Tracking Applications Research Articles

An Integrated Application of Motion Sensing and Eye Movement Tracking Techniques in Perceiving User Behaviors in a Large Display Interaction

In public use of a large display, it is a usual phenomenon that multiple users individually participate in respective tasks on a common device. Previous studies have categorized such activity as independent interaction that involves little group engagement. However, by investigating how users approach, participate in, and interact with large displays, we found that parallel use is affected by group factors such as group size and between-user relationship. To gain a thorough understanding of individual and group behaviors, as well as parallel interaction task performance, one 70-inch display-based information searching task and experiment was conducted, in which a mobile eye movement tracking headset and a motion sensing RGB-depth sensor were simultaneously applied. The results showed that (1) a larger group size had a negative influence on the group users’ concentration on the task, perceived usability, and user experience; (2) a close relationship between users contributed to occasional collaborations, which was found to improve the users’ task completion time efficiency and their satisfaction on the large display user experience. This study proves that an integrated application of eye movement tracking and motion sensing is capable of understanding individual and group users’ behaviors simultaneously, and thus is a valid and reliable scheme in monitoring public activities that can be widely used in public large display systems.

Fundamental limits to depth imaging with single-photon detector array sensors

Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth images with millimeter precision. Such sensors are a key enabling technology for future autonomous systems as they provide guidance and situational awareness. However, to fully exploit the capabilities of SPAD array sensors, it is crucial to establish the quality of depth images they are able to generate in a wide range of scenarios. Given a particular optical system and a finite image acquisition time, what is the best-case depth resolution and what are realistic images generated by SPAD arrays? In this work, we establish a robust yet simple numerical procedure that rapidly establishes the fundamental limits to depth imaging with SPAD arrays under real world conditions. Our approach accurately generates realistic depth images in a wide range of scenarios, allowing the performance of an optical depth imaging system to be established without the need for costly and laborious field testing. This procedure has applications in object detection and tracking for autonomous systems and could be easily extended to systems for underwater imaging or for imaging around corners.

Application of Point Tracking Technology in 360 Degree Panorama Virtual Tour Applications for Introduction to Siliwangi University Campus

Most of the campus area introduction media use brochures to provide information to prospective students. This media among teenagers is no longer attractive. Most prospective students prefer information through multimedia such as short videos. Due to the limited time of video media, very little content is provided so that delivery will be very less. The use of Virtual Tour 360 multimedia technology will help to provide clear information in the form of text and the application of Point tracking technology will make it easier for users to feel like they are in a campus environment. The methodology used in making this application is the Luther-Sutopo version of the Multimedia Development Life Cycle (MDLC). The 360 Degree Panorama Virtual Tour Application Introduction to the Siliwangi University Campus is expected to make it easier to convey information that can be easily accepted by users. This application can see a real environment simulation on the Siliwangi University campus by representing information in the form of 360° panoramic images making it easy to display information visually. The test results obtained from the alpha test of the application of Point tracking can make it easier for users to run the application and the beta test results that the application functions very well get a score of 83.75%.

Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network (WSN) includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications. It comprises a massive set of nodes with restricted energy and processing abilities. Energy dissipation is a major concern involved in the design of WSN. Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms. In order to design an energy aware cluster-based route planning scheme, this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing (HBAC-AVOR) protocol for WSN. The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way. The presented HBAC-AVOR model follows a two stage process. At the initial stage, the HBAC technique is exploited to choose an optimal set of cluster heads (CHs) utilizing a fitness function involving many input parameters. Next, the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN. A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol. On comparing with existing techniques, the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Modified Adhoc On-Demand Distance Vector for Trust Evaluation And Attack Detection

Recently, Wireless Sensor Network (WSN) becomes most potential technologies for providing improved services to several data gathering and tracking applications. Because of the wireless medium, multi-hop communication, absence of physical protectivity, and accumulated traffic, WSN is highly vulnerable to security concerns. Therefore, this study explores a specific type of DoS attack identified as a selective forwarding attack where the misbehaving node in the network drops packet on a selective basis. It is challenging to determine if packet loss is caused by a collision in the medium access path, poor channel quality, or a selective forwarding assault. Identifying misbehaving nodes at the earliest opportunity is an acceptable solution for performing secure routing in such networks. As a result, in this study effort, we present a unique Modified Ad Hoc On-Demand Distance Vector (AODV) Routing protocol depending upon the One time password (OTP) method that employs the RSA algorithm. Finally, a trust evaluation process determines which approach is the most optimal. According to the simulation findings of the suggested routing protocol and comparison with existing routing protocols provided in this article, the proposed work is both efficient and cost-effective.

