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This paper presents a bottom-up tracking algorithm for surveillance applications where speed and reliability in the case of multiple matches and occlusions are major concerns. The algorithm is divided into four steps. First, moving objects are detected using an accurate hybrid scheme with selective Gaussian modeling. Simple object features balancing speed, reliability, and complexity are then extracted...
A robust Hinfin controller design in an LMI framework has been carried out for a load-frequency control problem of a single-area uncertain power system model. For carrying out this design, the uncertainties have been restructured by considering norm-bounded uncertainty instead of rank-1 uncertainty structure of . The proposed design is simple and can be easily solved to obtain optimal values of the...
The expectation maximization algorithm is a popular approach to learning Gaussian mixture models from unlabeled data. In addition to the unlabeled data, in many applications, additional sources of information such as a-priori knowledge of mixing proportions are also available. We present a weakly supervised approach, in the form of a penalized expectation maximization algorithm that uses a-priori...
A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered...
Delay failures are becoming a dominant failure mechanism in nanometer technologies. Diagnosis of such failures is important to ensure yield and robustness of the design. However, the increasing circuit size limits the granularity of diagnosis, resulting in large suspect fault list. In this paper, we present a methodology for improving delay fault localization in test-per-scan BIST using on-die delay...
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