Search results for: tracking task

Authors: Sello Mokwena, Monyepao Thabang

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In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques.

Keywords: camera network, convolutional neural network topology, person tracking, person re-identification, siamese

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Authors: Ailing Yang, Penghan Song, Aihua Cai

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The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: Aslı Yörük, Mevla Yahya, Banu Tavat

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It is well established that bilinguals outperform monolinguals on executive function tasks. However, the effects of bilingualism on prospective memory (PM), which also requires executive functions, have not been investigated vastly. This study aimed to compare bi and monolingual participants' PM performance in focal and non-focal PM tasks. Considering that bilinguals have greater executive function abilities than monolinguals, we predict that bilinguals’ PM performance would be higher than monolinguals on the non-focal PM task, which requires controlled monitoring processes. To investigate these predictions, we administered the focal and non-focal PM task and measured the PM and ongoing task performance. Forty-eight Turkish-English bilinguals residing in North Macedonia and forty-eight Turkish monolinguals living in Turkey between the ages of 18-30 participated in the study. They were instructed to remember responding to rarely appearing PM cues while engaged in an ongoing task, i.e., spatial working memory task. The focality of the task was manipulated by giving different instructions for PM cues. In the focal PM task, participants were asked to remember to press an enter key whenever a particular target stimulus appeared in the working memory task; in the non-focal PM task, instead of responding to a specific target shape, participants were asked to remember to press the enter key whenever the background color of the working memory trials changes to a specific color (yellow). To analyze data, we performed a 2 × 2 mixed factorial ANOVA with the task (focal versus non-focal) as a within-subject variable and language group (bilinguals versus monolinguals) as a between-subject variable. The results showed no direct evidence for a bilingual advantage in PM. That is, the group’s performance did not differ in PM accuracy and ongoing task accuracy. However, bilinguals were overall faster in the ongoing task, yet this was not specific to PM cue’s focality. Moreover, the results showed a reversed effect of PM cue's focality on the ongoing task performance. That is, both bi and monolinguals showed enhanced performance in the non-focal PM cue task. These findings raise skepticism about the literature's prevalent findings and theoretical explanations. Future studies should investigate possible alternative explanations.

Keywords: bilingualism, executive functions, focality, prospective memory

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Authors: Lídia E. O. Cruz, Adriano P. C. Calvo, Renato J. Soares, Regiane A. Carvalho

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Individuals with non-specific chronic low back pain may present altered movement patterns during functional activities. However, muscle behavior before and after performing a functional task with different load conditions is not yet fully understood. The aim of this study is to analyze lumbar muscle activity before and after performing the functional task of picking up and placing an object on the ground (with and without load) in individuals with nonspecific chronic low back pain. 20 subjects with nonspecific chronic low back pain and 20 healthy subjects participated in this study. A surface electromyography was performed in the ilio-costal, longissimus and multifidus muscles to evaluate lumbar muscle activity before and after performing the functional task of picking up and placing an object on the ground, with and without load. The symptomatic participants had greater lumbar muscle activation compared to the asymptomatic group, more evident in performing the task without load, with statistically significant difference (p = 0,033) between groups for the right multifidus muscle. This study showed that individuals with nonspecific chronic low back pain have higher muscle activation before and after performing a functional task compared to healthy participants.

Keywords: chronic low back pain, functional task, lumbar muscles, muscle activity

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Authors: Abdulmohsen A. AlBesher, Richard T. Stone

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Star rating for mobile applications is a very useful way to differentiate between the best and worst rated applications. However, the question is whether the rating reflects the level of usability or not. The aim of this paper is to find out if the user’ star ratings on mobile apps correlate with the usability of those apps. Thus, we tested three mobile apps, which have different star ratings: low, medium, and high. Participating in the study, 15 mobile phone users were asked to do one single task for each of the three tested apps. After each task, the participant evaluated the app by answering a survey based on the System Usability Scale (SUS). The results found that there is no major correlation between the star rating and the usability. However, it was found that the task completion time and the numbers of errors that may happen while completing the task were significantly correlated to the usability.

