IRIS Pol. Torinohttps://iris.polito.itIl sistema di repository digitale IRIS acquisisce, archivia, indicizza, conserva e rende accessibili prodotti digitali della ricerca.Tue, 31 Mar 2020 20:00:12 GMT2020-03-31T20:00:12Z10491GPU-accelerated algorithms for compressed signals recovery with application to astronomical imagery deblurringhttp://hdl.handle.net/11583/2693597Titolo: GPU-accelerated algorithms for compressed signals recovery with application to astronomical imagery deblurring
Abstract: Compressive sensing promises to enable bandwidth-efficient onboard
compression of astronomical data by lifting the encoding
complexity from the source to the receiver. The signal is recovered
off-line, exploiting graphical processing unit (GPU)’s parallel computation
capabilities to speedup the reconstruction process.
However, inherent GPU hardware constraints limit the size of the
recoverable signal and the speedup practically achievable. In this
work, we design parallel algorithms that exploit the properties of
circulant matrices for efficient GPU-accelerated sparse signals
recovery. Our approach reduces the memory requirements, allowing
us to recover very large signals with limited memory. In addition,
it achieves a 10-fold signal recovery speedup, thanks to adhoc
parallelization of matrix–vector multiplications and matrix
inversions. Finally, we practically demonstrate our algorithms in a
typical application of circulant matrices: deblurring a sparse astronomical
image in the compressed domain
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/11583/26935972018-01-01T00:00:00ZIn-network reconstruction of jointly sparse signals with ADMMhttp://hdl.handle.net/11583/2624989Titolo: In-network reconstruction of jointly sparse signals with ADMM
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/11583/26249892015-01-01T00:00:00ZA distributed classification/estimation algorithm for sensor networkshttp://hdl.handle.net/11583/2547736Titolo: A distributed classification/estimation algorithm for sensor networks
Abstract: In this paper, we address the problem of simultaneous classification and estimation of hidden parameters in a sensor network with communications constraints. In particular, we consider a network of noisy sensors which measure a common scalar unknown parameter. We assume that a fraction of the nodes represent faulty sensors, whose measurements are poorly reliable. The goal for each node is to simultaneously identify its class (faulty or non-faulty) and estimate the common parameter.
We propose a novel cooperative iterative algorithm which copes with the communication constraints imposed by the network and shows remarkable performance. Our main result is a rigorous proof of the convergence of the algorithm, under a fixed communication graph, and a characterization of the limit behavior as the network size goes to infinity. In particular, we prove that, in the limit when the number of sensors goes to infinity, the common unknown parameter is estimated with arbitrary small error, while the classification error converges to that of the optimal centralized maximum likelihood estimator. We also show numerical results that validate the theoretical analysis and support their possible generalization. We compare our strategy with the Expectation-Maximization
algorithm and we discuss trade-offs in terms of robustness, speed of convergence and implementation simplicity.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/11583/25477362014-01-01T00:00:00ZSpectral Analysis in the Solar Wind and Heliosheathhttp://hdl.handle.net/11583/2630435Titolo: Spectral Analysis in the Solar Wind and Heliosheath
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/11583/26304352015-01-01T00:00:00ZOnline Optimization in Dynamic Environments: A Regret Analysis for Sparse Problemshttp://hdl.handle.net/11583/2729891Titolo: Online Optimization in Dynamic Environments: A Regret Analysis for Sparse Problems
Abstract: Time-varying systems are a challenge in many scientific and engineering areas. Usually, estimation of time-varying parameters or signals must be performed online, which calls for the development of responsive online algorithms. In this paper, we consider this problem in the context of the sparse optimization; specifically, we consider the Elastic-net model. Following the rationale in [1], we propose a novel online algorithm and we theoretically prove that it is successful in terms of dynamic regret. We then show an application to recursive identification of time-varying autoregressive models, in the case when the number of parameters to be estimated is unknown. Numerical results show the practical efficiency of the proposed method.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/11583/27298912018-01-01T00:00:00ZSpectra and correlations in the solar wind from Voyager 2 around 5AUhttp://hdl.handle.net/11583/2562542Titolo: Spectra and correlations in the solar wind from Voyager 2 around 5AU
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/11583/25625422014-01-01T00:00:00ZDeconvolution of quantized-input linear systems: Analysis via Markov Processes of a low-complexity algorithmhttp://hdl.handle.net/11583/2505271Titolo: Deconvolution of quantized-input linear systems: Analysis via Markov Processes of a low-complexity algorithm
Abstract: This paper is concerned with the problem of the
deconvolution, which consists in recovering the unknown input
of a linear system from a noisy version of the output. The
case of a system with quantized input is considered and a
low-complexity algorithm, derived from decoding techniques,
is introduced to tackle it. The performance of such algorithm
is analytically evaluated through the Theory of Markov Processes. In this framework, results are shown which prove the
uniqueness of an invariant probability measure of a Markov
Process, even in case of non-standard state space. Finally, the
theoretic issues are compared with simulations’ outcomes.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/11583/25052712010-01-01T00:00:00ZEnergy-Saving Gossip Algorithm for Compressed Sensing in Multi-Agent Systemshttp://hdl.handle.net/11583/2546944Titolo: Energy-Saving Gossip Algorithm for Compressed Sensing in Multi-Agent Systems
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/11583/25469442014-01-01T00:00:00ZA large scale analysis of a classification algorithm over sensor networkshttp://hdl.handle.net/11583/2519027Titolo: A large scale analysis of a classification algorithm over sensor networks
Abstract: This paper is devoted to study an iterative estimation/classification algorithm over a sensor network with faulty units recently appeared in the literature. We here present a complete analysis of the performance of the algorithm when the number of units goes to infinity both in terms of estimation and of classification error. In particular it is shown that the algorithm solution converges to the optimal Maximum Likelihood estimator.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/11583/25190272012-01-01T00:00:00ZInput driven consensus algorithm for distributed estimation and classification in sensor networkshttp://hdl.handle.net/11583/2476779Titolo: Input driven consensus algorithm for distributed estimation and classification in sensor networks
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/11583/24767792011-01-01T00:00:00Z