donoho, D., and Saunders, M. A. 1999. Journal on Scientific Computing, 20( 1): 33-61), and sparse coding (Olshausen, B. A. and Field, D. J. 1996. Nature, 381: 607-609;Lewicki, M. S. and Olshausen, B. A. 1999. JOSA, A. 16( 7): 1587-1601). In this paper, we demonstrate the integrated framework by constructing a class of hierarchical models for texton patterns ( the term "texton" was coined by psychologist Julesz in the early 80s). At the bottom level of the model, we assume that an observed texture image is generated by multiple hidden "texton maps", and textons on each map are translated, scaled, stretched, and oriented versions of a window function, like mini-templates or wavelet bases. The texton maps generate the observed image by occlusion or linear superposition. This bottom level of the model is generative in nature. At the top level of the model, the spatial arrangements of the textons in the texton maps are characterized by minimax entropy principle, which leads to embellished versions of Gibbs point process models (Stoyan, D., Kendall, W. S., and Mecke, J. 1985. stochastic Geometry and its Applications). The top level of the model is descriptive in nature. We demonstrate the integrated model by a set of experiments.

关键词： descriptive models generative models Gibbs point processes Markov chain Monte Carlo Markov random fields minimax entropy learning perceptual organization texton models visual learning

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neural and stochastic methods in image and signal processing ii

Neural and Stochastic Methods in Image and Signal Processing...

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neural and stochastic methods in image and signal processing ii 1993

The proceedings contain 29 papers. The topic discussed include: algorithm for classification of multispectral data and its implementation on a massively parallel computer;convergence measure and some parallel aspects of Markov-chain Monte Carlo algorithms;evidential reasoning based on Dempster-Shafer theory and its application to medical image analysis;cluster approximations for statistical image processing;image recovery and segmentation using competitive learning in a layered network;feature competition and domain of attraction in artificial-perceptron pattern recognizer;and probabilistic spectral feature extraction technique for neural networks.

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image COMPRESSION USING BOLTZMANN MACHINES 2

IMAGE COMPRESSION USING BOLTZMANN MACHINES

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： LIU, Y Savannah State College (United States)

ISBN: (纸本)0819412813

In this paper, the idea of image compression using the Boltzmann machines will be developed. We will first introduces (theta) -transformation and show it is complete for a certain class of images. Then we will show that the Boltzmann machine can implement the first few terms in the (theta) -transformation; therefore, the Boltzmann machines can be used as image encoders. The compression ratio and distortion of this new image compression method are discussed.

关键词： image compression Neurons Distortion Computer programming Data compression Fractal analysis Polonium neural networks Promethium stochastic processes

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FAST stochastic GLOBAL OPTIMIZATION 2

FAST STOCHASTIC GLOBAL OPTIMIZATION

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： BILBRO, GL North Carolina State Univ. (United States)

ISBN: (纸本)0819412813

A new stochastic optimization algorithm is introduced in which a pipeline of many biased stochastic procedures cooperate to concurrently sample the usual Boltzmann distribution for different temperatures. Convergence and efficiency of the pipeline algorithm is proved under certain conditions. Experimental confirmation is provided using seven standard test problems in nonlinear optimization.

关键词： stochastic processes Optimization (mathematics) Algorithms Algorithm development Astatine Detection and tracking algorithms signal processing Computer engineering Computer simulations Differential equations

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STATISTICAL TEXTURE GUIDED ADAPTIVE image-CODING BY WAVELET TRANSFORMS 2

STATISTICAL TEXTURE GUIDED ADAPTIVE IMAGE-CODING BY WAVELET ...

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： GUAN, L Univ. of Sydney (Canada)

ISBN: (纸本)0819412813

An adaptive image coding scheme is introduced in this paper. The proposed method explores the relationship between wavelet transform and statistical texture analysis, decomposing an image into octal signal bands and utilizing statistical texture information as a guiding tool to control the levels of signal decomposition necessary in coding in order to achieve high compression ratio and image reconstruction with superior quality.

关键词： image compression Wavelet transforms

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CLUSTER APPROXIMATIONS FOR STATISTICAL image-processing 2

CLUSTER APPROXIMATIONS FOR STATISTICAL IMAGE-PROCESSING

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： WU, CH DOERSCHUK, PC Purdue Univ. (Taiwan) Purdue Univ. (United States)

ISBN: (纸本)0819412813

A disadvantage of using discrete-state Markov random field models of images is that optimal estimators for reconstruction problems require excessive and typically random amounts of computation. In one approach the key task is the computation of the conditional mean of the field given the data or equivalently the unconditional mean of the a posteriori field. In this paper we describe a hierarchy of deterministic parallelizable methods for such computations.

关键词： image restoration image processing Binary data Magnetorheological finishing Performance modeling Radon Computer simulations Crystallography X-rays Algorithms

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EVOLVING neural-NETWORK PATTERN CLASSIFIERS 2

EVOLVING NEURAL-NETWORK PATTERN CLASSIFIERS

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： MCDONNELL, JR WAAGEN, DE PAGE, WC Naval Command Control and Ocean Surveillance Ctr. (United States)

ISBN: (纸本)0819412813

This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements a parallel nonlinear regression technique and represents a powerful method for evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis & Wets random optimization method thereby maintaining the integrity of the stochastic search while taking into account empirical information about the response surface. A network architecture is proposed which is motivated by the structures generated in projection pursuit regression and the cascade-correlation learning architecture. Results are given for the 3-bit parity, normally distributed data, and the T-C classifier problems.

关键词： Computer programming neural networks Network architectures stochastic processes Neurons Evolutionary optimization Autoregressive models image classification Brain mapping Chemical elements

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A PROBABILISTIC SPECTRAL FEATURE-EXTRACTION TECHNIQUE FOR neural NETWORKS 2

A PROBABILISTIC SPECTRAL FEATURE-EXTRACTION TECHNIQUE FOR NE...

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CONF ON neural AND stochastic methods IN image AND signal processing 2

作者： YOON, YR ERSOY, OK NCCOSC RDT and E DIV CODE 4504 San Diego 92152-5142 CA United States Purdue University School of Electrical Engineering West Lafayette 47907 IN United States

ISBN: (纸本)0819412813

Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech, image recognition and pattern recognition. For high performance and for controlling the size of the network, the input information must be preprocessed before being fed in to the neural network. In this paper, a probabilistic spectral feature extraction technique (PSFET) with multiview spectral representations and its applications are described. During training and testing, the PSFET allows efficient extraction of useful information in addition to generating an input vector size for best classification performance by the following neural network. Experimental results indicate that the performance of the neural network increases in classification accuracy when PSFET is used at the input. The network also generalizes better. © 1993 SPIE. All rights reserved.

关键词： image recognition

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