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Search Results: 1 - 10 of 23050 matches for " Paolo Di Lorenzo "
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Diffusion Adaptation Strategies for Distributed Estimation over Gaussian Markov Random Fields
Paolo Di Lorenzo
Computer Science , 2014, DOI: 10.1109/TSP.2014.2356433
Abstract: The aim of this paper is to propose diffusion strategies for distributed estimation over adaptive networks, assuming the presence of spatially correlated measurements distributed according to a Gaussian Markov random field (GMRF) model. The proposed methods incorporate prior information about the statistical dependency among observations, while at the same time processing data in real-time and in a fully decentralized manner. A detailed mean-square analysis is carried out in order to prove stability and evaluate the steady-state performance of the proposed strategies. Finally, we also illustrate how the proposed techniques can be easily extended in order to incorporate thresholding operators for sparsity recovery applications. Numerical results show the potential advantages of using such techniques for distributed learning in adaptive networks deployed over GMRF.
Distributed Estimation and Control of Algebraic Connectivity over Random Graphs
Paolo Di Lorenzo,Sergio Barbarossa
Computer Science , 2013, DOI: 10.1109/TSP.2014.2355778
Abstract: In this paper we propose a distributed algorithm for the estimation and control of the connectivity of ad-hoc networks in the presence of a random topology. First, given a generic random graph, we introduce a novel stochastic power iteration method that allows each node to estimate and track the algebraic connectivity of the underlying expected graph. Using results from stochastic approximation theory, we prove that the proposed method converges almost surely (a.s.) to the desired value of connectivity even in the presence of imperfect communication scenarios. The estimation strategy is then used as a basic tool to adapt the power transmitted by each node of a wireless network, in order to maximize the network connectivity in the presence of realistic Medium Access Control (MAC) protocols or simply to drive the connectivity toward a desired target value. Numerical results corroborate our theoretical findings, thus illustrating the main features of the algorithm and its robustness to fluctuations of the network graph due to the presence of random link failures.
Uncertainty Principle and Sampling of Signals Defined on Graphs
Mikhail Tsitsvero,Sergio Barbarossa,Paolo Di Lorenzo
Mathematics , 2015,
Abstract: In many applications, from sensor to social networks, gene regulatory networks or big data, observations can be represented as a signal defined over the vertices of a graph. Building on the recently introduced Graph Fourier Transform, the first contribution of this paper is to provide an uncertainty principle for signals on graph. As a by-product of this theory, we show how to build a dictionary of maximally concentrated signals on vertex/frequency domains. Then, we establish a direct relation between uncertainty principle and sampling, which forms the basis for a sampling theorem of signals defined on graph. Based on this theory, we show that, besides sampling rate, the samples' location plays a key role in the performance of signal recovery algorithms. Hence, we suggest a few alternative sampling strategies and compare them with recently proposed methods.
Signals on Graphs: Uncertainty Principle and Sampling
Mikhail Tsitsvero,Sergio Barbarossa,Paolo Di Lorenzo
Mathematics , 2015,
Abstract: In many applications of current interest, the observations are represented as a signal defined over a graph. The analysis of such signals requires the extension of standard signal processing tools. Building on the recently introduced Graph Fourier Transform, the first contribution of this paper is to provide an uncertainty principle for signals on graph. As a by-product of this theory, we show how to build a dictionary of maximally concentrated signals on vertex/frequency domains. Then, we establish a direct relation between uncertainty principle and sampling, which forms the basis for a sampling theorem for graph signals. Since samples location plays a key role in the performance of signal recovery algorithms, we suggest and compare a few alternative sampling strategies. Finally, we provide the conditions for perfect recovery of a useful signal corrupted by sparse noise, showing that this problem is also intrinsically related to vertex-frequency localization properties.
Sparse Distributed Learning Based on Diffusion Adaptation
Paolo Di Lorenzo,Ali H. Sayed
Computer Science , 2012, DOI: 10.1109/TSP.2012.2232663
Abstract: This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the detection of sparsity via a diffusive process over the network. The resulting algorithms endow networks with learning abilities and allow them to learn the sparse structure from the incoming data in real-time, and also to track variations in the sparsity of the model. We provide convergence and mean-square performance analysis of the proposed method and show under what conditions it outperforms the unregularized diffusion version. We also show how to adaptively select the regularization parameter. Simulation results illustrate the advantage of the proposed filters for sparse data recovery.
Distributed Detection and Estimation in Wireless Sensor Networks
Sergio Barbarossa,Stefania Sardellitti,Paolo Di Lorenzo
Computer Science , 2013,
Abstract: In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.
Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing
Paolo Di Lorenzo,Sergio Barbarossa,Stefania Sardellitti
Computer Science , 2013,
Abstract: The aim of this paper is to propose a computation offloading strategy, to be used in mobile cloud computing, in order to minimize the energy expenditure at the mobile handset necessary to run an application under a latency constraint. We exploit the concept of call graph, which models a generic computer program as a set of procedures related to each other through a weighted directed graph. Our goal is to derive the partition of the call graph establishing which procedures are to be executed locally or remotely. The main novelty of our workis th at the optimal partition is obtained jointly with the selection of the transmit power and constellation size, in order to minimize the energy consumption at the mobile handset, under a latency constraint taking into account transmit time, packet drops, and execution time. We consider both a single channel and a multi-channel transmission strategy, thus proving that a globally optimal solution can be achieved in both cases with affordable complexity. The theoretical findings are corroborated by numerical results and are aimed to show under what conditions, in terms of call graph topology, communication strategy, and computation parameters, the proposed offloading strategy can provide a significant performance gain.
A new station for monitoring electromagnetic fields in Duronia (Italy): experimental setup and first results
Paolo Palangio,Fabrizio Masci,Manuele Di Persio,Cinzia Di Lorenzo
Annals of Geophysics , 2009, DOI: 10.4401/ag-4601
Abstract: Since the end of 2007 a new electromagnetic field monitoring station has been in operation in Central Italy in the area of a village called Duronia. The station was created in the framework of the MEM (Magnetic and Electric fields Monitoring) Project composed of a team headed by the Abruzzo region. The main target of the MEM Project is to create in the Adriatic Area a network of observatories to monitor the environmental electromagnetic signals in the frequency band from 0.001Hz to 100kHz (ULF-ELF-VLF). The peculiarity of the Duronia installation is the low electromagnetic background noise of the site and the low noise of the instrumentation. Here we show the experimental setup, with a brief discussion on the installed instrumentation and on the preliminaresults obtained in the first months of operation. The research activity is mainly focused on the analysis of the spectral structure of the Schumann Resonance in the range of frequencies [5.0-35.0]Hz, and the Ionospheric Alfvén Resonator in the range of frequencies [0.1-7.0]Hz and their evolution in time. Another target concerns the long-term monitoring of local magnetic field anomalies possiblelated to the local geodynamical processes.
CLOE: Identification of putative functional relationships among genes by comparison of expression profiles between two species
Maurizio Pellegrino, Paolo Provero, Lorenzo Silengo, Ferdinando Di Cunto
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-179
Abstract: We demonstrate the capabilities of the approach by testing its predictive power on three proteomically-defined mammalian protein complexes, in comparison with single and multiple species meta-analysis approaches. Our results show that CLOE can find candidate partners for a greater number of genes, if compared to multiple species co-expression analysis, but retains a comparable specificity even when applied to species as close as mouse and human. On the other hand, it is much more specific than single organisms co-expression analysis, strongly reducing the number of potential candidate partners for a given gene of interest.CLOE represents a simple and effective data mining approach that can be easily used for meta-analysis of cDNA microarray experiments characterized by very heterogeneous coverage. Importantly, it produces for genes of interest an average number of high confidence putative partners that is in the range of standard experimental validation techniques.The availability of genome sequences from several model organisms, including humans, and of high-throughput technologies to study gene function is dramatically changing the approach to biological problems. In particular, the consolidated reductionist gene-by-gene strategy is being replaced by a 'modular approach', in which several genes are studied simultaneously to gather a more comprehensive picture of the many different cellular processes [1]: in living organisms, the majority of gene products are part of intricate molecular circuits, composed of physical, functional and regulatory interactions. In higher eukaryotes, the study of gene function is further complicated by the alternative use of transcriptional units, frequently resulting in the production of proteins with different or even antagonistic activities from the same genes [2,3].It is well recognized that one of the most important and widespread mechanisms used by cells to regulate functional modules is the coordinate transcriptional and/or post-
Functional Annotation and Identification of Candidate Disease Genes by Computational Analysis of Normal Tissue Gene Expression Data
Laura Miozzi, Rosario Michael Piro, Fabio Rosa, Ugo Ala, Lorenzo Silengo, Ferdinando Di Cunto, Paolo Provero
PLOS ONE , 2008, DOI: 10.1371/journal.pone.0002439
Abstract: Background High-throughput gene expression data can predict gene function through the “guilt by association” principle: coexpressed genes are likely to be functionally associated. Methodology/Principal Findings We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. Conclusions/Significance We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several genetic diseases of unknown molecular basis.
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