The human body characteristic parameters extraction and disease tendency prediction based on multi-sensing fusion algorithms

2016 ◽  
Author(s):  
Guangyi Shi ◽  
Chao Geng ◽  
Hailiang Liu ◽  
Hang Su ◽  
Yufeng Jin ◽  
...  
Keyword(s):  
Human Body ◽  
Characteristic Parameters ◽  
Parameters Extraction

Extract Characteristic Parameters of FID Signal Combining Autocorrelation Function with the Least Absolute Value

2012 ◽  
Vol 239-240 ◽  
pp. 1108-1112
Author(s):  
Hui Cui Hao ◽  
Jun Lin ◽  
Bao Feng Tian ◽  
Qi Wan
Keyword(s):  
Autocorrelation Function ◽  
Least Squares Method ◽  
Measurement Data ◽  
Characteristic Parameters ◽  
Absolute Value ◽  
Nonlinear Fitting ◽  
Least Absolute Value ◽  
Function Fitting ◽  
Parameters Extraction ◽  
Fitting Method

FID signal is the envelope of magnetic resonance signal, and the extraction accuracy of characteristic parameters directly influence the accuracy of the hydrogeologic parameters of the inverse interpretation. In order to improve the accuracy of characteristic parameters extraction, made simulation and study combined the autocorrelation function fitting with the least absolute value nonlinear fitting method in different SNR and different noise in this paper. The simulation results showed that, the characteristic parameters fitting error using this method was smaller than that using linear, nonlinear fitting method or the autocorrelation function with the least squares method, within 7% in lower SNR. The field measurement data and inversion results verified the method validity.

Extraction Algorithm of Lip Characteristic Parameters Based on Interpolation

2014 ◽  
Vol 536-537 ◽  
pp. 235-240
Author(s):  
Ying Jie Meng ◽  
Li Xin Bai ◽  
Wen Jun Liu ◽  
Ming Wen Liu
Keyword(s):  
High Efficiency ◽  
Parameter Extraction ◽  
Characteristic Parameters ◽  
Motion Feature ◽  
Identity Recognition ◽  
Features Fusion ◽  
Parameters Extraction ◽  
Key Points ◽  
Extraction Algorithm ◽  
Motion Features

In the research of identity recognition based on lip motion features, there are limitations for the existing algorithms of lip characteristic parameters extraction. This paper uses the strategy of lip static/dynamic geometric features fusion, designs the lip feature parameter extraction program based on interpolation, and implements the major aspects of processing algorithm of the program. The solution is based on the speaker's key six primitives spelling lip sequence image, firstly generates the lip key point coordinates in the image, then based on Lagrange interpolation obtains function curve coefficient of upper and lower lips' key points , lastly the two curve coefficients are combined to form lip motion feature information of human speaker's some specific sounds; Simulation results show that the extraction of characteristic parameters of the program not only have a high efficiency and availability, but also have the advantages of good storage.

The Characteristic Parameters Extraction of the Drawing Forming Cracks Based on Time-Series Analysis and ICA

2012 ◽  
Vol 239-240 ◽  
pp. 1259-1263
Author(s):  
Zhi Gao Luo ◽  
Jing Jing Zhang ◽  
Jun Li Zhao ◽  
Xu Dong Li
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Time Series Analysis ◽  
Deep Drawing ◽  
Characteristic Parameters ◽  
Distribution Maps ◽  
Part Quality ◽  
Series Analysis ◽  
Parameters Extraction ◽  
Ae Signals

The purpose of the study is to extract the characteristic parameters of the forming crack acoustic emission (AE) signals generated by the metal deep drawing. Time-series analysis and MATLAB were used to adopt independent component analysis (ICA) to isolate the crack AE signals and extracted the characteristic parameters of AE signals. This study isolate the crack AE signals of the drawing parts by the FastICA method based on the maximum negative entropy, the data was processed by MATLAB and the regression model of the various decomposition established by time-series analysis to extract the characteristic parameters of the crack AE signals. The results suggested that this method can isolate the crack AE signals of the deep drawing successfully and can extract the characteristic parameters and distribution maps of the crack AE signals of the metal drawing parts effectively, provide a favorable basis for the judgment of the molding part quality.

Noninvasive real-time breath test for controlling hormonal background of the human body: Detection of serotonin and melatonin with quantum point-contact sensors

2021 ◽  
Author(s):  
Lyudmila Kamarchuk ◽  
Alexander Pospelov ◽  
Dmytro Harbuz ◽  
Victor Belan ◽  
Yuliya Volkova ◽  
...  
Keyword(s):  
Real Time ◽  
Human Body ◽  
Breath Test ◽  
High Efficiency ◽  
Point Contact ◽  
Diagnostic Tools ◽  
Blood Collection ◽  
Characteristic Parameters ◽  
Energy Characteristics ◽  
Contact Sensors

Abstract Significant progress in development of noninvasive diagnostic tools based on breath analysis can be expected if one employs a real-time detection method based on finding a spectral breath profile which would contain some energy characteristics of the analyzed gas mixture. Using the fundamental energy parameters of a quantum system, it is possible to determine with a high accuracy its quantitative and qualitative composition. Among the most efficient tools to measure energy characteristics of quantum systems are sensors based on Yanson point contacts. This paper reports the results of serotonin and melatonin detection as an example of testing the human hormonal background with point-contact sensors, which have already demonstrated their high efficiency in detecting carcinogenic strains of Helicobacter pylori and selective detection of complex gas mixtures. When comparing the values of serotonin and melatonin with the characteristic parameters of the spectral profile of the exhaled breath of each patient, high correlation dependences of the concentration of serotonin and melatonin with a number of characteristic parameters of the response curve of the point-contact sensor were found. The performed correlation analysis was complemented with the regression analysis. As a result, empiric regression relations were proposed to realize in practice the new non-invasive breath test for evaluation of the human hormonal background. Registration of the patient’s breath profile using point-contact sensors makes it possible to easily monitor the dynamics of changes in the human hormonal background and perform a quantitative evaluation of serotonin and melatonin levels in the human body in real time without invasive interventions (blood collection) and expensive equipment or reagents.

Research of Feature Extraction in Mongolian Speech Based on an Improved Algorithm of MFCC Parameter

2012 ◽  
Vol 542-543 ◽  
pp. 833-837 ◽  
Author(s):  
Chomorlig ◽  
Ze Zhang ◽  
Yan Li Xiang
Keyword(s):  
Technology Development ◽  
Recognition System ◽  
Parameter Vector ◽  
Characteristic Parameters ◽  
Order Difference ◽  
First Order ◽  
Front End ◽  
Parameters Extraction ◽  
Feature Parameter ◽  
New Feature

The extraction of characteristic parameters is extremely important front-end of the speech recognition system, accurate parameters extraction of Mongolian speech have the important meaning to its recognition technology development. This paper clarified Mel frequency cepstrum coefficients(MFCC) features and extraction method at first, then it will treated with the first-order and second-order difference, after that, combining these parameters will get a new feature parameter vector. Analyze and extract this eigenvector from Mongolian speech signal by means of MATLAB. Experimental results reveal that the more the feature parameter is, the more precise the description for static and dynamic characteristics of ear to speech is.

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