Particle Swarm Optimization Framework for Low Power Testing of VLSI Circuits

Power dissipation in sequential circuits is due to increased toggling count of Circuit under Test, which depends upon test vectors applied. If successive test vectors sequences have more toggling nature then it is sure that toggling rate of flip flops is higher. Higher toggling for flip flops results more power dissipation. To overcome this problem, one method is to use GA to have test vectors of high fault coverage in short interval, followed by Hamming distance management on test patterns. This approach is time consuming and needs more efforts. Another method which is purposed in this paper is a PSO based Frame Work to optimize power dissipation. Here target is to set the entire test vector in a frame for time period 'T', so that the frame consists of all those vectors strings which not only provide high fault coverage but also arrange vectors in frame to produce minimum toggling

Vision enhancement through single image fog removal

Contrast and color of the captured pictures are degraded under foggy weather conditions and this degradation is often attributed to attenuation and airlight. To reduce the number of road accidents through vision enhancement in turbid weather, an efficient fog removal technique plays a vital role as fog greatly reduces the visibility and hence affects the computer vision algorithms such as surveillance, tracking and Fog Vision Enhancement System (FVES). In this paper, a novel and effective algorithm is proposed for single image fog removal that’s capable of handling images of gray and color channels. The proposed algorithm introduces Dark Channel Prior (DCP) followed by Weighted Least Square (WLS) and High Dynamic Range (HDR) based fog removal scheme. The qualitative and quantitative analysis is applied for the assessment of defogged images obtained from the proposed methodology and is additionally compared with the different fog removal algorithms to establish its superiority. The foremost dominant advantage of the proposed algorithm is its capability to preserve sharp details whereas maintaining the color quality

QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases

The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection