Publications (76)94.86 Total impact
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Dataset: First Cross-Correlation Analysis of Interferometric and Resonant-Bar Gravitational-Wave Data for Stochastic Backgrounds - Phys. Rev. D 76 (2007) 022001
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Dataset: Upper limit map of a background of gravitational waves - PHYSICAL REVIEW D 76, 082003 (2007)
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Conference Proceeding: Multitarget-Multisensor ML and PHD: Some Asymptotics
Proc. of Intern. Conf. on Information Fusion (FUSION); 07/2012 -
Article: Single-Transmission Distributed Detection via Order Statistic
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ABSTRACT: Consider a sensor network made of remote nodes connected to a common fusion center. In a recent work, Blum and Sadler proposed the idea of ordered transmissions-sensors with more informative measurements deliver their messages first-and they proved that optimal detection performance can be achieved using only a subset of the measurements available to the system. Taking to one extreme this approach, we show that using only one transmission the detection error can be made as small as desired, provided that the network size n is large enough. Indeed, we design a distributed detection scheme and prove its asymptotic consistency with respect to n, when the decision is made using just one-but the best-out of n collected samples.IEEE Transactions on Signal Processing 01/2012; 60(4):2042 - 2048. · 2.63 Impact Factor -
Conference Proceeding: Quickest Distributed Detection Via Running Consensus
19th European Signal Processing Conference 2011 (EUSIPCO); 08/2011 -
Conference Proceeding: Asymptotically Consistent One-Bit Detection in Large Sensor Networks
19th European Signal Processing Conference 2011 (EUSIPCO); 08/2011 -
Conference Proceeding: Embedding covert information flow
[show abstract] [hide abstract]
ABSTRACT: The problem of embedding a covert information flow in independent renewal cover traffic is considered. Such embedding provides maximum anonymity against traffic analysis. The maximum embedding efficiency is characterized, and an accurate approximation is obtained by formulating the problem as a Riemann-Hilbert boundary value problem.Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on; 12/2010 -
Article: Selective Measurement Transmission in Distributed Estimation With Data Association
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ABSTRACT: In distributed multisensor estimation/tracking the problem of fusion is complicated by that of data association (i.e., with false alarms and missed detections): not only is it of concern to provide an estimation-efficient sensor level quantization of the “target-originated” measurement, but it is also unclear which among each sensor's measurements this might be, if any at all. The former issue has been studied previously; in this paper we address only the latter concern. At first we assume that each sensor is tasked to communicate exactly one of its observations to a Fusion Center (FC) for a global estimate, and we work in one dimension. Via order statistics we show that, surprisingly, the nearest neighbor (NN) is not always the most appropriate measurement to share. We also expand our bandwidth to allow for transmission of multiple measurements, for example the nearest and third-nearest: it turns out that a single-measurement transmission is more bandwidth efficient than multiple. The analysis and results are further extended to two dimensions, but the moral-that sharing of the NNs is not always a good idea-remains.IEEE Transactions on Signal Processing 09/2010; · 2.63 Impact Factor -
Article: Optimal Relay Function in the Low-Power Regime for Distributed Estimation Over a MAC
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ABSTRACT: A random parameter is estimated by a distributed network of sensors that communicate over a common multiple-access channel (MAC). A MAC implies an additive fusion rule, and the goal here is to design a power-constrained forwarding strategy and fusion center post-processing. To get an explicit solution we appeal to asymptotics, meaning that we design the locally optimal scheme for the limiting case that the received power goes to zero.IEEE Transactions on Signal Processing 06/2010; · 2.63 Impact Factor -
Article: Refining Decisions After Losing Data: The Unlucky Broker Problem
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ABSTRACT: Consider a standard statistical hypothesis test, leading to a binary decision made by exploiting a certain dataset. Suppose that, later, part of the data is lost, and we want to refine the test by exploiting both the surviving data and the previous decision. What is the best one can do? Such a question, here referred to as the unlucky broker problem, can be addressed by very standard tools from detection theory, but the solution gives intriguing insights and is by no means obvious. We provide the general form of the optimal detectors and discuss in depth their modus operandi , ranging from simple likelihood ratio tests to more complex behaviors. Limiting cases, where either the surviving data or the initial decision is almost useless, are also discussed.IEEE Transactions on Signal Processing 05/2010; · 2.63 Impact Factor -
