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• A combination of standard and dilated convolution provide sharper segmentation results. • Tongue contour can be automatically tracked in real-time from ultrasound data using deep learning methods. • BowNet models can delineate a region of interest from input data using the capability of dilated convolution. • A large number of learnable parameters in a CNN model is not always an indication of a better generalization. • Ground truth labels of ultrasound data should be in binary instead of gray-scale format. One application of medical ultrasound imaging is to visualize and characterize human tongue shape and motion in real-time to study healthy or impaired speech production. Due to the low-contrast characteristic and noisy nature of ultrasound images, it requires knowledge about the tongue structure and ultrasound data interpretation for users to recognize tongue locations and gestures easily. Moreover, quantitative analysis of tongue motion needs the tongue contour to be extracted, tracked and visualized instead of the whole tongue region. Manual tongue contour extraction is a cumbersome, subjective, and error-prone task. Furthermore, it is not a feasible solution for real-time applications where the tongue contour moves rapidly with nuance gestures. This paper presents two new deep neural networks (named BowNet models) that benefit from the ability of global prediction of encoding–decoding fully convolutional neural networks and the capability of full-resolution extraction of dilated convolutions. Both qualitatively and quantitatively studies over datasets from two ultrasound machines disclosed the outstanding performances of the proposed deep learning models in terms of performance speed and robustness. Experimental results also revealed a significant improvement in the accuracy of prediction maps due to the better exploration and exploitation ability of the proposed network models. [ABSTRACT FROM AUTHOR]

Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to recognize and to predict the behavior of organisms. In this paper, we first develop a method for underwater real-time recognition and tracking of multi-objects, which we call "You Only Look Once: YOLO". This method provides us with a very fast and accurate tracker. At first, we remove the haze, which is caused by the turbidity of the water from a captured image. After that, we apply YOLO to allow recognition and tracking of marine organisms, which include shrimp, squid, crab and shark. The experiments demonstrate that our developed system shows satisfactory performance. [ABSTRACT FROM AUTHOR]

Continuous jumping behavior, a kind of endurance locomotion, plays important roles in insect ecological adaption and survival. However, the methods used for the efficient evaluation of insect jumping behavior remain largely lacking. Here, we developed a locomotion detection system named JumpDetector with automatic trajectory tracking and data analysis to evaluate the jumping of insects. This automated system exhibits more accurate, efficient, and adjustable performance than manual methods. By using this automatic system, we characterized a gradually declining pattern of continuous jumping behavior in 4th‐instar nymphs of the migratory locust. We found that locusts in their gregarious phase outperformed locusts in their solitary phase in the endurance jumping locomotion. Therefore, the JumpDetector could be widely used in jumping behavior and endurance locomotion measurement. [ABSTRACT FROM AUTHOR]

The analysis of the dynamic behavior of cells in time-lapse microscopy sequences requires the development of reliable and automatic tracking methods capable of estimating individual cell states and delineating the lineage trees corresponding to the tracks. In this paper, we propose a novel approach, i.e., an ant colony inspired multi-Bernoulli filter, to handle the tracking of a collection of cells within which mitosis, morphological change and erratic dynamics occur. The proposed technique treats each ant colony as an independent one in an ant society, and the existence probability of an ant colony and its density distribution approximation are derived from the individual pheromone field and the corresponding heuristic information for the approximation to the multi-Bernoulli parameters. To effectively guide ant foraging between consecutive frames, a dual prediction mechanism is proposed for the ant colony and its pheromone field. The algorithm performance is tested on challenging datasets with varying population density, frequent cell mitosis and uneven motion over time, demonstrating that the algorithm outperforms recently reported approaches. [ABSTRACT FROM AUTHOR]

The intelligent tracking car can realize self-perception, behavioral decision-making, automatic driving, etc. for environmental information, and has a wide application in our lives. In this paper, a new intelligent car is designed based on STM32F407ZGT6 MCU. The track information is collected by the OV7725 camera. The fast OTSU adaptive threshold algorithm is used to obtain the path guiding center line, which can realize image collection, image analysis and sensor data fusion, blocked path identification and intelligent judgment, automatic tracking function. The test results show that the algorithm is effective and feasible. When there are obstacles or partial loss on the track, it can intelligently identify the effective and obstacles and realize the independent decision making. It has good dynamic and robustness. [ABSTRACT FROM AUTHOR]

