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Environmental quality is a great concern to everyone, in order to realize the collection, upload, management, and visualization of parameters of atmospheric environment in real time. We propose a cheap, low-power, and fast deployment wireless sensor node for environmental monitoring, consisting of STM32 MCU, ESP8266, light sensor, rain sensor, UV sensor, seven-in-one sensor (including temperature, humidity, PM2.5, PM10, CO2, formaldehyde, and TVOC), and solar automatic tracking module. A customized μC/OS-III runs on the node, which controls the transmission of environment parameters collected by each sensor to the cloud server through the wireless network, and then the server receives, stores, and visualizes the data. In actual test, the node collects data once an hour, and the running power of the node is low and stable. Experimental results show that the node could achieve accurate collection and transmission and display the environmental data, and solar automatic tracking module could meet long-term running of the node in the night and continuous rainy days. [ABSTRACT FROM AUTHOR]

By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl lengths increase with time. Numerical simulations show that cells inside the tissue have smaller area than those at the interface, which has been observed in recent experiments. In the antagonistic migration assay, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce the experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to results of our numerical simulations. We use time series of data generated by numerical simulations to automatically group, track and classify the advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These techniques of topological data analysis are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration. Author summary: Confluent motion of cells in tissues plays a crucial role in wound healing, tissue repair, development, morphogenesis and in numerous pathological processes such as tumor invasion and metastatic cancer. For such complex processes, controlled experiments help clarifying the roles of chemical, mechanical and biological cues. Among them, spreading of cellular tissues on an empty space and antagonistic migration assays between cancerous and normal cells are quite revealing. The interfaces between confluent cellular aggregates uncover properties thereof when a combination of modeling, numerical simulation and data analysis is used. Here we have modified an active vertex model with a dynamics that includes inertia, friction and active forces that tend to align cells based on interaction with its immediate neighborhood. Selecting appropriately junction tensions among cells and using the SAMoS software, we have succeed in simulating assays of cellular tissue spreading on an empty space and the invasion of healthy tissue by cancerous one. We have introduced topological data analysis to characterize, track and compare in an automatic manner the interfaces of the tissue both in numerical simulations and from experimental data of normal and Ras modified precancerous Human Embryonic Kidney cells. We find good agreement when normal cells are solidlike and modified cells are liquidlike according to their shape parameters. In addition, cell variability means that a fraction of randomly distributed cells favor mixing, the others segregation. Topological data analysis techniques are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant in ascertaining how the biophysical features of materials may affect tissue and organ regeneration. [ABSTRACT FROM AUTHOR]

Tracking emotional responses as they unfold has been one of the hallmarks of applied neuroscience and related disciplines, but recent studies suggest that automatic tracking of facial expressions have low validation. In this study, we focused on the direct measurement of facial muscles involved in expressions such as smiling. We used single-channel surface electromyography (sEMG) to evaluate the muscular activity from the Zygomaticus Major face muscle while participants watched music videos. Participants were then tasked with rating each video with regard to their thoughts and responses to each of them, including their judgment of emotional tone ("Valence"), personal preference ("Liking") and rating of whether the video displayed strength and impression ("Dominance"). Using a minimal recording setup, we employed three ways to characterize muscular activity associated with spontaneous smiles. The total time spent smiling (ZygoNum), the average duration of smiles (ZygoLen), and instances of high valence (ZygoTrace). Our results demonstrate that Valence was the emotional dimension that was most related to the Zygomaticus activity. Here, the ZygoNum had higher discriminatory power than ZygoLen for Valence quantification. An additional investigation using fractal properties of sEMG time series confirmed previous studies of the Facial Action Coding System (FACS) documenting a smoother contraction of facial muscles for enjoyment smiles. Further analysis using ZygoTrace responses over time to the video events discerned "high valence" stimuli with a 76% accuracy. Additional validation of this approach came against previous findings on valence detection using features derived from a single channel EEG setup. We discuss these results in light of both the recent replication problems of facial expression measures, and in relation to the need for methods to reliably assess emotional responses in more challenging conditions, such as Virtual Reality, in which facial expressions are often covered by the equipment used. [ABSTRACT FROM AUTHOR]

