Neuro signal has many more advantages than myoelectricity in providing information for prosthesis control, and can be an ideal source for developing new prosthesis. In this work, by implanting intrafascicular electrod...Neuro signal has many more advantages than myoelectricity in providing information for prosthesis control, and can be an ideal source for developing new prosthesis. In this work, by implanting intrafascicular electrode clinically in the amputee’s upper extremity, collective signals from fascicules of three main nerves (radial nerve, ulnar nerve and medium nerve) were suc- cessfully detected with sufficient fidelity and without infection. Initial analysis of features under different actions was performed and movement recognition of detected samples was attempted. Singular value decomposition features (SVD) extracted from wavelet coefficients were used as inputs for neural network classifier to predict amputee’s movement intentions. The whole training rate was up to 80.94% and the test rate was 56.87% without over-training. This result gives inspiring prospect that col- lective signals from fascicules of the three main nerves are feasible sources for controlling prosthesis. Ways for improving accu- racy in developing prosthesis controlled by neuro signals are discussed in the end.展开更多
With high spatial resolution,on-demand-flying ability,and the capacity for obtaining threedimensional measurements,unmanned aerial vehicle(UAV)photogrammetry is widely used for detailed investigations of single landsl...With high spatial resolution,on-demand-flying ability,and the capacity for obtaining threedimensional measurements,unmanned aerial vehicle(UAV)photogrammetry is widely used for detailed investigations of single landslides,but its effectiveness for landslide detection and monitoring in a large area needs to be investigated.The Heifangtai terrace in the Loess Plateau of China is a loess terrace that is extremely susceptible to irrigation-induced loess landslides.This paper used UAV-based photogrammetry for a series of highresolution images spanning over 30 months for landslide detection and monitoring of the terrace with an area of 32 km^2.Dense and evenly distributed ground control points were established and measured to ensure the high accuracy of the photogrammetry results.The structure-from-motion(Sf M)technique was used to convert overlapping images into orthographic images,3D point clouds,digital surface models(DSMs)and mesh models.Using multitemporal differential mesh models,landslide vertical movements and potential landslides were detected and monitored.The results indicate that a combination of UAV-based orthophotos and differential mesh models can be used for flexible and accurate detection and monitoring of potential loess landslides in a large area.展开更多
The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood.Several cyber-attacks lead to the compromise of data security.The proposed system offers c...The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood.Several cyber-attacks lead to the compromise of data security.The proposed system offers complete data protection from Advanced Persistent Threat(APT)attacks with attack detection and defence mechanisms.The modified lateral movement detection algorithm detects the APT attacks,while the defence is achieved by the Dynamic Deception system that makes use of the belief update algorithm.Before termination,every cyber-attack undergoes multiple stages,with the most prominent stage being Lateral Movement(LM).The LM uses a Remote Desktop protocol(RDP)technique to authenticate the unauthorised host leaving footprints on the network and host logs.An anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of LM.After extracting various feature sets from the logs,the RDP sessions are classified using machine-learning techniques with high recall and precision.It is found that the AdaBoost classifier offers better accuracy,precision,F1 score and recall recording 99.9%,99.9%,0.99 and 0.98%.Further,a dynamic deception process is used as a defence mechanism to mitigateAPTattacks.A hybrid encryption communication,dynamic(Internet Protocol)IP address generation,timing selection and policy allocation are established based on mathematical models.A belief update algorithm controls the defender’s action.The performance of the proposed system is compared with the state-of-the-art models.展开更多
Objective:Explore the feasibility of the high precision accelerometer for measuring the human respiratory displacement.Methods:A wireless acceleration acquisition system with the low power consumption and the high pre...Objective:Explore the feasibility of the high precision accelerometer for measuring the human respiratory displacement.Methods:A wireless acceleration acquisition system with the low power consumption and the high precision was designed with the high precision acceleration sensor ADXL355 as the core device.Based on the frequency characteristics of the breathing motion and the principle that the displacement can be calculated by the acceleration quadratic integration,two displacement measurement algorithms for the quasi-periodic weak motion are designed.Results:The simulation results show that the proposed algorithm is effective.The experimental results show that the designed acquisition system and algorithm can calculate the human respiratory displacement.Conclusion:The high precision accelerometer can be used to measure the human respiratory displacement,which provides a new method for the measurement of the human respiratory displacement.展开更多
Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment...Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment, and traffic demand estimation. However, it is very time consuming and costly to obtain vehicle turning movement information manually. Previous efforts to simplify this process were focused on solving the problem using an O-D matrix, but this method proved to be inaccurate and unreliable with the existing data acquisition system. Another study involved the identification of vehicle turning movements from the detector information, but the presence of shared lanes led to uncertainties in vehicle matching, thus limiting application of the method only to intersections without shared lanes. In light of those unsuccessful attempts, this paper develops and tests a system called the Automatic Turning Movement Identification System (ATMIS), which estimates vehicle turning movements at a signalized intersection in real time, regardless of its geometry. The results from lab experiments as well as a field test show that the algorithm is very promising and may potentially be expanded for field applications.展开更多
A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental e...A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.展开更多
With the rapid advancements in artificial intelligence and computer vision technology,thefield of visual-based human pose detection has emerged as a highly sought-after research area in recent years.The identification of...With the rapid advancements in artificial intelligence and computer vision technology,thefield of visual-based human pose detection has emerged as a highly sought-after research area in recent years.The identification of human poses has practical applications in diverse domains,ranging from motion-sensing games for human-computer interaction to activity prediction and medical rehabil-itation.The present study is focused on the utilization of human pose detection forfitness movement counting.The ultimate aim of the system design is to accurately detect the skeletal key points of each body part in the image and subsequently con-nect them to form a human pose skeleton,which serves as a vital representation of the characteristics of human motion,particularly in the context of video data,where multiple human poses can be linked to form a certain movement trajectory.By judging the trajectory and angle changes,the system can determine whether people’sfitness movements are correct and help them improve theirfitness effec-tiveness.Hence,an increasing number of researchers are investing time and effort in thisfield.One common approach for human pose detection is OpenPose,but this model has a large and complex structure and low detection accuracy.Therefore,thisfitness movement detection and counting system uses a lightweight MediaPipe model and improves it to enhance the algorithm’s accuracy and recognition speed.The specific work in this paper includes three main points:(1)a suitable network structure to detect human skeletal points;(2)the appropriate skeletal structure forfitness movements through experiments to obtain accurate results;and(3)a Qt interface for human-computer interaction.展开更多
基金Project (No. 39930070) supported by the National Natural Science Foundation of China
文摘Neuro signal has many more advantages than myoelectricity in providing information for prosthesis control, and can be an ideal source for developing new prosthesis. In this work, by implanting intrafascicular electrode clinically in the amputee’s upper extremity, collective signals from fascicules of three main nerves (radial nerve, ulnar nerve and medium nerve) were suc- cessfully detected with sufficient fidelity and without infection. Initial analysis of features under different actions was performed and movement recognition of detected samples was attempted. Singular value decomposition features (SVD) extracted from wavelet coefficients were used as inputs for neural network classifier to predict amputee’s movement intentions. The whole training rate was up to 80.94% and the test rate was 56.87% without over-training. This result gives inspiring prospect that col- lective signals from fascicules of the three main nerves are feasible sources for controlling prosthesis. Ways for improving accu- racy in developing prosthesis controlled by neuro signals are discussed in the end.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.41521002,41941019,41630640)the Major R&D projects of Sichuan Science and Technology Plan(Grant No.2018SZ0339)the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Grant No.SKLGP2014Z004)。
文摘With high spatial resolution,on-demand-flying ability,and the capacity for obtaining threedimensional measurements,unmanned aerial vehicle(UAV)photogrammetry is widely used for detailed investigations of single landslides,but its effectiveness for landslide detection and monitoring in a large area needs to be investigated.The Heifangtai terrace in the Loess Plateau of China is a loess terrace that is extremely susceptible to irrigation-induced loess landslides.This paper used UAV-based photogrammetry for a series of highresolution images spanning over 30 months for landslide detection and monitoring of the terrace with an area of 32 km^2.Dense and evenly distributed ground control points were established and measured to ensure the high accuracy of the photogrammetry results.The structure-from-motion(Sf M)technique was used to convert overlapping images into orthographic images,3D point clouds,digital surface models(DSMs)and mesh models.Using multitemporal differential mesh models,landslide vertical movements and potential landslides were detected and monitored.The results indicate that a combination of UAV-based orthophotos and differential mesh models can be used for flexible and accurate detection and monitoring of potential loess landslides in a large area.
