Automatic Tracking System Research Articles

A Muscle Load Feedback Application for Strength Training: A Proof-of-Concept Study.

Muscle overload injuries in strength training might be prevented by providing personalized feedback about muscle load during a workout. In the present study, a new muscle load feedback application, which monitors and visualizes the loading of specific muscle groups, was developed in collaboration with the fitness company Gymstory. The aim of the present study was to examine the effectiveness of this feedback application in managing muscle load balance, muscle load level, and muscle soreness, and to evaluate how its actual use was experienced. Thirty participants were randomly distributed into 'control', 'partial feedback', and 'complete feedback' groups and monitored for eight workouts using the automatic exercise tracking system of Gymstory. The control group received no feedback, while the partial feedback group received a visualization of their estimated cumulative muscle load after each exercise, and the participants in the complete feedback group received this visualization together with suggestions for the next exercise to target muscle groups that had not been loaded yet. Generalized estimation equations (GEEs) were used to compare muscle load balance and soreness, and a one-way ANOVA was used to compare user experience scores between groups. The complete feedback group showed a significantly better muscle load balance (β = -18.9; 95% CI [-29.3, -8.6]), adhered better to the load suggestion provided by the application (significant interactions), and had higher user experience scores for Attractiveness (p = 0.036), Stimulation (p = 0.031), and Novelty (p = 0.019) than the control group. No significant group differences were found for muscle soreness. Based on these results, it was concluded that personal feedback about muscle load in the form of a muscle body map in combination with exercise suggestions can effectively guide strength training practitioners towards certain load levels and more balanced cumulative muscle loads. This application has potential to be applied in strength training practice as a training tool and may help in preventing muscle overload.

Design and analyses of shipborne multifunctional Automatic Identification System (AIS) antenna

ABSTRACT This paper is introducing analyses of new design of the automatic identification system (AIS) ships antenna that will serve for both VHF-band radio and satellite tracking and identification system for maritime applications. The current AIS network is an automatic tracking system used by ships of vessel traffic service for identification and location of vessels by electronically exchanging data between nearby ships and on-shore base station. In the AIS system for the electronic data exchange, the very high frequency (VHF) range is used. Utilizing of the VHF range makes the AIS as short-range communication, identification and collision avoidance system between AIS equipped ships and base station is possible in the antennas line-of-sight . In order to widen the range of the AIS from short range up to long or global range, AIS communication should be used via a satellite. In order to provide communication via satellite as well should be designed multifunctional antenna for both radio AIS (R-AIS) and satellite AIS (S-AIS) communication antenna. The analyses and design of a new multifunctional antenna, calculation and development, including implementation and testing of helical VHF antenna for R-AIS and S-AIS equipment are discussed.

Conditioned place avoidance in the planaria Schmidtea mediterranea: A pre-clinical invertebrate model of anxiety-related disorders

The objective of the present study was to develop a model of avoidance learning and its extinction in planarians (Schmidtea mediterranea). Based on previous experiments showing conditioned place preference, we developed a procedure to investigate conditioned place avoidance (CPA) using shock as an unconditioned stimulus (US) and an automated tracking system to record the animals’ behaviour. In Experiment 1, we assessed the unconditioned properties of different shock intensities by measuring post shock activity. In two subsequent experiments we investigated CPA using different designs, surfaces as conditioned stimuli (CSs; rough and smooth), and different US intensities (5 V and 10 V). In general, we observed the successful development of CPA. However, CPA was stronger with higher shock intensities, and we found that, in our preparation, a rough surface is best at entering into an association with the shock than a smooth surface. Finally, we also observed extinction of CPA. The evidence of CPA and its extinction in flatworms validates the planaria as a pre-clinical model for the study of avoidance learning, a hallmark of anxiety disorders.