An active distributed sonar multi-target automatic tracking method based on sequential Monte Carlo and probability hypothesis density( SMC-PHD) filtering is proposed to solve the problems of large number of clutter,targets uncertainty and observation uncertainty. In the proosed method,the random finite set( RFS) model is used to characterize the target state and observation,and the importance sampling and resampling strategy of sequential Monte Carlo( SMC) method is used to realize the transferring and filtering of probability hypothesis density of multi-target posterior. The multi-target tracking based on SMC-PHD filtering with different number of observation nodes is simulated. The results show that the proposed method can be used to effectively realize multi-target automatic tracking in real time in clutter environment with unknown and time-varying multi-targets. In active distributed sonar system with 4 nodes,the proposed method achieves the high-accuracy tracking with distance estimation relative error less than 0. 05 and the completely accurate estimation of targets number. [ABSTRACT FROM AUTHOR]

A robotic system for automatically navigating ultrasound (US) imaging catheters can provide real-time intra-cardiac imaging for diagnosis and treatment while reducing the need for clinicians to perform manual catheter steering. Clinical deployment of such a system requires accurate navigation despite the presence of disturbances including cyclical physiological motions (e.g., respiration). In this work, we report results from in vivo trials of automatic target tracking using our system, which is the first to navigate cardiac catheters with respiratory motion compensation. The effects of respiratory disturbances on the US catheter are modeled and then applied to four-degree-of-freedom steering kinematics with predictive filtering. This enables the system to accurately steer the US catheter and aim the US imager at a target despite respiratory motion disturbance. In vivo animal respiratory motion compensation results demonstrate automatic US catheter steering to image a target ablation catheter with 1.05 mm and 1.33° mean absolute error. Robotic US catheter steering with motion compensation can improve cardiac catheterization techniques while reducing clinician effort and X-ray exposure. [ABSTRACT FROM AUTHOR]

We propose a tracking-by-detection algorithm to track the movements of meeting participants from an overhead camera. An advantage of using overhead cameras is that all objects can typically be seen clearly, with little occlusion; however, detecting people from a wide-angle overhead view also poses challenges such as people's appearance significantly changing due to their position in the wide-angle image, and generally from a lack of strong image features. Our experimental datasets do not include empty meeting rooms, and this means that standard motion based detection techniques (e.g., background subtraction or consecutive frame differencing) struggle since there is no prior knowledge for a background model. Additionally, standard techniques may perform poorly when there is a wide range of movement behaviours (e.g. periods of no movement and periods of fast movement), as is often the case in meetings. Our algorithm uses a novel coarse-to-fine detection and tracking approach, combining motion detection using adaptive accumulated frame differencing (AAFD) with Shi-Tomasi corner detection. We present quantitative and qualitative evaluation which demonstrates the robustness of our method to track people in environments where object features are not clear and have similar colour to the background. We show that our approach achieves excellent performance in terms of the multiple object tracking accuracy (MOTA) metrics, and that it is particularly robust to initialisation differences when compared with baseline and state of the art trackers. Using the Online Tracking Benchmark (OTB) videos we also demonstrate that our tracker is very strong in the presence of background clutter, deformation and illumination variation. [ABSTRACT FROM AUTHOR]

This study investigates the information content of ship noise received on a drifting volumetric array of hydrophones in shallow water marine environments for the purposes of conducting acoustic thermometry or other environmental inversions. Passive inversions for physical oceanographic parameters are conducted using travel time differences, determined by cross-correlating ship noise received on hydrophones suspended beneath drifting buoys. Ships are tracked using the automatic identification system. Information content gained from the inversion is assessed using traditional a posteriori error analysis. Numerical simulations using a standard normal mode propagation model are used to test limitations of the proposed approach with respect to frequency band, drifting receiver configuration, precision, and accuracy of the inversion results, along with sensitivity to environmental and position mismatch. Performance predictions using this model are compared with results from a field experiment using at-sea data collected off the coast of New London, CT, USA, in Long Island Sound. Information gathered using passive acoustic inversion methods on drifting arrays can be used to constrain general circulation models, in coastal environments, where ship noise is ubiquitous, environmental data is sparse, and the oceanography is dynamic and important for understanding large-scale ocean processes. [ABSTRACT FROM AUTHOR]

