46 research outputs found
Virtual laboratories for electrical engineering students: Student perspectives and design guidelines
No full textDo New Mobile Devices in Enterprises Pose A Serious Security Threat?
No full textAbstract The purpose of this paper is to introduce a research proposal designed to explore the network security issues concerning mobile devices protection
Performance of Iterative Coded CDMA Receivers with APP Feedback: A Use of a Weighted Delay Filter
No full textThe prohibitive computational complexity of optimal coded multiuser detection necessitates using suboptimal detectors in practical implementations. The filter is very computationally simple and is also demonstrated to provide faster convergence and superior bit error rate (BER) performance. Further investigation of the weighted delay filter concept produces a second filter—derived via the joint likelihood function. It is analytically demonstrated that extrinsic feedback systems will not benefit from weighted delay filtering. A system model is provided that introduces the notion of feedback ‘residue’, which is shown to be the key difference between a-posterior probability (APP) and extrinsic systems when determining the parallel interference cancellation (PIC) output statistics. It is analytically shown that the weighted delay filter derived via a maximum signal-to-noise ratio (SNR) approach is identical to a weighted delay filter derived via the joint likelihood function. It is analytically shown that when extrinsic feedback is used in a coded-code division multiple access (C-CDMA) system, no benefit will be realised by weighted delay filtering, as soft outputs from previous cycles are a merely scaled, noisy version of the most recent data. The notion of a ‘feedback residue’ for systems with APP feedback is introduced, and it is empirically shown that this residue term is a key consideration when determining the PIC output statistics. Using the ‘residual feedback’ model, it is shown that when APP feedback is utilised, data from previous cycles is not simply “a scaled, noisy version” of the current data. For this reason, benefits may be realised by APP feedback use. The simulation results shows that the residue may be trivial at small loads, the residue builds to the substantial value of nearly 0.4 at a reasonably modest load of K/N=15/10, and continues to grow as the load increases
Learning Outcomes of Educational Usage of Social Media: The Moderating Roles of Task–Technology Fit and Perceived Risk
No full textThis study aims to explore the moderating roles of task–technology fit (TTF) and perceived risk (PR) in the relationships between the educational usage of social media (SM) platforms and its use outcomes. This is to better understand the potential benefits of using SM for educational purposes and to provide thorough insights on how SM usage would influence students’ use outcomes. We conceptualize the potential use outcomes through three-dimensional factors: perceived satisfaction, perceived academic performance, and perceived impact on learning. We further hypothesize that TTF and PR have negative moderation effects on the relationships between SM usage and the variables of use outcomes. In addition, we examine gender differences using multi-group analysis. Data were collected from a state college in Palestine using a self-administered survey, and Smart-PLS was used for data analysis and model testing using partial least square–structural equation modeling. The findings reveal that TTF has significant negative effects on the relationships between SM usage and its outcomes, whereas PR has insignificant negative moderation effects. Despite the significant negative interaction effects of TTF, the educational usage of SM has a positive impact on use outcomes. Furthermore, the findings only indicate significant gender differences in three variables: information sharing, TTF, and PR
Correction: Anthropological responses to environmental challenges in SAARC nations: A comparative analysis.
No full text[This corrects the article DOI: 10.1371/journal.pone.0296516.]
Dynamical analysis and soliton solutions of a variety of quantum nonlinear Zakharov–Kuznetsov models via three analytical techniques
No full textSome new types of truncated M-fractional exact soliton solutions of the two important quantum plasma physics models, extended quantum Zakharov–Kuznetsov and extended quantum nonlinear Zakharov–Kuznetsov, are successfully achieved by applying the expa function technique, the improved (G′/G)-expansion technique, and the Sardar sub-equation technique. These two models have many useful applications when explaining the waves in the quantum electron-positron-ion magnetoplasmas as well as weakly nonlinear ion-acoustic waves in plasma. The obtained results are in the form of dark, bright, periodic, and other soliton solutions. The results are verified and represented by two-dimensional, three-dimensional, and contour graphs. The results are newer than the existing results in the literature due to the use of fractional derivatives. Hence, the solutions will be fruitful in future studies on these models. The solutions obtained are useful in the areas of applied physics, applied mathematics, dynamical systems, and nonlinear waves in plasmas and in dense space plasma. The applied techniques are simple, fruitful, and reliable for solving other models in mathematical physics
Intelligent Time Delay Control of Telepresence Robots Using Novel Deep Reinforcement Learning Algorithm to Interact with Patients
No full textTelepresence robots are gaining more popularity as a means of remote communication and human–robot interaction, allowing users to control and operate a physical robot remotely. However, controlling these robots can be challenging due to the inherent delays and latency in the communication systems. In this research paper, we propose a novel hybrid algorithm exploiting deep reinforcement learning (DRL) with a dueling double-deep Q-network (DDQN) and a gated recurrent unit (GRU) to assist and maneuver the telepresence robot during the delayed operating signals from the operator. The DDQN is used to learn the optimal control policy for the telepresence robot in a remote healthcare environment during delayed communication signals. In contrast, the GRU is employed to model the control signals’ temporal dependencies and handle the variable time delays in the communication system. The proposed hybrid approach is evaluated analytically and experimentally. The results demonstrate the approach’s effectiveness in improving telepresence robots’ tracking accuracy and stability performance. Multiple experiments show that the proposed technique depicts improved controlling efficiency with no guidance from the teleoperator. It can control and manage the operations of the telepresence robot during the delayed communication of 15 seconds by itself, which is 2.4% better than the existing approaches. Overall, the proposed hybrid approach demonstrates the potential implementation of RL and deep learning techniques in improving the control and stability of the telepresence robot during delayed operating signals from the operator
Anthropological responses to environmental challenges in SAARC nations: A comparative analysis.
No full textThe purpose of the study is to investigate the relationships and potential impacts of environmental pollutants, human resources, GDP, sustainable power sources, financial assets, and SAARC countries from 1995 to 2022. Board cointegration tests, D-H causality, cross-sectional reliance (CSD), Saville and Holdsworth Restricted (SHL), and the DSK Appraisal Strategy were among the logical techniques employed to discover long-term connections between these components. Results demonstrate that GDP growth, renewable energy sources (REC), and environmental pollution (ENP) all contribute to SAARC countries' progress. However, future opportunities and HR are negatively impacted by increased ecological pollution. The results of the two-way causality test demonstrate a strong correlation between HR and future possibilities. Opportunities for the SAARC countries are closely related to the growth of total national output, the use of green electricity, and public support sources. Ideas for tackling future projects are presented in the paper's conclusion. These include facilitating financial development, reducing ecological pollution, financing the progress of human resources, and promoting the use of sustainable power sources
Parameterization and Design of Telepresence Robot to Avoid Obstacles
No full textBackground: The development of telepresence robots is getting much attention in various areas of human–robot interaction, healthcare systems and military applications because of multiple advantages such as safety improvement, lower energy and fuel consumption, exploitation of road networks, reduced traffic congestion and greater mobility. Methods: In the critical decision-making process during the motion of a robot, intelligent motion planning takes an important and challenging role. It includes obstacle avoidance, searching for the safest path to follow, generating appropriate behavior and comfortable trajectory generation by optimization while keeping road boundaries and traffic rules as important concerns. Results: This paper presents a state machine algorithm for avoiding obstacles and speed control design to a cognitive architecture named auto-MERLIN. This research empirically tested the proposed solutions by providing implementation details and diagrams for establishing the path planning and obstacle tests. Conclusions: The results validate the usability of our approach and show auto-MERLIN as a ready robot for short- and long-term tasks, showing better results than using a default system, particularly when deployed in highly interactive scenarios. The stable speed control of the auto-MERLIN in case of detecting any obstacle was shown
Telepresence Robot with DRL Assisted Delay Compensation in IoT-Enabled Sustainable Healthcare Environment
No full textTelepresence robots have become popular during the COVID-19 era due to the quarantine measures and the requirement to interact less with other humans. Telepresence robots are helpful in different scenarios, such as healthcare, academia, or the exploration of certain unreachable territories. IoT provides a sensor-based environment wherein robots acquire more precise information about their surroundings. Remote telepresence robots are enabled with more efficient data from IoT sensors, which helps them to compute the data effectively. While navigating in a distant IoT-enabled healthcare environment, there is a possibility of delayed control signals from a teleoperator. We propose a human cooperative telecontrol robotics system in an IoT-sensed healthcare environment. The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) offered improved control of the telepresence robot to provide assistance to the teleoperator during the delayed communication control signals. The proposed approach can stabilize the system in aid of the teleoperator by taking the delayed signal term out of the main controlling framework, along with the sensed IOT infrastructure. In a dynamic IoT-enabled healthcare context, our suggested approach to operating the telepresence robot can effectively manage the 30 s delayed signal. Simulations and physical experiments in a real-time healthcare environment with human teleoperators demonstrate the implementation of the proposed method
