Tracking Behavior Research Articles

Effects of simulated natural light brightness on visual perception in virtual reality forests: An eye-tracking study

Although natural light is essential for human health and well-being, the potential effects of different brightness levels of light on the visual perception of forests have not been discussed. Using controlled eye-tracking experiments, this study explored the impact of seven simulated natural brightness levels, from the darkest to the brightest, on the visual perception of 118 participants in a virtual reality (VR) forest environment. One of the most significant findings of this study was the marginal effect of natural light brightness levels on visual physiological stress in VR forests, with the pupillary unrest index decreasing significantly with the increase of natural light brightness; Secondly, the study analyzed the influence of natural light brightness levels in VR forests on the visual engagement behavior of eye tracking, that is, bright forest environments showed an overall trend associated with longer fixation duration, less saccade duration, and greater fixation direction ratio; Thirdly, in terms of visual perception evaluation, the study found an inverted U-shaped trend of mood, restorative perception, perceived safety, and natural light brightness level in VR forests. In addition, the study also found a significant correlation between visual physiological and psychological indicators in VR forest natural light visual perception experiments. These findings help us understand the visual perception effect of forest nature light exposure as a whole, and provided important clues and a basis for future research on improving the visual comfort of forest natural light.

The effects of water flow velocity and odor release frequency on the odor-tracking behavior of swimming crabs (Portunus trituberculatus)

Chemical-guided tracking is vital for critical activities such as foraging and mating in swimming crabs (Portunus trituberculatus), as it facilitates the detection and localization of odor sources. While water flow and odor release patterns are known to influence odor-tracking behavior, the specific mechanisms of their impact remain poorly understood. This study quantified the tracking behaviors of juvenile swimming crabs in response to varying water flow velocities (0.7 cm/s, 1.6 cm/s, and 2.5 cm/s) and odor release frequencies (0.5 Hz, 1.0 Hz, and 1.5 Hz). Results indicate that (1) at a flow velocity of 1.6 cm/s, crabs exhibited a higher proportion of tracking and success. Successful trackers at this flow velocity also demonstrated better tracking performance, including higher net-to-gross displacement ratios (NGDR), reduced gross displacements, shorter exit and track durations, and simpler tracking trajectories. (2) While odor release frequency did not significantly affect the proportion of tracking or success, the successful trackers at a higher frequency (1.5 Hz) had lower NGDR and higher gross displacements compared to those exposed to a lower frequency (0.5 Hz). (3) The successful trackers were not stastistically affected by the interaction between flow velocities and release frequencies. (4) In contrast to successful trackers, unsuccessful ones showed highly variable responses to flow velocity and odor release frequency, with generally inferior tracking performance. These findings provide valuable insights into the olfactory ecology of swimming crabs and can inform the optimized of aquaculture management and formulated feeds production.

A transfer learning model for cognitive electronic reconnaissance of unmanned aerial vehicle: Experiments

Applying Deep Reinforcement Learning (DRL) technologies to Unmanned Aerial Vehicle (UAV) electronic reconnaissance is one of the current research hotspots. However, simulation and engineering practice show that due to poor generalization of DRL models, the performance of Cognitive Electronic Reconnaissance (CER) policies based on training will significantly decrease when the mission scene undergoes slight changes. To address this issue, we thoughtfully combine the mission area segmentation technique with transfer DRL and propose a difference-adaptive transfer DRL algorithm. This algorithm involves mission subarea segmentation, subarea pre-training, multi-subarea policy transfer, and multi-subarea splicing, providing an efficient solution to the convergence problem of the DRL algorithm caused by mission space expansion and reward sparsity. Additionally, a general CER transfer learning simulator is developed based on the analysis of the capabilities of the maneuvering platform and electronic reconnaissance payload. Multiple sets of CER policy transfer learning experiments are designed for different mission spaces, mission difficulties, and UAV characteristics. Compared with the algorithm baseline, our designed policy model significantly outperforms: the mission completion rate of UAVs in multi-scale mission spaces improves by up to 37.4%, reaching 97.5%, while the training time is reduced by 2.46 h. Further behavior analysis shows that this policy model enables UAVs to exhibit target tracking behaviors such as hovering and sustained approach.

Novel Efficiency Calculation Model Based on Fine Particle Tracking Behavior

The underflow entrainment of fine particles occurs during the hydrocyclone separation process, resulting in a “fishhook” effect on grade efficiency. Traditional efficiency models fail to address this phenomenon. This study examines the tracking behavior of fine particles, using variations in centrifugal settling velocity to characterize separation performance. The effect of this behavior on particle separation is quantified through a tracking coefficient for small particles and an entrainment coefficient for large particles, together forming a novel efficiency calculation model. The experimental research shows that the new model is applicable for the efficiency calculation of particles with different shapes, and can calculate grade efficiency curves with fishhook segments. By comparing with the existing research results, the accuracy and universality of the new model have been demonstrated. This model facilitates the accurate computation of grade efficiency curves, thereby significantly enhancing the precision of efficiency calculations, which provides guidance for the design and selection of hydrocyclones.