The aim of the present study was to analyze the time series of team spread during futsal official matches in the frequency domain for different categories. Using an automatic tracking method, trajectories of 258 players (excluding goalkeepers) were obtained, composed of 79 players from the under-15 (U15) category, 86 from the under-18 (U18), and 93 from the professional. We calculated the team spread defined as the Euclidean norm of the distance-between-player vector as a function of time. We applied the Fast Fourier Transformation method and calculated the median frequency for each time series of spread. The results of mean ± SD of the median frequency of the time series of spread from the first to the second half were significantly different only for the U15 category (first half, 1.04 ± 0.46, second half 1.40 ± 0.34). The frequency values differed significantly between the categories. The younger categories presented a higher frequency of spread oscillation than the professional category, which reflects the dynamics of the game between attack and defense sequences. The results provide insights into the features of the sport and present a basis for appropriate training interventions for players in each category, planning for future transition to the following category. [ABSTRACT FROM AUTHOR]

Multi activity-multi object recognition (MAMO) is a challenging task in visual systems for monitoring, recognizing and alerting in various public places, such as universities, hospitals and airports. While both academic and commercial researchers are aiming towards automatic tracking of human activities in intelligent video surveillance using deep learning frameworks. This is required for many real time applications to detect unusual/suspicious activities like tracking of suspicious behaviour in crime events etc. The primary purpose of this paper is to render a multi class activity prediction in individuals as well as groups from video sequences by using the state-of-the-art object detector You Look only Once (YOLOv3). By optimum utilization of the geographical information of cameras and YOLO object detection framework, a Deep Landmark model recognize a simple to complex human actions on gray scale to RGB image frames of video sequences. This model is tested and compared with various benchmark datasets and found to be the most precise model for detecting human activities in video streams. Upon analysing the experimental results, it has been observed that the proposed method shows superior performance as well as high accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Rupture of an arterosclerotic plaque in the carotid artery is a major cause of stroke. Biomechanical analysis of plaques is under development aiming to aid the clinician in the assessment of plaque vulnerability. Patient‐specific three‐dimensional (3D) geometry assessment of the carotid artery, including the bifurcation, is required as input for these biomechanical models. This requires a high‐resolution, 3D, noninvasive imaging modality such as ultrasound (US). In this study, a high‐resolution two‐dimensional (2D) linear array in combination with a magnetic probe tracking device and automatic segmentation method was used to assess the geometry of the carotid artery. The advantages of using this system over a 3D ultrasound probe are its higher resolution (spatial and temporal) and its larger field of view. Methods: A slow sweep (v = ± 5 mm/s) was made over the subject's neck so that the full geometry of the bifurcated geometry of the carotid artery is captured. An automated segmentation pipeline was developed. First, the Star‐Kalman method was used to approximate the center and size of the vessels for every frame. Images were filtered with a Gaussian high‐pass filter before conversion into the 2D monogenic signals, and multiscale asymmetry features were extracted from these data, enhancing low lateral wall‐lumen contrast. These images, in combination with the initial ellipse contours, were used for an active deformable contour model to segment the vessel lumen. To segment the lumen–plaque boundary, Otsu's automatic thresholding method was used. Distension of the wall due to the change in blood pressure was removed using a filter approach. Finally, the contours were converted into a 3D hexahedral mesh for a patient‐specific solid mechanics model of the complete arterial wall. Results: The method was tested on 19 healthy volunteers and on 3 patients. The results were compared to manual segmentation performed by three experienced observers. Results showed an average Hausdorff distance of 0.86 mm and an average similarity index of 0.91 for the common carotid artery (CCA) and 0.88 for the internal and external carotid artery. For the total algorithm, the success rate was 89%, in 4 out of 38 datasets the ICA and ECA were not sufficient visible in the US images. Accurate 3D hexahedral meshes were successfully generated from the segmented images. Conclusions: With this method, a subject‐specific biomechanical model can be constructed directly from a hand‐held 2D US measurement, within 10 min, with a minimal user input. The performance of the proposed segmentation algorithm is comparable to or better than algorithms previously described in literature. Moreover, the algorithm is able to segment the CCA, ICA, and ECA including the carotid bifurcation in transverse B‐mode images in both healthy and diseased arteries. [ABSTRACT FROM AUTHOR]