Design of Automatic Underwater Object Detection and Tracking System for Penta Tubular ROV in Blue Economy Environment

The development of underwater vehicles has opportunity to be further improved. One of the most challenging for developing underwater vehicle is developing automatic tracking system for Autonomous Underwater Vehicle (AUV). This research develops AUV with automatic object detection and tracking systems. These systems are developed by using vision-based method. The AUV is designed with Penta Tubular type whereas the automatic object detection system is started from captured image taken by underwater camera in the form of Blue-Green-Red (BGR) color space, which then will go through a masking process. The object resulted by object detection system is then to be used as followed object for automatic tracking system. Meanwhile, Automatic object tracking system is established by using two vertical lines that are used as motion parameters of the AUV. By considering the object position compared with the two vertical lines, the automatic tracking guidance system can be generated. The developed automatic object detection and tracking systems can be used well for AUV, in which verage travel time for forward and backward motion are 14.20 seconds in 0.14 m/s, and 19.82 seconds in 0.1 m/s while average travel time is 15.36 seconds and 13.58 seconds for right and left motion test respectively.

A Review of Time-Based Solar Photovoltaic Tracking Systems

Solar energy is the cleanest and most abundant form of energy that can be obtained from the Sun. Solar panels convert this energy to generate solar power, which can be used for various electrical purposes, particularly in rural areas. Maximum solar power can be generated only when the Sun is perpendicular to the panel, which can be achieved only for a few hours when using a fixed solar panel system, hence the development of an automatic solar tracking system. Over the years, different solar tracking systems have been proposed and developed, and a few have been reviewed in the literature. However, the existing review works have not adequately provided a comprehensive survey and taxonomies of these solar tracking systems to show the trends and possible further research direction. This paper aims to bridge these gaps by extensively reviewing these time-based solar tracking systems based on axis rotation and drive types. Lessons learned from the comprehensive review have been highlighted and discussed. Finally, critical open research issues are identified and elaborated.

Metabolic power and energy expenditure in the German Bundesliga.

The aims of the study were to analyze metabolic power (MP) and MP derived parameters for different positions in the German Soccer Bundesliga and to evaluate if classification of high-intensity is more suited using the metabolic power approach instead of using traditional speed-based methods. 1,345 video match analysis (25Hz) datasets from 380 players in 96 matches of the German first Bundesliga were gathered by an automatic player tracking system. Displacement (speed, acceleration, distance) and energetic (MP, energy expenditure) variables were determined. Intensity was classified utilizing conventional thresholds. Metabolic and running profiles were compared among six positional groups and between the halves of the match respectively (one-way ANOVA). Further, time spent, distance covered and energy expended at high speed (>15.5kmh-1) and high acceleration (>2ms-2) were compared to those at high MP (>20Wkg-1) (one-way ANOVA) for evaluating if metabolic power is more suited to describe intensity in team sports. Main findings are that central-attacking midfielders (CAM) and central midfielders (CM) expended more energy (CAM: 59.8 ± 4.2kJkg-1; CM: 59.6 ± 3.6kJkg-1) and covered more distance (CAM: 11,494 ± 765m; CM: 11,445 ± 638m) than all other positions (p < 0.001). In the whole group of players, less time (t) and less energy were expended (EE) in high speed (t: 302 ± 84s; EE: 10.1 ± 2.9kJkg-1) and at high acceleration zones (t: 147 ± 24s; EE: 5.5 ± 1.2kJkg-1) when compared to high MP zone (t: 617 ± 141s; EE: 20.0 ± 4.4kJkg-1) (p < 0.001). Furthermore, players covered more distance at high MP (2,464 ± 597m) than at high speed (1,638 ± 458m) and much less at high acceleration (359 ± 67m) (p < 0.001). The higher activity profiles of CAM and CM compared to the other positions indicate the need for higher developed physiological performance in players of these positions. High intensity activities should be interpreted differently when using MP and displacement parameters as indicators of high intensity in soccer.

