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adaptive algorithm-Based Fused Bayesian Maximum Entropy-Variational Analysis Methods for Enhanced Radar Imaging

2nd Mexican Conference on Pattern Recognition

作者： Vazquez-Bautista, R. F. Morales-Mendoza, L. J. Ortega-Almanza, R. Blanco-Ortega, A. FIEC Univ Veracruzana Av Venustiano Carranza S-N Poza Rica 93390 Ver Mexico CENIDET Ingn Mecatron Cuernavaca 62490 Morelos Mexico

ISBN: (纸本)9783642159916

In this paper we address an adaptive computational algorithm to improve the Bayesian maximum entropy variational analysis (BMEVA) performance for high resolution radar imaging and denoising. Furthermore, the variational analysis (VA) approach is aggregated by imposing the metrics structures in the corresponding signal spaces. Then, the formalism for combining the Bayesian maximum entropy strategy with the VA paradigm is presented. Finally, the image enhancement and denoising benefits produced by the proposed adaptive Bayesian maximum entropy variational analysis (ABMEVA) method are showed via simulations with real-world radar scene

关键词： Bayesian Maximum Entropy data fusion adaptive algorithm variational analysis

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Regarding an adaptive algorithm for testing multivariate linear dependence

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LINEAR ALGEBRA AND ITS APPLICATIONS 2011年第2期434卷 605-613页

作者： Walker, Ronald A. Penn State Harrisburg Dept Comp Sci & Math Sci Middletown PA 17057 USA

For a finite collection of functions within some differential field of several variables, there exists an adaptive algorithm for calculating a basis of their linear relations. We study the complexity of this algorithm, noting how it compares to some other existing techniques. Also we demonstrate some modifications for improving implementation. In the course of our analysis, we define the marginal set of a Young-like set and show how the size of the former can be bounded in terms of the size of the latter. (C) 2010 Elsevier Inc. All rights reserved.

关键词： adaptive algorithm Complexity analysis Linear dependence Differential fields Young-like sets Marginal set Binomial coefficient expansions

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adaptive algorithm and Tool Flow for Accelerating SystemC on Many-Core Architectures 17

Adaptive Algorithm and Tool Flow for Accelerating SystemC on...

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17th Euromicro Conference on Digital System Design (DSD)

作者： Roth, Christoph Reder, Simon Bucher, Harald Sander, Oliver Becker, Juergen Karlsruhe Inst Technol Inst Informat Proc Technol ITIV D-76021 Karlsruhe Germany

ISBN: (纸本)9781479957934

Within this paper an adaptive approach for parallel simulation of SystemC RTL models on future many-core architectures like the Single-chip Cloud Computer (SCC) from Intel is presented. It is based on a configurable parallel SystemC kernel that preserves the partial order defined by the SystemC delta cycles while avoiding global synchronization as far as possible. The underlying algorithm relies on a classification of existing communication relations between parallel processes. The type and topology of communication relations determines the type and number of causality conditions that need to be fulfilled during runtime. The parallel kernel is complemented by an automated tool flow that allows detecting relevant model-specific properties, performing a fine-grained model partitioning, classifying communication relations and configuring the kernel. Experiments by means of a MPSoC model show, that pure local synchronization can provide significant performance gains compared to global synchronization. Furthermore, the combination of local synchronization with fine-grained partitioning provides additional degrees of freedom for optimization.

关键词： Adaptation models Computer architecture Delays Kernel Runtime Synchronization System recovery adaptive algorithm Many-Core Parallel Simulation SystemC system recovery Runtime Computer Architecture adaptive algorithms core construction Kernel Seeds Adaptation models Degree of freedom delay ARCHITECTURE

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Combination of "generalized Trotter operator splitting" and "quadratic adaptive algorithm" method for tradeoff among speedup, stability, and accuracy in the Markov chain model of sodium ion channels in the ventricular cell model

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2020年第9期58卷 2131-2141页

作者： Chen, Xing-Ji Luo, Ching-Hsing Chen, Min-Hung Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Natl Cheng Kung Univ Dept Math 1 Univ Rd Tainan 701 Taiwan

