Continuous Tracking Research Articles

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Region-based convolutional neural networks for occluded person re-identification

In a variety of applications, including intelligent surveillance systems, targeted tracking, and assistive human-following robots, the ability to accurately identify individuals even when they are partially obscured is imperative. Such Continuous person tracking is complicated by the close similarity between the appearance of people and target occlusions. This study addresses this significant challenge by proposing a two-step, detection-first approach that uses a region-based convolutional neural network (R-CNN) as the re-identification (re-ID)solution. The model is specifically trained to detect occluded persons at different levels of occlusion before forwarding the image for the re-ID process. Three occluded-specific datasets are selected to evaluate the model's effectiveness in detecting occluded people. There are 379 distinct people in total, and each has five images obstructed from different angles. A sample of the data is taken to simulate various environment settings, and new data points are generated with different degrees of occlusion to assess how well the model performs under varying levels of obstruction. The findings demonstrate that the proposed person re-ID model is reliable in most circumstances, correctly re-identifying at 74% (Rank-1) and 90% (Rank-5). Although there is a decrease in accuracy as the number of distinctive people in the dataset increases, this does not significantly impact the tracking performance in various applications, which are expected to recognize a single person or a small group of individuals. Future works will explore refining similarity matching algorithms by delving into robust image comparison techniques, thereby addressing the challenges presented by occlusions. A critical aspect is to assess the model under diverse lighting conditions and investigate scenarios with multiple individuals in a frame. It is also beneficial to exploit high-resolution datasets, such as DukeMTMC-reID, and integrate finer contextual details, like clothing or carried objects. These collective efforts are essential for optimizing the model’s efficacy in practical applications and advancing person re-ID technologies.

Comparative structural analysis of MecA encoded beta-lactam resistance in MRSA

As the level of antibiotic resistance in bacteria rises, the threat of patients facing fatal consequences from a simple cut or infection grows dramatically. One example of an emerging antibiotic-resistant threat is MRSA, methicillin-resistant staphylococcus aureus, which is a growing cause of hospital-acquired pneumonia, skin infections, and even sepsis through cross-contamination in health care settings. However, methicillin resistance like that exploited by MRSA is also highly conserved with that of another species of staphylococcus bacteria, Staphylococcus sciuri which is the ancestral bacterium of S. aureus. S. sciuri is a typically animal-associated bacterium with increasing clinical relevance as the amount of human infections from it has grown. Both these bacteria share the commonality of having the mecA gene that determines methicillin resistance transported through the mobile Staphylococcal cassette chromosome. This study focuses on the relationship between the mecA mobile element in S. sciuri compared to S. aureus and explores the specific role of S. sciuri in the creation of the mecA cassette. Understanding the cassette evolution and similarities and differences between S. sciuri and S. aureus will enable the continued tracking of S. sciuri as an emerging clinical threat. To answer all these questions, BLAST analysis, InterProScan, and additional computational analysis were used to characterize differences in homology, structure, function, and effect on infectivity, robustness of antibiotic resistance, and morbidity. The overall goal of this analysis is to characterize the differences between mecA and methicillin-resistant genes in clinically relevant MRSA as well as environmental samples such as S. sciuri.

Collaborative strategy research of target tracking based on natural intelligence by UAV swarm

Regarding the regional area target collaborative tracking problem widely existing in intelligent scenarios, this paper built a distributed UAV swarm framework inspired by natural intelligence to heighten intricate missions’ efficiency. Also, a standoff collaboratively continuous tracking strategy was proposed based on a lateral guidance law with an improved Reference Point Guidance (RPG) and a longitudinal guidance law with an improved phase collaboration. Under an uncertain environment, this framework used an improved bat algorithm (IBA) to optimize the speed allocation of the UAV swarm’s online control strategy with information consensus estimation. Compared with a case without the designed transformation, statistically, the results demonstrate that the framework operates efficiently and robustly in phase error convergence, swarm flight distance, and fuel consumption, where a dynamic target exists.

Changes in muscle strength and risk of cardiovascular disease among middle-aged and older adults in China: Evidence from a prospective cohort study.