Insomnia is a common sleep disorder with a high prevalence and substantial adverse consequences. There is growing interest in identifying novel therapeutics from herbal medicine. Tenuifolin is a major constituent of the well-known anti-insomnia herb Radix Polygala. The present study investigated the neural activity in response to tenuifolin during rest/wake behaviour in zebrafish and identified the potential biological signalling pathways involved. An automatic video tracking system was used to monitor the behavioural response of zebrafish larvae for 24 h after treatment with tenuifolin. In total, six rest/wake parameters were measured and visualized with a behavioural fingerprint. Time series analysis was conducted by averaging the total rest and waking activity in 10 min intervals. A correlation analysis was performed between tenuifolin and well-known compounds to analyse the underlying biological signalling pathways. Reverse transcription-quantitative PCR was also performed to detect the effects of tenuifolin on the transcription of interesting genes associated with the signalling pathways that were potentially involved. The present results suggested tenuifolin significantly increased the total rest time during the dark phase, with a slight effect on the waking activity in zebrafish larvae. This behavioural phenotype induced by tenuifolin is similar to that of selective serotonin reuptake inhibitors and gamma-aminobutyric acid (GABA) agonists. Furthermore, the expression levels of GABA transporter 1 were significantly increased after tenuifolin treatment. No significant difference was determined in other associated genes in untreated control and tenuifolin-treated larvae. The present results suggested that tenuifolin caused sleep-promoting activity in zebrafish and that these effects may be mediated by the serotoninergic systems and the GABAergic systems. [ABSTRACT FROM AUTHOR]

UAV can realize track planning and automatic navigation flight due to its small size and flexible operation performance. It has been widely used in the agricultural field for its advantages of high obstacle crossing performance, stability and operation efficiency comparing to ground machines. At present, most UAV based aerial seeding devices use core parts like horizontal centrifugal disc or outer groove wheel to scatter seeds, fertilizers and other materials in the air. Due to the high seeding height and the disturbance of UAV rotor air flow, the randomness of material particles after landing is very large, and they are generally scattered on the whole plane. Although it has high seeding efficiency, it belongs to a relatively extensive seeding mode for that it is not easy to control the seed uniformity affected by many factors. In view of the above problems, a kind of rapeseed aerial seeding device based on an electrically driven centrifugal seeding meter was designed and tested for XAG P20 which is a commercial crop protection UAV. The aerial seeding device can seed in lines instead of wide area, and is more conducive to improve the uniformity of seedlings and the convenience of field management during crop growth.In this study, we first improved the design of the concave cone centrifugal seed metering device and determined the structural parameters of the key components such as seed metering tray and seed metering port. Then, based on ANSYS Fluent simulation software, the distribution of down wash air flow field of the XAG P20 drone was analyzed. An auxiliary seed guiding device used in combination with the centrifugal seed metering device was proposed and built. Three experiments including the seed metering performance test, the prototype test and the field test were carried out to analyze and study the functions of the aerial seeding device. The results showed that the total seeding quantity per unit time increased when the seeding rotation speed increased from 40 to 190 r/min, and the maximum total seeding quantity reached 179.65 g/min at 190 r/min, which can meet the requirements of 0-5m/s operation speed of the drone; the variation coefficient of consistency and stability of apiecerow seeding quantity decreased first and then increased, which were distributed in the range of 4.5%-12.6% and 0.7%-6.2% respectively. The damage rate of seeds increased gradually, but all of which were within 2%. The prototype test showed that when the height of the seed guiding device was in the range from 1.5 to 2.5 m, the width of the seed strips was less than 1/4 of the row spacing, and the striping index of the seed had no significant correlation with it. However, considering the operational risk and convenience of the personnel in the actual operation process, the height of 2m was selected as the appropriate height of the seed guiding device. Under that condition, the field experiment results showed that the strip index was 35.0%, the variation coefficient of sowing uniformity was 19.26%. The device designed in this study can meet the technical requirements of rape drilling. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) networks with the frequency spectrum ranging from 30 GHz (with wavelength 10 mm) to 300 GHz (with wavelength 1 mm), can support massive wireless data in fifth-generation (5G) systems. Importantly, large colocated antenna elements can be exploited at the base station (BS) to facilitate beam-steering synthesis along the users' directions. This paper proposes the Butler matrix (BM) frequency diverse retrodirective array (BM-FDRA) beamforming network at the BS for multiuser communications in mmWave networks. We utilize the orthogonal feature of the M × M BM with M elements of the FDRA to create directional beams for concurrent transmission towards different users in range-angle locations. The proposed scheme has the following merits: (a) there is beam-steering orthogonality without beam interferences and (b) there is automatic tracking functionality, i.e., the prior knowledge on the user location is not required by the proposed BM-FDRA at the BS. The proposed method can serve multiple users concurrently using the beam-steering orthogonality property. Furthermore, performance metrics such as the multiaccess secrecy sum rate (SSR) model, bit error rate (BER), system capacity, and energy efficiency are examined. The proposed BM-FDRA scheme achievements are highlighted via simulation results. [ABSTRACT FROM AUTHOR]

