A Compressed sensing analog-to-information converter with edge-triggered SAR ADC Core

Trakimas, Michael; Hancock, Timothy; Sonkusale, Sameer

doi:10.1109/iscas.2012.6271993

Cited by 17 publications

(10 citation statements)

References 3 publications

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“…Recent theoretical work related to compressive sensing and sparse recovery has shown that it is possible to relax some of the ADC characteristics by transforming the signal basis to a sparse domain [5]. Modulated wide-band converters (MWC) proposed in [6] also reduce ADC rate by performing sub-Nyquist sampling over multiple channel.…”

Section: Review Of Frequency Domain Samplingmentioning

confidence: 99%

An ultra-wideband frequency Domain receiver for software defined radio applications

Adhikari

Jain

Jamadagni

2015

2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

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This work presents a frequency domain ultrawideband receiver architecture spanning from 500 MHz to 10.5 GHz. Proposed architecture uses Transform Domain (TD) sampling method which relaxes the ADC performance bottleneck for digitizing 500 MHz baseband signal. To achieve wideband operation a RF front end consisting of wideband LNA, VGA, baseband mixer and integrator is designed using UMC 130nm technology. Due to wide frequency operation this architecture is suitable for software defined radio applications.

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Section: Review Of Frequency Domain Samplingmentioning

confidence: 99%

An ultra-wideband frequency Domain receiver for software defined radio applications

Adhikari

Jain

Jamadagni

2015

2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

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“…At 65nm CMOS technology, the lowest power consummation by SAR ADC is 108nW with SNDR value of 59.19db and minimum sampling rate of 0.5KS/s [26]. The power consumption is 550μW for 90nm CMOS process with SNDR of 57.6db [22].…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…Compressed sensing (CS) technique used to improve the energy efficiency. Compressed sensing takes M samples out of N input samples to reduce the power consumption [12][17] [22][25] [26].…”

Section: Review Of Related Literaturementioning

confidence: 99%

SAR Analog to Digital Converter for bio-potential signals: A review

Singh

Kumar

Kumar³

2015

2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI)

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“…At present, the application of the compressed sensing theory has got involved with many fields, such as: compressed sensing radar [2][3][4][5][6], Distributed Compressed Sensing (DCS) theory [7,8,9], wireless sensor network [10,11], image acquisition equipment development [12,13], medical image processing [14,15], Analog-to-Information [16,17], spectral analysis [18][19], hyperspec-tral image processing and remote sensing image processing [20,21,22]. However research on the compressed sensing of the speech signal is still in the early stage.…”

Section: Introductionmentioning

confidence: 99%

Speech Compression and Reconstruction Algorithm Based on Multi-Scale Wavelet Packet and Compressed Sensing

Cao¹,

Xiong²,

Yuan³

2015

IJIES

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Wavelet transform is widely applied in the field of the speech signal processing, but the wavelet decomposition is only decomposing the low frequency coefficients, so higher resolution can not be obtained for the high frequency components. In order to further decompose the high frequency coefficient, in this paper, the speech signal was decomposed by multi-scale wavelet packet. By analyzing the sparsity of the decomposed coefficients, the reconstruction algorithm based on compressed sensing was proposed by using the sparse representation of speech signal, at the same time, the effect of compression ratio on the reconstruction quality was analyzed. In determining the compression ratio, speech signal reconstruction quality and coding speed were taken into account, so the proposed compressed and reconstruction method had good real-time property. A large number of simulation experiments showed that the proposed method in this paper had excellent reconstruction quality and efficient coding efficiency.

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A Compressed sensing analog-to-information converter with edge-triggered SAR ADC Core

Cited by 17 publications

References 3 publications

An ultra-wideband frequency Domain receiver for software defined radio applications

An ultra-wideband frequency Domain receiver for software defined radio applications

SAR Analog to Digital Converter for bio-potential signals: A review

Speech Compression and Reconstruction Algorithm Based on Multi-Scale Wavelet Packet and Compressed Sensing

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