Automatic Tracking System Research Articles

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

<span lang="EN-US">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.</span>

Motion Pattern Classification on Online/Active Data-Machine Learning

Abstract: Ship behaviour recognition and prediction is very important for the early warning of risky behaviour, identifying potential ship collision, improving maritime traffic efficiency etc., and thus is a very active topic in the intelligent maritime navigation community. The high flow of vessel traffic affects the difficulty of monitoring vessel in the middle of the sea because of limited human visibility, occurrence of vessel accidents at the sea and other illegal activities that illustrate abnormal vessel behaviour such as oil bunkering, piracy, illegal fishing and other crimes that will continue and will certainly have an impact on losses in several aspects. An existing system involves, Automatic Identification System (AIS) for short-range operation, Long-Range Identification and Tracking (LRIT), Vessel Monitoring System (VMS) are widely used automatic reporting systems for the ship/vessels. Further few classification algorithms like Bayesian, CNN and many other methods which does not permit to draw definite conclusions about the overall effectiveness of the identification procedure because of noise level. Automatic identification system (AIS) trajectory will collect data from multiple sensors that record dynamic and static ship information. AIS sequences (and records) are affected by subjective ship-officer behavior such as collision-avoidance decisionmaking and good seamanship

Developing a dual axis photoelectric tracking module using a multi quadrant photoelectric device

To determine the distance, presence, or absence of the object by using a transmitter or photoelectric receiver the photoelectric device is used. The potential of a photovoltaic (PV) panel to produce power is roughly dependent on the intensity of sunlight falling on it. This study planned and constructed a dual-axis solar programmable logical controller (PLC) based automatic tracking system, as well as its management and signal conditioning. The suggested automated monitoring system regulates the elevation and orientation angles of solar panels to keep them transverse to the sun at all times. The capacity of the pouring source to right path the position of the sunlight is detracting for the proper functioning of warm excited lasers, which is an essential resource of absorbing light part of every 24 h. The current pursuing electronics, in another way, has poor strength and pursuing accuracy. To view the same issue, a photoelectric tracking system accompanying qual pivot is ingrained. The proposed system is based on the idiosyncratic multi quadrant photoelectric detector is implemented and a tracking scheme with a solar orbital method is included. The cosmic at fixed intervals monitoring device’s control design, in pair with the fine bringing into the harmony of the multi-one of four equal parts photoelectric indicator, substantially increases the effectiveness of the projected model. The pump source’s accurate positioning and tracking capabilities to the solar azimuth enable the pump source’s system reliability.

Economics and Environment Assessment for Design and Implementation of An Automatic Solar Tracking System

Economics and Environment Assessment for Design and Implementation of An Automatic Solar Tracking System

Gain-Scheduled Steering and Braking Coordinated Control in Path Tracking of Intelligent Heavy Vehicles

Abstract In the context of intelligent transportation system and advanced vehicle control system, higher requirements for intelligent control and coordinated control of heavy vehicles (HVs) have been proposed. Automatic path tracking control is essential for automatic driving of heavy vehicles. However, uncertain lateral disturbances and time-varying characteristics of system parameters make it difficult to guarantee roll stability and path tracking accuracy during automatic path tracking. This paper investigates the coordination of active front steering (AFS) and direct yaw moment control (DYC) in the automatic path tracking system for intelligent heavy vehicles. The main idea is to adjust the braking action according to the rollover risk evaluated by a specific rollover index (RI). The coordination of steering and braking systems and the robustness of the system against time-varying parameters are achieved by a gain-scheduled linear parameter varying (LPV) controller. Based on the linear matrix inequality (LMI) framework, the LPV controller is synthesized to ensure the robust H∞ performance against external disturbances. Simulation results show that the proposed gain-scheduled LPV/H∞ control strategy can enhance roll stability and path tracking accuracy in the path tracking process of intelligent heavy vehicles.

Improved design of automatic solar tracking system based on integration of accelerometer, compass sensor, and GPS

Improved design of automatic solar tracking system based on integration of accelerometer, compass sensor, and GPS

Vector analysis of Contoura Vision for the correction of myopia and myopic astigmatism.

