Sampling Reconstruction of Stochastic Signals– The Roots in the Fifties

Draščić, Biserka

doi:10.17713/ajs.v36i1.321

Cited by 3 publications

(1 citation statement)

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“…Despite these facts Ville and Oswald gave introductory results to the sampling reconstruction procedure for bandlimited stochastic processes years earlier, see [14]. In his article [6] Belyaev emphasized that "... a rigorous proof of the Formula (2) for the sample functions of random processes with bounded spectra has not been given..." before his work for both the mean-square (and a fortiori almost sure) sampling reconstruction formulae [6] (p. 411).…”

Section: Introductionmentioning

confidence: 99%

Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work

Pogány

2019

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The sampling reconstruction theory is one of the great areas of the analysis in which Paul Leo Butzer earned longstanding and excellent theoretical results. Thus, we are forced either by earlier exhaustive presentations of his research activity and/or the highly voluminous material to restrict ourselves to a more narrow and precise sub-area in consideration; we discuss here, giving deeper insight, Paul Butzer’s sampling theoretical work with special attention concerning sampling stochastic signals.

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Section: Introductionmentioning

confidence: 99%

Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work

Pogány

2019

Axioms

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Foundations of Signal Processing

2014

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This comprehensive and engaging textbook introduces the basic principles and techniques of signal processing, from the fundamental ideas of signals and systems theory to real-world applications. • Introduces students to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation, and compression. • Discusses issues in real-world use of these tools such as effects of truncation and quantization, limitations on localization, and computational costs. • Includes over 160 homework problems and over 220 worked examples, specifically designed to test and expand students' understanding of the fundamentals of signal processing. • Accompanied by extensive online materials designed to aid learning, including Mathematica resources and interactive demonstrations.

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Reproducible coherence characterization of superconducting quantum devices

McRae

Stiehl

Wang

et al. 2021

Applied Physics Letters

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As the field of superconducting quantum computing approaches maturity, optimization of single-device performance is proving to be a promising avenue toward large-scale quantum computers. However, this optimization is possible only if performance metrics can be accurately compared among measurements, devices, and laboratories. Currently, such comparisons are inaccurate or impossible due to understudied errors from a plethora of sources. In this Perspective, we outline the current state of error analysis for qubits and resonators in superconducting quantum circuits and discuss what future investigations are required before superconducting quantum device optimization can be realized.

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Sampling Reconstruction of Stochastic Signals– The Roots in the Fifties

Cited by 3 publications

References 10 publications

Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work

Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work

Foundations of Signal Processing

Reproducible coherence characterization of superconducting quantum devices

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