The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve ex...The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient’s mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 25% decrease in sampling-rate and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best compression performance with the highest energy efficiency for random sensing matrix.展开更多
This paper presents a novel method to improve the performance of high-DOF image base visual servoing （IBVS） with an uncalibrated camera. Firstly, analysis and comparison between point-based and moment-based features...This paper presents a novel method to improve the performance of high-DOF image base visual servoing （IBVS） with an uncalibrated camera. Firstly, analysis and comparison between point-based and moment-based features are carried out with respect to a 4-DOF positioning task. Then, an extended interaction matrix （IM） related to the digital image, and a Kalman filter （KF）-based estimation algorithm of the extended IM without calibration and IM model are proposed. Finally, the KF-based algorithm is extended to realize an approximation to decoupled control scheme. Experimental results conducted on an industrial robot show that our proposed methods can provide accurate estimation of IM, and achieve similar performance compared with traditional calibration-based method. Therefore, the proposed methods can be applied to any robot control system in variational environments, and can realize instant operation to planar object with complex and unknown shape at large displacement.展开更多
Boundary Element Method (BEM) is widely used in electrocardiographic (ECG) problem. Formulations of these problems based on mathematical and numerical approximations of the known source in heart and the volume conduct...Boundary Element Method (BEM) is widely used in electrocardiographic (ECG) problem. Formulations of these problems based on mathematical and numerical approximations of the known source in heart and the volume conductor that can transfer voltages on the surface of the body. To analyze the electric potentials on body surface or epicardial surface, a set of discrete equations derived from a boundary integral equations need to be solved. Solving these equations means to get the potential distribution eventually. In the process of solving, transfer matrix of discrete equations has received considerable attention, how to get an appropriate transfer matrix is an important issue. This paper found that the direction of normal vector could affect the results when calculating the transfer matrix and presents a method analogous to Mesh Current Method to deal with this direction problem. Several simulations have been carried out to verify the accurate results with the correct direction of normal vector using new method within a torso model given simultaneous epicardial and body surface potential recordings.展开更多
Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects th...Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.展开更多
文摘The main drawback of current ECG systems is the location-specific nature of the systems due to the use of fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient’s mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 25% decrease in sampling-rate and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best compression performance with the highest energy efficiency for random sensing matrix.
基金supported by the National Natural Science Foundation of China (No. 60675048)
文摘This paper presents a novel method to improve the performance of high-DOF image base visual servoing （IBVS） with an uncalibrated camera. Firstly, analysis and comparison between point-based and moment-based features are carried out with respect to a 4-DOF positioning task. Then, an extended interaction matrix （IM） related to the digital image, and a Kalman filter （KF）-based estimation algorithm of the extended IM without calibration and IM model are proposed. Finally, the KF-based algorithm is extended to realize an approximation to decoupled control scheme. Experimental results conducted on an industrial robot show that our proposed methods can provide accurate estimation of IM, and achieve similar performance compared with traditional calibration-based method. Therefore, the proposed methods can be applied to any robot control system in variational environments, and can realize instant operation to planar object with complex and unknown shape at large displacement.
文摘Boundary Element Method (BEM) is widely used in electrocardiographic (ECG) problem. Formulations of these problems based on mathematical and numerical approximations of the known source in heart and the volume conductor that can transfer voltages on the surface of the body. To analyze the electric potentials on body surface or epicardial surface, a set of discrete equations derived from a boundary integral equations need to be solved. Solving these equations means to get the potential distribution eventually. In the process of solving, transfer matrix of discrete equations has received considerable attention, how to get an appropriate transfer matrix is an important issue. This paper found that the direction of normal vector could affect the results when calculating the transfer matrix and presents a method analogous to Mesh Current Method to deal with this direction problem. Several simulations have been carried out to verify the accurate results with the correct direction of normal vector using new method within a torso model given simultaneous epicardial and body surface potential recordings.
基金supported by National Natural Science Foundation of China(No.62076151)Natural Science Foundation of Shandong Province,China(No.ZR2020 MF052)the NSFC-Xinjiang Joint Fund,China(No.U1903127)。
文摘Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.
