Multiperson Target Dynamic Tracking Method for Athlete Training Based on Wireless Body Area Network

Wireless Body Area Network (WBAN) is a pioneer trend in healthcare technology.A WBAN consists of small sensors that may be worn or implanted on the patient's body or around them.These sensors are responsible for sending wireless, real-time physiological signs of the patient's body (such as blood pressure, heart rate, blood sugar, temperature, breathing, etc.) to an intermediate device.An intermediate device (such as a mobile phone) collects and prepares data to send and display to the doctor.In the WBAN, since data is sent through all broadcasts, any cyber attack such as eavesdropping and data modification/ sabotage can be done.Any cyber-attack on a WBAN could jeopardize the patient's health; therefore, securing the WBAN plays a crucial role in healthcare applications.Due to the limitation of energy, memory, and processing power of sensors in WBAN, it is not possible to use traditional security methods, such as encryption and security protocols.For this reason, one of the methods to provid security in the WBAN is the use of intrusion detection systems.An intrusion detection system (IDS), as a second-line defense, is one of the security methods in computer networks.Intrusion detection is the intelligent monitoring of network or computer systems to find any security breach.These systems are divided into two groups, signature-based and anomaly-based.One of the advantages 1 55 160 of anomaly-based systems is the detection of zero-day attacks.Statistical, knowledge-based, data mining and machine learning methods can be used to implement anomaly-based intrusion detection systems.In this paper, a new IDS has been presented which is able to detect denial of service (DoS) attacks in a WBAN.The proposed IDS is a hybrid anomaly-based system using machine learning and feature engineering methods.In the proposed IDS, a genetic algorithm is used to select features of collected data, in a way that increases the performance of the IDS, and as a result the WBAN (increasing detection rate and reducing energy consumption in the node).Then, using support vector machine and k nearest neighbor algorithms, the data classification is performed to detect DoS traffic from regular data traffic.Simulation results indicate that the proposed IDS has effective performance with a 90% detection rate.

This study examines the dependence of the tropical cyclone (TC) intensity errors on the track errors in the Weather Research and Forecasting (WRF-ARW) model. By using the National Centers for Environmental Prediction global final analysis as the initial and boundary conditions for cloud-resolving simulations of TC cases that have small track errors, it is found that the 2- and 3-day intensity errors in the North Atlantic basin can be reduced to 15 and 19 % when the track errors decrease to 55 and 76 %, respectively, whereas the 1-day intensity error shows no significant reduction despite more than 30 % decrease of the 1-day track error. For the North-Western Pacific basin, the percentage of intensity reduction is somewhat similar with the 2- and 3-day intensity errors improved by about 15 and 19 %, respectively. This suggests that future improvement of the TC track forecast skill in the WRF-ARW model will be beneficial to the intensity forecast. However, the substantially smaller percentages of intensity improvement than those of the track error improvement indicate that ambient environment tends to play a less important role in determining the TC intensity as compared to other factors related to the vortex initialization or physics representations in the WRF-ARW model.

Lateral Control for UAVs using Sliding Mode Technique

This paper presents sliding mode based lateral control for UAVs using piecewise linear sliding surface. A single linear surface is replaced by piecewise linear sliding surface to ensure the performance during small track errors or divergence and a bounded heading error in the case of large track error, thus avoiding the actuator saturations. Stability of the proposed sliding surface is analyzed with piecewise analytical solution of the proposed sliding surface. The proposed scheme uses only the readily available sensor information that is lateral track error and heading error angle and no succeeding mission information are required here. The proposed scheme is implemented on the 6-degrees-of-freedom (6-dof) simulation and different cases of large and small track errors are simulated in the presence/absence of wind to successfully validate the proposed control scheme.

Purpose: To assess the geometrical accuracy and estimate adequate PTV margins for liver treatments using the Synchrony respiratory tracking system.Material and methods: Treatment log files are analyzed for 72 liver patients to assess tracking accuracy. The tracking error is calculated as the quadratic sum of the correlation, the predictor and the beam positioning errors. Treatment target rotations and rigid body errors reported by the system are also evaluated. The impact of uncorrected rotations is assessed by rotating the planned dose distribution and reassessing target coverage. Total PTV margins are estimated by summing in quadrature tracking errors and rigid body errors. Relationships are explored between tracking errors, model linearity and motion amplitudes of internal and external markers.Results: Margins of 3, 2, 2 mm in SUP-INF, LT-RT and ANT-POST directions, respectively, are sufficient to account for tracking and beam positioning errors for 95% of patients. If rigid body error is also considered, margins increase to 4 mm isotropic. Rotations could not be corrected for 92% of patients due to imperfect fiducial implantation and limitations in the magnitude of corrections that the system can apply. Uncorrected rotations would lead to average estimated dose reductions of 2.7% ± 5.8% of the prescribed dose for D99 of GTVs (5 mm PTV expansion) in which the target was well covered in the original plan (28 of 31 GTVs). 80% of tracking models exhibit near linear correlation between internal and external marker motions with small tracking errors (<2.2 mm).Conclusions: Isotropic PTV margins considering tracking errors and target rigid body errors could be used for liver SBRT treatments if rotational corrections can be calculated accurately so that systematic rotational offsets can be avoided. The linearity of the internal and external breathing motions might be useful for other types of treatment modalities for liver cancer.

