To achieve high performance and reliability in video streaming over wireless local area networks （WLANs）, one must jointly consider both optimized association to access points （APs） and handover management based o...To achieve high performance and reliability in video streaming over wireless local area networks （WLANs）, one must jointly consider both optimized association to access points （APs） and handover management based on dynamic scanning of alternate APs. In this article, we propose a new architecture within the software-defined networking （SDN） framework, which allows stations to be connected to several APs simultaneously and to switch fast between them. We evaluate our system in a real-time testbed and demonstrate that our SDN-based handover mechanism significantly reduces the number and duration of video freeze events and allows for smaller playout buffers.展开更多
This paper presents a video-tracking system composed of two cameras. One camera acts for producing an in-focus image of the object and the other one ensures an extended vision field under monitoring. Such a system sch...This paper presents a video-tracking system composed of two cameras. One camera acts for producing an in-focus image of the object and the other one ensures an extended vision field under monitoring. Such a system scheme can achieve an excellent tracking performance,especially under various unfavorable conditions. A projection algorithm is used for the calculation of the displacement of the moving object. As a result, the computational cost is reduced greatly.In order that the tracking action of the system persists even if the object is sheltered or ns acceleration is larger than a specified threshold, a dedicated tracking algorithm on the basis of the α-β filtering is designed. Experiments show that the algorithm is efficient and the system works very well.展开更多
Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to r...Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.展开更多
Objective: To characterise flash visual evoked potentials(FVEPs) in 20 patient s with Creutzfeldt-Jacob disease (CJD),and assess the relationships between spo ntaneous EEG patterns and the responses to individual stim...Objective: To characterise flash visual evoked potentials(FVEPs) in 20 patient s with Creutzfeldt-Jacob disease (CJD),and assess the relationships between spo ntaneous EEG patterns and the responses to individual stimuli. Methods: We analy sed the shape and time course of periodic sharp wave complexes(PSWCs) and respon ses to 1 Hz flashes. In nine patients, we applied an algorithm based on an autor egressive model with exogenous input (ARX) to estimate responses to individual r andom flashes and their interaction with PSWCs. Results:The FVEPs included P1 an d N1 components in all patients, and the P2 peak in 18. Eight patients showed gi ant FVEPs (N1-P2 > 60 V), all of whom had an MM polymorphism in codon129 of the prion protein gene; in seven cases, the presence of giant FVEPs correlated with a prominent and almost continuous periodic EEG pattern. Giant N1-P2 abnormally spread on the anterior scalp regions, and had a different waveform distribution from that of the PSWCs. In five patients with a normal or slightly enlarged ave rage N1-P2 amplitude, single sweep (ARX) analysis revealed a period of relative refractoriness following individual PSWCs. In four patients with‘giant’FVEPs, the individual responses occurred regardless of the interval between the stimul us and previous PSWC, but their amplitude had an inverse relationship with the i nterval length.Conclusions: Giant responses to flash stimuli are a common findin g in CJD patients (40%of our cases). Single sweep ARX analysis showed that PSWC s were followed by a period of partial refractoriness, which prevented most of t he individual responses to flashes, but not giant FVEPs. The association between prominent spontaneous paroxysms and giant FVEPs suggests that both are due to a common hyperexcitable change favouring neuronal synchronisation. Significance: Our data contribute to clarifying the debated problem of the occurrence of giant FVEPs in CJD and their relationships with the spontaneous periodic EEG pattern.展开更多
文摘To achieve high performance and reliability in video streaming over wireless local area networks （WLANs）, one must jointly consider both optimized association to access points （APs） and handover management based on dynamic scanning of alternate APs. In this article, we propose a new architecture within the software-defined networking （SDN） framework, which allows stations to be connected to several APs simultaneously and to switch fast between them. We evaluate our system in a real-time testbed and demonstrate that our SDN-based handover mechanism significantly reduces the number and duration of video freeze events and allows for smaller playout buffers.
文摘This paper presents a video-tracking system composed of two cameras. One camera acts for producing an in-focus image of the object and the other one ensures an extended vision field under monitoring. Such a system scheme can achieve an excellent tracking performance,especially under various unfavorable conditions. A projection algorithm is used for the calculation of the displacement of the moving object. As a result, the computational cost is reduced greatly.In order that the tracking action of the system persists even if the object is sheltered or ns acceleration is larger than a specified threshold, a dedicated tracking algorithm on the basis of the α-β filtering is designed. Experiments show that the algorithm is efficient and the system works very well.
文摘Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.
文摘Objective: To characterise flash visual evoked potentials(FVEPs) in 20 patient s with Creutzfeldt-Jacob disease (CJD),and assess the relationships between spo ntaneous EEG patterns and the responses to individual stimuli. Methods: We analy sed the shape and time course of periodic sharp wave complexes(PSWCs) and respon ses to 1 Hz flashes. In nine patients, we applied an algorithm based on an autor egressive model with exogenous input (ARX) to estimate responses to individual r andom flashes and their interaction with PSWCs. Results:The FVEPs included P1 an d N1 components in all patients, and the P2 peak in 18. Eight patients showed gi ant FVEPs (N1-P2 > 60 V), all of whom had an MM polymorphism in codon129 of the prion protein gene; in seven cases, the presence of giant FVEPs correlated with a prominent and almost continuous periodic EEG pattern. Giant N1-P2 abnormally spread on the anterior scalp regions, and had a different waveform distribution from that of the PSWCs. In five patients with a normal or slightly enlarged ave rage N1-P2 amplitude, single sweep (ARX) analysis revealed a period of relative refractoriness following individual PSWCs. In four patients with‘giant’FVEPs, the individual responses occurred regardless of the interval between the stimul us and previous PSWC, but their amplitude had an inverse relationship with the i nterval length.Conclusions: Giant responses to flash stimuli are a common findin g in CJD patients (40%of our cases). Single sweep ARX analysis showed that PSWC s were followed by a period of partial refractoriness, which prevented most of t he individual responses to flashes, but not giant FVEPs. The association between prominent spontaneous paroxysms and giant FVEPs suggests that both are due to a common hyperexcitable change favouring neuronal synchronisation. Significance: Our data contribute to clarifying the debated problem of the occurrence of giant FVEPs in CJD and their relationships with the spontaneous periodic EEG pattern.