Keywords: mobile applications, SUS, star rating, usability

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Authors: Wajiha Fatima

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Various approaches and methods for second language classroom teaching have been proposed since the nineteenth century. Task Based Language Teaching has been prevailing approach in a second language classroom context. It is an approach which immerses students in a naturalistic setting. Tasks are the core unit of planning and instruction. This paper aims at expounding the concept of Task Based Language Teaching and how it has been evolved. In this study, researcher will highlight the usefulness of TBLT and the role it played as a powerful tool for learning and teaching in a second language setting. The article will reflect the implementation of various tasks based activities as well as the roles played by learners and teachers and the problems faced by them. In the end, researcher will discuss how TBLT can be implemented in second language classroom pedagogy.

Keywords: implementation, second language classroom, tasks, task based language teaching

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

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Authors: N. Ould cherchali, M. S. Boucherit, L. Barazane, A. Morsli

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Photovoltaic power is widely used to supply isolated or unpopulated areas (lighting, pumping, etc.). Great advantage is that this source is inexhaustible, it offers great safety in use and it is clean. But the dynamic models used to describe a photovoltaic system are complicated and nonlinear and due to nonlinear I-V and P–V characteristics of photovoltaic generators, a maximum power point tracking technique (MPPT) is required to maximize the output power. In this paper, two online techniques of maximum power point tracking using robust controller for photovoltaic systems are proposed, the first technique use fuzzy logic controller (FLC) and the second use sliding mode controller (SMC) for photovoltaic systems. The two maximum power point tracking controllers receive the partial derivative of power as inputs, and the output is the duty cycle corresponding to maximum power. A Photovoltaic generator with Boost converter is developed using MATLAB/Simulink to verify the preferences of the proposed techniques. SMC technique provides a good tracking speed in fast changing irradiation and when the irradiation changes slowly or is constant the panel power of FLC technique presents a much smoother signal with less fluctuations.

Keywords: fuzzy logic controller, maximum power point, photovoltaic system, tracker, sliding mode controller

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Authors: Jesika A. Walker, Mohammed Aswad, Guy Lacroix, Denis Cousineau

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There is a fundamental disconnect between the experience of time passing and the chronometric units by which time is quantified. Specifically, there appears to be no relationship between the passage of time judgments (PoTJs) and verbal duration estimates at short durations (e.g., < 2000 milliseconds). When a duration is longer than several minutes, however, evidence suggests that a slower feeling of time passing is predictive of overestimation. Might the length of a task moderate the relation between PoTJs and duration estimates? Similarly, the estimation paradigm (prospective vs. retrospective) and the mental effort demanded of a task (task demand) have both been found to influence duration estimates. However, only a handful of experiments have investigated these effects for tasks of long durations, and the results have been mixed. Thus, might the length of a task also moderate the effects of the estimation paradigm and task demand on duration estimates? To investigate these questions, 273 participants performed either an easy or difficult visual and memory search task for either eight or 58 minutes, under prospective or retrospective instructions. Afterward, participants provided a duration estimate in minutes, followed by a PoTJ on a Likert scale (1 = very slow, 7 = very fast). A 2 (prospective vs. retrospective) × 2 (eight minutes vs. 58 minutes) × 2 (high vs. low difficulty) between-subjects ANOVA revealed a two-way interaction between task demand and task duration on PoTJs, p = .02. Specifically, time felt faster in the more challenging task, but only in the eight-minute condition, p < .01. Duration estimates were transformed into RATIOs (estimate/actual duration) to standardize estimates across durations. An ANOVA revealed a two-way interaction between estimation paradigm and task duration, p = .03. Specifically, participants overestimated the task more if they were given prospective instructions, but only in the eight-minute task. Surprisingly, there was no effect of task difficulty on duration estimates. Thus, the demands of a task may influence ‘feeling of time’ and ‘estimation time’ differently, contributing to the existing theory that these two forms of time judgement rely on separate underlying cognitive mechanisms. Finally, a significant main effect of task duration was found for both PoTJs and duration estimates (ps < .001). Participants underestimated the 58-minute task (m = 42.5 minutes) and overestimated the eight-minute task (m = 10.7 minutes). Yet, they reported the 58-minute task as passing significantly slower on a Likert scale (m = 2.5) compared to the eight-minute task (m = 4.1). In fact, a significant correlation was found between PoTJ and duration estimation (r = .27, p <.001). This experiment thus provides evidence for a compensatory effect at longer durations, in which people underestimate a ‘slow feeling condition and overestimate a ‘fast feeling condition. The results are discussed in relation to heuristics that might alter the relationship between these two variables when conditions range from several minutes up to almost an hour.

