Jongmin Lee
Arizona State University
Publications 8
#1Jongmin LeeH-Index: 3
Last.Andreas SpaniasH-Index: 26
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A quantile is defined as a value below which random draws from a given distribution falls with a given probability. In a centralized setting where the cumulative distribution function (CDF) is unknown, the empirical CDF (ECDF) can be used to estimate such quantiles after aggregating the data. In a fully distributed sensor network, however, it is challenging to estimate quantiles. This is because each sensor node observes local measurement data with limited storage and data transmission power whi...
#1Sunil Rao (ASU: Arizona State University)H-Index: 2
#2Sameeksha Katoch (ASU: Arizona State University)H-Index: 1
Last.Mahesh K. Banavar (Clarkson University)H-Index: 12
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In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled senso...
Dec 1, 2016 in GLOBECOM (Global Communications Conference)
#1Sai Zhang (ASU: Arizona State University)H-Index: 4
#2Jongmin Lee (ASU: Arizona State University)H-Index: 3
Last.Andreas SpaniasH-Index: 26
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A distributed consensus algorithm for estimating the degree distribution of a graph is proposed. The proposed algorithm is based on average consensus and in-network empirical mass function estimation. It is fully distributed in the sense that each node in the network only needs to know its own degree, and nodes do not need to be labeled. The algorithm works for any connected graph structure in the presence of communication noise. The performance of the algorithm is analyzed. A discussion on how ...
Dec 1, 2016 in ISSPIT (International Symposium on Signal Processing and Information Technology)
#1Jongmin Lee (ASU: Arizona State University)H-Index: 3
#2Michael Stanley (NXP Semiconductors)H-Index: 1
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
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Interpreting sensor data in Internet-of-Things applications is a challenging problem particularly in embedded systems. We consider sensor data analytics where machine learning algorithms can be fully implemented on an embedded processor/sensor board. We develop an efficient real-time realization of a Gaussian mixture model (GMM) for execution on the NXP FRDM-K64F embedded sensor board. We demonstrate the design of a customized program and data structure that generates real-time sensor features, ...
#1Sai Zhang (ASU: Arizona State University)H-Index: 4
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 26
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System size estimation in distributed wireless sensor networks is important in various applications such as network management and maintenance. One popular method for system size estimation is to use distributed consensus algorithms with randomly generated initial values at nodes. In this paper, the performance of such methods is studied and Fisher information and Cramer-Rao bounds (CRBs) for different consensus algorithms are derived. Errors caused by communication noise and lack of convergence...
Apr 1, 2016 in MELECON (Mediterranean Electrotechnical Conference)
#1Sunil Rao (ASU: Arizona State University)H-Index: 2
#2David Ramirez Dominguez (ASU: Arizona State University)H-Index: 3
Last.Yoshitaka MorimotoH-Index: 1
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Monitoring utility-scale solar arrays was shown to minimize cost of maintenance and help optimize the performance of the array under various conditions. In this paper, we describe the design of an 18 kW experimental facility that consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchan...
Jun 1, 2015 in ICC (International Conference on Communications)
#1Jongmin Lee (ASU: Arizona State University)H-Index: 3
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 26
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This paper introduces diffusion adaptation strategies over distributed networks with nonlinear transmissions, motivated by the necessity for bounded transmit power. Local information is exchanged in real-time with neighboring nodes in order to estimate a common parameter vector via constrained nonlinear transmissions, using an adaptive learning algorithm. We propose nonlinear diffusion strategies for such an adaptive estimation. We will study convergence properties of the proposed algorithm in t...
#1Shwetang Peshin (ASU: Arizona State University)H-Index: 1
#2Andreas SpaniasH-Index: 26
Last.Devarajan SrinivansanH-Index: 1
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