Tracking Technique Research Articles

An Approach Based on Golden Eagle Optimization Algorithm for Maximum Power Point Tracking of PV Panel Under Partial Shading Conditions

This study introduces an innovative Maximum Power Point Tracking (MPPT) technique utilizing the Golden Eagle Optimization (GEO) method, specifically designed to enhance the efficiency of photovoltaic (PV) systems under partial shading conditions. Unlike traditional MPPT approaches that struggle with local peaks in power-voltage curves caused by shading, the GEO method leverages the hunting behavior-inspired algorithm to accurately locate the global maximum power point (GMPP). The effectiveness of the GEO MPPT technique is demonstrated through extensive simulations across three diverse case scenarios, each representing different partial shading patterns. In all scenarios, the GEO method outperforms conventional MPPT techniques, showcasing its adaptability and superior performance in challenging conditions. The successful implementation of GEO MPPT leads to substantial improvements in PV panel energy extraction efficiency, even when faced with the complexities of partial shading. This research contributes significantly to the advancement of solar PV systems, enhancing their reliability and performance in real-world environments. By mitigating the impact of partial shading, this work promotes the wider adoption of solar energy as a viable and sustainable power solution.

Prompt Assessment of Facial Expressions to Foresee Behavioral Aptitudes in Video Interviews

This endeavor seeks to engineer a sophisticated real-time image and video processor, augmented with an advanced artificial intelligence (AI) agent, capable of prognosticating a candidate's behavioral competencies through the nuanced analysis of their facial expressions. This achievement leverages a real-time, video-recorded interview utilizing a histogram of oriented gradients and support vector machine (HOG-SVM) in conjunction with convolutional neural network (CNN) recognition. Diverging from the traditional paradigm of emotional state recognition, this avant-garde prototype system is designed to autonomously decipher a job candidate’s behaviors through their micro expressions, grounded in the behavioral ecology view of facial displays (BECV), within the framework of employment interviews. This manuscript introduces a pioneering methodology for evaluating a candidate's performance in a video interview. It elucidates a comprehensive study of sentiment analysis and eye-tracking techniques, whereby results can be synthesized on a single display to facilitate the selection of the most suitable candidate for hire. An empirical study was executed at a enterprise, wherein video recordings and competency evaluations were gathered from the organization's staff and hiring executives. The findings revealed that our proposed system exhibits superior predictive capabilities compared to traditional human-structured interviews, personality assessments, occupational interest evaluations, and assessment center. Consequently, our innovative methodology can be effectively employed as a screening tool, leveraging a personal-value-based competency framework. Key Words: Behavioural ecology view of facial displays (BECV) · Convolutional neural network (CNN) · Employment selection · Histogram of oriented gradients (HOG) · Real-time image and video processing · Support vector machine (SVM), , Eye Tracking Technique, AVI(Automatic Visual Inspection).

System that Facilitates Communication for People With Speech Difficulties With Artificial Intelligence Techniques

Objective: To develop a communication system for people with speech difficulties that allows them to express their needs by issuing instructions to the computer with minimal eye blinks, using a model created with MediaPipe and Deep Learning techniques. Theoretical Framework: The research is based on concepts of eye position tracking, convolutional networks and media pipe technology, with a focus on applying it to the communication needs of people with speech difficulties. Method: Qualitative and exploratory study, convolutional networks and media pipe techniques were used. To create the dataset, web scraping techniques were combined with manual image collection, the model was trained by comparing the performance of two CNN architectures. Results and Discussion: The incorporation of AI in the eye blink detection process is relatively recent, with more publications since 2020. It was found that the system is capable of processing facial gestures in real time with an average delay of 0.5 seconds, users reported improvements in their ability to communicate independently and reducing the effort their relatives had to make to interpret their needs, an accuracy of 94.5% was achieved in standard lighting conditions and 92% in variable conditions. Research Implications: The research reveals how AI with the incorporation of continuously emerging methods can improve the task of detecting images for eye tracking, obtaining increasingly better results in precision. Originality/Value: The application of emerging AI techniques in eye tracking to apply it in the development of a system that helps people with speech problems communicate.

