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Journal articles on the topic 'Power Spectral Density'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Shabat, Hafedh Ali, Khamael Raqim Raheem, and Wafaa Mohammed Ridha Shakir. "Blind Steganalysis Method Using Image Spectral Density and Differential Histogram Correlative Power Spectral Density." Journal of Image and Graphics 12, no. 1 (2024): 10–15. http://dx.doi.org/10.18178/joig.12.1.10-15.

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Recent research has demonstrated the success of employing neural networks for the purpose of detecting image tampering. Nevertheless, the utilization of reference-free steganalysis has become increasingly popular as a result of the challenges associated with obtaining an annotated dataset. This dataset is crucial for the classification process using neural networks, which aims to detect and identify instances of tampering. This paper introduces a robust approach to blind steganalysis, utilizing image spectral density and differential histogram correlative power spectral density. The proposed m

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2

Bayan, Tony, and Nabamita Deb. "Effect of Mantra Chanting on Power Spectral Density." Indian Journal Of Science And Technology 18, no. 2 (2025): 95–101. https://doi.org/10.17485/ijst/v18i2.3780.

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Objectives: Mantra chanting a spiritual form of meditation helps to calm the human brain. Mantra chanting is a meditative practice known to influence brainwave activities that are linked to increased theta and alpha brain rhythms. This paper investigates the variations in Power Spectral Density of experienced Mantra chanting individuals and evaluates the paired t-test before and during mantra chanting. Methods: The research is inspired by existing works on how meditation affects the activities of brain waves. This paper particularly emphasizes the effect of “OM” mantra chanting on experienced

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3

Xu, H. W., R. S. Zhao, Erbil Gugercinoglu, et al. "Statistical Analysis of Pulsar Flux Density Distribution." Astrophysical Journal 970, no. 2 (2024): 148. http://dx.doi.org/10.3847/1538-4357/ad5001.

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Abstract This study presents a comprehensive analysis of the spectral properties of 886 pulsars across a wide frequency range from 20 MHz–343.5 GHz, including a total of 86 millisecond pulsars (MSPs). The majority of the pulsars exhibit power-law behavior in their spectra, although some exceptions are observed. Five different spectral models, namely, simple power law, broken power law, low-frequency turnover, high-frequency cutoff, and double turnover, were employed to explore the spectral behaviors. The average spectral index for pulsars modeled with a simple power law is found to be −1.64 ±

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4

da Cunha Lima, A. T., I. C. da Cunha Lima, and M. P. de Almeida. "Analysis of turbulence power spectra and velocity correlations in a pipeline with obstructions." International Journal of Modern Physics C 28, no. 02 (2017): 1750019. http://dx.doi.org/10.1142/s012918311750019x.

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We calculate the power spectral density and velocity correlations for a turbulent flow of a fluid inside a duct. Turbulence is induced by obstructions placed near the entrance of the flow. The power spectral density is obtained for several points at cross-sections along the duct axis, and an analysis is made on the way the spectra changes according to the distance to the obstruction. We show that the differences on the power spectral density are important in the lower frequency range, while in the higher frequency range, the spectra are very similar to each other. Our results suggest the use o

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5

Christian, John T. "Generating Seismic Design Power Spectral Density Functions." Earthquake Spectra 5, no. 2 (1989): 351–68. http://dx.doi.org/10.1193/1.1585526.

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The most widely used way to describe earthquake motions for purposes of design is the response spectrum, but it is often difficult to apply a response spectrum when dealing with multiple degrees of freedom or with complex representations of structural behavior. The power spectral density function, which is a more fundamental description of the frequency content of ground motion, has found increasing use and is essential in the most popular methods of developing artificial earthquake time histories. Although in theory the response spectrum and the power spectral density are closely related, in

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6

Mack, Chris A. "Reaction-diffusion power spectral density." Journal of Micro/Nanolithography, MEMS, and MOEMS 11, no. 4 (2012): 043007. http://dx.doi.org/10.1117/1.jmm.11.4.043007.

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7

S, Sreelekha, and Sabi S. "Modified Welch Power Spectral Density Computation with Fast Fourier Transform." International Journal of Scientific Engineering and Research 4, no. 10 (2016): 92–96. https://doi.org/10.70729/ijser151016.

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8

Sahbudin, Murtadha Arif Bin. "Audio Fingerprint based on Power Spectral Density and Hamming Distance Measure." Journal of Advanced Research in Dynamical and Control Systems 12, no. 04-Special Issue (2020): 1533–44. http://dx.doi.org/10.5373/jardcs/v12sp4/20201633.