In the last years Autonomous vehicle navigation has had considerable improvements; however, this technology has not yet been able to effectively be integrated with mobile systems that work inside buildings, houses or without the influence of absolute references such as GPS. It is for this reason that this paper provides a methodological proposal that allows the integration of the use of object detection methods such as YOLO (You Only Look Once) or R-CNN (Region Convolutional Neural Networks), with laser scanning systems for the measurement of spatial coordinates and an INS system to obtain spatial positioning through relative references. This method integrates the principle of dynamic triangulation and the methodological proposal IKZ (Inertial Navigation with Kalman and Zero Update) that allow to obtain acceptable mobile autonomy times in which the sector of interest is re-scanned. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel reweighted price relative tracking (RPRT) system for automatic portfolio optimization (APO). In the price prediction stage, it automatically assigns separate weights to the price relative predictions according to each asset’s performance, and these weights will also be automatically updated. In the portfolio optimizing stage, a novel tracking system with a generalized increasing factor is proposed to maximize the future wealth of next period. Besides, an efficient algorithm is designed to solve the portfolio optimization objective, which is applicable to large-scale and time-limited situations. Extensive experiments on six benchmark datasets from real financial markets with diverse assets and different time spans are conducted. RPRT outperforms other state-of-the-art systems in cumulative wealth, mean excess return, annual percentage yield, and some typical risk metrics. Moreover, it can withstand considerable transaction costs and runs fast. It indicates that RPRT is an effective and efficient APO system. [ABSTRACT FROM AUTHOR]

For a visual surveillance in a crowd, multiple human tracking is essential and of course a challenging task. Real-world applications require multiple cameras to capture crowd scenes so that a keen tracking is observed. Automatic tracking in a crowded environment is very important criteria for the surveillance. Accurate and real-time tracking in a crowd, the number of people present in the public places and shopping mall are some of the vital information for monitoring traffic violations. To provide human safety and security, surveillance like theft prevention and automated checkout provides the necessary consumer information to the managers. The conventional tracking algorithm does not handle the complex background, multi-view points, various illumination changes and severe occlusion occurring in a crowd. The above problem can be effectively handled by using the proposed Adaptive Hybrid Multiple Human Tracking (AHMHT) method. The proposed work utilizes the Adaptive Hybrid Gaussian Mixture Model (AHGMM) (Karpagavalli and Ramprasad, International Journal of Multimedia Tools and Application 76(12):14129–14149, 12) detected output, so that, the proposed algorithm tracks all the blobs in each frame on the basis of motion information along with the width and height information of exact blob. The experimental results demonstrate that the proposed method performs well compared to other methods. The multiple human tracking rates are improved with maximum of 91% using the proposed frame work compared with other methods. The proposed method is efficient in terms of computational time (CT) using an adaptive hybrid tracking. [ABSTRACT FROM AUTHOR]

The catadromous life cycle of the European eel encompasses a spawning migration to the Sargasso Sea. Prior to their migration, eels develop a silver coloration being referred to as "silver eels." Due to the dramatic European eel recruitment decline, it is crucial to quantify silver eel escapement to evaluate the success of measures taken under Eel Management Plans (Regulation EC (1100/2007)). Thus, the percentage of silver eels escaping from the River Mondego (Portugal) was estimated during two consecutive spawning seasons (2014–2016) and their migratory behaviour was studied. Thirty‐six male silver eels were tagged with acoustic transmitters and their downstream migration was tracked using automatic receivers deployed at four locations along the river course, from the first impassable obstacle up to the river mouth (67 km). Among the tagged eels, 15 (42%) escaped to the sea. In both years, individuals displayed faster migration speed in the downstream reaches of the river. BRT ("Boosted Regression Trees") models were used to explain downstream migration and escapement of silver eels. The analyses revealed that the downstream migration is triggered by several environmental variables with water conductivity as the most influential predictor. Escapement of eels from the estuary occurred a few hours after their arrival and was primarily explained by the offshore wind intensity, which promotes a current in the coast that may favour their transport to the open sea. These findings contribute to a better understanding of silver eel migration in southern latitudes and highlight and reinforce the need to monitor spawner escapement. [ABSTRACT FROM AUTHOR]

This paper presents a robust tracking controller for the automatic voltage regulator (AVR) system, where both system uncertainties and external disturbances are taken into account. The proposed control scheme combines optimal feedback control with robust control. The parameter gains of the proposed controller have been selected based on Lyapunov theorem to ensure the stability of the closed-loop control system. Moreover, the parameters of the feedback control term are optimized using the Harris Hawks Optimization (HHO) algorithm. The performance of the proposed controller is compared with several Proportional Integral Derivative (PID) controllers optimized with different optimization algorithms. In addition, the fractional order PID (FOPID) controller is used for comparison. The validity of the proposed control scheme is illustrated through the simulation of the AVR system with three cases of simulation have been discussed. [ABSTRACT FROM AUTHOR]