Keywords: duration estimates, long durations, passage of time judgements, task demands

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Authors: Osamede Asowata

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Optimized gain in respect to output power of stand-alone photovoltaic (PV) systems is one of the major focus of PV in recent times. This is evident in its low carbon emission and efficiency. Power failure or outage from commercial providers, in general, does not promote development to public and private sector; these basically limit the development of industries. The need for a well-structured PV system is of importance for an efficient and cost effective monitoring system. The purpose of this paper is to validate the maximum power point of an off-grid PV system taking into consideration the most effective tilt and orientation angles for PV's in the southern hemisphere. This paper is based on analyzing the system using a solar charger with maximum power point tracking (MPPT) from a pulse width modulation (PWM) perspective. The power conditioning device chosen is a solar charger with MPPT. The practical setup consists of a PV panel that is set to an orientation angle of 0°N, with a corresponding tilt angle of 36°, 26°, and 16°. Preliminary results include regression analysis (normal probability plot) showing the maximum power point in the system as well the best tilt angle for maximum power point tracking.

Keywords: poly-crystalline PV panels, solar chargers, tilt and orientation angles, maximum power point tracking, MPPT, Pulse Width Modulation (PWM).

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Authors: Ravindra B. Patil, P. Krishnamoorthy, Shriram Sethuraman

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This work proposes a novel Gaussian Mixture Model (GMM) based approach for accurate tracking of the arterial wall and subsequent computation of the distension waveform using Radio Frequency (RF) ultrasound signal. The approach was evaluated on ultrasound RF data acquired using a prototype ultrasound system from an artery mimicking flow phantom. The effectiveness of the proposed algorithm is demonstrated by comparing with existing wall tracking algorithms. The experimental results show that the proposed method provides 20% reduction in the error margin compared to the existing approaches in tracking the arterial wall movement. This approach coupled with ultrasound system can be used to estimate the arterial compliance parameters required for screening of cardiovascular related disorders.

Keywords: distension waveform, Gaussian Mixture Model, RF ultrasound, arterial wall movement

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Authors: Van Trieu, Shouhuai Xu, Yusheng Feng

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Tracking attack trajectories can be difficult, with limited information about the nature of the attack. Even more difficult as attack information is collected by Intrusion Detection Systems (IDSs) due to the current IDSs having some limitations in identifying malicious and anomalous traffic. Moreover, IDSs only point out the suspicious events but do not show how the events relate to each other or which event possibly cause the other event to happen. Because of this, it is important to investigate new methods capable of performing the tracking of attack trajectories task quickly with less attack information and dependency on IDSs, in order to prioritize actions during incident responses. This paper proposes a two-level graph causality framework for tracking attack trajectories in internet networks by leveraging observable malicious behaviors to detect what is the most probable attack events that can cause another event to occur in the system. Technically, given the time series of malicious events, the framework extracts events with useful features, such as attack time and port number, to apply to the conditional independent tests to detect the relationship between attack events. Using the academic datasets collected by IDSs, experimental results show that the framework can quickly detect the causal pairs that offer meaningful insights into the nature of the internet network, given only reasonable restrictions on network size and structure. Without the framework’s guidance, these insights would not be able to discover by the existing tools, such as IDSs. It would cost expert human analysts a significant time if possible. The computational results from the proposed two-level graph network model reveal the obvious pattern and trends. In fact, more than 85% of causal pairs have the average time difference between the causal and effect events in both computed and observed data within 5 minutes. This result can be used as a preventive measure against future attacks. Although the forecast may be short, from 0.24 seconds to 5 minutes, it is long enough to be used to design a prevention protocol to block those attacks.

