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IEEE International On-Line Testing Workshop

作者： E. Kalligeros X. Kavousianos D. Bakalis D. Nikolos Department of Computer Engineering & Informatics University of Patras Patras Greece Computer Technology Institute Patras Greece

ISBN: (纸本)0769512909

Presents a new reseeding technique for LFSR-based test pattern generation suitable for circuits with random-pattern resistant faults. Our technique eliminates the need of a ROM for storing the seeds since the LFSR jumps from a state to the required state (seed) by inverting the logic value of some of the bits of its next state. An efficient algorithm for selecting reseeding points is also presented, which targets complete fault coverage and minimization of the cardinality of the test set and the hardware required for the implementation of the test pattern generator. The application of the proposed technique to ISCAS '85 and the combinational part of ISCAS '89 benchmark circuits shows its superiority against the already known reseeding techniques with respect to the length of the test sequence and, in the majority of cases, the hardware required for their implementation.

关键词： Test pattern generators Circuit testing Circuit faults Automatic testing Built-in self-test Integrated circuit testing Polynomials Read only memory Hardware System testing

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A robust hybrid evolutionary method for ARMA model identification

A robust hybrid evolutionary method for ARMA model identific...

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International Symposium on Image and Signal Processing and Analysis

作者： G.N. Beligiannis S.D. Likothanassis E.N. Demiris Department of Computer Engineering and Informatics University of Patras Patras Greece Computer Technology Institute Patras Greece

ISBN: (纸本)9539676940

A new hybrid evolutionary method is proposed. This method alleviates the dependency of pure evolutionary algorithms on the complexity of a given time series and turns out to be very reliable in identifying the correct order and estimation the true parameters' values of a given system model. It combines the effectiveness of the multi-model partitioning theory with the robustness of evolutionary algorithms. Although the system structure is a bit complicated, simulation results show that the proposed method gives better results compared to the conventional multi-model adaptive filter algorithm and the pure evolutionary ones, since it has not only the ability to perform well in searching the whole parameter space, but also to cope with the complexity of the model and reliably lead to the correct order and the true parameters' values. The method can be implemented in a parallel environment thus increasing the computational speed.

关键词： Robustness Parameter estimation Evolutionary computation Adaptive filters Signal processing algorithms Partitioning algorithms System identification Adaptive signal processing Time series analysis Costs

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Augmenting experience reports with lightweight postmortem reviews

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Lecture Notes in computer Science

作者： Dingsøyr, Torgeir Moe, Nils Brede Nytrø, Øystein Department of Computer and Information Science Norwegian University of Science and Technology Norway SINTEF Telecom and Informatics Trondheim7491 Norway Fraunhofer Institute for Experimental Software Engineering Sauerwiesen 6 Kaiserslautern67661 Germany

ISBN: (纸本)3540425713

Many small and medium-sized companies that develop software experience the same problems repeatedly, and have few systems in place to learn from their own mistakes as well as their own successes. Here, we propose a lightweight method to collect experience from completed software projects, and compare the results of this method to more widely applied experience reports. We find that the new method captures more information about core processes related to software development in contrast to experience reports that focus more on management processes. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Artificial intelligence

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A clustering method for nominal and numerical data based on rough set theory 15th

A clustering method for nominal and numerical data based on ...

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15th International Workshop on Japanese Society for Artificial Intelligence, JSAI 2001

作者： Hirano, Shoji Tsumoto, Shusaku Okuzaki, Tomohiro Hata, Yutaka Department of Medical Informatics Shimane Medical University School of Medicine 89-1 Enya-cho Izumo693-8501 Japan Department of Computer Engineering Himeji Institute of Technology 2176 Shosha Himeji671-2201 Japan

ISBN: (纸本)9783540455486

This paper describes a new clustering method based on rough set theory. This method classifies objects according to the indiscernibility relations defined on the basis of relative similarity. First, an initial equivalence relation, which evaluates local similarity of objects, is assigned to every object. Then modification of the initial equivalence relations is performed by examining global relationships among them. An initial equivalence relation will be modified if it gives excessively fine classification to the objects. Consequently, generation of small category is suppressed and adequately coarse clusters are formed. Experimental results on the artificial data showed that this method produced good clustering results for both of nominal and numerical data. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Rough set theory

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On the design of modulo 2/sup n//spl plusmn/1 adders

On the design of modulo 2/sup n//spl plusmn/1 adders

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： C. Efstathiou H.T. Vergos D. Nikolos Department of Informatics TEI Athens Athens Greece Computer Technology Institute CTI Patras Greece Computer Engineering & Informatics Department University of Patras Patras Greece

In this paper we present new architectures for the design of modulo 2/sup n//spl plusmn/1 adders, which are based on the use of the same design block. Our design block incorporates a parallel-prefix carry computation unit with a carry increment stage. VLSI implementations of the proposed architectures in a static CMOS technology reveal their superiority against all already known architectures when the area * time/sup 2/ product is used as a metric and n > 8.

关键词： computer architecture Tree data structures Logic Algorithm design and analysis Costs Concurrent computing Tin Delay

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CONSTRUCTING MODULAR HYBRID RULE BASES FOR EXPERT SYSTEMS

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International Journal on Artificial Intelligence Tools 2001年第1N02期10卷 87-105页

作者： I. HATZILYGEROUDIS J. PRENTZAS University of Patras School of Engineering Department of Computer Engineering & Informatics 26500 Patras Hellas Greece Computer Technology Institute P.O. Box 1122 26110 Patras Hellas Greece

Neurules are a kind of hybrid rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Thus, the corresponding neurule base consists of a number of autonomous adaline units (neurules). Due to this fact, a modular and natural knowledge base is constructed, in contrast to existing connectionist knowledge bases. In this paper, we present a method for generating neurules from empirical data. To overcome the difficulty of the adaline unit to classify non-separable training examples, the notion of 'closeness' between training examples is introduced. In case of a training failure, two subsets of 'close' examples are produced from the initial training set and a copy of the neurule for each subset is trained. Failure of training any copy, leads to production of further subsets as far as success is achieved.

