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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13 Nov 2010TL;DR: An in-depth analysis of the impact of system noise on large-scale parallel application performance in realistic settings shows that not only collective operations but also point-to-point communications influence the application's sensitivity to noise.
Abstract: This paper presents an in-depth analysis of the impact of system noise on large-scale parallel application performance in realistic settings. Our analytical model shows that not only collective operations but also point-to-point communications influence the application's sensitivity to noise. We present a simulation toolchain that injects noise delays from traces gathered on common large-scale architectures into a LogGPS simulation and allows new insights into the scaling of applications in noisy environments. We investigate collective operations with up to 1 million processes and three applications (Sweep3D, AMG, and POP) with up to 32,000 processes.We show that the scale at which noise becomes a bottleneck is system-specific and depends on the structure of the noise. Simulations with different network speeds show that a 10x faster network does not improve application scalability. We quantify noise and conclude that our tools can be utilized to tune the noise signatures of a specific system.
236 citations
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TL;DR: In this paper, the high electron mobility transistor's (HEMT's) noise behavior is presented from theoretical and experimental points of view, and a comparison between the noise performance of both MESFETs and HEMTs is carried out.
Abstract: The high electron mobility transistor's (HEMT's) noise behavior is presented from theoretical and experimental points of view. The general method used in the high-frequency noise analysis is described and the different approximations commonly used in the derivation of the noise parameter expressions are discussed. A comparison between the noise performance of both MESFETs and HEMTs is carried out. The measurement techniques providing the noise figure and the other noise parameters are then described and compared. >
235 citations
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14 May 2006TL;DR: This paper demonstrates how CS principles can solve signal detection problems given incoherent measurements without ever reconstructing the signals involved, and proposes an incoherent detection and estimation algorithm (IDEA) based on matching pursuit.
Abstract: The recently introduced theory of compressed sensing (CS) enables the reconstruction or approximation of sparse or compressible signals from a small set of incoherent projections; often the number of projections can be much smaller than the number of Nyquist rate samples. In this paper, we show that the CS framework is information scalable to a wide range of statistical inference tasks. In particular, we demonstrate how CS principles can solve signal detection problems given incoherent measurements without ever reconstructing the signals involved. We specifically study the case of signal detection in strong inference and noise and propose an incoherent detection and estimation algorithm (IDEA) based on matching pursuit. The number of measurements and computations necessary for successful detection using IDEA is significantly lower than that necessary for successful reconstruction. Simulations show that IDEA is very resilient to strong interference, additive noise, and measurement quantization. When combined with random measurements, IDEA is applicable to a wide range of different signal classes
233 citations
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TL;DR: The main advantage of this object-based method is its robustness to background artefacts such as ghosting, and within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.
229 citations
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TL;DR: Both objective (global SNR) and subjective mean opinion score (MOS) evaluations demonstrate consistent superiority of the HMM-based enhancement systems that incorporate the innovations described in this paper over the conventional spectral subtraction method.
Abstract: An improved hidden Markov model-based (HMM-based) speech enhancement system designed using the minimum mean square error principle is implemented and compared with a conventional spectral subtraction system. The improvements to the system are: (1) incorporation of mixture components in the HMM for noise in order to handle noise nonstationarity in a more flexible manner, (2) two efficient methods in the speech enhancement system design that make the system real-time implementable, and (3) an adaptation method to the noise type in order to accommodate a wide variety of noise expected under the enhancement system's operating environment. The results of the experiments designed to evaluate the performance of the HMM-based speech enhancement systems in comparison with spectral subtraction are reported. Three types of noise-white noise, simulated helicopter noise, and multitalker (cocktail party) noise-were used to corrupt the test speech signals. Both objective (global SNR) and subjective mean opinion score (MOS) evaluations demonstrate consistent superiority of the HMM-based enhancement systems that incorporate the innovations described in this paper over the conventional spectral subtraction method.
229 citations