Publications related to The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics

Martin Vetterli, Luciano Sbaiz, Yue Lu, Feng Yang

We study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversam ...

EPFL2011

A 16-channel 220 µW neural recording IC with embedded delta compression

Alexandre Schmid, Yusuf Leblebici, Vahid Majidzadeh Bafar

A 16-channel neural action potential recording IC suitable for large-scale integration with multi-electrode arrays (MEAs) is presented. A closed-loop gain of 60 dB in the action potential band is achieved by cascading differential gain-stages utilizing a n ...

2011

Clock jitter estimation in noise

Ali H. Sayed

Clock timing jitter refers 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 ...

IEEE2011

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

Sensing and Recovery under Sparsity Constraints

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Laurent Jacques

The theory of Compressed Sensing (CS) is based on reconstructing sparse signals from random linear measurements. As measurement of continuous signals by digital devices always involves some form of quantization, in practice devices based on CS encoding mus ...

2009

Dequantizing Compressed Sensing with non-Gaussian Constraints

Laurent Jacques

The theory of Compressed Sensing (CS) is based on reconstructing sparse signals from random linear measurements. As measurement of continuous signals by digital devices always involves some form of quantization, in practice devices based on CS encoding mus ...

2009

Compressive sensing recovery of spike trains using a structured sparsity model

Volkan Cevher

The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a length-N signal x with sparsity level K, M = O(K log(N/K)) ...

2009

Sampling Bounds for Sparse Support Recovery in the Presence of Noise

Michael Christoph Gastpar

It is well known that the support of a sparse signal can be recovered from a small number of random projections. However, in the presence of noise all known sufficient conditions require that the per-sample signal-to-noise ratio (SNR) grows without bound w ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Differences between observation and sampling error in sparse signal reconstruction

Michael Christoph Gastpar

The field of Compressed Sensing has shown that a relatively small number of random projections provide sufficient information to accurately reconstruct sparse signals. Inspired by applications in sensor networks in which each sensor is likely to observe a ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing

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Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics

A 16-channel 220 µW neural recording IC with embedded delta compression

Clock jitter estimation in noise

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

Sensing and Recovery under Sparsity Constraints

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Dequantizing Compressed Sensing with non-Gaussian Constraints

Compressive sensing recovery of spike trains using a structured sparsity model

Sampling Bounds for Sparse Support Recovery in the Presence of Noise

Differences between observation and sampling error in sparse signal reconstruction

A 16-channel 220 µW neural recording IC with embedded delta compression

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

Compressive sensing recovery of spike trains using a structured sparsity model

Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics

Sensing and Recovery under Sparsity Constraints

Clock jitter estimation in noise

Sampling Bounds for Sparse Support Recovery in the Presence of Noise

Dequantizing Compressed Sensing with non-Gaussian Constraints

Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

Differences between observation and sampling error in sparse signal reconstruction