scholarly journals Experimental Study on Sampling Theorem in Signal Processing

2021 ◽  
Vol 1 (6) ◽  
pp. 6-11
Author(s):  
Nyein Mynt ◽  
Zaw Aung ◽  
Kyaw Lin
Keyword(s):  
Experimental Study ◽  
Signal Processing ◽  
Sample Size ◽  
Output Signal ◽  
Recovery Process ◽  
Sampling Theorem ◽  
Signal Recovery ◽  
Test Information ◽  
Initial Signal

This practicum is to define the study properties of the sampling theorem. Understand the effect of selecting the sample size and its effect on the signal recovery process. The experiment utilizes a computer or portable workstation to run an examination of the hypothesis reenactment program. From the test information gotten, it can be concluded that the more noteworthy the frequency of the signal to be inspected, the closer the signal will be to the initial signal. The time and frequency of the examining signal are conversely relative. The higher the frequency, the lower the time will be. The magnitude of the amplitude of the output signal is indeterminate.

Experimental Investigation on the Use of Bispectral Analysis in Detecting Nonlinear Faults in Hydraulic Machines

2014 ◽  
Vol 606 ◽  
pp. 147-151 ◽  
Author(s):  
M.S. Somia Alfatih ◽  
M. Salman Leong ◽  
L.M. Hee
Keyword(s):  
Experimental Study ◽  
Experimental Investigation ◽  
Signal Processing ◽  
Spectral Analysis ◽  
Centrifugal Pump ◽  
Nonlinear Behavior ◽  
Suction Side ◽  
Bispectral Analysis ◽  
Detection And Identification ◽  
Hydraulic Machines

Bispectral analysis is one of the relatively more recent tools in signal processing used for detection and identification of higher harmonics in a signal. It is also acknowledged to be one of Higher Order Spectral Analysis (HOSA) effective tools for detecting nonlinear behavior in mechanical systems. In this study, vibration sources in a hydraulic machine which may have features of nonlinear behavior were investigated. An experimental study was undertaken to formulate a more sensitive and effective method using Bispectral analysis to diagnose cavitation in a centrifugal pump facility. Cavitation was induced on the suction side of the pump. The cavitation signal was analyzed with and without induced cavitation conditions at different locations on the pump, and analyzed using FFT and bispectrum methods. It was observed that bispectral analysis could be used as an early indicator of cavitation with changes for severity of cavitation.

Signal Recovery

2009 ◽  
Author(s):  
Robert J Marks II
Keyword(s):  
Interpolation Problem ◽  
Super Resolution ◽  
Sampling Theorem ◽  
Signal Recovery ◽  
Continuous Sampling ◽  
Bandlimited Signals ◽  
Special Cases ◽  
Extrapolation Problem ◽  
Definition Of

The literature on the recovery of signals and images is vast (e.g., [23, 110, 112, 257, 391, 439, 791, 795, 933, 934, 937, 945, 956, 1104, 1324, 1494, 1495, 1551]). In this Chapter, the specific problem of recovering lost signal intervals from the remaining known portion of the signal is considered. Signal recovery is also a topic of Chapter 11 on POCS. To this point, sampling has been discrete. Bandlimited signals, we will show, can also be recovered from continuous samples. Our definition of continuous sampling is best presented by illustration.Asignal, f (t), is shown in Figure 10.1a, along with some possible continuous samples. Regaining f (t) from knowledge of ge(t) = f (t)Π(t/T) in Figure 10.1b is the extrapolation problem which has applications in a number of fields. In optics, for example, extrapolation in the frequency domain is termed super resolution [2, 40, 367, 444, 500, 523, 641, 720, 864, 1016, 1099, 1117]. Reconstructing f (t) from its tails [i.e., gi(t) = f (t){1 − Π(t/T)}] is the interval interpolation problem. Prediction, shown in Figure 10.1d, is the problem of recovering a signal with knowledge of that signal only for negative time. Lastly, illustrated in Figure 10.1e, is periodic continuous sampling. Here, the signal is known in sections periodically spaced at intervals of T. The duty cycle is α. Reconstruction of f (t) from this data includes a number of important reconstruction problems as special cases. (a) By keeping αT constant, we can approach the extrapolation problem by letting T go to ∞. (b) Redefine the origin in Figure 10.1e to be centered in a zero interval. Under the same assumption as (a), we can similarly approach the interpolation problem. (c) Redefine the origin as in (b). Then the interpolation problem can be solved by discarding data to make it periodically sampled. (d) Keep T constant and let α → 0. The result is reconstructing f (t) from discrete samples as discussed in Chapter 5. Indeed, this model has been used to derive the sampling theorem [246]. Figures 10.1b-e all illustrate continuously sampled versions of f (t).

