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

Deep neural networks are rapidly emerging as data analysis tools, often outperforming the conventional techniques used in complex microfluidic systems. One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets. Specifically, droplet tracking in dense emulsions is challenging due to inherently small droplets moving in tightly packed configurations. Sometimes, the individual droplets in these dense clusters are hard to resolve, even for a human observer. Here, two deep learning-based cutting-edge algorithms for object detection [you only look once (YOLO)] and object tracking (DeepSORT) are combined into a single image analysis tool, DropTrack, to track droplets in the microfluidic experiments. DropTrack analyzes input microfluidic experimental videos, extracts droplets' trajectories, and infers other observables of interest, such as droplet numbers. Training an object detector network for droplet recognition with manually annotated images is a labor-intensive task and a persistent bottleneck. In this work, this problem is partly resolved by training many object detector networks (YOLOv5) with several hybrid datasets containing real and synthetic images. We present an analysis of a double emulsion experiment as a case study to measure DropTrack's performance. For our test case, the YOLO network trained by combining 40% real images and 60% synthetic images yields the best accuracy in droplet detection and droplet counting in real experimental videos. Also, this strategy reduces labor-intensive image annotation work by 60%. DropTrack's performance is measured in terms of mean average precision of droplet detection, mean squared error in counting the droplets, and image analysis speed for inferring droplets' trajectories. The fastest configuration of DropTrack can detect and track the droplets at approximately 30 frames per second, well within the standards for a real-time image analysis. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, the application of intelligent algorithms to low-power embedded chips has become a new research topic today. Based on this, this study optimizes the YOLOv2 algorithm by tailoring and successfully deploys it on the K210 chip to train the face object detection algorithm model separately. The intelligent fan with YOLOv2 model deployed in K210 chip can detect the target of the character and obtain the position and size of the character in the machine coordinates. Based on the obtained information of character coordinate position and size, the fan's turning Angle and the size of air supply are intelligently perceived. The experimental results show that the intelligent fan design method proposed here is a new embedded chip intelligent method of cutting and improving the YOLOv2 algorithm. It innovatively designed solo tracking, crowd tracking, and intelligent ranging algorithms, which perform well in human perception of solo tracking and crowd tracking and automatic air volume adjustment, improve the accuracy of air delivery and user comfort, and also provide good theoretical and practical support for the combination of AI and embedded in other fields. [ABSTRACT FROM AUTHOR]

Linguistic phrases are tracked in sentences even though there is no one-to-one acoustic phrase marker in the physical signal. This phenomenon suggests an automatic tracking of abstract linguistic structure that is endogenously generated by the brain. However, all studies investigating linguistic tracking compare conditions where either relevant information at linguistic timescales is available, or where this information is absent altogether (e.g., sentences versus word lists during passive listening). It is therefore unclear whether tracking at phrasal timescales is related to the content of language, or rather, results as a consequence of attending to the timescales that happen to match behaviourally relevant information. To investigate this question, we presented participants with sentences and word lists while recording their brain activity with magnetoencephalography (MEG). Participants performed passive, syllable, word, and word-combination tasks corresponding to attending to four different rates: one they would naturally attend to, syllable-rates, word-rates, and phrasal-rates, respectively. We replicated overall findings of stronger phrasal-rate tracking measured with mutual information for sentences compared to word lists across the classical language network. However, in the inferior frontal gyrus (IFG) we found a task effect suggesting stronger phrasal-rate tracking during the word-combination task independent of the presence of linguistic structure, as well as stronger delta-band connectivity during this task. These results suggest that extracting linguistic information at phrasal rates occurs automatically with or without the presence of an additional task, but also that IFG might be important for temporal integration across various perceptual domains. [ABSTRACT FROM AUTHOR]

Linguistic phrases are tracked in sentences even though there is no one-to-one acoustic phrase marker in the physical signal. This phenomenon suggests an automatic tracking of abstract linguistic structure that is endogenously generated by the brain. However, all studies investigating linguistic tracking compare conditions where either relevant information at linguistic timescales is available, or where this information is absent altogether (e.g., sentences versus word lists during passive listening). It is therefore unclear whether tracking at phrasal timescales is related to the content of language, or rather, results as a consequence of attending to the timescales that happen to match behaviourally relevant information. To investigate this question, we presented participants with sentences and word lists while recording their brain activity with magnetoencephalography (MEG). Participants performed passive, syllable, word, and word-combination tasks corresponding to attending to four different rates: one they would naturally attend to, syllable-rates, word-rates, and phrasal-rates, respectively. We replicated overall findings of stronger phrasal-rate tracking measured with mutual information for sentences compared to word lists across the classical language network. However, in the inferior frontal gyrus (IFG) we found a task effect suggesting stronger phrasal-rate tracking during the word-combination task independent of the presence of linguistic structure, as well as stronger delta-band connectivity during this task. These results suggest that extracting linguistic information at phrasal rates occurs automatically with or without the presence of an additional task, but also that IFG might be important for temporal integration across various perceptual domains. [ABSTRACT FROM AUTHOR]