The existing polyester stable fiber production can not separate the bag contain the end of fiber effectively, resulting in the uneven quality of the finished package, a set of automatic tracking isolation system who can effectively separate the package containing head and tail wire was designed. The system can record the generation time of head and tail wire accurately, re-positioning the lead wire according to this timestamp after the stretch operation, and achieving automatic isolation of the finished package containing the head and tail wire. The experimental results and the practical situation on the spot show that the number of packets is reduced by more than 50%, and the automatic isolation system of head and tail wire works efficiently and reliably. [ABSTRACT FROM AUTHOR]

Poultry tracking is primarily used for evaluating abnormal behaviour and predicting disease in poultry. Offline video is often used to track and record poultry behaviour. However, poultry are group-housed animals. The difficulty of accurately monitoring large-scale poultry farms lies in the automatic tracking of individual poultry. To this end, this paper demonstrates the use of a deep regression network to track single poultry based on computer vision technology. By referring to the Alexnet network, the broiler chicken area of the previous frame and the search area of the next frame were input into the convolutional layer respectively, and the coordinates of the prediction area were obtained by full-connection layer regression. The method was compared with some existing tracking algorithms. Preliminary tests revealed that when compared with MeanShift Algorithm (MS), Multitask learning Algorithm (MIL), Kernel Correlation Filter (KCF), Adaptive Correlation Filters (ACF) and tracking-learn-detection (TLD), the poultry tracking algorithm named TBroiler tracker proposed in this paper has better performance on the overlap ratio, pixel error and the failure rate. TBroiler achieved a mixed tracking performance evaluation (MTPE) of 0.730. The evaluation scores of other methods were 0.362 (MS), 0.355 (MIL), 0.434 (KCF), 0.051 (ACF), and 0.248 (TLD). In addition, the method can be further optimised to improve the overall success rate of verification. • Method for tracking broilers was put forward. • The method has better performance than traditional tracking algorithms. • The method can meet the tracking needs of poultry farms. [ABSTRACT FROM AUTHOR]

In order to reduce the traffic congestion of driverless vehicles in rail transit and reduce the probability of traffic accidents of driverless vehicles, a coordinated regional control method based on driverless rail transit signal is proposed. The operation structure of driverless rail transit is designed based on vehicle network technology. The vehicle information is obtained by radio frequency identification equipment and the wireless communication technology is used. Instructions are conveyed to the built-in speed controller and steering controller of the vehicle. Vehicle speed, steering and running phase are controlled by the unmanned automatic tracking control module and the signal priority control module of the unmanned vehicle. The photoelectric sensor is used in the unmanned automatic tracking control module to convert the test results of the collected rail traffic signal into the lateral deviation to obtain the driving advance of the vehicle. Aiming at the information, adding the "driver" model to automatically control the state of the vehicle; the signal priority control module of the driverless vehicle chooses the vehicle to restore the priority request through the signal priority request and processing flow, adjusts the vehicle operation phase, and ensures the driverless vehicle to pass as first as possible according to the signal priority strategy of the rail transit. The experimental results show that the difference between the angle command and the actual angle is 20°. Within the range, the front wheel angle error does not exceed 1.45°. The average stability of the method is 0.9747, and the stability is good. [ABSTRACT FROM AUTHOR]

