SelectedWorks - Hao Chen

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Selected Works of Hao Chen

Associate Professor, Department of Electrical and Computer Engineering

Prior to coming to Boise State in 2010, Dr. Hao Chen worked at Syracuse University as a research assistant professor. He has a Ph.D. in Electrical Engineering from Syracuse University and master’s and bachelor’s degrees in Electrical Engineering from the University of Science and Technology of China. Dr. Chen’s research interests include noise-enhanced signal processing (NESP), stochastic resonance (SR), distributed inference, detection and estimation, image processing and cognitive radio. He recently received the All University Doctoral Prize at Syracuse University. Dr. Chen has many professional affiliations, including membership in the Institute of Electrical and Electronics Engineers (IEEE) and American Society for Engineering Education.

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About Hao Chen

Prior to coming to Boise State in 2010, Dr. Hao Chen worked at Syracuse University as a research assistant professor. He has a Ph.D. in Electrical Engineering from Syracuse University and master’s and bachelor’s degrees in Electrical Engineering from the University of Science and Technology of China. Dr. Chen’s research interests include noise-enhanced signal processing (NESP), stochastic resonance (SR), distributed inference, detection and estimation, image processing and cognitive radio. He recently received the All University Doctoral Prize at Syracuse University. Dr. Chen has many professional affiliations, including membership in the Institute of Electrical and Electronics Engineers (IEEE) and American Society for Engineering Education.

Present	Assistant Professor, Boise State University ‐ Department of Electrical and Computer Engineering
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Electrical and Computer Engineering

Statistical Signal and Image Processing and Communications

All University Doctoral Prize, Syracuse University, 2007
Finalist for the Best Student Paper Prize, 2006 IEEE International Conference on Acoustic, Speech, and Signal Processing (IACSSP 2006)
Shing-Tung Yau Scholarship, USTC, Hefei, China, 2002

E-mail: haochen@boisestate.edu
Tel : (208) 426-1020

Articles (15)

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks (Jose Cadena, Priyadip Ray, Hao Chen, et al.) IEEE Transactions on Signal Processing (2021)

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network ...

Optimal Channel-Aware Bayesian Estimation with 1-Bit Quantization (Santosh Paudel and Hao Chen) 2021 55th Asilomar Conference on Signals, Systems, and Computers (2021)

We address the optimal quantizer design problem for distributed Bayesian parameter estimation where each sensor quantizes its local observation into one bit and transmit it through non-ideal channels to the Fusion Center. We first develop ...

Secrecy Constrained Distributed Detection in Sensor Networks (Jun Guo, Uri Rogers, Xia Li, et al.) IEEE Transactions on Signal and Information Processing Over Networks (2018)

The data collected by sensor networks often contain sensitive information and care must be taken to prevent that information from being leaked to malicious third parties, e.g., eavesdroppers. Under both the Neyman–Pearson and Bayesian frameworks, ...

Objective Assessment of Strength Training Exercises Using a Wrist-Worn Accelerometer (Scott A. Conger, Jun Guo, Scott M. Fulkerson, et al.) Medicine & Science in Sports & Exercise (2016)

The 2008 Physical Activity Guidelines for Americans recommend that all adults perform muscle-strengthening exercises to work all of the major muscle groups of the body on at least 2 d·wk-1 , in addition to aerobic ...

Distributed Detection Performance Under Dependent Observations and Nonideal Channels (Ying Lin and Hao Chen) IEEE Sensors Journal (2015)

In this paper, we explore the impact of wireless channel capacity on asymptotic detection performance for distributed detection in wireless sensor networks (WSNs). As a key performance metric for large-scale WSNs, the asymptotic detection performance ...

Localization in Wireless Sensor Networks: Byzantines and Mitigation Techniques (Aditya Vempaty, Onur Ozdemir, Keshav Agrawal, et al.) IEEE Transactions on Signal Processing (2013)

Wireless Sensor Networks (WSNs) are vulnerable to Byzantine attacks in which malicious sensors send falsified information to the Fusion Center (FC) with the goal of degrading inference performance. In this paper, we consider Byzantine attacks ...

Localization in Wireless Sensor Networks: Byzantines and Mitigation Techniques (Aditya Vempaty, Onur Ozdemir, Keshav Agrawal, et al.) IEEE Transactions on Signal Processing (2013)

Wireless Sensor Networks (WSNs) are vulnerable to Byzantine attacks in which malicious sensors send falsified information to the Fusion Center (FC) with the goal of degrading inference performance. In this paper, we consider Byzantine attacks ...

Optimal Identical Binary Quantizer Design for Distributed Estimation (Swarnendu Kar, Hao Chen and Pramod K. Varshney) IEEE Transactions on Signal Processing (2012)

We consider the design of identical one-bit probabilistic quantizers for distributed estimation in sensor networks. We assume the parameter-range to be finite and known and use the maximum Crameŕ–Rao lower bound (CRB) over the parameter-range ...

Optimal Identical Binary Quantizer Design for Distributed Estimation (Swarnendu Kar, Hao Chen and Pramod K. Varshney) IEEE Transactions on Signal Processing (2012)

We consider the design of identical one-bit probabilistic quantizers for distributed estimation in sensor networks. We assume the parameter-range to be finite and known and use the maximum Crameŕ–Rao lower bound (CRB) over the parameter-range ...

