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Efficient reconstruction of block-sparse signals

Published in:
IEEE Statistical Signal Processing Workshop, 28-30 June 2011.

Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N > M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved using an l2 optimization with an l1 penalty on the sparsity of the solution. There are practical applications in which multiple measurements can be grouped together, so that K x P data must be estimated from M x P observations, where the l1 sparsity penalty is taken with respect to the vector formed using the l2 norms of the rows of the data matrix. In this paper we develop a computationally efficient block partitioned homotopy method for reconstructing K x P data from M x P observations using a grouped sparsity constraint, and compare its performance to other block reconstruction algorithms.
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Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N > M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved...

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Matched filtering for subgraph detection in dynamic networks

Published in:
2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, pp. 509-512.

Summary

Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic setting a previous method for the detection of anomalously dense subgraphs in a large background. In simulation, we show that this temporal integration technique enables the detection of weak subgraph anomalies than are not detectable in the static case. We also demonstrate background/foreground separation using a real background graph based on a computer network.
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Summary

Graphs are high-dimensional, non-Euclidean data, whose utility spans a wide variety of disciplines. While their non-Euclidean nature complicates the application of traditional signal processing paradigms, it is desirable to seek an analogous detection framework. In this paper we present a matched filtering method for graph sequences, extending to a dynamic...

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An active filter achieving 43.6dBm OIP3

Published in:
IEEE Radio Frequency Integrated Circuits Symp., RFIC, 5-7 June 2011.

Summary

An active filter with a 50 omega buffer suitable as an anti-alias filter to drive a highly linear ADC is implemented in 0.13 um SiGe BiCMOS. This 6th-order Chebyshev filter has a 3 dB cutoff frequency of 28.3 MHz and achieves 36.5 dBm OIP3. Nonlinear digital equalization further improves OIP3 to 43.6 dBm. Measurements show 92 dB of rejection at the stopband and a gain of 49 dB. The measured in-band OIP3 of 43.6 dBm is 19 dB higher than previously published designs.
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Summary

An active filter with a 50 omega buffer suitable as an anti-alias filter to drive a highly linear ADC is implemented in 0.13 um SiGe BiCMOS. This 6th-order Chebyshev filter has a 3 dB cutoff frequency of 28.3 MHz and achieves 36.5 dBm OIP3. Nonlinear digital equalization further improves OIP3...

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Identification and compensation of Wiener-Hammerstein systems with feedback

Published in:
ICASSP 2011, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 22-27 May 2011, pp. 4056-4059.

Summary

Efficient operation of RF power amplifiers requires compensation strategies to mitigate nonlinear behavior. As bandwidth increases, memory effects become more pronounced, and Volterra series based compensation becomes onerous due to the exponential growth in the number of necessary coefficients. Behavioral models such as Wiener-Hammerstein systems with a parallel feedforward or feedback filter are more tractable but more difficult to identify. In this paper, we extend a Wiener-Hammerstein identification method to such systems showing that identification is possible (up to inherent model ambiguities) from single- and two-tone measurements. We also calculate the Cramer-Rao bound for the system parameters and compare to our identification method in simulation. Finally, we demonstrate equalization performance using measured data from a wideband GaN power amplifier.
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Summary

Efficient operation of RF power amplifiers requires compensation strategies to mitigate nonlinear behavior. As bandwidth increases, memory effects become more pronounced, and Volterra series based compensation becomes onerous due to the exponential growth in the number of necessary coefficients. Behavioral models such as Wiener-Hammerstein systems with a parallel feedforward or...

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Subgraph detection using eigenvector L1 norms

Published in:
23rd Int. Conf. on Neural Info. Process. Syst., NIPS, 6-9 December 2010, pp. 1633-41.

Summary

When working with network datasets, the theoretical framework of detection theory for Euclidean vector spaces no longer applies. Nevertheless, it is desirable to determine the detectability of small, anomalous graphs embedded into background networks with known statistical properties. Casting the problem of subgraph detection in a signal processing context, this article provides a framework and empirical results that elucidate a "detection theory" for graph-valued data. Its focus is the detection of anomalies in unweighted, undirected graphs through L1 properties of the eigenvectors of the graph's so-called modularity matrix. This metric is observed to have relatively low variance for certain categories of randomly-generated graphs, and to reveal the presence of an anomalous subgraph with reasonable reliability when the anomaly is not well-correlated with stronger portions of the background graph. An analysis of subgraphs in real network datasets confirms the efficacy of this approach.
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Summary

When working with network datasets, the theoretical framework of detection theory for Euclidean vector spaces no longer applies. Nevertheless, it is desirable to determine the detectability of small, anomalous graphs embedded into background networks with known statistical properties. Casting the problem of subgraph detection in a signal processing context, this...