Keywords: causality, multilevel graph, cyber-attacks, prediction

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Authors: Li-Kai Kuo, Shyh-Hau Wang

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The kinetic trajectory of the median nerve (MN) in the wrist has shown to be capable of being applied to assess the carpal tunnel syndrome (CTS), and was found able to be detected by high-frequency ultrasound image via motion tracking technique. Yet, previous study may not quickly perform the measurement due to the use of a single element transducer for ultrasound image scanning. Therefore, previous system is not appropriate for being applied to clinical application. In the present study, B-mode ultrasound images of the wrist corresponding to movements of fingers from flexion to extension were acquired by clinical applicable real-time scanner. The kinetic trajectories of MN were off-line estimated utilizing two dimensional Baysian speckle tracking (TDBST) technique. The experiments were carried out from ten volunteers by ultrasound scanner at 12 MHz frequency. Results verified from phantom experiments have demonstrated that TDBST technique is able to detect the movement of MN based on signals of the past and present information and then to reduce the computational complications associated with the effect of such image quality as the resolution and contrast variations. Moreover, TDBST technique tended to be more accurate than that of the normalized cross correlation tracking (NCCT) technique used in previous study to detect movements of the MN in the wrist. In response to fingers’ flexion movement, the kinetic trajectory of the MN moved toward the ulnar-palmar direction, and then toward the radial-dorsal direction corresponding to the extensional movement. TDBST technique and the employed ultrasound image scanner have verified to be feasible to sensitively detect the kinetic trajectory and displacement of the MN. It thus could be further applied to diagnose CTS clinically and to improve the measurements to assess 3D trajectory of the MN.

Keywords: baysian speckle tracking, carpal tunnel syndrome, median nerve, motion tracking

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Authors: Yonatan Sverdlov, Shimon Ullman

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Artificial neural networks encounter a notable challenge known as continual learning, which involves acquiring knowledge of multiple tasks over an extended period. This challenge arises due to the tendency of previously learned weights to be adjusted to suit the objectives of new tasks, resulting in a phenomenon called catastrophic forgetting. Most approaches to this problem seek a balance between maximizing performance on the new tasks and minimizing the forgetting of previous tasks. In contrast, our approach attempts to maximize the performance of the new task, while ensuring zero forgetting. This is accomplished through the introduction of task-specific modulation parameters for each task, and only these parameters are learned for the new task, after a set of initial tasks have been learned. Through comprehensive experimental evaluations, our model demonstrates superior performance in acquiring and retaining novel tasks that pose difficulties for other multi-task models. This emphasizes the efficacy of our approach in preventing catastrophic forgetting while accommodating the acquisition of new tasks.

Keywords: continual learning, life-long learning, neural analogies, adaptive modulation

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Authors: Marcus Maia

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The present study reports and discusses the use of eye tracking qualitative data in reading workshops in Brazilian middle and high schools and in Generative Syntax and Sentence Processing courses at the undergraduate and graduate levels at the Federal University of Rio de Janeiro, respectively. Both endeavors take the sentential level as the proper object to be metacognitively explored in language education (cf. Chomsky, Gallego & Ott, 2019) to develop innate science forming capacity and knowledge of language. In both projects, non-discrepant qualitative eye tracking data collected and quantitatively analyzed in experimental syntax and psycholinguistic studies carried out in Lapex (Experimental Psycholinguistics Laboratory of the Federal University of Rio de Janeiro) were displayed to students as a point of departure, triggering discussions. Classes would generally start with the display of videos showing eye tracking data, such as gaze plots and heatmaps from several studies in Psycholinguistics and Experimental Syntax that we had already developed in our laboratory. The videos usually triggered discussions with students about linguistic and psycholinguistic issues, such as the reading of sentences for gist, garden-path sentences, syntactic and semantic anomalies, the filled-gap effect, island effects, direct and indirect cause, and recursive constructions, among other topics. Active, problem-solving based methodologies were employed with the objective of stimulating student participation. The communication also discusses the importance of developing full literacy, epistemic vigilance and intellectual self-defense in an infodemic world in the lines of Maia (2022).

