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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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neural and stochastic methods in image and signal processing iii

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

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neural and stochastic methods in image and signal processing iii 1994

The proceedings contain 28 papers. The topics discussed include: general learning algorithm for robot vision;variational versus Markov random field methods for image segmentation;knowledge-directed electroencephalography (EEG) signal analysis with recurrent context-learning neural networks;logarithmic convergence of random heuristic search;statistical multiscale approach to mapping altimetric data;panchromatic band sharpening of multispectral image data to improve machine exploitation accuracy;comparison of fractal methods with discrete cosine transform (DCT) and wavelets;and matching of road segments using probabilistic relaxation: a hierarchical approach.

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A COMPARISON OF FRACTAL methods WITH DCT AND WAVELETS 3

A COMPARISON OF FRACTAL METHODS WITH DCT AND WAVELETS

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Conference on neural and stochastic methods in image and signal processing iii

作者： FISHER, Y ROGOVIN, D SHEN, TP UNIV CALIF SAN DIEGO INST NONLINEAR SCILA JOLLACA 92093

ISBN: (纸本)0819416282

The paper contains a brief description of fractal image compression methods with sample compression results. We also present comparative results between two fractal schemes, discrete cosine transform, and a wavelet method. We show that, with the PSNR as a measure of image quality, some fractal schemes perform best over the range of compressions of most interest. © 1994 SPIE.

关键词： FRACTAL image COMPRESSION DCT JPEG EPIC WAVELETS COMPARISON

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DATA DE-DUPLICATION USING neural NETWORKS 3

DATA DE-DUPLICATION USING NEURAL NETWORKS

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Conference on neural and stochastic methods in image and signal processing iii

作者： LIU, J NANYANG TECHNOL UNIV SCH ELECT & ELECTR ENGNSINGAPORE 2263SINGAPORE

ISBN: (纸本)0819416282

In this paper, we present the result of using neural network to search for duplicate addresses in a database. The problem is called data de-duplication of database. The problem of duplicated data appears when a distribution company maintains and updates its large-scale customer database. Back-propagation network has been used in our project. Several encoding approaches are developed and tested. Testing results are presented in the paper.

关键词： neural NETWORKS DATA processing DATA DE-DUPLICATION

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LOGARITHMIC CONVERGENCE OF RANDOM HEURISTIC-SEARCH 3

LOGARITHMIC CONVERGENCE OF RANDOM HEURISTIC-SEARCH

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Conference on neural and stochastic methods in image and signal processing iii

作者： VOSE, MD UNIV TENNESSEE DEPT COMP SCIKNOXVILLETN 37996

A general class of stochastic search algorithms, random heuristic search, is reviewed. A general convergence theorem for this class is proved. Since the simple genetic algorithm is an instance of random heuristic sear... 详细信息

ISBN: (纸本)0819416282

关键词： Genetic algorithms Binary data stochastic processes Computer science Genetics Optical spheres Polonium Transition metals

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VISUAL neural DYNAMICS 3

VISUAL NEURAL DYNAMICS

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Conference on neural and stochastic methods in image and signal processing iii

作者： CHEN, SS UNIV N CAROLINA CHARLOTTENC 28223

ISBN: (纸本)0819416282

The mental process has fascinated human beings for a long time. neural dynamics which studies the dynamics of neurons and cell assemblies should play a key role in the mental process. Only recently, the relationship between neural dynamics and mental process are beginning to be established. This relies upon the interdisciplinary collaboration of behavioral, computational, and neurobiological sciences. This paper studies the relationship for the visual process or perceptual processes in general.

关键词： Visualization Dynamical systems Neurons Scanning tunneling microscopy Brain Electroencephalography Information visualization neural networks Retina Visual cortex

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KNOWLEDGE-DIRECTED EEG signal ANALYSIS WITH RECURRENT CONTEXT-LEARNING neural NETWORKS 3

KNOWLEDGE-DIRECTED EEG SIGNAL ANALYSIS WITH RECURRENT CONTEX...

