- Communication and Sensing aspects of Sensor Networks, Wireless and Ad-Hoc Networks
- Communication Systems and Applications
- MIMO Communications and Signal Processing
- Signal Transmission and Reception
- Read more about SAMPLING AND DISTORTION TRADEOFFS FOR INDIRECT SOURCE RETRIEVAL
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We study the problem of remote reconstruction of a continuous signal from its multiple corrupted versions. We are interested in the optimal number of samples and their locations for each corrupted signal to minimize the total reconstruction distortion of the remote signal. The correlation among the corrupted signals can be utilized to reduce the sampling rate.
Poster3.pdf
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- Read more about Generalized Approximate Message Passing for One-Bit Compressed Sensing with AWGN
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Compressed sensing recovery techniques allow for reconstruction of an unknown sparse vector from an underdetermined system of linear equations. Recently, a lot of attention was drawn to the problem of recovering the sparse vector from quantized CS measurements. Especially interesting is the case, when extreme quantization is enforced that captures only the sign of the measurements. The problem becomes even more difficult if the measurements are corrupted by noise. In this paper we consider \ac{AWGN}.
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- Read more about In-Network Linear Regression with Arbitrarily Split Data Matrices
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We address for the first time the question of how networked agents can collaboratively fit a Morozov-regularized linear model when each agent knows a summand of the regression data. This question generalizes previously studied data-splitting scenarios, which require that the data be partitioned among the agents. To answer the question, we introduce a class of network-structured problems, which contains the regularization problem, and by using the Douglas-Rachford splitting algorithm, we develop a distributed algorithm to solve these problems.
Poster.pdf
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- Read more about A New Perspective on Randomized Gossip Algorithms
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In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how the Randomized Block Kaczmarz (RBK) method—a method for solving linear systems—works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case.
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- Read more about A New Perspective on Randomized Gossip Algorithms
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In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how the Randomized Block Kaczmarz (RBK) method—a method for solving linear systems—works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case.
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- Read more about A New Perspective on Randomized Gossip Algorithms
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In this short note we propose a new approach for the design and analysis of randomized gossip algorithms which can be used to solve the average consensus problem. We show how the Randomized Block Kaczmarz (RBK) method—a method for solving linear systems—works as gossip algorithm when applied to a special system encoding the underlying network. The famous pairwise gossip algorithm arises as a special case.
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- Read more about Construction of Complementary Sets of Sequences with Low Aperiodic Correlation and Complementary Correlation
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The construction of complementary sets of unimodular sequences of length N, with low correlation and complementary correlation coefficients is addressed. The design criterion is based on the minimisation of a cost function that penalizes the integrated side lobe as well as the sum of the complementary correlations of the sequences in the set. Numerical solution to the proposed cost function is obtained using conventional optimization methods.
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- Read more about IEEE SP Cup 2016 Report - Team MGLS
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Extracting the Electric Network Frequency (ENF) fluctuations from an audio recording and comparing it to a reference database is a new approach in performing forensic digital audio authentication. The problem statement of the IEEE SP cup 2016 competition relates to time-varying location-dependent signature of power grids as it becomes intrinsically captured in media recordings, due to direct or indirect influences from the respective power grid. In this project signal processing and information security/forensics are collectively elaborated.
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- Read more about Graph Frequency Analysis of Brain Signals
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This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations.
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