250579
Live neural network formations on electronic chips
Abstract Live neuron behavior on an electronic chip was recorded by Nippon Telephone & Telegraph (NTT) with a time-lapsed video under the microscope. The dissociated chick embryonic brain neurons (telencephalic neurons) were cultured, and individual neurons were placed on silicon glass plates deposited with metal oxide strips about 10 μm width for possible electronegativity neurite guidance. We have attempted to map biological neural networks (BNN) to artificial neural networks (ANN) to determine whether the connectivity patterns dictate the information processing efficiency, or vice versa. By an image processing technique, we discovered the smallest size of intelligent BNN from those NTT video tapes that happened to fail their neurite guidance experiments. The neurite growth connecting other neurons was accomplished intentionally through selective paring in time rather than mechanically following the external guidance. Such a dynamic interconnection has inspired a model of energy landscaping, of which a general convergence theorem that has unified Hopfield-Cohen-Grossberg and Werbos-Rumelhart theorems is given, and a nonlinear Hebbian learning is derived capable for self-determination of ANN architectures.
35585526
On the Design of Fault-Tolerant Two-Dimensional Systolic Arrays for Yield Enhancement
The authors propose a unified approach to the design of the fault-tolerant systolic arrays incorporating design for testability, a testing scheme, a reconfiguration algorithm, time-complexity analysis of the proposed reconfiguration algorithm, and yield analysis. A main feature of the proposed designs is that multiple processing elements in a 2-D array can be tested simultaneously, thus reducing the testing time significantly. Another feature is that with the introduction of delay registers, the proposed reconfiguration algorithm reconfigures a faulty 2-D systolic array into a fault-free array without reducing throughput. The overall aim is to provide a design for a 2-D systolic array that produces high yield in VLSI/WSI implementations. >
10981925
Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field
A geometrical expansion learning algorithm for multilayer neural networks using unipolar binary neurons with integer connection weights, which guarantees convergence for any Boolean function, is introduced. Neurons in the hidden layer develop as necessary without supervision. In addition, the computational amount is much less than that of the backpropagation algorithm. >
31990528
Diagnosing Multiple Bridge Faults in Baseline Multistage Interconnection Networks
27298271
The Rule-Based Approach to Reconfiguration of 2-D Processor Arrays
The authors propose to set up assignment rules based on the available interconnection resources of 2-D processor arrays. The reconfiguration of 2-D processor arrays is guided by the assignment rules, such that logical cells are always connected through the available interconnection resources. The advantage of the proposed approach is the easy adaptation to various system requirements, by adjusting assignment rules. The proposed reconfiguration algorithm can be extended to arrays with clustered faulty cells, overcoming the weakness of fixed domain approaches. >
38004947
On-Line Detection of Errors in Homogeneous Multiprocessor Systems
14673300
A Polynomial Time Approximation Scheme for the Problem of Interconnecting Highways
AbstractThe objective of the Interconnecting Highways problem is to construct roads of minimum total length to interconnect n given highways under the constraint that the roads can intersect each highway only at one point in a designated interval which is a line segment. We present a polynomial time approximation scheme for this problem by applying Arora's framework (Arora, 1998; also available from http:www.cs.princeton.edu/~arora). For every fixed c > 1 and given any n line segments in the plane, a randomized version of the scheme finds a 
$$\left( {1 + \frac{1}{c}} \right)$$
-approximation to the optimal cost in O(nO(c)log(n) time.
46221811
On Energy-Aware Dynamic Clustering for Hierarchical Sensor Networks
This paper proposes an energy-efficient nonlinear programming based dynamic clustering protocol (NLP-DC) unique to sensor networks to reduce the consumption of energy of cluster heads and to prolong the sensor network lifetime. NLP-DC must cover the entire network, which is another basic functionality of topology control. To achieve these goals, NLP-DC dynamically regulates the radius of each cluster for the purpose of minimizing energy consumption of cluster heads while the entire sensor network field is still being covered by each cluster. We verify both energy-efficiency and guarantee of perfect coverage. Through simulation results, we show that NLP-DC achieves the desired properties.
12043862
A method and tool support for variant requirements analysis: goal and scenario based approach
Identifying and representing variant requirements among products in a product family are crucial activities for a successful software reuse. The variant requirements should be not only identified based on the business goal, which drives marketing plan, product plan, and differences among products, but also represented as familiar notations in order to support developing a particular product in the product family. Thus, our proposal is to identify the variant requirements through goals and scenarios, and represent them as variable use cases for a product family. Especially, for identification of the variant requirements, we propose four abstraction levels of requirements in the product family, and goal and scenario modeling. For representation of them, variable use case model is suggested, and also the use case transfer rules are proposed so as to bridge the gap between the identification and representation activity. The paper illustrates the application of the approach within a supporting tool, namely DREAM (Domain REquirements Analysis Method) using the ATM example.
7572098
New Usage of SOM for Genetic Algorithms
Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantizations, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.
16441621
Tree-Dependent Components of Gene Expression Data for Clustering
Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metabolic shift data, and yeast sporulation data; shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.