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles. This paper uses wireless communication to find the optimal route planning problem. Using geometric methods, we develop a model of the working environment of the mobile automatic guided vehicle and develop a route finding algorithm. Based on the kinematic model, an advanced routing controller is designed to conduct experimental simulation of two trajectories and verify the effectiveness of the trajectory tracking controller. When the time is after 2 s, the position error is almost completely zero. In the path planning, when the number of iterations is greater than 10, the path length remains constant, verifying the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

Automatic driving has received a broad of attention from academia and industry since it is effective in greatly reducing the severity of potential traffic accidents and achieving the ultimate automobile safety and comfort. This article presents a back‐stepping sliding mode controller (BSMC) through linear matrix inequalities (LMIs) for the highly automatic driving vehicle on sloped roads. It includes three key modules, namely, an extended Kalman filter (EKF)‐based road slope estimation module, a robust BSMC‐LMIs velocity‐tracking controller based on the input–output feedback linearization, as well as a longitudinal inverse vehicle dynamics module. The nonlinear combined slip tire model with the transient behavior is introduced to calculate the tire forces properly, which would be further proven to offer more accurate road slope estimations even in a fierce acceleration or deceleration situation. The proposed BSMC‐LMIs controller for velocity tracking can handle the lumped uncertainties which include the modeling error, the parameter perturbation, external disturbances, and noises, and guarantee the reachability of the sliding surface, meanwhile, alleviating the chattering phenomenon inherited from the sliding mode structure. Besides, a sufficient condition for the existence of the proposed BSMC is derived by using the LMIs, which ensures the t−α$$ {t}^{-\alpha } $$ asymptotical stability on the sliding surface. Finally, the robustness, feasibility, and effectiveness of the proposed BSMC‐LMIs controller for velocity‐tracking are verified by simulation tests in various working scenarios, which shows satisfying results when dealing with the lumped uncertainties on sloped roads. Moreover, the comparative study also shows that the proposed BSMC‐LMIs controller has the best tracking performance when compared to the model predictive control, conventional sliding mode control, and the cubic proportional‐integral controller. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) is the primary cause of severe respiratory infection in infants worldwide. Replication of RSV genomic RNA occurs in cytoplasmic inclusions generating viral ribonucleoprotein complexes (vRNPs). vRNPs then reach assembly and budding sites at the plasma membrane. However, mechanisms ensuring vRNPs transportation are unknown. We generated a recombinant RSV harboring fluorescent RNPs allowing us to visualize moving vRNPs in living infected cells and developed an automated imaging pipeline to characterize the movements of vRNPs at a high throughput. Automatic tracking of vRNPs revealed that around 10% of the RNPs exhibit fast and directed motion compatible with transport along the microtubules. Visualization of vRNPs moving along labeled microtubules and restriction of their movements by microtubule depolymerization further support microtubules involvement in vRNPs trafficking. Approximately 30% of vRNPs colocalize with Rab11a protein, a marker of the endosome recycling (ER) pathway and we observed vRNPs and Rab11-labeled vesicles moving together. Transient inhibition of Rab11a expression significantly reduces vRNPs movements demonstrating Rab11 involvement in RNPs trafficking. Finally, Rab11a is specifically immunoprecipitated with vRNPs in infected cells suggesting an interaction between Rab11 and the vRNPs. Altogether, our results strongly suggest that RSV RNPs move on microtubules by hijacking the ER pathway. Author summary: Respiratory syncytial virus is the leading cause of severe lower respiratory infection in children worldwide and is increasingly recognized as a major respiratory pathogen in the elderly and frail. Yet, no curative treatment or vaccine is currently marketed. The late stages of RSV multiplication remain poorly understood despite they being potential targets for the development of antiviral strategies. In the infected cell, the viral genome is encapsidated and associated to the viral polymerase, to form the viral ribonucleoprotein (vRNP). The vRNPs are produced and assembled in cytoplasmic viral factories. The process ensuring their transport to the budding sites, at the plasma membrane, awaits to be precisely defined. Here we explored these mechanisms by tracking moving vRNPs in living infected cells. We developed an automated imaging pipeline allowing us to characterize the movements of vRNPs, at an unprecedented throughput. We then exploited the potential of our method to monitor the behaviour of the vRNPs during the infection. Using this approach, we document substantial trafficking of RSV along the microtubule network and demonstrate that RSV hijacks the recycling endosome pathway to promote the mobility of its vRNPs. Altogether, this work provides a cutting-edge approach allowing for live visualization of RSV RNP trafficking and critical data toward the understanding of RSV RNPs movements. [ABSTRACT FROM AUTHOR]

