To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transfo...To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.展开更多
Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities.Wearable medical devices have realized the po...Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities.Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring,however,relatively little attention is paid to accuracy and reliability.In previous study,a wearable respiration biofeedback system was designed.In this work,three kinds of signals were mixed to extract respiratory rate,i.e.,respiration inductive plethysmography(RIP),3D-acceleration and ECG.In-situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate.It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37%of a reference gold standard.Similarly studies derive respiratory rate from single-lead ECG within mean absolute percentage error 17%of a reference gold standard.展开更多
ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal ...ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RL5 adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84% of LM5 and 88% of RLS while PhysioNet was up to 98% and 96% for LMS and RLS respectively.展开更多
基金The Cultivation Fund of the Key Scientific and Technical Innovation Project of Higher Education of Ministry of Education (No.705020)
文摘To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.
基金Project(2012M510207)supported by the China Postdoctoral Science FoundationProjects(60932001,61072031)supported by the National Natural Science Foundation of China+2 种基金Project(2012AA02A604)supported by the National High Technology Research and Development Program of ChinaProject(2013ZX03005013)supported by the Next Generation Communication Technology Major Project of National Science and Technology,ChinaProject supported by the"One-hundred Talent"and the"Low-cost Healthcare"Programs of Chinese Academy of Sciences
文摘Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities.Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring,however,relatively little attention is paid to accuracy and reliability.In previous study,a wearable respiration biofeedback system was designed.In this work,three kinds of signals were mixed to extract respiratory rate,i.e.,respiration inductive plethysmography(RIP),3D-acceleration and ECG.In-situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate.It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37%of a reference gold standard.Similarly studies derive respiratory rate from single-lead ECG within mean absolute percentage error 17%of a reference gold standard.
文摘ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RL5 adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84% of LM5 and 88% of RLS while PhysioNet was up to 98% and 96% for LMS and RLS respectively.
