SPECTRAL FEATURES OF THE ALTERNATING CLUSTER PROCESS

The alternating cluster process is a Poisson process the rate of which is modulated by an underlying two-state process. We derive the power spectral density of the alternating cluster process; besides random noise we obtain excess noise due to the impact of modulation.

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An Intermittent Poisson Process Generating 1/f Noise with Possible Application to Fluorescence Intermittency

Fluctuation and Noise Letters ◽

10.1142/s0219477514500151 ◽

2014 ◽

Vol 13 (02) ◽

pp. 1450015 ◽

Cited By ~ 5

Author(s):

Ferdinand Grüneis

Keyword(s):

Power Spectrum ◽

Spectral Density ◽

Poisson Process ◽

Power Spectral Density ◽

High Frequency ◽

Frequency Region ◽

Excess Noise ◽

State Process ◽

Power Spectral ◽

Fluorescence Intermittency

Quantum dots (QD) and other nanoparticles exhibit fluorescence intermittency switching irregularly between bright ("on") and dark ("off") states. On- and off-times follow a power-law statistics with exponents ranging from -1 to -2. The empirical power spectral density of this two-state process shows a 1/fx shape with an exponent x reverting from ≈1 at low frequencies to ≈2 at high frequency. Based on theoretical considerations, the low frequency region can be attributed to the on-state; however, there are some discrepancies in attributing the off-states to the high frequency region. This difficulty can be overcome by introducing a Poisson process which is gated by the two-state process giving rise to an intermittent Poisson process (IPP); in this way, the statistical features of the two-state process are transferred to the IPP. The power spectral density of the IPP can be derived in closed form for arbitrarily distributed on/off-states. Besides shot noise the power spectrum of the IPP exhibits excess noise with two scaling regions which can be attributed to the respective on/off-states. The results are applied to interpret the power spectrum of fluorescence intermittency in QDs.

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Analysing Random Vibration of KLT Box during Transporting by Finite Element Method

10.29007/b1th ◽

2022 ◽

Author(s):

Cong Hoa Vu ◽

Ngoc Thien Ban Dang

Keyword(s):

Finite Element ◽

Spectral Density ◽

Power Spectral Density ◽

Automotive Industry ◽

Random Vibration ◽

Element Model ◽

Vibration Testing ◽

Time Data ◽

Power Spectral ◽

The Impact

Today, freight is an extremely important industry for the world we are living. Fast transportation, large volume...will optimize the cost, time and effort. Besides, ensuring the products safety is a matter of concern. During transporting, it is inevitable that the vibration caused by the engine, rough road surface...the cargo inside can be damaged. Automobile industries have prime importance to vibration testing. Sine vibration testing is performed when we have been given with only one frequency at given time instant. Trend to perform random vibration testing has been increased in recent times. As random vibration considers all excited frequencies in defined spectrum at known interval of time, it gives real-time data of vibration severities. The vibration severity is expressed in terms of Power Spectral Density (PSD). KLT box is an industrial stacking container conforming to the VDA 4500 standard that was defined by German Association of the Automotive Industry (VDA) for the automotive industry. The aim of this paper is study about random vibration and power spectral density analysis, how it can be used to predict the impact of hash road to the KLT box on container / truck during transportation. Finite element model is developed in ANSYS, modal analysis and random vibration analysis were done.

