Tracking Mechanism Research Articles

PhloVer: A Modular and Integrated Tracking Photovoltaic Shading Device for Sustainable Large Urban Spaces—Preliminary Study and Prototyping

In this work, a flower-shaped shading system with integrated tracking photovoltaic, suitable for sustainable extensive urban coverages, is designed. Detailed photovoltaic energy yield simulations with a single-diode model approach are performed to disclose the potential of the proposed tracking photovoltaic shading device (PVSD). Simulations are performed with reference to a case study. A double-layer space truss is used to house the innovative modular photovoltaic tracking system, and the first application is envisaged for the coverage of a public market area of a sunny municipality in Southern Italy. By comparing it with the traditional photovoltaic fixed system, the results of the simulations show a steadier energy generation of the new PVSD, and it also provides better coverage with renewable energy during the hours of the day when the traditional system produces low electric energy. Lastly, an early interactive prototype of the PVSD system is presented. The tracking mechanism is carefully designed, 3D-printed at a small scale and tested with a motorized dynamic system controlled by a microcontroller board. The realization of the physical prototype and the engineering of the movement mechanism confirmed the feasibility and the correct functioning of the conceived system opening to real-scale applications.

Tracking-Integrated CPV Technology: State-of-the-Art and Classification

Concentrator photovoltaic (CPV) technology offers an alternative to conventional photovoltaic systems, focusing on the concentration of solar radiation through the optics of the system onto smaller and more efficient solar cells. CPV technology captures direct radiation and requires precise module orientation. Traditional CPV systems use robust and heavy solar trackers to achieve the necessary alignment, but these trackers add to the installation and operating costs. To address this challenge, tracking-integrated CPV systems have been developed, eliminating the need for conventional trackers. These systems incorporate tracking mechanisms into the CPV module itself. This review presents a detailed classification of existing designs in the literature and provides an overview of this type of system with different approaches to integrated tracking including tracking concentrator elements, using external trackers, or employing internal trackers (the most researched). These approaches enable the automatic adjustment of the CPV system components to follow the movement of the Sun. The various tracking-integrated systems have different designs and performance characteristics. Significant progress has been made in developing tracking-integrated CPV systems with the aim to make CPV technology more competitive and expand its applications in markets where traditional CPV has been excluded.

Learning Modality Complementary Features with Mixed Attention Mechanism for RGB-T Tracking.

RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on a mixed-attention mechanism to achieve a complementary fusion of modalities (referred to as MACFT) is proposed in this paper. In the feature extraction stage, we utilize different transformer backbone branches to extract specific and shared information from different modalities. By performing mixed-attention operations in the backbone to enable information interaction and self-enhancement between the template and search images, a robust feature representation is constructed that better understands the high-level semantic features of the target. Then, in the feature fusion stage, a modality shared-specific feature interaction structure was designed based on a mixed-attention mechanism, effectively suppressing low-quality modality noise while enhancing the information from the dominant modality. Evaluation on multiple RGB-T public datasets demonstrates that our proposed tracker outperforms other RGB-T trackers on general evaluation metrics while also being able to adapt to long-term tracking scenarios.

Modified Siamese Network Based on Feature Enhancement and Dynamic Template for Low-Light Object Tracking in UAV Videos

Unmanned aerial vehicles (UAVs) visual object tracking under low-light conditions serves as a crucial component for applications, such as night surveillance, indoor searches, night combat, and all-weather tracking. However, the majority of the existing tracking algorithms are designed for optimal lighting conditions. In low-light environments, images captured by UAV typically exhibit reduced contrast, brightness, and a signal-to-noise ratio, which hampers the extraction of target features. Moreover, the target’s appearance in low-light UAV video sequences often changes rapidly, rendering traditional fixed template tracking mechanisms inadequate, and resulting in poor tracker accuracy and robustness. This study introduces a low-light UAV object tracking algorithm (SiamLT) that leverages image feature enhancement and a dynamic template-updating Siamese network. Initially, the algorithm employs an iterative noise filtering framework-enhanced low-light enhancer to boost the features of low-light images prior to feature extraction. This ensures that the extracted features possess more critical target characteristics and minimal background interference information. Subsequently, the fixed template tracking mechanism, which lacks adaptability, is enhanced by dynamically updating the tracking template through the fusion of the reference and base templates. This improves the algorithm’s capacity to address challenges associated with feature changes. Furthermore, the Average Peak-to-Correlation Energy (APCE) is utilized to filter the templates, mitigating interference from low-quality templates. Performance tests were conducted on various low-light UAV video datasets, including UAVDark135, UAVDark70, DarkTrack2021, NAT2021, and NAT2021L. The experimental outcomes substantiate the efficacy of the proposed algorithm in low-light UAV object-tracking tasks.

