scholarly journals Emotion Identification Using Extremely Low Frequency Components of Speech Feature Contours

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chang-Hong Lin ◽  
Wei-Kai Liao ◽  
Wen-Chi Hsieh ◽  
Wei-Jiun Liao ◽  
Jia-Ching Wang
Keyword(s):  
Low Frequency ◽  
Principal Component ◽  
Support Vector ◽  
Emotional Speech ◽  
Emotion Identification ◽  
Identification Rate ◽  
Extremely Low Frequency ◽  
Frequency Components ◽  
Speech Feature ◽  
Speech Identification

The investigations of emotional speech identification can be divided into two main parts, features and classifiers. In this paper, how to extract an effective speech feature set for the emotional speech identification is addressed. In our speech feature set, we use not only statistical analysis of frame-based acoustical features, but also the approximated speech feature contours, which are obtained by extracting extremely low frequency components to speech feature contours. Furthermore, principal component analysis (PCA) is applied to the approximated speech feature contours so that an efficient representation of approximated contours can be derived. The proposed speech feature set is fed into support vector machines (SVMs) to perform multiclass emotion identification. The experimental results demonstrate the performance of the proposed system with 82.26% identification rate.

scholarly journals Investigation of the Surrounding Environment's Influence on Gait Sensing Using a Plant as a Sensor

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Shigeki Hirobayashi ◽  
Yusuke Tamura ◽  
Kazuhiro Yamamoto
Keyword(s):  
Rubber Tree ◽  
Low Frequency ◽  
Principal Component ◽  
Bioelectric Potential ◽  
Surrounding Environment ◽  
Frequency Components ◽  
Foliage Plant ◽  
Number Of Leaves ◽  
Bioelectric Potentials ◽  
Peak Value

Some animals and plants function as bioantennas in that changes in their surrounding environment produce variations in their bioelectric potentials. While the bioelectric potential is affected by living activities of the plant, it has been observed that the bioelectric potential can be reduced using plants. Thus, the influence of the life activity of a plant on the reception signal must be accounted for when a plant is used as a sensor. In this study, we produced an environmental change near a foliage plant growing in an indoor environment and examined the directivity of the plant's sensing ability. The sensitivity of the plant was a roughly circular area centered on the location of the plant. We also investigated the influence of the number of leaves on the plant on its sensing ability and found that it decreased with a reduction in the number of leaves. In addition, we monitored the effect of a person walking on the spot near the plant on the bioelectric potential of the plant. Six subjects stepped on the spot 50 cm from a rubber tree and we measured the variation in the bioelectric potential of the tree produced by this stepping motion. The results confirmed that stepping motion produces a measurable response in the bioelectric potential of a plant and that this response varies in synchrony with the subject's stepping rate. Moreover, by conducting principal component analysis using the peak value of the spectrum characteristics of the measured bioelectric potential, cumulative proportion was found to reach nearly 97% at low-frequency components up to the fifth peak.

scholarly journals Rainfall and runoff forecasting with SSA–SVM approach

2001 ◽  
Vol 3 (3) ◽  
pp. 141-152 ◽  
Author(s):  
C. Sivapragasam ◽  
Shie-Yui Liong ◽  
M. F. K. Pasha
Keyword(s):  
Linear Prediction ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Support Vector ◽  
Runoff Forecasting ◽  
Time Operation ◽  
Real Time Operation ◽  
Frequency Components ◽  
Hydrologic Variable ◽  
Original Time

Real time operation studies such as reservoir operation, flood forecasting, etc., necessitates good forecasts of the associated hydrologic variable(s). A significant improvement in such forecasting can be obtained by suitable pre-processing. In this study, a simple and efficient prediction technique based on Singular Spectrum Analysis (SSA) coupled with Support Vector Machine (SVM) is proposed. While SSA decomposes original time series into a set of high and low frequency components, SVM helps in efficiently dealing with the computational and generalization performance in a high-dimensional input space. The proposed technique is applied to predict the Tryggevælde catchment runoff data (Denmark) and the Singapore rainfall data as case studies. The results are compared with that of the non-linear prediction (NLP) method. The comparisons show that the proposed technique yields a significantly higher accuracy in the prediction than that of NLP.

scholarly journals Modeling and Analysis of Inductive Coil ELF Sensor

2015 ◽  
Vol 16 (1) ◽  
pp. 38
Author(s):  
Rajendra Aparnathi ◽  
Vedvyas Dwivedi
Keyword(s):  
Magnetic Field ◽  
Low Frequency ◽  
Magnetic Loop ◽  
Magnetic Field Effects ◽  
Modeling And Analysis ◽  
Extremely Low Frequency ◽  
Frequency Components ◽  
Loop Coil ◽  
Normal Spectrum ◽  
Ferromagnetic Effect