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CLASSIFICATION OF ICTAL EEG USING MODELING BASED SPECTRAL AND TEMPORAL FEATURES ON INSTANTANEOUS AMPLITUDE-FREQUENCY COMPONENTS OF IMFs

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237218500424 ◽

2018 ◽

Vol 30 (06) ◽

pp. 1850042 ◽

Cited By ~ 1

Author(s):

K. S. Biju ◽

M. G. Jibukumar

Keyword(s):

Spectral Density ◽

Power Spectral Density ◽

Classification Accuracy ◽

Spectral Features ◽

Eeg Signal ◽

Instantaneous Amplitude ◽

Eeg Signals ◽

Hilbert Huang Transform ◽

Temporal Features ◽

Power Spectral

In the present study, a method for classifying the different ictal stages in electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are indicated by ictal activities, which trigger widespread neurological disorders other than stroke and thus affect the world population. In this work, a novel ictal classification method that combines the spectral and temporal features of twin components in Hilbert–Huang transform is proposed. Spectral features of instantaneous amplitude (IA) function are obtained based on the power spectral density of autoregressive (AR) modeling. Here four different cases of ictal activities of EEG signal are classified. In each case first and second intrinsic mode function of Hilbert–Huang transform are tabulated. The power spectral density of AR(6) and AR(10) model are done for IA1 and IA2 components of each case. Temporal features of either instantaneous frequency (IF) function or IA are computed. The feature vectors are tested in a well-known database of different classes in interictal, ictal, and normal activities of EEG signals. The discriminating power of each vector is evaluated through one-way analysis of variance, and the classification results are verified using an artificial neural network (ANN) classifier. The performance of the classifier was assessed in term of sensitivity, specificity, and total classification accuracy. The spectral features of the AR(10) of IA and the temporal features of IA yielded 100% accuracy, 100% sensitivity, and 100% specificity in the ictal classification. By contrast, these features obtained only 83.33% of the total classification accuracy in ictal and interictal EEG signal.

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Recognition of human emotion with spectral features using multi layer-perceptron

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200044 ◽

2020 ◽

Vol 24 (3) ◽

pp. 227-233

Author(s):

A. Pramod Reddy ◽

Vijayarajan V

Keyword(s):

Support Vector Machine ◽

Spectral Density ◽

Emotion Recognition ◽

Power Spectral Density ◽

Multilayer Perceptron ◽

Support Vector ◽

Spectral Features ◽

Emotional States ◽

Multi Layer Perceptron ◽

Power Spectral

For emotion recognition, here the features extracted from prevalent speech samples of Berlin emotional database are pitch, intensity, log energy, formant, mel-frequency ceptral coefficients (MFCC) as base features and power spectral density as an added function of frequency. In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered in our study. Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model. The extracted features are analyzed using Support Vector Machine (SVM) and with multilayer perceptron (MLP) a class of feed-forward ANN classifiers is/are used to classify different emotional states. We observed 91% accuracy for Angry and Boredom emotional classes by using SVM and more than 96% accuracy using ANN and with an overall accuracy of 87.17% using SVM, 94% for ANN.

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Análisis del consumo energético de la herramienta de un torno CNC a diferentes velocidades de corte usando la densidad del espectro de potencia

Revista de Simulación y Laboratorio ◽

10.35429/jsl.2019.19.6.1.8 ◽

2019 ◽

pp. 1-8

Author(s):

Miguel Angel Hernández-Epigmenio ◽

Carlos Juárez-Toledo ◽

Irma Martínez-Carrillo

Keyword(s):

Energy Consumption ◽

Spectral Density ◽

Power Spectral Density ◽

Metal Cutting ◽

Cutting Tool ◽

Electrical Current ◽

Power Spectral ◽

The Impact ◽

The Relationship ◽

A Company

In a company, income must exceed expenses for the business to be profitable. The choices a company makes about its energy sourcing and consumption can profoundly influence its cost of production. The purpose of this paper is to examine the energy consumption of the cutting tool of a lathe using a numerical tool CNC. This study was designed to examine the relationship between speed of cutting and the spectral modes of the power spectral density. This work shows the impact of the current for different cutting speeds, the results of this study indicate that the power spectral density of the current of cutting, may be more than enough to determinate the energy consumption of the manufacturing process. The main contribution of this paper is the experimental validation of the attenuation of higher order modes of the electrical current at metal cutting process. These results can be used in the design and tuning of the speed control of the cutting tool.

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