New machine learning-based automatic high-throughput video tracking system for assessing water toxicity using Daphnia Magna locomotory responses

Daphnia magna is an important organism in ecotoxicity studies because it is sensitive to toxic substances and easy to culture in laboratory conditions. Its locomotory responses as a biomarker are highlighted in many studies. Over the last several years, multiple high-throughput video tracking systems have been developed to measure the locomotory responses of Daphnia magna. These high-throughput systems, used for high-speed analysis of multiple organisms, are essential for efficiently testing ecotoxicity. However, existing systems are lacking in speed and accuracy. Specifically, speed is affected in the biomarker detection stage. This study aimed to develop a faster and better high-throughput video tracking system using machine learning methods. The video tracking system consisted of a constant temperature module, natural pseudo-light, multi-flow cell, and an imaging camera for recording videos. To measure Daphnia magna movements, we developed a tracking algorithm for automatic background subtraction using k-means clustering, Daphnia classification using machine learning methods (random forest and support vector machine), and tracking each Daphnia magna location using the simple online real-time tracking algorithm. The proposed tracking system with random forest performed the best in terms of identification (ID) precision, ID recall, ID F1 measure, and ID switches, with scores of 79.64%, 80.63%, 78.73%, and 16, respectively. Moreover, it was faster than existing tracking systems such as Lolitrack and Ctrax. We conducted an experiment to observe the impact of toxicants on behavioral responses. Toxicity was measured manually in the laboratory and automatically using the high-throughput video tracking system. The median effective concentration of Potassium dichromate measured in the laboratory and using the device was 1.519 and 1.414, respectively. Both measurements conformed to the guideline provided by the Environmental Protection Agency of the United States; therefore, our method can be used for water quality monitoring. Finally, we observed Daphnia magna behavioral responses in different concentrations after 0, 12, 18, and 24 h and found that there was a difference in movement according to the concentration at all hours.

The Application of Big Data and Artificial Intelligence in Sports Industry

In the age of technology, intelligent and innovative sports events present a brand new look, which has affected how many enterprises operate worldwide. Traditional sports events are upgraded to artificial intelligence. While broadcasting baseball, basketball, and other games, the automatic tracking system is used to perform real-time technical analysis, and the player's course can be analyzed on the spot. In the event broadcast part, the simulation technology, virtual and real integration are widely used, and the events could also be broadcasted live on the streaming audio-visual platform. Therefore, how to use big data analysis has become a key factor for the success of professional teams or sports marketing companies. This research explores the application classification and significance of big data analysis and artificial intelligence in the field of sports by analyzing relevant literature. Through this study, the significance and application of big data, the importance of big data analysis and artificial intelligence in sports industry, and the development trend of sports big data are preliminarily understood. It is also expected that the application and development trend of sports intelligence technology could help sports industry-related companies improve their competitiveness in terms of development strategies, thereby enhancing the visibility of Taiwan's sports industry in the world and making the research and development strength of Taiwan's sports enterprises visible to the world.

Intelligent metasurface system for automatic tracking of moving targets and wireless communications based on computer vision

The fifth-generation (5G) wireless communication has an urgent need for target tracking. Digital programmable metasurface (DPM) may offer an intelligent and efficient solution owing to its powerful and flexible controls of electromagnetic waves and advantages of lower cost, less complexity and smaller size than the traditional antenna array. Here, we report an intelligent metasurface system to perform target tracking and wireless communications, in which computer vision integrated with a convolutional neural network (CNN) is used to automatically detect the locations of moving targets, and the dual-polarized DPM integrated with a pre-trained artificial neural network (ANN) serves to realize the smart beam tracking and wireless communications. Three groups of experiments are conducted for demonstrating the intelligent system: detection and identification of moving targets, detection of radio-frequency signals, and real-time wireless communications. The proposed method sets the stage for an integrated implementation of target identification, radio environment tracking, and wireless communications. This strategy opens up an avenue for intelligent wireless networks and self-adaptive systems.