Performance Limits of Compressive Sensing-Based Signal Classification (Thakshila Wimalajeewa, Hao Chen and Pramod K. Varshney) IEEE Transactions on Signal Processing (2012)

Most of the recent compressive sensing (CS) literature has focused on sparse signal recovery based on compressive measurements. However, exact signal recovery may not be required in certain signal processing applications such as in inference ...

A New Framework for Distributed Detection with Conditionally Dependent Observations (Hao Chen, Biao Chen and Pramod K. Varshney) IEEE Transactions on Signal Processing (2012)

Distributed detection with conditionally dependent observations is known to be a challenging problem in decentralized inference. This paper attempts to make progress on this problem by proposing a new framework for distributed detection that builds ...

An Image Fusion Approach Based on Markov Random Fields (Min Xu, Hao Chen and Pramod K. Varshney) IEEE Transactions on Geoscience and Remote Sensing (2011)

Markov random field (MRF) models are powerful tools to model image characteristics accurately and have been successfully applied to a large number of image processing applications. This paper investigates the problem of fusion of remote ...

Development of a Novel Methodology for Indoor Emission Source Identification (K. H. Han, J. S. Zhang, H. N. Knudsen, et al.) Atmospheric Environment (2011)

The objective of this study was to develop and evaluate a methodology to identify individual sources of emissions based on the measurements of mixed air samples and the emission signatures of individual materials previously determined ...

Development of a Novel Methodology for Indoor Emission Source Identification (K. H. Han, J. S. Zhang, H. N. Knudsen, et al.) Atmospheric Environment (2011)

The objective of this study was to develop and evaluate a methodology to identify individual sources of emissions based on the measurements of mixed air samples and the emission signatures of individual materials previously determined ...

Ziv–Zakai Bounds on Image Registration (M. Xu, Hao Chen and P. K. Varshney) IEEE Transactions on Signal Processing (2009)

Image registration is a fundamental and important task in image processing. The goal essentially is to estimate the transformation that aligns two images. We focus on the general rigid body transformation case. In this paper, ...

Asymptotic Perfect Secrecy in Distributed Estimation for Large Sensor Networks (Jun Guo, Hao Chen and Uri Rogers) 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings (2017)

This paper considers asymptotic perfect secrecy and asymptotic perfect estimation in distributed estimation for large sensor networks under threat of an eavesdropper, which has access to all sensor outputs. To measure secrecy, we compare the ...

Analyzing and Improving MIMO Radar Detection Performance in the Presence of Cybersecurity Attacks (Hao Chen and Braham Himed) 2016 IEEE Radar Conference (RadarConf) (2016)

A distributed MIMO radar system consists of multiple transmitters, receivers, and a fusion center where the final target detection decision is made. It has previously been shown that a distributed MIMO radar offers improved performance ...

Asymptotic Optimal Quantizer Design for Distributed Bayesian Estimation (Xia Li, Jun Guo, Uri Rogers, et al.) 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings (2016)

In this paper, we address the optimal quantizer design problem for distributed Bayesian parameter estimation with one-bit quantization at local sensors. A performance limit obtained for any distributed parameter estimator with a known prior is ...

Asymptotic Perfect Secrecy in Distributed Detection Against a Global Eavesdropper (Jun Guo, Xia Li, Uri Rogers, et al.) 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings (2016)

This paper examines the secrecy in distributed detection under threat of a global eavesdropper (Eve) which has access to all sensors decisions. To measure secrecy, we compare the detection performance at the fusion center (FC) ...

Impact of Common Observations in Parallel Distributed Detection (Hao Chen and Tsang-Yi Wang) 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE) (2015)

Distributed detection with dependent observations is always a challenging problem. The problem of detection with shared information has many applications when sensors have overlapped measurements, e.g., when distributed detection is performed in a security system ...

The Performance Limit for Distributed Bayesian Estimation with Identical One-Bit Quantizers (Xia Li, Jun Guo, Hao Chen, et al.) 2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE) (2015)

In this paper, a performance limit is derived for a distributed Bayesian parameter estimation problem in sensor networks where the prior probability density function of the parameter is known. The sensor observations are assumed conditionally ...

Noise-Enhanced Information Systems (Hao Chen, Lav R. Varshney and Pramod K. Varshney) Proceedings of the IEEE (2014)

Noise, traditionally defined as an unwanted signal or disturbance, has been shown to play an important constructive role in many information processing systems and algorithms. This noise enhancement has been observed and employed in many ...

Random Distributed Detection with an Application to Cognitive Radio Byzantine Attack (Uri Rogers, Jun Guo, Xia Li, et al.) 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014)

In this paper, a distributed detection model is introduced for m-ary hypotheses testing where the local sensors quantize their decisions to messages with alphabet size of D and the number of local sensors is random ...

On the Performance of Decentralized Detection Under Transmission Constraints (Uri Rogers and Hao Chen) 2014 National Wireless Research Collaboration Symposium (2014)

We consider the problem of distributed detection in a wireless network consisting of a large number of sensors having either ideal or non-ideal communication links to their respective fusion or relay node. The detection performance ...

Dimensionality Reduction for Registration of High-Dimensional Data Sets (Min Xu, Hao Chen and Pramod K. Varshney) IEEE Transactions on Image Processing (2013)

Registration of two high-dimensional data sets often involves dimensionality reduction to yield a single-band image from each data set followed by pairwise image registration. We develop a new application-specific algorithm for dimensionality reduction of high-dimensional ...