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State Restoration Ratio (SRR) has been the de facto standard for evaluating quality of signals selected for post-silicon tracing and debug. Given a set S of selected signals, SRR measures the fraction of (gate-level) design states that can be inferred from observing signals in S at each cycle. Unfortunately, in spite of its widespread use, we found that SRR is intrinsically unsuitable as a metric...
The ℓ1 norm regularization problem, the ℓ1 norm minimization problem and the ℓ1 norm constraint problem are known collectively as the ℓ1 norm based Basic Sparse Representation Problems (BSRPs), and have been popular basic models in the field of signal processing and machine learning. The equivalence of the above three problems is one of the crucial bases for the corresponding algorithms design. However,...
Under the cloud manufacturing environment, with the increasing number of manufacturing cloud services, one of the issues needs to be resolved is to find a manufacturing cloud service that meets users' demand. However, until now, there is not an outstanding manufacturing cloud discovery mechanism to help users to find the appropriate solution among numerous existing manufacturing cloud services. In...
when initializing the background model with ViBe algorithm, the prospects for moving target in background area will also be treated as a background model, and Video stream in the next area will appear the phenomenon of ghost. Although ViBe algorithm provide the method of updated background model, slowed to eliminate ghost region. When the foreground object passed these areas, the detection accuracy...
Variable Speed Limits (VSL) is a control tool of Intelligent Transportation Systems (ITS) which can enhance traffic safety and which has the potential to contribute to traffic efficiency. This study presents the results of a calibration and operational analysis of a candidate VSL algorithm for high flow conditions on an urban motorway of Queensland, Australia. The analysis was done using a framework...
Genetictic Algorithm (GA) is a widely-used high-efficiency random search and optimization method, which is geneticrated from evolution theory. This paper aims at overcoming the drawbacks of local convergence and not converging well to the globally-optimal solution, which usually exist in the course of solving optimization problems using Basic Genetic Algorithm(GA),and proposes a modified algorithm-...
In this paper, in view of the slowness and the locality of convergence for simple particle swarm optimization (PSO) in solving the complex system optimization, a two stage composite particle swarm optimization (TS-CPSO) is proposed as an improved PSO with the strategy of gradual range contraction. The designing ideas and the implementation of TS-CPSO are also given, and the convergence is analyzed...
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