To evaluate the visual outcomes of Contoura Vision (CV) with automatic eye tracking system in eyes with myopia and myopic astigmatism. This prospective study included 160 eyes (80 patients) with moderate myopia and irregular astigmatism between January and August 2018. Subjects were randomly divided into CV group (80 eyes) that underwent CV femtosecond laser-assisted in situ keratomileusis (FS-LASIK) and a control group (80 eyes) that underwent wavefront-optimized FS-LASIK. Visual outcomes and astigmatic vector analysis were evaluated and compared between preoperatively and 3mo postoperatively. Basic details were similar in both groups (P>0.05). At 3mo postoperatively, uncorrected distance visual acuity was 20/16, 20/20, and 20/25 in 24, 76, and 80 eyes of patients in CV group, respectively. The CV group was better in predictability of astigmatism correction at 3mo postoperatively. In CV group, 64 eyes had deviation of astigmatic axis within 15° and 28 eyes had deviation of astigmatic axis within 5°, both were better than those in the control group. The number of eyes with residual astigmatism within 0.5 D were less in CV group (48 eyes) than the control group (40 eyes). Compared with the preoperative, C7 significantly reduced to 0.056±0.030 in CV group at 3mo after the procedure (P<0.05), and were significantly lower than those in the control group (P<0.05). CV with automatic eye tracking system is safe and effective for the correction of myopia and myopic astigmatism.

A Literature Survey on Automated Cargo Tracking System

Abstract: The automated asset tracking system is designed to track lost or lost property in public and elsewhere. As goods are transported by plane or train from one country to another, there is always a risk of theft or negligence of the goods or luggage in which the proposed system is considered. The automatic property tracking system works with an alarm where the alarm is set via the Arduino UNO board and GPS module. Also, the alarm goes off as soon as the bag is misplaced and goes out some distance. In addition, a map is created where we can track the location of the bag as it travels, as the symbols are lowered in a way that gives us the location of the bag as it moves from the desired location. In this way, the components are used as an Arduino board and GPS Module to track cargo and build a front-end or mobile system to monitor lost property. Keywords: Arduino UNO, GPS module

Improved Vessel Trajectory Prediction Model Based on Stacked-BiGRUs

An intelligent maritime navigation system is expected to play an important role in the realm of Internet of Vessels (IoV). As a key technology in navigation systems, vessel trajectory prediction technology is critical to the IoV. Automatic identification system (AIS), an automated tracking system, is used extensively for vessel trajectory prediction. However, certain characteristics in the AIS data, such as the large number of anchored trajectories in the area, anomalous sharp turns of some trajectories, and the behavioral differences of vessels in different segments, limit the prediction accuracy. In this study, we propose a novel vessel trajectory prediction model for accurate prediction with the following characteristics: (1) an anchor trajectory elimination algorithm to eliminate anchor trajectories; (2) a statistical trajectory restoration algorithm to repair sharp turning; (3) a two-stage clustering algorithm (D-KMEANS) to distinguish vessel behavior; and (4) a deep bidirectional gate recurrent unit (Stacked-BiGRUs) model to predict vessel trajectory and compare the accuracy of the model before and after improvement. The results show that the mean square error and the mean absolute error of the improved model are reduced by 27% and 46%, respectively. This research shows good potential for maritime navigation early warning and safety.

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance.

The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.

Environmentally relevant concentration of caffeine—effect on activity and circadian rhythm in wild perch

We studied the ecological consequences of widespread caffeine contamination by conducting an experiment focused on changes in the behavioral traits of wild perch (Perca fluviatilis) after waterborne exposure to 10 μg L−1 of caffeine. We monitored fish swimming performance during both light and dark conditions to study the effect of caffeine on fish activity and circadian rhythm, using a novel three-dimensional tracking system that enabled positioning even in complete darkness. All individuals underwent three behavioral trials—before exposure, after 24 h of exposure, and after 5 days of exposure. We did not observe any effect of the given caffeine concentration on fish activity under light or dark conditions. Regardless of caffeine exposure, fish swimming performance was significantly affected by both the light-dark conditions and repeating of behavioral trials. Individuals in both treatments swam significantly more during the light condition and their activity increased with time as follows: before exposure < after 24 h of exposure < after 5 days of exposure. We confirmed that the three-dimensional automated tracking system based on infrared sensors was highly effective for conducting behavioral experiments under completely dark conditions.