Keywords: reading, educational psycholinguistics, eye-tracking, active methodology

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Sneha Kumari, Ravi Krishnan Elangovan

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This abstract describes the development of a high-speed tracking method by modification in motor components for nanoparticle attachment. Myosin motors are nano-sized protein machines powering movement that defines life. These miniature molecular devices serve as engines utilizing chemical energy stored in ATP to produce useful mechanical energy in the form of a few nanometre displacement events leading to force generation that is required for cargo transport, cell division, cell locomotion, translated to macroscopic movements like running etc. With the advent of in vitro motility assay (IVMA), detailed functional studies of the actomyosin system could be performed. The major challenge with the currently available IVMA for tracking actin filaments is a resolution limitation of ± 50nm. To overcome this, we are trying to develop Single Molecule IVMA in which nanoparticle (GNP/QD) will be attached along or on the barbed end of actin filaments using CapZ protein and visualization by a compact TIRF module called ‘cTIRF’. The waveguide-based illumination by cTIRF offers a unique separation of excitation and collection optics, enabling imaging by scattering without emission filters. So, this technology is well equipped to perform tracking with high precision in temporal resolution of 2ms with significantly improved SNR by 100-fold as compared to conventional TIRF. Also, the nanoparticles (QD/GNP) attached to actin filament act as a point source of light coffering ease in filament tracking compared to conventional manual tracking. Moreover, the attachment of cargo (QD/GNP) to the thin filament paves the way for various nano-technological applications through their transportation to different predetermined locations on the chip

Keywords: actin, cargo, IVMA, myosin motors and single-molecule system

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Authors: Kazushige Wada, Mayuko Ueda

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In the present study, we reanalyzed lapse errors in the last phase of a job, by re-counting near lapse errors and increasing the number of participants. We also examined the results of this study from the perspective of prospective memory (PM), which concerns future actions. This study was designed to investigate whether perceiving the importance of PM tasks caused lapse errors in the last phase of a job and to determine if such errors could be explained from the perspective of PM processing. Participants (N = 34) conducted a computerized clicking task, in which they clicked on 10 figures that they had learned in advance in 8 blocks of 10 trials. Participants were requested to click the check box in the start display of a block and to click the checking off box in the finishing display. This task was a PM task. As a measure of PM performance, we counted the number of omission errors caused by forgetting to check off in the finishing display, which was defined as a lapse error. The perceived importance was manipulated by different instructions. Half the participants in the highly important task condition were instructed that checking off was very important, because equipment would be overloaded if it were not done. The other half in the not important task condition was instructed only about the location and procedure for checking off. Furthermore, we controlled workload and the emotion of surprise to confirm the effect of demand capacity and attention. To manipulate emotions during the clicking task, we suddenly presented a photo of a traffic accident and the sound of a skidding car followed by an explosion. Workload was manipulated by requesting participants to press the 0 key in response to a beep. Results indicated too few forgetting induced lapse errors to be analyzed. However, there was a weak main effect of the perceived importance of the check task, in which the mouse moved to the “END” button before moving to the check box in the finishing display. Especially, the highly important task group showed more such near lapse errors, than the not important task group. Neither surprise, nor workload affected the occurrence of near lapse errors. These results imply that high perceived importance of PM tasks impair task performance. On the basis of the multiprocess framework of PM theory, we have suggested that PM task performance in this experiment relied not on monitoring PM tasks, but on spontaneous retrieving.

Keywords: prospective memory, perceived importance, lapse errors, multi process framework of prospective memory.

