Journal of Advanced Research Design

Air–Water Jet Wiper System for Continuous Visibility and Driver Safety

Wed, 03 Dec 2025 00:00:00 +0800

Ensuring continuous driver visibility during rainfall remains a persistent challenge, as conventional blade-based systems cause intermittent vision loss, uneven coverage, and high maintenance requirements. This study proposes and validates a bladeless smart wiper washing system that integrates synchronized micro-jet water spraying with high-pressure air-jet drying to eliminate temporary blindness and enhance safety. The system was conceptualized using CAD modeling, structurally validated via finite element analysis (von Mises stress), and fabricated into a working prototype consisting of a hollow aluminum rod with twenty-one 1 mm micro-nozzles, a 12 V pump, a compressor, and a synchronized control unit. Experimental testing demonstrated more than 85% uniform spray coverage, rapid cleaning cycles of 2–4 s compared to 5–10 s for conventional wipers, reduced fluid consumption (~100 mL per cycle versus 200–300 mL), and markedly lower operational noise. Comparative evaluations confirmed that the bladeless configuration eliminated blade streaking, minimized mechanical wear, and provided uninterrupted visibility even under simulated rainfall. Structural simulations were consistent with prototype behavior, indicating robust performance within the tested pressure range. Collectively, the findings establish that integrating micro-jet spraying with air-jet clearing offers a viable pathway toward safer, quieter, and more resource-efficient windshield cleaning. The approach is particularly suited for next-generation vehicles and ADAS platforms where continuous visibility is essential. Future research will focus on adaptive sensing and closed-loop control strategies to optimize jet activation under real-world environmental conditions.

Islamic Inheritance System: Smart Mobile Application for Declaration of Assets and Liabilities

Thu, 15 Jan 2026 00:00:00 +0800

Islamic inheritance law mandates a fair distribution of assets and liabilities among heirs, requiring careful record-keeping and adherence to religious principles. However, traditional methods of asset declaration often prove cumbersome and error-prone. This paper presents the characteristics of a smart mobile application that integrates RFID technology in determining the geolocation of inheritance land to facilitate the process of declaring property and liability. Existing methods of declaring assets and liabilities for Islamic inheritance lack efficiency and security, leading to potential inaccuracies and disputes. Additionally, the absence of robust geolocation and RFID capabilities hinders the tracking and verification of declared assets, creating additional challenges in ensuring transparency and fairness. This study aims to develop a mobile application that streamlines the declaration of assets and liabilities for Islamic inheritance while increasing security through geolocation and RFID technology. The integration of geolocation and RFID technology into a mobile application for the declaration of Islamic inheritance is a significant advance in ensuring the security and transparency of asset distribution. By leveraging these features, the application not only streamlines the declaration process but also increases trust and adherence to Islamic principles.

A Robust Iris Localization and Texture Extraction Scheme for Iris Authentication Systems

Tue, 12 Aug 2025 00:00:00 +0800

Biometrics systems accurately identify and distinguish individuals based on their characteristics. Due to the high accuracy and stability, iris has been widely used in the authentication process of authorized persons. One of the most important processes in developing an iris recognition system is iris segmentation since it has a substantial impact on the accuracy of iris matching.