Cardiac Magnetic Resonance Imaging in Diagnostics and Cardiovascular Risk Assessment

Cardiac magnetic resonance (CMR) allows for analysis of cardiac function and myocardial tissue characterization. Increased left ventricular mass (LVM) is an independent predictor of cardiovascular events; however, the diagnosis of left ventricular hypertrophy and its prognostic value strongly depend on the LVM indexation method. Evaluation of the quantity and distribution of late gadolinium enhancement assists in clinical decisions on diagnosis, cardiovascular assessment, and interventions, including the placement of cardiac implantable electronic devices and the choice of an optimal procedural approach. Novel CMR techniques, such as T1 and T2 mapping, may be used for the longitudinal follow-up of myocardial fibrosis and myocardial edema or inflammation in different groups of patients, including patients with systemic sclerosis, myocarditis, cardiac sarcoidosis, amyloidosis, and both ischemic and non-ischemic cardiomyopathy, among others. Moreover, CMR tagging and feature tracking techniques might improve cardiovascular risk stratification in patients with different etiologies of left ventricular dysfunction. This review summarizes the knowledge about the current role of CMR in diagnostics and cardiovascular risk assessment to enable more personalized approach in clinical decision making.

Site-Specific Hydrogeological Characterization for Radiological Safety: Integrating Groundwater Dynamics and Transport

The radiological impact of radionuclide transport via groundwater pathways at the Wolsong Low- and Intermediate-Level Waste (LILW) Disposal Center was estimated by considering site-specific characteristics, including hydrogeology, geochemistry, and land use. Human intrusion scenarios, such as groundwater well development, were analyzed to evaluate potential pumping volumes and radionuclide migration pathways. Particular attention was given to the hydrological and geochemical aspects of radionuclide transport, with a focus on local aquifer heterogeneity, flow dynamics, and interactions with engineered barriers and surrounding rock formations that delay radionuclide migration through sorption and other retention mechanisms. Sorption coefficients (Kd), calibrated using site-specific geochemical data, were incorporated to ensure realistic modeling of radionuclide behavior. A hierarchical approach integrating scenario screening, particle tracking techniques, and mass transfer modeling was employed. Numerical simulations using FEFLOW ver. 7.3 and GoldSim ver. 14.0 software provided insights into near-field and far-field transport phenomena under well pumping conditions. The results revealed distinct spatial flux behaviors, where carbon-14 (14C) dominated near-field flux due to its high inventory, while technetium-99 (99Tc) emerged as the primary dose contributor in the far-field flux, owing to its anionic nature and limited sorption capacity. Additionally, under high-pH conditions near concrete barriers, cellulose degradation into isosaccharinic acid was identified, enhancing radionuclide mobility through complex formation. These findings underscore the importance of site-specific sorption and speciation parameters in safety assessment and highlight the need for accurate geochemical modeling to optimize waste placement and ensure long-term disposal safety. The outcomes provide valuable insights for optimizing waste placement and contribute to the development of evidence-based safety strategies for long-term performance assessment.

Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.

Research on Visual Appeal in Element Design for Virtual Reality Thematic Digital Exhibitions

With the rapid development of information technology, the application of virtual reality (VR) technology in the field of digital exhibition has attracted wide attention, providing visitors with a new interactive experience. The purpose of this study is to explore the influence of different elements design on audience visual attraction in virtual reality digital exhibition. By using eye tracking techniques and in-depth interviews, this study analyzed the visual behavior patterns of visitors during the exhibition to assess the specific effects of different design elements on attention allocation. The study involved 30 graduate students in the School of Design and Art, using the aSee Pro desktop eye tracker to collect data. Based on the grounded theory, the interview data were coded and analyzed in depth, so as to further verify the conclusions of the eye tracker data analysis. By combining interview and eye-tracking data analysis, this study provides empirical support and in-depth understanding for the planning and optimization of digital exhibitions, and offers a new theoretical perspective and methodological reference for related fields, possessing significant value.

Brief communication: Training of AI-based nowcasting models for rainfall early warning should take into account user requirements

Abstract. In the field of precipitation nowcasting, deep learning (DL) has emerged as an alternative to conventional tracking and extrapolation techniques. However, DL struggles to adequately predict heavy precipitation, which is essential in early warning. By taking into account specific user requirements, though, we can simplify the training task and boost predictive skill. As an example, we predict the cumulative precipitation of the next hour (instead of 5 min increments) and the exceedance of thresholds (instead of numerical values). A dialogue between developers and users should identify the requirements to a nowcast and how to consider these in model training.