The National Institute of Information and Communications Technology in Japan has developed vehicle earth stations equipped with an antenna system for the automatic tracking of the Wideband InterNetworking engineering test and Demonstration Satellite "KIZUNA" (WINDS), making it possible to form a satellite link while moving. However, shadowing can have a large impact on satellite communications using directional Ka-band antennas with high rectilinearity. It is estimated that the degree of shadowing due to trees varies with seasons, which is a function of seasonal leaf cover. Therefore, we measured the potential seasonal difference by comparing the attenuation of the received signal from the satellite during a year-long experiment with shadowing from several kinds of trees. No attenuation fluctuations were observed from evergreen trees, whereas seasonal attenuation fluctuations were confirmed from deciduous trees. [ABSTRACT FROM AUTHOR]

Real-time automatic detection and tracking of moving vehicles in videos acquired by airborne cameras is a challenging problem due to vehicle occlusion, camera movement, and high computational cost. This paper presents an efficient and robust real-time approach for automatic vehicle detection and tracking in aerial videos that employ both detections and tracking features to enhance the final decision. The use of Top-hat and Bottom-hat transformation aided by the morphological operation in the detection phase has been adopted. After detection, background regions are eliminated by motion feature points' analysis of the obtained object regions using a combined technique between KLT tracker and K-means clustering. Obtained object features are clustered into separate objects based on their motion characteristic. Finally, an efficient connecting algorithm is introduced to assign the vehicle labels with their corresponding cluster trajectories. The proposed method was tested on videos taken in different scenarios. The experimental results showed that the recall, precision, and tracking accuracy of the proposed method were about 95.1 %, 97.5%, and 95.2%, respectively. The method also achieves a fast processing speed. Thus, the proposed approach has superior overall performance compared to newly published approaches. [ABSTRACT FROM AUTHOR]

Long-term automatic tracking of coronal bright points has made it possible to study their proper motions using data from the AIA 193 Å channel of SDO/AIA. Fits files for 95 visually long-lived bright points uniformly distributed in latitude have been chosen for processing. A specially developed program automatically fixes the heliographic coordinates of the centroid of the chosen coronal bright points in a series of Fits files. It was found that the proper motions of all the coronal bright points studied here have a distinct oscillatory character. The periods of the oscillations range from 5 to 60 h with an average of 20 h. Some tendencies in the latitudinal dependence of the oscillatory characteristics have been found. A possible explanation of these oscillations in terms of the propagation of helical waves along the magnetic loops that form the structure of the coronal bright points is pointed out. Linear trends in the latitudinal and longitudinal components of the motion of the coronal points are used to determine the rotational velocity and meridional migration of the coronal bright points. An analysis of the rotational velocities confirms the differential character of the latitudinal dependence. The problem of determining the direction and magnitude of the meridional migration is discussed. [ABSTRACT FROM AUTHOR]

In this article, a microcontroller-based high-accuracy dew-point (DP) instrument using a quartz crystal resonance (QCR) sensor is developed. The sensor is a symmetrical double cooled QCR DP sensor, which has the characteristics of efficient cooling and stable oscillation. Based on the proportional—integral—differential control method, the logic level signal and the oscillation amplitude outputted by the drive circuit are used as control feedback to obtain an optimized expert strategy control system, and the automatic DP tracking measurement is realized by the microcontroller. It is proved by experiments that the measurement error of the instrument in the range of 0–15 °C dew point (DP) environment is less than 0.3 °C DP, and it has a good performance of tracking measurement in a dynamic DP environment. [ABSTRACT FROM AUTHOR]

Direct measurements of spatially distributed vertical strain within ice masses are scientifically valuable but challenging to acquire. We use manual marker tracking and automatic cross correlation between two repeat optical televiewer (OPTV) images of an ~100 m-long borehole at Derwael Ice Rise (DIR), Antarctica, to reconstruct discretised, vertical strain rate and velocity at millimetre resolution. The resulting profiles decay with depth, from −0.07 a−1 at the surface to ~−0.002 a−1 towards the base in strain and from −1.3 m a−1 at the surface to ~−0.5 m a−1 towards the base in velocity. Both profiles also show substantial local variability. Three coffee-can markers installed at different depths into adjacent boreholes record consistent strain rates and velocities, although averaged over longer depth ranges and subject to greater uncertainty. Measured strain-rate profiles generally compare closely with output from a 2-D ice-flow model, while the former additionally reveal substantial high-resolution variability. We conclude that repeat OPTV borehole logging represents an effective means of measuring distributed vertical strain at millimetre scale, revealing high-resolution variability along the uppermost ~100 m of DIR, Antarctica. [ABSTRACT FROM AUTHOR]