A pulse array image sensor (PAIS) is a bionic image sensor that converts light intensity into a pulse sequence to reduce the data volume. The traditional tracking method is not suitable for the sparse data form because of the absence of grayscale information. A trajectory tracking method based on a Bayesian classifier is proposed to maximize the property of the pulse data. First, candidate points with the smallest interval distances are selected in the area of interest. Then the total pulse numbers in a specified period at different positions are gathered to compose the positive and negative feature vectors, which are used to train a naive Bayesian classifier. The classifier can exactly obtain positions from the candidate points, and features in the new tracking positions train and update the classifier. The two-step filtering target point tracking algorithm using the interval distance and Bayesian classifier requires only the raw pulse data without processing, which can maximize the advantages of the special data format and improve computing efficiency. In this way, the position information can be directly obtained from pulse data, and the problem of wasting computing resources on reconstructing grayscale images and treating them in the traditional way is avoided. Experiments were performed on both real filmed data and the public event camera datasets. Our method can obtain trajectories with a high accuracy and long tracking time. The tracking errors are in single digits. In the comparison experiments, although our method has a smaller data volume than other models that obtain both frame and event data, the results still show that it has a comparable performance to the state-of-the-art methods.

Tracking error minimization is commonly used by the traditional passive fund managers as well as alternative portfolio (for example, hedge fund) managers. We propose a graduated non-convexity method to minimize portfolio tracking error with the total number of assets no greater than a specified integer K. The solution of this tracking error minimization problem is a global minimizer of a minimization problem with a discontinuous counting function in a constraint. We attempt to track the globally minimal tracking error portfolio by approximating the discontinuous counting function with a sequence of continuously differentiable non-convex functions, a graduated non-convexity process. We discuss the advantages of this approach, present numerical results, and compare it with two methods from recent literature.

We present a successive interference cancellation (SIC) scheme for a multicarrier (MC) DS CDMA system. In every subband, the SIC receiver successively detects the interferers' signals and substracts them from the user-of-interest. A comparison is made among SIC, matched filter detection with an equal gain combiner (MF-EGC), matched filter followed by maximal-ratio combining (MF-MRC) and minimum mean-squared error (MMSE) receivers with optimal tap coefficients. For this latter receiver, we determine the performance both when the number of taps equals the processing gain, denoted by N, and when the number of taps is roughly one-half of the processing gain. By analysis and simulation, it is shown that the SIC has better ability to suppress multiple-access interference (MAI) than do MF and [N/2]-tap MMSE in single carrier (SC) CDMA, and MF-EGC, MF-MRC and [N/2]-tap MMSE in MC CDMA, and this translates to increased capacity and better fading resistance. Further, SIC and N-tap MMSE yield approximately the same performance in SC CDMA, but SIC has a poorer performance than does an N-tap MMSE in MC CDMA. In addition, we derive results for the performance degradation that a SIC scheme experiences in MC CDMA due to both phase and timing tracking errors. It is shown that with small tracking errors, SIC can still retain a performance advantage over MF-EGC and MF-MRC, although the advantage of SIC decreases with increasing tracking errors.

