98 research outputs found
Modified Topology-Based proactive routing protocols with indoor testbed design and development for Quality-Of-Service support in Vehicular AD HOC Network
No full textVehicular Ad-hoc Networks (VANETs) can produce scalable and cost-effective solutions for both safety-related and non-safety applications that rely on wireless communication. In VANETs, vehicles may disseminate helpful information about vital incidents, such as traffic conditions, route congestion, and incident warnings, providing extra efficient and circulated traffic management. For example, vehicles can receive the aforementioned information from their immediate surroundings in order to determine traffic delays or hazards. During this crucial event, the topology of such network shifts fast since the nodes are in continuous exchange at varying speeds. This results in several challenges that must be discussed accordingly in order to establish VANET effectively. Fast topological transitions and constant disconnection makes it difficult to produce an efficient routing performance for broadcasting data across vehicles, notably in vehicle to vehicle (V2V) communication. Many related investigations have been performed to identify routing protocols that have been proposed as a data dissemination approach. Routing protocols are classified based on how they transmit the packet from the source to the destination. In VANETs, routinely evaluating routing protocols can be challenging due to the lack of readily available installation packages and resources. The lack of universal support for many routing protocols has been indicated as a problem in previous literature. The evaluation of VANET protocols and applications relies largely on simulations. There are challenges in measuring performance in VANET environments, which is the lack of a unified platform for collecting and analysing performance data. The present study focuses on proactive routing protocols and the performance of VANET through the deployment of indoor microcontroller testbeds in motionless and motion states. Modifications have been proposed to the default Optimised Link State Routing (OLSR), Better Approach To Mobile Ad-Hoc Networking (BATMAN) and BABEL routing protocols. In the development of the testbed, Raspberry Pi 4, a two-wheel-drive (2WD) car chassis, and a number of components were used. Several scenarios were represented to determine the optimum performance incorporating both default and modified routing protocols. The Quality of Service (QoS) index measurement is used to measure the performance of the default and modified routing protocols based on throughput, delay, jitter, packet delivery ratio, and packet loss. According to the results of the study, BABEL routing has significantly better results than OLSR and BATMAN. The contribution of this study could lead to a more practical implementation of V2V communication
Adaptive fusion based deep learning framework for restoring underwater image quality using multi scale attention features
No full textReal-Time Heart Rate Classification: Advancements and Challenges
Full text linkThis study focuses on the classification of heart rate, a condition with significant implications for health. The challenge lies in selecting an appropriate algorithm that can handle various types and severities of arrhythmia, enabling informed decisions and effective management. Factors such as accuracy, scalability, and efficiency are crucial for individuals without medical expertise. The selected algorithm should provide reliable classifications across different levels of severity, allowing individuals to monitor their heart rates in real-time and seek medical attention when needed. By addressing these challenges, this research aims to contribute to early diagnosis, treatment, and improved management of heart rate arrhythmia
Real-Time Heart Rate Classification: Advancements and Challenges
No full textThis study focuses on the classification of heart rate, a condition with significant implications for health. The challenge lies in selecting an appropriate algorithm that can handle various types and severities of arrhythmia, enabling informed decisions and effective management. Factors such as accuracy, scalability, and efficiency are crucial for individuals without medical expertise. The selected algorithm should provide reliable classifications across different levels of severity, allowing individuals to monitor their heart rates in real-time and seek medical attention when needed. By addressing these challenges, this research aims to contribute to early diagnosis, treatment, and improved management of heart rate arrhythmia.