A Visual Target Representation using Saliency Detection Approach

The task of saliency detection is to identify the most important and informative part of a scene. Saliency detection is broadly applied to numerous vision problems, including image segmentation, object recognition, image compression, content-based image retrieval, and moving object detection. Existing saliency detection methods suffer a low accuracy rate because of missing components of saliency regions. This study proposes a visual saliency detection method for the target representation to represent targets more accurately. The proposed method consists of five modules. In the first module, the salient region is extracted through manifold ranking on a graph, which incorporates local grouping cues and boundary priors. Secondly, using a region of interest (ROI) algorithm and the subtraction of the salient region from the original image, other parts of the image, either related or nonrelated to the interested target, are segmented. Lastly, those related and non-related regions are classified and distinguished using our proposed algorithm. Experimental result shows that proposed salient region accurately represent the interested target which can be used for object detection and tracking applications.

Photophysics of the red-form Kaede chromophore.

The green fluorescent protein (GFP) drove revolutionary progress in bioimaging. Photoconvertible fluorescent proteins (PCFPs) are an important branch of the FP family, of which Kaede is the prototype. Uniquely, PCFPs can be permanently switched from green to red emitting forms on UV irradiation, facilitating applications in site-specific photolabelling and protein tracking. Optimisation and exploitation of FPs requires understanding of the photophysical and photochemical behaviour of the chromophore. Accordingly, the principal GFP chromophore has been the subject of intense experimental and theoretical investigation. In contrast, the photophysics of the red emitting PCFP chromophore are largely unstudied. Here we present a detailed investigation of the excited-state properties of the Kaede chromophore in solution, utilising steady state measurements, ultrafast time-resolved electronic and vibrational spectroscopies, and electronic structure theory. Its excited state dynamics are very different to those of the parent GFP. Most remarkably, the PCFP chromophore has highly complex wavelength-dependent fluorescence decays and a mean lifetime an order of magnitude longer than the GFP chromophore. Transient electronic and vibrational spectroscopies suggest that these dynamics arise from a range of excited-state conformers that are spectrally and kinetically distinct but chemically similar. These conformers are populated directly by excitation of a broad thermal distribution of ground state structures about a single conformer, suggesting an excited-state potential surface with several minima. Temperature-dependence confirms the existence of barriers on the excited-state surface and reveals the radiationless decay mechanism to be internal conversion. These experimental observations are consistent with a model assuming a simple ground state potential energy surface accessing a complex excited state possessing multiple minima.

Review on Recent Advances and Applications of Passive Harmonic RFID Systems.

Radio frequency identification (RFID) has gained significant attention because it provides a highly versatile platform for identifying, tracking, and monitoring objects. An emerging trend in this technology is the use of nonlinear RFID, such as passive harmonic tags, which have been demonstrated to be effective against clutters, echoes, crosstalk, and other electromagnetic interferences. This article presents a comprehensive review of recent advances and applications of passive harmonic RFIDs and integrated systems. A passive harmonic RFID exploits the frequency orthogonality of the transmitted (fundamental tone) and received (harmonics) radio-frequency (RF) signals to enable robust interrogation in noisy and cluttered environments, not possible with traditional passive linear RFIDs. This review article evaluates passive harmonic RFID systems in comparison to traditional systems and highlights their pros and cons. Several state-of-the-art chipless and chip-based harmonic RFIDs are presented, and their novel applications in identification, tracking, sensing, and biotelemetry are discussed. The review summarizes the key successes and challenges of passive harmonic RFID systems and provides insights into their future development, implementation, and optimization.