Despite considerable progress in the field of automatic multi-target tracking, several problems such as data association remained challenging. On the other hand, cognitive studies have reported that humans can robustly track several objects simultaneously. Such circumstances happen regularly in daily life, and humans have evolved to handle the associated problems. Accordingly, using brain-inspired processing principles may contribute to significantly increase the performance of automatic systems able to follow the trajectories of multiple objects. In this paper, we propose a multiple-object tracking algorithm based on dynamic neural field theory which has been proven to provide neuro-plausible processing mechanisms for cognitive functions of the brain. We define several input neural fields responsible for representing previous location and orientation information as well as instantaneous linear and angular speed of the objects in successive video frames. Image processing techniques are applied to extract the critical object features including target location and orientation. Two prediction fields anticipate the objects' locations and orientations in the upcoming frame after receiving excitatory and inhibitory inputs from the input fields in a feed-forward architecture. This information is used in the data association and labeling process. We tested the proposed algorithm on a zebrafish larvae segmentation and tracking dataset and an ant-tracking dataset containing non-rigid objects with spiky movements and frequently occurring occlusions. The results showed a significant improvement in tracking metrics compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Amur tiger(Panthera tigris altaica)is one of the first-grade state key protected animals. The accurate tracking of the Amur tiger cubs and their mother in the artificial rearing and breeding environment is the basis for analyzing the individual behavior of the Amur tiger cubs during in the growing period and for observing health condition of the Amur tiger. In this study, a lightweight GhostNet-DeepSort network is proposed to realize multi-target tracking of juvenile Amur tigers group activities in surveillance video. According to the test results of the algorithm, the recall rate and average accuracy of the Amur tiger target detection are 94. 9% and 96. 2% respectively, and the tracking accuracy of the Amur tiger target MOTA and MOTP are 91. 6% and 88. 2% respectively. MOTP increases by 1. 25% while MOTA remaines unchanged by GhostNet-DeepSORT algorithm after the lightweight operation, and the memory footprint of the model decreases from 45. 4 MB to 6. 5 MB. Therefore, with respect to DeepSORT algorithm, GhostNet-DeepSORT algorithm can ensure the tracking accuracy and is more suitable for small equipment with insufficient computing resources. Compared with the re-identification network composed of wide residual network, GhostNet network is used to replace the model with light weight to re-identify Amur tigers in video. The implementation of this tracking algorithm also provides necessary technical support for subsequent study on the convenient and rapid evaluation of individual behavior recognition and individual health of captive Amur tiger cubs. [ABSTRACT FROM AUTHOR]

Bear time records, which are the accumulations of spatial spectrum estimates on the time axis, are often employed for passive sonar information processing. Multi-target jamming is a common difficulty in this approach due to the constraints of Rayleigh limit, and neither the conventional beamforming (CBF) nor minimum variance distortionless response (MVDR) technique can handle it well. This work presents a post-processing tracking framework based on visual pattern recognition algorithms to track weak acoustic targets within jamming environments, which includes target motion analysis, matched filtering, and principal component analysis-based denoising, and we call this 'P-Gabor' algorithm. The simulations and sea-trial experiments show that the proposed method can track a weak target successfully under −23 dB (signal-to-interference ratio) SIR, which is more effective than the references, especially in terms of using real-world data from sea trials. We further demonstrate that the method also has stable tracking performance at even −25 dB SNR (signal-to-noise ratio) circumstances. [ABSTRACT FROM AUTHOR]

The American heart association recommends examining four heart angles, i.e., the long axis, short axis, two-chamber, and a four-chamber section of the left ventricle, during the inspection of hearts' condition. Currently, left ventricle observation to assess the heart's condition is commonly done manually. No studies have performed an extraction of features from the movement of the left ventricular wall in the four-chamber and two-chamber views. The presence of an automatic method in observing and evaluating the left ventricle condition will help experts diagnose heart conditions. Methods: This study proposes an automatic tracking method for the left ventricular heart cavity, thereby obtaining the value of heart movement to build a classification system. It involves several stages: image preprocessing, image segmentation, tracking, feature extraction, classification, and validation. Results: The initial preprocessing stage produces images that have clear differences between the heart wall and the left ventricle. This clear distinction becomes the characteristic in separating the left ventricle from the heart wall, thus obtaining a contour using the segmentation method. The obtained contour line is then used as a good-feature using the intersection line. This research uses 24 good-features points which are only defined in the first frame. Furthermore, all frames will be processed using the optical flow Lucas-Kanade method to track the movement of the heart wall. Conclusion: Each good-feature produces four features values: direction (positive and negative) and distance (positive and negative); thus, a total of 96 features are obtained for the entire process. In its implementation, the support vector machine method obtains the highest accuracy value of 94,762 % and 94,977 % with validation techniques, k-folds, and leave-one-out. Significance: This study proposes a method for extracting features from left ventricular movement. [ABSTRACT FROM AUTHOR]

Meso-scale eddies with a diameter of less than 150 km and a time-scale of about one month are in fact rotations (distortions) which are observed in the continuous spatio-temporal fluid field as an evolving pattern over space and time. Ubiquitous eddy fields, including coherent vortices, are important in all aspects of oceanography such as biological, chemical and physical and have major effects on marine ecosystem and adjacent lands. In the present study, using pattern mining approach, the eddy identification automatic algorithm in the Persian Gulf called "Persian Gulf Eddy Scan" was developed and implemented in MATLAB. In order to prepare the input data for the aforementioned algorithm, first the ROMS numerical model with climate forcings was implemented for the Persian Gulf. The algorithm input data include average daily free surface water height, sea surface temperature and salinity with a grid resolution of 5 km. Finally, using this algorithm, 48 meso-scale eddies (including cyclonic and anti-cyclonic) with a lifespan of at least 3 weeks were identified and tracked automatically in the Persian Gulf for one year. The high compliance of the results with the former studies shows its capability in detecting meso-scale eddies. [ABSTRACT FROM AUTHOR]