HVD-Net: A Hybrid Vehicle Detection Network for Vision-Based Vehicle Tracking and Speed Estimation

Visual surveillance of on-road vehicles has emerged as a vibrant research topic within the fields of computer vision and Intelligent Transportation Systems (ITSs) to mitigate traffic problems. Road accidents result in traffic congestion, vehicle damage, injuries, and fatalities. The likelihood, severity, and fatality of automobile collisions are all increased by speeding. Autonomous Speed Limit Violation Detection (SLVD) mechanism offers the best way to estimate speed with high precision using Intelligent Transportation Systems (ITSs) technology. Vision-based Vehicle Speed Monitoring (VSM) pipeline is proposed in this work, which includes mechanisms for vehicle detection, tracking, and speed measurement. VSM pipeline is based on a three-tier architecture, exploiting a single RSU-camera. In the first tier, a real-time CNN-based Hybrid Vehicle Detection Network (HVD-Net) is designed for vehicle detection. The HVD-Net utilizes multi-level and multi-scale features to minimize the impact of vehicle scale variation and maximize detection accuracy. Secondly, a Simple Online Real-time Tracker (SORT) along with HVD-Net is adopted to track vehicles' trajectories. The tracker utilizes a Kalman filter for vehicle state estimation and a Hungarian algorithm to solve the multi-vehicle association problem. Finally, a mechanism is presented to estimate the vehicle speed in the motion plane to mitigate speed overestimation. Empirical evaluations on three datasets demonstrate that the proposed vehicle detection, tracking, and speed estimation schemes perform better when compared with the relevant and state-of-the-art schemes. The estimated pipeline vehicle speed is achieved with accuracy of 87.242%.

State-flipped control and Q-learning for finite horizon output tracking of Boolean control networks

This article explores the state-flipped control mechanism for the finite horizon output tracking of Boolean control networks (BCNs) subject to a time-varying reference output trajectory. Firstly, the concept of joint control pair consisting of state-flipped control and traditional control is proposed, based on which, the joint reachability set of BCNs with joint control pair is given. Secondly, combining the joint reachability set with the output-correlation set, a necessary and sufficient condition is presented to determine the solvability of the finite horizon output tracking problem. Thirdly, a Q-learning based algorithm is developed to find both the minimum combination flip set and joint control pair sequence. Finally, an example is given to illustrate the proposed theoretical results.

Only Once Attack: Fooling the Tracker With Adversarial Template

Adversarial attacks in visual object tracking aims to fool trackers via injecting invisible perturbations for the video frames. Most adversarial methods advocate generating perturbations for each video frame, but frequent attacks may increase the computational load and the risk of exposure. Unfortunately, less works are about only attacking the initial frame and their attack effects are insufficient. To tackle this, we focus on the initialization phase of tracking and propose an only once attack framework. It can effectively fool the tracker via only generating invisible perturbations for the initial template, rather than each frame. Specifically, considering the tracking mechanism of the Siamese-based trackers, we design the minimum score-based and the minimum IoU-based loss functions. Both of them are used for training the UNet-based perturbation generator instead of the tracker, achieving the non-targeted attack. Additionally, we propose the location and direction offsets as the base attacks of sophisticated targeted attack. Combined with the two basic attacks, the tracker can be easily hijacked to move towards the fake target predefined by users. Extensive experimental results demonstrate that our only once attack framework costs the least number of attacks yet achieves better attack effect, with the maximum drop of 68.7%. The transferability experiments illustrate that our attack framework with good generalization ability can be directly applicable to the CNN-based, Siamese-based, deep discriminative-based and Transformer-based trackers, without retraining.

Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning.

Cattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. While promising progress has been made in this field, monitoring individual cattle, especially those with similar visual characteristics, remains challenging due to numerous factors such as occlusion, scale variations, and pose changes. Accurate and consistent individual identification over time is therefore essential to overcome these challenges. To address this issue, this paper introduces an approach for multiview monitoring of individual cattle behavior based on action recognition using video data. The proposed system takes an image sequence as input and utilizes a detector to identify hierarchical actions categorized as part and individual actions. These regions of interest are then inputted into a tracking and identification mechanism, enabling the system to continuously track each individual in the scene and assign them a unique identification number. By implementing this approach, cattle behavior is continuously monitored, and statistical analysis is conducted to assess changes in behavior in the time domain. The effectiveness of the proposed framework is demonstrated through quantitative and qualitative experimental results obtained from our Hanwoo cattle video database. Overall, this study tackles the challenges encountered in real farm indoor scenarios, capturing spatiotemporal information and enabling automatic recognition of cattle behavior for precision livestock farming.

Investigating the exchange of plant material between European and African botanical institutions for research and development

Social Impact StatementThe exchange of plant material and data is essential for supporting collaborative research efforts to address global challenges. In order to promote collaboration between Plant Genetic Resource (PGR) organizations in conservation research and development and to improve the sustainable utilization of PGRs, constraints to plant material and data exchange must be addressed. This can only be achieved through the introduction of policy measures that will build trust among institutions and encourage facilitated plant material exchange, equitable benefit sharing, and compliance with Access and Benefit Sharing (ABS) instruments. The resulting improvements in the conservation and sustainable utilization of PGRs will improve food security, public health, livelihoods, sustainable development, and ecological sustainability.Summary The aim of this study was to assess the extent of plant material exchange between European and African Plant Genetic Resource (PGR) organizations for research and development. Data were collected from Index Seminum databases and through a survey questionnaire. They were statistically analyzed using a chi‐square test and Spearman correlation coefficient using SPSS, version 25. The study indicated that the extent of plant material exchange between African and European PGR organizations for research and development is insignificant compared with the exchange between European institutions. Plant material exchange usually included benefit sharing, and the most commonly shared benefits were knowledge transfer, participation in research, access to research results, and joint authorship of publications. Many of the respondents were not very familiar with Access and Benefit Sharing (ABS) principles and regulations. The major constraints to plant material exchange were found to be cumbersome bureaucratic procedures, poor knowledge of compliance requirements, lack of national ABS regulations, poor quality of data associated with collections, and lack of tracking mechanisms. The low level of plant material exchange between African and European institutions is also most probably due to limited human and technological capacity in African institutions that restrict their involvement in research and development. Therefore, there is a need to build trust and to increase human and technological capacities for research in African institutions to strengthen collaboration by ensuring facilitated exchange and equitable benefit sharing.

A novel technique of schedule tracker for parabolic dish concentrator.

Tracking is important in a system that harnesses solar energy. Single axis tracking mechanism is cheaper and simple to develop but because of the limitation of tracking axis, this system is less efficient than dual axis. Dual-axis tracking systems necessitate a large number of equipment, sensors, motors, and a lengthy computer program to function properly. Therefore, in the present study, a novel method of solar tracking has been discussed where each tracking point has the impact of both the azimuth and altitude angle at a single point. This method is an average axis tracking method (AATM). HelioScope software was used to extract the hourly solar altitude and azimuth angles for each day and month for the site of Bhopal, India. The average method was then used to get the hourly average solar tracking angle (ASTA) for each month. The parabolic dish concentrator was designed in SolidWorks to apply and simulate the newly developed tracking points on SolTrace software. The graphical analysis was presented along with proper validation of the proposed method, and the single and dual axes were compared with AATM. The graphical study shows that the average axis tracking points have a smoother slop of wave than the single axis. From June to September, the proposed method's error was estimated between 0.85 and 0.95. It can be concluded that by making slight adjustments to the seasonal angle, this error could be minimized and the concept could be successfully applied to a parabolic dish or a solar PV system.