The fast hybrid operator splitting (HOS) and stable uniformization (UNI) methods have been proposed to save computation cost and enhance stability for Markov chain model in cardiac cell simulations. Moreover, Chen-Chen-Luo's quadratic adaptive algorithm (CCL) combined with HOS or UNI was used to improve the tradeoff between speedup and stability, but without considering accuracy. To compromise among stability, acceleration, and accuracy, we propose a generalized Trotter operator splitting (GTOS) method combined with CCL independent of the asymptotic property of a particular ion-channel model. Due to the accuracy underestimation of the mixed root mean square error (MRMSE) method, threshold root mean square error (TRMSE) is proposed to evaluate computation accuracy. With the fixed time-step RK4 as a reference, the second-order GTOS combined with CCL (30.8-fold speedup) for the wild-type Markov chain model with nine states (WT-9 model) or (7.4-fold) for the wild-type Markov chain model with eight states (WT-8 model) is faster than UNI combined with CCL (15.6-fold) for WT-9 model or (1.2-fold) for WT-8 model, separately. Besides, the second-order GTOS combined with CCL has 3.81% TRMSE for WT-9 model or 4.32% TRMSE for WT-8 model more accurate than 72.43% TRMSE for WT-9 model or 136.17% TRMSE for WT-8 model of HOS combined with CCL. To compromise speedup and accuracy, low-order GTOS combined with CCL is suggested to have the advantages of high precision and low computation cost. For high-accuracy requirements, high-order GTOS combined with CCL is recommended.

关键词： Ventricular cell model Sodium ion channel Markov chain model adaptive algorithm Operator splitting

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A Partial Update adaptive algorithm for Sparse System Identification

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 240-255页

作者： Wen, Hao-Xiang Yang, Sen-Quan Hong, Yuan-Quan Luo, Huan Shaoguan Univ Coll Phys & Mech & Elect Engn Shaoguan 512000 Guangdong Peoples R China

A sparse partial update (SPU) algorithm and its improved version improved SPU (ISPU) algorithm, are proposed in this paper for sparse system identification. The SPU first categorizes its filter coefficients into active and inactive coefficients. Then all the active coefficients are included in each adaptation, while only a small portion of the inactive coefficients is periodically chosen to be included in the adaptation. The SPU emphasizes convergence of the active coefficients by updating them at every adaptation, while the periodical adaptation of the inactive coefficients ensures its robustness and tracking capability. By eliminating most of the inactive coefficients from adaptation, the SPU significantly reduces the number of adapting coefficients in each adaptation. The decline in the number of adapting coefficients eventually leads to improvement in both computation and convergence speed. To avoid performance degradation in the case of identifying a dispersive system, an ISPU is further proposed by making modifications to the SPU. The simulation results demonstrate that the ISPU not only outperforms other sparse adaptive algorithms in identifying a sparse system but also performs at least as well as the NLMS algorithm in identifying a dispersive system.

关键词： Filtering algorithms adaptive filters Dispersion Convergence adaptive algorithms Robustness System identification adaptive algorithm partial update algorithm sparse impulse response sparse system identification minimum disturbance principle independence assumption

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Self-tuning fast adaptive algorithm for impulsive noise suppression in color images

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2020年第4期17卷 1067-1087页

作者： Malinski, Lukasz Smolka, Bogdan Silesian Tech Univ Div Ind Informat Krasinskiego 8 PL-40019 Katowice Poland Silesian Tech Univ Inst Automat Control Akad 16 PL-44100 Gliwice Poland

In this paper, a self-tuning version of the newly introduced Fast adaptive Switching Trimmed Arithmetic Mean Filter, which is a very efficient technique for impulsive noise suppression, is elaborated. Most of the methods presented in the rich literature have numerous parameters, whose proper settings are crucial for efficient noise suppression. Although researchers often provide recommended values for their algorithms' parameters, the actual choice remains in the hands of the user. Our goal is to free the operator from parameter selection dilemma and to propose an algorithm which includes required expert knowledge within itself. The only obligatory inputs of the proposed algorithm (from the user perspective) are the image itself and the size of the operating window.

