scholarly journals The method of discretization signals to minimize the fallibility of information recovery

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Oleksandr Laptiev ◽  
Serhii Yevseiev ◽  
Larysa Hatsenko ◽  
Olena Daki ◽  
Vitaliy Ivanenko ◽  
...  
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Digital Signal ◽  
Information Recovery ◽  
Discrete Signal ◽  
Optimal Discretization ◽  
Discrete Values ◽  
Joint Consideration ◽  
Discretization Interval ◽  
Digital Block

The paper proposes a fundamentally new approach to the formulation of the problem of optimizing the discretization interval (frequency). The well-known traditional methods of restoring an analog signal from its discrete implementations consist of sequentially solving two problems: restoring the output signal from a discrete signal at the output of a digital block and restoring the input signal of an analog block from its output signal. However, this approach leads to methodical fallibility caused by interpolation when solving the first problem and by regularizing the equation when solving the second problem. The aim of the work is to develop a method for the signal discretization to minimize the fallibility of information recovery to determine the optimal discretization frequency.The proposed method for determining the optimal discretization rate makes it possible to exclude both components of the methodological fallibility in recovering information about the input signal. This was achieved due to the fact that to solve the reconstruction problem, instead of the known equation, a relation is used that connects the input signal of the analog block with the output discrete signal of the digital block.The proposed relation is devoid of instabilities inherent in the well-known equation. Therefore, when solving it, neither interpolation nor regularization is required, which means that there are no components of the methodological fallibility caused by the indicated operations. In addition, the proposed ratio provides a joint consideration of the properties of the interference in the output signal of the digital block and the frequency properties of the transforming operator, which allows minimizing the fallibility in restoring the input signal of the analog block and determining the optimal discretization frequency.A widespread contradiction in the field of signal information recovery from its discrete values has been investigated. A decrease in the discretization frequency below the optimal one leads to an increase in the approximation fallibility and the loss of some information about the input signal of the analog-to-digital signal processing device. At the same time, unjustified overestimation of the discretization rate, complicating the technical implementation of the device, is not useful, since not only does it not increase the information about the input signal, but, if necessary, its restoration leads to its decrease due to the increase in the effect of noise in the output signal on the recovery accuracy. input signal. The proposed method for signal discretization based on the minimum information recovery fallibility to determine the optimal discretization rate allows us to solve this contradiction.

Design of Class D Amplifiers Using Zero Crossing Auto Gain Control

2012 ◽  
Vol 198-199 ◽  
pp. 1157-1161
Author(s):  
Hai Shi Wang ◽  
Bo Zhang ◽  
Jiang Sun
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Gain Control ◽  
Digital Signal ◽  
Alternating Current ◽  
Road Test ◽  
Zero Crossing ◽  
Class D Amplifier ◽  
Large Gain ◽  
Class D

This paper proposes a method to control the gain of an amplifier. A 6-bit digital signal is used to control the gain of the amplifier by adjusting the resistance of a potentiometer. The change of the digital signal is allowed to change the gain of the amplifier only if the input signal of the amplifier crosses alternating current (AC) zero. The gain of the amplifier could be reduced or restored, which is based on whether the output signal of the amplifier exceeds a predetermined value or not. The method is verified in a class D amplifier. Road test shows that the method may eliminate the glitch caused by gain change, and the clamp caused by a too large gain.

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.

A Comparison Study of Three of Input Buffer Designed Using 0.35µm CMOS Technology

2013 ◽  
Vol 646 ◽  
pp. 184-190
Author(s):  
Shinta Kisriani ◽  
Eri Prasetyo Wibowo ◽  
Busono Soerowirdjo ◽  
Hamzah Afandi ◽  
Veronica Ernita Kristianti
Keyword(s):  
Input Signal ◽  
Delay Time ◽  
Digital Signal ◽  
Cmos Technology ◽  
Memory Device ◽  
Comparison Study ◽  
Comparison Analysis ◽  
Input Buffer ◽  
Technology Analysis ◽  
Parallel Input