Facilitators and inhibitors of Agile methods adoption: Practitioners view

AbstractThis study provides empirical evidence to the body of knowledge in Agile methods adoption in small, medium, and large organizations in the global context. This research explores facilitators and inhibitors of Agile methods adoption in software development organizations. A survey was conducted among Agile professionals to gather survey data from 52 software organizations in seven countries across the world. This study found many facilitators of Agile adoption to be significant such as customers’ dominant issues, encouragement, project champion, highly competent team, use of tools, etc. Similarly a correlation analysis revealed multiple inhibitors as significant: absence of a full set of right Agile practices, absence of customer presence, absence of tracking mechanisms during Agile progress, and failure to determine the role of the client. The present study identifies that an Agile team with high expertise and competence leads to higher quality in software, customer satisfaction along with return on investment (ROI) while a small Agile team increases ease in handling changing requirements, customer satisfaction, reduced delivery time, and increased ROI. Frequent delivery accelerates better control over work, adds to software quality, customer satisfaction, and in shortening delivery time along with increase ROI. It has also been observed that providing essential features early leads to increase in software quality and customer satisfaction. This study confirms that active customer focus leads to better control over work. Further, absence of customer decreases dealing with changing requirements, and customer satisfaction while absence of progress tracking lowers customer satisfaction.

Multidisciplinary teams, efficient communication, procedure services, and telehealth improve cirrhosis care: A qualitative study.

Cirrhosis care and outcomes are improved with access to subspecialty gastroenterology and hepatology care. In qualitative interviews, we investigated clinicians' perceptions of factors that optimize or impede cirrhosis care. We conducted 24 telephone interviews with subspecialty clinicians at 7 Veterans Affairs medical centers with high- and low-complexity services. Purposive sampling stratified Veterans Affairs medical centers on timely post-hospitalization follow-up, a quality measure. We asked open-ended questions about facilitators and barriers of care coordination, access to appointments, procedures, transplantation, management of complications, keeping up to date with medical knowledge, and telehealth use. Key themes that facilitated care were structural: multidisciplinary teams, clinical dashboards, mechanisms for appointment tracking and reminders, and local or virtual access to transplant and liver cancer specialists through the "specialty care access network extension for community health care outcomes" program. Coordination and efficient communication between transplant and non-transplant specialists and between transplant and primary care facilitated timely care. Same-day access to laboratory, procedural, and clinical services is an indicator of high-quality care. Barriers included lack of on-site procedural services, clinician turnover, patient social needs related to transportation, costs, and patient forgetfulness due to HE. Telehealth enabled lower complexity sites to obtain recommendations for complex patient cases. Barriers to telehealth included lack of credit (eg, VA billing equivalent), inadequate staff, lack of audiovisual technology support, and patient and staff discomfort with technology. Telehealth was optimal for return visits, cases where physical examination was nonessential, and where distance and transportation precluded in-person care. Rapid telehealth uptake during the COVID-19 pandemic was a positive disruptor and facilitated use. We identify multi-level factors related to structure, staffing, technology, and care organization to optimize cirrhosis care delivery.

Dropout prediction and decision feedback supported by multi temporal sequences of learning behavior in MOOCs

The temporal sequence of learning behavior is multidimensional and continuous in MOOCs. On the one hand, it supports personalized learning methods, achieves flexible time and space. On the other hand, it also makes MOOCs produce a large number of dropouts and incomplete learning behaviors. Dropout prediction and decision feedback have become an important issue of MOOCs. This study carries out sufficient method design and decision analysis on the dropout trend. Based on a large number of learning behavior instances, we construct a multi behavior type association framework, design dropout prediction model to analyze the temporal sequence of learning behavior, then discuss the corresponding intervention measures, in order to provide adaptive monitoring mechanism for long-term tracking and short-term learning method selection, and enable adaptive decision feedback. the full experiment shows that the designed model might improve the performance of the dropout prediction, which achieves the reliability and feasibility. The whole research can provide key technical solution and decision, which has important theoretical and practical value for dropout research of MOOCs.