文摘The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood.Several cyber-attacks lead to the compromise of data security.The proposed system offers complete data protection from Advanced Persistent Threat(APT)attacks with attack detection and defence mechanisms.The modified lateral movement detection algorithm detects the APT attacks,while the defence is achieved by the Dynamic Deception system that makes use of the belief update algorithm.Before termination,every cyber-attack undergoes multiple stages,with the most prominent stage being Lateral Movement(LM).The LM uses a Remote Desktop protocol(RDP)technique to authenticate the unauthorised host leaving footprints on the network and host logs.An anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of LM.After extracting various feature sets from the logs,the RDP sessions are classified using machine-learning techniques with high recall and precision.It is found that the AdaBoost classifier offers better accuracy,precision,F1 score and recall recording 99.9%,99.9%,0.99 and 0.98%.Further,a dynamic deception process is used as a defence mechanism to mitigateAPTattacks.A hybrid encryption communication,dynamic(Internet Protocol)IP address generation,timing selection and policy allocation are established based on mathematical models.A belief update algorithm controls the defender’s action.The performance of the proposed system is compared with the state-of-the-art models.
文摘Objective:Explore the feasibility of the high precision accelerometer for measuring the human respiratory displacement.Methods:A wireless acceleration acquisition system with the low power consumption and the high precision was designed with the high precision acceleration sensor ADXL355 as the core device.Based on the frequency characteristics of the breathing motion and the principle that the displacement can be calculated by the acceleration quadratic integration,two displacement measurement algorithms for the quasi-periodic weak motion are designed.Results:The simulation results show that the proposed algorithm is effective.The experimental results show that the designed acquisition system and algorithm can calculate the human respiratory displacement.Conclusion:The high precision accelerometer can be used to measure the human respiratory displacement,which provides a new method for the measurement of the human respiratory displacement.
文摘Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment, and traffic demand estimation. However, it is very time consuming and costly to obtain vehicle turning movement information manually. Previous efforts to simplify this process were focused on solving the problem using an O-D matrix, but this method proved to be inaccurate and unreliable with the existing data acquisition system. Another study involved the identification of vehicle turning movements from the detector information, but the presence of shared lanes led to uncertainties in vehicle matching, thus limiting application of the method only to intersections without shared lanes. In light of those unsuccessful attempts, this paper develops and tests a system called the Automatic Turning Movement Identification System (ATMIS), which estimates vehicle turning movements at a signalized intersection in real time, regardless of its geometry. The results from lab experiments as well as a field test show that the algorithm is very promising and may potentially be expanded for field applications.
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2014AA123103)
文摘A Wi-Fi fingerprinting localization approach has attracted increasing attention in recent years due to the ubiquity of Access Point( AP). However,typical fingerprinting localization methods fail to resist accidental environmental changes,such as AP movement. In order to address this problem,a robust fingerprinting indoor localization method is initiated. In the offline phase,three attributes of Received Signal Strength Indication( RSSI) —average,standard deviation and AP's response rate—are computed to prepare for the subsequent computation. In this way,the underlying location-relevant information can be captured comprehensively. Then in the online phase, a three-step voting scheme-based decision mechanism is demonstrated, detecting and eliminating the part of AP where the signals measured are severely distorted by AP 's movement. In the following localization step,in order to achieve accuracy and efficiency simultaneously,a novel fingerprinting localization algorithm is applied. Bhattacharyya distance is utilized to measure the RSSI distribution distance,thus realizing the optimization of MAximum Overlapping algorithm( MAO). Finally,experimental results are displayed,which demonstrate the effectiveness of our proposed methods in eliminating outliers and attaining relatively higher localization accuracy.
文摘With the rapid advancements in artificial intelligence and computer vision technology,thefield of visual-based human pose detection has emerged as a highly sought-after research area in recent years.The identification of human poses has practical applications in diverse domains,ranging from motion-sensing games for human-computer interaction to activity prediction and medical rehabil-itation.The present study is focused on the utilization of human pose detection forfitness movement counting.The ultimate aim of the system design is to accurately detect the skeletal key points of each body part in the image and subsequently con-nect them to form a human pose skeleton,which serves as a vital representation of the characteristics of human motion,particularly in the context of video data,where multiple human poses can be linked to form a certain movement trajectory.By judging the trajectory and angle changes,the system can determine whether people’sfitness movements are correct and help them improve theirfitness effec-tiveness.Hence,an increasing number of researchers are investing time and effort in thisfield.One common approach for human pose detection is OpenPose,but this model has a large and complex structure and low detection accuracy.Therefore,thisfitness movement detection and counting system uses a lightweight MediaPipe model and improves it to enhance the algorithm’s accuracy and recognition speed.The specific work in this paper includes three main points:(1)a suitable network structure to detect human skeletal points;(2)the appropriate skeletal structure forfitness movements through experiments to obtain accurate results;and(3)a Qt interface for human-computer interaction.
