This paper presents a novel method for designing TSC m-out-of-n code checkers taking into account a realistic fault model including stuck-at, transistor stuck-on, transistor stuck-open, resistive bridging faults and b...
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This paper presents a novel method for designing TSC m-out-of-n code checkers taking into account a realistic fault model including stuck-at, transistor stuck-on, transistor stuck-open, resistive bridging faults and breaks. The proposed design method is the first method in the open literature that takes into account a realistic fault model and can be applied for all practical values of m and n. Apart from the above the proposed checkers are very compact and very fast. Another benefit of the proposed TSC checkers is that all faults are tested by single pattern tests thus the probability of achieving the TSC goal is greater than in checkers requiring two-pattern tests.
Investigates a novel approach to image structure segmentation based on detecting the critical image edges by formulating the problem as a classification task. The main goal of such a research effort is to better ident...
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Investigates a novel approach to image structure segmentation based on detecting the critical image edges by formulating the problem as a classification task. The main goal of such a research effort is to better identify abrupt image changes without increasing the presence of noise in the resulting image. The suggested methodology is based on the discrete 2D wavelet transform applied to the original image in an attempt to extract more informative features for facilitating the decision-making process subsequently involved. This process is considered as a classification procedure employing supervised training techniques and, more specifically, multivariate stepwise discriminant analysis. The feasibility of this novel proposed approach is preliminarily studied by applying it to the image structure segmentation problem of a brain slice MRI image.
The recursive least squares (RLS) algorithm is one of the most well known algorithms used for adaptive filtering and system identification. We consider the convergence properties of the forgetting factor RLS algorithm...
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The recursive least squares (RLS) algorithm is one of the most well known algorithms used for adaptive filtering and system identification. We consider the convergence properties of the forgetting factor RLS algorithm in a stationary data environment. We study the dependence of the speed of convergence of RLS with respect to the initialization of the input sample covariance matrix and with respect to the observation noise level. By obtaining estimates of the settling time we show that RLS, in a high SNR environment, when initialized with a matrix of small norm, has a very fast convergence. The convergence speed decreases as we increase the norm of the initialization matrix. In a medium SNR environment the optimum convergence speed of the algorithm is reduced, but the RLS becomes more insensitive to initialization. Finally in a low SNR environment it is preferable to start the algorithm with a matrix of large norm.
There have been a large number of systems that integrate logic and objects (frames or classes) for knowledge representation and reasoning. Most of those systems give pre-eminence to logic and their objects lack the st...
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There have been a large number of systems that integrate logic and objects (frames or classes) for knowledge representation and reasoning. Most of those systems give pre-eminence to logic and their objects lack the structure of frames. These choices imply a number of disadvantages, as the inability to represent exceptions and perform default reasoning, and the reduction in the naturalness of representation. In this paper, aspects of knowledge representation and reasoning in SILO, a system integrating logic in objects, are presented. SILO gives pre-eminence to objects. A SILO object comprises elements from both frames and classes. A kind of many-sorted logic is used to express object internal knowledge. Message passing, alongside inheritance, plays a significant role in the reasoning process. Control knowledge, concerning both deduction and inheritance. is separately and explicitly represented via definitions of certain functions, called meta-functions.
In this paper we are comparing (via simulations) under real conditions, several routing algorithms based either on oblivious wormhole routing or deflection routing. Although these two techniques differ significally in...
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FIR filters obtained with the classical L/sub 2/ method have performance that is very sensitive to the form of the ideal response selected for the transition region. In this paper we propose a means for selecting the ...
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FIR filters obtained with the classical L/sub 2/ method have performance that is very sensitive to the form of the ideal response selected for the transition region. In this paper we propose a means for selecting the unknown part of a complex ideal response optimally. By selecting a proper L/sub 2/ criterion and using variational techniques we succeed in minimizing the criterion with respect to the ideal response and thus obtain its corresponding optimum form. The complete solution to the problem can be obtained by solving a simple system of linear equations suggesting a reduced complexity for the proposed method. Using the optimum form of the ideal response we also propose a new suboptimal method for the design of weighted FIR filters. Design examples are presented to illustrate the performance of the proposed method.
The problem of estimating the relative position of an underwater maneuvering target is treated as an estimation problem when an unknown and time varying bias is present in the plant noise process. Pilot-initiated mane...
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The problem of estimating the relative position of an underwater maneuvering target is treated as an estimation problem when an unknown and time varying bias is present in the plant noise process. Pilot-initiated maneuvers are modeled as impulsive unknown inputs affecting the bias term at times unknown to the observer. A new algorithm, capable of efficiently handling the problem of state estimation with time varying unknown bias, is derived by using the Lainiotis multimodel partitioning theory coupled with conventional constant bias estimation algorithms, Simulation results show that the proposed algorithm performs very well under adverse operating conditions, such as high measurement noise, long target to observer range and large-scale target maneuvers.
A constraint network is arc consistent if any value of its variables is compatible with at least one value of any other variable. The Arc Consistency Problem (ACP) consists in filtering out values of the variables of ...
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The goal of a fugitive-search game on a graph is to trap a fugitive that hides on the vertices of the graph by systematically placing searchers on the vertices. The fugitive is assumed to have complete knowledge of th...
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