scholarly journals Signal processing of acoustic signals in the time domain with an active nonlinear nonlocal cochlear model

2006 ◽  
Vol 86 (2) ◽  
pp. 360-374 ◽  
Author(s):  
M. Drew LaMar ◽  
Jack Xin ◽  
Yingyong Qi
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Acoustic Signals ◽  
Cochlear Model ◽  
The Time Domain

scholarly journals Instrumentation for mechanical vibrations analysis in the time domain and frequency domain using the Arduino platform

2016 ◽  
Vol 38 (1) ◽  
Author(s):  
Marcus Varanis ◽  
Anderson Langone Silva ◽  
Pedro Henrique Ayres Brunetto ◽  
Rafael Ferreira Gregolin
Keyword(s):  
Signal Processing ◽  
Finite Elements ◽  
Time Domain ◽  
Good Accuracy ◽  
Low Cost ◽  
Analytical Models ◽  
Experimental Setup ◽  
Mechanical Vibrations ◽  
Python Language ◽  
The Time Domain

In this paper, we use the Arduino platform together with sensors as accelerometer, gyroscope and ultrasound, to measure vibrations in mechanical systems. The main objective is to assemble a signals acquisition system easy to handle, of low cost and good accuracy for teaching purposes. It is also used the Python language and its numerical libraries for signal processing. This paper proposes the study of vibrations of a beam, which is measured by position, velocity and acceleration. An experimental setup was implemented. The results obtained are compared with analytical models and computer simulations using finite elements. The results are in agreement with the literature.

Second-Order Fourier Synthesis of Broadband Acoustic Signals Using Normal Modes

1997 ◽  
Vol 05 (04) ◽  
pp. 355-370 ◽  
Author(s):  
E. K. Skarsoulis
Keyword(s):  
Time Domain ◽  
Normal Modes ◽  
Acoustic Signals ◽  
Second Order ◽  
Single Frequency ◽  
Closed Form Expression ◽  
Fourier Synthesis ◽  
Eigenvalues And Eigenfunctions ◽  
Intermediate Order ◽  
The Time Domain

A scheme for approximate normal-mode calculation of broadband acoustic signals in the time domain is proposed based on a second-order Taylor expansion of eigenvalues and eigenfunctions with respect to frequency. For the case of a Gaussian impulse source a closed-form expression is derived for the pressure in the time domain. Using perturbation theory, analytical expressions are obtained for the involved first and second frequency-derivatives of eigenvalues and eigenfunctions. The proposed approximation significantly accelerates arrival-pattern calculations, since the eigenvalues, the eigenfunctions and their frequency-derivatives need to be calculated at a single frequency, the central frequency of the source. Furthermore, it offers a satisfactory degree of accuracy for the lower and intermediate order modes. This is due to the fact that essential wave-theoretic mechanisms such as dispersion and frequency dependence of mode amplitudes are contained in the representation up to a sufficient order. Numerical results demonstrate the efficiency of the method.

Comparison of Meteorological Radar Signal Detectability with Noncoherent and Spectral-Based Processing

2016 ◽  
Vol 33 (4) ◽  
pp. 723-739 ◽  
Author(s):  
James B. Mead
Keyword(s):  
Signal Processing ◽  
False Alarm ◽  
False Alarm Rate ◽  
Time Domain ◽  
Spectral Width ◽  
Probability Of Detection ◽  
Spectral Domain ◽  
Processing Gain ◽  
Meteorological Radar ◽  
The Time Domain

AbstractDetection of meteorological radar signals is often carried out using power averaging with noise subtraction either in the time domain or the spectral domain. This paper considers the relative signal processing gain of these two methods, showing a clear advantage for spectral-domain processing when normalized spectral width is less than ~0.1. A simple expression for the optimal discrete Fourier transform (DFT) length to maximize signal processing gain is presented that depends only on the normalized spectral width and the time-domain weighting function. The relative signal processing gain between noncoherent power averaging and spectral processing is found to depend on a variety of parameters, including the radar wavelength, spectral width, available observation time, and the false alarm rate. Expressions presented for the probability of detection for noncoherent and spectral-based processing also depend on these same parameters. Results of this analysis show that DFT-based processing can provide a substantial advantage in signal processing gain and probability of detection, especially when the normalized spectral width is small and when a large number of samples are available. Noncoherent power estimation can provide superior probability of detection when the normalized spectral width is greater than ~0.1, especially when the desired false alarm rate exceeds 10%.

Discrete Time, Discrete Frequency

2021 ◽  
pp. 106-155
Author(s):  
Victor Lazzarini
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Discrete Time ◽  
Time Domain ◽  
Continuous Time ◽  
Time Varying ◽  
Discrete Frequency ◽  
The Fourier Transform ◽  
Definition Of ◽  
The Time Domain

This chapter is dedicated to exploring a form of the Fourier transform that can be applied to digital waveforms, the discrete Fourier transform (DFT). The theory is introduced and discussed as a modification to the continuous-time transform, alongside the concept of windowing in the time domain. The fast Fourier transform is explored as an efficient algorithm for the computation of the DFT. The operation of discrete-time convolution is presented as a straight application of the DFT in musical signal processing. The chapter closes with a detailed look at time-varying convolution, which extends the principles developed earlier. The conclusion expands the definition of spectrum once more.

Real-time fractal signal processing in the time domain

2013 ◽  
Vol 392 (1) ◽  
pp. 89-102 ◽  
Author(s):  
András Hartmann ◽  
Péter Mukli ◽  
Zoltán Nagy ◽  
László Kocsis ◽  
Péter Hermán ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Time Domain ◽  
Fractal Signal ◽  
The Time Domain

Application of Mathematical Morphological Filter in Vibration Signal Processing

2011 ◽  
Vol 71-78 ◽  
pp. 4564-4567
Author(s):  
Ai Jun Hu ◽  
Jing Jing Sun ◽  
Wan Li Ma
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Nonlinear Filtering ◽  
Vibration Signal ◽  
Morphological Operations ◽  
Morphological Filter ◽  
Original Signal ◽  
Low Pass ◽  
The Time Domain

The morphological filter as a nonlinear filtering method has been widely used for image (or signal) processing. Unlike the traditional digital filters, mathematical morphological operations are shape-based computing. Feature extraction of signals is entirely in the time domain without the transforming of the signal from the time domain to frequency domain. The vibration signal contaminated with noise is processed using morphological filter and Butterworth filter respectively. To compare the outputs of the two filters, we find that morphological filter shows better performance. It is effective in suppressing noise while maintaining the original signal both in the time and frequency domain. In addition, an outstanding advantage of morphological filter is its ability to keep the phase of the original signal. Its computing speed is faster. In the end, its low-pass characteristic is verified by processing vibration signal.

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