Petros T. Boufounos
Mitsubishi Electric Research Laboratories
250 Papers
2K Citations
Petros T. Boufounos is an academic researcher from Mitsubishi Electric Research Laboratories. The author has contributed to research in topics: Compressed sensing & Signal. The author has an hindex of 31, co-authored 223 publications. Previous affiliations of Petros T. Boufounos include Mitsubishi Electric & Chalmers University of Technology.
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Papers
Learning Model-Based Sparsity via Projected Gradient Descent
TL;DR: In this paper, the projected gradient descent with a non-convex structured-sparse parameter model as the constraint set is studied, where the cost function has a stable model-restricted Hessian and the algorithm produces an approximation for the desired minimizer.
Patent
Method for Privacy Preserving Hashing of Signals with Binary Embeddings
Petros T. Boufounos,Shantanu Rane +1 more
- 08 Nov 2011
TL;DR: In this paper, a hash of signal is determined by dithering and scaling random projections of the signal, and then the dithered and scaled random projections are quantized using a non-monotonic scalar quantizer to form the hash.
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Coded aperture compressive 3-D LIDAR
Achuta Kadambi,Petros T. Boufounos +1 more
- 19 Apr 2015
TL;DR: A new depth sensing architecture is proposed that exploits a fixed coded aperture to significantly reduce the number of sensors compared to conventional systems, and a modeling and reconstruction framework is developed, based on model-based compressed sensing, which characterizes a large variety of depth sensing systems.
Economics of Dynamic Pricing in a Reputation Brokered Agent Mediated Marketplace
TL;DR: A framework to study the microeconomic effects in a reputation brokered Agent mediated Knowledge Marketplace, when the agents use dynamic pricing algorithms based on "dynamically" updated reputations is presented.
Learning Model-Based Sparsity via Projected Gradient Descent
TL;DR: This paper studies the projected gradient descent with a non-convex structured-sparse parameter model as the constraint set and elaborates on application of the main results to estimation in generalized linear models.
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