Separation of Individual Noise Sources from Compound Noise Measurements in Digital Circuits

2006 ◽  
Author(s):  
Vivek P. Nigam ◽  
Masud H. Chowdhury ◽  
Roland Priemer
Keyword(s):  
Digital Circuits ◽  
Noise Sources ◽  
Noise Measurements

Airframe noise measurements on JAXA Jet Flying Test Bed “Hisho” using a phased microphone array

2017 ◽  
Vol 16 (4-5) ◽  
pp. 255-273 ◽  
Author(s):  
Takehisa Takaishi ◽  
Hiroki Ura ◽  
Kenichiro Nagai ◽  
Yuzuru Yokokawa ◽  
Mitsuhiro Murayama ◽  
...  
Keyword(s):  
Microphone Array ◽  
Low Frequency ◽  
Landing Gear ◽  
Frequency Resolution ◽  
Test Bed ◽  
Noise Sources ◽  
Low Frequencies ◽  
Airframe Noise ◽  
Noise Measurements ◽  
Wooden Platform

In 2015, the Japan Aerospace Exploration Agency launched the Flight demonstration of QUiet technology to Reduce nOise from High-lift configurations project to verify by flight demonstration the feasibility of practical noise-reducing aircraft modification concepts. In order to serve as a baseline for comparison before modification, airframe noise sources of the JAXA Jet Flying Test Bed “Hisho” were measured with a 30 m diameter array of 195 microphones mounted on a wooden platform built temporary beside the runway of Noto Satoyama Airport in Japan. A classical Delay and Sum in the time domain beamforming algorithm was adapted for the present study, with weight factors introduced to improve the low-frequency resolution and autocorrelations eliminated to suppress wind noise at high frequencies. In the landing configuration at idle thrust, the main landing gear, nose landing gear, and side edges of the six extended flap panels were found to be the dominant “Hisho” airframe noise sources. Deconvolution by the DAMAS and CLEAN-SC algorithms provided clearer positions of these sound sources at low frequencies. Integration of acoustical maps agreed well with the sound pressure level measured by a microphone placed at the center of the microphone array and gave detailed information about the contribution of each noise source.

scholarly journals SCLC Degradation in 980 nm Pump Laser by Using Electrical Noise

ISRN Optics ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-4
Author(s):  
B. Orsal ◽  
I. Asaad
Keyword(s):  
Defect Density ◽  
Optical Power ◽  
Longitudinal Mode ◽  
Pump Laser ◽  
Electrical Noise ◽  
Noise Sources ◽  
Noise Spectral Density ◽  
Noise Current ◽  
Noise Measurements ◽  
Pump Lasers

The knowledge of the noise levels is important for pump laser diodes as it allows to study and to locate the noise sources and their origin. 980 nm fresh and aged pump lasers have been characterized by using electrical noise measurements. At 10 Hz, the spectra are dominated by () noise. Current noise spectral density (CNSD) is dominated by (). The trapping defect density near the n+n- and p+p- interfaces is related to pinching of the space-charge-limited current (SCLC) effect. An excess electrical noise due to longitudinal mode hopping is correlated with optical power fluctuations.

Internally Generated Noise From Gas Turbine Engines. Measurement and Prediction

1967 ◽  
Vol 89 (2) ◽  
pp. 177-185 ◽  
Author(s):  
M. J. T. Smith ◽  
M. E. House
Keyword(s):  
Gas Turbine ◽  
Full Scale ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Test Results ◽  
Radiation Patterns ◽  
Noise Sources ◽  
Prediction Technique ◽  
Noise Measurements ◽  
Engine Compressor

The noise sources from gas turbine engines are defined and their radiation patterns identified from test results. Examination of single-stage and full-scale engine compressor noise measurements leads to a prediction technique being evolved for inlet and efflux levels.

Noise at Workplaces in the Call Center

2010 ◽  
Vol 35 (2) ◽  
pp. 253-264 ◽  
Author(s):  
Bożena Smagowska
Keyword(s):  
Hearing Loss ◽  
Call Center ◽  
Call Centers ◽  
European Countries ◽  
Assessment Criteria ◽  
Noise Sources ◽  
Work Activities ◽  
Basic Work ◽  
Noise Measurements

AbstractIn the last years the number of new forms of workplaces, such as call centers, increases. It is defined as a workstation where the basic tasks of a worker are carried out with the use of a phone and a computer. According to statistics, about 1.3-4% of workers are employed in call centers in the European countries. The noise is one of the harmful and annoying hazards of call center workstations. The paper presents the noise sources in call center rooms, assessment criteria of noise and results of noise measurements in call center workstations. The results of measurements show that the noise at call center workstations (during the use of handset receiver phone by operators) can be harmful (causing the risk of hearing loss) and annoying, as it makes it difficult to carry out the basic work activities and causes additionally auditory disadvantageous changes in health.

