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Research Objective
Ischemic stroke accounts for 87% of all strokes and is a leading cause of morbidity and mortality in the United States. Timely administration of thrombolysis (rtPA) and proper supportive care are essential to recovery of IS patients. Recent data show that compared to standard ambulances, use of Mobile Stroke Units (MSUs)—ambulances equipped with telemedicine, a stroke care team,...
We describe a new type or neural network for object recognition which we call a Cresceptron. The term “Cresceptron” was coined from Latin cresco (grow) and perceptio (perception). The primary objective of the Cresceptron framework is to automatically handle manually intractable tasks: such as constructing a network that can recognize many objects from real world images. The Cresceptron uses a hierarchical...
We present a new hierarchical texture segmentation method that partitions an image into textured regions. A textured region is viewed as a set of uniformly distributed primitives. A primitive is a region with constant gray values. Gray values within a primitive can be corrupted by noise. Any noisy primitive contains gray values from a δ-wide interval (δ-homogeneous primitive. The noisy primitive is...
This paper deals with the remarkable linear phase and stability property of FIR filter. It is appealing for wide FIR filter applications. Constant filter coefficients are procured from MATLAB R2011b and further the optimization operation is performed on the constant coefficients using XILINX ISE13.1 Tool. Constant multiplication can be secured by a technique that is widely used and accepted as MCM...
Given a sequence of observable features of a linear dynamical system (LDS), we propose the problem of finding a representation of the LDS which is sparse in terms of a given dictionary of LDSs. Since LDSs do not belong to Euclidean space, traditional sparse coding techniques do not apply. We propose a probabilistic framework and an efficient MAP algorithm to learn this sparse code. Since dynamic textures...
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