Hybrid proton acceleration with an ultra-intense laser of low contrast

Proton acceleration from the interaction of an ultra-intense femtosecond-class laser pulse at varying picosecond contrast levels with an ultrathin-foil target is investigated numerically. It is found that lower contrast increases the optimal target thickness without reducing the proton cutoff energy at an ultrahigh laser intensity of I∼8×1021 W/cm2, contrasting with previous experimental results at low laser intensity I<1×1021 W/cm2. By employing particle tracking techniques, we show that due to the intense radiation pressure of the main pulse, the acceleration of protons in the pre-expanded target via the Hole-Boring mechanism under low contrast surpasses that in the steep-edge target under high contrast, which is driven by a hybrid regime of light-sail and sheath acceleration before the target is penetrated. After that, a thinner optimal target thickness under high contrast results in stronger electron heating, enabling the proton energy to catch up gradually in relativistic-induced transparency enhanced acceleration. Ultimately, a similar cutoff energy is obtained for both scenarios. Our work demonstrates that high laser intensity can offer some advantages to proton acceleration at the radiation-pressure-dominated stage when laser contrast control on the picosecond level is challenging, and a thicker target is necessary. This implies that the demanding requirement for laser contrast could potentially be relaxed for multi-petawatt laser facilities, simplifying experimental setups and enhancing proton energy.

Detection of Microcalcifications with Vessels, Using Linear Structure Analysis and Uncertainty Tracking Technique in Mammography

As a potential biomarker for women’s cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide a more objective measurement for vascular structures with calcium depositions in mammography, this paper introduces a new computerized method to delineate calcified vessels. Specifically, we leverage two underlying cues—calcification and vesselness—within a multiple seeded tracking with uncertainty scheme. This new vessel-tracking scheme generates numerous sampling paths to describe the complicated topology of vascular structures with calcium depositions. A compiling and linking process is further employed to organize these sampling paths into vessel segments that are likely part of the same vessel tract. The proposed method has been evaluated on 63 mammograms, comparing manual delineations from two experts using various assessment metrics. The experimental results confirm the efficacy and stability of the proposed method, indicating its potential use as a convenient BAC measurement tool, replacing the trivial and tedious manual delineation tasks

Implementasi Live Recording Motion Tracking pada Pergerakan Karakter Animasi 2D

2D animation is currently still in demand and produced by various countries. There are many techniques developed in 2D animation, such as the common techniques, namely frame by frame and cut out, which each have their own advantages and disadvantages. As technology develops, several new techniques are starting to be developed, one of which is the motion tracking technique. Motion Tracking is a way to record the movement of a particular object in a scene using a camera. The movement recording process includes the X and Y axes on 2-Dimensional objects, as well as the X, Y and Z axes on 3-Dimensional objects. In this research, 2D animated character modeling will be carried out which will be moved using camera recording and will later be recorded using the Live2D application by applying motion tracking techniques so that efficiency and excellence will be obtained in using this technique, especially in the movements that can be achieved in 2D animation.

Eye tracking techniques and medical applications: A comprehensive review

Eye tracking is a powerful tool that has been widely used to study human behavior and cognition. This article provides a comprehensive review of eye tracking techniques, their efficiencies in accurately evaluating eye movements, and their medical applications. The article discusses different eye tracking techniques, including video-based electrooculography (EOG), and scleral search coil methods, and their advantages and limitations. It also reviews the medical conditions that can be evaluated with eye tracking, including ophthalmological and neurological disorders.

Optimization of solar water pumping systems through a combined approach based on MPPT-Bat and DTC.

This paper investigates enhancing the efficiency of solar water pumping systems (SWPS) by implementing a Maximum Power Point Tracking technique based on the Bat Metaheuristic Optimizer (MPPT-bat) for the photovoltaic generator (PVG) side, coupled with Direct Torque Control (DTC) for the induction motor powering the pump. Unlike traditional techniques, which make no compromise between tracking speed, oscillation and robustness. The integration of the MPPT-bat represents a significant advance, making it possible to improve PVG performance whatever the weather conditions. The main objective remains to improve the energy efficiency of this type of application by maximizing the electrical power allocated to the SWPS. At the same time, a DTC controller applied to the pump motor aims to optimize the use of the energy generated by the MPPT-bat. This intelligent approach adjusts the motor power according to the power extracted from the PVG, thus avoiding inappropriate profiles for the pumping system. The study confirms that optimizing SWPS using this approach based on MPPT-bat and DTC, significantly improves overall performances in terms of tracking error, oscillations, tracking speed and robustness, promotes more efficient pump rotation and, consequently, increases the flow rate of pumped water, and that these improvements persist under different climate conditions.

