Topic
Noise measurement
About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.
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TL;DR: In this paper, the authors proposed a nonstationary extension of the standard 1/f noise model, which allows them to analyze 1/F noise in switched MOSFET circuits more accurately.
Abstract: Analysis of 1/f noise in MOSFET circuits is typically performed in the frequency domain using the standard stationary 1/f noise model. Recent experimental results, however, have shown that the estimates using this model can be quite inaccurate especially for switched circuits. In the case of a periodically switched transistor, measured 1/f noise power spectral density (psd) was shown to be significantly lower than the estimate using the standard 1/f noise model. For a ring oscillator, measured 1/f-induced phase noise psd was shown to be significantly lower than the estimate using the standard 1/f noise model. For a source follower reset circuit, measured 1/f noise power was also shown to be lower than the estimate using the standard 1/f model. In analyzing noise in the follower reset circuit using frequency-domain analysis, a low cutoff frequency that is inversely proportional to the circuit on-time is assumed. The choice of this low cutoff frequency is quite arbitrary and can cause significant inaccuracy in estimating noise power. Moreover, during reset, the circuit is not in steady state, and thus frequency-domain analysis does not apply. This paper proposes a nonstationary extension of the standard 1/f noise model, which allows us to analyze 1/f noise in switched MOSFET circuits more accurately. Using our model, we analyze noise for the three aforementioned switched circuit examples and obtain results that are consistent with the reported measurements.
98 citations
TL;DR: A noise reduction DCSK system as a solution to reduce the noise variance present in the received signal in order to improve performance, and computer simulation results are compared to relevant theoretical findings to validate the accuracy of the proposed system and demonstrate the performance improvement.
Abstract: One of the major drawbacks of the conventional differential chaos shift keying (DCSK) system is the addition of channel noise to both the reference signal and the data-bearing signal, which deteriorates its performance. In this brief, we propose a noise reduction DCSK system as a solution to reduce the noise variance present in the received signal in order to improve performance. For each transmitted bit, instead of generating $\beta$ different chaotic samples to be used as a reference sequence, $\beta/P$ chaotic samples are generated and then duplicated $P$ times in the signal. At the receiver, $P$ identical samples are averaged, and the resultant filtered signal is correlated to its time-delayed replica to recover the transmitted bit. This averaging operation of size $P$ reduces the noise variance and enhances the performance of the system. Theoretical bit error rate expressions for additive white Gaussian noise and multipath fading channels are analytically studied and derived. Computer simulation results are compared to relevant theoretical findings to validate the accuracy of the proposed system and to demonstrate the performance improvement compared to the conventional DCSK, the improved DCSK, and the differential-phase-shift-keying systems.
98 citations
19 Apr 2015
TL;DR: It is found that system performance can be improved significantly, with relative improvements up to 75% in far-field conditions, by employing a combination of multi-style training and a proposed novel formulation of automatic gain control that estimates the levels of both speech and background noise.
Abstract: We explore techniques to improve the robustness of small-footprint keyword spotting models based on deep neural networks (DNNs) in the presence of background noise and in far-field conditions. We find that system performance can be improved significantly, with relative improvements up to 75% in far-field conditions, by employing a combination of multi-style training and a proposed novel formulation of automatic gain control (AGC) that estimates the levels of both speech and background noise. Further, we find that these techniques allow us to achieve competitive performance, even when applied to DNNs with an order of magnitude fewer parameters than our base-line.
98 citations
TL;DR: A practical noise model is proposed that best describes the noise conditions on an orthogonal frequency-division multiplexing PLC system and it is tested on a PLC channel and its performance in terms of bit-error rate versus the E b/N 0 value is obtained.
Abstract: Power-line communications (PLC) have gained a lot of scientific interest over the past years. In this paper, a practical noise model is proposed that best describes the noise conditions on an orthogonal frequency-division multiplexing PLC system. The noise present on a power-line system is divided in five categories which are grouped into two classes: 1) the generalized background and 2) the impulsive noise. In this paper, all of the components comprising the noise are precisely depicted on a computer simulation system. The statistical properties regarding all component parameters are taken into account and used in our model. By this way, the real conditions on a PLC channel can be portrayed in the most precise way. This model is tested on a PLC channel and its performance in terms of bit-error rate versus the E b/N 0 value is obtained. For reasons of completeness, we examine how two of the model's components affect the system's performance by altering their vital parameters. In order to accomplish this, we take various values for these parameters and we check their influence on the system. Furthermore, we apply a popular noise model, such as Middleton's noise model and we compare the performance obtained by both noise models.
98 citations
TL;DR: Two algorithms, UN-MUSIC and UN-CLE, are developed to estimate the DOA of signals in unknown spatially correlated noise based on the utilization of these properties and computer simulations show that these methods are superior in performance compared to conventional methods.
Abstract: A new approach is proposed for the consistent estimation of the directions of arrival (DOA) of signals in an unknown spatially-correlated noise environment. The signal and noise model used is based on the assumption that the data are received by two arrays well separated so that their noise outputs are uncorrelated. The generalized correlation decomposition of the cross-correlation matrix between the two arrays is then introduced. Of particular interest is the canonical correlation decomposition. The analysis of the generalized correlation leads to various interesting geometric and asymptotic properties of the eigenspace structure. Two algorithms, UN-MUSIC and UN-CLE, are developed to estimate the DOA of signals in unknown spatially correlated noise based on the utilization of these properties. Computer simulations show that these methods are superior in performance compared to conventional methods. Furthermore, it is demonstrated that the new methods are equally effective even when only one sensor array is employed. >
98 citations