Mathematical statistical analysis of inductive loop coil-sensor is carried out for its magnetic field effects operating on extremely low frequency (<30Hz). A system using resister, inductor, and capacitor effects finds resonance frequency for this loop sensor and its sensitivity as ferromagnetic effect. The design methods for these coils with air and ferromagnetic cores are technically compared and summarized, which are also known and used as search coils or pickup coils or magnetic loop coil sensors. The amplitude and bandwidth of the frequency components are compared to the standardized normal spectrum. This paper also presents the applications of coil sensor as magnetic coil.

scholarly journals A Terahertz Spectroscopy Nondestructive Identification Method for Rubber Based on CS-SVM

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Xianhua Yin ◽  
Wei Mo ◽  
Qiang Wang ◽  
Binyi Qin
Keyword(s):  
Terahertz Spectroscopy ◽  
Search Algorithm ◽  
Principal Component ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Function Parameter ◽  
Support Vector ◽  
Identification Rate ◽  
Nondestructive Identification ◽  
Factor C

A method is proposed for rubber identification based on terahertz time-domain spectroscopy (THz-TDS) and support vector machine (SVM). In order to improve the accuracy, the cuckoo search algorithm (CS) is used to optimize the penalty factor C and kernel function parameter g of SVM. The SVM model optimized by the cuckoo search algorithm is abbreviated as CS-SVM. Principal component analysis (PCA) is applied to decrease the dimension of the spectral data. The top ten principal component factors, whose accumulated variance contribution rate reaches 93.93%, are extracted from the original spectra data and then are applied to CS-SVM. The identification rate of testing sets for CS-SVM is 100%, which is significantly higher than 96.67% identification rate of testing sets for PSO-SVM and Grid search. Experimental results show that CS-SVM can accomplish nondestructive identification for different rubber. This method lays a theoretical foundation for the application of terahertz spectroscopy in rubber classification and identification.

Recaptured Image Forensics Algorithm Based on Multi-Resolution Wavelet Transformation and Noise Analysis

2017 ◽  
Vol 32 (02) ◽  
pp. 1854003 ◽  
Author(s):  
Yanjun Sun ◽  
Xuanjing Shen ◽  
Yingda Lv ◽  
Changming Liu
Keyword(s):  
Wavelet Transformation ◽  
Rapid Development ◽  
Noise Analysis ◽  
Low Frequency ◽  
Image Forensics ◽  
Digital Image Forensics ◽  
Support Vector ◽  
Identification System ◽  
Identification Rate ◽  
Noise Characteristics

With the rapid development of digital cameras and smart phones, the image identification system in current times will be of a great impact. This will cause the form of image information to increase serious security issues. Especially, the emergence of the recaptured image makes conventional digital image forensics algorithm invalid. Therefore, a new image forensics algorithm is urgently needed to identify the recaptured image. In this paper, a new recaptured image identifying algorithm is put forward based on wavelet transformation and noise analysis by analyzing the differences between the real and recaptured images generated in the imaging process. First, the proposed algorithm extracts mean value, variance and skewness as wavelet characteristic from the high-frequency images and low-frequency images by wavelet transformation. Meanwhile, the proposed algorithm analyzes the noise image by means of local binary pattern to extract noise characteristic. Finally, the support vector machine is applied to classify the recaptured image with wavelet characteristics and noise characteristics. The results show the presented method can not only identify the recaptured image obtained from different media but also have better identification rate, and the dimension of the characteristic vector is also lower than those obtained by other algorithms.

scholarly journals Multi-Scale Principal Component Analysis for the Fault Detection and Isolation in Induction Motors

2018 ◽  
Vol 7 (3.31) ◽  
pp. 86
Author(s):  
Naga Venkata Navya Repaka ◽  
Vidya Sagar Yellapu
Keyword(s):  
Principal Component Analysis ◽  
Induction Motors ◽  
Low Frequency ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Isolation ◽  
Multi Scale ◽  
Detection And Identification ◽  
Wear And Tear ◽  
Frequency Components

Induction motors, though rugged, undergo faults due to wear and tear in their operation. Some faults have the characteristic property of influencing the stator current frequencies. Some side-band frequencies can be observed in the case of such faults. In this paper, a Multi-Scale Principal Component Analysis which combines wavelet analysis with principal component analysis has been applied to the data obtained from the simulation model of an induction motor. A 3-level decomposition of the data is performed and the principal component analysis is applied to high-frequency and low-frequency components of the data at various levels. The results suggest the use of the scheme for timely detection and identification of the faults which would endanger the motor from the otherwise possible destruction. It has also been proved that the scheme has the capability of detecting the sensor faults also, in addition to the motor faults.  