scholarly journals Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging

2020 ◽  
Vol 19 ◽  
pp. 153601212091369
Author(s):  
Asmaysinh Gharia ◽  
Efthymios P. Papageorgiou ◽  
Simeon Giverts ◽  
Catherine Park ◽  
Mekhail Anwar
Keyword(s):  
Tumor Cells ◽  
Imaging System ◽  
Signal To Noise Ratio ◽  
Size Variation ◽  
Pixel Size ◽  
Signal To Noise ◽  
Noise Sources ◽  
Noise Measurements ◽  
Noise Ratio ◽  
The Impact

Real-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells; however, visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal to noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system and optimize factors such as pixel size. Variation in the background (noise) is due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, and diffusion). Here, we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a Monte Carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.

Noise assessment of taxibotted versus conventional taxiing operations using a phased microphone array

2021 ◽  
Vol 263 (5) ◽  
pp. 1152-1163
Author(s):  
Bieke von den Hoff ◽  
Mirjam Snellen ◽  
Dick G. Simons
Keyword(s):  
Noise Source ◽  
Microphone Array ◽  
Noise Levels ◽  
Noise Sources ◽  
Operational Testing ◽  
Electric Aircraft ◽  
Noise Source Identification ◽  
Noise Measurements ◽  
Noise Assessment ◽  
Lower Noise

In sustainable aviation the focus is mostly applied to the greenhouse gas emissions during flight. However airports have an increasing interest in reducing emissions during ground operations such as taxiing for example to improve the local air quality. Amsterdam Airport Schiphol started a pilot for sustainable taxiing with a pilot-controlled hybrid-electric aircraft towing vehicle called TaxiBot in 2020. The COVID-19 pandemic created an opportunity for extensive operational testing on a near-empty airport. Due to the low background noise levels in this situation, also a noise assessment of taxiing with the TaxiBot versus conventional two-engine taxiing was performed. This assessment can be used to evaluate the noise levels to which ground workers or neighbouring communities are exposed due to TaxiBot operations. For the noise measurements a phased microphone array was used, which allowed not only for a noise level and directionality assessment, but also for noise source identification. This paper compares the noise emissions and noise sources between a taxibotted and conventional taxiing operation. The results show that a taxibotted taxiing operation produces significantly lower noise levels. Additionally, acoustic imaging shows that the TaxiBot engine is the main noise source for a taxibotted pass-by manoeuvre.

scholarly journals Circuit Models and Experimental Noise Measurements of Micropipette Amplifiers for Extracellular Neural Recordings from Live Animals

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chang Hao Chen ◽  
Sio Hang Pun ◽  
Peng Un Mak ◽  
Mang I Vai ◽  
Achim Klug ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Intrinsic Noise ◽  
Model Systems ◽  
Noise Model ◽  
Noise Sources ◽  
Extracellular Recordings ◽  
Neural Recordings ◽  
Noise Measurements ◽  
Glass Micropipette

Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise forin vivoexperiments.

Innovation in Noise Mapping in Natural Gas Compressor Stations

2010 ◽  
Author(s):  
K. K. Botros ◽  
A. Hawryluk ◽  
J. Geerligs ◽  
B. Huynh ◽  
R. Phernambucq
Keyword(s):  
Sound Pressure Level ◽  
Gas Turbines ◽  
Sound Pressure ◽  
Dominant Frequency ◽  
Pressure Level ◽  
Multiple Point ◽  
Noise Sources ◽  
Noise Mapping ◽  
Noise Measurements ◽  
Future Work

Noise is generated at gas turbine-based compressor stations from a number of sources, including turbomachinery (gas turbines and compressors), airflow through inlet ducts and scrubbers, exhaust stacks, aerial coolers, and auxiliary systems. Understanding these noise sources is necessary to ensure that the working conditions on site are safe and that the audible noise at neighbouring properties is acceptable. Each noise source has different frequency content, and the overall sound pressure level (OSPL) at any location in the station yard or inside the compressor building is the result of a superposition of these noise sources. This paper presents results of multiple-point spectral noise measurements at three of TransCanada’s compressor stations on the Alberta System. A method is described to determine the overall noise map of the station yard using Delaunay Triangulation and Natural-Neighbour Interpolation techniques. The results are presented in OSPL maps, as well as animated pictures of the sound pressure level (SPL) in frequency domain which will be shown on a video at the conference. The latter will be useful in future work to determine the culprit sources and the respective dominant frequency range that contributes the most to the OSPL.

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