The application of eye tracking and artificial intelligence in contemporary marketing communication management

Purpose: Paper aims to fill the cognitive gap regarding the role, importance and potential of integrating eye tracking research and artificial intelligence. The main goal of the work was to design a proposal for the synergistic use of eye tracking and artificial intelligence for marketing communication management. Design/methodology/approach: To achieve the planned goal and answer the research questions, methods of systematic literature review, online content analysis, expert interviews and pilot eye tracking studies using the Gazepoint GP3 device and Gazepoint Analysis V6.9.0 software were used. The considerations are conducted in the field of behavioral economics. Findings: During the discussion and analysis of various aspects related to the use of eye tracking, artificial intelligence and neuro research in the context of marketing communication management, the following was established: the importance, applications and synergy of eye tracking and AI in marketing communication; the future of new technologies in marketing; key benefits, challenges and potential of the tools discussed, with particular emphasis on the need for their responsible use along with continuous development in this field. Research limitations/implications: Suggestions for future research on the issues discussed include: ethics and privacy; technological limitations; future dynamics changes. Suggestions for future research include broader analysis of user experience, integration of methods with other technologies, longitudinal research on the impact of AI-generated content personalization, measurement of consumers' emotional engagement. The indicated research areas may contribute to better use of technology in marketing and expand knowledge about non- declarative consumer behavior. Originality/value: The value of the article lies in providing conclusions and practical recommendations as well as identifying areas for further research, which may contribute to a better understanding and use of eye tracking, AI and neuro research in the field of marketing. The entire research project allowed for the design of a proposal for the synergistic use of eye tracking and artificial intelligence for marketing communication management. The article may be valuable for marketing specialists, researchers, students and people interested in the use of modern technologies in marketing. Thanks to its comprehensive perspective, it can be a guide for people interested in introducing these technologies into marketing practice. Keywords: neuromarketing, eye tracking, artificial intelligence, marketing communication. Category of the paper: Research paper.

AIR POLLUTION PREDICTION USING MACHINE LEARNING

The fact that environment tracking is focused largely on the fundamental rights of people, lifestyles, and health makes it so important. As a result, this device tracks the quality of the air using excellent sensor nodes within that check for CO2, NOx, UV light, temperature, and humidity. The gad get is able to categorize automatically if a certain geographic area is going above the established gas emission restrictions thanks to the statistics assessment using device mastering algorithms. In order to choose the most contaminated sectors, the DB SCAN with LR, SVM, and NB set of rules delivered a noteworthy category overall performance. Monitoring air quality is a crucial concern in many commercial and physical areas of the world. In areas with serious difficulties with air pollution, Air Quality Operational Centers (AQOCs) are established specifically for this purpose. The AQOCs are operational units responsible for managing tracking networks, analyzing the gathered data, and eventually disseminating online assessments of air pollutants and their short- and long-term evolution. Up until recently, modelling of air pollution events has been focused mostly on dispersion models, which approximate the complex physicochemical processes at play. Although the intricacy and complexity of these models have increased over time, their application in real-time atmospheric pollution tracking appears to no longer be acceptable in terms of performance, input data requirements, and compliance with the problem's time limitations.

Impact of Data Resolution on Tracking Southern Ocean Cyclones

Abstract The ERA5 new generation of high-resolution reanalysis provides a possibility to obtain more accurate cyclone tracks in the Southern Ocean. With a commonly used cyclone tracking algorithm, this study evaluates the impact of data resolution on Southern Ocean cyclone tracks for the period from 1980 to 2020 by preprocessing the ERA5 dataset at different spatial and temporal resolutions. A new track-matching method is proposed to assure an accurate comparison of different track datasets, considering the multiple match pairs and best match pair for each track. It is found that the number, distribution, and characteristics of cyclones are considerably different for various resolution scenarios. The higher spatial resolution captures more tracks, while the increased temporal resolution decreases the number, as well as the lifetime and the moving distance of tracks. The shared cyclones of different track datasets show different characteristics, influenced by both spatial and temporal resolutions. Higher spatial resolution schemes tend to identify more additional track points after the overlapping time of shared tracks rather than before. The spatial distribution pattern of additional track points is consistent when increasing temporal or spatial resolution separately. These results are a reference for the application of objective tracking algorithms in the Southern Ocean using input data with higher resolution. Significance Statement Automatic tracking algorithms are important tools for the research of cyclones and their associated weather phenomena. High-resolution input data are available now for cyclone tracking but it also brings more noise that may affect the results. The problem can be solved by a preprocess (smoothing) process using suitable spatial and temporal resolutions. This study compared the features of Southern Ocean cyclones obtained by different spatial and temporal resolution schemes. We further study the shared cyclones in different track datasets with a new track-matching method and discuss the different impacts of spatial and temporal resolution, as well as provide a reference for the application and improvement of cyclone tracking in the future.