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Authors: Bruk Gebregziabher

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The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multiobject tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

Keywords: autonomous mobile robots, multi-object tracking, predictive collision avoidance, ensemble Kalman filter, lidar sensors

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Authors: Kikuo Asai, Yuji Sugimoto

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We developed a prototype system for multiplayer RC-car driving in a collaborative Augmented Reality (AR) environment. The tele-existence environment is constructed by superimposing digital data onto images captured by a camera on an RC-car, enabling players to experience an augmented coexistence of the digital content and the real world. Marker-based tracking was used for estimating position and orientation of the camera. The plural RC-cars can be operated in a field where square markers are arranged. The video images captured by the camera are transmitted to a PC for visual tracking. The RC-cars are also tracked by using an infrared camera attached to the ceiling, so that the instability is reduced in the visual tracking. Multimedia data such as texts and graphics are visualized to be overlaid onto the video images in the geometrically correct manner. The prototype system allows a tele-existence sensation to be augmented in a collaborative AR environment.

Keywords: multiplayer, RC-car, collaborative environment, augmented reality

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Authors: Rumeysa Gunduz, Dirk Koester, Thomas Schack

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Previous behavioral studies have shown that working memory (WM) and manual actions share limited capacity cognitive resources, which in turn results in execution costs of manual actions in WM. However, to the best of our knowledge, there is no study investigating the neurophysiology of execution costs. The current study aims to fill this research gap investigating the neurophysiology of execution costs of grasping in WM phases (encoding, maintenance, retrieval) considering verbal and visuospatial domains of WM. A WM-grasping dual task paradigm was implemented to examine execution costs. Baseline single task required performing verbal or visuospatial version of a WM task. Dual task required performing the WM task embedded in a high precision grasp to place task. 30 participants were tested in a 2 (single vs. dual task) x 2 (visuo-spatial vs. verbal WM) within subject design. Event related potentials (ERPs) were extracted for each WM phase separately in the single and dual tasks. Memory performance for visuospatial WM, but not for verbal WM, was significantly lower in the dual task compared to the single task. Encoding related ERPs in the single task revealed different ERPs of verbal WM and visuospatial WM at bilateral anterior sites and right posterior site. In the dual task, bilateral anterior difference disappeared due to bilaterally increased anterior negativities for visuospatial WM. Maintenance related ERPs in the dual task revealed different ERPs of verbal WM and visuospatial WM at bilateral posterior sites. There was also anterior negativity for visuospatial WM. Retrieval related ERPs in the single task revealed different ERPs of verbal WM and visuospatial WM at bilateral posterior sites. In the dual task, there was no difference between verbal WM and visuospatial WM. Behavioral and ERP findings suggest that execution of grasping shares cognitive resources only with visuospatial WM, which in turn results in domain specific execution costs. Moreover, ERP findings suggest unique patterns of costs in each WM phase, which supports the idea that each WM phase reflects a separate cognitive process. This study not only contributes to the understanding of cognitive principles of manual action control, but also contributes to the understanding of WM as an entity consisting of separate modalities and cognitive processes.

Keywords: dual task, grasping execution, neurophysiology, working memory domains, working memory phases

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Authors: Mohamed Amine Haraoubia, Abdelaziz Hamzaoui, Najib Essounbouli

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The pursuit of the MPPT has led to the development of many kinds of controllers, one of which is the Fuzzy Logic Controller, which has proven its worth. To further tune this controller this paper will discuss and analyze the use of Genetic Algorithms to tune the Fuzzy Logic Controller. It will provide an introduction to both systems, and test their compatibility and performance.