Conference Proceeding: Asymptotically optimal power-constrained distributed estimation
[show abstract] [hide abstract]
ABSTRACT: A random parameter is estimated by a distributed network of sensors that communicate over a common MAC. The channel implies an enforced additive fusion rule, and the goal here is to design a power-constrained forwarding strategy and the post-processing by the fusion center. To get an explicit solution we appeal to asymptotics, meaning that we design the locally optimal scheme for the limiting case that the received power goes to zero.Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010 · 4.63 Impact Factor -
Article: Estimation of Target Location Via Likelihood Approximation in Sensor Networks
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ABSTRACT: A fully decentralized sensor network, without fusion center, is deployed to estimate the position of a target. Taking advantage of the limited communication range of the nodes, and exploiting their (unknown) location inside the surveyed area, the likelihood profile is approximately reconstructed. A distributed ML-like estimator is, therefore, proposed and its asymptotic performance is investigated analytically, while computer experiments assess the behavior of the estimator in nonasymptotic regimes. The differences between one- and two-dimensional scenarios are also discussed.IEEE Transactions on Signal Processing 04/2010; · 2.63 Impact Factor -
Article: Asymptotic Optimality of Running Consensus in Testing Binary Hypotheses
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ABSTRACT: Consensus in sensor networks is a procedure to corroborate the local measurements of the sensors with those of the surrounding nodes, and leads to a final agreement about a common value that, in detection applications, represents the decision statistic. As the amount of collected data increases, the convergence toward the final statistic is ruled by suitable scaling laws, and the question arises if the asymptotic (large sample) properties of a detection statistic are retained when this statistic is approximated via consensus algorithms. We investigate the asymptotic properties of running consensus detectors both under the Neyman-Pearson paradigm (fixed number of data) and in the sequential case. An appropriate asymptotic framework is developed, and exact theoretical results are provided, showing the asymptotic optimality of the running consensus detector. In addition, numerical experiments are performed to address nonasymptotic scenarios.IEEE Transactions on Signal Processing 03/2010; · 2.63 Impact Factor -
Conference Proceeding: Distributed estimation with data association: Is the nearest neighbor the most informative?
[show abstract] [hide abstract]
ABSTRACT: In distributed multi-sensor estimation/tracking the problem of measurement fusion arises. In large sensor networks (SN), each sensor is constrained by bandwidth to communicate only one of its observations to a fusion center (FC) for a global estimate. We study the problem of distributed estimation with data association, where the FC ldquooptimallyrdquo combines the ldquobestrdquo measurements from the sensors, instead of suboptimally combining the local estimates. Using order statistics, we show that, surprisingly, the nearest neighbor (NN) is not always the most informative measurement. Simulations corroborate our analysis.Information Fusion, 2009. FUSION '09. 12th International Conference on; 08/2009 -
Conference Proceeding: Decentralized asymptotic detection by running consensus
[show abstract] [hide abstract]
ABSTRACT: A Wireless Sensor Network is engaged in a binary detection task and the operational modality is that known as running consensus: Each node continuously collects data and simultaneously exchanges informations with its neighbors, with the final aim of reaching agreement about a common detection statistic. This paper exploits an appropriate framework for investigating the detection performance of the system, in the limit of large sample sizes, under both the Neyman-Pearson and the sequential detection paradigms. The main result is that the running consensus detector is asymptotically optimal, in the sense that its performance approaches that of an ideal centralized entity to which all the data globally sensed by the network are made available.Signal Processing Advances in Wireless Communications, 2009. SPAWC '09. IEEE 10th Workshop on; 07/2009 -
Article: Distributed Detection With Censoring Sensors Under Physical Layer Secrecy
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ABSTRACT: We consider distributed binary detection problems in which the remote sensors of a network implement a censoring strategy to fulfill energy constraints, and the network works under the attack of an eavesdropper. The attacker wants to discover the state of the nature scrutinized by the system, but the network implements appropriate countermeasures to make this task hopeless. The goal is to achieve perfect secrecy at the physical layer, making the data available at the eavesdropper useless for its detection task. Adopting as performance metric certain Ali-Silvey distances, we characterize the detection performance of the system under physical layer secrecy. Two communication scenarios are addressed: parallel access channels and a multiple access channel. In both cases the optimal operative points from the network perspective are found. The most economic operative solution is shown to lie in the asymptote of low energy regime. How the perfect secrecy requirement impacts on the achievable performances, with respect to the absence of countermeasures, is also investigated.IEEE Transactions on Signal Processing 06/2009; · 2.63 Impact Factor -