Manuscript Received: 21 July 2023, Accepted: 30 August 2023, Published: 15 March 2024, ORCiD: 0000-0002-5151-230
Preventing Impaired Driving Using IoT on Steering Wheels Approach
Full text linkTo drive safely, one must be attentive, coordinated, have good judgment, and be able to respond quickly to changing conditions. In certain countries, improving safety may depend largely on reducing the number of impaired drivers on the road. Therefore, solutions are required to reduce the risk that is posed on the road by drivers who have been consuming alcohol while driving. Previous research has proposed the use of sensors for detecting driver impairment caused by alcohol intoxication. However, relying on a gas sensor alone may not be appropriate for detection. To reduce drunk driving, this study proposes an Internet of Things (IoT)-based tool that measures heart rate and analyzes the breath of a driver for traces of alcohol. The tool represents a vehicle that is made up of a DC motor. In the circumstance that the tool detects a higher than resting heart rate in the driver as well as an amount of alcohol in the driver’s breath sample, the tool will immediately power down the DC motor and send an SMS to the registered emergency contact with the driver’s precise position using the GPS module. The initial prototype demonstrates the tool as a potential aftermarket accessory for vehicles. The implication of this paper is that the designed tool might be of practical use to researchers in their attempts to determine and obtain information on alcohol intoxication
Lane Changing Models: A Short Review
No full textIn general, drivers tend to change lane to have a convenient journey. Lane changing process give notable impact in traffic flow especially in congested road. Over the last decades, driving models are being develop with the intention to illustrate the behavior of vehicles. This paper intends to review the process of lane changing in the context of vehicle communication. In addition, existing lane changing models are reviewed and classified based on their characteristic. For this section, few studies on development of the models are summarized and to build new behavior model that can manage lane changes. The limitations of earlier lane-changing models are then addressed. Last, the findings and conclusions are presented, and future work are proposed
RPA-based Bots for Managing Online Learning Materials
No full textThe global COVID-19 pandemic has seen a rise in digital learning materials being shared with students of all levels of education. Learning institutions usually provide a learning management system where all the notes, tutorial and example past year examination questions are provided for students to support their learning activities in courses throughout their studies. Students usually download the learning materials either as .zip or individual files in various file formats. The steps are repetitive for each registered course, therefore can be time consuming for students. Students also need to have a sense of appropriate file management skill in order to organize downloaded materials for easy access whenever necessary. When the number of courses grow throughout the years, improper files organization may result in loss of access or unidentifiable files in student machine or devices. Hence, the purpose of this paper is to investigate the potential of Robotic Process Automation (RPA) to address related challenges faced by students in managing the amount of learning materials provided through a learning management system or portal. A RPA-based bot was developed and integrated with a learning management system to accomplish the goals. The integration shows that RPA-based bots can minimize student’s effort in managing their learning materials efficiently
Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment
No full textDriver acceptance studies are vital from the manufacturer’s perspective as well as the driver’s perspective. Most empirical investigations are limited to populations in the United States and Europe. Asian communities, particularly in Southeast Asia, which make for a large proportion of global car users, are underrepresented. To better understand the user acceptance toward in-vehicle applications, additional factors need to be included in order to complement the existing constructs in the Technology Acceptance Model (TAM). Hypotheses were developed and survey items were designed to validate the constructs in the research model. A total of 308 responses were received among Malaysians via convenience sampling and analyzed using linear and non-linear regression analyses. Apart from that, a mediating effect analysis was also performed to assess the indirect effect a variable has on another associated variable. We extended the TAM by including personal characteristics, system characteristics, social influence and trust, which could influence users’ intention to use the in-vehicle applications. We found that users from Malaysia are more likely to accept in-vehicle applications when they have the information about the system and believe that the applications are reliable and give an advantage in their driving experience. Without addressing the user acceptance, the adoption of the applications may progress more slowly, with the additional unfortunate result that potentially avoidable crashes will continue to occur
Lane change decision aid and warning system using LoRa-based vehicle-to-vehicle communication technology
No full textAmong the most severe crash scenarios are those caused by driver’s decisions to manoeuvre the vehicle to the adjacent lanes. In most scenarios, drivers’ intentionally change lanes to take over another slower vehicle and preserving the current vehicle speed especially on highway road. The decision may be fatal for drivers of incoming or approaching vehicles which are not aware of the intention and fail to reduce their vehicle speed to avoid lane change collision. Hence, this study proposes a lane change decision aid and warning system which aims to support the driver’s decision prior to performing the lane change on highway road where vehicles are travelling in a single direction. The system implements vehicle-to-vehicle communication (V2V) via long-range (LoRa) communication technology to alert the host vehicle of approaching vehicles and warns the approaching vehicle when a host vehicle intends to change lane. Visual and audible warning will be triggered as precaution mechanism for both host and approaching vehicle drivers. Experiments shows that V2V using LoRa can provide contextual information which are useful to assist drivers in deciding whether to change lane or not on highway use case settings
Prioritizing Ethical Conundrums in the Utilization of ChatGPT in Education through an Analytical Hierarchical Approach
No full textThe transformative integration of artificial intelligence (AI) into educational settings, exemplified by ChatGPT, presents a myriad of ethical considerations that extend beyond conventional risk assessments. This study employs a pioneering framework encapsulating risk, reward, and resilience (RRR) dynamics to explore the ethical landscape of ChatGPT utilization in education. Drawing on an extensive literature review and a robust conceptual framework, the research identifies and categorizes ethical concerns associated with ChatGPT, offering decision-makers a structured approach to navigate this intricate terrain. Through the Analytic Hierarchy Process (AHP), the study prioritizes ethical themes based on global weights. The findings underscore the paramount importance of resilience elements such as solidifying ethical values, higher-level reasoning skills, and transforming educative systems. Privacy and confidentiality emerge as critical risk concerns, along with safety and security concerns. This work also highlights reward elements, including increasing productivity, personalized learning, and streamlining workflows. This study not only addresses immediate practical implications but also establishes a theoretical foundation for future AI ethics research in education
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