Tracking Process Research Articles

Feline sacroiliac luxation: comparison of fluoroscopy-controlled freehand vs. computer-navigated drilling in the sacrum—a cadaveric study

IntroductionSacroiliac luxation is a common traumatic feline injury, with the small size of the sacral body being a challenge for surgical stabilization. This study compared an innovative computer-guided drilling method with the conventional fluoroscopy-controlled freehand technique. Neuronavigation, using CT-based planning and real-time tracking, was evaluated against the freehand method for accuracy and time efficiency.Materials and methodsBilateral sacroiliac luxation was induced in 20 feline cadavers. One side of the sacral body was drilled using fluoroscopy, and the other with neuronavigation (Stealth Station S8). A reference frame was affixed to the sacral spinous process for tracking. Ten cats were operated on by an ECVS diplomate and 10 by a resident. Postoperative cone beam CT images were used to assess both techniques, comparing the accuracy of the planned corridor vs. the actual drill hole in the sacrum. A learning curve for both methods was estimated by measuring procedure time.ResultsCT scan assessments showed all 40 drill holes achieved “surgically satisfactory” results. The computer-navigated technique demonstrated an average deviation of 1.9 mm (SD 1.0 mm) at the entry point and 1.6 mm (SD 0.8 mm) at the exit point. The pins of 3/20 reference frames penetrated the vertebral canal, creating a risk for potential clinical complications. The neuronavigation-guided procedures took an average of 23 min and 37 s (SD 8 min 34 s), significantly longer than the freehand technique, which averaged 9 min and 47 s (SD 3 min 26 s). A steep learning curve was observed with neuronavigation.DiscussionThe neuronavigation-guided technique achieved accuracy is comparable to the fluoroscopy-controlled method, is offering real-time feedback and has potential for highly precise surgeries near critical anatomical structures. However, significant attention must be given to the placement of the reference frame, as it is placed blindly and presents a potential risk for errors and complications. Despite its longer duration, the neuronavigation method shows promise for improving precision in complex surgical scenarios.

Research on Underground Track Tracking of a New Type Shaft Boring Machine

A trajectory tracking control scheme is proposed in this paper to achieve unmanned underground construction of a new type of shaft boring machine. The body motion model is constructed, and the control relationship between the driving system and the body motion is deduced. MATLAB and Simulink simulate the joint of the track tracking process in the new shaft boring machine. The actual effect of the track tracking control scheme is validated by the track tracking field test of the new shaft boring machine. The results show that the new shaft boring machine can complete various complex track tracking tasks, with the maximum position error being less than 0.005 m. The running track and excavation areas of the boring machine are observed along with the track tracking field test of the new shaft boring machine, demonstrating that the track tracking control scheme makes the new shaft boring machine realize unmanned construction and verifies the co-simulation accuracy. The PID (proportional–integral–derivative control) algorithm can complete complex trajectory tracking tasks and promote the development of unmanned construction technology.

Integrated Gaussian Processes for Tracking

Integrated Gaussian Processes for Tracking

FUZZY REGION MERGING WITH HIERARCHICAL CLUSTERING TO FIND OPTIMAL INITIALIZATION OF FUZZY REGION IN IMAGE SEGMENTATION

One of the most important goals in image segmentation is the process of separating the object parts from the image background. Image segmentation is also a fundamental stage in the development of other image applications such as object recognition, target tracking, computer vision, and biomedical image processing. Interactive image segmentation methods with additional user interaction are still popular in research. Interactive image segmentation aims to provide additional information through simple interactions, especially in images with complex objects. Interactive image segmentation with region merging processes has drawbacks, one of which is suboptimal region splitting due to soft color shades, blurred contours, and uneven lighting, referred to in this study as ambiguous regions. However, in the fuzzy region initialization stage after obtaining values from the marker process, there is a possibility of missing or suboptimal determination of fuzzy regions. This is because it only takes the highest gray level value for the background marker and the lowest gray level value for the object marker. In this study, fuzzy region merging using hierarchical clustering is proposed to find optimal initialization for fuzzy regions in image segmentation. Based on the experimental results, the proposed method can achieve optimal segmentation with an average misclassification error value of 2.62% for Natural Images and 9.33% for Dental Images.