Detection and Tracking of Broiler Flock Movements in The Chicken Coop using YOLO

Observation of the movement of broilers in the chicken coop is done to monitor the welfare and health condition of broilers. Currently, observing broiler flock activity in the chicken coop is generally still done conventionally, with manual observations made by farmers. But on a large scale, this observation method takes a lot of time and manpower, and is subjective. Therefore, an automatic observation system is needed that continuously monitors broiler activity, so as to increase the efficiency of farmer resources and reduce operational costs for observing broiler activity. This study developed an automatic detection and tracking system for broiler chicken movements using You Only Look Once, Version 4 (YOLOv4) as the base model, Yolo Weights as the Transfer Learning Pretrained Model, and Deep Sort as the Tracker Model. For comparison of base models, use Single Shot Multibox Detector (SSD), You Only Look Once, Version 3 (YOLOv3), You Only Look Once, Version 4 - tiny (YOLOv4-tiny). For comparison, the Network Model uses MobileNet and MobileNet v2. For comparison of Transfer Learning Pretrained Model using Caffe Model Weights and Tensorflow Weights. For comparison of Tracker Models using Centroid Tracker, Centroid Kalman Filter Tracker, Simple Online and Realtime Tracking (SORT) and Intersection over Union (IOU). The results showed that Model You Only Look Once, Version 4 (YOLOv4) with Transfer Learning Pretrained Model = Yolo Weights, and Tracker Model = Deep SORT was able to detect and track the most chicken herds in cages compared to others, with the number of broilers detected as many as 17.

Disturbance Rejection Extremum Seeking: A Sliding Mode Control Approach

This paper investigates the sliding mode extremum seeking control problem for nonlinear systems with disturbances to improve the disturbance rejection capability, delivering promising searching accuracy and efficiency under disturbances. The existing results in sliding mode extremum seeking control suffer from the drawback of unsatisfactory transient performance and slow convergence speed when disturbances, especially unmatched disturbances, are injected into the system. To this end, a disturbance rejection sliding mode extremum seeking control approach is proposed in this paper. A nonlinear disturbance observer is developed for disturbance estimation, and then a disturbance rejection sliding mode extremum seeking control algorithm is proposed by exploiting the disturbance estimation. Under the proposed approach, the disturbances can be online estimated and compensated. As a result, the cost function is less disturbed by unknown disturbances, and faster convergence speed as well as shorter recovery time can be obtained. The proposed method is applied for directional antenna automatic tracking system. Simulation results show that the newly proposed searching control approach outperforms the baseline extremum searching approaches in terms of much more reliable and high efficiency searching performance under disturbances.

A Lightweight Transformer-Based Approach of Specific Emitter Identification for the Automatic Identification System

The automatic identification system (AIS) is the automatic tracking system for automatic traffic control and collision avoidance services, which plays an important role in maritime traffic safety. However, it faces a possible security threat when the maritime mobile service identity (MMSI) that specifies the vessels’ identity in AIS is illegally counterfeited. To guarantee the communication security of AIS for preventing fraudulent devices, we design a novel lightweight Transformer-based network GLFormer for specific emitter identification (SEI) to provide an extra security layer for AIS terminal emitters. Concretely, the gated local attention unit (GLAU) and the gated sliding local attention unit (GSLAU) modules that combine a simplified gated attention unit (GAU) and a sliding local self-attention (SLA) are developed in GLFormer to extract the radio frequency fingerprint (RFF) features automatically from the raw in-phase signals. Especially, the simplified GAU focuses on more critical RFF features and filters out the irrelevant information from the raw signal to improve performance, which is also a single-head self-attention module with fewer parameters for lightweight. Meanwhile, the SLA limits self-attention operation to a window, introducing the inductive bias of local information to enhance performance further and reducing the quadratic computational complexity to linearity for efficiency. Experimental results demonstrate that the GLFormer achieves 96.31% and 89.38% identification accuracy in the constructed AIS transient and AIS steady-state datasets with 50 vessels, respectively. The 99.90% identification accuracy is achieved in the universal software radio peripheral (USRP) dataset with ten devices. It is not only better than the existing methods but requires much fewer parameters and lower computational complexity; besides, it is also suitable for working with long signal sequences.

ICACE2022] Automatic hand tracking system control with 4-axis light stand for surgical light

ICACE2022] Automatic hand tracking system control with 4-axis light stand for surgical light

Improving the performance of a modified solar distiller with phase change material and parabolic trough collector.

