Publications related to Nyquist–Shannon sampling theorem

Model-based compressive sensing

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K < N elements from an N-dimensional basis. Instead of taking periodic samples, we measure inner ...

Institute of Electrical and Electronics Engineers2010

Multichannel Sampling with Unknown Gains and Offsets: A Fast Reconstruction Algorithm

Martin Vetterli, Yue Lu

We study a multichannel sampling scheme, where different channels observe scaled and shifted versions of a common bandlimited signal. The channel gains and offsets are unknown a priori, and each channel samples at sub-Nyquist rates. This setup appears in m ...

2010

Learning Sparse Systems at Sub-Nyquist Rates: A Frequency-Domain Approach

Martin Vetterli, Yue Lu

We propose a novel algorithm for sparse system identification in the frequency domain. Key to our result is the observation that the Fourier transform of the sparse impulse response is a simple sum of complex exponentials, whose parameters can be efficient ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Sampling clock jitter estimation and compensation in ADC circuits

Ali H. Sayed

Clock timing jitters refer to random perturbations in the sampling time in analog-to-digital converters (ADCs). The perturbations are caused by circuit imperfections in the sampling clock. This paper analyzes the effect of sampling clock jitter on the acqu ...

IEEE2010

Continuous-Time AR Model Identification: Does Sampling Rate Really Matter?

Michaël Unser, Hagay Kirshner, Simona Maggio

We address the problem of identifying continuous-time auto regressive (CAR) models from sampled data. The exponential nature of CAR autocorrelation functions is taken into account by means of exponential B-splines modelling, allowing one to associate the a ...

EURASIP2010

Sparse Signal Acquisition and Recovery with Graphical Models

Volkan Cevher

A great deal of theoretic and algorithmic research has revolved around sparsity view of signals over the last decade to characterize new, sub-Nyquist sampling limits as well as tractable algorithms for signal recovery from dimensionality reduced measuremen ...

Institute of Electrical and Electronics Engineers2010

Robust Controller Design by Convex Optimization based on Finite Frequency Samples of Spectral Models

Alireza Karimi, Roland Longchamp, Gorka Galdos Sanz de Galdeano

Some frequency-domain controller design problems are solved using a finite number of frequency samples. Consequently, the performance and stability conditions are not guaranteed for the frequencies between the frequency samples. In this paper, all possible ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Signal Processing in Space and Time

Francisco Pereira Correia Pinto

Sound waves propagate through space and time by transference of energy between the particles in the medium, which vibrate according to the oscillation patterns of the waves. These vibrations can be captured by a microphone and translated into a digital sig ...

EPFL2010

Registration for Super-Resolution: Theory, Algorithms, and Applications in Image and Mobile Video Enhancement

Martin Vetterli, Luciano Sbaiz, Patrick Vandewalle

The relation between sensor resolution and the optics of a digital camera is determined by the Nyquist sampling theorem: the sampling frequency should be larger than twice the maximum frequency of the image content coming out of the optical system. If a lo ...

Taylor& Francis / CRC Press2010

Subthreshold Current-Mode Delta-Sigma Quantizer with 3-Decade Scalable Sampling Rate and pico-Ampere Range Resolution

Seyed Armin Tajalli, Yusuf Leblebici

A current-mode

\Delta\Sigma

analog-to-digital data converter (ADC) for biomedical readout front-end systems is being presented. The core of this circuit is a current-controlled ring oscillator (CCO) whose oscillation frequency is proportional to the inpu ...

2010

Nyquist–Shannon sampling theorem

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Model-based compressive sensing

Multichannel Sampling with Unknown Gains and Offsets: A Fast Reconstruction Algorithm

Learning Sparse Systems at Sub-Nyquist Rates: A Frequency-Domain Approach

Sampling clock jitter estimation and compensation in ADC circuits

Continuous-Time AR Model Identification: Does Sampling Rate Really Matter?

Sparse Signal Acquisition and Recovery with Graphical Models

Robust Controller Design by Convex Optimization based on Finite Frequency Samples of Spectral Models

Signal Processing in Space and Time

Registration for Super-Resolution: Theory, Algorithms, and Applications in Image and Mobile Video Enhancement

Subthreshold Current-Mode Delta-Sigma Quantizer with 3-Decade Scalable Sampling Rate and pico-Ampere Range Resolution

Sampling clock jitter estimation and compensation in ADC circuits

Robust Controller Design by Convex Optimization based on Finite Frequency Samples of Spectral Models

Model-based compressive sensing

Registration for Super-Resolution: Theory, Algorithms, and Applications in Image and Mobile Video Enhancement

Signal Processing in Space and Time

Continuous-Time AR Model Identification: Does Sampling Rate Really Matter?

Multichannel Sampling with Unknown Gains and Offsets: A Fast Reconstruction Algorithm

Sparse Signal Acquisition and Recovery with Graphical Models

Learning Sparse Systems at Sub-Nyquist Rates: A Frequency-Domain Approach

Subthreshold Current-Mode Delta-Sigma Quantizer with 3-Decade Scalable Sampling Rate and pico-Ampere Range Resolution