FACIAL BIOMETRICS USING NONTENSOR PRODUCT WAVELET AND 2D DISCRIMINANT TECHNIQUES

2009 ◽  
Vol 23 (03) ◽  
pp. 521-543 ◽  
Author(s):  
DAN ZHANG ◽  
XINGE YOU ◽  
PATRICK WANG ◽  
SVETLANA N. YANUSHKEVICH ◽  
YUAN YAN TANG
Keyword(s):  
Tensor Product ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
Feature Representation ◽  
Support Vector ◽  
Facial Features ◽  
Linear Discriminant ◽  
Illumination Changes ◽  
Frequency Components

A new facial biometric scheme is proposed in this paper. Three steps are included. First, a new nontensor product bivariate wavelet is utilized to get different facial frequency components. Then a modified 2D linear discriminant technique (M2DLD) is applied on these frequency components to enhance the discrimination of the facial features. Finally, support vector machine (SVM) is adopted for classification. Compared with the traditional tensor product wavelet, the new nontensor product wavelet can detect more singular facial features in the high-frequency components. Earlier studies show that the high-frequency components are sensitive to facial expression variations and minor occlusions, while the low-frequency component is sensitive to illumination changes. Therefore, there are two advantages of using the new nontensor product wavelet compared with the traditional tensor product one. First, the low-frequency component is more robust to the expression variations and minor occlusions, which indicates that it is more efficient in facial feature representation. Second, the corresponding high-frequency components are more robust to the illumination changes, subsequently it is more powerful for classification as well. The application of the M2DLD on these wavelet frequency components enhances the discrimination of the facial features while reducing the feature vectors dimension a lot. The experimental results on the AR database and the PIE database verified the efficiency of the proposed method.

ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OF MOTOR IMAGERY-INDUCED ELECTROENCEPHALOGRAMS

2012 ◽  
Vol 05 (02) ◽  
pp. 1250006 ◽  
Author(s):  
JIANING ZHENG ◽  
LIYU HUANG ◽  
JING ZHAO
Keyword(s):  
Motor Imagery ◽  
Low Frequency ◽  
New Combination ◽  
Support Vector ◽  
Frequency Noise ◽  
Intrinsic Mode Functions ◽  
Identification Rate ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Optimal Kernel

The precise classification for the electroencephalogram (EEG) in different mental tasks in the research on brain–computer interface (BCI) is the key for the design and clinical application of the system. In this paper, a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments. First, to eliminate the low frequency noise in the original EEGs, the signals were decomposed by empirical mode decomposition (EMD) and then the optimal kernel parameters for support vector machine (SVM) were determined, the energy features of the first three intrinsic mode functions (IMFs) of every signal were extracted and used as input vectors of the employed SVM. The output of the SVM will be classification result for different mental task EEG signals. The study shows that mean identification rate of the proposed algorithm is 95%, which is much better than the present traditional algorithms.

Quick Test for Transgenic Components in Rice Using Terahertz Spectra

2018 ◽  
Vol 73 (2) ◽  
pp. 171-181
Author(s):  
Xin-gang Ju ◽  
Yuan Zhang ◽  
Fei-yu Lian ◽  
Mai-xia Fu
Keyword(s):  
Gaussian Function ◽  
Back Propagation ◽  
Principal Component ◽  
Basis Functions ◽  
Support Vector ◽  
Thz Spectroscopy ◽  
Identification Rate ◽  
Positive Identification ◽  
Radial Basis ◽  
Cumulative Rate

The terahertz (THz) spectrum of 0.2–1.6 THz (6.6–52.8 cm−1) was used to identify the existence of transgenic rice Bt63 contents in non-GMO rice using a THz time-domain spectroscopy system. Principal component analysis (PCA) was used to extract the feature data based on the cumulative rate of information contribution ( > 90%); the top four principal components were selected and a radial basis function neural network (RBFNN) method was then trained and used. Three selection radial basis functions including a Gaussian function were used to identify the three types (strong positive, weak positive, and negative). The results show that the samples were identified with an accuracy of nearly 90%; additionally, the positive identification rate was > 87.5% and the negative identification rate reached 100% using the proposed method (PCA-RBF). The proposed approach was then compared with other methods, including back propagation (BP) neural networks and support vector machine (SVM). The results of the comparison show that the accuracy of PCA-RBF method reaches 92% in total and all the rest are < 90% using 100 samples. Obviously, the proposed approach outperforms the other methods and also indicates that the proposed method, in combination with THz spectroscopy, is efficient and practical for transgenic ingredient identification in rice.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

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