scholarly journals Enhancing the quality of communication of cellular networks using big data applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mozamel Musa Saeed ◽  
Mohammed Alsharidah
Keyword(s):  
Big Data ◽  
Spectral Density ◽  
Cellular Networks ◽  
Power Spectral Density ◽  
Spectral Estimation ◽  
Estimation Methods ◽  
Spectrum Estimation ◽  
Spectral Densities ◽  
Power Spectral ◽  
Welch Method

AbstractBoth software-defined networking and big data have gained approval and preferences from both industry and academia. These two important realms have conventionally been addressed independently in wireless cellular networks. The discussion taken into consideration in this study was to analyze the wireless cellular technologies with the contrast of efficient and enhanced spectral densities at a reduced cost. To accomplish the goal of this study, Welch's method has been used as the core subject. With the aid of previous research and classical techniques, this study has identified that the spectral densities can be enhanced at reduced costs with the help of the power spectral estimation methods. The Welch method gives the result on power spectrum estimation. By reducing the effect of noise, the Welch method is used to calculate the power spectral density of a signal. When data length is increased, Welch's method is considered the best as a conclusion to this paper because excellent results are yielded by it in the area of power spectral density estimation.

Analysis of earth motions and seismic sources by power spectral density

1969 ◽  
Vol 59 (3) ◽  
pp. 1071-1091
Author(s):  
Dean V. Power
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
High Explosive ◽  
Peak Velocity ◽  
Test Site ◽  
Nevada Test Site ◽  
Spectral Densities ◽  
Power Spectral ◽  
Power Spectral Densities ◽  
Velocity Spectra

abstract Ground motion records from six high-explosive cratering events in northeastern Montana, ten contained nuclear explosive events at the Nevada Test Site, and motions of an earth-fill dam during the Gasbuggy underground nuclear explosion in New Mexico were analyzed for power spectral density, peak velocity and velocity spectra. The high-explosive events included four 20-ton single charges at depths of burst which varied between 42 to 57 feet, a 140-ton row charge consisting of three 20-ton and two 40-ton charges at optimum cratering depths of burst, and a 0.5-ton charge at the optimum depth of burst. It was found that at these depths and charge weights an increase in depth of burst resulted in an increase in peak velocities and power-spectral densities as measured at distant points (> 5 km). Power spectral density was found to be approximately proportional to the first power of yield. For this region it was determined that power spectral density varied inversely as radial distance to the 3.55 power. Three analysis techniques—peak velocity, velocity spectra and power spectral density—are compared, and it is shown that power spectral density is the most consistent method when comparing records from different measuring stations. An analysis of power-spectral density measured at one station for the ten events at the Nevada Test Site shows that a significant shift in the frequency of the energy in the seismogram occurs when the source location changes. For events in the Yucca Flat area the peak energy at Mercury was consistently at 1.0 Hz, while for events in the Pahute Mesa area this peak occurs at 2.5 Hz. A comparison of the power spectral densities on and near the Navajo Dam revealed that the natural frequencies and first harmonics of the dam are 1.4, 2.0 and 2.5 Hz in the mode where motion is parallel to the canyon axis. A simple model makes use of these frequencies to calculate a shear-wave velocity of 1130 ft/sec. A method of using power spectral density to measure earthquake magnitudes and measure the yield of underground explosions is proposed.

Resolving the Issues of Capon and APES Approach for Projecting Enhanced Spectral Estimation

2016 ◽  
Vol 6 (2) ◽  
pp. 725
Author(s):  
Kantipudi MVV Prasad ◽  
H.N. Suresh
Keyword(s):  
Signal Processing ◽  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Simple Technique ◽  
Spectral Estimation ◽  
Research Work ◽  
Research Community ◽  
The Past ◽  
Power Spectral

There are various applications on signal processing that is highly dependent on preciseness and accuracy of the outcomes in spectrum of signals. Hence, from the past two decades the research community has recognized the benefits, significance, as well as associated problems in carrying out a model for spectral estimation. While in-depth investigation of the existing literatures shows that there are various attempts by the researchers to solve the issues associated with spectral estimations, where majority of teh research work is inclined towards addressing problems associated with Capon and APES techniques of spectral analysis. Therefore, this paper introduces a very simple technique towards resolving the issues of Capon and APES techniques. The outcome of the study was analyzed using correlational factor and power spectral density to find the proposed system offers better spectral estimations compared to existing system.

A time-domain procedure for non-Gaussian stationary environmental testing using zero-memory nonlinear transformation

2020 ◽  
Vol 26 (15-16) ◽  
pp. 1197-1213 ◽  
Author(s):  
Song Cui ◽  
Enlai Zheng ◽  
Min Kang
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Time Domain ◽  
Finite Impulse Response ◽  
Numerical Test ◽  
Nonlinear Transformation ◽  
Spectral Densities ◽  
Power Spectral ◽  
Power Spectral Densities ◽  
Non Gaussian

This article proposes a time-domain procedure for a non-Gaussian stationary random vibration test with prescribed power spectral densities. Previous procedures for generating non-Gaussianity suffered from certain defects. For example, zero-memory nonlinear transformation, an algorithm frequently applied to transform Gaussian signals into non-Gaussian signals, often produces changes in both auto-power spectral densities and cross-power spectral densities, which might result in control instability under certain circumstances. In this article, the authors propose a different approach for the zero-memory nonlinear function. First, a time-domain procedure for a non-Gaussian random test is introduced. Second, a rescaling method is applied to correct the magnitude amplification on the auto-power spectral density because of zero-memory nonlinear transformation. We offer experience formulas in this method to adjust the auto-power spectral density of both super-Gaussian and sub-Gaussian responses. Third, a control strategy using a finite impulse response filter is proposed to further improve the auto-power spectral density if the shape of the auto-power spectral density is distorted. The kurtosis loss induced by the filtering process is also analysed and a corresponding solution is put forward to ease the reduction. Numerical test and a biaxial shaker table test are conducted to validate the availability and superiority of the proposed procedure.

