We present simple methods to enhance a recently proposed class of fully adaptive algorithms for fault tolerant wormhole routing. These algorithms are based on J. Duato's (1993) theory and are being used in several...
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We present simple methods to enhance a recently proposed class of fully adaptive algorithms for fault tolerant wormhole routing. These algorithms are based on J. Duato's (1993) theory and are being used in several research projects. We show that with three virtual channels per physical channel, multiple rectangular shaped fault blocks can be tolerated in two dimensional meshes. There is no restriction on the number of faults, and maintaining fault information locally is sufficient. The logic for fault tolerant routing is used only in the presence of faults, and there is no performance degradation in the absence of faults. The proposed technique incorporates fault tolerance into wormhole algorithms with simple logic and low virtual channel requirements.< >
The paper establishes several robustness, optimality, and convergence properties of the widely used class of instantaneous-gradient adaptive algorithms. The analysis is carried out in a purely deterministic framework ...
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The paper establishes several robustness, optimality, and convergence properties of the widely used class of instantaneous-gradient adaptive algorithms. The analysis is carried out in a purely deterministic framework and assumes no apriori statistical information. It starts with a simple Cauchy-Schwarz inequality for vectors in an Euclidean space and proceeds to derive local and global energy bounds that are shown here to highlight, as well as explain, several relevant aspects of this important class of algorithms.< >
In this paper, a novel adaptive soft morphological gradient (ASMG) filter is proposed, based on a combination of the idea of the soft morphological filtering and the adaptive technique. ASMG filtering is an efficient ...
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In this paper, a novel adaptive soft morphological gradient (ASMG) filter is proposed, based on a combination of the idea of the soft morphological filtering and the adaptive technique. ASMG filtering is an efficient nonlinear sharpening method, which can be applied for edge detection. By employing four directional structuring elements, ASMG filtering has the capability of selecting the directional structuring element with the maximum response, whose direction varies depending on the change of directional edges. In addition, by comparing the variance of the moving structuring window with the base variance, the ASMG filter provides an adaptive algorithm for morphological operations. The experimental results show that the ASMG filter is better than the traditional operators in edge detection and noise suppression.
Eigenstructure decomposition of correlation matrices is an important pre-processing stage in many modern signal processing applications. In an unknown and possibly changing environment, adaptive algorithms that are ef...
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Eigenstructure decomposition of correlation matrices is an important pre-processing stage in many modern signal processing applications. In an unknown and possibly changing environment, adaptive algorithms that are efficient and numerically stable as well as readily implementable in hardware for eigendecomposition are highly desirable. Most modern real-time signal processing applications involve processing large amounts of input data and require high throughput rates in order to fulfil the needs of tracking and updating. The authors consider the use of a novel systolic array architecture for the high throughput online implementation of the adaptive simultaneous iteration method (SIM) algorithm for the estimation of the p largest eigenvalues and associated eigenvectors of quasi-stationary or slowly varying correlation matrices.< >
In this paper, we propose a new nonlinear principal component analysis based on a generalized correlation function which we call correntropy. The data is nonlinearly transformed to a feature space, and the principal d...
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In this paper, we propose a new nonlinear principal component analysis based on a generalized correlation function which we call correntropy. The data is nonlinearly transformed to a feature space, and the principal directions are found by eigen-decomposition of the correntropy matrix, which has the same dimension as the standard covariance matrix for the original input data. The correntropy matrix characterizes the nonlinear correlations between the data. With the correntropy function, one can efficiently compute the principal components in the feature space by projecting the transformed data onto those principal directions. We give the derivation of the new method and present simulation results.
Although many successful industrial and semi-industrial applications of the adaptive systems have been reported in the literature, the gap between theory and practice still exists. More specifically it is easy to get ...
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Although many successful industrial and semi-industrial applications of the adaptive systems have been reported in the literature, the gap between theory and practice still exists. More specifically it is easy to get adaptive algorithm to perform well under an idealized simulation framework. In the real world life the adaptive controller should be able to handle non-linearities, non-minimum phase behaviour, load-disturbance, and unmodeled and time-varying dynamics over all the range of operating conditions. In this paper, some practical implications of recent theoretical results in robustness of adaptive controllers are discussed from a design engineer point of view using an adaptive LQ controller. Simulation studies involving a realistic mathematical model of an electrical furnace are given to show the applicability of the considered adaptive controller.
This paper describes an adaptive filter structure which may be used in multi-channel noise-cancelling applications. The proposed structure differs from those presented previously in that it incorporates a lattice filt...
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This paper describes an adaptive filter structure which may be used in multi-channel noise-cancelling applications. The proposed structure differs from those presented previously in that it incorporates a lattice filter framework, rather than tapped-delay-lines. The successive orthogonalization provided by the lattice offers advantages in adaptive convergence rate which cannot be achieved with tapped-delay-lines. In the sections below, we present an explicit description of the general noise-cancelling lattice structure, together with the appropriate adaptive algorithms.
We consider a low-complexity adaptive MIMO transmission approach for spatially correlated channels. The proposed scheme adaptively switches between different transmission modes depending on the changing channel condit...
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We consider a low-complexity adaptive MIMO transmission approach for spatially correlated channels. The proposed scheme adaptively switches between different transmission modes depending on the changing channel conditions, as a means to enhance system capacity. Each mode is a combination of a transmission technique (i.e. statistical beamforming, double space-time transmit diversity and spatial multiplexing) and a modulation/coding scheme. We first motivate our adaptive algorithm by deriving new closed-form capacity expressions, and demonstrating significant information theoretic improvements over non-adaptive transmission. We then present a practical method to switch between different modes, based on the channel statistics. Our approach is shown to yield significant improvements in spectral efficiency for typical channel scenarios
We propose a family of novel affine projection algorithms (APA) with adaptive regularization matrix. Conventional regularized APA (R-APA) uses a fixed and weighted identity matrix for regularization. The proposed algo...
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We propose a family of novel affine projection algorithms (APA) with adaptive regularization matrix. Conventional regularized APA (R-APA) uses a fixed and weighted identity matrix for regularization. The proposed algorithms incorporate a non-identity regularization matrix which is also dynamically updated. The matrix adaptation is based on the normalized stochastic-gradient of mean-square error. As a result, the efficient and robust algorithms are derived. Throughout experiments, we illustrate that the proposed algorithms show better performance than the conventional R-APA and comparable to the RLS algorithm in terms of the convergence rate and the misadjustment error
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