Automated recognition and analysis of head thrashes behavior in C. elegans

BackgroundLocomotive behaviors are a rapid evaluation indicator reflecting whether the nervous system of worms is damaged, and has been proved to be sensitive to chemical toxicity. In many toxicological studies, C. elegans head thrashes is a key indicator of locomotive behaviors to measure the vitality of worms. In previous studies, the number of head thrashes was manually counted, which is time-consuming and labor-intensive.ResultsThis paper presents an automatic recognition and counting method for head thrashes behavior of worms from experimental videos. First, the image processing algorithm is designed for worm morphology features calculation, mean gray values of head and tail are used to locate the head of worm accurately. Next, the worm skeleton is extracted and divided into equal parts. The angle formulas are used to calculate the bending angle of the head of worm. Finally, the number of head thrashes is counted according to the bending angle of the head in each frame. The robustness of the proposed algorithm is evaluated by comparing the counting results of the manual counting. It is proved that the proposed algorithm can recognize the occurrence of head thrashes of C. elegans of different strains. In addition, the difference of the head thrashes behavior of different worm strains is analyzed, it is proved that the relationship between worm head thrashes behavior and lifespan.ConclusionsA new method is proposed to automatically count the number of head thrashes of worms. This algorithm makes it possible to count the number of head thrashes from the worm videos collected by the automatic tracking system. The proposed algorithm will play an important role in toxicological research and worm vitality research. The code is freely available at https://github.com/hthana/HTC.

An adaptive kernelized correlation filters with multiple features in the tracking application

Automatic target detection and tracking systems are used extensively in complex scenes. In long-term tracking, some visual attributes of objects are changing, such as illumination, size, profile, and so on. To address the issue, it is particularly important to describe the essential properties of the objects in tracking. An enhanced kernelized correlation filter tracking strategy fused multiple features with location prediction is proposed. To make the object appearance models more accuracy and robustness, based on the original histogram of oriented gradient features, we integrate the hue, saturation, value, and grayscale information to construct a new descriptor to represent the target appearance. Moreover, location prediction and bi-linear interpolation are employed to obtain the more accurate target position. Experiments show that the proposed strategy can obtain superior or competitive performance in challenging benchmark data sets. In practice, the algorithm is applied to track shuttle bus targets in the airport apron.

Evaluation of the Clinical Application of Multi-Color Optical Coherence Tomography as a Diagnostic Tool for Different Retinal Pathologies

Objective: To assess clinical application of multi-color OCT (Optical Coherence Tomography) with the utilization of the CSLO (Confocal Scanning Laser-Ophthalmoscopy) in various pathologies of retina. Methodology: This study was conducted at in Benazir Bhutto Shaheed Teaching Hospital Abbottabad and the duration of this study was from April 2021 to June 2021. There were thirty-six patients in this research study who were suffering from various retinal pathologies as disorders of vitreomacular interface, diabetic retinopathy and macular degeneration related with age with the utilization of multi-color OCT as a tool of screening. Results: This study discovered that ophthalmologist were able to get high resolution images of CSLO reflectance because of the automatic tracking system of this particular tool (new version). Confocal optics may be used to avoid the scattering of light. Some of the differences were presence of hemorrhages and presences of pigment changes when comparison with the conventional CFP was performed. Approximately 20.0% patients with AMD, 37.50% patients with diabetes and 100% patients suffering from disorders of vitreomacular interface could have been missed easily with the utilization of CFP. Conclusions: The findings conclude that multi-color OCT is able to deliver information &amp; figures far more confident was compared to conventional method of CFP, because it is much influenced by the media opacities. For the best interpretation of the Multi-color OCT, more watchfulness of the ophthalmologists is necessary with high level of comprehensiveness. Keywords: Retinal Pathologies, Multi-Color, Diabetic Retinopathy, Optical Coherence Tomography.

An ultra‐wideband 3 × 3 Butler matrix with unique Vivaldi array structure for high‐range resolution radars

Steerable antenna beam capability is an outstanding expectation for the development of automatic tracking systems and target detection. A switched beam array antenna, which can generate multiple fixed beams with enhanced sensitivity, emerges as a solution for beamforming tracking. On the other hand, it is known that the target can be detected with high-range resolution thanks to ultra-wideband (UWB) systems. In this work, a compact switched beam Vivaldi array antenna based on a Butler matrix for a UWB application capable of target detection with accurate target tracking is presented to meet the high gain and low side lobe level requirements. Based on a three-element antenna array, two methods have been applied to obtain small grating lobes and it has been shown that significant improvements can be achieved. Designed with a 3 × 3 UWB Butler matrix, the UWB array antenna can direct its beams in three directions in a direction range from −42 to 42°. The measured result showed that the group delay was less than about 4.5 ns with variation of less than ±0.7 ns over the entire operating frequency band.

Automated Tracking Systems for the Assessment of Farmed Poultry.