Research and Design of Laser Displacement Measurement Based on the PSD

2015 ◽  
Vol 719-720 ◽  
pp. 580-583
Author(s):  
Xin Wen Duan ◽  
Ye Sun
Keyword(s):  
Signal Processing ◽  
Output Signal ◽  
Position Sensitive Detector ◽  
Measuring System ◽  
Two Dimensional ◽  
Sensitive Detector ◽  
Position Measurement ◽  
Arithmetic Circuit ◽  
Position Information ◽  
Position Sensitive

This paper introduces a kind of measuring system based on two-dimensional position sensitive detector PSD and single-chip signal processing, and have discussed the key technology. Two-dimensional position sensitive detector (2 D-PSD) is a kind of testing device of light spot position information and the output signal is a kind of current signal related to the location information. PSD output signal processing circuits are usually made by the I/V conversion circuit ,the addition, the subtraction and division arithmetic circuit. System can quickly, accurately and easily realize the position measurement, with a simple implementation, low power consumption, fast response, etc, It can be applied to many remote laser center positioning measurement and general industrial control occasions.

Design and performance analysis of live model of Bessel beamformer for adaptive array system

2014 ◽  
Vol 33 (4) ◽  
pp. 1434-1447 ◽  
Author(s):  
M. Yasin ◽  
Pervez Akhtar
Keyword(s):  
Experimental Data ◽  
Signal Processing ◽  
Base Station ◽  
Real System ◽  
Signal Recovery ◽  
Mean Square ◽  
Content Type ◽  
Live Model ◽  
Audio Data ◽  
Directive Gain

Purpose – The purpose of this paper is to design and analyze the performance of live model of Bessel beamformer for thorough comprehension of beamforming in adaptive environment and compared with live model of least mean square (LMS) in terms of gain and mean square error (MSE). It presents the principal elements of communication system. The performance of designed live model is tested for its efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format into the MATLAB workspace. These adaptive techniques are illustrated by appropriate examples. Design/methodology/approach – The proposed algorithm framework relies on MATLAB software with the goal to obtain high efficiency in terms of signal recovery, directive gain by minimizing MSE using the “wavrecord” function to bring live audio data in WAV format. It is assumed that this audio signal is only the message or the baseband signal received by the computer. Here the authors consider computer (laptop) as a base station containing adaptive signal processing algorithm and source (mobile phone) as a desired user, so the experiment setup is designed for uplink application (user to base station) to differentiate between desired signal, multipath and interfering signals as well as to calculate their directions of arrival. Findings – The presented adaptive live model is reliable, robust and lead to a substantial reduction of MSE, signal recovery in comparison with the LMS technique. The paper contains experimental data. Obtained results are presented clearly and the conclusion comes directly from the presented experimental data. The paper shows that the presented method leads to superior results in comparison with the popular LMS method and can be used as a better alternative in many practical applications. Research limitations/implications – The adaptive processes described in the paper are still limited to simulation. It is because of the non-availability of real system for testing, therefore chosen research approach that is platform of MATLAB is opted for simulation. Therefore, researchers are encouraged to test the proposed algorithms on real system if possible. Practical implications – The paper contains experimental data. The paper's impact on the society is acceptable. These implications are consistent with the findings and the conclusions of the paper. However, there is a need to extend this paper to a next level by implementing the proposed algorithms in the real time environment using FPGA technology. Social implications – This research will improve the signal quality of wireless cellular system by increasing capacity and will reduce the total cost of the system so that cost toward subscribers be decreased. Originality/value – The live model presented in this paper is shown to provide better results. It is the original work and can provide scientific contribution to signal processing community.

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