Results 1  10
of
12
Design and exploration of lowpower analog to information conversion based on compressed sensing
 IN CIRCUITS SYST
, 2012
"... The longstanding analogtodigital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures constraints are simply not manageable. Compresse ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
The longstanding analogtodigital conversion paradigm based on Shannon/Nyquist sampling has been challenged lately, mostly in situations such as radar and communication signal processing where signal bandwidth is so large that sampling architectures constraints are simply not manageable. Compressed sensing (CS) is a new emerging signal acquisition/compression paradigm that offers a striking alternative to traditional signal acquisition. Interestingly, by merging the sampling and compression steps, CS also removes a large part of the digital architecture and might thus considerably simplify analogtoinformation (A2I) conversion devices. This socalled “analog CS,” where compression occurs directly in the analog sensor readout electronics prior to analogtodigital conversion, could thus be of great importance for applications where bandwidth is moderate, but computationally complex, and power resources are severely constrained. In our
A Compressed Sensing Parameter Extraction Platform for Radar Pulse Signal Acquisition
, 2009
"... In this paper we present a complete (hardware/software) subNyquist rate (×13) wideband signal acquisition chain capable of acquiring radar pulse parameters in an instantaneous bandwidth spanning 100 MHz–2.5 GHz with the equivalent of 8 ENOB digitizing performance. The approach is based on the alte ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
(Show Context)
In this paper we present a complete (hardware/software) subNyquist rate (×13) wideband signal acquisition chain capable of acquiring radar pulse parameters in an instantaneous bandwidth spanning 100 MHz–2.5 GHz with the equivalent of 8 ENOB digitizing performance. The approach is based on the alternative sensingparadigm of CompressedSensing (CS). The hardware platform features a fullyintegrated CS receiver architecture named the randommodulation preintegrator (RMPI) fabricated in Northrop Grumman’s 450 nm InP HBT bipolar technology. The software backend consists of a novel CS parameter recovery algorithm which extracts information about the signal without performing full timedomain signal reconstruction. This approach significantly reduces the computational overhead involved in retrieving desired information which demonstrates an avenue toward employing CS techniques in powerconstrained realtime applications. The developed techniques are validated on CS samples physically measured by the fabricated RMPI and measurement results are presented. The parameter estimation algorithms are described in detail and a complete description of the physical hardware is given.
A 100MHz2GHz 12.5x subNyquist Rate Receiver in 90nm CMOS
"... called the random modulation preintegrator is realized in IBM 90nm digital CMOS. It achieves an effective instantaneous bandwidth of 2GHz, with>54dB dynamic range. Most notably, the aggregate digitization rate is fs =320MSPS, 12.5 × below the Nyquist rate. Signal recovery can be accomplished for ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
(Show Context)
called the random modulation preintegrator is realized in IBM 90nm digital CMOS. It achieves an effective instantaneous bandwidth of 2GHz, with>54dB dynamic range. Most notably, the aggregate digitization rate is fs =320MSPS, 12.5 × below the Nyquist rate. Signal recovery can be accomplished for any signal with a concise representation. The system is validated using radarpulses and tones as the input and recovering the timedomain waveforms.
Compression limits for random vectors with linearly parameterized secondorder statistics,” arXiv:1311.0737 [math.ST
, 2013
"... Abstract — The class of complex random vectors whose covariance matrix is linearly parameterized by a basis of Hermitian Toeplitz (HT) matrices is considered, and the maximum compression ratios that preserve all secondorder information are derived—the statistics of the uncompressed vector must be ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
(Show Context)
Abstract — The class of complex random vectors whose covariance matrix is linearly parameterized by a basis of Hermitian Toeplitz (HT) matrices is considered, and the maximum compression ratios that preserve all secondorder information are derived—the statistics of the uncompressed vector must be recoverable from a set of linearly compressed observations. This kind of vectors arises naturally when sampling widesense stationary random processes and features a number of applications in signal and array processing. Explicit guidelines to design optimal and nearly optimal schemes operating both in a periodic and nonperiodic fashion are provided by considering two of the most common linear compression schemes, which we classify as dense or sparse. It is seen that the maximum compression ratios depend on the structure of the HT subspace containing the covariance matrix of the uncompressed observations. Compression patterns attaining these maximum ratios are found for the case without structure as well as for the cases with circulant or banded structure. Universal samplers are also proposed to compress unknown HT subspaces. Index Terms — Compressive covariance sensing, covariance matching, compression matrix design.
MAKING DO WITH LESS: AN INTRODUCTION TO COMPRESSED SENSING
"... Abstract. This article offers an accessible but rigorous and essentially selfcontained account of the main ideas in compressed sensing (also known as compressive sensing or compressive sampling), ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract. This article offers an accessible but rigorous and essentially selfcontained account of the main ideas in compressed sensing (also known as compressive sensing or compressive sampling),
Giannakis, “Spectrum cartography using quantized observations
 IEEE Int. Conf. Acoust., Speech, Signal Process. (Accepted
, 2015
"... This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by lowcomplexity sensing hardware and stringent communication constraints, compressed and quantized measurements are consider ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by lowcomplexity sensing hardware and stringent communication constraints, compressed and quantized measurements are considered. Setting out from a nonparametric regression framework, it is shown that a sensible approach leads to a support vector machine formulation. The simulated tests verify that accurate spectrum maps can be constructed using a simple sensing architecture with significant savings in the feedback. 1.