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9

Davis, B. R., and A. G. Thompson. "Power Spectral Density of Road Profiles." Vehicle System Dynamics 35, no. 6 (2001): 409–15. http://dx.doi.org/10.1076/vesd.35.6.409.2039.

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10

Thompson, R. S., and G. K. Aldis. "Flow spectra from spectral power density calculations for pulsed Doppler." Ultrasonics 40, no. 1-8 (2002): 835–41. http://dx.doi.org/10.1016/s0041-624x(02)00223-8.

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11

Thomson, Nicholas, and Joana Rocha. "Comparison of Semi-Empirical Single Point Wall Pressure Spectrum Models with Experimental Data." Fluids 6, no. 8 (2021): 270. http://dx.doi.org/10.3390/fluids6080270.

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This study presents an evaluation of semi-empirical single-point wall pressure spectrum models by comparing model predictions with wind tunnel and flight test data. The mean squared error was used to compare the power spectral density of the wall pressure fluctuations predicted by semi-empirical models with a large amount of experimental data. Results show that the models proposed by Goody and Smol’yakov have the lowest mean squared error when predicting the power spectral density for wind tunnel experiments and the Rackl and Weston model has the lowest mean squared error when predicting the p

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12

Nyeina, Oo Kyaw. "MEASUREMENT OF VIBRATION POWER FLOW IN THIN PLATE STRUCTURE WITH CROSS POWER SPECTRAL DENSITY-BASED TECHNIQUE." International Journal of Psychosocial Rehabilitation 24, no. 4 (2020): 4703–11. http://dx.doi.org/10.37200/ijpr/v24i4/pr201570.

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13

Yoon, Tae Hyun, and Eon Kyeong Joo. "Butterworth Window for Power Spectral Density Estimation." ETRI Journal 31, no. 3 (2009): 292–97. http://dx.doi.org/10.4218/etrij.09.0108.0260.

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14

Xu, Hui Bin, and Kui Zhang. "The UWB Signals of Power Spectral Density." Advanced Materials Research 472-475 (February 2012): 2748–51. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.2748.

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System or the waveform is energy, or has the power value. Generally, periodic signal and random signal is power signal,while the determine nonperiodic signal is energy signal. For the energy signal,we can use the energy spectrum density to describe the signal on the energy unit bandwidth,the unit is the joule/Hertz.For the power signal,we can use the power spectral density to describe the signal on the energy unit bandwidth,the unit for w/Hertz.

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15

Parhi, Keshab K., and Manohar Ayinala. "Low-Complexity Welch Power Spectral Density Computation." IEEE Transactions on Circuits and Systems I: Regular Papers 61, no. 1 (2014): 172–82. http://dx.doi.org/10.1109/tcsi.2013.2264711.

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16

Aldis, G. K., and R. S. Thompson. "Calculation of Doppler spectral power density functions." IEEE Transactions on Biomedical Engineering 39, no. 10 (1992): 1022–31. http://dx.doi.org/10.1109/10.161334.

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17

Yin, Jun, Chris Peter Diduch, and Liucheng Chang. "Islanding Detection Using Proportional Power Spectral Density." IEEE Transactions on Power Delivery 23, no. 2 (2008): 776–84. http://dx.doi.org/10.1109/tpwrd.2007.915907.

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18

Hossen, A. "Power spectral density estimation via wavelet decomposition." Electronics Letters 40, no. 17 (2004): 1055. http://dx.doi.org/10.1049/el:20045235.

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19

Boudraa, Abdel-Ouahab, Thierry Chonavel, and Jean-Christophe Cexus. "-energy operator and cross-power spectral density." Signal Processing 94 (January 2014): 236–40. http://dx.doi.org/10.1016/j.sigpro.2013.05.022.

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20

Thompson, R. S., and G. K. Aldis. "Spectral power density calculations for pulsed Doppler." Ultrasonics 39, no. 10 (2002): 703–14. http://dx.doi.org/10.1016/s0041-624x(02)00385-2.

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21

Rassaian, Mostafa. "Power Spectral Density Conversions and Nonlinear Dynamics." Shock and Vibration 1, no. 4 (1994): 349–56. http://dx.doi.org/10.1155/1994/903103.

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To predict the vibration environment of a payload carried by a ground or air transporter, mathematical models are required from which a transfer function to a prescribed input can be calculated. For sensitive payloads these models typically include linear shock isolation system stiffness and damping elements relying on the assumption that the isolation system has a predetermined characteristic frequency and damping ratio independent of excitation magnitude. In order to achieve a practical spectral analysis method, the nonlinear system has to be linearized when the input transportation and hand

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22

Broersen, P. M. T., and S. de Waele. "Generating data with prescribed power spectral density." IEEE Transactions on Instrumentation and Measurement 52, no. 4 (2003): 1061–67. http://dx.doi.org/10.1109/tim.2003.814824.