关键词： Impulsive noise reduction Color image enhancement and restoration Image quality adaptive algorithm Switching filter

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On-line estimation of variable parameters of synchronous machines using a novel adaptive algorithm - Estimation and experimental verification - Discussion

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IEEE TRANSACTIONS ON ENERGY CONVERSION 1997年第3期12卷 208-210页

作者： Zhao, Z Jiang, J Xu, L OHIO STATE UNIV DEPT ELECT ENGNCOLUMBUSOH 43210

A novel adaptive algorithm is presented for the online estimation of the variable parameters of a synchronous machine (SM) as a function of the operating conditions. The concept of a synthesized information factor (SIF) is proposed as the core of the novel adaptive algorithm. For a continuous process, the SIF optimally combines information from the past with that at the present. adaptive principles based on the SIF are discussed and adaptive estimation procedures are developed. Computer simulation results are given to highlight the advantages of the novel adaptive algorithm over conventional least mean square and recurrence least square algorithms.

关键词： Parameter estimation Synchronous machines adaptive algorithm Samarium Resonance light scattering Power system transients Least squares approximation Power system dynamics Power system simulation adaptive estimation

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NEW PROCESSING TECHNIQUES BASED ON THE CONSTANT MODULUS adaptive algorithm

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1985年第2期33卷 420-431页

作者： TREICHLER, JR LARIMORE, MG ARGOSYST INC SUNNYVALECA 94086

This paper presents three extensions of the constant modulus algorithm (CMA) introduced in an earlier paper as a means of correcting degradations in constant enyelope waveforms. As originally formulated, the CMA employs an FIR filter with complex coefficients and accepts complex (quadrature) input data. In this paper, first a real input, real coefficient version of the algorithm is shown to perform arbitrarily close to the fully complex version. Secondly, the algorithm is extended for the enhancement of signals having a nonconstant but known envelope, as might arise in data signals with pulse shaping. Lastly, a multichannel version of CMA, wherein several observations are linearly combined, is presented for joint adaptation of multiple filters. This approach can be used, for example, as a means of spatial or polarization "beamsteering" to reject additive interferers and compensate for channel-induced polarization rotation.

关键词： adaptive algorithm Finite impulse response filter Signal processing algorithms adaptive filters Polarization Interference Signal processing Degradation Pulse shaping methods Filtering algorithms

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A WEIGHTED NORMALIZED FREQUENCY-DOMAIN LMS adaptive algorithm

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1988年第7期36卷 1002-1007页

作者： FLORIAN, S BERSHAD, NJ RAFAEL HAIFAISRAEL

A general filtering scheme is presented for obtaining an input power estimate for setting the convergence parameter mu separately in each frequency bin of a frequency-domain LMS adaptive filter (FDAF) algorithm. A linear filtering operation is performed on the magnitude square of the input data and incorporated directly into the algorithm as a data-dependent time-varying stochastic mu (n). The mean performance of the weighted normalized frequency domain LMS algorithm (WNFDAF) is analyzed using independent and identically distributed Gaussian data, and the results are validated by the Monte Carlo simulations of the algorithm. The simulations are also used to study the weight transient behavior. The simulations suggest that sort smoothing times are sufficient for rapid weight convergence without large fluctuations in the power estimates significantly affecting transient weight behavior.< >

关键词： Frequency domain analysis Least squares approximation adaptive algorithm adaptive filters Frequency estimation Convergence Filtering algorithms Nonlinear filters Maximum likelihood detection Stochastic processes

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NONLINEAR QUANTIZATION EFFECTS IN THE FREQUENCY-DOMAIN COMPLEX SCALAR LMS adaptive algorithm

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1986年第1期34卷 140-151页

作者： SHERWOOD, DT BERSHAD, NJ UNIV CALIF IRVINE DEPT ELECT ENGNIRVINECA 92717

Nonlinear quantization effects in the frequency domain complex scalar LMS adaptive algorithm are analyzed by using conditional expectations. The probability density function of the quantizer input, conditioned on the weight, is derived. The density is applied to finding the conditional characteristic function and the Mth conditional moment at the quantizer output. The first and second conditional moments of the quantizer output are used to derive difference equations that approximate the dynamical behavior of the first and second weight moments. These difference equations are solved numerically and compare favorably to simulation results. A model of the quantizer as an additive noise source is of no analytical value since the quantizatian noise has negligible effect on the mean square error when the model is valid. Finally, a design approach is proposed for selecting the number of bits in the weight accumulator. The moment equations are also used to determine the algorithm mean square error for different quantizer step sizes and the optimum algorithm step size μ when a fixed amount of input data is available for adaptation.

关键词： Quantization Frequency domain analysis Least squares approximation adaptive algorithm Difference equations Additive noise Mean square error methods algorithm design and analysis Probability density function Differential equations

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