Prahalad K. Rao

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BACKGROUND Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. This paper presents the application of the Classification and Regression Tree (CART) technique for detecting spontaneous termination or sustenance of AF with sparse data. METHOD Electrocardiogram (ECG) recordings were obtained from the PhysioNet (AF Termination Challenge(More)
Availability of only limited or sparse experimental data impedes the ability of current models of chemical mechanical planarization (CMP) to accurately capture and predict the underlying complex chemomechanical interactions. Modeling approaches that can effectively interpret such data are therefore necessary. In this paper, a new approach to predict the(More)
The aim of this paper is to detect the incipient anomalies in a ultraprecision machining (UPM) process by integrating multiple in situ sensor signals. To realize this aim we forward a Bayesian non-parametric Dirichlet Process (DP) decision-making approach for real-time monitoring of UPM process using the data gathered from multiple, heterogeneous sensors.(More)
The goal of this work is to use sensor data for online detection and identification of process anomalies (faults). In pursuit of this goal, we propose Dirichlet process Gaussian mixture (DPGM) models. The proposed DPGM models have two novel outcomes: 1) DP-based statistical process control (SPC) chart for anomaly detection and 2) unsupervised recurrent(More)
Testing of digital VLSI circuits entails many challenges as a consequence of rapid growth of semiconductor manufacturing technology and the unprecedented levels of design complexity and the gigahertz range of operating frequencies. These challenges include keeping the average and peak power dissipation and test application time within acceptable limits.(More)
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