scholarly journals Development of an innovative 9 GHz EPR surface detection method and its application to non-invasive human fingers and nails investigation

2015 ◽  
Vol 150 ◽  
pp. 461-464 ◽  
Author(s):  
Kouichi Nakagawa
Keyword(s):  
Detection Method ◽  
Non Invasive ◽  
Surface Detection

Removing Blinks in Video-Oculography

2012 ◽  
Vol 239-240 ◽  
pp. 1165-1168
Author(s):  
Xue Jun Chen ◽  
Chen Hua Zhang
Keyword(s):  
Kalman Filter ◽  
Eye Movement ◽  
Detection Method ◽  
Threshold Level ◽  
Experimental Results ◽  
Video Oculography ◽  
Non Invasive ◽  
Pupil Center ◽  
Simulation And Experiment ◽  
Blink Artifact

Video-oculography (VOG) is a non-invasive detection method used for eye movement. However, during testing, if object blinks, VOG would be difficult to acquire eye movement. A removing blink method based on Kalman Filter was presented. A cubic spline was employed to patch the removed data. Then simulation and experiment were done. The experimental results show that the method well predicts the next state. Compared to a threshold level, it eliminates blink artifact and patches the removed data. The method is a viable means of predicting pupil center for blink in VOG.

A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data

2014 ◽  
Vol 140 ◽  
pp. 704-716 ◽  
Author(s):  
J.-F. Pekel ◽  
C. Vancutsem ◽  
L. Bastin ◽  
M. Clerici ◽  
E. Vanbogaert ◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Water Surface ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Surface Detection

scholarly journals Application of a New Non-Invasive Skin Cholesterol Detection Technology in Atherosclerosis Screening

2020 ◽  
Author(s):  
Jingshu Ni ◽  
Haiou Hong ◽  
Yang Zhang ◽  
Shiqi Tang ◽  
Yongsheng Han ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
High Risk ◽  
Roc Curve ◽  
Detection Method ◽  
Risk Group ◽  
Cholesterol Content ◽  
Normal Group ◽  
Disease Group ◽  
Non Invasive ◽  
Cholesterol Detection

Abstract Background: Establishing a high-accuracy and non-invasive method is essential for evaluating cardiovascular disease. Skin cholesterol is a novel marker for assessing the risk of atherosclerosis and can be used as an independent risk factor for early assessment of atherosclerotic risk.Methods: we propose a non-invasive skin cholesterol detection method based on absorption spectroscopy. Detection reagents specifically bind to skin cholesterol and react with indicator to produce colored products, the skin cholesterol content can be obtained through absorption spectrum information of colored products detected by noninvasive technology. Gas chromatography is used to measure cholesterol extracted from the skin to verify the accuracy of the noninvasive test method. A total of 163 subjects were divided into normal group(n=58), disease group (n=26) and risk group(n=79). All subjects underwent noninvasive skin cholesterol test. The diagnostic accuracy of the measured value was analyzed by receiver-operating characteristic (ROC) curve.Results: The proposed method is able to identify porcine skin containing gradient concentration of cholesterol and the values measured by non-invasive detection method were significantly correlated with gas chromatography measured results (r=0.9074, n=73, p<0.001). We further evaluated the method on patients with atherosclerosis and high risk population as well as normal group, patients and high risk atherosclerosis group exhibited higher skin cholesterol content than normal group (all P<0.001). The area under the ROC curve for distinguishing Normal/Disease group was 0.8243(95% confidence interval, 0.7165 to 0.9321), however, the area under the ROC curve for distinguishing Normal/Risk group was 0.8488(95% confidence interval, 0.7793 to 0.9182). Conclusions: The method demonstrated its capability of detecting different concentration of skin cholesterol. This non-invasive skin cholesterol detection system may potentially be used as a risk assessment tool for atherosclerosis screening, especially in a large population.

scholarly journals Automating, Analyzing and Improving Pupillometry with Machine Learning Algorithms

2019 ◽  
Vol 24 (2) ◽  
pp. 197-209
Author(s):  
György Kalmár ◽  
Alexandra Büki ◽  
Gabriella Kékesi ◽  
Gyöngyi Horváth ◽  
László G. Nyúl
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Nervous System ◽  
Detection Method ◽  
Infrared Camera ◽  
Machine Learning Algorithms ◽  
Invasive Technique ◽  
Non Invasive ◽  
Pupillary Light ◽  
Pupil Detection

The investigation of the pupillary light reflex (PLR) is a well-known method to provide information about the functionality of the autonomic nervous system. Pupillometry, a non-invasive technique, was applied to study the PLR alterations in a new, schizophrenia-like rat substrain, named WISKET. The pupil responses to light impulses were recorded with an infrared camera; the videos were automatically processed and features were extracted. Besides the classical statistical analysis (ANOVA), feature selection and classification were applied to reveal the significant differences in the PLR parameters between the control and WISKET animals. Based on these results, the disadvantages of this method were analyzed and the measurement process was redesigned and improved. The automated pupil detection method has also been adapted to the new videos. 2564 images were annotated manually and used to train a fully-convolutional neural network to produce pupil mask images. The method was evaluated on 329 test images and achieved 4% median relative error. With the new setup, the pupil detection became reliable and the new data acquisition offers robustness to the experiments.

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