Recently, various Internet of Things (IoT) platforms offer cloud-based AI using their services like Machine Learning and Neural Networks that operate in their powerful computer engines and provide functionality to IoT devices. Artificial intelligence is widely used in the topics of computer vision for developing smart technologies and intelligent systems. The aim of this research is to create an automatic tool for tracking human's behavior in stores to identify the most popular places. Based on this information, more efficient product or commercial arrangements could be applied that are based on the latest tendencies. In this paper all humans as objects were localized by identifying human features using Gaussian mixture probability density model. After all images were collected and humans were labeled, the real-time human detection and tracking methods were realized. Several NN architectures were analyzed and compared. It appears that the pre-trained Custom Vision model reaches 80% accuracy in 1 hour of training at Microsoft Azure platform. The proposed technique allows not only to track people's behavior but also create a heatmap of the store providing the most visited places, where customers stop and pay more attention. [ABSTRACT FROM AUTHOR]

Automatic detection and tracking of individual animals is important to enhance their welfare and to improve our understanding of their behaviour. Due to methodological difficulties, especially in the context of poultry tracking, it is a challenging task to automatically recognise and track individual animals. Those difficulties can be, for example, the similarity of animals of the same species which makes distinguishing between them harder, or sudden changes in their body shape which may happen due to putting on or spreading out the wings in a very short period of time. In this paper, an automatic poultry tracking algorithm is proposed. This algorithm is based on the well-known tracktor approach and tackles multi-object tracking by exploiting the regression head of the Faster R-CNN model to perform temporal realignment of object bounding boxes. Additionally, we use a multi-scale re-identification model to improve the re-association of the detected animals. For evaluating the performance of the proposed method in this study, a novel dataset consisting of seven image sequences that show chicks in an average pen farm in different stages of growth is used. [ABSTRACT FROM AUTHOR]

Background. Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited. Methods. We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days and the probability of elimination. Results. Effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective. Conclusions. For digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals. [ABSTRACT FROM AUTHOR]

Aiming at problems of complex structure and no overload of articulated automatic cloth cropping trolley, an automatic multi load cloth cropping trolley composed of AGV and chain track was designed and developed. The chain track was composed of three tangent arcs, parametric design was carried out according to the loom type and workshop working conditions. Parametric equation of track curve was derived by D-H method, design variables were determined based on the geometric relationship of parameters, the single objective optimization model with multiple constraints was established, the optimization solution of track parameters was obtained by genetic algorithm, interactive interface was developed for inputting known parameters and outputting track curve. Parts of the driving unit were calculated and selected, the engineering prototype was made and the function test was completed. The results showed that the time consuming of cloth cropping process was about 20 s, and the average speed of the trolley during transportation was 15 m/s, the time consuming was basically same to the skilled cloth cropping worker. It is considered that multi load function can be realized by adopting chain track automatic cloth cropping trolley, the purpose of reducing labor intensity, reducing staff and increasing efficiency can be achieved. [ABSTRACT FROM AUTHOR]

This paper describes a concept for the construction and application of the Counter-UAV Defence System as developed by the Polish consortium of Zakłady Mechaniczne “Tarnów” S.A. (ZMT) and Military University of Technology (MUT). The article describes a system designed to counteract unmanned aerial vehicles, which consists of a multi-barrel machine gun and a radiolocation system. The Counter-UAV Defence System enables the combating of aerial targets, including unmanned aerial vehicles (UAVs).

• A resonant frequency tracking method based on phase control method is proposed. • A shunted piezoelectric transducer which could tune itself by digitally programmable inductance is presented. • Fuzzy PI-based frequency tracking algorithm is introduced with the advantage of eliminating the percent overshoot. • An impedance matching network is designed to accommodate piezoelectric transducers with different parameters. Piezoelectric ultrasonic transducers, vital in medical devices and aerospace, often face challenges like resonant frequency shifts and impedance variations affecting their operational efficiency. This paper introduces a shunted piezoelectric transducer which could tune itself by digitally programmable inductance. A transformer and inductance-capacitance matching network ensures enhanced compatibility and impedance management. Proposing a fuzzy PI-based phase control method achieves resonant frequency tracking, synchronizing operational frequency with the transducer. In contrast to traditional methods, our approach enables faster and more precise fine-tuning, detecting and rectifying real-world deviations for optimal performance. A comprehensive experimental validation, based on fundamental knowledge analysis, confirms the feasibility and superiority of our proposed method, and the commonly encountered issues of resonance frequency deviation and impedance variation in high-power piezoelectric transducer applications can be effectively mitigated. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-sensor distributed fusion with memory configuration for multi-target automatic tracking (MTAT) in clutter. Aimed at fully addressing the problems of both track origin ambiguity and the strong-correlation among system track and its paired local tracks in the framework, this paper derives an Exact Unified Multisensory Tracks Fusion with Memory (EU-MTFwM) method that fuses the system track with enumerated combinatorial tracks to achieve fusing all sensors' local tracks at once. It deploys track-to-track probabilistic association (T2TPA) and target-existence based track automatic management (TAM) to mitigate track origin ambiguity caused by clutter disturbance and the presence of multiple targets. Additionally, it implements the exact distributed kinematic state estimation (exact DKSE) to remove strong correlation stemming from common historical measurements and process noise. However, as the number of sensors to be fused increases, the proposed EU-MTFwM suffers from the combinatorial explosion. An Implementable Unified Multisensory Tracks Fusion with Memory (IU-MTFwM) method is then derived to sequentially fuse the system track with origin-ambiguous local tracks from multiple sensors one-by-one, providing a unified but computationally efficient alternative to the EU-MTFwM. Furthermore, a novel system track initialization technology is proposed to trigger the multisensory tracks fusion recursion. Numerical results show the proposed two unified methods achieve comparable tracking performance to the centralized fusion benchmark but with significantly reduced communication consumption, and demonstrate intensive improvement over the enhanced state-of-the-art that incorporates our proposed system track initialization into its fusion recursion. [ABSTRACT FROM AUTHOR]