Olfactory sensory neuron population expansions influence projection neuron adaptation and enhance odour tracking

The evolutionary expansion of sensory neuron populations detecting important environmental cues is widespread, but functionally enigmatic. We investigated this phenomenon through comparison of homologous olfactory pathways of Drosophila melanogaster and its close relative Drosophila sechellia, an extreme specialist for Morinda citrifolia noni fruit. D. sechellia has evolved species-specific expansions in select, noni-detecting olfactory sensory neuron (OSN) populations, through multigenic changes. Activation and inhibition of defined proportions of neurons demonstrate that OSN number increases contribute to stronger, more persistent, noni-odour tracking behaviour. These expansions result in increased synaptic connections of sensory neurons with their projection neuron (PN) partners, which are conserved in number between species. Surprisingly, having more OSNs does not lead to greater odour-evoked PN sensitivity or reliability. Rather, pathways with increased sensory pooling exhibit reduced PN adaptation, likely through weakened lateral inhibition. Our work reveals an unexpected functional impact of sensory neuron population expansions to explain ecologically-relevant, species-specific behaviour.

Application of Video Abnormal Behavior Detection Algorithm in Evaluation of Track and Field Teaching and Training Effect

With the development of track and field, people pay more and more attention to the quality of classroom teaching of track and field technology, and the evaluation of teaching quality plays a key role in it. In today's educational reform, teaching evaluation plays an important role as an important method to test teachers' teaching and students' learning. With the rapid development of machine learning, especially deep learning, image-based individual abnormal behavior detection technology is becoming more and more mature, but there are still many difficulties to be solved in video-based group abnormal behavior detection technology. Therefore, it is necessary to study the detection algorithm. Based on machine vision theory, image processing theory and video analysis technology, this paper studies three key technologies involved in human abnormal behavior detection in video: moving target detection, moving target tracking and abnormal behavior detection.

Sensorimotor confidence for tracking eye movements.

For successful interactions with the world, we often have to evaluate our own performance. Although eye movements are one of the most frequent actions we perform, we are typically unaware of them. Here, we investigated whether there is any evidence for metacognitive sensitivity for the accuracy of eye movements. Participants tracked a dot cloud as it followed an unpredictable sinusoidal trajectory and then reported if they thought their performance was better or worse than their average tracking performance. Our results show above-chance identification of better tracking behavior across all trials and also for repeated attempts of the same target trajectories. Sensitivity in discriminating performance between better and worse trials was stable across sessions, but judgements within a trial relied more on performance in the final seconds. This behavior matched previous reports when judging the quality of hand movements, although overall metacognitive sensitivity for eye movements was significantly lower.

Performance evaluation of MPPT algorithm of VIPV systems in realistic urban routes using image processing

The assessment of the power that Vehicle Integrated Photovoltaic (VIPV) systems produce during the vehicle displacement in urban environments requires to know the effect of the dynamic shadowing over the photovoltaic (PV) surface and the response that the DC/DC electronics have over these situations of fast-evolving shadows. This paper poses a novel methodology that evaluate the shadowing profile on the vehicle through images and estimates the PV output power and the behavior of a classical Maximum Power Point Tracking (MPPT) algorithm through simulations. Through this methodology, the efficiency (intended as the ratio between the maximum power generated by the PV module and the power delivered by the MPPT algorithm) has been evaluated in a real dynamic shadowing situation, where images of the vehicle roof in a 22 km route have been acquired at high frequency.

Vehicle Monitoring and Tracking System

In this paper we present a vehicle monitoring and tracking system. Previous systems focus solely on either theft detection or car/driver monitoring. In this paper we provide an integrated solution that provide both theft detection, tracking and driver behavior monitoring. Our system performs well in detecting theft attempts through RFID and tracking location by GPS. It also monitors the driver behavior using OBD reader. Combining both features in one system has great advantages like cost efficiency, space saving and the possibility of adding new features.

State anticipation and task serialization attenuate embodied decision biases when deciding while moving.