Rancang Bangun Sistem Informasi Pencatatan dan Pengelolaan Data Pekerjaan Karyawan di PT Aplic Digital Teknologi

The design and development of an information system for recording and managing employee work data aim to simplify the process of tracking and monitoring employee tasks, including when tasks are assigned and when they are expected to be completed. This system is designed to replace manual administrative processes, enabling work-related information to be accessed more quickly and accurately. The method used is System Development Life Cycle (SDLC) with system development using a waterfall method approach which involves the stages of needs analysis, design, design, and implementation. With this system, management can regularly monitor the progress of employee tasks and evaluate the efficiency of task completion. Additionally, the system records work history, which can be used for reporting and further analysis. The implementation results show that the system improves the accuracy of work data recording and supports more effective decision-making.

A novel Adaptive Jellyfish Search algorithm (AJFS) for maximum power optimization of photovoltaic systems: performance evaluation and analysis

ABSTRACT In solar photovoltaic (SPV) systems, optimizing output under various meteorological conditions relies on the Maximum Power Point (MPP) tracking controller. However, the presence of multiple peaks due to partial shading complicates this tracking process. While traditional and soft computing methods are commonly employed for MPP tracking, they face limitations such as the fixed step size in conventional methods and a lack of randomness in soft computing approaches once they reach a certain MPP. To address these challenges, a unique optimization technique known as the Adaptive Jellyfish Search (AJFS) method has been proposed. This method conducts both global and local searches simultaneously in a single step, enhancing the effectiveness of MPP tracking. To evaluate its performance and resilience, zero, weak, moderate, and strong shading patterns are employed in simulations, with comparisons made against traditional Jellyfish Search (JFS) and Particle Swarm Optimization (PSO) techniques. The newly suggested AJFS approach demonstrates significant improvements over traditional methods. It reduces convergence time by 49% and offers additional motivating features, including zero risk of failure, minimized oscillation in power parameters, and enhanced energy production by 1.5%. Moreover, it enables smooth tracking of the MPP, particularly in dynamically changing shading patterns. Overall, the AJFS method presents a promising solution for efficient MPP tracking in SPV systems, overcoming limitations of conventional approaches and demonstrating superior performance under various shading conditions. Its ability to simultaneously conduct global and local searches in a single step makes it well suited for optimizing energy production in real-world scenarios.

Aircraft Position Estimation Using Deep Convolutional Neural Networks for Low SNR (Signal-to-Noise Ratio) Values.

The safety of the airspace could be improved by the use of visual methods for the detection and tracking of aircraft. However, in the case of the small angular size of airplanes and the high noise level in the image, sufficient use of such methods might be difficult. By using the ConvNN (Convolutional Neural Network), it is possible to obtain a detector that performs the segmentation task for aircraft images that are very small and lost in the background noise. In the learning process, a database of actual aircraft images was used. Using the Monte Carlo method, four types of Max algorithms, i.e., Pixel Value, Min. Pixel Value, and Max. Abs. Pixel Value, were compared with ConvNN's forward architecture. The obtained results showed superior detection with ConvNN. For example, if the standard deviation equals 0.1, it was twice as large. Deep dream analysis for network layers is presented, which shows a preference for images with horizontal contrast lines. The proposed solution uses the processed image values for the tracking process with the raw data using the Track-Before-Detect method.

How Do They Decide? Performance Support Staff Perceptions of Decision-Making in Elite-Sport Environments.