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23

Jones, A. E., T. Doumi, and J. G. Gardiner. "Power spectral density of imperfectly generated MSK." Electronics Letters 28, no. 3 (1992): 324. http://dx.doi.org/10.1049/el:19920201.

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24

Laevens, K. "Power spectral density of on-off sources." Electronics Letters 33, no. 7 (1997): 559. http://dx.doi.org/10.1049/el:19970417.

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25

Zaman, K. B. M. Q., and J. C. Yu. "Power spectral density of subsonic jet noise." Journal of Sound and Vibration 98, no. 4 (1985): 519–37. http://dx.doi.org/10.1016/0022-460x(85)90259-7.

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26

Lakshmi, V. Anantha, Satheesh G, and T. Bramhananda Reddy. "Power Spectral Density Estimation and THD of Motor Line Currents of AZSPWM Based DTC of Induction Motor Drive." International Journal of Engineering & Technology 7, no. 4.24 (2018): 42. http://dx.doi.org/10.14419/ijet.v7i4.24.21853.

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Estimating the power distribution over a given frequency range and the total harmonic distortion of the line currents of the AZSPWM based induction motor drive is discussed in this paper. Applying a spectrum analysis on the motor line currents and inspecting the spectrum amplitudes at different switching frequencies for abnormality is a well-known procedure for acoustic noise detection and diagnosis. Among the spectrum analysis techniques for acoustic noise detection, the Fast Fourier Transform (FFT) is the most widely used technique. There are other spectrum techniques, which are based on the

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27

Kang, J. M., S. E. Cho, and S. G. Kang. "Difference in spectral power density of sleep electroencephalography in individuals with or without insomnia." European Psychiatry 65, S1 (2022): S120. http://dx.doi.org/10.1192/j.eurpsy.2022.332.

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Introduction Power spectral analysis is the most common method of quantitative electroencephalogram (qEEG) techniques and enables investigation of the microstructure of insomnia. Previous spectral analysis studies on insomnia have shown inconsistent results due to their heterogeneity and small sample sizes. Objectives We compared the difference of electroencephalogram (EEG) spectral power during sleep among participants without insomnia, insomniacs with no hypnotic use, hypnotic users with no insomnia complaints, and hypnotic users with insomnia complaints. Methods We used the Sleep Heart Heal

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28

Treumann, Rudolf A., Wolfgang Baumjohann, and Yasuhito Narita. "On the ion-inertial-range density-power spectra in solar wind turbulence." Annales Geophysicae 37, no. 2 (2019): 183–99. http://dx.doi.org/10.5194/angeo-37-183-2019.

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Abstract. A model-independent first-principle first-order investigation of the shape of turbulent density-power spectra in the ion-inertial range of the solar wind at 1 AU is presented. Demagnetised ions in the ion-inertial range of quasi-neutral plasmas respond to Kolmogorov (K) or Iroshnikov–Kraichnan (IK) inertial-range velocity–turbulence power spectra via the spectrum of the velocity–turbulence-related random-mean-square induction–electric field. Maintenance of electrical quasi-neutrality by the ions causes deformations in the power spectral density of the turbulent density fluctuations.

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29

Mizin, Valeriia, and Olena Severynovska. "The effect of caffeine on alpha activity in the cerebral cortex of rats in different models of depression." ScienceRise: Biological Science, no. 1 (42) (April 10, 2025): 4–11. https://doi.org/10.15587/2519-8025.2025.325272.

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The aim of the study is to determine changes in the power spectral density of the alpha-like rhythm of the cerebral cortex in models of contagious depression and chronic unpredictable stress, as well as to study the effect of caffeine on these indicators. Materials and methods. The study was conducted on white sexually mature male rats weighing 230–300 grams. Six groups were formed according to the type of depression and caffeine consumption. The electroencephalographic method was used, and the power spectral density analysis in the alpha range was performed to assess the depressive-like state

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30

Orcioni, Simone, Alessandra Paffi, Francesca Apollonio, and Micaela Liberti. "Revealing Spectrum Features of Stochastic Neuron Spike Trains." Mathematics 8, no. 6 (2020): 1011. http://dx.doi.org/10.3390/math8061011.