Improved Drosophila Visual Neural Network Application in Vehicle Target Tracking and Collision Warning

Improved Drosophila Visual Neural Network Application in Vehicle Target Tracking and Collision Warning

Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks

Wireless sensor network (WSN) plays a vital part in real time tracking and data collection applications. WSN incorporates a set of numerous sensor nodes (SNs) commonly utilized to observe the target region. The SNs operate using an inbuilt battery and it is not easier to replace or charge it. Therefore, proper utilization of available energy in the SNs is essential to prolong the lifetime of the WSN. In this study, an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection (TFL-BOARS) has been developed for clustered WSN. The TFL-BOARS technique intends to optimally select the cluster heads (CHs) and routes in the clustered WSN. Besides, the TFL-BOARS technique incorporates Type-II Fuzzy Logic (T2FL) technique with distinct input parameters namely residual energy (RE), link quality (LKQ), trust level (TRL), inter-cluster distance (ICD) and node degree (NDE) to select CHs and construct clusters. Also, the butterfly optimization algorithm based route selection (BOARS) technique is derived to select optimal set of routes in the WSN. In addition, the BOARS technique has computed a fitness function using three parameters such as communication cost, distance and delay. In order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN, a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

Infrared Small Target Detection Based on Local Contrast-Weighted Multidirectional Derivative

Realizing robust infrared small target detection in complex backgrounds is of great essence for infrared search and tracking (IRST) applications. However, the high-intensity structures in background regions, such as the sharp edges, make it a challenging task, especially when the target is with low signal-to-clutter ratio (SCR). To address this issue, we propose an infrared small target detection method using local contrast-weighted multidirectional derivative (LCWMD). It is a robust detector that comprehensively considers the target property, background information, and the relation between them. First, we take into account the approximate isotropy of the infrared small target and present a new multidirectional derivative with penalty factors based on the Facet model to develop the target salience in the local region. Second, a dual local contrast fusion model with the tri-layer design is introduced to amplify the difference between the target and the background, so as to further suppress the high-intensity structural clutters. Finally, the LCWMD map is obtained by weighting the above two filtered maps, after which an adaptive segmentation operation is applied to accomplish the target detection. The results of comparative experiments implemented on real infrared images demonstrate that our method outperforms other state-of-the-art detectors by several times in terms of signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

A Nonlinear Observer-Based Approach to Robust Cooperative Tracking for Heterogeneous Spacecraft Attitude Control and Formation Applications

This article proposes a nonlinear observer-based approach to address the robust cooperative tracking problem and application for heterogeneous spacecraft systems. By introducing appropriate assumptions, a weight average estimation is proposed to construct a class of distributed nonlinear observers for heterogeneous spacecraft systems with a target whose dynamics are available only to a few chasers. By synthesizing the observer dynamics among the neighbors, a novel cooperative tracking controller is developed to address nonidentical parameters both in model uncertainties and nonlinear dynamics. Then, the robust consensus tracking problem can be solved if the cooperative behaviors between the chasers and their observers can be achieved synchronically. A special case of the identical model uncertainty and nonlinear dynamics scenario is also discussed. As applications of the proposed nonlinear observer-based approaches, the controller and observer designs of heterogeneous spacecraft attitude control and formation flying systems are revisited. Numerical simulations with a nonlinear dynamic model of spacecraft attitude control and formation flying systems demonstrate the effectiveness of the proposed approaches.

Development and implementation of in-line segmentation for continuous electrode production in lithium-ion battery cell manufacturing for traceability applications

One of the biggest challenges in battery cell production is reducing manufacturing costs. Production errors and manufacturing inaccuracies due to a complex process chain and cost-intensive material flows lead to high scrap rates and costs. In order to detect and eliminate production errors early in the manufacturing process, traceability approaches have been increasingly referred to in the literature. Here, process and product parameters are already assigned to the individual intermediate products and used to evaluate whether further processing is possible. When transferring traceability concepts to battery cell production, the challenge is to identify individual sections in the continuous processes of electrode production. These process steps have a decisive influence on the later electrochemical performance of the battery cells and have so far only been performed in batches to reduce possible defects. This paper presents two approaches to segmenting the produced electrodes in-line in the coating process to provide discrete sections for tracking and tracing applications. Production data are allocated to single electrodes instead of the entire electrode coil. Laser and ink marking systems are used to apply unique markings that are subsequently read out for product and process data assignment. Appropriate integration requirements must be met for both marking systems to apply readable markings. The requirements are derived for both marking systems from the respective mode of operation and compared with each other. It is shown that laser systems are more technically challenging and cost-intensive to integrate than ink systems but offer greater geometrical flexibility. Furthermore, it is shown how both marking technologies are integrated into the respective coating lines for lithium-ion battery electrodes within the pilot production lines at two research facilities. The traceability of product data to individual electrode sections is demonstrated using in-line and hand-measured electrode areal mass loading data.

