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Andreas Spanias
Researcher at Arizona State University
Publications - 512
Citations - 8918
Andreas Spanias is an academic researcher from Arizona State University. The author has contributed to research in topics: Speech coding & Speech processing. The author has an hindex of 36, co-authored 490 publications receiving 7895 citations. Previous affiliations of Andreas Spanias include Arizona's Public Universities & Intel.
Papers
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Book
Image Understanding using Sparse Representations
TL;DR: The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition.
Optimality and Stability of the K-Hyperline Clustering
TL;DR: In this paper, the authors proved that the K-hyperline clustering algorithm converges to a locally optimal solution for a given set of training data, based on Lloyd's optimality conditions, and developed an Expectation-Maximization procedure for learning dictionaries to be used in sparse representations.
Book
Machine Learning for Solar Array Monitoring, Optimization, and Control
Sunil Rao,Sameeksha Katoch,Vivek Sivaraman Narayanaswamy,Gowtham Muniraju,Cihan Tepedelenlioglu,Andreas Spanias,Pavan Turaga,Raja Ayyanar,Devarajan Srinivasan +8 more
TL;DR: The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance, as well as monitoring utility-scale solar array efficiency.
Posted Content
Kernel Sparse Models for Automated Tumor Segmentation
Jayaraman J. Thiagarajan,Karthikeyan Natesan Ramamurthy,Deepta Rajan,Anup Puri,David H. Frakes,Andreas Spanias +5 more
TL;DR: A low complexity segmentation approach based on kernel sparse codes, which allows the user to initialize the tumor region, and the proposed methods lead to accurate tumor identification are presented.
Journal ArticleDOI
An overview of recent advances on distributed and agile sensing algorithms and implementation
Mahesh K. Banavar,Jun Jason Zhang,Bhavana Chakraborty,Homin Kwon,Ying Li,Huaiguang Jiang,Andreas Spanias,Cihan Tepedelenlioglu,Chaitali Chakrabarti,Antonia Papandreou-Suppappola +9 more
TL;DR: An overview of recent work on distributed and agile sensing algorithms and their implementation is provided, which includes methods for adapting the sensor transmit waveform to match the environment and to optimize the selected performance metric.