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Conference on neural and stochastic methods in image and signal processing iii

作者： FU, LM UNIV FLORIDA DEPT COMP & INFORMAT SCIGAINESVILLEFL 32611

ISBN: (纸本)0819416282

EEG signal analysis is a key to the understanding of brain activities. Traditionally, this process involves quantifying the signal in terms of frequency and amplitude, on which basis a number of waveforms have been identified. The complexity of EEG signals warrants the construction of a computer program for automatic interpretation. Symbolic knowledge is being built up for correlating the quantity of certain waveforms and brain behavior, and this knowledge can be readily programmed into a knowledge-based system (expert system) for various purposes such as cognitive research, neurological evaluation, and clinical diagnosis. The presented approach employs a knowledge-based neural network in conjunction with a recurrent neural network model as a memory device which conducts context processing. This research emphasizes the need for the exploitation of "knowledge" and "context" in signal analysis. © 1994 SPIE.

关键词： EEG CONTEXT KNOWLEDGE-BASED neural NETWORKS PATTERN ANALYSIS

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A stochastic APPROACH FOR MOTION VECTOR ESTIMATION IN VIDEO CODING 3

A STOCHASTIC APPROACH FOR MOTION VECTOR ESTIMATION IN VIDEO ...

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Conference on neural and stochastic methods in image and signal processing iii

作者： KIM, SG KUO, CCJ UNIV SO CALIF DEPT ELECT ENGN SYSTINST SIGNAL & IMAGE PROCLOS ANGELESCA 90089

ISBN: (纸本)0819416282

Motion vector (MV) estimation plays an important role in motion compensated video coding. We propose in this research a new fast MV estimation algorithm by using a statistical approach. Our algorithm consists of two components. First, instead of using a fixed search window centered at the origin, we allow the center and the size of the search window to vary according to the motion vectors obtained in coding previous frames. Second, instead of performing an exhaustive block matching within the search window, we introduce a probability density function which has a peak at the window center and decays as the distance becomes larger, and generate a few search positions according to the probability density. Experiments are performed to illustrate the excellent performance of the proposed algorithm. © 1994 SPIE.

关键词： image coding

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image CLASSIFICATION BY CLASSIFIER COMBINING TECHNIQUE 3

IMAGE CLASSIFICATION BY CLASSIFIER COMBINING TECHNIQUE

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Conference on neural and stochastic methods in image and signal processing iii

作者： LIU, K HUANG, YS SUEN, CY CONCORDIA UNIV CTR PATTERN RECOGNIT & MACHINE INTELLIGENCEMONTREAL H3G 1M8QUEBECCANADA

ISBN: (纸本)0819416282

A classification method has been proposed to recognize images of multiple classes based on algebraic feature extraction and classifier combining techniques. First, an image algebraic feature extraction method is applied to all pairs of classes to extract the image features. Then, a nearest neighbor classifier with a small number of prototypes is designed for each pair of classes based on the algebraic features of training samples. Finally, a neural network technique is used to combine the measurement values of paired classes. Experiments on the U.S. zip code data base show that the method is effective.

关键词： COMBINATION OF PATTERN CLASSIFIERS OPTIMAL DISCRIMINANT ANALYSIS FEATURE EXTRACTION CHARACTER RECOGNITION CLASSIFIER DESIGN neural NETWORK

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EVOLUTIONARY OPTIMIZATION OF CASCADED-NETWORKS 3

EVOLUTIONARY OPTIMIZATION OF CASCADED-NETWORKS

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Conference on neural and stochastic methods in image and signal processing iii

作者： MCDONNELL, JR WAAGEN, DE NCCOSC RDT&E DIVSAN DIEGOCA 92152

ISBN: (纸本)0819416282

This work investigates the application of evolutionary search to cascade-correlation learning architectures. Evolutionary programming is used to generate the hidden weights of each candidate hidden unit in the cascade-correlation learning paradigm. The output weights are adapted using deterministic techniques. Evolutionary search is also used to modify the connectivity of each candidate unit so that parsimonious structures may be generated during the neural network construction process. This approach is appealing from a computational perspective since only a population of hidden nodes is being optimized as opposed to a population of neural networks. Results are given for selected low-dimensional examples.

关键词： neural NETWORK EVOLUTIONARY OPTIMIZATION CASCADE-CORRELATION CLASSIFIER DESIGN

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