Featured Application: The eye-in-hand adaptive gripper can be used in intelligent manufacturing applications. With its eye-in-hand feature and adaptive mechanism, the smart gripper can offer both visual and mechanical adaption in grasping objects of any shape. It also offers automatic identification and tracking of a moving object with its vision servoing feature. With the advancement of robotic technologies, more and more tasks in industrial and commercial applications rely on the use of robots to assist or even replace humans. To fulfill the needs of grasping and handling different objects, the development of a universal grasping device acting as an end-effector to a robotic manipulator has been one of the main robotic research and development focuses. Therefore, this study was aimed at the development of a general robotic gripper with three fingers for adaptive actuation and an eye-in-hand vision system. With the adaptive actuation feature, each finger of the robotic gripper contained multiple degrees of freedom that allowed the finger to change its shape to wrap around an object's geometry adaptively for stable grasping. With the eye-in-hand configuration in the adaptive gripper, it offered advantages including occlusion avoidance, intuitive teleoperation, imaging from different angles, and simple calibration. This study proposed and integrated a plug-and-play gripper module, controller module, and visual calculation module all in the model smart gripper, of which the gripper was further validated by calibrated experiments. The proposed gripper featured mechanical adaptation and visual servoing adaptivity to achieve 100% gripping success rate when gripping a moving target of any shape that was carried by conveyor belt with moving speed less than 70 mm/s. By integrating mechanical and visual adaptivity, the proposed gripper enabled the inclusion of intelligence in robotic applications and can further be used in smart manufacturing and intelligent robotic applications. [ABSTRACT FROM AUTHOR]

The usage of unmanned aerial vehicles (UAVs) is rapidly increasing in the current era as these devices are capable enough in providing unique solutions in applications such as inspection of environment, identification of disaster, rescue operations, and defense systems. For the governance of the flight missions in a complex defense environment, the usage of these systems necessitated a sound command over data mining process. The large volume of data is generated by UAVs and processing of this data is a challenging issue. The existing data tracking and management systems are expensive and complex. For defense systems, smarter solutions are needed to process the large volume of data at low cost and with high accuracy. Therefore, a technique of tracking the data generated by UAV and automatic measurement of trajectory based on micro-electro-mechanical systems sensor in UAV has been proposed in this paper to provide inexpensive solutions to overcome the problems of the existing data tracking and processing systems. An iterative learning control algorithm is utilized in UAV to ascertain the disturbance and modeling errors. The finest characteristics of the Kalman filter technique are used for estimations of UAV trajectory. The quadratic performance function is introduced in discrete equation to solve the model error disturbance. Then on the basis of gyroscope data, the quaternion differential equation is formulated. The gradient descent process is also used to speed up the processing of UAV data. The results depict that the proposed technique has the lowest data tracking error of the UAV trajectory (0.09%) and has good measurement accuracy of 92%. The proposed method also reduces time complexity and searches the solution space in a faster manner. [ABSTRACT FROM AUTHOR]

Seamounts are prominent features of the seafloor that are often located in Areas Beyond National Jurisdiction (ABNJs). Whilst comprehensive biological information is lacking on most of these features, they have been recognised for hosting high biodiversity across multiple trophic levels. Technological advancements have enabled greater exploitation of biological resources further offshore with increasing concern over the long‐term impacts of anthropogenic activities on vulnerable distant and deep‐sea habitats. Analysis of ex situ vessel tracking technologies such as Automatic Identification Systems (AIS) have enabled spatial patterns of fishing activity to be monitored over large geographical areas. In this study, analysis of fishing activity within 30 km of seamount summits at the global scale found that these features within the waters of the Pacific Island Group and the Mediterranean Sea were subject to the highest levels of longlining and trawling activities respectively. Fishing in proximity to seamounts is dominated by the flag states of Taiwan, China, Japan, South Korea and Spain. Furthermore, our results reveal that the majority of sea areas managed by many Regional Fishery Management Organisations (RFMOs) have experienced increased fishing activity at seamounts compared to areas in the same ocean basin without management. This study demonstrates how free web‐accessible data can be used to gain insights into remote areas where in situ research is prohibitively expensive and logistically challenging. [ABSTRACT FROM AUTHOR]