scholarly journals Power Spectral Density Computation and Dominant Frequencies Identification from the Vibration Sensor Output under Random Vibration Environment

2020 ◽  
Vol 70 (6) ◽  
pp. 692-700
Author(s):  
R. Srinivasan ◽  
Tessy Thomas ◽  
Bopanna Lakshmi
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Model Simulation ◽  
Dynamic Properties ◽  
Vibration Sensor ◽  
Mode Shapes ◽  
Reference Spectrum ◽  
Power Spectral ◽  
Welch Method ◽  
Dominant Frequencies

The objective of the modal and spectral analysis is to determine the vibration characteristics of structures such as natural frequencies, dominant frequencies and mode shapes. Such modal and spectral analyses have major relevance to the study of the dynamic properties of the structures undergoing dynamic vibration. Methods for the estimation of the power spectral density and identification of the dominant frequencies from the sensor responses under random vibrating environment are presented in this paper. Periodogram using FFT, Welch Method and MUSIC algorithm are used to analyse the known frequency sinusoids with additive white noise and output of the vibration sensor mounted on the test object. The resultant spectra obtained using the methods and their corresponding errors with the reference spectrum are analysed. The Welch method is further studied with three different windows, namely, Hann, Hamming and Blackman-Harris and with three different overlapping criteria viz. 0%, 25% and 50%. The same algorithm and methodology were adopted and compared in two different platforms: Mathematical Model Simulation and Hardware-In-Loop-Simulation. It is observed from the results that Welch Method with 25% overlap used in combination either with Hann or Blackman-Harris window yields more accurate results, compared to other combinations. Also, 25% overlap provides better execution time trade-off compared to 50% overlap.

Influence of Estimation Methods of Power Spectral Density Function on the Calculated Fatigue Life with Spectral Method

2014 ◽  
Vol 224 ◽  
pp. 118-123 ◽  
Author(s):  
Adam Niesłony ◽  
Michał Böhm ◽  
Agnieszka Materac
Keyword(s):  
Fatigue Life ◽  
Spectral Density ◽  
Spectral Method ◽  
Power Spectral Density ◽  
Probability Distributions ◽  
Spectral Density Function ◽  
Estimation Methods ◽  
Fundamental Parameters ◽  
Power Spectral ◽  
Matlab Package

Spectral method is one of the most effective methods of fatigue life estimation. In this method fundamental parameters characterizing the load process are determined on the basis of Power Spectral Density (PSD) function. PSD function can be obtained in many ways. Several functions from parametric methods, nonparametric methods and subspace method from Matlab package were discussed in this paper in terms of their use for the determination of fatigue life. Finally, calculated fatigue life was determined using spectral method and cycle counting method and compared using probability distributions of amplitudes according to Dirlik and Miner rule for damage summation.

scholarly journals Resolving the Issues of Capon and APES Approach for Projecting Enhanced Spectral Estimation

2016 ◽  
Vol 6 (2) ◽  
pp. 725
Author(s):  
Kantipudi MVV Prasad ◽  
H.N. Suresh
Keyword(s):  
Signal Processing ◽  
Spectral Analysis ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Simple Technique ◽  
Spectral Estimation ◽  
Research Work ◽  
Research Community ◽  
The Past ◽  
Power Spectral

There are various applications on signal processing that is highly dependent on preciseness and accuracy of the outcomes in spectrum of signals. Hence, from the past two decades the research community has recognized the benefits, significance, as well as associated problems in carrying out a model for spectral estimation. While in-depth investigation of the existing literatures shows that there are various attempts by the researchers to solve the issues associated with spectral estimations, where majority of teh research work is inclined towards addressing problems associated with Capon and APES techniques of spectral analysis. Therefore, this paper introduces a very simple technique towards resolving the issues of Capon and APES techniques. The outcome of the study was analyzed using correlational factor and power spectral density to find the proposed system offers better spectral estimations compared to existing system.

scholarly journals Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation

2019 ◽  
Vol 3 (1) ◽  
pp. 17-25
Author(s):  
Nursuci Putri Husain ◽  
Nurseno Bayu Aji
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Principle Component Analysis ◽  
Visual Analysis ◽  
Component Analysis ◽  
Eeg Signal ◽  
Multi Layer Perceptron ◽  
Principle Component ◽  
Power Spectral ◽  
Welch Method

Electroencephalogram (EEG) signal is a signal that could become an information for study about disorders of brain function  such as Epilepsi. EEG that detected in epileptic seizures produce patterns that allow doctors to distinguish it from normal conditions. However, a visual analysis can not be done continuously. This study proposed a new hybrid method of EEG signal classification using Power Spectral Density (PSD) based on Welch method, Principle Component Analysis (PCA), and Multi Layer Perceptron Backpropagation.There are 3 main stages in this study, firstly preprocessing the dataset of EEG signals by Power Spectral Density (PSD) based on Welch method, then Principle Component Analysis (PCA) as a method of  dimensionallity reduction of the EEG signal data and the Multi Layer Perceptron Backpropagation for classifying a signal. Based on experimental results, the proposed method is successfully obtain high accuracy for the 80-20% training-testing partition (99.68%).  

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