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Physical layer considerations for wideband cognitive radio

Published in:
MILCOM 2010, IEEE Military Communications Conference , 31 October-3 November 2010, pp. 2113-2118.

Summary

Next generation cognitive radios will benefit from the capability of transmitting and receiving communications waveforms across many disjoint frequency channels spanning hundreds of megahertz of bandwidth. The information theoretic advantages of multi-channel operation for cognitive radio (CR), however, come at the expense of stringent linearity requirements on the analog transmit and receive hardware. This paper presents the quantitative advantages of multi-channel operation for next generation CR, and the advanced digital compensation algorithms to mitigate transmit and receive nonlinearities that enable broadband multi-channel operation. Laboratory measurements of the improvement in the performance of a multi-channel CR communications system operating below 2 GHz in over 500 MHz of instantaneous bandwidth are presented.
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Summary

Next generation cognitive radios will benefit from the capability of transmitting and receiving communications waveforms across many disjoint frequency channels spanning hundreds of megahertz of bandwidth. The information theoretic advantages of multi-channel operation for cognitive radio (CR), however, come at the expense of stringent linearity requirements on the analog transmit...

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Toward signal processing theory for graphs and non-Euclidean data

Published in:
ICASSP 2010, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 15 March 2010, pp. 5415-5417.

Summary

Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be developed. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing framework by way of a practical example: detection of a subgraph embedded in a background graph. We describe an approach based on detection theory and provide empirical results indicating that the test statistic proposed has reasonable power to detect dense subgraphs in large random graphs.
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Summary

Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be...

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A multi-sensor compressed sensing receiver: performance bounds and simulated results

Published in:
43rd Asilomar Conf. on Signals, Systems, and Computers, 1-4 November 2009, pp. 1571-1575.

Summary

Multi-sensor receivers are commonly tasked with detecting, demodulating and geolocating target emitters over very wide frequency bands. Compressed sensing can be applied to persistently monitor a wide bandwidth, given that the received signal can be represented using a small number of coefficients in some basis. In this paper we present a multi-sensor compressive sensing receiver that is capable of reconstructing frequency-sparse signals using block reconstruction techniques in a sensor-frequency basis. We derive performance bounds for time-difference and angle of arrival (AoA) estimation of such a receiver, and present simulated results in which we compare AoA reconstruction performance to the bounds derived.
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Summary

Multi-sensor receivers are commonly tasked with detecting, demodulating and geolocating target emitters over very wide frequency bands. Compressed sensing can be applied to persistently monitor a wide bandwidth, given that the received signal can be represented using a small number of coefficients in some basis. In this paper we present...

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A log-frequency approach to the identification of the Wiener-Hammerstein model

Published in:
IEEE Sig. Proc. Lett., Vol. 16, No. 10, October 2009, pp. 889-892.

Summary

In this paper we present a simple closed-form solution to the Wiener-Hammerstein (W-H) identification problem. The identification process occurs in the log-frequency domain where magnitudes and phases are separable. We show that the theoretically optimal W-H identification is unique up to an amplitude, phase and delay ambiguity, and that the nonlinearity enables the separate identification of the individual linear time invariant (LTI) components in a W-H architecture.
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Summary

In this paper we present a simple closed-form solution to the Wiener-Hammerstein (W-H) identification problem. The identification process occurs in the log-frequency domain where magnitudes and phases are separable. We show that the theoretically optimal W-H identification is unique up to an amplitude, phase and delay ambiguity, and that the...

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Compressed sensing arrays for frequency-sparse signal detection and geolocation

Published in:
Proc. of the 2009 DoD High Performance Computing Modernization Program Users Group Conf., HPCMP-UGC, 15 June 2009, pp. 297-301.

Summary

Compressed sensing (CS) can be used to monitor very wide bands when the received signals are sparse in some basis. We have developed a compressed sensing receiver architecture with the ability to detect, demodulate, and geolocate signals that are sparse in frequency. In this paper, we evaluate detection, reconstruction, and angle of arrival (AoA) estimation via Monte Carlo simulation and find that, using a linear 4- sensor array and undersampling by a factor of 8, we achieve near-perfect detection when the received signals occupy up to 5% of the bandwidth being monitored and have an SNR of 20 dB or higher. The signals in our band of interest include frequency-hopping signals detected due to consistent AoA. We compare CS array performance using sensor-frequency and space-frequency bases, and determine that using the sensor-frequency basis is more practical for monitoring wide bands. Though it requires that the received signals be sparse in frequency, the sensor-frequency basis still provides spatial information and is not affected by correlation between uncompressed basis vectors.
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Summary

Compressed sensing (CS) can be used to monitor very wide bands when the received signals are sparse in some basis. We have developed a compressed sensing receiver architecture with the ability to detect, demodulate, and geolocate signals that are sparse in frequency. In this paper, we evaluate detection, reconstruction, and...

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