Book ChapterDOI
A compressive sensing algorithm for many-core architectures
Alexandre Borghi,Jérôme Darbon,Sylvain Peyronnet,Tony F. Chan,Stanley Osher +4 more
- pp 678-686
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TLDR
In this article, a parallel algorithm for solving the l1- compressive sensing problem is presented, which takes advantage of shared memory, vectorized, parallel and many-core microprocessors such as GPUs and standard vectorized multi-core processors (e.g. quad-core CPUs).Abstract:
This paper describes a parallel algorithm for solving the l1- compressive sensing problem. Its design takes advantage of shared memory, vectorized, parallel and many-core microprocessors such as Graphics Processing Units (GPUs) and standard vectorized multi-core processors (e.g. quad-core CPUs). Experiments are conducted on these architectures, showing evidence of the efficiency of our approach.read more
Citations
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Journal ArticleDOI
Overlaying Classifiers: A Practical Approach to Optimal Scoring
TL;DR: The goal of this paper is to propose a statistical learning method for constructing a scoring function with nearly optimal ROC curve by proposing a discretization approach, consisting of building a finite sequence of N classifiers by constrained empirical risk minimization and then constructing a piecewise constant scoring function sN(x) by overlaying the resulting classifiers.
Proceedings ArticleDOI
A scalable implementation of sparse approximation on a field programmable analog array
TL;DR: A Hopfield-Network-like analog system is proposed as a solution, using the Locally Competitive Algorithm (LCA) to solve an overcomplete l1 sparse approximation problem, and a scalable system architecture using sub-threshold currents is described.
Design of Energy-efficient Sensing Systems with Direct Computations on Compressively-sensed Data
TL;DR: This thesis develops methodologies that enable us to directly perform analysis on embedded signals that are compressively sensed and shows that the optimized coprocessor reduces the computational energy of an embedded signal-analysis platform by over three orders of magnitude compared to that of a low-power processor with custom instructions alone.
Dissertation
Adaptation de l’algorithmique aux architectures parallèles
TL;DR: In this paper, the authors propose an approach for the adaptation of l'algorithmique to parallelism in parallel architectures paralleles, and propose an algorithm for the factorization of compressive sensing problems.
Parallelization of Fast 1-Minimization for Face Recognition
TL;DR: In this article, the augmented Lagrangian method (ALM) solvers are used for face recognition in both GPU and CPU hardware, and the proposed implementations significantly out-perform naive library-based implementations.
References
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Journal ArticleDOI
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Journal ArticleDOI
Sparse MRI: The application of compressed sensing for rapid MR imaging.
TL;DR: Practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference and demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography.
Journal ArticleDOI
Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
TL;DR: This paper proposes gradient projection algorithms for the bound-constrained quadratic programming (BCQP) formulation of these problems and test variants of this approach that select the line search parameters in different ways, including techniques based on the Barzilai-Borwein method.
Book
Convex analysis and minimization algorithms
TL;DR: In this article, the cutting plane algorithm is used to construct approximate subdifferentials of convex functions, and the inner construction of the subdifferential is performed by a dual form of Bundle Methods.
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