Giannakis, “Online spectrum cartography via quantized measurements
, 2015
"... Abstract—An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging interpolation task is formulated as a nonparametric regression problem in a reproducing kernel Hi ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Abstract—An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging interpolation task is formulated as a nonparametric regression problem in a reproducing kernel Hilbert space (RKHS) of vectorvalued functions, and solved using a stochastic gradient descent iteration. Numerical tests verify the map estimation performance of the proposed technique. I.
1 ModelBased Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing
"... Abstract—The random demodulator is a recent compressive sensing architecture providing efficient subNyquist sampling of sparse bandlimited signals. The compressive sensing paradigm requires an accurate model of the analog frontend to enable correct signal reconstruction in the digital domain. In ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract—The random demodulator is a recent compressive sensing architecture providing efficient subNyquist sampling of sparse bandlimited signals. The compressive sensing paradigm requires an accurate model of the analog frontend to enable correct signal reconstruction in the digital domain. In practice, hardware devices such as filters deviate from their desired design behavior due to component variations. Existing reconstruction algorithms are sensitive to such deviations, which fall into the more general category of measurement matrix perturbations. This paper proposes a modelbased technique that aims to calibrate filter model mismatches to facilitate improved signal reconstruction quality. The mismatch is considered to be an additive error in the discretized impulse response. We identify the error by sampling a known calibrating signal, enabling leastsquares estimation of the impulse response error. The error estimate and the known system model are used to calibrate the measurement matrix. Numerical analysis demonstrates the effectiveness of the calibration method even for highly deviating lowpass filter responses. The proposed method performance is also compared to a state of the art method based on discrete Fourier transform trigonometric interpolation.
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 1 VLSI Design of a Monolithic CompressiveSensing Wideband AnalogtoInformation Converter
"... Abstract—One of the key tasks in cognitive radio and communications intelligence is to detect active bands in the radiofrequency (RF) spectrum. In order to perform spectral activity detection in wideband RF signals, expensive and energyinefficient highrate analogtodigital converters (ADCs) in ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract—One of the key tasks in cognitive radio and communications intelligence is to detect active bands in the radiofrequency (RF) spectrum. In order to perform spectral activity detection in wideband RF signals, expensive and energyinefficient highrate analogtodigital converters (ADCs) in combination with sophisticated digital detection circuitry are typically used. In many practical situations, however, the RF spectrum is sparsely populated, i.e., only a few frequency bands are active at a time. This property enables the design of socalled analogtoinformation (A2I) converters, which are capable of acquiring and directly extracting the spectral activity information at low cost and low power by means of compressive sensing (CS). In this paper, we present the first VLSI design of a monolithic wideband CSbased A2I converter that includes a signal acquisition stage capable of acquiring RF signals having large bandwidths and a highthroughput spectral activity detection unit. Lowcost wideband signal acquisition is obtained via CSbased randomized temporal subsampling in combination with a 4bit flash ADC. Highthroughput spectrum activity detection from the coarsely quantized and compressive measurements is achieved by means of a massivelyparallel VLSI design of a novel accelerated sparse spectrum dequantization (ASSD) algorithm. The resulting monolithic A2I converter is designed in 28nm CMOS, acquires RF signals up to 6GS/s, and the onchip ASSD unit detects the active RF bands at a rate 30 × below realtime. Index Terms—Analogtoinformation (A2I) conversion, cognitive radio, compressive sensing, flash analogtodigital converter (ADC), randomized subsampling, sparse signal dequantization, wideband spectrum sensing, verylargescale integration (VLSI). I.
HigherOrder Methods for LargeScale Optimization
, 2015
"... To everyone who, both directly or indirectly, piqued my interest in research ii Declaration I declare that this thesis was composed by myself and that the work contained therein is my own, except where explicitly stated otherwise in the text. (Kimon Fountoulakis) iii There has been an increased inte ..."
Abstract
 Add to MetaCart
(Show Context)
To everyone who, both directly or indirectly, piqued my interest in research ii Declaration I declare that this thesis was composed by myself and that the work contained therein is my own, except where explicitly stated otherwise in the text. (Kimon Fountoulakis) iii There has been an increased interest in optimization for the analysis of largescale data sets which require gigabytes or terabytes of data to be stored. A variety of applications originate from the fields of signal processing, machine learning and statistics. Seven representative applications are described below. Magnetic Resonance Imaging (MRI): A medical imaging tool used to scan the anatomy and the physiology of a body [80]. Image inpainting: A technique for reconstructing degraded parts of an image [14]. Image deblurring: Image processing tool for removing the blurriness of a