The non-orthogonal shafting design is motived by the growing demand for large-scale measurement instrument, and the matching laser tracking technology is required to be completed to improve the automation and flexibility. While, due to the complexity and nonlinearity of non-orthogonal shafting architecture, it is difficult to calculate the rotation angles of the two axes based on the tracking deviation in the absence of distance information, and traditional proportional, integral and differential method cannot enable the system to adapt to unknown sudden target movement efficiently. Thus, the enhanced cascade control method for non-orthogonal shafting laser tracking system based on fuzzy control theories is proposed in this paper. Firstly, a position loop fuzzy prediction and tracking algorithm is proposed. Based on the current motion state of the target, the fuzzy controller predict the motion trend and calculate the tracking speed of the two axes. By implementing a two-stage model of coarse adjustment and fine adjustment, the responsiveness and stability of the laser tracking system have been improved. Secondly, the particle swarm optimization algorithm is adopted to conduct offline iterative optimization of the parameters of the scaling factor in the variable universe fuzzy control, achieving the rapid adaptability of the speed loop. The simulation results show that, compared with cascade PI control, this method has a faster response speed and a smaller tracking error. The experimental results show that the average dynamic tracking error and average static tracking error of this method are 1.873 58 mm and 0.026 89 mm respectively, and the fastest tracking speed of the non-orthogonal system is better than 7° s −1 .

To overcome the high position and posture angle tracking error, long tracking loss time and posture tracking update response time, and low fitness problem of traditional human motion posture tracking methods, in this paper, a three-dimensional (3D) human motion posture tracking method using multilabel transfer learning is proposed. According to the human structure composition and degree of freedom constraints, the 3D human joint skeleton model is constructed to generate the 3D human pose image and perform the noise reduction operation. The background difference is used to detect the 3D human moving target. Using multilabel transfer learning, human motion posture features are extracted from joint position and joint angle, and the estimation results of 3D human motion posture are obtained. The tracking error of human motion posture is corrected by three-step search, and the visual 3D human motion posture tracking results are output. The results show that, compared with the traditional human motion posture tracking method, the position and posture angle tracking errors of the proposed method are 2.18 mm and 0.178 deg, respectively. The tracking loss time and posture tracking update response time are shorter, which proves that the proposed method has more advantages in tracking accuracy and higher adaptability.

Model-predictive current control (MPCC) is a promising candidate for modular multilevel converter (MMC) control due to its advantages of direct modeling and fast dynamic response. The conventional MPCC, which obtains the optimal control input by evaluating a cost function for all the possible switching states, may make the MPCC impractical due to the exponentially increasing computation burden with the increasing number of submodules (SMs). On the other hand, the MPCC experiences high load current and circulating current tracking errors, since only one switching state is selected and applied during one control period. To address these issues, this paper proposes an MPCC with phase-shifted pulsewidth modulation (PS-PWM) for improving the steady-state control performance. The arm voltages are considered as a whole to implement the proposed MPCC. The optimal duty cycle is obtained based on the load and circulating current tracking error minimization and applied using the PS-PWM. As a result, the computation burden is unrelated to the number of SMs by avoiding the exhaustive evaluation process for all the possible switching states. A better steady-state performance with smaller tracking errors is achieved with the similar switching frequency, and the tedious tuning process of the weighting factor is eliminated. Experimental results are presented to demonstrate the effectiveness of the proposed MPCC.

This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.

Recently, compressed sensing (CS) has been an effective data compression technique for telemonitoring of multichannel electroencephalogram (EEG) signals through wireless body-area networks. Most of the existing multichannel EEG CS methods ignore the noise or only consider the Gaussian noise. However, there are also some other types of noise, such as heavy-tailed impulsive noise. In this paper, to overcome the above mentioned problems, we propose a novel multichannel EEG CS method based on sparse and low rank representation in the presence of mixed noise (SLRMN), which can take both Gaussian noise and impulsive noise into consideration. Moreover, we develop the alternative direction method of multipliers (ADMM) to solve the proposed SLRMN. The experimental results demonstrate the advantage of the proposed SLRMN over state-of-the-art multichannel EEG CS methods in the presence of mixed noise.

A limited-preview filtered B-spline approach to tracking control – With application to vibration-induced error compensation of a 3D printer

This paper presents the design of a sliding mode controller to compensate hysteresis nonlinearity and achieve precision motion tracking for a novel piezoelectric tip/tilt platform driven by a PZT actuator. The sliding mode control scheme is based on the unique physical characteristics of the piezoelectric tip/tilt platform. The proposed scheme effectively guides the platform state onto a predefined sliding surface and ensures its sustained movement along this manifold. This approach reduces tracking errors compared to conventional methodologies. The stability of the sliding mode control scheme is demonstrated by the Lyapunov theory framework. It achieves precise motion control with minimal tracking error on a piezoelectric tip/tilt platform. The properties of the controller have been confirmed through experimental tests. The proposed control scheme enhances the robust tracking and stability performance on the piezoelectric tip/tilt platform, outperforming traditional control schemes. Compared with the P562.6CD produced by PI Germany, the proposed innovative approach not only boosts the platform’s resolution by 32% but also implements a 33% reduction in cost.