Evidence indicates that low muscle strength is associated with an increased cardiovascular diseases (CVDs) risk. However, the association between muscle strength changes based on repeated measurements and CVD incidence remains unclear. The study used data from the China Health and Retirement Longitudinal Study in 2011 (Wave 1), 2013 (Wave 2), 2015 (Wave 3), and 2018 (Wave 4). Low muscle strength was defined as handgrip strength <28kg for men or <18kg for women, or chair-rising time ≥12 s. Based on changes in muscle strength from Waves 1 to 2, participants were categorized into four groups of Normal-Normal, Low-Normal, Normal-Low, and Low-Low. CVD events, including heart disease and stroke, were recorded using a self-reported questionnaire during Waves 3 and 4 visits. Cox proportional hazards models were used to investigate the association between muscle strength changes and CVD incidence after multivariable adjustments. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated with the Normal-Normal group as the reference. A total of 1164 CVD cases were identified among 6608 participants. Compared to participants with sustained normal muscle strength, the CVD risks increased progressively across groups of the Low-Normal (HR=1.20, 95% CI: 1.01-1.43), the Normal-Low (HR=1.35, 95% CI: 1.14-1.60), and the Low-Low (HR=1.76, 95% CI: 1.49-2.07). Similar patterns were observed for the significant associations between muscle strength status and the incidence risks of heart disease and stroke. Subgroup analyses showed that the significant associations between CVD and muscle strength changes were consistent across age, sex, and body mass index (BMI) categories. The study found that muscle strength changes were associated with CVD risk. This suggests that continuous tracking of muscle status may be helpful in screening cardiovascular risk.

Recent advances in smart wearable sensors for continuous human health monitoring

In recent years, the biochemical and biological research areas have shown great interest in a smart wearable sensor because of its increasing prevalence and high potential to monitor human health in a non-invasive manner by continuous screening of biomarkers dispersed throughout the biological analytes, as well as real-time diagnostic tools and time-sensitive information compared to conventional hospital-centered system. These smart wearable sensors offer an innovative option for evaluating and investigating human health by incorporating a portion of recent advances in technology and engineering that can enhance real-time point-of-care-testing capabilities. Smart wearable sensors have emerged progressively with a mixture of multiplexed biosensing, microfluidic sampling, and data acquisition systems incorporated with flexible substrate and bodily attachments for enhanced wearability, portability, and reliability. There is a good chance that smart wearable sensors will be relevant to the early detection and diagnosis of disease management and control. Therefore, pioneering smart wearable sensors into reality seems extremely promising despite possible challenges in this cutting-edge technology for a better future in the healthcare domain. This review presents critical viewpoints on recent developments in wearable sensors in the upcoming smart digital health monitoring in real-time scenarios. In addition, there have been proactive discussions in recent years on materials selection, design optimization, efficient fabrication tools, and data processing units, as well as their continuous monitoring and tracking strategy with system-level integration such as internet-of-things, cyber-physical systems, and machine learning algorithms.

Diammonium cation pre-intercalation into inorganic framework for efficient Dion-Jacobson perovskite solar cells

The two-dimensional and three-dimensional (2D/3D) perovskite heterojunction has garnered significant attention in perovskite solar cells (PSCs) owing to the superior stability, modified band alignment and suppressed defects. Here, a pre-intercalation strategy involving bulky cations into inorganic framework to construct a buried 2D/3D perovskite heterojunction is proposed. The incorporation of bulky cation into PbI2 not only leads to the formation of well-oriented 2D nanoplates, but also disrupts the compact inorganic structure. It results in substantially increased grain boundaries at the buried interface, facilitating the complete reaction with small cations. Additionally, the pre-intercalated bulky cation acts as a template for the oriented growth of perovskite layer, leading to significantly reduced defects, enlarged crystal size and enhanced orientation. Furthermore, the advanced formation of buried 2D phases constructs a bottom-up 2D/3D perovskite heterojunction, enhancing carrier extraction and reducing energy loss in the PSCs. The resulting PSC achieves a champion power conversion efficiency of 23.40 %, surpassing the control device of 20.64 %. Moreover, the PSC maintains 88 % of its initial efficiency after 1050 h continuous efficiency tracking and retains over 97 % of its performance for 2232 h under a low humidity condition. Therefore, the pre-intercalation strategy represents a promising approach for both efficiency and stability challenges in PSCs.

