Broadband cross-correlation processing—Taking advantage of high-frequency impulse response envelope at low frequency

2016 ◽  
Vol 139 (4) ◽  
pp. 2195-2195
Author(s):  
Paul Hursky
Keyword(s):  
Impulse Response ◽  
High Frequency ◽  
Cross Correlation ◽  
Low Frequency ◽  
Correlation Processing ◽  
Response Envelope ◽  
Frequency Impulse

Applied Research on Segmented-Hilbert-Huang Transform Algorithm in Fault Feature Extraction of Main Reducer

2013 ◽  
Vol 823 ◽  
pp. 417-421 ◽  
Author(s):  
Feng Yun Huang ◽  
Huan Huan Sun ◽  
Hao Pan ◽  
Wei Ru Zhang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Vibration Signal ◽  
Model Decomposition ◽  
Frequency Dictionary ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
Scale Characteristics ◽  
Frequency Impulse

For the multi-time scale characteristics of vibration signal, a composite multi-frequency dictionary combining the low-frequency Fourier dictionary and the high-frequency impulse time-frequency dictionary is constituted, to decompose multi-component vibration signal into the combination of several one-component signals. The use of empirical model decomposition (EDM) in high-frequency impulse Component signal including feature information is to realize segmented Hilbert-Huang transform of signal and to acquire the time-frequency representation of every one-component signal, which is the process of fault information extraction of vibration signal. The application of the method in main reducer fault diagnosis verifies the engineering practicability and validity of the new algorithm.

On a Design of Narrowband FIR Low-Pass Filters

2011 ◽  
pp. 2882-2887
Author(s):  
Gordana Jovanovic Dolecek ◽  
Javier Diaz Carmona
Keyword(s):  
Impulse Response ◽  
High Frequency ◽  
Digital Filters ◽  
Stop Band ◽  
Low Frequency ◽  
Pass Band ◽  
Low Pass ◽  
Frequency Components ◽  
Band Pass ◽  
High Pass

Stearns and David (1996) states that “for many diverse applications, information is now most conveniently recorded, transmitted, and stored in digital form, and as a result, digital signal processing (DSP) has become an exceptionally important modern tool.” Typical operation in DSP is digital filtering. Frequency selective digital filter is used to pass desired frequency components in a signal without distortion and to attenuate other frequency components (Smith, 2002; White, 2000). The pass-band is defined as the frequency range allowed to pass through the filter. The frequency band that lies within the filter stop-band is blocked by the filter and therefore eliminated from the output signal. The range of frequencies between the pass-band and the stop-band is called the transition band and for this region no filter specification is given. Digital filters can be characterized either in terms of the frequency response or the impulse response (Diniz, da Silva & Netto, 2002). Depending on its frequency characteristic, a digital filter is either low-pass, high-pass, band-pass, or band-stop filters. A low-pass (LP) filter passes low frequency components to the output, while eliminating high-frequency components. Conversely, the high-pass (HP) filter passes all high-frequency components and rejects all low-frequency components. The band-pass (BP) filter blocks both low- and high-frequency components while passing the intermediate range. The band-stop (BS) filter eliminates the intermediate band of frequencies while passing both low- and high-frequency components. In terms of their impulse responses digital filters are either infinite impulse response (IIR) or finite impulse response (FIR) digital filters. Each of four types of filters (LP, HP, BP, and BS) can be designed as an FIR or an IIR filter (Ifeachor & Jervis, 2001; Mitra, 2005; Oppenheim & Schafer, 1999).

scholarly journals Triangular Vertex Transform Based Watermarking on Contourlet Coefficients for Improved Robustness

2021 ◽  
Vol 12 (3) ◽  
pp. 5434-5442
Author(s):  
Reena Thomas Et. al.
Keyword(s):  
High Frequency ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Standard Test ◽  
Embedding Capacity ◽  
Signal To Noise ◽  
Watermark Embedding ◽  
Hybrid Watermarking ◽  
Watermarking Scheme

A hybrid watermarking scheme based on Triangular Vertex Transform (TVT) and Contourlet coefficients for high robustness is implemented. During watermark embedding, the cover image is first decomposed using Contourlet Transform to obtain high frequency and low frequency coefficients. The lower frequency coefficients are applied with TVT. Then, the W coefficients obtained from TVT are again subdivided. The watermark bit is then embedded on the subdivided coefficients to obtain the watermarked image. Reverse operation is followed in the extraction phase. The performance of this algorithm is evaluated using embedding capacity, Normalized cross correlation (Ncc) and Peak Signal to Noise Ratio (PSNR) using standard test images. These evaluation results disclose the domination of proposed scheme over traditional schemes

scholarly journals Voiceless Stop Consonant Modelling and Synthesis Framework Based on MISO Dynamic System

2017 ◽  
Vol 42 (3) ◽  
pp. 375-383 ◽  
Author(s):  
Gražina Korvel ◽  
Bożena Kostek
Keyword(s):  
Impulse Response ◽  
High Frequency ◽  
Low Frequency ◽  
Stop Consonant ◽  
Frequency Component ◽  
High Frequency Component ◽  
Frequency Range ◽  
Modelling Framework ◽  
Voiceless Stop ◽  
Consonant Phoneme

AbstractA voiceless stop consonant phoneme modelling and synthesis framework based on a phoneme modelling in low-frequency range and high-frequency range separately is proposed. The phoneme signal is decomposed into the sums of simpler basic components and described as the output of a linear multiple-input and single-output (MISO) system. The impulse response of each channel is a third order quasi-polynomial. Using this framework, the limit between the frequency ranges is determined. A new limit point searching three-step algorithm is given in this paper. Within this framework, the input of the low-frequency component is equal to one, and the impulse response generates the whole component. The high-frequency component appears when the system is excited by semi-periodic impulses. The filter impulse response of this component model is single period and decays after three periods. Application of the proposed modelling framework for the voiceless stop consonant phoneme has shown that the quality of the model is sufficiently good.

