Search Results for author: Edwin K. P. Chong

Found 9 papers, 0 papers with code

Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning

no code implementations14 Jul 2022 Stephen L. H. Lau, Edwin K. P. Chong

Single-pixel imaging (SPI) is a novel imaging technique whose working principle is based on the compressive sensing (CS) theory.

Compressive Sensing Image Reconstruction

Well-Conditioned Linear Minimum Mean Square Error Estimation

no code implementations6 Jan 2022 Edwin K. P. Chong

Using this framework, we explore an important structural property of constrained LMMSE filters involving a certain prefilter.

Information-Theoretic Approach to Navigation for Efficient Detection and Classification of Underwater Objects

no code implementations9 Jul 2020 Christopher Robbiano, Edwin K. P. Chong, Mahmood R. Azimi-Sadjadi

This paper addresses an autonomous exploration problem in which a mobile sensor, placed in a previously unseen search area, utilizes an information-theoretic navigation cost function to dynamically select the next sensing action, i. e., location from which to take a measurement, to efficiently detect and classify objects of interest within the area.

General Classification

Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network

no code implementations7 Jan 2020 Stephen L. H. Lau, Edwin K. P. Chong, Xu Yang, Xin Wang

In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images.

Crack Segmentation Feature Engineering +2

Bayesian Learning of Occupancy Grids

no code implementations18 Nov 2019 Christopher Robbiano, Edwin K. P. Chong, Mahmood R. Azimi-Sadjadi, Louis L. Scharf, Ali Pezeshki

Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells.

Decision Automation for Electric Power Network Recovery

no code implementations1 Oct 2019 Yugandhar Sarkale, Saeed Nozhati, Edwin K. P. Chong, Bruce R. Ellingwood

We propose a novel decision technique that addresses the massive number of decision choices for large-scale real-world problems; in addition, our method also features an experiential learning component that adaptively determines the utilization of the computational resources based on the performance of a small number of choices.

Decision Making Multi-Armed Bandits

Controlled Tracking in Urban Terrain: Closing the Loop

no code implementations1 May 2018 Patricia R. Barbosa, Yugandhar Sarkale, Edwin K. P. Chong, Yun Li, Sofia Suvorova, Bill Moran

We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain.

Signal Processing Systems and Control

Ranking and Selection as Stochastic Control

no code implementations7 Oct 2017 Yijie Peng, Edwin K. P. Chong, Chun-Hung Chen, Michael C. Fu

Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation.

Hypothesis Testing in Feedforward Networks with Broadcast Failures

no code implementations19 Nov 2012 Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran

In the case where the flipping probabilities converge to 1/2, we derive a necessary condition on the convergence rate of the flipping probabilities such that the decisions still converge to the underlying truth.

Two-sample testing

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