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

Background: The potential for using ultrasound by speech and language therapists (SLTs) as an adjunct clinical tool to assess swallowing function has received increased attention during the COVID‐19 pandemic, with a recent review highlighting the need for further research on normative data, objective measurement, elicitation protocol and training. The dynamic movement of the hyoid, visible in ultrasound, is crucial in facilitating bolus transition and protection of the airway during a swallow and has shown promise as a biomarker of swallowing function. Aims: To examine the kinematics of the hyoid during a swallow using ultrasound imaging and to relate the patterns to the different stages of a normal swallow. To evaluate the accuracy and robustness of two different automatic hyoid tracking methods relative to manual hyoid position estimation. Methods & Procedures: Ultrasound data recorded from 15 healthy participants swallowing a 10 ml water bolus delivered by cup or spoon were analysed. The movement of the hyoid was tracked using manually marked frame‐to‐frame positions, automated hyoid shadow tracking and deep neural net (DNN) tracking. Hyoid displacement along the horizontal image axis (HxD) was charted throughout a swallow, and the maximum horizontal displacement (HxD max) and maximum hyoid velocity (HxV max) along the same axis were automatically calculated. Outcomes & Results: The HxD and HxV of 10 ml swallows are similar to values reported in the literature. The trajectory of the hyoid movement and its location at significant swallow event time points showed increased hyoid displacement towards the peak of the swallow. Using an interclass correlation coefficient, HxD max and HxV max values derived from the DNN tracker and shadow tracker are shown to be in high agreement and moderate agreement, respectively, when compared with values derived from manual tracking. Conclusions & Implications: The similarity of the hyoid tracking results using ultrasound to previous reports based on different instrumental tools supports the possibility of using hyoid movement as a measure of swallowing function in ultrasound. The use of machine learning to automatically track the hyoid movement potentially provides a reliable and efficient way to quantify swallowing function. These findings contribute towards improving the clinical utility of ultrasound as a swallowing assessment tool. Further research on both normative and clinical populations is needed to validate hyoid movement metrics as a means of differentiating normal and abnormal swallows and to verify the reliability of automatic tracking. WHAT THIS PAPER ADDS: What is already known on this subject: There is growing interest in the use of ultrasound as an adjunct tool for assessing swallowing function. However, there is currently insufficient knowledge about the patterning and timing of lingual and hyoid movement in a typical swallow. We know that movement of the hyoid plays an essential role in bolus transition and airway protection. However, manual tracking of hyoid movement is time‐consuming and restricts the extent of large‐scale normative studies. What this study adds: We show that hyoid movement can be tracked automatically, providing measurable continuous positional data. Measurements derived from this objective data are comparable with similar measures previously reported using videofluoroscopy and of the two automatic trackers assessed, the DNN approach demonstrates better robustness and higher agreement with manually derived measures. Using this kinematic data, hyoid movement can be related to different stages of swallowing. Clinical implications of this study: This study contributes towards our understanding of the kinematics of a typical swallow by evaluating an automated hyoid tracking method, paving the way for future studies of typical and disordered swallow. The challenges of image acquisition highlight issues to be considered when establishing clinical protocols. The application of machine learning enhances the utility of ultrasound swallowing assessment by reducing the labour required and permitting a wider range of hyoid measurements. Further research in normative and clinical populations is facilitated by automatic data extraction allowing the validity of prospective hyoid measures in differentiating different types of swallows to be rigorously assessed. [ABSTRACT FROM AUTHOR]

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Fractures play a vital role in reservoir transformation, but the distribution of faults and fractures is difficult to obtain by observing cores, which ultimately limits the effective development of gas dolomite reservoirs. We propose an integrated method that incorporates thin-section observations, three-dimensional (3D) seismic data, and image logs to interpret the distribution of faults and fractures of Cambrian Longwangmiao Carbonate Formation to predict potential development areas in the Moxi-Gaoshiti area of the Sichuan Basin, South China. Firstly, the faults were well interpreted by using the automatic tracking and 3D visualization technique based on the new seismic combination attribute of symmetry and ant tracking. Secondly, a comprehensive analysis was conducted using the thin sections, paleogeomorphology, and in situ test results to determine the fracture types (corrosion and structural fractures). The results help us to find potential sweet spot zones with good permeabilities, which is of great significance in reducing the risk of water production of drilled wells in the field development. [ABSTRACT FROM AUTHOR]

Most research aimed at measuring biomarkers on the skin is only concerned with sensing chemicals in sweat using electrical signals, but these methods are not truly non-invasive nor non-intrusive because they require substantial amounts of sweat to get a reading. This project aims to create a truly non-invasive wearable sensor that continuously detects the gaseous acetone (a biomarker related to metabolic disorders) that ambiently comes out of the skin. Composite films of polyaniline and cellulose acetate, exhibiting chemo-mechanical actuation upon exposure to gaseous acetone, were tested in the headspaces above multiple solutions containing acetone, ethanol, and water to gauge response sensitivity, selectivity, and repeatability. The bending of the films in response to exposures to these environments was tracked by an automatic video processing code, which was found to out-perform an off-the-shelf deep neural network-based tracker. Using principal component analysis, we showed that the film bending is low dimensional with over 90% of the shape changes being captured with just two parameters. We constructed forward models to predict shape changes from the known exposure history and found that a linear model can explain 40% of the observed variance in film tip angle changes. We constructed inverse models, going from third order fits of shape changes to acetone concentrations where about 45% of the acetone variation and about 30% of ethanol variation are captured by linear models, and non-linear models did not perform substantially better. This suggests there is sufficient sensitivity and inherent selectivity of the films. These models, however, provide evidence for substantial hysteretic or long-time-scale responses of the PANI films, seemingly due to the presence of water. Further experiments will allow more accurate discrimination of unknown exposure environments. Nevertheless, the sensor will operate with high selectivity in low sweat body locations, like behind the ear or on the nails. [ABSTRACT FROM AUTHOR]