Simple SummaryWith the advent of artificial intelligence, the poultry sector is gearing up to adopt and embrace sensor technologies to enhance the production and the welfare of birds. Automated tracking and tracing of poultry birds has several advantages in poultry farms: overcoming the subjectivity of human measurements, enhancing the ability to provide quality care for the birds during their life on the farm, providing the ability to predict events and thereby enabling timely interventions, and many more. However, the technologies behind automated tracking systems are not ripe due to the lags in algorithms and practical implementation issues. This mini review provides a brief critical assessment of the current and recent advancements of automated tracking systems in the poultry industry and offers an outlook on future directions.The world’s growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animals on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of artificial intelligence (AI)-assisted technology individualised, and per-herd assessments of livestock became possible and accurate. Various studies have shown that cameras linked with specialised systems of AI can properly analyse flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on recent advancements, this review explores the aspects of AI in the detection, counting, and tracking of poultry in commercial and research-based applications.

Illegal fishing, anomalous vessel behavior detection through automatic identification system

Border cross fishing and illegal fishing are big problems around the world. Observations show, there are many records for intensive and often illegal fishing of West Africa’s waters by Asian and European hasty that reduce the regular catch for the native people. They are in poverty levels and then illegal fishing impacts rare spices and life in the oceans and threatens the food supply of coastal communities. This paper aims to reduce and decline illegal fishing and cross fishing. Automatic Identification System (AIS) which is collected by satellites and terrestrial receivers, to identify distinct fishing behavior based on the movement of vessels over time. Using the Automatic tracking System(ATS) or AIS each vessels periodically reports information including the Vessel’s identity, type, positions, speed Navigational status and other safety-related information By tracking ship moments, the data is receivers by the Global Fishing watch(GFW) the result will be GFW Detector the vessel’s location. Frequently analyst and terminate the illegal fishing and monitor vessel moments in the oceans.

Experimental Verification of the Modified PSA Algorithm

Being abundant and non-polluted, solar energy is an increasingly desirable energy source. Growing research is concerned about maximum solar energy acquisition from solar panels, especially in the automatic sun-tracking of the solar panels. This paper presents a modified algorithm based on the &#x201C;PSA Algorithm&#x201D; proposed in the 2001. And presents a new automatic solar tracking system with a two-axis is designed based on the algorithm. The PSA modified algorithm is used to calculate the sun&#x2019;s horizontal azimuth and zenith angle combined with time, longitude, latitude, and other information. We designed a prototype mainly composed of a microcontroller, a Global Positioning System (GPS) module, a digital compass, a tilt sensor, a 555 timer, and gates, optical couples, an H-bridge and two stepper motors. Then using the proposed prototype, a series of experiments are conducted in Tianjin City. Measured zenith angle values were collected and recorded based on the PSA modified algorithm and the original PSA algorithm, compared with the actual Zenith Angle of the Sun in Tianjin City. The comparison curves of Zenith Angle show that the measurement accuracy of the modified PSA algorithm is higher than that of the PSA original algorithm.

Energy and economic evaluation of a middle-sized CSP parabolic field in an agro-industrial company

The energy assessment carried out in a middle-sized CSP parabolic field of 120 m2 is presented; it provides pre-heating water at 70°C to the boilers of the lemon essential oil extraction process in an agro-industrial company located in Piura, at the border of Sechura desert in Peru. The parabolic cylindrical solar concentrators are made with glass reflectors, an automatic solar tracking system, and collector tubes through which water flows. It was measured that each collector converts solar energy and transfer it as a thermal energy to the water with an average efficiency of 18.1%. Considering this result, theoretically the whole solar field could provide 550.37 GJ/year for water preheating, which is equivalent to saving 28151.8 liters of LPG, and 46.87 tons of CO2 released to the environment. However, after a year of operation, the solar field provided only 99.59 GJ for water preheating, saving 4 705.26 liters of LPG, equivalent to stop emitting 7.69 tons of CO2 to the environment. An overall thermal energy efficiency of the CSP solar field in 18.1% was estimated. According to the financial assessment with the actual annual data, the period of recovery of the investment is estimated to be 4.4 years, 3.6 more years than theoretical projection.

A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming-Application to Sheep Husbandry.

The automated quantification of the behaviour of freely moving animals is increasingly needed in applied ethology. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilization, for instance in genetic selection programs of animal breeding. Here we introduce a new automated tracking system based on millimetre-wave radars for real time robust and high precision monitoring of untagged animals. In contrast to conventional video tracking systems, radar tracking requires low processing power, is independent on light variations and has more accurate estimations of animal positions due to a lower misdetection rate. To validate our approach, we monitored the movements of 58 sheep in a standard indoor behavioural test used for assessing social motivation. We derived new estimators from the radar data that can be used to improve the behavioural phenotyping of the sheep. We then showed how radars can be used for movement tracking at larger spatial scales, in the field, by adjusting operating frequency and radiated electromagnetic power. Millimetre-wave radars thus hold considerable promises precision farming through high-throughput recording of the behaviour of untagged animals in different types of environments.