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 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 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.  

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.

ANALYSIS OF STAND RESEARCH RESULTS OF AXIAL DYNAMIC FORCE IN DRILLING BY THREE ROLLER CONE BIT

2019 ◽  
pp. 81-93
Author(s):  
В. М. Мойсишин ◽  
M. V. Lyskanych ◽  
R. A. Zhovniruk ◽  
Ye. P. Majkovych
Keyword(s):  
Low Frequency ◽  
Maximum Energy ◽  
Dynamic Force ◽  
Low Frequency Oscillation ◽  
Drilling Tool ◽  
Rock Destruction ◽  
Frequency Components ◽  
Harmonic Components ◽  
Experimental Values ◽  
Harmful Effects

The purpose of the proposed article is to establish the causes of oscillations of drilling tool and the basic laws of the distribution of the total energy of the process of changing the axial dynamic force over frequencies of spectrum. Variable factors during experiments on the classical plan were the rigidity of drilling tool and the hardness of the rock. According to the results of research, the main power of the process of change of axial dynamic force during drilling of three roller cone bits is in the frequency range 0-32 Hz in which three harmonic frequency components are allocated which correspond to the theoretical values of low-frequency and gear oscillations of the chisel and proper oscillations of the bit. The experimental values of frequencies of harmonic components of energy and normalized spectrum as well as the magnitude of the dispersion of the axial dynamic force and its normalized values at these frequencies are presented. It has been found that with decreasing rigidity of the drilling tool maximum energy of axial dynamic force moves from the low-frequency oscillation region to the tooth oscillation area, intensifying the process of rock destruction and, at the same time, protecting the tool from the harmful effects of the vibrations of the bit. Reducing the rigidity of the drilling tool protects the bit from the harmful effects of the vibrations generated by the stand. The energy reductions in these fluctuations range from 47 to 77%.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Underwater Image Enhancement Method Combining color and Luminance

2020 ◽  
Vol 13 ◽  
Author(s):  
ZHAO Baiting ◽  
WANG Feng ◽  
JIA Xiaofen ◽  
GUO Yongcun ◽  
WANG Chengjun
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Low Frequency ◽  
Brightness Enhancement ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method ◽  
Frequency Components ◽  
Image Contour ◽  
Objective Indicators

Background:: Aiming at the problems of color distortion, low clarity and poor visibility of underwater image caused by complex underwater environment, a wavelet fusion method UIPWF for underwater image enhancement is proposed. Methods:: First of all, an improved NCB color balance method is designed to identify and cut the abnormal pixels, and balance the color of R, G and B channels by affine transformation. Then, the color correction map is converted to CIELab color space, and the L component is equalized with contrast limited adaptive histogram to obtain the brightness enhancement map. Finally, different fusion rules are designed for low-frequency and high-frequency components, the pixel level wavelet fusion of color balance image and brightness enhancement image is realized to improve the edge detail contrast on the basis of protecting the underwater image contour. Results:: The experiments demonstrate that compared with the existing underwater image processing methods, UIPWF is highly effective in the underwater image enhancement task, improves the objective indicators greatly, and produces visually pleasing enhancement images with clear edges and reasonable color information. Conclusion:: The UIPWF method can effectively mitigate the color distortion, improve the clarity and contrast, which is applicable for underwater image enhancement in different environments.

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

scholarly journals Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance

2021 ◽  
Vol 13 (3) ◽  
pp. 480
Author(s):  
Jingang Zhan ◽  
Hongling Shi ◽  
Yong Wang ◽  
Yixin Yao
Keyword(s):  
Principal Component Analysis ◽  
Ice Sheet ◽  
Low Frequency ◽  
Principal Component ◽  
Equatorial Pacific ◽  
Mass Change ◽  
Frequency Variation ◽  
Periodic Signal ◽  
Time Frequency ◽  
The Antarctic

Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery and Climate Experiment (GRACE) satellite time-varying solutions to study the principal components (PCs) of the Antarctic ice sheet mass change and their time-frequency variation. This assessment was based on complex principal component analysis (CPCA) and the wavelet amplitude-period spectrum (WAPS) method to study the PCs and their time-frequency information. The CPCA results revealed the PCs that affect the ice sheet balance, and the wavelet analysis exposed the time-frequency variation of the quasi-periodic signal in each component. The results show that the first PC, which has a linear term and low-frequency signals with periods greater than five years, dominates the variation trend of ice sheet in the Antarctic. The ratio of its variance to the total variance shows that the first PC explains 83.73% of the mass change in the ice sheet. Similar low-frequency signals are also found in the meridional wind at 700 hPa in the South Pacific and the sea surface temperature anomaly (SSTA) in the equatorial Pacific, with the correlation between the low-frequency periodic signal of SSTA in the equatorial Pacific and the first PC of the ice sheet mass change in Antarctica found to be 0.73. The phase signals in the mass change of West Antarctica indicate the upstream propagation of mass loss information over time from the ocean–ice interface to the southward upslope, which mainly reflects ocean-driven factors such as enhanced ice–ocean interaction and the intrusion of warm saline water into the cavities under ice shelves associated with ice sheets which sit on retrograde slopes. Meanwhile, the phase signals in the mass change of East Antarctica indicate the downstream propagation of mass increase information from the South Pole toward Dronning Maud Land, which mainly reflects atmospheric factors such as precipitation accumulation.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

Assessment of Damage Progression in Automotive Electronics Assemblies Subjected to Temperature and Vibration

2018 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Instantaneous Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Large Data ◽  
Data Sets ◽  
Automotive Electronics ◽  
Damage Progression ◽  
Mode Decomposition ◽  
Electronic Assemblies ◽  
Strain Signal

Electronics in automotive underhood environments is used for a number of safety critical functions. Reliable continued operation of electronic safety systems without catastrophic failure is important for safe operation of the vehicle. There is need for prognostication methods, which can be integrated, with on-board sensors for assessment of accrued damage and impending failure. In this paper, leadfree electronic assemblies consisting of daisy-chained parts have been subjected to high temperature vibration at 5g and 155°C. Spectrogram has been used to identify the emergence of new low frequency components with damage progression in electronic assemblies. Principal component analysis has been used to reduce the dimensionality of large data-sets and identify patterns without the loss of features that signify damage progression and impending failure. Variance of the principal components of the instantaneous frequency has been shown to exhibit an increasing trend during the initial damage progression, attaining a maximum value and decreasing prior to failure. The unique behavior of the instantaneous frequency over the period of vibration can be used as a health-monitoring feature for identifying the impending failures in automotive electronics. Further, damage progression has been studied using Empirical Mode Decomposition (EMD) technique in order to decompose the signals into Independent Mode Functions (IMF). The IMF’s were investigated based on their kurtosis values and a reconstructed strain signal was formulated with all IMF’s greater than a kurtosis value of three. PCA analysis on the reconstructed strain signal gave better patterns that can be used for prognostication of the life of the components.

Sign in / Sign up

Export Citation Format

Share Document