Wilson, PJ, Roe, G, and Kiely, J. How do they decide? Performance support staff perceptions of decision-making in elite-sport environments. J Strength Cond Res XX(X): 000-000, 2024-Decision-making is a critical component of performance support staff responsibilities, yet research exploring staff perceptions relating to key facets of decision-making is lacking. This study aimed to explore perceptions of performance support staff relating to; the importance of decision-making in their role, the value of team decision-making, strategies to enhance decision-making skills, monitoring the process and outcome of decisions, and the obstacles to good decision-making within their professional contexts. A mixed-methods approached was used to explore perceptions of decision-making in 191 performance support staff (male staff = 179, female staff = 12) working in professional (n = 147) or international (n = 44) sport. Performance support staff reported decision-making as extremely important to their role; however, only 31% had received formal decision-making education and revealed underdeveloped decision-making capabilities within their organizations as a barrier to good decision-making. Most performance support staff conveyed that team decision-making is a valuable endeavor and augments decision outcomes; yet articulated dysfunctional team dynamics as an obstacle to good decision-making in their environments. Largely, performance support staff reported an absence of organizational strategy and workflow systems and processes for tracking and making decisions, respectively, whereas only 22% had a consistent reviewing and updating process. When making decisions, performance support staff faced several different cognitive biases and were challenged by the environmental pressures. The perspectives documented here suggest that performance support staff would likely benefit from undertaking evidence-based training and implementing scientifically informed strategies to enhance decision-making processes within their organizations.

COLLABORATIVE PROJECT MANAGEMENT TOOL

This project presents a robust Collaborative Project Management Tool developed using the Django framework to streamline project tracking and user management processes[1]. The system incorporates essential features, including user authentication for registration, login, profile updates, and secure password management, alongside a user-friendly dashboard that enhances navigation and usability. Users can create and manage projects [5], assign tasks,[5] and monitor progress through a comprehensive project history. Additionally, the platform supports the generation of project reports by converting HTML content into PDF format for printing and archival purposes [4]. A centralized admin panel allows administrators to oversee user authentication and authorization while managing project lifecycles[9]. Admins are empowered to modify project statuses [5], such as transitioning projects from "Started" to "Completed," ensuring accurate and up-to-date records [5]. The tool is built using Django for backend development [1], and employs technologies like HTML, CSS, JavaScript, and Bootstrap for a responsive frontend [3]. Libraries such as XHTML facilitate seamless PDF generation [4], while Django’s built-in authentication[1] system ensures secure user management. The database layer relies on SQLite (SQLite3) for robust data storage. By prioritizing accessibility, functionality, and ease of use, this system serves as a valuable resource for teams and organizations, fostering collaboration and ensuring efficient project management [2].

ADSTrack: adaptive dynamic sampling for visual tracking

The most common method for visual object tracking involves feeding an image pair comprising a template image and search region into a tracker. The tracker uses a backbone to process the information in the image pair. In pure Transformer-based frameworks, redundant information in image pairs exists throughout the tracking process and the corresponding negative tokens consume the same computational resources as the positive tokens while degrading the performance of the tracker. Therefore, we propose to solve this problem using an adaptive dynamic sampling strategy in a pure Transformer-based tracker, known as ADSTrack. ADSTrack progressively reduces irrelevant, redundant negative tokens in the search region that are not related to the tracked objectand the effect of noise generated by these tokens. The adaptive dynamic sampling strategy enhances the performance of the tracker by scoring and adaptive sampling of important tokens, and the number of tokens sampled varies according to the input image. Moreover, the adaptive dynamic sampling strategy is a parameterless token sampling strategy that does not use additional parameters. We add several extra tokens as auxiliary tokens to the backbone to further optimize the feature map. We extensively evaluate ADSTrack, achieving satisfactory results for seven test sets, including UAV123 and LaSOT.