关键词： hybrid knowledge representation symbolic representation connectionist representation production rules neural networks modularity naturalness

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AN OBJECT-ORIENTED TOOLBOX FOR ADAPTIVE NEUraL NETWORKS' IMPLEMENTATION

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International Journal on Artificial Intelligence Tools 2001年第3期10卷 345-371页

作者： GEORGE D. MANIOUDAKIS SPIRIDON D. LIKOTHANASSIS University of Patras Department of Computer Engineering and Informatics Patras 26500 Greece Computer Technology Institute (C.T.I.) 3 Kolokotroni Str. Patras 26221 Greece

Neural Networks are massively parallel processing systems, that require expensive and usually not available hardware, in order to be realized. Fortunately, the development of effective and accessible software, makes their simulation easy. Thus, various neural network's implementation tools exist in the market, which are oriented to the specific learning algorithm used. Furthermore, they can simulate only fixed size networks. In this work, we present some object-oriented techniques that have been used to defined some types of neuron and network objects, that can be used to realize, in a localized approach, some fast and powerful learning algorithms which combine results of the optimal filtering and the multi-model partitioning theory. Thus, one can build and implement intelligent learning algorithms that face both, the training as well as the on-line adjustment of the network size. Furthermore, the design methodology used, results to a system modeled as a collection of concurrent executable objects, making easy the parallel implementation. The whole design results in a general purpose tool box which is characterized by maintainability, reusability, and increased modularity. The provided features are shown by the presentation of some practical applications.

关键词： Neural Networks (NN) Object-Oriented (OO) Localized approaches of EKF (MEKA, EBP) Multi-model partitioning theory

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On distributed information retrieval in virtual enterprises

On distributed information retrieval in virtual enterprises

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IEEE International Conference on Systems, Man and Cybernetics

作者： K. Giotopoulos B. Vassiliadis N. Bogonikolos A. Tsakalidis S. Likothanassis Computer Technology Institute Patras Greece Department of Computer Engineering and Informatics University of Patras Patras Greece ZEUS Consulting S.A Patras Greece

A new emerging area of research and technological development, set forward by the requirements of today's progressive industries, is the paradigm of virtual organisations. A meta-data repository is the logical place for uniformly retaining and managing corporate knowledge within or across different members of such organisations. Many meta-data architectures are not optimized for information retrieval purposes. Taking into consideration the existence of different data types and attributes, many problems arise when we try to successfully merge search results from heterogeneous sources. Motivated by these observations, this paper investigates a new architecture that can support multiple meta-data abstractions and that is readily extensible. Modularity and extensibility are the design emphases.

关键词： Information retrieval Virtual enterprises Information management Knowledge management informatics Research and development management technology management Management information systems Information analysis Information technology

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On the design of self-testing checkers for modified Berger codes

On the design of self-testing checkers for modified Berger c...

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IEEE International On-Line Testing Workshop

作者： S.J. Piestrak D. Bakalis X. Kavousianos Institute of Engineering Cybernetics Wroclaw University of Technology Wroclaw Poland Department of Computer Engineering & Informatics University of Patras Patras Greece

ISBN: (纸本)0769512909

One of several approaches for designing highly-reliable systems relies on using error detecting codes (EDCs) and implementing digital circuits as self-checking. One class of EDCs that has been very often used to implement self-checking circuits are Berger codes. Although several self-testing checkers (STCs) for Berger codes have been proposed in the past, they mostly present area and delay results based on gate counts and gate levels and not on real implementations. In this work we consider real implementations and present and evaluate the area, delay and power characteristics of STCs for modified Berger codes that are based on: (a) parallel counters and (b) sorting networks. Preliminary results indicate that STCs based on parallel counters are smaller and consume less power than the STCs based on sorting networks.

关键词： Built-in self-test Power dissipation Aerospace electronics Circuit faults CMOS logic circuits Sorting Aerodynamics computer errors Digital circuits Circuit testing

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A rough set-based clustering method with modification of equivalence relations 5th

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5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001

作者： Hirano, Shoji Okuzaki, Tomohiro Hata, Yutaka Tsumoto, Shusaku Tsumoto, Kouhei Department of Computer Engineering Himeji Institute of Technology 2167 Shosha Himeji671-2201 Japan Department of Medical Informatics Shimane Medical University School of Medicine 89-1 Enya-cho IzumoShimane693-8501 Japan Department of Bimolecular Engineering Graduate School of Engineering Tohoku University Aoba-yama07 Sendai980-8579 Japan

ISBN: (纸本)3540419101

This paper presents a clustering method for nominal and numerical data based on rough set theory. We represent relative simi- larity between objects as a weighted sum of two types of distances: the Hamming distance for nominal data and the Mahalanobis distance for numerical data. On assigning initial equivalence relations to every ob- ject, modification of slightly difierent equivalence relations is performed to suppress excessive generation of categories. The optimal clustering re- sult can be obtained by evaluating the cluster validity over all clusters generated with various values of similarity thresholds. After classifica- tion has been performed, features of each class are extracted based on the concept of value reduct. Experimental results on artificial data and amino acid data show that this method can deal well with both types of attributes. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Rough set theory

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