Opportunistic Sampling by Level-Crossing

Guan, K.; Singer, Andrew C.

doi:10.1109/icassp.2007.367136

Cited by 28 publications

(24 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LCSS adapts its sampling rate according to the input signal local variations [6,7]. Thus, it drastically reduces the post processing chain activity, because it only samples the relevant information [8,9].…”

Section: Introductionmentioning

confidence: 99%

Power efficient analog to digital conversion for the Li-ion battery voltage monitoring and measurement

Qaisar¹,

Dallet

Benjamin

et al. 2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

In modern era the utilization of battery empowered systems is growing exponentially. In the goal to achieve an optimal system performance, the employment of a BMS (Battery Management System) is inevitable. The recent sophistications in the area of BMSs are demanding more and more processing resources. Reducing the BMS power consumption is becoming one of the most difficult industrial challenges. Most of efforts to achieve this goal are focused on improving the embedded systems design, but very few studies target to exploit the input signal time-varying nature. This work aims to achieve power efficiency by smartly adapting the system activity to the input signal local variations. In this context a novel A/D conversion approach is derived. The proposed solution, based on the LCSS (Level Crossing Sampling Scheme) presents an ADC (A/D Converter), able to adapt its acquisition rate according to the input signal variations.In fact, the principle is to smartly exploit the signal local characteristics to acquire only the relevant signal parts at relevant sampling rate. The idea offers a significant reduction in acquired number of samples and hence promises a drastic reduction in the system power consumption compared to the classical approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Power efficient analog to digital conversion for the Li-ion battery voltage monitoring and measurement

Qaisar¹,

Dallet

Benjamin

et al. 2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

show abstract

“…Here, L is the effective length in seconds of the window function w n and Fs is the sampling frequency. The STFT can be expressed mathematically by (9). In Equation (9), f is the frequency index, which is normalized with respect to Fs.…”

Section: Adaptive Resolution Analysismentioning

confidence: 99%

“…The LCSS drastically reduces the activity of the post processing chain, because it only captures the relevant information [7][8][9]. In this context, analog to digital converters based on the LCSS have been developed [10][11][12].…”

Section: Asynchronous Analog To Digital Converter (Aadc)mentioning

confidence: 99%

An Adaptive Resolution Computationally Efficient Short‐Time Fourier Transform

Qaisar

Fesquet

Renaudin

2008

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

The short-time Fourier transform (STFT) is a classical tool, used for characterizing the time varying signals. The limitation of the STFT is its fixed time-frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross-level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local characteristics. Therefore, it provides an adaptive resolution time-frequency representation of the input signal. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power.

show abstract

“…The random convolution based sampling strategy has been investigated by [7], [8] and [9] to solve this drawback. To avoid hardware realization challenges, we acquire less samples from analog signal directly by using Level-crossing (LC) sampling [10,11,12,13,14,15,16,17], in which an analog input signal is compared with a set of quantization levels (can also be called reference levels) and a sample is produced only when the input analog signal changes enough to cross a level, thus it results in nonuniform sampling and saves dynamic power in the analog-to-digital conversion (ADC) and following DSP for those powered by a very small battery and that involve "bursty" signals with varying activity over time, possibly including long periods of silence. Sampling by LC can mimic the behavior of such input signals.…”

Section: Introductionmentioning

confidence: 99%

Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics

Chen¹,

Wan²,

Fan³

et al. 2015

RADIOENGINEERING

View full text Add to dashboard Cite

Abstract. Traditional direction-of-arrival (DOA) estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC) sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analogto-digital converter (LC ADC) has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an 1 minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing normbased constrained optimization compressive sensing (CS) algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal.

show abstract

Opportunistic Sampling by Level-Crossing

Cited by 28 publications

References 10 publications

Power efficient analog to digital conversion for the Li-ion battery voltage monitoring and measurement

Power efficient analog to digital conversion for the Li-ion battery voltage monitoring and measurement

An Adaptive Resolution Computationally Efficient Short‐Time Fourier Transform

Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics

Contact Info

Product

Resources

About