New Findings: What is the central question of this study?Is the proposed semi‐automatic algorithm suitable for tracking the medial gastrocnemius muscle–tendon junction in ultrasound images collected during passive and active conditions?What is the main finding and its importance?The validation of a method allowing efficient tracking of the muscle–tendon junction in both passive and active conditions, in healthy as well as in pathological conditions. This method was tested in common acquisition conditions and the developed software made freely available. Clinically relevant information can be extracted from ultrasound (US) images by tracking the displacement of the junction between muscle and tendon. This paper validated automatic methods for tracking the location of muscle–tendon junction (MTJ) between the medial gastrocnemius and the Achilles tendon during passive slow and fast stretches, and active ankle rotations while walking on a treadmill. First, an automatic algorithm based on an optical flow approach was applied on collected US images. Second, results of the automatic algorithm were evaluated and corrected using a quality measure that indicated which critical images need to be manually corrected. US images from 12 typically developed (TD) children, 12 children with spastic cerebral palsy (SCP) and eight healthy adults were analysed. Automatic and semi‐automatic tracking methods were compared to manual tracking using root mean square errors (RMSE). For the automatic tracking, RMSE was less than 3.1 mm for the slow stretch and 5.2 mm for the fast stretch, the worst case being for SCP. The tracking results in the fast stretch condition were improved (especially in SCP) by using the semi‐automatic approach, with an RMSE reduction of about 30%. During walking, the semi‐automatic method also reduced errors, with a final RMSE of 3.6 mm. In all cases, data processing was considerably shorter using the semi‐automatic method (2 min) compared to manual tracking (20 min). A quick manual correction considerably improves tracking of the MTJ during gait and allows to achieve results suitable for further analyses. The proposed algorithm is freely available. [ABSTRACT FROM AUTHOR]

A novel control scheme is proposed in this paper for the sensorless control of interior permanent magnet synchronous machines (IPMSMs) with the automatic tracking of the maximum-torque-per-ampere (MTPA) trajectory. Differing from the conventional method that the estimation of the rotor position and the tracking of MTPA trajectory are two separate processes, the proposed control scheme can achieve sensorless MTPA control of IPMSMs in one step by defining and constructing a proper virtual q-axis inductance to directly calculate the current space vector angle along the MTPA trajectory rather than the rotor position, which greatly simplifies the whole implementation process. Meanwhile, considering that the virtual q-axis inductance is changing with the machine parameters and the applied load, a simple signal processing technique based on the virtual high-frequency signal injection is developed for the real time and accurate acquisition of the virtual q-axis inductance. Furthermore, in order to ensure the reliability and stability of the proposed control scheme, the effective range of the virtual q-axis inductance is analyzed in detail. Finally, the experimental results on an IPMSM demonstrate the proposed control scheme can achieve the high-performance sensorless control with accurate MTPA self-tracking operation. [ABSTRACT FROM AUTHOR]

Image-guided radiation therapy (IGRT) allows radiation dose deposition with a high degree of geometric accuracy. Previous studies have demonstrated that such therapies may benefit from the employment of deformable image registration (DIR) algorithms, which allow both the automatic tracking of anatomical changes and accumulation of the delivered radiation dose over time. In order to ensure patient care and safety, however, the estimated deformations must be subjected to stringent quality assurance (QA) measures. In the present study we propose to extend the state-of-the-art methodology for QA of DIR algorithms by a set of novel biomechanical criteria. The proposed biomechanical criteria imply the calculation of the normal and shear mechanical stress, which would occur within the observed tissues as a result of the estimated deformations. The calculated stress is then compared to plausible physiological limits, providing thus the anatomical plausibility of the estimated deformations. The criteria were employed for the QA of three DIR algorithms in the context of abdominal conebeam computed tomography and magnetic resonance radiotherapy guidance. An initial evaluation of organ boundary alignment capabilities indicated that all three algorithms perform similarly. However, an analysis of the deformations within the organ boundaries with respect to the proposed biomechanical QA criteria revealed different degrees of anatomical plausibility. Additionally, it was demonstrated that violations of these criteria are also indicative of errors within the dose accumulation process. The proposed QA criteria, therefore, provide a tissue-dependent assessment of the anatomical plausibility of the deformations estimated by DIR algorithms, showcasing potential in ensuring patient safety for future adaptive IGRT treatments. [ABSTRACT FROM AUTHOR]

Combining the current development status of intelligent robots, obstacle avoidance and automatic tracking are the focus of robot travel problems.Based on the development status of domestic intelligent control systems, current technology, etc, this paper uses arduino as the core control system, combined with infrared tracking module. Four modules, such as ultrasonic obstacle avoidance module, motor drive module and power module, have designed a good control scheme, thus realizing the intelligent tracking and obstacle avoidance function of the wheeled robot. [ABSTRACT FROM AUTHOR]