On the vertical effect of the Non-linear behavior of track–bridge interaction in simply supported railway bridges

The present paper is devoted to studying the influence of the nonlinear behavior of the ballasted track on the dynamic response of a simply supported, non-skewed single ballasted track railway bridge acted by moving trains at constant speeds. Based on a double-beam model the rail track and the bridge deck are modeled as a simply supported Euler–Bernoulli upper beam and lower beam, respectively, which are connected between them through a vertical nonlinear continuous layer with viscous damping, which schematizes the mechanism of the vertical track–bridge interaction. The moving trains are modeled as a series of moving vertical forces (Moving Load Model). The nonlinear viscoelastic layer model describing the behavior of the rail bed is assumed to be a cubic-plus-bilinear type foundation; in particular, a bilinear constitutive law is introduced by the fact that this connection layer is characterized by two stiffness coefficients in compression and in tension at vertical zero deflection. The nonlinear partial differential equations that govern the transverse motion of the coupled system are determined. The Galerkin method and the fourth-order Runge–Kutta method are used to discretize and determine numerically the time–history response of the considered bridge. The obtained numerical results at the first order have shown that the dynamics of the coupled ballasted track–bridge system is governed by a Duffing like oscillator. The effect of the nonlinear behavior of the ballasted track cannot be neglected and yields decreased critical speeds and decreasing resonant amplitudes.

The Dilemma and Solution of Socialized Extension Work in China's Judicial Administrative Drug Rehabilitation

The Anti-Drug Law has established a four-in-one drug rehabilitation system, including voluntary drug rehabilitation, community-based drug rehabilitation, compulsory isolation drug rehabilitation, and community rehabilitation. Community rehabilitation, as a consolidation measure after drug rehabilitation, is constrained by factors such as personnel, funding, and technology, which prevent it from fully exerting its function of consolidating drug rehabilitation effects. Social extension, as an extension of the function of compulsory isolation drug rehabilitation facilities, has compensated for the shortcomings of community rehabilitation in terms of professional talent and technical support. However, due to inadequate legal support, different supervisory departments, and overlapping business responsibilities, social extension work currently faces many difficulties. With the development of society and the improvement of understanding of drug rehabilitation laws, the focus of compulsory isolation drug rehabilitation work will not be limited to fixed locations and a period of about two years. Instead, it should leverage advantages in talent, technology, and information to reshape institutions and optimize processes around reducing relapse rates. Through on-site investigations of the current social extension work, it is found that there are currently issues such as incomplete laws and regulations, unconsolidated business processes, no established follow-up care and tracking mechanisms, low social extension service levels, insufficient publicity efforts, and deviation from guiding principles. Based on China's current political, economic, and technological foundations, there is still a lot of room for improvement in the effectiveness of social extension work. From a legal perspective, the legislative process should be accelerated and current laws and regulations should be improved. From a process perspective, business processes should be re-engineered and organizational structures optimized. From a talent perspective, incentive mechanisms should be improved, and professional development strengthened. From a service perspective, employment issues should be emphasized to create a return environment. From an information perspective, digital construction should be accelerated to meet audience needs. From an acceptance perspective, drug prevention publicity should be expanded, and guiding principles clarified. From an iterative perspective, a negotiation platform should be established, and a corrective mechanism constructed.

From Godown to Warehouse Management: Transformation at eKart, An Illustrative Case Study

The growth in Ecommerce has transformed warehouse and distribution centers to mega-fulfilment centers. The focus is increasingly on innovative storage solutions, better tracking mechanisms coupled with technology and training. Companies who do not realize this have little chance of surviving (Rachid Fergati, MD Indian subcontinent, UPS). The purpose of this case is to highlight the process, people, and operations improvements undertaken by an Indian e-commerce giant with the help of the research team at the CII School of Logistics for enhanced efficiency and customer service. The researchers spent 50 days onsite (at the company’s warehouse located in Binola) observing the processes, identifying issues, providing solutions, and tracking and recording improvements as per the action research methodology (Reason, 2001). Thus, two-fold objectives were achieved: (a) improved cost efficiency and material handling processes in the warehouse and (b) knowledge building in the strategic operations management area. The case is a representative illustration of how the Indian logistics industry is moving from fragmented, unorganized, and inefficient systems to a more organized, structured sector in an ultra-competitive global space.

