16922921
Wire space estimation and routability analysis
This paper presents a new stochastic model for two-dimensional layouts of large size. Two problems are addressed. (1) Under the condition that the number of wires emanating from a block is Poisson distributed, determine the distribution of channel width, thus estimating the average channel width. (2) Given T tracks for each channel, determine the success probability of routing an M/spl times/M cell array chip. For both problems, closed formulas are obtained. The experiment demonstrates the effectiveness of our approach. The results can be applied to gate array, field-programmable gate array design, etc.
44707452
Diagnosis of clustered faults for identical degree topologies
Huang et al, [1998] studied the diagnosis of clustered faults and wafer testing. They proposed a diagnosis algorithm for a probabilistic fault model in simple rectangular grid structures. In this paper, we extend their results and study the diagnosis algorithm for arbitrary identical degree topologies. Our results are useful and valid for a large class of topologies. We investigate the local and global performance of the algorithm under several important fault distributions: Bernoulli failure distribution, Gamma failure distribution, and exponential failure distribution. We demonstrate that the diagnosis scheme can identify almost all nodes successfully even if the percentage of fault-free units is low (much lower than 50%) while almost all units are guaranteed to be correctly identified. In addition, we show that the performance of the algorithm is insensitive to the changes of the percentage.
10079260
The sum number and integral sum number of complete bipartite graphs
Let N denote the set of positive integers. The sum graph G + (S) of a 1nite subset S of N is the graph (S; E) with vertex set S and edge set E such that for u; v ∈ S; uv ∈ E if and only if u + v ∈ S. A graph G is called a sum graph if it is isomorphic to the sum graph G + (S) of some 1nite subset S of N . The sum number � (G) of a graph G is de1ned as the smallest nonnegative integer m for which G ∪ mK1 is a sum graph. Let Z be the set of all integers. By extending the set N to Z in the above de1nitions of sum graphs and sum numbers, Harary [3] introduced the corresponding notions of integral sum graphs and integral sum number of a graph. In this paper, we evaluate the value of the sum number and integral sum number of the complete bipartite graph Kr; s. While the former one corrects the result given in [4], the latter settles completely a problem proposed in [3]. c � 2001 Elsevier Science B.V. All rights reserved.
3032688
Statistical Identification of Key Phrases for Text Classification
Algorithms for text classification generally involve two stages, the first of which aims to identify textual elements (words and/or phrases) that may be relevant to the classification process. This stage often involves an analysis of the text that is both language-specific and possibly domain-specific, and may also be computationally costly. In this paper we examine a number of alternative keyword-generation methods and phrase-construction strategies that identify key words and phrases by simple, language-independent statistical properties. We present results that demonstrate that these methods can produce good classification accuracy, with the best results being obtained using a phrase-based approach.
14934450
Combining Statistical and Knowledge-Based Spoken Language Understanding in Conditional Models
Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive --- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. A generative HMM/CFG composite model, which integrates easy-to-obtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. We also study and compare conditional random fields (CRFs) with perceptron learning for SLU. Experimental results show that the conditional models achieve more than 20% relative reduction in slot error rate over the HMM/CFG model, which had already achieved an SLU accuracy at the same level as the best results reported on the ATIS data.
13343820
The error concealment feature in the H.26L test model
This paper presents the error concealment (EC) feature implemented by the authors in the test model of the draft ITU-T video coding standard H.26L. The selected EC algorithms are based on weighted pixel value averaging for INTRA. pictures and boundary-matching-based motion vector recovery for INTER pictures. The specific concealment strategy and some special methods, including handling of B-pictures, multiple reference frames and entire frame losses, are described. Both subjective and objective results are given based on simulations under Internet conditions. The feature was adopted and is now included in the latest H.26L reference software TML-9.0.
15897925
Error exponents for channel coding with side information
Capacity formulas and random-coding and sphere-packing exponents are derived for a generalized family of Gel'fand-Pinsker coding problems. Information is to be reliably transmitted through a noisy channel with random state sequence. Partial information about the state sequence is available to the encoder and decoder. Two families of channels are considered: (1) compound discrete memoryless channels (C-DMC); and (2) channels with arbitrary memory, subject to an additive cost constraint, or more generally to a constraint on the conditional type of the channel output given the input. Both problems are closely connected. For the C-DMC case, our random-coding and sphere-packing exponents coincide at high rates, thereby determining the reliability function of the channel family. The random-coding exponent is achieved using a 3D binning scheme and a maximum penalized mutual information decoder. In the case of arbitrary channels, with memory, a larger random-coding error exponent than in the C-DMC case is obtained. Applications of this study include watermarking, data hiding, communication in presence of partially known interferers, and problems such as broadcast channels, all of which involve the fundamental idea of binning.