A novel quaternion linear matrix equation solver through zeroing neural networks with applications to acoustic source tracking

<abstract><p>Due to its significance in science and engineering, time-varying linear matrix equation (LME) problems have received a lot of attention from scholars. It is for this reason that the issue of finding the minimum-norm least-squares solution of the time-varying quaternion LME (ML-TQ-LME) is addressed in this study. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. In light of that, two new ZNN models are introduced to solve the ML-TQ-LME problem for time-varying quaternion matrices of arbitrary dimension. Two simulation experiments and two practical acoustic source tracking applications show that the models function superbly.</p></abstract>

Early childhood tracking application: Correspondence between crowd-based developmental percentiles and clinical tools.

Barriers to child developmental screening lead to delayed diagnosis and intervention. babyTRACKS, a mobile application for tracking developmental milestones, presents parents their child's percentiles computed relative to crowd-based data. This study evaluated correspondence between crowd-based percentiles and traditional development measures. Research analyzed babyTRACKS diaries of 1951 children. Parents recorded attainment age for milestones across Gross Motor, Fine Motor, Language, Cognitive, and Social domains. Fifty-seven parents completed the Ages and Stages Questionnaire (ASQ-3), and 13 families participated in the Mullen Scales of Early Learning (MSEL) expert assessment. Crowd-based percentiles were compared with: Centers for Disease Control (CDC) norms for comparable milestones, ASQ-3 and MSEL scores. babyTRACKS percentiles correlated with the percentage of unmet CDC milestones, and with higher ASQ-3 and MSEL scores across several domains. Children who did not meet CDC age thresholds had lower babyTRACKS percentiles by about 20 points and those at ASQ-3 risk had lower babyTRACKS Fine Motor and Language scores. Repeated measures tests showed significantly higher MSEL versus babyTRACKS percentiles in the Language domain. Although ages and milestones in diary varied, the app percentiles corresponded with traditional measures, particularly in fine motor and language domains. Future research is needed for determining referral thresholds while minimizing false alarms.

Multi-Stage Estimation Algorithm for Vehicle Trajectory Tracking

This paper develops a multi-stage estimation algorithm for vehicle trajectory tracking applications. Previously designed nonlinear observers for vehicle trajectory tracking lack either the ability to handle variable velocity or have a high sensitivity to sensor noise. To overcome these shortcomings, the original model of the non-ego vehicle is translated into three separate models for speed, orientation, and position. Three stable observers are designed for these models which are all shown to be stable and robust to uncertainties. The new estimation algorithm outperforms previous high-gain and LMI-based nonlinear observers. The developed observer is useful for collision prediction and avoidance applications.

Comparing the accuracy of estimated time of arrival in mobile navigation applications

Accuracy of estimated time of arrival (ETA) for mobile vehicle tracking applications (apps) remains as a key issue to be discussed. Most of the mobile vehicle tracking apps such as WAZE is using GPS to track the current location of the vehicles. However, the accuracy of data provided by the mobile apps and factors affecting the accuracy remain as an unexplored area. This study examines the ETA accuracy by comparing ETA of a self-developed vehicle tracking app namely UCSITravel and a popular navigation app namely WAZE. The comparison test is performed by using t-test to determine if there is a statistically significant difference between the means of the datasets. Factors affecting ETA accuracy are determined in this study. The comparison results show that UCSITravel, which uses mean-value calculation (MAP), produces as accurate ETA as WAZE, which calculates ETA based on historic and real-time crowdsourced traffic data. The results shed light to mobile application developers to explore and understand more options of prediction models in developing mobile vehicle tracking app. In addition, this study has demonstrated several factors that were found to affect the accuracy of ETA in both apps as in line with literature. The results yield theoretical and practical implications.