Article: Distributed Detection in the Presence of Byzantine Attacks
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ABSTRACT: Distributed detection in the presence of cooperative (Byzantine) attack is considered. It is assumed that a fraction of the monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitious observations aimed at confusing the decision maker at the fusion center. For detection under binary hypotheses with quantized sensor observations, the optimal attacking distributions for Byzantine sensors that minimize the detection error exponent are obtained using a ldquowater-fillingrdquo procedure. The smallest error exponent, as a function of the Byzantine sensor population, characterizes the power of attack. Also obtained is the minimum fraction of Byzantine sensors that destroys the consistency of detection at the fusion center. The case when multiple measurements are made at the remote nodes is also considered, and it is shown that the detection performance scales with the number of sensors differently from the number of observations at each sensor.IEEE Transactions on Signal Processing 02/2009; · 2.63 Impact Factor -
Article: Stochastic Resonance in Sequential Detectors
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ABSTRACT: Stochastic resonance (SR) is a nonlinear phenomenon known in physics that has attracted recent interest in the signal-processing literature, and specifically in the context of detection. We investigate the SR effect arising in sequential detectors for shift-in-mean binary hypothesis testing and characterize the optimal resonance as the solution of specific optimization problems. One particular (and at first glance perhaps counterintuitive) finding is that certain sequential detection procedures can be made more efficient by randomly adding or subtracting a suitable constant value to the data at the input of the detector.IEEE Transactions on Signal Processing 02/2009; · 2.63 Impact Factor -
Article: Erratum: All-sky search for periodic gravitational waves in LIGO S4 data (Physical Review D (2008) 77 (022001))
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ABSTRACT: Export Date: 10 December 2012, Source: Scopus, Art. No.: 129904, CODEN: PRVDA, doi: 10.1103/PhysRevD.80.129904, Language of Original Document: EnglishPhysical Review D - Particles, Fields, Gravitation and Cosmology. 01/2009; 80(12). -
Article: Search of S3 LIGO data for gravitational wave signals from spinning black hole and neutron star binary inspirals
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ABSTRACT: We report on the methods and results of the first dedicated search for gravitational waves emitted during the inspiral of compact binaries with spinning component bodies. We analyze 788 hours of data collected during the third science run (S3) of the LIGO detectors. We searched for binary systems using a detection template family specially designed to capture the effects of the spin-induced precession of the orbital plane. We present details of the techniques developed to enable this search for spin-modulated gravitational waves, highlighting the differences between this and other recent searches for binaries with nonspinning components. The template bank we employed was found to yield high matches with our spin-modulated target waveform for binaries with masses in the asymmetric range 1.0M⊙<m1<3.0M⊙ and 12.0M⊙<m2<20.0M⊙ which is where we would expect the spin of the binary’s components to have a significant effect. We find that our search of S3 LIGO data has good sensitivity to binaries in the Milky Way and to a small fraction of binaries in M31 and M33 with masses in the range 1.0M⊙<m1, m2<20.0M⊙. No gravitational wave signals were identified during this search. Assuming a binary population with spinning components and Gaussian distribution of masses representing a prototypical neutron star–black hole system with m1≃1.35M⊙ and m2≃5M⊙, we calculate the 90%-confidence upper limit on the rate of coalescence of these systems to be 15.9 yr-1L10-1, where L10 is 1010 times the blue light luminosity of the Sun.Phys. Rev. D. 08/2008; 78(4).
Top co-authors
Top Journals
Institutions
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2007–2010
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University of Connecticut
- Department of Electrical and Computer Engineering
Storrs, CT, USA
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2003–2009
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Università degli Studi di Salerno
- Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM)
Fisciano, Campania, Italy
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