In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different la...In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different landmarks were placed to gauge the spatial memory of C. sphinx. We changed the number of landmarks every day with 0 landmarks again on the fifth day （from 0, 2, 4, 8 to 0）. Individuals from the control group were exposed to the identical artificial foraging environment, but without landmarks. The results indicated that there was significant correlation between the time of the first foraging and the experimental days in both groups （Pearson Correlation： experimental group： r=-0.593, P〈0.01; control group： r=-0.581, P〈0.01）. There was no significant correlation between the success rates of foraging and the experimental days in experimental groups （Pearson Correlation： r=0.177, P〉0.05）, but there was significant correlation between the success rates of foraging and the experimental days in the control groups （Pearson Correlation： r=0.445, P〈0.05）. There was no significant difference for the first foraging time between experimental and control groups （GLM： F0.05,1=4.703, P〉0.05）; also, there was no significant difference in success rates of foraging between these two groups （GLM： F0.05,1=0.849,P〉0.05）. The results of our experiment suggest that spatial memory in C. sphinx was formed gradually and that the placed landmarks appeared to have no discernable effects on the memory of the foraging space.展开更多
The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this ...In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers.However,the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped.Therefore,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark.Machinelearning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space.Besides,the particle filter algorithm is also introduced to smooth the sample points in crowd paths.We implemented the approach on off-the-shelf smartphones and evaluate the performance.Experimental results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.展开更多
基金supported by the National Natural Science Foundation of China(NSFC,No30800102)Natural Science Foundation of Hainan Province(309026)
文摘In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different landmarks were placed to gauge the spatial memory of C. sphinx. We changed the number of landmarks every day with 0 landmarks again on the fifth day （from 0, 2, 4, 8 to 0）. Individuals from the control group were exposed to the identical artificial foraging environment, but without landmarks. The results indicated that there was significant correlation between the time of the first foraging and the experimental days in both groups （Pearson Correlation： experimental group： r=-0.593, P〈0.01; control group： r=-0.581, P〈0.01）. There was no significant correlation between the success rates of foraging and the experimental days in experimental groups （Pearson Correlation： r=0.177, P〉0.05）, but there was significant correlation between the success rates of foraging and the experimental days in the control groups （Pearson Correlation： r=0.445, P〈0.05）. There was no significant difference for the first foraging time between experimental and control groups （GLM： F0.05,1=4.703, P〉0.05）; also, there was no significant difference in success rates of foraging between these two groups （GLM： F0.05,1=0.849,P〉0.05）. The results of our experiment suggest that spatial memory in C. sphinx was formed gradually and that the placed landmarks appeared to have no discernable effects on the memory of the foraging space.
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
基金partially sponsored by National Key Project of China (No.2012ZX03001013-003)
文摘In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers.However,the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped.Therefore,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark.Machinelearning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space.Besides,the particle filter algorithm is also introduced to smooth the sample points in crowd paths.We implemented the approach on off-the-shelf smartphones and evaluate the performance.Experimental results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.