We analysed the interpersonal interaction between players during official matches considering the coupling of different playing positions. 2D coordinates and technical actions of 204 male professional football players during eight matches were obtained using a video-based tracking system. Dyads were composed of the nearest opponents and the interpersonal distance and frequency were computed. Dyadic distances were used to estimate the pressure index of defensive teams and the relationship with technical performance. Full-backs presented the greatest distances of their opponents. Dyads between defenders and forwards presented the shortest distances and were more frequent. In moments with ball possession the midfielders and forwards have more than one defensive player as the nearest opponent to them. The pressure index of defensive teams was significantly negatively and positively correlated with tackles performed by them, and successful passes of offensive teams, respectively, suggesting that defensive players more distant from the offensive players performed less tackles and provided better conditions for their opponents to perform passes. Interpersonal interaction is influenced by playing positions and maintaining proximity to the opponents can improve defensive technical performance. Information about the interpersonal interactions in real match situations provides the fundamentals for future research and assists coaches in planning tactical training. [ABSTRACT FROM AUTHOR]

Bioenergy plants are widely used as a form of renewable energy. It is important to monitor the vegetation and accurately estimate the yield before harvest in order to maximize the profit and reduce the costs of production. The automatic tracking of plant development by traditional methods is quite difficult and labor intensive. Nowadays, the application of Unmanned Aerial Vehicles (UAV) became more and more popular in precision agriculture. Detailed, precise, three-dimensional (3D) representations of energy forestry are required as a prior condition for an accurate assessment of crop growth. Using a small UAV equipped with a multispectral camera, we collected imagery of 1051 pictures of a study area in Kompolt, Hungary, then the Pix4D software was used to create a 3D model of the forest canopy. Remotely sensed data was processed with the aid of Pix4Dmapper to create the orthophotos and the digital surface model. The calculated Normalized Difference Vegetation Index (NDVI) values were also calculated. The aim of this case study was to do the first step towards yield estimation, and segment the created orthophoto, based on tree species. This is required, since different type of trees have different characteristics, thus, their yield calculations may differ. However, the trees in the study area are versatile, there are also hybrids of the same species present. This paper presents the results of several segmentation algorithms, such as those that the widely used eCognition provides and other Matlab implementations of segmentation algorithms. [ABSTRACT FROM AUTHOR]

Automatic tracking of particles in time-lapse fluorescence microscopy images is essential for quantifying the dynamic behavior of subcellular structures and virus structures. We introduce a novel particle tracking approach based on a deep recurrent neural network architecture that exploits past and future information in both forward and backward direction. Assignment probabilities are determined jointly across multiple detections, and the probability of missing detections is computed. In addition, existence probabilities are determined by the network to handle track initiation and termination. For correspondence finding, track hypotheses are propagated to future time points so that information at later time points can be used to resolve ambiguities. A handcrafted similarity measure and handcrafted motion features are not necessary. Manually labeled data is not required for network training. We evaluated the performance of our approach using image data of the Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods. [ABSTRACT FROM AUTHOR]

This paper focuses on automatic locking of tracking filters used in optical frequency transfer systems. General concept of such a system is briefly described and the problems with its automatic startup, originating in the use of the analog phase locked loop to filter weak, received signal, are discussed. A supervisory circuitry and algorithm to solve these problems is proposed. The frequency of the signal to be filtered is measured indirectly and the output frequency of the tracking filter is monitored. In the case of lack of synchronism (i:e: after the startup) a significant difference of these frequencies is measured and the supervisory algorithm forces the filter to tune into the right frequency and then allows it to synchronize. A system with the proposed solution was implemented and tested experimentally on a fiber optic link with high attenuation and multiple optical connectors. Transient signals during locking were recorded to investigate the system's behavior in real environment. The system was evaluated in the link causing synchronization losses every 17 min on average. During measurements over 3 days, the whole system was synchronized for over 99.98% of time despite these difficult conditions. [ABSTRACT FROM AUTHOR]