Real-time traffic monitoring and parking are very important aspects for a better social and economic system. Python-based Intelligent Parking Management System (IPMS) module using a USB camera and a canny edge detection method was developed. The current situation of real-time parking slot was simultaneously checked, both online and via a mobile application, with a message of Parking "Available" or "Not available" for 10 parking slots. In addition, at the time entering in parking module, gate open and at the time of exit parking module, the gate closes automatically using servomotor and sensors. Results are displayed in figures with the proposed method flow chart. [ABSTRACT FROM AUTHOR]

In this study, the thermostatically controlled loads (TCLs) are employed to track automatic generation control signal through changing the temperature setpoint offset in smart grid. The costs minimisation problem is formulated for the power company in demand-side energy management. The costs are caused by the imbalance of supply and demand and consumer discomfort, which achieve a tradeoff by using a weighting coefficient. The forward method and the tracking-differentiator are applied to achieve the costs optimisation. In the simulation, the root-mean-square error (RMSE) and the costs are measured by changing the weighting coefficient. Simulation results demonstrate that the RMSE and the costs of the power company are increased with the weighting coefficient and the tracking error can be reduced. Further, the impact of dividing TCLs into clusters on RMSE and costs is considered. It is shown that the RMSE and the costs are increased with the number of groups. [ABSTRACT FROM AUTHOR]

As an important format of multimedia, music has filled almost everyone's life. Automatic analyzing of music is a significant step to satisfy people's need for music retrieval and music recommendation in an effortless way. Thereinto, downbeat tracking has been a fundamental and continuous problem in Music Information Retrieval (MIR) area. Despite significant research efforts, downbeat tracking still remains a challenge. Previous researches either focus on feature engineering (extracting certain features by signal processing, which are semi-automatic solutions); or have some limitations: they can only model music audio recordings within limited time signatures and tempo ranges. Recently, deep learning has surpassed traditional machine learning methods and has become the primary algorithm in feature learning; the combination of traditional and deep learning methods also has made better performance. In this paper, we begin with a background introduction of downbeat tracking problem. Then, we give detailed discussions of the following topics: system architecture, feature extraction, deep neural network algorithms, data sets, and evaluation strategy. In addition, we take a look at the results from the annual benchmark evaluation—Music Information Retrieval Evaluation eXchange—as well as the developments in software implementations. Although much has been achieved in the area of automatic downbeat tracking, some problems still remain. We point out these problems and conclude with possible directions and challenges for future research. [ABSTRACT FROM AUTHOR]

A widely used form of renewable energy are bioenergy crops. One form of it is the energy forestry that includes short rotation coppice plantations in which fast growing species of tree or woody shrub are grown (e.g. poplar, willow). The accurate prediction of forest biomass and volume can be used for the evaluation of plant breeding efficiency as well. The automatic tracking of plant development by traditional methods is quite difficult and labor intensive. Since energy forestries often contain different trees for estimating their volume it is essential to find segments containing the same tree species in the image.We investigated the applicability of a low cost UAV and an intermediate cost UAV in the field of agricultural image segmentation that is the first stage of biomass estimation (Gatziolis et al., 2015, Gaulton et al., 2015).This paper is a case study that shows the results of several segmentation algorithms applied on imagery obtained by a low cost UAV with low-cost camera, and imagery gathered by a UAV and camera set that are of higher quality and price. In the case study, we have observed two small forestry areas that contained six different tree species and their hybrids. Our results show that more expensive, better-equipped drone shots do not necessary provide significantly better segmentation. [ABSTRACT FROM AUTHOR]

Movement ecology of small wild animals is often reliant on radio‐tracking methods due to the size and weight restrictions of available transmitters. In manual radio telemetry, large errors in spatial position and infrequent relocations prevent the effective analysis of small‐scale movement patterns and dynamic aspects of habitat selection. Automatic radio‐tracking systems present a potential solution for overcoming these drawbacks. However, existing systems use customized electronics and commercial software or exclusively record presence/absence data instead of triangulating the position of tagged individuals.We present a low‐cost automatic radio‐tracking system built from consumer electronic devices that can locate the position of radio transmitters under field conditions. We provide information on the hardware components, describe mobile and stationary set‐up options, and offer open‐source software solutions.We describe the workflow from hardware setup and antenna calibration, to recording and processing the data and present a proof of concept for forest‐dwelling bats using a mixed forest as study area. With an average bearing error of 6.8° and a linear error of 21 m within a distance ranging from 65 m to 190 m, the accuracy of our system exceeds that of both traditional methods as well as manual telemetry.This affordable and easy‐to‐use automatic radio‐tracking system complements existing tools in movement ecology research by combining the advantages of lightweight and cost‐efficient radio telemetry with an automatic tracking set‐up. [ABSTRACT FROM AUTHOR]