We examined whether and how embodied decision biases-related to motor costs (MC) as well as cognitive crosstalk (CC) due to the body state-are influenced by extended deliberation time. Participants performed a tracking task while concurrently making reward-based decisions, with rewards being presented with varying preview time. In Experiment 1 (N = 58), we observed a reduced CC bias with extended preview time. Partially, this was due to participants slightly adapting tracking to serialize it in relation to decision making. However, the influence of MC was only marginal and not subject to anticipatory state adjustments. In Experiment 2 (N = 67), we examined whether participants integrated the immediate state at reward presentation or anticipated state when a decision could be implemented when adapting their tracking and decision behavior. Results were most compatible with the anticipated state being integrated. We conclude that humans anticipate the body state when a decision must be implemented and consider the corresponding motor and cognitive demands when adapting their decision behavior. However, anticipatory state adaptations targeting the influence of MC with extended preview time were absent, suggesting that anticipatory adaptations are starkly limited in low-practice tasks compared to more overlearned behavior like walking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A dynamic scale-mixture model of motion in natural scenes.

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.

Social media behaviors and body type ideals predict weight loss and food tracking behaviors among recreational climbers

Social media behaviors and body type ideals predict weight loss and food tracking behaviors among recreational climbers

The role of prediction and visual tracking strategies during manual interception: An exploration of individual differences.

The interception (or avoidance) of moving objects is a common component of various daily living tasks; however, it remains unclear whether precise alignment of foveal vision with a target is important for motor performance. Furthermore, there has also been little examination of individual differences in visual tracking strategy and the use of anticipatory gaze adjustments. We examined the importance of in-flight tracking and predictive visual behaviors using a virtual reality environment that required participants (n = 41) to intercept tennis balls projected from one of two possible locations. Here, we explored whether different tracking strategies spontaneously arose during the task, and which were most effective. Although indices of closer in-flight tracking (pursuit gain, tracking coherence, tracking lag, and saccades) were predictive of better interception performance, these relationships were rather weak. Anticipatory gaze shifts toward the correct release location of the ball provided no benefit for subsequent interception. Nonetheless, two interceptive strategies were evident: 1) early anticipation of the ball's onset location followed by attempts to closely track the ball in flight (i.e., predictive strategy); or 2) positioning gaze between possible onset locations and then using peripheral vision to locate the moving ball (i.e., a visual pivot strategy). Despite showing much poorer in-flight foveal tracking of the ball, participants adopting a visual pivot strategy performed slightly better in the task. Overall, these results indicate that precise alignment of the fovea with the target may not be critical for interception tasks, but that observers can adopt quite varied visual guidance approaches.

Performance-Based Hierarchical Fault-Tolerant Control for Closed-Loop Systems With Multiplicative Faults: A Data-Driven Design Method.

Fault-tolerant control (FTC) is vital for the safety and reliability of automatic systems. Most of the existing FTC methods are developed for open-loop systems subject to additive faults, regardless of the widely present control loops and multiplicative faults within systems. In this article, a performance-based FTC strategy is proposed for the closed-loop systems with multiplicative faults. Considering the high efforts in modeling complex systems, the proposed FTC strategy is realized in the data-driven context. Specifically, a nominal feedback-feedforward controller is first established for the fault-free systems. By selecting the system stability and reference tracking behavior as the key performance indices, two performance evaluators are constructed to detect and classify the occurred multiplicative faults based on the fault-induced effects on the system performance. Then, with the aid of the coprime factorization technique, the multiplicative faults, in the form of additive perturbations to the system coprime factors, are estimated utilizing the closed-loop process data. Furthermore, based on the fault knowledge, a hierarchical fault-tolerant tracking controller is developed according to the levels of system performance degradations, where the functional controller parameters are reconfigured with different priorities. Finally, case studies are provided to validate the effectiveness of the proposed method.

Treatment of Hypertension in Children.

Hypertension is a real problem in children. It shows a tracking behaviour, representing a key risk factor for hypertension, cardiovascular disease, and end-organ failure in adulthood. However, the importance of addressing arterial hypertension in children is not limited to its risk of tracking into adulthood. Thus, early detection and management are crucial. Hypertension may be primary or due to secondary causes, and identification of this distinction is very important for the treatment setting. Importantly, the management of hypertension in children is crucial to prevent the well-known cardiovascular effects in adulthood. As demonstrated in the literature, healthy eating habits, together with regular physical activity, can have a major impact on reducing high blood pressure and preventing organ damage in children and adolescents. However, suppose these are not sufficient to treat hypertension. In that case, if patients are symptomatic and/or have additional metabolic conditions such as obesity, type diabetes mellitus, or chronic kidney disease, anti-hypertensive medication must be started. Recent guidelines have provided clear guidance on the treatment of hypertension and hypertensive crisis in pediatric age. On the other hand, there are currently few specific recommendations on the treatment of isolated nocturnal hypertension and treatment- resistant hypertension. This review aims to summarize the most recent recommendations for the treatment of hypertension and hypertensive crisis in children and the last years' knowledge and experience in treating childhood isolated nocturnal hypertension and resistant hypertension of childhood.

