An Investigation of the Sources and Characteristics of the Noise Component in SeaMarc II Echo Signals.

1996 ◽  
Author(s):  
Stanley Zisk
Keyword(s):  
Noise Component ◽  
Seamarc Ii

Noise properties of thin-film Ni-P resistors embedded in printed circuit boards

2013 ◽  
Vol 61 (3) ◽  
pp. 731-735
Author(s):  
A.W. Stadler ◽  
Z. Zawiślak ◽  
W. Stęplewski ◽  
A. Dziedzic
Keyword(s):  
Thin Film ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Noise Component ◽  
Printed Circuit ◽  
Resistance Bridge ◽  
Different Types ◽  
Signal Path ◽  
Resistance Fluctuations ◽  
Made In

Abstract. Noise studies of planar thin-film Ni-P resistors made in/on Printed Circuit Boards, both covered with two different types of cladding or uncladded have been described. The resistors have been made of the resistive-conductive-material (Ohmega-Ply©) of 100 Ώ/sq. Noise of the selected pairs of samples has been measured in the DC resistance bridge with a transformer as the first stage in a signal path. 1/f noise caused by resistance fluctuations has been found to be the main noise component. Parameters describing noise properties of the resistors have been calculated and then compared with the parameters of other previously studied thin- and thick-film resistive materials.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

2021 ◽  
Vol 11 (14) ◽  
pp. 6288
Author(s):  
Hang Su ◽  
Chang-Myung Lee
Keyword(s):  
Speech Signal ◽  
Output Signal ◽  
Cross Correlation ◽  
Acoustic Noise ◽  
Audio Signal ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Experiment Data ◽  
Noise Component

The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

scholarly journals Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis

2021 ◽  
Vol 11 (10) ◽  
pp. 4385
Author(s):  
Kun Qian ◽  
Zhichao Hou ◽  
Jie Liang ◽  
Ruixue Liu ◽  
Dengke Sun
Keyword(s):  
Electric Vehicles ◽  
Sound Quality ◽  
Interior Noise ◽  
Experimental Comparison ◽  
Noise Component ◽  
Acoustic Environment ◽  
Evaluation Indexes ◽  
Transfer Path ◽  
New Ideas ◽  
Path Synthesis

The interior sound quality (SQ) of pure electric vehicles (PEVs) has become an important consideration for users purchasing vehicles. At present, it is insufficient to take the sound pressure level as the interior acoustics design index of PEVs. Transfer path analysis (TPA) and transfer path synthesis (TPS) that take the SQ of interior noise as the improvement target remains in the preliminary exploration stage. In this paper, objective psychoacoustic parameters of SQ were taken as evaluation indexes of interior PEV noise. A virtual interior SQ synthesis model was designed on the basis of TPA and TPS, which combines experimentation and simulation. The SQ synthesis model demonstrates each noise component contribution in a PEV by new SQ separation technology. First, the interior noise transfer path and noise source of the PEV were determined in a synthesis analysis method of the interior PEV noise. Second, on the basis of the composition mechanism of interior noise and the basic principle of TPA, the excitation signal and transfer function of each interior noise path in the PEV were tested. On the basis of TPS, the interior SQ synthesis model of PEV was then established. Finally, the accuracy of the prediction model was verified in simulation and experimental comparison studies on the psychoacoustic objective parameters of SQ. The SQ objective parameter value of each transfer path was quantified by using contribution analysis. The results are expected to improve the comfort of the interior acoustic environment and enhance the competitiveness of vehicle products. They also provide an effective reference and new ideas for the development of interior SQ in PEVs.

scholarly journals Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

2009 ◽  
Vol 19 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Tomasz Barszcz
Keyword(s):  
Fault Detection ◽  
Adaptive Filter ◽  
Complex Structure ◽  
Reference Signal ◽  
The Self ◽  
Noise Component ◽  
Vibration Signals ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Self Adaptive

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and IdentificationThe paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied—first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

scholarly journals NOISE SPECTROSCOPY OF RESISTIVE COMPONENTS AT ELEVATED TEMPERATURE

2014 ◽  
Vol 21 (1) ◽  
pp. 15-26 ◽  
Author(s):  
Adam Witold Stadler ◽  
Zbigniew Zawiślak ◽  
Andrzej Dziedzic ◽  
Damian Nowak
Keyword(s):  
Thick Film ◽  
Entire Range ◽  
Slow Rate ◽  
Noise Intensity ◽  
Voltage Fluctuations ◽  
Noise Component ◽  
Conductive Materials ◽  
Noise Spectroscopy ◽  
Order Of Magnitude ◽  
Resistance Fluctuations

Abstract Studies of electrical properties, including noise properties, of thick-film resistors prepared from various resistive and conductive materials on LTCC substrates have been described. Experiments have been carried out in the temperature range from 300 K up to 650 K using two methods, i.e. measuring (i) spectra of voltage fluctuations observed on the studied samples and (ii) the current noise index by a standard meter, both at constant temperature and during a temperature sweep with a slow rate. The 1/f noise component caused by resistance fluctuations occurred to be dominant in the entire range of temperature. The dependence of the noise intensity on temperature revealed that a temperature change from 300 K to 650 K causes a rise in magnitude of the noise intensity approximately one order of magnitude. Using the experimental data, the parameters describing noise properties of the used materials have been calculated and compared to the properties of other previously studied thick-film materials.

1/f Noise in Mott Variable Range Hopping Conduction in p-type Amorphous Silicon

2015 ◽  
Vol 1770 ◽  
pp. 25-30 ◽  
Author(s):  
V.C. Lopes ◽  
A.J. Syllaios ◽  
D. Whitfield ◽  
K. Shrestha ◽  
C.L. Littler
Keyword(s):  
Electrical Conductivity ◽  
Amorphous Silicon ◽  
Chemical Vapor ◽  
Variable Range Hopping ◽  
Temperature Dependent ◽  
Noise Component ◽  
Variable Range ◽  
Noise Measurements ◽  
P Type ◽  
Hydrogenated Amorphous

ABSTRACTWe report on electrical conductivity and noise measurements made on p-type hydrogenated amorphous silicon (a-Si:H) thin films prepared by Plasma Enhanced Chemical Vapor Deposition (PECVD). The temperature dependent electrical conductivity can be described by the Mott Variable Range Hopping mechanism. The noise at temperatures lower than ∼ 400K is dominated by a 1/f component which follows the Hooge model and correlates with the Mott conductivity. At high temperatures there is an appreciable G-R noise component.

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