Cellular Phone Number Tracking System Based on ArcGIS and Google Maps

Over 4 billion individuals are projected to own a mobile device, with smartphone use continuing to rise around the world. Associated with this trend is the collection and usage of location information provided by these devices. The procedure for tracking and evaluating a mobile phone number is crucial for crime and terrorist attacks preventing concerns, especially in countries with deteriorated security conditions. The locational tracking information can enable real-time operational awareness and can save people's lives. In this aspect, the Geographic Information System (GIS) system is frequently used to provide intensive spatial information along the tracking process. This research aims to examine, follow up on, and trace a certain person's phone number inside a certain area using a combination of ArcGIS and Google Maps application software they are programs used to track and determine locations through the use of various types of coordinates, including latitude and longitude. In the ArcGIS program, there are several methods and algorithms used in various fields, including (locating people, determining tourist places, determining water and sewage networks, forecasting in the event of storms or floods, and many other areas), an example of these methods in the program is (Kriging method, Idw method, Spline method) and many other methods, each method is used for a specific purpose and field. In this research, the kriging method was used, which is a design algorithm that is used in many analysis and tracking processes and gives clear and accurate results. The process of determining locations in the maps used in the search depends on taking the longitude and latitude coordinates and downloading them to those maps. The maps that have been worked on in the research provide a set of basic concepts adopted in the process of tracking a particular person’s phone number, such as (the person’s location, the numbers connected to him, and the duration of a call to numbers in his contact list). The tracking system used is tested by creating a virtual contact list for a specific person’s phone number and making use of all the data in the contact list and applying it in the ArcGIS program, knowing that the duration of the contact list used is 9 months (from January to October 2021). The obtained results are analyzed and sorted in different ways to provide a wide model of the locational communication and movement activity of the tracked person. They showed that the system is efficient and can be applied to enhance the security in Iraq for tracking suspected people in crimes like terrorism, kidnapping, drugs, extortion … etc.

Eye Tracker With In-Pixel Signal Processing for Fast Eye Tracking

Eye Tracker With In-Pixel Signal Processing for Fast Eye Tracking

The Relationship Between Soil and Gut Microbiota Influences the Adaptive Strategies of Goitered Gazelles in the Qaidam Basin.

The gut microbiota is integral to the health and adaptability of wild herbivores. Interactions with soil microbiota can shape the composition and function of the gut microbiota, thereby influencing the hosts' adaptive strategies. As a result, soil microbiota plays a pivotal role in enabling wild herbivores to thrive in extreme environments. However, the influence of soil microbiota from distinct regions on host's gut microbiota has often been overlooked. We conducted the first comprehensive analysis of the composition and diversity of gut and soil microbiota in goitered gazelles across six regions in the Qaidam Basin, utilizing source tracking and ecological assembly process analyses. Significant differences were observed in the composition and diversity of soil and gut microbiota among the six groups. Source tracking analysis revealed that soil microbiota in the GangciGC (GC) group contributed the highest proportion to fecal microbiota (8.94%), while the Huaitoutala (HTTL) group contributed the lowest proportion (1.80%). The GC group also exhibited the lowest α-diversity in gut microbiota. The observed differences in gut microbial composition and diversity among goitered gazelles from six regions in the Qaidam Basin were closely tied to their adaptive strategies. Ecological assembly process analysis indicated that the gut microbiota were primarily influenced by stochastic processes, whereas deterministic processes dominated most soil microbial groups. Both the differences and commonalities in gut and soil microbiota play essential roles in enabling these gazelles to adapt to diverse environments. Notably, the utilization pattern of soil microbiota by gut microbiota did not align with regional trends in gut microbial α-diversity. This discrepancy may be attributed to variations in environmental pressures and the gut's filtering capacity, allowing gazelles to selectively acquire microbiota from soil to maintain homeostasis. This study highlights the significant regional variation in gut and soil microbiota diversity among goitered gazelle populations in the Qaidam Basin and underscores the critical role of soil-derived microbiota in their environmental adaptation.