Continual online learning-based optimal tracking control of nonlinear strict-feedback systems: application to unmanned aerial vehicles

A novel optimal trajectory tracking scheme is introduced for nonlinear continuous-time systems in strict feedback form with uncertain dynamics by using neural networks (NNs). The method employs an actor-critic-based NN backstepping technique for minimizing a discounted value function along with an identifier to approximate unknown system dynamics that are expressed in augmented form. Novel online weight update laws for the actor and critic NNs are derived by using both the NN identifier and Hamilton-Jacobi-Bellman residual error. A new continual lifelong learning technique utilizing the Fisher Information Matrix via Hamilton-Jacobi-Bellman residual error is introduced to obtain the significance of weights in an online mode to overcome the issue of catastrophic forgetting for NNs, and closed-loop stability is analyzed and demonstrated. The effectiveness of the proposed method is shown in simulation by contrasting the proposed with a recent method from the literature on an underactuated unmanned aerial vehicle, covering both its translational and attitude dynamics.

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.

Automatised Bioreactor to Produce Biogas from Biomass: Development and Evaluation

Brazil's abundant biomass from agricultural and industrial activities offers significant potential for biogas production, aligning with the ONU Sustainable Development Goals. This study aims to explore anaerobic digestion for biogas production from biomass, investigating key factors influencing the process and advancing efficient technologies. A literature review utilizing biogas as a sustainable energy source was conducted, and an Arduino-controlled bioreactor was built to automatically monitor and control crucial variables in anaerobic digestion, ensuring high-quality and efficient biogas production. The developed bioreactor efficiently enabled continuous tracking of pressure, temperature, hydrogen, and methane in the bioreactor, contributing to energy transition and decarbonization.

Performance and accuracy of cross‐section tracking methods for hydromorphological habitat assessment in wadable rivers with sparse canopy conditions

AbstractThis article investigates the performance and accuracy of continuous Real‐Time Kinematic (RTK) Global Navigation Satellite System (GNSS) position tracking for hydromorphological surveys, based on a comprehensive river restoration monitoring campaign. The aim of the research was to assess the method's suitability for efficient data collection in turbid, wadable rivers with sparse canopy conditions, and responds to the water management sector's increasing demand for efficient, low‐cost, and robust survey techniques. The methodological approach involved comparing manual, cross‐sectional water depth measurements to water depth estimations obtained by applying different interpolation methods to the continuous tracking data. The results demonstrate good agreement between both datasets (R2 = 0.77, RMSE = 0.13 m). When using a local standard deviation filter to remove noisy RTK‐GNSS measurements, estimation performance increased significantly (R2 = 0.96, RMSE = 0.06 m). The filter's influence on the hydromorphological habitat statistics mean water depth and coefficient of variation was limited but proved to be relevant for reach‐scale assessments of hydromorphological diversity. Based on a correlation analysis of &gt;106 RTK‐GNSS position logs, we furthermore assessed the impact of tree canopy on RTK‐GNSS measurement accuracy and observed a strong influence within 6.5 m from the canopy border. Estimated accuracy deteriorated noticeably when canopy penetration exceeded 1 m, and accuracies &gt;1 m were common beyond 4 m penetration. The study highlights the efficiency gains achieved with RTK‐GNSS tracking, and showcases its potential for hydromorphological surveys and streamgaging applications in challenging conditions, making it a promising alternative to traditional methods and remote sensing techniques.

Investigating cross-orientation inhibition with continuous tracking.

We investigated cross-orientation inhibition with the recently developed continuous tracking technique. We designed an experiment where participants tracked the horizontal motion of a narrow vertical grating. The target was superimposed on one of three different backgrounds, in separate sessions: a uniform gray background or a sinusoidal grating oriented either parallel or orthogonal to the target. Both mask and target where phase reversed. We cross-correlated target and mouse movements and compared the peaks and lags of response with the different masks. Our results are in agreement with previous findings on cross-orientation inhibition: The orthogonal mask had a weak effect on the peaks and lags of correlation as a function of target contrast, consistently with a divisive effect of the mask, while the parallel mask acted subtractively on the response. Interestingly, lags of correlation decreased approximately linearly with contrast, with decrements of the order of 100ms, even at 10 times the detection threshold, confirming that it is possible to investigate behavioral differences above threshold using the continuous tracking paradigm.

AI-Embedded UAV System for Detecting and Pursuing Unwanted UAVs

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather.&#x0D; Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications.&#x0D; This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.

Advances and Challenges in Wearable Glaucoma Diagnostics and Therapeutics.