The use of Engineered Heart Tissues (EHT) as in vitro model for disease modeling and drug screening has increased, as they provide important insight into the genetic mechanisms, cardiac toxicity or drug responses. Consequently, this has highlighted the need for a standardized, unbiased, robust and automatic way to analyze hallmark physiological features of EHTs. In this study we described and validated a standalone application to analyze physiological features of EHTs in an automatic, robust, and unbiased way, using low computational time. The standalone application "EHT Analysis" contains two analysis modes (automatic and manual) to analyzes the contractile properties and the contraction kinetics of EHTs from high speed bright field videos. As output data, the graphs of displacement, contraction force and contraction kinetics per file will be generated together with the raw data. Additionally, it also generates a summary file containing all the data from the analyzed files, which facilitates and speeds up the post analysis. From our study we highlight the importance of analyzing the axial stress which is the force per surface area (μN/mm2). This allows to have a readout overtime of tissue compaction, axial stress and leave the option to calculate at the end point of an experiment the physiological cross-section area (PSCA). We demonstrated the utility of this tool by analyzing contractile properties and compaction over time of EHTs made out of a double reporter human pluripotent stem cell (hPSC) line (NKX2.5EGFP/+-COUP-TFIImCherry/+) and different ratios of human adult cardiac fibroblasts (HCF). Our standalone application "EHT Analysis" can be applied for different studies where the physiological features of EHTs needs to be analyzed under the effect of a drug compound or in a disease model. [ABSTRACT FROM AUTHOR]

The assessment of Tropical Cyclones (TC) statistics requires the direct, objective and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of algorithms, often referred to as trackers, that aim to detect the positions of tropical cyclones in gridded datasets. This paper compares four trackers with very different formulations in detail. We assess their performances by tracking tropical cyclones in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. The first section of the paper finds typical detection rates of the trackers ranging from 75 to 85%. At the same time, false alarm rates (FAR) greatly vary across the four trackers and can sometimes exceed the number of detected genuine cyclones. Based on the finding that many of these false alarms are extra-tropical cyclones, we adapt two existing filtering methods common to all trackers. Both post-treatments dramatically impact FARs, which range from 9 to 36% in our final catalogs of tropical cyclones tracks. We then show that different traditional metrics can be very sensitive to the particular choice of the tracker, which is particularly true for the TC frequencies and their durations. By contrast, all trackers identify a robust negative bias in ERA5 tropical cyclones intensities, a result already noted in previous studies. We conclude by advising against using as many trackers as possible and averaging the results. A more efficient approach would involve selecting one or a few trackers with well-known properties. [ABSTRACT FROM AUTHOR]

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to solve the problem of path planning and path tracking in the automatic parking assistant system. Design/methodology/approach: This paper first uses the method of reverse driving to confirm few control points based on the constraints of the construction of the vehicle and the environment information, then a reference path with free-collision and continuous curvature is designed based on the Bézier curve. According to the principle of the discrete linear quadratic regulator (LQR), a tracking controller that combines feedforward control and feedback control is designed. Findings: Finally, simulation analysis are carried out in Simulink and CARSIM. The results show that the proposed method can obtain a better path tracking effect when the parking space size is appropriate. Originality/value: According to the principle of the discrete LQR, a tracking controller that combines feedforward control and feedback control is designed. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic optic neuropathy characterized by irreversible damage to the retinal nerve fiber layer (RNFL), resulting in changes in the visual field (VC). Glaucoma screening is performed through a complete ophthalmological examination, using images of the optic papilla obtained in vivo for the evaluation of glaucomatous characteristics, eye pressure, and visual field. Identifying the glaucomatous papilla is quite important, as optical papillary images are considered the gold standard for tracking. Therefore, this article presents a review of the diagnostic methods used to identify the glaucomatous papilla through technology over the last five years. Based on the analyzed works, the current state-of-the-art methods are identified, the current challenges are analyzed, and the shortcomings of these methods are investigated, especially from the point of view of automation and independence in performing these measurements. Finally, the topics for future work and the challenges that need to be solved are proposed. [ABSTRACT FROM AUTHOR]

In shipbuilding, there are many drain holes in the cabin, which lead to the discontinuity of the weld. The existing large welding manipulator cannot move independently and automatically to identify and track this kind of weld seam. At present, this kind of work is mainly done manually. In order to solve this problem, an autonomous mobile welding robot is developed, which can automatically identify all kinds of discontinuous welds seam and complete automatic welding. The tasks, structure, and working principle of welding robot are introduced. The mathematical model of the system is established, and the theoretical analysis and field application verification of related tasks are carried out. The results show that the welding robot can complete the automatic recognition and tracking welding of discontinuous welds seam with high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Our work targets automated analysis to quantify the growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate the proposed automatic cell tracking algorithm using (i) recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps and (ii) real data. On a batch of 1100 simulated image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences (i.e., real data) of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%. [ABSTRACT FROM AUTHOR]