HUMAN FOLLOWER ROBOT

The development of a Human Follower Robot incorporating IR and Ultrasonic Sensors represents an innovative venture in robotics and automation, aimed at creating a versatile machine capable of tracking and following targets while navigating obstacles. This project is fuelled by the vision of combining sensor technology, microcontroller programming, and robotic design to birth a robot capable of graceful traversal and intelligent target pursuit. At its core, the project relies on the integration of two essential sensor technologies: Infrared (IR) and Ultrasonic sensors. IR sensors, functioning akin to human eyesight, detect objects by emitting and receiving infrared light, enabling the robot to recognize the presence of a target within its line of sight. Concurrently, ultrasonic sensors enhance the robot's perception by determining distances through sound waves, facilitating obstacle avoidance and maintaining a safe tracking distance from the target. The project's foundation comprises key components working in tandem to fulfil the robot's objectives. A microcontroller, such as Arduino or Raspberry Pi, orchestrates actions by processing sensor data and regulating movements. IR sensors act as vigilant eyes, detecting the target, while ultrasonic sensors serve as the echolocation system, enabling seamless navigation in cluttered environments. Motors and wheels propel the robot, providing motion and directional control, while a reliable power supply, such as batteries, sustains its operations. Four pivotal milestones define the project's core goals. Firstly, the development of algorithms and code for effective object detection using IR sensors enables the robot to discern the presence of a target. Secondly, obstacle avoidance mechanisms are implemented through ultrasonic sensors, ensuring adept manoeuvring around barriers and hazards. Thirdly, proximity control is prioritized to maintain an optimal and safe following distance from the target, preventing collisions. Lastly, efforts focus on enabling smooth and precise movement to ensure accurate and responsive tracking behaviour. The potential applications of such a robot are diverse. Apart from assisting visually impaired individuals by guiding and avoiding obstacles, these robots can serve as automated tour guides, offering informative experiences in museums or exhibitions. Additionally, their utility extends to surveillance and security, where they can monitor areas and track intruders. Ultimately, this project holds promise for a future where intelligent robots seamlessly follow, guide, and protect, enriching lives with their remarkable capabilities. Key Words: Human Follower Robot, IR Sensors, Human Follower Robot, IR Sensors, Ultrasonic Sensors, Robotics, Automation, Sensor Technology etc

Impact assessment of major operating factors on internal tracking behavior in a phase-separated ditch

Impact assessment of major operating factors on internal tracking behavior in a phase-separated ditch

A novel simulation-based analysis of a stochastic HIV model with the time delay using high order spectral collocation technique

The economic impact of Human Immunodeficiency Virus (HIV) goes beyond individual levels and it has a significant influence on communities and nations worldwide. Studying the transmission patterns in HIV dynamics is crucial for understanding the tracking behavior and informing policymakers about the possible control of this viral infection. Various approaches have been adopted to explore how the virus interacts with the immune system. Models involving differential equations with delays have become prevalent across various scientific and technical domains over the past few decades. In this study, we present a novel mathematical model comprising a system of delay differential equations to describe the dynamics of intramural HIV infection. The model characterizes three distinct cell sub-populations and the HIV virus. By incorporating time delay between the viral entry into target cells and the subsequent production of new virions, our model provides a comprehensive understanding of the infection process. Our study focuses on investigating the stability of two crucial equilibrium states the infection-free and endemic equilibriums. To analyze the infection-free equilibrium, we utilize the LaSalle invariance principle. Further, we prove that if reproduction is less than unity, the disease free equilibrium is locally and globally asymptotically stable. To ensure numerical accuracy and preservation of essential properties from the continuous mathematical model, we use a spectral scheme having a higher-order accuracy. This scheme effectively captures the underlying dynamics and enables efficient numerical simulations.

A Wireless Sensor-Brain Interface System for Tracking and Guiding Animal Behaviors Through Closed-Loop Neuromodulation in Water Mazes

A Wireless Sensor-Brain Interface System for Tracking and Guiding Animal Behaviors Through Closed-Loop Neuromodulation in Water Mazes

A comprehensive comparison of the two MPPT techniques (P&O, SMC) for photovoltaic systems

This paper aims to study the behavior of different maximum power point tracking (MPPT) techniques applied to PV systems. In this work, we evaluate and compare perturbation and observation (P&O) and sliding mode control (SMC) techniques. a DC/DC boost converter is used between the PV system and load to transfer the maximum possible power. the conventional MPPT methods Perturb and Observe (P&O) cannot detect and track the highest peak. Thus, this causes an important loss of power, so a technique is suggested sliding mode control, which offers many benefits such as durability against parameter changes, minimal current output distortion, and excellent reference tracking. The simulation results shown in MATLAB confirm that the sliding mode control method allows to have a faster response compared to P&O, to reduce the steady-state fluctuations and better track the maximum power point with fewer power losses.