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Power spectra of spike trains reveal important properties of neuronal behavior. They exhibit several peaks, whose shape and position depend on applied stimuli and intrinsic biophysical properties, such as input current density and channel noise. The position of the spectral peaks in the frequency domain is not straightforwardly predictable from statistical averages of the interspike intervals, especially when stochastic behavior prevails. In this work, we provide a model for the neuronal power spectrum, obtained from Discrete Fourier Transform and expressed as a series of expected value of sin

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31

Lee, Duehee, and Ross Baldick. "Future Wind Power Scenario Synthesis Through Power Spectral Density Analysis." IEEE Transactions on Smart Grid 5, no. 1 (2014): 490–500. http://dx.doi.org/10.1109/tsg.2013.2280650.

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32

Zhao, Wen Li, and De Gang Liu. "A Method of Simulating Computation for the Fatigue Life Prediction." Advanced Materials Research 139-141 (October 2010): 2582–86. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2582.

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The fatigue life prediction and reliability analysis of dynamic systems under random excitement are important topics in modern engineering design. However, the stress power spectral density and the formulation of fatigue life prediction of a component in a dynamic system must be known when one predicts its fatigue life. A method of simulating computation for the fatigue life of a dynamic structure is presented. The method is based on the concept of unit load stress matrix. According to it, the relationship between the stress power spectral density of a structure in a system and the response sp

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33

Zavgorodnii, Aleksei S. "The global navigation systems satellites signals partial powers measuring method." Izmeritel`naya Tekhnika, no. 9 (2021): 16–22. http://dx.doi.org/10.32446/0368-1025it.2021-9-16-22.

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Different consumers of global navigation satellite systems signals are able to receive different combinations of them. For this reason, it is important to monitor the signal parameters separately. The existing partial signals characteristics measuring methods are based on correlation processing, which leads to the loss of measurement information and signal averaging. A method for measuring the partial powers of navigation signals with frequency and code division was developed. The initial data are the total spectral power density measurements results, as well as information on the shape of the

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34

Tony, Bayan, and Deb Nabamita. "Effect of Mantra Chanting on Power Spectral Density." Indian Journal of Science and Technology 18, no. 2 (2025): 95–101. https://doi.org/10.17485/IJST/v18i2.3780.

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Abstract <strong>Objectives:</strong> Mantra chanting a spiritual form of meditation helps to calm the human brain. Mantra chanting is a meditative practice known to influence brainwave activities that are linked to increased theta and alpha brain rhythms. This paper investigates the variations in Power Spectral Density of experienced Mantra chanting individuals and evaluates the paired t-test before and during mantra chanting. <strong>Methods:</strong> The research is inspired by existing works on how meditation affects the activities of brain waves. This paper particularly emp

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35

Dr., Rahul Pachauri. "Spectral Variability in Fixed Windows using Fractional Fourier Transform: Application in Power Spectral Density Estimation." Indian Journal of Signal Processing (IJSP) 3, no. 3 (2023): 1–9. https://doi.org/10.54105/ijsp.C1015.083323.

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<strong>Abstract: </strong>In statistical signal processing, power spectral density estimation is a frequency domain analysis in which power contents of a signal are measured with respect to frequency components of that signal. The power estimation of a signal can be carried out more precisely by using a window with a narrower 3-dB bandwidth and higher side-lobe attenuation. Theoretically, these two spectral parameters show trade-off in variable windows and remain constant in fixed windows. In this work, spectral behavior of fixed windows has been elaborated using Fractional Fourier Transform

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36

Kumar, Rajendra. "Theoretical Analysis of the Noise Power Ratio of Nonlinear Power Amplifiers." Journal of Applied Mathematics 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8710860.

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This paper presents a theoretical analysis and derives the amplifier output noise power spectral density result in a closed form when the input to the amplifier is a band limited Gaussian noise. From the computed power spectral density the NPR is evaluated by a simple subtraction. The method can be applied to any amplifier with known input-output characteristics. The method may be applied to analyze various other important characteristics of the nonlinear amplifier such as spectral regrowth that refers to the spreading of the signal bandwidth when a band limited signal is inputted to the nonli

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37

Fang, Sheng En, Ricardo Perera, and Maria Consuelo Huerta. "Damage Localization Based on Power Spectral Density Analysis." Key Engineering Materials 347 (September 2007): 589–94. http://dx.doi.org/10.4028/www.scientific.net/kem.347.589.

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An environmental excitation having random characteristics may be more effective and cost-efficient than other excitation means for non-destructive damage identification purpose on most of the large-scale engineering structures under operation. In general, many existing damage indexes are constructed based on the modal properties derived firstly from the power spectral density (PSD) analysis of the structures under random excitation. However, the derivation procedures for the modal parameters usually introduce some extra errors into the indexes. This paper aims to propose a simple and feasible

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38

Babich, Ekaterina, Sergey Scherbak, Ekaterina Lubyankina, Valentina Zhurikhina, and Andrey Lipovskii. "Power Spectral Density Analysis for Optimizing SERS Structures." Sensors 22, no. 2 (2022): 593. http://dx.doi.org/10.3390/s22020593.

