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Power dissipation during testing is substantially higher than during normal operations due to increased switching activity. Test vector ordering is an effective method to reduce switching activity in combinational circuits and scan chain reordering has been often cited as an effective technique for reducing power dissipation in the scan chain during testing. This paper describes a techniquefor re-ordering of test vectors and scan cells to minimize power dissipation in full scan combinational circuits during test application. The reduction is achieved by decreasing the switching activity and spurious transitions between consequent test vectors and scan cells. We formulate the test vector and scan reordering problem as a travel salesman problem (TSP) using hamming distance between test vectors and scan cells. One of the successful approaches to solve TSP is using genetic algorithm (GA) and we use standard genetic algorithm to solve this problem. Experiments performed on the ISCAS-85 and ISCAS-89 benchmark suite show a reduction in power test applying (41] for s298) as well as a reduction in power test vector inserting (25o for s298).

Self-similarity is a phenomenon which has come into computer networks literatures during last two decades and plays a significant role in modeling of computer network traffics. It is generally accepted that computer network traffics are self-similar and they are dissimilar to Poissonbased traffics. Computer network models exert a considerable influence on improving quality of service. Therefore, selfsimilarity should be considered in traffic models in order to acquire more appropriate QoS. In this paper, we propose a novel model for generating self-similar traffic. Our model includes a multi-layer perceptron neural network and a random error generator. This model has two phases: Firstly, the model is trained with real network traffic. Secondly, with the assistance of the random error generator, it generates traffic which is as self-similar as the real traffic. The implementation and the results validate this model through drawing a comparison between the Hurst parameter of the generated traffic and the real traffic.