Glaucoma is a leading cause of irreversible blindness, and early detection and treatment are crucial for preventing vision loss. This review aims to provide an overview of current diagnostic and treatment standards, recent medical and technological advances, and current challenges and future outlook for wearable glaucoma diagnostics and therapeutics. Conventional diagnostic techniques, including the rebound tonometer and Goldmann Applanation Tonometer, provide reliable intraocular pressure (IOP) measurement data at single-interval visits. The Sensimed Triggerfish and other emerging contact lenses provide continuous IOP tracking, which can improve diagnostic IOP monitoring for glaucoma. Conventional therapeutic techniques include eye drops and laser therapies, while emerging drug-eluting contact lenses can solve patient noncompliance with eye medications. Theranostic platforms combine diagnostic and therapeutic capabilities into a single device. Advantages of these platforms include real-time monitoring and personalized medication dosing. While there are many challenges to the development of wearable glaucoma diagnostics and therapeutics, wearable technologies hold great potential for enhancing glaucoma management by providing continuous monitoring, improving medication adherence, and reducing the disease burden on patients and healthcare systems. Further research and development of these technologies will be essential to optimizing patient outcomes.

Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

Pedestrian tracking in surveillance videos is crucial and challenging for precise personnel management. Due to the limited coverage of a single video, the integration of multiple surveillance videos is necessary in practical applications. In the realm of pedestrian management using multiple surveillance videos, continuous pedestrian tracking is quite important. However, prevailing cross-video pedestrian matching methods mainly rely on the appearance features of pedestrians, resulting in low matching accuracy and poor tracking robustness. To address these shortcomings, this paper presents a cross-video pedestrian tracking algorithm, which introduces spatial information. The proposed algorithm introduces the coordinate features of pedestrians in different videos and a linear weighting strategy focusing on the overlapping view of the tracking process. The experimental results show that, compared to traditional methods, the method in this paper improves the success rate of target pedestrian matching and enhances the robustness of continuous pedestrian tracking. This study provides a viable reference for pedestrian tracking and crowd management in video applications.

Infants on the move: bibliometric analyses of observational vs. digital means of screening infant development.

Neurodevelopmental disorders are on the rise, yet their average diagnosis is after 4.5 years old. This delay is partly due to reliance on social-communication criteria, which require longer maturation than scaffolding elements of neuromotor control. Much earlier criteria could include reflexes, monitoring of the quality of spontaneous movements from central pattern generators and maturation of intentional movements and their overall sensation. General Movement Assessment (GMA) studies these features using observational means, but the last two decades have seen a surge in digital tools that enable non-invasive, continuous tracking of infants' spontaneous movements. Despite their importance, these tools are not yet broadly used. In this work, using CiteSpace, VOSViewer and SciMAT software, we investigate the evolution of the literature on GMA and the methods in use today, to estimate how digital techniques are being adopted. To that end, we created maps of key word co-occurrence networks, co-author networks, document co-citation analysis and strategic diagrams of 295 publications based on a search in the Web of Science, Dimensions and SCOPUS databases for: 'general movement assessment' OR 'general movements assessment'. The nodes on the maps were categorized by size, cluster groups and year of publication. We found that the state-of-the-art methodology to diagnose neurodevelopmental disorders still relies heavily on observation. Several groups in classical GMA research have branched out to incorporate new techniques, but few groups have adopted digital means. We report on additional analyses of methods and biosensors usage and propose that combining traditional clinical observation criteria with digital means may allow earlier diagnoses and interventional therapies for infants.

Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial.