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The problem of optimizing the topography of metal structures allowing Surface Enhanced Raman Scattering (SERS) sensing is considered. We developed a model, which randomly distributes hemispheroidal particles over a given area of the glass substrate and estimates SERS capabilities of the obtained structures. We applied Power Spectral Density (PSD) analysis to modeled structures and to atomic force microscope images widely used in SERS metal island films and metal dendrites. The comparison of measured and calculated SERS signals from differing characteristics structures with the results of PSD a

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39

Xiong, Jiecheng, and Jun Chen. "Power Spectral Density Function for Individual Jumping Load." International Journal of Structural Stability and Dynamics 18, no. 02 (2018): 1850023. http://dx.doi.org/10.1142/s0219455418500232.

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Modern slender structures such as long-span floors and cantilever stands are sensitive to jumping-induced vibrations. A conventional deterministic Fourier series model for the human jumping load may overestimate a structure's responses in resonance condition. This paper suggests a power spectral density (PSD) function for the individual jumping load, which was treated as a narrowband stationary stochastic process. Experiments were conducted on individual jumping loads resulting in 334 records from 73 subjects. Statistical analysis of the records led to experimental PSD curves on which a symmet

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40

Lorbeer, Raoul-Amadeus, Jan Pastow, Michael Sawannia, Peter Klinkenberg, Daniel Förster, and Hans-Albert Eckel. "Power Spectral Density Evaluation of Laser Milled Surfaces." Materials 11, no. 1 (2017): 50. http://dx.doi.org/10.3390/ma11010050.

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41

Vatel, Olivier, Philippe Dumas, Frederic Chollet, Franck Salvan, and Elie André. "Roughness Assessment of Polysilicon Using Power Spectral Density." Japanese Journal of Applied Physics 32, Part 1, No. 12A (1993): 5671–74. http://dx.doi.org/10.1143/jjap.32.5671.

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42

Pimentel, Cecilio, and Valdemar C. da Rocha. "On the Power Spectral Density of Constrained Sequences." IEEE Transactions on Communications 55, no. 3 (2007): 409–16. http://dx.doi.org/10.1109/tcomm.2007.892443.

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43

Ramirez, David, Daniel Romero, Javier Via, Roberto Lopez-Valcarce, and Ignacio Santamaria. "Testing Equality of Multiple Power Spectral Density Matrices." IEEE Transactions on Signal Processing 66, no. 23 (2018): 6268–80. http://dx.doi.org/10.1109/tsp.2018.2875884.

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44

Kanazawa, Kenji, and Kazuta Hirata. "Parametric estimation of the cross-power spectral density." Journal of Sound and Vibration 282, no. 1-2 (2005): 1–35. http://dx.doi.org/10.1016/j.jsv.2004.02.009.

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45

Yoshida, Keiko. "Power spectral density peak estimation from broadband data." Journal of Sound and Vibration 312, no. 4-5 (2008): 893–905. http://dx.doi.org/10.1016/j.jsv.2007.11.017.

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46

Mohanty, R. K., C. Joenathan, and R. S. Sirohi. "Power spectral density of a subjective speckle pattern." Applied Optics 25, no. 5 (1986): 595. http://dx.doi.org/10.1364/ao.25.000595.

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47

Chen, Jun, Jinping Wang, and James M. W. Brownjohn. "Power Spectral-Density Model for Pedestrian Walking Load." Journal of Structural Engineering 145, no. 2 (2019): 04018239. http://dx.doi.org/10.1061/(asce)st.1943-541x.0002248.

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48

Etherington, N. J., D. S. L. Lloyd, and C. J. Watkins. "EEG monitoring with power spectral density band analysis." Electroencephalography and Clinical Neurophysiology 61, no. 3 (1985): S232—S233. http://dx.doi.org/10.1016/0013-4694(85)90879-x.

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49

Chen, Wei, Hanmin Yao, Fan Wu, Shibin Wu, and Qiang Chen. "Power spectral density measurement for large aspheric surfaces." Frontiers of Optoelectronics in China 1, no. 1-2 (2008): 197–200. http://dx.doi.org/10.1007/s12200-008-0032-2.

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50

Na, Cheolhun, and Sangjin Ryoo. "Cancer cell discrimination by power spectral density function." Contemporary Engineering Sciences 7 (2014): 719–24. http://dx.doi.org/10.12988/ces.2014.4680.

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