Daily walking time effects of the opening of a multifunctional facility “ONIKURU” using propensity score matching and GPS tracking techniques

Urban design focused on improving walkability has received attention as a method of increasing physical activity among the population. However, only a few studies have examined the effect of walking time of opening multifunctional facilities as an architecture-scale intervention. This study aimed to clarify the effect of opening a multifunctional facility on residents’ daily walking time. In addition, this study analyzed the gender and age subgroups. The natural experiment was conducted using the case of the Ibaraki City Cultural and Childcare Complex “ONIKURU,” a public multifunctional facility. This study used GPS-trajectory data based on GPS tracking techniques, which is anonymized location data for smartphone users. The causal relationship was analyzed using propensity score matching and difference-in-differences analysis. The results showed that the opening of ONIKURU significantly increased the average walking time of visitors to 3.165 [− 1.697, 8.027] min/day compared with that of non-visitors. Specifically, visitors’ average daily walking time improved to a level comparable to that of non-visitors after the opening of ONIKURU. In addition, opening ONIKURU significantly increased female young adults’ average walking time to 3.385 [− 4.906, 11.676] min/day. Therefore, this study provides theoretical contributions to a health-promoting built environment significantly affecting walking at an architecture-scale intervention.

Subclinical myocardial dysfunction of the left ventricular in patients with systemic lupus erythematosus

The aim – to study the frequency of left ventricular (LV) myocardial dysfunction and its relationship with disease activity in patients with systemic lupus erythematosus (SLE).Materials and methods. The study included 100 patients with SLE who met the criteria of SLICC/ACR 2012, 87% were women, the average age was 33 [25; 40] years, the average duration of the disease was 1 [1; 9] year, patients with varying degrees of activity according to SLEDAI-2K (low/moderate/high) – 30/45/25 (30%/45%/25%). All the subjects had not previously received biological therapy and had no previously diagnosed cardiovascular diseases and other rheumatic diseases. All patients received glucocorticoids (GCs) and hydroxychloroquine therapy in different dosages depending on the severity of the disease, according to the standards recommended by the Association of Rheumatologists of Russia from 2021. Methotrexate was received by 6% of patients, azathioprine – 14%, cyclophosphamide – 3%, nonsteroidal anti-inflammatory drugs – 81.2%. The control group consisted of 20 healthy individuals, having no signs of rheumatic diseases and CVD comparable in age and gender. All the subjects underwent echocardiography (ECHO) with tissue dopplerography and left ventricle global longitudinal strain (LV GLS) assessment by speckle tracking.Results. Violation of LV GLS was observed in 65 (65%) patients with SLE. In the SLE group, compared with the control group, a significantly more damaged LV GLS was revealed. In all patients with impaired diastolic function of the left ventricular myocardium, deterioration of LV GLS parameters is observed. Impairment of LV GLS correlated with clinical and immunological parameters in patients with SLE: the degree of SLE activity according to SLEDAI-2K (r=0.219), the level of antibodies to double-stranded DNA (a/b to ds-DNA) (r=0.316), the C3 level of the complement component (r=–0.389), the C4 level of the complement component (r=–0.238), the hemoglobin level (r=–0.255), the number of red blood cells (r=–0.286), the level of C-reactive protein (r=–0.284) and CRP (r=–0.927). Also, patients with SLE with diagnosed nephritis (n=26) had a significant violation of LV GLS parameters.Conclusions. In patients with SLE, according to ECHO using the Speckle tracking technique, violation of LV GLS occurs with a high frequency (65%). Violation of LV GLS parameters is associated with SLE activity, immunological and hematological disorders. In all patients with impaired diastolic function of the left ventricular myocardium, LV GLS was obviously decreased. The presence of lupus nephritis is associated with a significant violation of the GLS parameters.

Dynamic Emerging Pathways in Entrance and Exit Detection: Integrating Deep Learning and Mathematical Modeling

Entrance and exit event detection in dynamic environments has a lot of real-world applications in security, crowd management, and retail analytics. Traditional methods used for this problem, namely Line Partition and Bounding Box Diameter methods often struggle in complex scenarios that contain less predictable movement patterns of individuals. This paper proposes a model that integrates deep learning-based object detection and tracking techniques with linear regression to enhance the overall performance of enter and exit detection in static and dynamic environments. This approach captures the movement patterns using advanced object detection and tracking algorithms, enabling the extraction of y-coordinate variations from bounding box centers which are used to calculate the tangent of the linear regression equation and determine if the event is entrance or exit. Experimentations were conducted on 132 video sequences and show the superiority of our approach over the traditional methods achieving an overall accuracy of 86.36% and an F1-score of 0.86. These results demonstrate the high efficiency of this approach to accurately detect entrance and exit events, making it highly reliable and applicable to this problem. This research contributes to computer vision by integrating object detection and tracking algorithms with linear regression offering a solution for enhancing entrance and exit events detection in dynamic environments.