Multiparametric remote measurement technologies (RMTs), which comprise smartphones and wearable devices, have the potential to revolutionize understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the utmost importance for the validity of predictive analytical methods and long-term use and can be conceptualized as both objective engagement (data availability) and subjective engagement (system usability and experiential factors). Positioning the design of user interfaces within the theoretical framework of the Behavior Change Wheel can help maximize effectiveness. In-app components containing information from credible sources, visual feedback, and access to support provide an opportunity to promote engagement with RMTs while minimizing team resources. Randomized controlled trials are the gold standard in quantifying the effects of in-app components on engagement with RMTs in patients with MDD. This study aims to evaluate whether a multiparametric RMT system with theoretically informed notifications, visual progress tracking, and access to research team contact details could promote engagement with remote symptom tracking over and above the system as usual. We hypothesized that participants using the adapted app (intervention group) would have higher engagement in symptom monitoring, as measured by objective and subjective engagement. A 2-arm, parallel-group randomized controlled trial (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms used the RADAR-base system, comprising a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50, 50%) also had access to additional in-app components. The primary outcome was objective engagement, measured as the percentage of weekly questionnaires completed during follow-up. The secondary outcomes measured subjective engagement (system engagement, system usability, and emotional self-awareness). The levels of completion of the Patient Health Questionnaire-8 (PHQ-8) were similar between the control (67/97, 69%) and intervention (66/97, 68%) arms (P value for the difference between the arms=.83, 95% CI -9.32 to 11.65). The intervention group participants reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19), and system usability (2.29, 95% CI -5.93 to 10.52) scores than the control group participants at follow-up; however, all CIs were wide and included 0. Process evaluation suggested that participants saw the in-app components as helpful in increasing task completion. The adapted system did not increase objective or subjective engagement in remote symptom tracking in our research cohort. This study provides an important foundation for understanding engagement with RMTs for research and the methodologies by which this work can be replicated in both community and clinical settings. ClinicalTrials.gov NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474. RR2-10.2196/32653.

Determination of the tracking system's trajectory considering the electric drive losses

This paper deals with optimal sun-tracking of a photovoltaic system considering the electric drive losses. The Sun tracking photovoltaic system assures that the highest possible share of the available solar radiation reaches the surface of the photovoltaic modules. The electric drive which enables tracking is considered as the loss of the energy produced in the photovoltaic system. The maximum of the energy produced in the photovoltaic system is achieved by the continuous tracking of the photovoltaic system. Since the electric drives are determined by constant speed and time, and angle quantization the maximum of the energy produced can only approximate. The results presented in this paper show, that the optimal trajectories can help to increase the electrical energy production within photovoltaic systems by sun tracking.

Functionalized Microwires‐Based Bundle Electrodes for Detection of Multiplexed Metabolites in Interstitial Space

Metabolic substances play a pivotal role in maintaining the body's regular physiological functions. When these processes are disrupted, it can lead to metabolic disorders which may cause severe damage to various organs. Diabetes mellitus, a prevalent metabolic disorder, arises from disturbances in sugar metabolism among other substances. Consequently, there's a pressing need to monitor metabolite levels for early diagnosis. To address this, in this study, a semi‐implantable metabolite sensing system developed around functionalized microwires‐based bundle electrodes (FMBE) is introduced. This FMBE device tracks glucose, hydrogen peroxide (H2O2), and uric acid (UA) levels in vivo in real time, sensitively, and continuously. The FMBE is coated with Au nanoclusters and carbon nanotube–2D carbides and nitride (MXene) nanocomposites to enhance sensing surface area. In vitro characterizations affirm the FMBE's linear responsivity, detection sensitivity, and selectivity toward glucose, H2O2, and UA sensing. In addition, in in vivo testing in healthy and diabetic rats, it is demonstrated that FMBE is able to continuously monitor interstitial glucose, H2O2, and UA concentrations after implantation. The FMBE system, thus, stands out as a promising platform for real‐time, in situ monitoring of metabolite concentrations, potentially assisting in the diagnosis of diabetes and associated complications.

Real-Time Object Localization Using a Fuzzy Controller for a Vision-Based Drone

This study proposes a drone system with visual identification and tracking capabilities to address the issue of limited communication bandwidth for drones. This system can lock onto a target during flight and transmit its simple features to the ground station, thereby reducing communication bandwidth demands. RealFlight is used as the simulation environment to validate the proposed drone algorithm. The core components of the system include DeepSORT and MobileNet lightweight models for target tracking. The designed fuzzy controller enables the system to adjust the drone’s motors, gradually moving the locked target to the center of the frame and maintaining continuous tracking. Additionally, this study introduces channel and spatial reliability tracking (CSRT) switching from multi-object to single-object tracking and multithreading technology to enhance the system’s execution speed. The experimental results demonstrate that the system can accurately adjust the target to the frame’s center within approximately 1.5 s, maintaining precision within ±0.5 degrees. On the Jetson Xavier NX embedded platform, the average frame rate (FPS) for the multi-object tracker was only 1.37, with a standard deviation of 1.05. In contrast, the single-object tracker CSRT exhibited a significant improvement, achieving an average FPS of 9.77 with a standard deviation of 1.86. This study provides an effective solution for visual tracking in drone systems that is efficient and conserves communication bandwidth. The validation of the embedded platform highlighted its practicality and performance.