2,253 research outputs found
Convexity in source separation: Models, geometry, and algorithms
Source separation or demixing is the process of extracting multiple
components entangled within a signal. Contemporary signal processing presents a
host of difficult source separation problems, from interference cancellation to
background subtraction, blind deconvolution, and even dictionary learning.
Despite the recent progress in each of these applications, advances in
high-throughput sensor technology place demixing algorithms under pressure to
accommodate extremely high-dimensional signals, separate an ever larger number
of sources, and cope with more sophisticated signal and mixing models. These
difficulties are exacerbated by the need for real-time action in automated
decision-making systems.
Recent advances in convex optimization provide a simple framework for
efficiently solving numerous difficult demixing problems. This article provides
an overview of the emerging field, explains the theory that governs the
underlying procedures, and surveys algorithms that solve them efficiently. We
aim to equip practitioners with a toolkit for constructing their own demixing
algorithms that work, as well as concrete intuition for why they work
Phaseless computational imaging with a radiating metasurface
Computational imaging modalities support a simplification of the active
architectures required in an imaging system and these approaches have been
validated across the electromagnetic spectrum. Recent implementations have
utilized pseudo-orthogonal radiation patterns to illuminate an object of
interest---notably, frequency-diverse metasurfaces have been exploited as fast
and low-cost alternative to conventional coherent imaging systems. However,
accurately measuring the complex-valued signals in the frequency domain can be
burdensome, particularly for sub-centimeter wavelengths. Here, computational
imaging is studied under the relaxed constraint of intensity-only measurements.
A novel 3D imaging system is conceived based on 'phaseless' and compressed
measurements, with benefits from recent advances in the field of phase
retrieval. In this paper, the methodology associated with this novel principle
is described, studied, and experimentally demonstrated in the microwave range.
A comparison of the estimated images from both complex valued and phaseless
measurements are presented, verifying the fidelity of phaseless computational
imaging.Comment: 18 pages, 18 figures, articl
Compressive wireless arrays for bearing estimation
Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the slim of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, low dimensional random projections of the microphone signals can be used to determine multiple source bearings by solving an l(1)-norm minimization problem. Field data results are shown where only 10 bits of information is passed from each microphone to estimate multiple target bearings
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