Sports biomechanics research includes the use of various technical methods to collect various sports biomechanical parameters and variables. Sports biomechanics is one of the branches of sports science, which is a new science for studying sports. In the process of teaching, the Kinect somatosensory camera is used for bone tracking to obtain spatial coordinate information of human nodes, and the teaching data for imitation learning is obtained after preprocessing. This paper builds a general sports auxiliary training system and designs some of the system's functions in detail, using the advantages of Microsoft's Kinect collector in image acquisition. The automatic tracking method of sports image sequence marker points is studied in this paper using Kinect. Kinect is used to collect bone point information, establish a virtual coordinate system, obtain each joint vector from the node information, calculate the included angle between the vectors, and then obtain the control angle of each joint, providing data support for imitation representation learning. Count and score this movement based on the distance between the jaw and the crossbar as well as the angle at which the arm bends. [ABSTRACT FROM AUTHOR]

Traditional PID controllers are widely used in industrial applications due to their simple computational architecture. However, the gain parameters of this simple computing architecture are fixed, and in response to environmental changes, the PID parameters must be continuously adjusted until the system is optimized. This research proposes to use the most important deep reinforcement learning (DRL) algorithm in deep learning as the basis and to modulate the gain parameters of the PID controller with fuzzy control. The research has the ability and advantages of reinforcement learning and fuzzy control and constructs a tracking unmanned wheel system. The mobile robotic platform uses a normalization system during computation to reduce the effects of reading errors caused by the wheeled mobile robot (WMR) of environment and sensor processes. The DRL-Fuzzy-PID controller architecture proposed in this paper utilizes degree operation to avoid the data error of negative input in the absolute value judgment, thereby reducing the amount of calculation. In addition to improving the accuracy of fuzzy control, it also uses reinforcement learning to quickly respond and minimize steady-state error to achieve accurate calculation performance. The experimental results of this study show that in complex trajectory sites, the tracking stability of the system using DRL-fuzzy PID is improved by 15.2% compared with conventional PID control, the maximum overshoot is reduced by 35.6%, and the tracking time ratio is shortened by 6.78%. If reinforcement learning is added, the convergence time of the WMR system will be about 0.5 s, and the accuracy rate will reach 95%. This study combines the computation of deep reinforcement learning to enhance the experimentally superior performance of the WMR system. In the future, intelligent unmanned vehicles with automatic tracking functions can be developed, and the combination of IoT and cloud computing can enhance the innovation of this research. [ABSTRACT FROM AUTHOR]

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

Currently, small payload and short endurance are the main problems of a single UAV in agricultural applications, especially in large-scale farmland. It is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative navigation. This study proposed a laser tracking leader-follower automatic cooperative navigation system for multi-UAVs. The leader in the cluster fires a laser beam to irradiate the follower, and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking device. Based on the existing kernel correlation filter (KCF) tracking algorithm, an improved KCF real-time spot tracking method was proposed. Compared with the traditional KCF tracking algorithm, the recognition and tracking rate of the optimized algorithm was increased from 70% to 95% in indoor environment, and was increased from 20% to 90% in outdoor environment. The navigation control method was studied from two aspects: the distance coordinate transformation model based on micro-gyroscope and navigation control strategy. The error of spot position was reduced from the maximum (3.12, -3.66) cm to (0.14, 0.12) cm by correcting the deviation distance of the spot at different angles through a coordinate correction algorithm. An image coordinate conversion model was established for a complementary metal-oxide-semiconductor (CMOS) camera and laser receiving device at different mounting distances. The laser receiving device was divided into four regions, S0-S3, and the speed of the four regions is calculated using an uncontrollable discrete Kalman filter. The outdoor flight experiments of two UAVs were carried out outdoors using this system. The experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm, and the coefficient of variation is 0.0181. The average flight error on the Z-axis is 7.3 cm, and the coefficient of variation is 0.0414. This study demonstrated the possibility and adaptability of the developed system to achieve multi-UAVs cooperative navigation. [ABSTRACT FROM AUTHOR]

Measuring the viscosity and density of petroleum products is important for their proper production, transportation and application. Viscosity and density are the main parameters determining the composition and structure of petroleum products. Therefore, in the industry, to control the quality of petroleum products during various technological processes, automatic and non-automatic devices are used for their measurement. The accuracy of measuring the viscosity and density of petroleum products is an important factor. The authors analyzed different methods of measuring the viscosity and density of petroleum products and synthesized three versions of throttle bridge transducers. These versions implement differential measurement methods and have different numbers of laminar and turbulent throttles. The authors obtained new equations of static conversion functions by channels of measuring the kinematic viscosity and density of petroleum products of the proposed throttle bridge transducers. The authors developed a block diagram and designed measuring equipment to study experimentally the static characteristics of the throttle bridge transducers. The authors determined that the maximal relative deviations of the results of experimental studies from numerical calculations of a static conversion factor by channels of kinematic viscosity and density were 5.88% and 8.76%, respectively. The authors developed two versions of automatic devices for measuring the kinematic viscosity and density of petroleum products based on the proposed throttle bridge transducers. The first version is an automatic analyzer with tracking astatic balancing. The second version is an automatic analyzer with deployment balancing. The authors developed constructions of both versions of automatic analyzers and obtained the results of experimental measuring of the kinematic viscosity and density of petroleum products in different ranges. [ABSTRACT FROM AUTHOR]