Rand PPM: A lowpower compressive sampling analog to digital converter

Yenduri, Praveen K.; Gilbert, Anna C.; Flynn, Michael P.; Naraghi, Shahrzad

doi:10.1109/icassp.2011.5947724

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…The largest components in B H y provide a good indication of the largest components in X [14]. The algorithm applies this idea iteratively to reconstruct an approximation to the signal X.…”

Section: The Reconstruction Algorithmmentioning

confidence: 99%

“…The matrix B is similar to the matrix used in [14] and hence we use the algorithm developed in [14] to estimate the indices of the dominant terms in X, that is, we identify the dominant frequencies in X. The largest components in B H y provide a good indication of the largest components in X [14].…”

Section: The Reconstruction Algorithmmentioning

confidence: 99%

“…One line of research assumes that the measurement matrix G satisfies a property called the restricted isometry (RIP) [11], and uses either greedy iterative algorithms ( [9], [10], [12]) or convex optimizations to obtainX. Another line of research designs matrices G and algorithms jointly, optimizing for reconstruction time [13], storage requirements of G, or physical realizability [14] of measurement with matrix G. The matrix G produced by an IAF time-encoding system (whether deterministic or random) does not necessarily satisfy the RIP condition. Hence, following the second line of research, we co-designed the measurement system (i.e.…”

Section: The Reconstruction Algorithmmentioning

confidence: 99%

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Integrate-and-fire neuron modeled as a low-rate sparse time-encoding device

Yenduri

Gilbert

Zhang

2012

2012 Third International Conference on Intelligent Control and Information Processing

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Neurons as Time Encoding Machines (TEMs) have been proposed to capture the information present in sensory stimuli and to encode it into spike trains [1], [2], [3]. These neurons, however, produce spikes at firing rates above Nyquist, which is usually much higher than the amount of information actually present in stimuli. We propose a low-rate spiking neuron which exploits the sparsity or compressibility present in natural signals to produce spikes at a firing rate proportional to the amount of information present in the signal rather than its duration. We consider the IAF (Integrateand-Fire) neuron model, provide appropriate modifications to convert it into a low-rate encoder and develop an algorithm for reconstructing the input stimulus from the low-rate spike trains. Our simulations with frequency-sparse signals demonstrate the superior performance of the Low-Rate IAF neuron operating at a sub-Nyquist rate, when compared with IAF neurons proposed earlier, which operate at and above Nyquist rates.

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“…The largest components in B H y provide a good indication of the largest components in X [14]. The algorithm applies this idea iteratively to reconstruct an approximation to the signal X.…”

Section: The Reconstruction Algorithmmentioning

confidence: 99%

Section: The Reconstruction Algorithmmentioning

confidence: 99%

Section: The Reconstruction Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Integrate-and-fire neuron modeled as a low-rate sparse time-encoding device

Yenduri

Gilbert

Zhang

2012

2012 Third International Conference on Intelligent Control and Information Processing

Self Cite

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“…Our ADC is based on a small modification of the well-known slope ADC and achieves full hardware utilization since no artificial delays enforcing a regular sampling grid are necessary. A random sampling ADC very similar to ours was developed independently and concurrently [8], which is also based on a slope ADC. In contrast to our implementation, wait periods of random length are introduced before a new slope is started.…”

Section: Introductionmentioning

confidence: 99%

Hardware-efficient random sampling of fourier-sparse signals

Maechler

Felber

Kaeslin

et al. 2012

2012 IEEE International Symposium on Circuits and Systems

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Spectrum sensing, i.e. the identification of occupied frequencies within a large bandwidth, requires complex sampling hardware. Measurements suggest that only a small fraction of the available spectrum is actually used at any time and place, which allows a sparse characterization of the frequency domain signal. Compressed sensing (CS) can exploit this sparsity and simplify measurements. We investigate the performance of a very simple hardware architecture based on the slope analogto-digital converter (ADC), which allows to sample signals at unevenly spaced points in time. CS algorithms are used to identify the occupied frequencies, which can be continuously distributed across a large bandwidth.

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“…The PPM and random PPM architectures are discussed in Section III. In contrast to [20], in which we present a mathematical model for random PPM, in this paper, we also discuss the motivation for and the design of the randomized ADC. A prototype random ADC, implemented as a custom complementary metal-oxide-semiconductor (CMOS) PPM ADC coupled to an field-programmable gate array (FPGA) is described.…”

Section: Introductionmentioning

confidence: 99%

A Low-Power Compressive Sampling Time-Based Analog-to-Digital Converter

Yenduri

Rocca

Rao

et al. 2012

IEEE J. Emerg. Sel. Topics Circuits Syst.

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This paper presents a low-power, time-based, compressive sampling architecture for analog-to-digital conversion. A random pulse-position-modulation (PPM) analog-to-digital conversion (ADC) architecture is proposed. A prototype 9-bit random PPM ADC incorporating a pseudo-random sampling scheme is implemented as proof of concept. This approach leverages the energy efficiency of time-based processing. The use of sampling techniques that exploit signal compressibility leads to further improvements in efficiency. The random PPM (pulse-position-modulation) ADC employs compressive sampling techniques to efficiently sample at sub-Nyquist rates. The sub-sampled signal is recovered using a reconstruction algorithm, which is tailored for practical hardware implementation. We develop a theoretical analysis of the hardware architecture and the reconstruction algorithm. Measurements of a prototype random PPM ADC and simulation, demonstrate this theory. The prototype successfully demonstrates a 90% reduction in sampling rate compared to the Nyquist rate for input signals that are 3% sparse in frequency domain.

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Rand PPM: A lowpower compressive sampling analog to digital converter

Cited by 9 publications

References 9 publications

Integrate-and-fire neuron modeled as a low-rate sparse time-encoding device

Integrate-and-fire neuron modeled as a low-rate sparse time-encoding device

Hardware-efficient random sampling of fourier-sparse signals

A Low-Power Compressive Sampling Time-Based Analog-to-Digital Converter

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