Hindi Syllable Segmentation Using ZCR and Dual Band Energy Ratio

2017 ◽  
Vol 7 (7) ◽  
pp. 581 ◽  
Author(s):  
Rubeena A. Khan ◽  
J. S. Chitode
Keyword(s):  
Energy Ratio ◽  
Dual Band ◽  
Band Energy ◽  
Syllable Segmentation

Prediction Method of Grinding Chatter Based on ARIMA

2014 ◽  
Vol 971-973 ◽  
pp. 1288-1291 ◽  
Author(s):  
Zi Liang Yao ◽  
Min Wang ◽  
Tao Zan ◽  
Guo Fu Liu
Keyword(s):  
Natural Frequency ◽  
Frequency Band ◽  
Prediction Method ◽  
Vibration Monitoring ◽  
Energy Ratio ◽  
Grinding Process ◽  
Band Energy ◽  
Grinding Chatter ◽  
Chatter Prediction ◽  
Frequency Band Energy

The process of grinding chatter is divided into three states: stable grinding state, chatter gestation state and chatter state. The vibration signals of grinding process contain chatter features can correspond well to the changes of grinding process. By analyzing the natural frequency band energy ratio of grinding process, a method of grinding chatter prediction is proposed. Experimental results show that the natural frequency band energy ratio is beyond a certain threshold, chatter occurred, otherwise no chatter happened. The method of grinding prediction can provide reference for vibration monitoring in practice.

The Research on Pain Mechanism Based on the Spectrum Analysis of EEG

2013 ◽  
Vol 380-384 ◽  
pp. 4202-4206 ◽  
Author(s):  
Ji Jun Tong ◽  
Yan Qin Kang ◽  
Guang Lei Zhang ◽  
Jin Liu ◽  
Qiang Cai
Keyword(s):  
Spectrum Analysis ◽  
Pain Treatment ◽  
Temporal Cortex ◽  
Isotonic Saline ◽  
Occipital Cortex ◽  
Energy Ratio ◽  
Placebo Control ◽  
Left Shoulder ◽  
Band Energy ◽  
Pain Stimulation

Pain is one of the most important sensations in daily life, it is necessary to get the essence and mechanism of pain in pain treatment and control. In this article, the pain was induced through injection of angelica on the left shoulder of 12 volunteers, as placebo-control stimulation, the isotonic saline was injected. And through the spectrum analysis of EEG, the EEG feature, the power percentile of δ ,θ, α, β were extracted during the experiment to study the modulation effect of pain on brain information and central mechanism. The research demonstrated that the δ band energy ratio increased and the θ, α, β band energy ratio decreased after pain stimulation, though the activated areas were not strictly same, they mainly located on the left frontal cortex, left temporal cortex, left parietal cortex, occipital cortex. It indicated that these areas were modulated significantly by pain stimulation.

scholarly journals Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 64
Author(s):  
Han Li ◽  
Yanzhu Hu ◽  
Song Wang
Keyword(s):  
Power Spectrum ◽  
Signal Detection ◽  
False Alarm ◽  
Probability Density ◽  
Beta Distribution ◽  
Detection Method ◽  
Energy Detection ◽  
Energy Ratio ◽  
Noise Variance ◽  
Band Energy

The power-spectrum sub-band energy ratio (PSER) has been applied in a variety of fields, but reports on its statistical properties and application in signal detection have been limited. Therefore, the statistical characteristics of the PSER were investigated and a signal detection method based on the PSER was created in this paper. By analyzing the probability and independence of power spectrum bins, as well as the relationship between F and beta distributions, we developed a probability distribution for the PSER. Our results showed that in a case of pure noise, the PSER follows beta distribution. In addition, the probability density function exhibited no relationship with the noise variance—only with the number of bins in the power spectrum. When Gaussian white noise was mixed with the signal, the resulting PSER followed a doubly non-central beta distribution. In this case, the probability density and cumulative distribution functions were represented by infinite double series. Under the constant false alarm strategy, we established a signal detector based on the PSER and derived the false alarm probability and detection probability of the PSER. The main advantage of this detector is that it did not need to estimate noise variance. Compared with time-domain energy detection and local spectral energy detection, we found that the PSER had better robustness under noise uncertainty. Finally, the results in the simulation and real signal showed that this detection method was valid.

scholarly journals A Novel Multidimensional Frequency Band Energy Ratio Analysis Method for the Pressure Fluctuation of Francis Turbine

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Weiyu Wang ◽  
Qijuan Chen ◽  
Xing Liang ◽  
Xuhui Yue ◽  
Jinzhou Dou
Keyword(s):  
Feature Extraction ◽  
Frequency Band ◽  
Pressure Fluctuation ◽  
Frequency Characteristics ◽  
Energy Ratio ◽  
Francis Turbine ◽  
Band Energy ◽  
Time Frequency ◽  
Frequency Band Energy ◽  
The Impact

The pressure fluctuation has multiple influence on the steady operation of Francis turbine, and the impact degree varies with the operation condition. In this paper, for the analysis of pressure fluctuation in the Francis turbine, a novel feature extraction method of multidimensional frequency bands energy ratio is proposed based on Hilbert Huang Transform (HHT). Firstly, the pressure fluctuation signal is decomposed into intrinsic mode functions (IMFs) by EEMD. Secondly, the Hilbert marginal spectrum is utilized to analyze the frequency characteristics of IMFs. Then, according to the inner frequency of IMFs, each of them is divided into high, medium, or low frequency band which are constructed based on the frequency characteristic of the pressure fluctuations in the Francis turbine. Afterward, the energy ratio of each frequency band to the original signal is calculated, which is to realize the feature extraction of multidimensional frequency band energy ratio. Actual applications verify that this method not only can extract the time-frequency characteristics but also can analyze the condition feature of the pressure fluctuation. It is a novel method for extracting the feature of pressure fluctuation in the Francis turbine.

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