Keywords: fuzzy logic controller, fuzzy logic, genetic algorithm, maximum power point, maximum power point tracking

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Authors: Toktam Khatibi

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Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

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Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

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The PUSPATI TRIGA Reactor (RTP), Malaysia reached its first criticality on June 28, 1982, with power capacity 1MW thermal. The Feedback Control Algorithm (FCA) which is conventional Proportional-Integral (PI) controller, was used for present power control method to control fission process in RTP. It is important to ensure the core power always stable and follows load tracking within acceptable steady-state error and minimum settling time to reach steady-state power. At this time, the system could be considered not well-posed with power tracking performance. However, there is still potential to improve current performance by developing next generation of a novel design nuclear core power control. In this paper, the dual-mode predictions which are proposed in modelling Optimal Model Predictive Control (OMPC), is presented in a state-space model to control the core power. The model for core power control was based on mathematical models of the reactor core, OMPC, and control rods selection algorithm. The mathematical models of the reactor core were based on neutronic models, thermal hydraulic models, and reactivity models. The dual-mode prediction in OMPC for transient and terminal modes was based on the implementation of a Linear Quadratic Regulator (LQR) in designing the core power control. The combination of dual-mode prediction and Lyapunov which deal with summations in cost function over an infinite horizon is intended to eliminate some of the fundamental weaknesses related to MPC. This paper shows the behaviour of OMPC to deal with tracking, regulation problem, disturbance rejection and caters for parameter uncertainty. The comparison of both tracking and regulating performance is analysed between the conventional controller and OMPC by numerical simulations. In conclusion, the proposed OMPC has shown significant performance in load tracking and regulating core power for nuclear reactor with guarantee stabilising in the closed-loop.

Keywords: core power control, dual-mode prediction, load tracking, optimal model predictive control

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Authors: Bang Young Kim, Sang Hoon Park, Chul Gyu Song

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A conventional optical coherence tomography (OCT) system has limited imaging depth, which is 1-2 mm, and suffers unwanted noise such as speckle noise. The motorized-stage-based OCT system, using a common-path Fourier-domain optical coherence tomography (CP-FD-OCT) configuration, provides enhanced imaging depth and less noise so that we can overcome these limitations. Using this OCT systems, OCT images were obtained from an onion, and their subsurface structure was observed. As a result, the images obtained using the developed motorized-stage-based system showed enhanced imaging depth than the conventional system, since it is real-time accurate depth tracking. Consequently, the developed CP-FD-OCT systems and algorithms have good potential for the further development of endoscopic OCT for microsurgery.

Keywords: common-path OCT, FD-OCT, OCT, tracking algorithm

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Authors: Graham Hancox, Saydia Razak, Sue Hignett, Jo Barnes, Jyri Silmari, Florian Kading

Abstract:

In the event of a Chemical, Biological, Radiological and Nuclear (CBRN) incident rapidly gaining, situational awareness is of paramount importance and advanced technologies have an important role to play in improving detection, identification, monitoring (DIM) and patient tracking. Understanding how these advanced technologies can fit into current response systems is essential to ensure they are optimally designed, usable and meet end-users’ needs. For this reason, Human Factors (Ergonomics) methods have been used within an EU Horizon 2020 project (TOXI-Triage) to firstly describe (map) the hierarchical structure in a CBRN response with adapted Accident Map (AcciMap) methodology. Secondly, Hierarchical Task Analysis (HTA) has been used to describe and review the sequence of steps (sub-tasks) in a CBRN scenario response as a task system. HTA methodology was then used to map one advanced technology, ‘Tag and Trace’, which tags an element (people, sample and equipment) with a Near Field Communication (NFC) chip in the Hot Zone to allow tracing of (monitoring), for example casualty progress through the response. This HTA mapping of the Tag and Trace system showed how the provider envisaged the technology being used, allowing for review and fit with the current CBRN response systems. These methodologies have been found to be very effective in promoting and supporting a dialogue between end-users and technology providers. The Human Factors methods have given clear diagrammatic (visual) representations of how providers see their technology being used and how end users would actually use it in the field; allowing for a more user centered approach to the design process. For CBRN events usability is critical as sub-optimum design of technology could add to a responders’ workload in what is already a chaotic, ambiguous and safety critical environment.

