A COMPUTER PROGRAM TO OBTAIN THE WEIGHTS OF A TIME‐DOMAIN WAVE‐SHAPING FILTER WHICH IS OPTIMUM IN AN ERROR‐DISTRIBUTION SENSE

Geophysics ◽  
1978 ◽  
Vol 43 (1) ◽  
pp. 197-215
Author(s):  
R. F. Mereu
Keyword(s):  
Computer Program ◽  
Time Domain ◽  
Input Signal ◽  
Output Signal ◽  
Error Distribution ◽  
Distribution Sense ◽  
Shaping Filter

The program presented in this paper (Appendix A) computes the weights of a time‐domain wave‐shaping filter F which will transform a given input signal into a desired output signal in an optimum “error‐distribution” sense (see Mereu, 1976).

Design and Application of a Low Frequency VCO

2014 ◽  
Vol 668-669 ◽  
pp. 808-811
Author(s):  
Hui Min Zhang ◽  
Qing Ping Wu ◽  
Zheng Yuan Zhou ◽  
Xun Wang
Keyword(s):  
Experimental Test ◽  
Input Signal ◽  
Operational Amplifier ◽  
Output Signal ◽  
Analog Circuit ◽  
Low Frequency ◽  
Voltage Controlled Oscillator ◽  
Frequency Range ◽  
Innovative Design ◽  
Design Cost

The low frequency voltage controlled oscillator (VCO) is designed using integrated operational amplifier. The frequency of the output signal of VCO changes with the magnitude of the input signal voltage, and show a linear relationship within a certain range through the experimental test. Experiments show that, under the input of certain amplitude and frequency range of the square wave, triangle wave, saw-tooth wave, the output waveform of VCO respectively is ambulance, fire siren and other kinds of ambulance siren Signal. This innovative design’ cost is low, realized by analog circuit. It can be used in the practice of teaching case, electronic production or development of sound panels.

scholarly journals Active Fault Diagnosis Based on Stochastic Tests

2008 ◽  
Vol 18 (4) ◽  
pp. 487-496 ◽  
Author(s):  
Niels Poulsen ◽  
Henrik Niemann
Keyword(s):  
Fault Diagnosis ◽  
Change Detection ◽  
Input Signal ◽  
Output Signal ◽  
Active Fault ◽  
Fast Change ◽  
Signal Injection ◽  
Cusum Method ◽  
Cusum Tests ◽  
Auxiliary Input

Active Fault Diagnosis Based on Stochastic TestsThe focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example.

scholarly journals Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

2021 ◽  
Vol 34 (1) ◽  
pp. 133-140
Author(s):  
Teimour Tajdari
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Adaptive Filters ◽  
Adaptive Method ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Filter Output ◽  
System Output ◽  
Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

Micromechanical Resonators With Near-Linear Response

2008 ◽  
Author(s):  
Krishna Vummidi ◽  
Eihab M. Abdel-Rahman ◽  
Bashar K. Hammad ◽  
Sanjay Raman ◽  
Ali H. Nayfeh
Keyword(s):  
Phase Noise ◽  
Closed Form ◽  
Input Signal ◽  
Output Signal ◽  
Linear Response ◽  
Low Frequency ◽  
Noise Performance ◽  
Micromechanical Resonators ◽  
Power Handling ◽  
Phase Noise Performance

We study the effect of bias voltage VDC on the effective nonlinearity of electrostatically clamped-clamped microbeam resonators. We identify three domains in the resonator response: hardening-type, softening-type, and near-linear behaviors. In the near linear domain we show that we can increase the power handling of the resonator without distorting its phase noise performance. We investigate the mixing of low frequency 1/f noise into the input signal. This causes phase distortion of the output signal and is quantized as its phase noise. We find that the amplitude and phase responses of the resonator’s displacement are coupled to each other through the effective non-linearity co-efficient (S), which distorts its phase response in the nonlinear regime. Finally we also present closed form expressions for resonator displacement and current in both linear and non-linear regimes.

scholarly journals Spectral mapping of natural signals

2020 ◽  
Author(s):  
Md. Shoaibur Rahman
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Spectral Component ◽  
Mapping Function ◽  
Original Signal ◽  
Spectral Mapping ◽  
Spectral Content ◽  
The Given

Here we present an algorithm to procedurally map spectral contents of natural signals. The algorithm takes in two inputs: a signal whose spectral component needs to be mapped and a warping or mapping function. The algorithm generates one output, which is a mapped version of the original signal. The input signal is mapped into the output signal in two steps. In the analysis step, the algorithm performs a series of operations to modify the spectral content, i.e., compute the warped phase of the signal according to the given mapping function. In the synthesis step, the modified spectral content is combined with the envelope information of the input signal to reconstruct the warped or mapped output signal.

scholarly journals Impact of feature selection on system identification by means of NARX-SVM

2019 ◽  
Vol 252 ◽  
pp. 03012
Author(s):  
Michał Awtoniuk ◽  
Marcin Daniun ◽  
Kinga Sałat ◽  
Robert Sałat
Keyword(s):  
Feature Selection ◽  
System Identification ◽  
Input Signal ◽  
Output Signal ◽  
Feature Vector ◽  
Classical Method ◽  
Support Vector ◽  
Peltier Module ◽  
Non Linear ◽  
Selection Of

Support Vector Machines (SVM) are widely used in many fields of science, including system identification. The selection of feature vector plays a crucial role in SVM-based model building process. In this paper, we investigate the influence of the selection of feature vector on model’s quality. We have built an SVM model with a non-linear ARX (NARX) structure. The modelled system had a SISO structure, i.e. one input signal and one output signal. The output signal was temperature, which was controlled by a Peltier module. The supply voltage of the Peltier module was the input signal. The system had a non-linear characteristic. We have evaluated the model’s quality by the fit index. The classical feature selection of SVM with NARX structure comes down to a choice of the length of the regressor vector. For SISO models, this vector is determined by two parameters: nu and ny. These parameters determine the number of past samples of input and output signals of the system used to form the vector of regressors. In the present research we have tested two methods of building the vector of regressors, one classic and one using custom regressors. The results show that the vector of regressors obtained by the classical method can be shortened while maintaining the acceptable quality of the model. By using custom regressors, the feature vector of SVM can be reduced, which means also the reduction in calculation time.

Sign in / Sign up

Export Citation Format

Share Document