Though the relationship between traumatic stress and social behavior, which has been explored for years, is dynamic and largely estimated between dyads, little is known about the causal effects of traumatic stress exposure on the time-dependent dynamic alterations in the social behaviors on a large-group level. We thus investigated the effect of a single prolonged stress (SPS) exposure, a classical animal model that recapitulates posttraumatic stress disorder (PTSD)-like symptoms in rodents, on the spatiotemporal, social behavior changes within a large group of cohabiting rats. One-half of thirty-two Sprague-Dawley rats were assigned to the experimental group and subjected to SPS treatment administered two weeks after baseline social behavior recording; the other half served as the controls. Each group of rats (n = 16) was housed in one of two large custom-made cylinders. We used an automatic tracking system to record the behavioral indices of social behavior of the rats before SPS exposure, on the SPS exposure day, during a 7-day-long quiescent period after SPS treatment, as well as during subsequent behavioral test days. In addition to SPS-induced PTSD-like behaviors, SPS induced a time-dependent, oscillating change in active/passive social behaviors that lasted for 3 weeks. SPS treatment decreased active social behaviors (especially affiliative behaviors) but increased passive social behaviors (e.g. huddling) immediately following stress exposure. Increased active social interactions were observed during the early phase after SPS treatment; while increased passive social behaviors were observed during the late phase after SPS treatment. These dynamic changes were repeatedly observed when the rats underwent subsequent stressful behavioral tests and challenges. SPS induced a long-term, time-dependent oscillating change in indices of the social behavior. These changes may serve as an adaptive mechanism, and their manifestations critically depended on the time course following the traumatic stress exposure. [ABSTRACT FROM AUTHOR]

Optical-based navigation for space is a field growing in popularity due to the appeal of efficient techniques such as Visual Simultaneous Localisation and Mapping (VSLAM), which rely on automatic feature tracking with low-cost hardware. However, low-level image processing algorithms have traditionally been measured and tested for ground-based exploration scenarios. This paper aims to fill the gap in the literature by analysing state-of-the-art local feature detectors and descriptors with a taylor-made synthetic dataset emulating a Non-Cooperative Rendezvous (NCRV) with a complex spacecraft, featuring variations in illumination, rotation, and scale. Furthermore, the performance of the algorithms on the Long Wavelength Infrared (LWIR) is investigated as a possible solution to the challenges inherent to on-orbit imaging in the visible, such as diffuse light scattering and eclipse conditions. The Harris, GFTT, DoG, Fast-Hessian, FAST, CenSurE detectors and the SIFT, SURF, LIOP, ORB, BRISK, FREAK descriptors are benchmarked for images of Envisat. It was found that a combination of Fast-Hessian with BRISK was the most robust, while still capable of running on a low resolution and acquisition rate setup. For large baselines, the rate of false-positives increases, limiting their use in model-based strategies. [ABSTRACT FROM AUTHOR]

• 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]

Tracking pedestrians in crowded scenes is a challenging task. Existing transformer-based tracking methods integrate detection and tracking into a unified model, which simplifies the tracking process. However, these methods also introduce complicated attention mechanisms that increase the model complexity and cost. To address this issue, we propose SOTTrack, a simple online transformer-based method for crowd tracking. Our method enhances feature learning and training strategies without sacrificing simplicity and efficiency. Specifically, we introduce the Sequential Feature Aggregation (SFA) module and the Hybrid Group Training (HGT) approach. The SFA module fuses features from sequential images to improve the temporal consistency of visual features within short time intervals. The HGT approach assigns different queries to multiple guided tasks, such as label assignment and de-noising, which are only used during training and do not incur any inference cost. We evaluate our method on the MOT17 and MOT20 datasets and demonstrate its competitive performance. • We propose a new online tracker based on Transformer that learns both detection and tracking pedestrians from static images. • We use sequential feature aggregation, SFA to enable the model to capture temporal information from historical frames. • We adopt a hybrid multi-group training strategy, HGT, that employs multiple query groups with concurrent label assignment training and de-noising training. [ABSTRACT FROM AUTHOR]

Background: The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images., Purpose: The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process., Methods: We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree., Results: Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter-observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter-observer agreement, 98.39%., Conclusions: We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice-doc.netlify.app) for further studies., (© 2023 American Association of Physicists in Medicine.)

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]

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]

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]

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]

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]