Gowtham Muniraju
Arizona State University
Machine learningErgodic processData miningDistributed algorithmSpectral radiusNoiseFault detection and isolationArtificial intelligenceLyapunov exponentLinear searchWireless sensor networkBayesian optimizationFusion centerApplied mathematicsComputer sciencePhotovoltaic systemSample (statistics)Node (networking)ConnectivityUpper and lower boundsHyperparameterSampling (statistics)
20Publications
6H-index
67Citations
Publications 18
Newest
Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning (ML), and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling, and hyperparameter optimization. Existing solutions attempt to adaptively trade off between global exploration and local exploitation, in which the initial exploratory sample is critical to their success. While discrepancy-based samples have bec...
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Abstract The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar array...
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#1Jongmin Lee (ASU: Arizona State University)H-Index: 6
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
view all 4 authors...
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#1Jie Fan (ASU: Arizona State University)H-Index: 3
#2Sunil Rao (ASU: Arizona State University)H-Index: 5
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
view all 5 authors...
In this paper, we address the problem of fault classification in PhotoVoltaic (PV) arrays using a semi-supervised graph signal processing approach. Traditional fault detection and classification methods require large amounts of labeled data for training. In utility scale solar arrays, obtaining labeled data for different fault classes is resource intensive. We propose a graph based classification technique that relies on a limited amount of labeled data. We compare our results with the well know...
2 CitationsSource
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
view all 3 authors...
A consensus based distributed algorithm to compute the spectral radius of a network is proposed. The spectral radius of the graph is the largest eigenvalue of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. Our distributed algorithm uses a simple update rule to reach consensus on the spectral radius, usi...
3 CitationsSource
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
view all 3 authors...
A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus a...
4 CitationsSource
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
view all 3 authors...
A distributed algorithm to compute the spectral radius of the graph in the presence of additive channel noise is proposed. The spectral radius of the graph is the eigenvalue with the largest magnitude of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. We devise an algorithm to reach consensus on the spec...
2 CitationsSource
#1Blaine Ayotte (Clarkson University)H-Index: 3
#2Justin Au-Yeung (Clarkson University)
Last. Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
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In this innovative practice work-in-progress paper, we discuss novel methods to teach machine learning concepts to undergraduate students. Teaching machine learning involves introducing students to complex concepts in statistics, linear algebra, and optimization. In order for students to better grasp concepts in machine learning, we provide them with hands-on exercises. These types of immersive experiences will expose students to the different stages of the practical uses of machine learning. Th...
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#1Xue Zhang (ASU: Arizona State University)H-Index: 7
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
view all 5 authors...
Abstract In this paper, localization using narrowband communication signals are considered in the presence of fading channels with time of arrival measurements. When narrowband signals are used for localization, due to existing hardware constraints, fading channels play a crucial role in localization accuracy. In a location estimation formulation, the Cramer–Rao lower bound for localization error is derived under different assumptions on fading coefficients. For the same level of localization ac...
7 CitationsSource
#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 6
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Mahesh K. Banavar (Clarkson University)H-Index: 17
view all 5 authors...
The analysis of a distributed consensus algorithm for estimating the maximum of the node initial state values in a network is considered in the presence of communication noise. Conventionally, the maximum is estimated by updating the node state value with the largest received measurements in every iteration at each node. However, due to additive channel noise, the estimate of the maximum at each node has a positive drift at each iteration and this results in nodes diverging from the true max val...
5 CitationsSource