In memory device that is contained in the digital application, there is a sequence of input buffer.The input buffer’s function is to improve a digital signal and remove noise. The buffer circuit take these input signal with imperfections and convert them in to full digital logic levels by slicing the signals at correct levels which depends upon the switching point voltage. In this paper,using three topologies, that are NMOS, PMOS and Parallel input buffer. It would be present into design, simulation and analysis of all topologies input buffer. The result in this paper to determine the best of the three topologies to used. The delay time used to determine the best of topologies. Mentor graphic is tools which used in this paper to design and simulation. The technology used in this paper is 0.35 µm CMOS Technology. Analysis of comparison all of topologies used in this paper based on six parameters. The result of comparison analysis can be seen in more details in this explanation.

scholarly journals THE USE OF DIGITAL SIGNAL PROCESSING AND A NEURAL NETWORK WHEN GENERATING A FORECAST OF TIME SERIES OF DATA FOR THE PURPOSE OF DETECTING ANOMALIES IN THE IN THE AUTOMATED CONTROL OF TECHNOLOGICAL PROCESSES

2020 ◽  
Vol 20 (1) ◽  
pp. 24-34
Author(s):  
A. N. Ragozin ◽  
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Input Signal ◽  
Forecast Error ◽  
Digital Signal ◽  
Processing Unit ◽  
Automated Control ◽  
Prediction Result ◽  
Low Pass

n order to detect anomalies and improve the quality of forecasting dynamic data flows observed from sensors in Industrial Control System (ACS)., it is proposed to use a predictive mod-ule consisting of a series-connected digital signal processing unit (DSP) and a predictive unit using a neural network (predictive autoencoder ( Auto Encoder), predictive Autoencoder (PAE)). The study showed that the preliminary DSP block of the predicted input signal, consisting of a parallel set (comb) of digital low-pass filters with finite impulse responses (FIR-LPF), leads to a non-equilibrium account of the correlation relationships of the time samples of the input signal and to increase the accuracy of the final prediction result. The predicted autoencoder (PAE) pro-posed and considered in the work, in addition to restoring the input signal or part of the input signal at the PAE output, also generates the predicted samples of the input signal for the speci-fied number of «forward» time steps at the output, which increases the accuracy of the predic-tion result. The reduction of the forecast error occurs due to the imposition of restrictions in the formation of the forecast, that is, an additional requirement to restore the input samples of the samples – «stabilizers» at the NS output. The introduction of «stabilizers» increases the accuracy of the prediction result.

scholarly journals SINUSOIDAL NOISE CANCELATION DENGAN MENGGUNAKAN DIGITAL SIGNAL PROCESSING STARTER KIT TMS320C6713

2012 ◽  
Vol 4 (2) ◽  
pp. 67-74
Author(s):  
Yultrisna Yultrisna ◽  
Andi Syofyan
Keyword(s):  
Output Signal ◽  
Audio Signal ◽  
Digital Signal ◽  
Noise Removal ◽  
Noise Signal ◽  
Fir Filter ◽  
Final Project ◽  
Voice Signal ◽  
Signal Output ◽  
Original Speech

Original speech signal is needed both in telecommunications and in some instruments in a variety of fields. Not infrequently, the original audio signal is damaged due to noise. This noise can cause the original signal changes in the actual form. In the final project will be designed FIR filter to remove noise by using TMS320C6713 DSK. Sound signal to be input to the mixed noise removal filter system noise. The mixed voice signal will be searched by subtracting the signal difference to noise signal output FIR filter to get the signal e (n), and then do an adaptation resulting filter coefficients. Results of the adaptive filter coefficients would be put back to calculate the noise signal output next FIR filter. Original voice signal used is the word "sinus" uttered by teenage boys, teenage girls, boys and girls. Girl's voice had the highest frequency with an average 522.50 Hz, the frequency of the sound of the boys 462.63 Hz, the sound frequency of 222.58 Hz girls and voice frequencies teenage boys at 201.49 Hz. Noise signal used is 100 Hz sinusoidal noise. From the test results obtained for the system output signal SNR sound input teenage boy was 19.94 dB. SNR output signal to the input of 21.39 dB girls. SNR signal input output system for boys was 34.70 dB. SNR output signal to the input system daughters of 35.52 dB

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