Publications
Efficient reconstruction of block-sparse signals
Summary
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...
Matched filtering for subgraph detection in dynamic networks
Summary
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...
An active filter achieving 43.6dBm OIP3
Summary
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...
Identification and compensation of Wiener-Hammerstein systems with feedback
Summary
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...
Subgraph detection using eigenvector L1 norms
Summary
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...
Physical layer considerations for wideband cognitive radio
Summary
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...
Toward signal processing theory for graphs and non-Euclidean data
Summary
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...
A multi-sensor compressed sensing receiver: performance bounds and simulated results
Summary
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 log-frequency approach to the identification of the Wiener-Hammerstein model
Summary
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...
Compressed sensing arrays for frequency-sparse signal detection and geolocation
Summary
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...