Real-time actuator fault detection and isolation for quadrotor UAV via directional residuals

Abstract Fault diagnosis is critical for the safe and reliable operation of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce its negative impact. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying (LPV) model is established to describe quadrotor UAV dynamics, which considers measurement noise and external disturbance. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and Η_/L∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L∞ performance condition, and actuator fault is detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residuals and the defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Three-Dimensional Monitoring of Zelongnong Glacier, China, with the PO-MSBAS Technique

High-precision monitoring of glacier motion provides crucial information for a thorough understanding of the dynamic characteristics and development patterns of glaciers, which serves as a scientific basis for the prevention and management of glacier-related disasters. Zelongnong Glacier, located in Tibet, China, has experienced glacier surges, collapse, and hazard chains four times in the last 70 years. On 10 September 2020, a major glacier hazard chain occurred in this region. To reveal the influencing factors of the glacier motion, we monitor the Zelongnong Glacier motions with 65 scenes of TerraSAR/PAZ images from 2022 to 2023, where the Pixel Offset Multidimensional Small Baseline Subset (PO-MSBAS) method is employed for three-dimensional time series inversion. As the registration window size directly affects the matching success rate, deformation accuracy, and signal-to-noise ratio (SNR) during the offset tracking processing, we adopt a variable window-weighted cross-correlation strategy. The strategy balances the advantages of different window sizes, effectively reducing noise while preserving certain details in the offset results. The standard deviation in stable areas is also significantly lower than that obtained using smaller window sizes in conventional methods. The results reveal that the velocity of the southern glacier tributary was larger than the one in the northern tributary. Specifically, the maximum velocity in the northern tributary reached 45.07 m/year in the horizontal direction and −7.45 m/year in the vertical direction, whereas in the southern tributary, the maximum velocity was 50.15 m/year horizontally and 50.66 m/year vertically. The southern tributary underwent two bends before merging with the mainstream, leading to a more complex motion pattern. Lastly, correlation reveals that the Zelongnong Glacier was affected by the combined influence of temperature and precipitation with a common period of around 90 days.

The Digital Divide and Cognitive Disparities Among Older Adults: Community-Based Cohort Study in China.

The widespread adoption of information and communication technologies (ICTs) further deepens disparities in resource access, particularly among the aging population. However, the relationship between these factors and their resulting impact on cognitive abilities remains uncertain. This study aims to investigate the potential impact of the digital divide on individuals' cognitive function and its association with the development and reversion of mild cognitive impairment (MCI). This cohort study used data from Beijing Aging Brain Rejuvenation (BABRI) study applying a multistage cluster sampling design between 2008 and 2020. The digital divide was quantified by the frequency of using ICTs. Analysis of covariance (ANCOVA), mixed linear models, and Cox proportional hazards models were used to model the association of digital divide and multidomain cognition. Among the 10098 participants, nearly half (n=4941, 48.9%) faced the digital divide, which was associated with a worse performance in processing speed (F10096=10.67; P<.001; effect size r=0.42), rather than memory, executive function, and language. The model indicated that individuals' physical and mental health, combined with their educational and occupational prestige, influenced the resources they attained, which ultimately caused the digital divide. Moreover, longitudinal data revealed that older adults who successfully crossed the digital divide during the tracking process and those who had already done so prior to tracking showed significantly slower rates of decline in processing speed (B=-1.98, P<.05; B=-2.62, P<.01) and general cognitive function (B=3.50, P<.001; B=3.13, P<.01). Additionally, overcoming the digital divide was also associated with a lower risk of developing MCI (hazard ratio [HR] 0.5, 95% CI 0.34-0.74; HR 0.43, 95% CI 0.29-0.62) and a greater probability of reversion from MCI to normal cognition (HR 6, 95% CI 3.77-9.56; HR 9.22, 95% CI 5.63-15.11). Overcoming the digital divide was significantly associated with improved cognitive function, a slower aging rate in cognitive performance, a reduced risk of developing MCI, and a higher likelihood of reverting from MCI to normal cognition.

UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review

Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments in electronics and communications systems have provided the basis for tracking and location systems inside buildings, so-called indoor positioning systems (IPSs). The ultra-wideband (UWB) technology is one of the important emerging solutions for IPSs. This radio communications technology provides important characteristics that distinguish it from other solutions, such as secure and robust communications, wide bandwidth, high data rate, and low transmission power. In this paper, we review the implementation of the most important real-time indoor positioning and tracking systems that use ultra-wideband technology for tracking and localizing moving objects. This paper reviews the newest in-market UWB modules and solutions, discussing several types of algorithms that are used by the real-time UWB-based systems to determine the location with high accuracy, along with a detailed comparison that saves the reader a lot of time and effort in choosing the appropriate UWB-module/method/algorithm for real-time implementation.

UAV Visual Object Tracking Based on Spatio-Temporal Context

To balance the real-time and robustness of UAV visual tracking on a single CPU, this paper proposes an object tracker based on spatio-temporal context (STCT). STCT integrates the correlation filter and Siamese network into a unified framework and introduces the target’s motion model, enabling the tracker to adapt to target scale variations and effectively address challenges posed by rapid target motion, etc. Furthermore, a spatio-temporal regularization term based on the dynamic attention mechanism is proposed, and it is introduced into the correlation filter to suppress the aberrance of the response map. The filter solution is provided through the alternating direction method of multipliers (ADMM). In addition, to ensure efficiency, this paper proposes the average maximum response value-related energy (AMRE) for adaptive tracking state evaluation, which considers the time context of the tracking process in STCT. Experimental results show that the proposed STCT tracker can achieve a favorable balance between tracking robustness and real-time performance for UAV object tracking while running at ∼38 frames/s on a low-cost CPU.

F-measure and Intra-operator Reliability of Aerial Imagery Tracking in Football

Current player tracking methods using multiple fixed stadium-based cameras and wearable sensors have limitations. To address this, a new computer vision system using aerial drone imagery has been developed to track football players. This approach is less expensive, has a wider field of view, and captures data not accessible through sensors. However, the visual performance has not been extensively evaluated. In this study, we aimed to determine the system’s tracking performance using an F-measure score, which is calculated based on the number of true positives, false positives, and false negatives identified during the tracking process. We also investigated the tracking reliability by comparing the intra-operator performance using the ICC for distance, speed, and time metrics by repeating the measurements five times. The aerial-imagery data were taken from a test match recorded using a drone that was hovered away from the touchline. Four players were tracked and measured simultaneously. The system demonstrates accuracy by performing admirably with average F-measure scores of 0.80, 0.80, 0.89, and 0.84 for player A0, player A1, player B0, and player B1, respectively. Meanwhile, the intra-operator reliability for distance and speed was deemed good to moderate with %MD &lt; 10%. The findings suggest that the system is a capable and reliable computer vision tracking tool with potential applications in performance analysis, training feedback, and injury prevention.

Research on photovoltaic MPPT under complex conditions based on an advanced great wall construction algorithm method

To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. We introduce the LGWGCA-P&O method, which synergistically combines an modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique. The LGWGCA is refined with a positional update mechanism inspired by the Grey Wolf Optimization (GWO) algorithm, optimizing the distribution of solution agents, while a Levy flight strategy is employed to reduce excessive randomness during agent replacement and recombination, thereby accelerating the tracking process. As the algorithm approaches the maximum power point, it seamlessly transitions to the P&O method, leveraging its rapid convergence to ensure precise and efficient power point identification. Experimental results demonstrate that the LGWGCA-P&O method achieves a PV conversion efficiency exceeding 99.96%, significantly minimizing power losses during MPPT. Furthermore, the method enhances convergence speed by approximately 40% compared to traditional GWO-P&O approaches, and it consistently outperforms Particle Swarm Optimization and Cuckoo Search Optimization algorithms under extreme conditions, demonstrating superior computational efficiency and robustness.