Non-invasive eye tracking and retinal view reconstruction in free swimming schooling fish

Eye tracking has emerged as a key method for understanding how animals process visual information, identifying crucial elements of perception and attention. Traditional fish eye tracking often alters animal behavior due to invasive techniques, while non-invasive methods are limited to either 2D tracking or restricting animals after training. Our study introduces a non-invasive technique for tracking and reconstructing the retinal view of free-swimming fish in a large 3D arena without behavioral training. Using 3D fish bodymeshes reconstructed by DeepShapeKit, our method integrates multiple camera angles, deep learning for 3D fish posture reconstruction, perspective transformation, and eye tracking. We evaluated our approach using data from two fish swimming in a flow tank, captured from two perpendicular viewpoints, and validated its accuracy using human-labeled and synthesized ground truth data. Our analysis of eye movements and retinal view reconstruction within leader-follower schooling behavior reveals that fish exhibit negatively synchronised eye movements and focus on neighbors centered in the retinal view. These findings are consistent with previous studies on schooling fish, providing a further, indirect, validation of our method. Our approach offers new insights into animal attention in naturalistic settings and potentially has broader implications for studying collective behavior and advancing swarm robotics.

Intelligent control of PV power conversion system connected to the grid

The integration of photovoltaic (PV) systems into the power grid has gained considerable attention due to the increasing demand for renewable energy sources. A critical aspect of grid-connected PV systems is the efficient extraction of maximum power from the PV modules under varying environmental conditions. Classical Maximum Power Point Tracking (MPPT) techniques, such as Perturb and Observe (PO) and Incremental Conductance (IC), often struggle with slow convergence and reduced accuracy under rapid changes in irradiance and temperature. To address these limitations, this study proposes the use of an advanced MPPT algorithm based on Artificial Neural Network (ANN), which offers improved tracking efficiency and faster response to dynamic conditions. The proposed MPPT method is implemented and tested within a grid-connected PV system using a three-level Neutral Point Clamped (NPC) inverter for power conversion. The NPC inverter is chosen for its advantages in reducing harmonic distortion and improving voltage quality compared to traditional two-level inverters. A detailed model of the PV system, including the inverter, is developed in MATLAB/Simulink to evaluate the effectiveness of the advanced MPPT method. Various scenarios are simulated to examine the system's performance under fluctuating irradiance changes. This study concludes that the integration of advanced MPPT algorithms with sophisticated inverter topologies is a viable solution for optimizing the performance of grid-connected PV systems. Future research will focus on validating the proposed approach in real-time environments and exploring hybrid control strategies that integrate energy storage systems.

Mars Gravity Field Determination Based on Satellite-to-Satellite Tracking Technique

The Martian gravity field serves as the primary constraint for modeling the interior structure and changes in the surface mass. Currently, the determination of the Martian gravity field relies on ground-based tracking measurements, such as range and Doppler, conducted between Earth stations and Mars orbiters. However, these tracking methods encounter limitations in terms of observation accuracy and signal attenuation. Given the successful application of the satellite-to-satellite tracking technique in determining Earth's gravity field, as well as improving the resolution of the lunar gravity field, this paper explores its potential application to the determination of the Martian static gravity field. The objective of this research is to employ numerical simulation to assess the improvement in the precision of the Martian static gravity field achievable with the low–low satellite-to-satellite tracking (ll-SST) technique, considering various conditions, including observation durations, measurement noises, and orbital altitudes. The findings show that the intersatellite ranging system considerably enhances the global resolution of the gravity field, using the ll-SST technique at an orbital altitude of 300 km and a SST measurement noise of 1 μm s−1, the global resolution can reach at least degree 120. Reducing the measurement noise by an order of magnitude can increase the global resolution of the gravity field by 20 degrees for 300 km altitudes, and 16 degrees for 400 km altitudes. Reducing the orbital altitude by 100 km (from 400 to 300 km) results in a 20 degrees improvement in the global resolution of the gravity field.

Mechanistic Insights and Technical Challenges in Sulfur-Based Batteries: A Comprehensive In Situ/Operando Monitoring Toolbox.

Batteries based on sulfur cathodes offer a promising energy storage solution due to their potential for high performance, cost-effectiveness, and sustainability. However, commercial viability is challenged by issues such as polysulfide migration, volume changes, uneven phase nucleation, limited ion transport, and sluggish sulfur redox kinetics. Addressing these challenges requires insights into the structural, morphological, and chemical evolution of phases, the associated volume changes and internal stresses, and ion and polysulfide diffusion within the battery. Such insights can only be obtained through real-time reaction monitoring within the battery's operational environment, supported by molecular dynamics simulations and advanced artificial intelligence-driven data analysis. This review provides an overview of in situ/operando techniques for real-time tracking of these processes in sulfur-based batteries and explores the integration of simulations with experimental data to provide a holistic understanding of the critical challenges, enabling advancements in their development and commercial adoption.