The solar stills can solve the problem of freshwater shortage for the people living in remote areas. Through this work, the performance of hybrid solar desalination arrangement was investigated. The system comprised of parabolic trough solar collector, automatic solar tracking system, separation room, modified solar still, two condensation units, feed water tank, and supplementary and measuring tools. The effect of using various water flow rates on the effectiveness of parabolic trough solar collector was tested. Also, the influences of integrating condenser to the modified solar still with and without phase change material were studied. The experimental results revealed that the best flow rate for parabolic trough solar collector was obtained as 10 L/h (100 L/day), where its effectivity was 61%. Under this condition, the obtained freshwater was around 61 L/day from a total saline water of 100 L/day. In addition, integrating a condenser to the modified solar still improved its productivity. So, the total yields of conventional and modified solar stills were 2500 and 5145mL/m2.day, respectively. Therefore, the distillate of modified solar still was improved by 105.8% as compared to that of the conventional solar still due to using the hot feed water and external condenser. Moreover, using phase change material enhanced the modified solar still yield. The total yield of conventional solar still and modified solar still with hot feed water, condenser, and phase change material was 2575 and 6150mL/m2.day, respectively. Consequently, the productivity of the modified solar still with hot feed water, condenser, and phase change material was improved by around 138.83% over that of the conventional solar still. Finally, the conventional solar still had an average efficiency of 31.5%. Also, when using the hot feed water and external condenser with the modified solar still, the thermal efficiency was reported as 49.7%. Also, the modified solar still with hot water, condenser, and phase change material had a thermal efficiency of 56.5%. Finally, the water costs of 1 L from the CSS and MSS are 0.063 $ and 0.050 $, respectively.

Solar tracker design on solar panel for stm32 microcontroller based on battery charging system

This paper describes about solar energy is one of the energy sources that won’t terminated. Solar energy can be easily converted into electrical energy by using solar panels. Solar panels that are placed horizontally on the ground, the solar panel cannot absorb the light perfectly. Therefore, solar panels require an automatic solar tracking system to increase the efficiency of the solar panels. In this study, a solar tracker has been designed using a light dependent resistor (ldr) sensor based on the stm32 microcontroller. From the results of the study, the increase in power obtained from the use of a solar tracker was 27.97%. In static solar panels, the time required to fully charge the 12v, 7ah lead acid battery is 5 hours 10 minutes, while using a solar tracker the lead acid battery charging time is 3 hours 30 minutes. The use of a solar tracker saves battery charging time for 1 hour 40 minutes compared to static solar panels.

Automatic Detection and Counting System for Pavement Cracks Based on PCGAN and YOLO-MF

The regular detection of pavement cracks is critical for life and property security. However, existing deep learning-based methods of crack detection face difficulties in terms of data acquisition and defect counting. An automatic intelligent detection and tracking system for pavement cracks is proposed. Our system is formed of a pavement crack generative adversarial network (PCGAN) and a crack detection and tracking network called YOLO-MF. First, PCGAN is used to generate realistic crack images, to address the problem of the small number of available images. Next, YOLO-MF is developed based on an improved YOLO v3 modified by an acceleration algorithm and median flow (MF) algorithm to count the number of cracks. In a counting loop, our improved YOLO v3 detects cracks and the MF algorithm tracks the cracks detected in a video. This improved algorithm achieves the best accuracy of 98.47% and F1 score of 0.958 among other algorithms, and the precision-recall curve was close to the top right. A tiny model was developed and an acceleration algorithm was applied, which improved the detection speed by factors of five and six, respectively. In on-site measurement, three cracks were detected and tracked, and the total count was correct. Finally, the system was embedded in an intelligent device consisting of a calculating module, an automated unmanned aerial vehicle, and other components.