scholarly journals The Correlated Multi-color Optical Variations of BL Lac Object S5 0716+714

2010 ◽  
Vol 27 (3) ◽  
pp. 296-301 ◽  
Author(s):  
Bingkai Zhang ◽  
Benzhong Dai ◽  
Li Zhang ◽  
Jiali Liu ◽  
Zhen Cao
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
Cross Correlation ◽  
Function Analysis ◽  
Low Frequency ◽  
Light Curves ◽  
Time Lags ◽  
Strongly Correlated ◽  
High Frequency Band ◽  
Bl Lac

AbstractS5 0716+714 is a well-studied BL Lac object in the sky. Verifying the existence of correlations among the flux variations in different bands serves as an important tool to investigate the emission processes. To examine the possible existence of a lag between variations in different optical bands on this source, we employ a discrete correlation function analysis on the light curves. In order to obtain statistically meaningful values for the cross-correlation time lags and their related uncertainties, we perform Monte Carlo simulations called ‘flux redistribution/random subset selection’. Our analysis confirms that the variations in different optical light curves are strongly correlated. The time lags show a hint of the variations in high frequency band leading those in low frequency band of the order of a few minutes.

scholarly journals Algorithm Used to Detect Weak Signals Covered by Noise in PIND

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
G. T. Wang ◽  
X. W. Liang ◽  
Y. Y. Xue ◽  
C. Li ◽  
Q. Ding
Keyword(s):  
High Frequency ◽  
Cross Correlation ◽  
Low Frequency ◽  
Detection Accuracy ◽  
Noise Detection ◽  
Electronic Components ◽  
Weak Signals ◽  
Detection Range ◽  
Impact Noise ◽  
Numerical Iteration

Detection of the loose particles is urgently required in the spacecraft production processes. PIND (particle impact noise detection) is the most commonly used method for the detection of loose particles in the aerospace electronic components. However, when the mass of loose particles is smaller than 0.01 mg, the weak signals are difficult to be detected accurately. In this paper, the aperiodic stochastic resonance (ASR) is firstly used to detect weak signals of loose particles. The loose particle signal is simulated by the oscillation attenuation signal. The influences of structure parameters on the potential height and detection performance of ASR are studied by a numerical iteration method. The cross-correlation coefficient C1 between input and output is chosen as a criterion for whether there is an existing a particle or not. Through normalization, the loose particle signal-labeled high frequency of 135 kHz is converted into the low-frequency band, which can be detected by the ASR method. According to the algorithm, weak signals covered by noise could be detected. The experimental results show that the detection accuracy is 66.7%. This algorithm improves the detection range of weak loose particle signals effectively.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

SEM image sharpness analysis

1996 ◽  
Vol 54 ◽  
pp. 142-143
Author(s):  
M. T. Postek ◽  
A. E. Vladar
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Quantitative Information ◽  
Primary Electron ◽  
Image Sharpness ◽  
Critical Dimensions ◽  
Sem Image ◽  
Frequency Changes ◽  
Electron Microscopes ◽  
Scanning Electron

Fully automated or semi-automated scanning electron microscopes (SEM) are now commonly used in semiconductor production and other forms of manufacturing. The industry requires that an automated instrument must be routinely capable of 5 nm resolution (or better) at 1.0 kV accelerating voltage for the measurement of nominal 0.25-0.35 micrometer semiconductor critical dimensions. Testing and proving that the instrument is performing at this level on a day-by-day basis is an industry need and concern which has been the object of a study at NIST and the fundamentals and results are discussed in this paper.In scanning electron microscopy, two of the most important instrument parameters are the size and shape of the primary electron beam and any image taken in a scanning electron microscope is the result of the sample and electron probe interaction. The low frequency changes in the video signal, collected from the sample, contains information about the larger features and the high frequency changes carry information of finer details. The sharper the image, the larger the number of high frequency components making up that image. Fast Fourier Transform (FFT) analysis of an SEM image can be employed to provide qualitiative and ultimately quantitative information regarding the SEM image quality.

Real Ear Sound Pressure Levels Developed by Three Portable Stereo System Earphones

1992 ◽  
Vol 1 (4) ◽  
pp. 52-55 ◽  
Author(s):  
Gail L. MacLean ◽  
Andrew Stuart ◽  
Robert Stenstrom
Keyword(s):  
High Frequency ◽  
Sound Pressure ◽  
Low Frequency ◽  
Test System ◽  
Output Frequency ◽  
Frequency Effects ◽  
Adult Men ◽  
Frequency Responses ◽  
Significant Difference ◽  
Sound Pressure Levels

Differences in real ear sound pressure levels (SPLs) with three portable stereo system (PSS) earphones (supraaural [Sony Model MDR-44], semiaural [Sony Model MDR-A15L], and insert [Sony Model MDR-E225]) were investigated. Twelve adult men served as subjects. Frequency response, high frequency average (HFA) output, peak output, peak output frequency, and overall RMS output for each PSS earphone were obtained with a probe tube microphone system (Fonix 6500 Hearing Aid Test System). Results indicated a significant difference in mean RMS outputs with nonsignificant differences in mean HFA outputs, peak outputs, and peak output frequencies among PSS earphones. Differences in mean overall RMS outputs were attributed to differences in low-frequency effects that were observed among the frequency responses of the three PSS earphones. It is suggested that one cannot assume equivalent real ear SPLs, with equivalent inputs, among different styles of PSS earphones.

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