Keywords: AcciMap, CBRN, ergonomics, hierarchical task analysis, human factors

Procedia PDF Downloads 188

Authors: Najm Alotaibi

Abstract:

Human motion capture has become one of the major area of interest in the field of computer vision. Some of the major application areas that have been rapidly evolving include the advanced human interfaces, virtual reality and security/surveillance systems. This study provides a brief overview of the techniques and applications used for the markerless human motion capture, which deals with analyzing the human motion in the form of mathematical formulations. The major contribution of this research is that it classifies the computer vision based techniques of human motion capture based on the taxonomy, and then breaks its down into four systematically different categories of tracking, initialization, pose estimation and recognition. The detailed descriptions and the relationships descriptions are given for the techniques of tracking and pose estimation. The subcategories of each process are further described. Various hypotheses have been used by the researchers in this domain are surveyed and the evolution of these techniques have been explained. It has been concluded in the survey that most researchers have focused on using the mathematical body models for the markerless motion capture.

Keywords: human motion capture, computer vision, vision-based, tracking

Procedia PDF Downloads 291

Authors: Fahad Y. Al-dawish

Abstract:

The current trend by a large number of organizations and individuals to use cloud computing. Many consider it a significant shift in the field of computing. Cloud computing are distributed and parallel systems consisting of a collection of interconnected physical and virtual machines. With increasing request and profit of cloud computing infrastructure, diverse computing processes can be executed on cloud environment. Many organizations and individuals around the world depend on the cloud computing environments infrastructure to carry their applications, platform, and infrastructure. One of the major and essential issues in this environment related to allocating incoming tasks to suitable virtual machine (cloud task scheduling). Cloud task scheduling is classified as optimization problem, and there are several meta-heuristic algorithms have been anticipated to solve and optimize this problem. Good task scheduler should execute its scheduling technique on altering environment and the types of incoming task set. In this research project a cloud task scheduling methodology based on ant colony optimization ACO algorithm, we call it ACO-TS Ant Colony Optimization for Task Scheduling has been proposed and compared with different scheduling algorithms (Random, First Come First Serve FCFS, and Fastest Processor to the Largest Task First FPLTF). Ant Colony Optimization (ACO) is random optimization search method that will be used for assigning incoming tasks to available virtual machines VMs. The main role of proposed algorithm is to minimizing the makespan of certain tasks set and maximizing resource utilization by balance the load among virtual machines. The proposed scheduling algorithm was evaluated by using Cloudsim toolkit framework. Finally after analyzing and evaluating the performance of experimental results we find that the proposed algorithm ACO-TS perform better than Random, FCFS, and FPLTF algorithms in each of the makespaan and resource utilization.

Keywords: cloud Task scheduling, ant colony optimization (ACO), cloudsim, cloud computing

Procedia PDF Downloads 394

Authors: Sultan Muhammed Fatih Apaydin

Abstract:

In this study, two types of robot were examined as being pioneer robot and follower robot for improving of the capabilities of tracking robots. Robots continue to tracking each other and measurement of the follow-up distance between them is very important for improvements to be applied. It was achieved that the follower robot follows the pioneer robot in line with intended goals. The tests were applied to the robots in various grounds and environments in point of performance and necessary improvements were implemented by measuring the results of these tests.

Keywords: mobile robot, remote and autonomous control, infra-red sensors, arduino

Procedia PDF Downloads 541

Authors: Rui Xing, Baolin Ye, Nan Zhou, Guohong Wang

Abstract:

Drawing upon a perspective of cognitive interaction, this study explores the relationship between team cognitive heterogeneity and team strategic decision-making flexibility, treating the transactive memory system as a mediator and task complexity as a moderator. The hypotheses were tested in linear regression models by using data gathered from 67 strategic decision-making teams in the new-energy vehicle industry. It is found that team cognitive heterogeneity has a positive impact on strategic decision-making flexibility through the mediation of specialization and coordination of the transactive memory system, which is positively moderated by task complexity.

Keywords: strategic decision-making flexibility, team cognitive heterogeneity, transactive memory system, task complexity

Procedia PDF Downloads 43