Ship Classification in Synthetic Aperture Radar Images Based on Multiple Classifiers Ensemble Learning and Automatic Identification System Data Transfer Learning

With the continuous development of earth observation technology, space-based synthetic aperture radar (SAR) has become an important source of information for maritime surveillance, and ship classification in SAR images has also become a hot research direction in the field of maritime ship monitoring. In recent years, the remote sensing community has proposed several solutions to the problem of ship object classification in SAR images. However, it is difficult to obtain an adequate amount of labeled SAR samples for training classifiers, which limits the application of machine learning, particularly deep learning methods, in SAR image ship object classification. In contrast, as a real-time automatic tracking system for monitoring ships at sea, a ship automatic identification system (AIS) can provide a large amount of relatively easy-to-obtain labeled ship samples. Therefore, to solve the problem of SAR image ship classification and improve the classification performance of learning models with limited samples, we proposed a SAR image ship classification method based on multiple classifiers ensemble learning (MCEL) and AIS data transfer learning. The core idea of our method is to transfer the MCEL model trained on AIS data to SAR image ship classification, which mainly includes three steps: first, we use the acquired global space-based AIS data to build a dataset for ship object classification models training; then, the ensemble learning model is constructed by combining multiple base classifiers; and finally, the trained classification model is transferred to SAR images for ship type prediction. Experiments show that the proposed method achieves a classification accuracy of 85.00% for the SAR ship classification, which is better than the performance of each base classifier. This proves that AIS data transfer learning can effectively solve the problem of SAR ship classification with limited samples, and has important application value in maritime surveillance.

Automatic Bug Classification System to Improve the Software Organization Product Performance

Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracking system (ABTS) to allocate the group for the revealed bug involving the text examination for bug naming and characterization AI calculation for anticipating designer. Hybrid natural language processing and machine learning techniques are used for automatic bug identification to improve the performance of software organization products.

Fast frequency relocking for synchronization enhanced resonant accelerometer

Synchronization, as a unique phenomenon, has been extensively studied in biology, chaotic systems, nonlinear dynamics, quantum information, and other fields. Benefiting from the characteristics of frequency amplification, noise suppression, and stability improvement, synchronization has been gradually applied in sensing, communication, time keeping, and other applications. In the sensing field, synchronization provides a new strategy to improve the performance of sensors. However, the performance improvement is only effective within the synchronization range, and the narrow synchronization range has become a great challenge for the wide application of synchronization-enhanced sensing mechanism. Here, we propose a frequency automatic tracking system (FATS) to widen the synchronization range and track the periodic acceleration signals by adjusting the frequency of the readout oscillator in real time. In addition, a high-precision frequency measurement system and fast response control system based on FPGA (Field Programmable Gate Array) are built, and the tracking performance of the FATS for static and dynamic external signals is analyzed to obtain the optimal control parameters. Experimental results show that the proposed automatic tracking system is capable of static acceleration measurement, the synchronization range can be expanded to 975 Hz, and the relocking time is shortened to 93.4 ms at best. By selecting the optimal PID parameters, we achieve a faster relocking time to meet the requirements of low-frequency vibration measurements, such as seismic detection and tidal monitoring.

Development and integration of laser sensor tracking system in robotic arm for path correction during welding operation

&lt;span lang="EN-US"&gt;An industrial robot is mainly used for manufacturing. Industrial robots are 6 or more axes, which can be automatically controlled by programming. Typical applications of robots include welding, painting, picking, and placing printed circuit boards, packaging and labelling, palletizing, product inspection, and testing with high accuracy, precision, and fast speed. Robotic welding is a complex, nonlinear and time varying process which can be affected by various natural or any random disturbances. Due to the effect of various factors, the actual welding path may differ. So, welding robots should be able to detect the actual welding path, then adjust the difference in welding path and complete the welding process accurately. Laser welding is one of the most important technologies in the manufacturing field. It is the most frequently used technology which has made new demands. So, the manufacturer ensures to meet the quality of laser welding and improve the production efficiency. Due to the increase in demand of quality, accuracy, precise, productivity, flexibility and adaptive control of welding robot, an automatic laser seam tracking system is developed with welding robot to precisely follow the welding path and make the necessary corrections during welding operations.&lt;/span&gt;