28,791 research outputs found
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed
representations of a sparse signal. Its low complexity is appealing for
resource-constrained scenarios like sensor networks. However, such scenarios
are often coupled with unreliable communication channels and providing robust
transmission of the acquired data to a receiver is an issue. Multiple
description coding (MDC) effectively combats channel losses for systems without
feedback, thus raising the interest in developing MDC methods explicitly
designed for the CS framework, and exploiting its properties. We propose a
method called Graded Quantization (CS-GQ) that leverages the democratic
property of compressive measurements to effectively implement MDC, and we
provide methods to optimize its performance. A novel decoding algorithm based
on the alternating directions method of multipliers is derived to reconstruct
signals from a limited number of received descriptions. Simulations are
performed to assess the performance of CS-GQ against other methods in presence
of packet losses. The proposed method is successful at providing robust coding
of CS measurements and outperforms other schemes for the considered test
metrics
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
Experimental analysis of computer system dependability
This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance
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