The main obstacle to applying aluminum oxide doped with carbon and magnesium (Al 2 O 3 :C,Mg)-based fluorescent nuclear track detectors (FNTDs) in radionuclide therapy with alpha emitters is that it has not been known how to achieve an accurate and in situ dose assessment for in vitro cellular experiments. In this study, we developed an automatic 3D track reanalysis algorithm (ATRA) to analyze the alpha energy spectra in FNTDs. The algorithm provides an excellent track identification rate and an excellent track linking rate (0.961) without significant variations in various track densities. The algorithm also achieves a reliable track detection rate exceeding 0.927 for track densities at the level of in situ measurement, and it successfully measures the alpha energy spectra for obtaining the absorbed dose. We also confirmed that the track overlap clearly interferes with accurate track reconstruction for track densities beyond 1.5 × 106 tracks cm−2. We provided a correction equation that can be used to obtain the actual alpha tracks for high track densities; the equation may also correct estimations of absorbed doses due to alpha particles. • Automatic 3D track reanalysis algorithm (ATRA) was developed. • The track detection rates of ATRA exceeded 0.927 at in situ measurement level. • The effect of track overlap on ATRA was quantified with various track densities. • Correction equation was provided for correcting absorbed dose due to alpha particle. [ABSTRACT FROM AUTHOR]

• A priori assumptions about the principles which guide mindreading are risky. • Explicit answers to belief questions mostly ignored the lack of perceptual access. • Lack of perceptual access is sometimes ignored by automatic mindreading. Reading other minds is a pervasive feature of human social life. A decade of research indicates that people can automatically track an agent's beliefs regardless of whether this is required. But little is known about the principles t guide automatic belief tracking. In six experiments adapting a false belief task introduced by Kovács et al. (2010) , we tested whether belief tracking is interrupted by either an agent's lack of perceptual access or else by an agent's constrained action possibilities. We also tested whether such manipulations create interruptions when participants were instructed to track beliefs. Our main finding: the agent's lack of perceptual access did not interrupt belief tracking when participants were not instructed to track beliefs. Overall, our findings raise a challenge: some of the phenomena that have been labelled mindreading are perhaps not mindreading at all, or—more likely—they are mindreading but not as we know it. [ABSTRACT FROM AUTHOR]

• An anchor-free deep learning model was proposed to count seedlings and flowers. • The tracking-based counting method didn't use complex motion estimation algorithms. • The counting results of our method highly correlated with ground truth. • Our method would significantly benefit plant breeding and replanting decision-making. Accurate counting of plants and their organs in natural environments is essential for breeders and growers. For breeders, counting plants during the seedling stage aids in selecting genotypes with superior emergence rates, while for growers, it informs decisions about potential replanting. Meanwhile, counting specific plant organs, such as flowers, forecasts yields for different genotypes, offering insights into production levels. The overall goal of this study was to investigate a deep convolutional neural network-based tracking method, CenterTrack, for cotton seedling and flower counting from video frames. The network is extended from a customized CenterNet, which is an anchor-free object detector. CenterTrack predicts the detections of the current frame and displacements of detections between the previous frame and the current frame, which are used to associate the same object in consecutive frames. The modified CenterNet detector achieved high accuracy on both seedling and flower datasets with an overall AP50 of 0.962. The video tracking hyperparameters were optimized for each dataset using orthogonal tests. Experimental results showed that seedling and flower counts with optimized hyperparameters highly correlated with those of manual counts ( R 2 = 0.98 and R 2 = 0.95) and the mean relative errors of 75 cotton seedling testing videos and 50 flower testing videos were 5.5 % and 10.8 %, respectively. An average counting speed of 20.4 frames per second was achieved with an input resolution of 1920 × 1080 pixels for both seedling and flower videos. The anchor-free deep convolution neural network-based tracking method provides automatic tracking and counting in video frames, which will significantly benefit plant breeding and crop management. [ABSTRACT FROM AUTHOR]