"We are half-doctors": Family Caregivers as Boundary Actors in Chronic Disease Management

Computer-Supported Cooperative Work (CSCW) and Human--Computer Interaction (HCI) research is increasingly investigating the roles of caregivers as ancillary stakeholders in patient-centered care. Our research extends this body of work to identify caregivers as key decision-makers and boundary actors in mobilizing and managing care. We draw on qualitative data collected via 20 semi-structured interviews to examine caregiving responsibilities in physical and remote care interactions within households in urban India. Our findings demonstrate the crucial intermediating roles family caregivers take on while situated along the boundaries separating healthcare professionals, patients and other household members, and online/offline communities. We propose design recommendations for supporting caregivers in intermediating patient-centered care, such as through training content and expert feedback mechanisms for remote care, collaborative tracking mechanisms integrating patient- and caregiver-generated health data, and caregiving-centered online health communities. We conclude by arguing for recognizing caregivers as critical stakeholders in patient-centered care who might constitute technologically assisted pathways to care.

Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors

Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible and efficient solution for many CSN applications. However, mobile camera sensor networks still face several issues, such as limited sensing range, the optimal deployment of camera sensors, and the energy consumption of the camera sensors. Therefore, mobile cameras should cooperate in order to improve the overall performance in terms of enhancing the tracking quality, reducing the moving distance, and reducing the energy consumed. In this paper, we propose a movement prediction algorithm to trace the moving object based on a cooperative relay tracking mechanism. In the proposed approach, the future path of the target is predicted using a pattern recognition algorithm by applying data mining to the past movement records of the target. The efficiency of the proposed algorithms is validated and compared with another related algorithm. Simulation results have shown that the proposed algorithm guarantees the continuous tracking of the object, and its performance outperforms the other algorithms in terms of reducing the total moving distance of cameras and reducing energy consumption levels. For example, in terms of the total moving distance of the cameras, the proposed approach reduces the distance by 4.6% to 15.2% compared with the other protocols that do not use prediction.

A blockchain-based traceable and secure data-sharing scheme.

The traditional data-sharing model relies on a centralized third-party platform, which presents challenges such as poor transaction transparency and unsecured data security. In this article, we propose a blockchain-based traceable and secure data-sharing scheme. Firstly, we designed an attribute encryption-based method to protect data and enable fine-grained shared access. Secondly, we developed a secure data storage scheme that combines on-chain and off-chain collaboration. The InterPlanetary File System (IPFS) is used to store encrypted data off-chain, and the hash value of encrypted data is stored on the blockchain. To improve data security, elliptic curve cryptography (ECC) encryption is performed before the hash value is stored. Finally, we designed a smart contract-based log tracking mechanism. The mechanism stores data sharing records on the blockchain and displays them in a visual form to meet the identity tracking needs of both data sharing parties. Experimental results show that our scheme can effectively secure data, track the identities of both parties sharing data in real-time, and ensure high data throughput.

MoErv14 mediates the intracellular transport of cell membrane receptors to govern the appressorial formation and pathogenicity of Magnaporthe oryzae.

Magnaporthe oryzae causes rice blasts posing serious threats to food security worldwide. During infection, M. oryzae utilizes several transmembrane receptor proteins that sense cell surface cues to induce highly specialized infectious structures called appressoria. However, little is known about the mechanisms of intracellular receptor tracking and their function. Here, we described that disrupting the coat protein complex II (COPII) cargo protein MoErv14 severely affects appressorium formation and pathogenicity as the ΔMoerv14 mutant is defective not only in cAMP production but also in the phosphorylation of the mitogen-activated protein kinase (MAPK) MoPmk1. Studies also showed that either externally supplementing cAMP or maintaining MoPmk1 phosphorylation suppresses the observed defects in the ΔMoerv14 strain. Importantly, MoErv14 is found to regulate the transport of MoPth11, a membrane receptor functioning upstream of G-protein/cAMP signaling, and MoWish and MoSho1 function upstream of the Pmk1-MAPK pathway. In summary, our studies elucidate the mechanism by which the COPII protein MoErv14 plays an important function in regulating the transport of receptors involved in the appressorium formation and virulence of the blast fungus.