Open Access
Statistical Signal Processing
TLDR
An understanding of the convergence and synchronization of statistical signal processing algorithms in continuous time is developed, and an understanding of linear and nonlinear circuits for analog memory is explored, and the “soft-multiplexer” is proposed.Abstract:
This thesis proposes an alternate paradigm for designing computers using continuoustime analog circuits. Digital computation sacrifices continuous degrees of freedom. A principled approach to recovering them is to view analog circuits as propagating probabilities in a message passing algorithm. Within this framework, analog continuous-time circuits can perform robust, programmable, high-speed, low-power, cost-effective, statistical signal processing. This methodology will have broad application to systems which can benefit from low-power, high-speed signal processing and offers the possibility of adaptable/programmable high-speed circuitry at frequencies where digital circuitry would be cost and power prohibitive. Many problems must be solved before the new design methodology can be shown to be useful in practice: Continuous-time signal processing is not well understood. Analog computational circuits known as “soft-gates” have been previously proposed, but a complementary set of analog memory circuits is still lacking. Analog circuits are usually tunable, rarely reconfigurable, but never programmable. The thesis develops an understanding of the convergence and synchronization of statistical signal processing algorithms in continuous time, and explores the use of linear and nonlinear circuits for analog memory. An exemplary embodiment called the Noise Lock Loop (NLL) using these design primitives is demonstrated to perform direct-sequence spread-spectrum acquisition and tracking functionality and promises order-of-magnitude wins over digital implementations. A building block for the construction of programmable analog gate arrays, the “soft-multiplexer” is also proposed. Thesis Supervisor: Neil Gershenfeld Title: Associate Professorread more
Citations
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Journal ArticleDOI
Hyperspectral Remote Sensing Data Analysis and Future Challenges
Jose M. Bioucas-Dias,Antonio Plaza,Gustau Camps-Valls,Paul Scheunders,Nasser M. Nasrabadi,Jocelyn Chanussot +5 more
TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.
Journal ArticleDOI
Hyperspectral Subspace Identification
TL;DR: This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery, which is eigen decomposition based, unsupervised, and fully automatic.
Journal ArticleDOI
Analog Beamforming in MIMO Communications With Phase Shift Networks and Online Channel Estimation
Vijay Venkateswaran,A. van Veen +1 more
TL;DR: This paper considers the design of the analog and digital beamforming coefficients, for the case of narrowband signals, and proposes the optimal analog beamformer to minimize the mean squared error between the desired user and its receiver estimate.
Proceedings ArticleDOI
Sensitivity to basis mismatch in compressed sensing
TL;DR: This paper establishes achievable bounds for the l1 error of the best k -term approximation and derives bounds, with similar growth behavior, for the basis pursuit l1 recovery error, indicating that the sparse recovery may suffer large errors in the presence of basis mismatch.
Journal ArticleDOI
Compact polarimetry overview and applications assessment
François Charbonneau,Brian Brisco,R. K. Raney,Heather McNairn,C. Liu,Paris W. Vachon,Jiali Shang,R. DeAbreu,Catherine Champagne,Amine Merzouki,Torsten Geldsetzer +10 more
TL;DR: A synthetic aperture radar (SAR) with hybrid-polarity (CL-pol) architecture transmits circular polarization and receives two orthogonal, mutually coherent linear polarizations, which is one manifestation of compact polarimetry as mentioned in this paper.
References
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TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.