Quickest Change Detection of a Markov Process Across a Sensor Array

Recent attention in quickest change detection in the multi-sensor setting has been on the case where the densities of the observations change at the same instant at all the sensors due to the disruption. In this work, a more general scenario is considered where the change propagates across the sensors, and its propagation can be modeled as a Markov process. A centralized, Bayesian version of this problem, with a fusion center that has perfect information about the observations and a priori knowledge of the statistics of the change process, is considered. The problem of minimizing the average detection delay subject to false alarm constraints is formulated as a partially observable Markov decision process (POMDP). Insights into the structure of the optimal stopping rule are presented. In the limiting case of rare disruptions, we show that the structure of the optimal test reduces to thresholding the a posteriori probability of the hypothesis that no change has happened. We establish the asymptotic optimality (in the vanishing false alarm probability regime) of this threshold test under a certain condition on the Kullback-Leibler (K-L) divergence between the post- and the pre-change densities. In the special case of near-instantaneous change propagation across the sensors, this condition reduces to the mild condition that the K-L divergence be positive. Numerical studies show that this low complexity threshold test results in a substantial improvement in performance over naive tests such as a single-sensor test or a test that wrongly assumes that the change propagates instantaneously.Comment: 40 pages, 5 figures, Submitted to IEEE Trans. Inform. Theor

Equivariant Giambelli and determinantal restriction formulas for the Grassmannian

The main result of the paper is a determinantal formula for the restriction to a torus fixed point of the equivariant class of a Schubert subvariety in the torus equivariant integral cohomology ring of the Grassmannian. As a corollary, we obtain an equivariant version of the Giambelli formula.Comment: 16 pages, 3 figures, LaTex, uses epsfig and psfrag; for the revised version: title changed; Proof of Theorem 3 changed; 3 references added and 1 deleted; other minor change

use natural gas consumption in 201Scenarios to decarbonize residential water heating in California

This paper presents the first detailed long-term stock turnover model to investigate scenarios to decarbonize the residential water heating sector in California, which is currently dominated by natural gas. We model a mix of water heating (WH) technologies including conventional and on-demand (tank-less) natural gas heating, electric resistance, existing electric heat pumps, advanced heat pumps with low global warming refrigerants and solar thermal water heaters. Technically feasible policy scenarios are developed by considering combinations of WH technologies with efficiency gains within each technology, lowering global warming potential of refrigerants and decreasing grid carbon intensity. We then evaluate energy demand, emissions and equipment replacement costs of the pathways. We develop multiple scenarios by which the annual greenhouse gas emissions from residential water heaters in California can be reduced by over 80% from 1990 levels resulting in an annual savings of over 10 Million Metric Tons by 2050. The overall cost of transition will depend on future cost reductions in heat pump and solar thermal water heating equipment, energy costs, and hot water consumption

Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

High precision laser radar tracking device

This thesis explores a relatively new Solid Silver Thin Film Source technology, for the implementation of a novel High Precision Laser Radar Tracking device. The process which consists of a Ag+-Na+ ion exchange, is designed in two steps. It utilizes an initial electric field-aided ion exchange step for a predeposition, and a subsequent second diffusion step to force the profile latitude necessary for optimization of the device. While the entire project of implementing this device, consists of analyzing, processing, polishing and testing, this thesis covers only the process aspect in detail. The success achieved by obtaining the required Power Coupling Ratio curve on a Simple Coupler, demonstrates a novel integrated optic multimode feed for a Monopulse LIDAR application