Malware Detection in Windows Using Deep Learning Classification Approach

Wed, 05 Nov 2025 00:00:00 +0800

Microsoft Windows is the most common operating system, and because of its global popularity, it is also the most popular platform for hackers to target. It is also susceptible to security flaws. According to the Common Vulnerabilities and Exposures (CVE) database, which tracks known system vulnerabilities, Microsoft had over 660 dangerous security holes, 357 of which were associated with Windows 10. Thus, users may also be at risk because of security flaws in the Windows applications they employ or because of attacks on connected devices. Windows malware has been a major threat to computer software for decades, putting millions of people in danger. An attacker creates it to disrupt computer operations, gather sensitive information, or gain access to private computer systems. The increasing number of zero-day vulnerabilities and the rapid growth of Windows malware require efficient and accurate malware detection. Thus, this paper discusses Windows malware detection using a deep learning classification approach. In this study, the samples of Windows malware were analysed using malware analysis tools such as HashMyFiles and CFF Explorer. Subsequently, the malware visualisation was used to convert the binaries of malware files to generate a grayscale dataset. The classification process implemented using CNN and RNN for malware detection was being evaluated. Using the Metric Formula Definition Accuracy, the performance of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) malware detection models in Windows has been tested. According to the models, CNN is doing better, providing an accuracy of 97.5 percent in detecting malware, whereas RNN provides an accuracy of 88.5 percent, respectively. This study evaluated the accuracy performance between the CNN and RNN architecture models.

Deep- Image: Automated Identification of Bacteria based on Deep Learning Model

Mon, 11 Aug 2025 00:00:00 +0800

Accurate Classification of bacteria plays a crucial role in microbiology and beyond. It helps to identify infectious agents during epidemiological investigations, food safety monitoring, and detection of biological threat agents. Convolutional Neural Network (CNN) is a deep learning technique that has proven reliable in the field of Classification of medical and biological diseases. In this study, CNNs are utilized to develop a bacterial classification system. Within this system, Classification is subjected to several modifications before the ResNet method is used in order to identify the kinds of Bactria from among sixteen different classes of bacterial images. The model was fine-tuned by training only the last two layers of the pre-trained ResNet101V2 network, which significantly improved the performance. A large-scale dataset and confusion matrix were used to evaluate the model's performance. The experimental results demonstrate that the accuracy rate reached a peak of 98.66%. Moreover, the suggested approach enhances the advancement of automated diagnostic tools for bacterial pictures that surpass the present state-of-the-art models and provide the groundwork for future enhancements in bacterial image classification utilizing CNNs.

Leveraging ECG Signals for Automated Diabetic Patient Detection using CNN

Wed, 05 Nov 2025 00:00:00 +0800

Increasing blood glucose (BG) levels can lead to diabetes, affecting millions of adults worldwide. Insulin facilitates glucose absorption into cells for energy, and severe hypoglycemia in insulin-treated diabetics may cause abnormal ECG changes. Therefore, continuous monitoring of BG levels is critical. Traditional monitoring involves invasive finger pricks, whereas non-invasive methods, such as this study's approach, avoid the need for blood samples. This research proposes an IoT-based, non-invasive BG monitoring system that uses near-infrared (NIR) light and ECG signals. The ECG data are preprocessed using a Butterworth filter and analysed with a convolutional neural network (CNN). Several machine learning algorithms were compared to thirty subjects' ECG readings to test their performance and achieved almost 95% accuracy in detecting diabetic (DM) or healthy (non-DM) status.

Numerical Analysis and Experimental Validation of a Smart Wiper Washing System Prototype

Wed, 03 Dec 2025 00:00:00 +0800

Conventional windshield wiper washer systems often suffer from non-uniform spray coverage, leakage under pressure, and limited structural endurance, reducing their effectiveness in maintaining driver visibility during adverse weather conditions. This study addresses these challenges through the design, structural validation, and experimental testing of a smart wiper washing system prototype. The development process began with conceptual sketches and parametric modeling in SolidWorks, followed by finite element analysis in ANSYS using the von Mises stress criterion to evaluate structural integrity under internal pressurization. The perforated aluminum spray pipe demonstrated negligible deformation (<0.2 mm) and maximum stress values of ~2.25 MPa, well below the material yield strength, confirming a wide safety margin. A physical prototype was fabricated using locally available materials, integrating mechanical, fluidic, and electrical subsystems into a dual-mode actuation cycle: water spraying during the upward stroke and compressed air discharge during the downward stroke. Experimental validation showed spray uniformity above 85% and stable, repeatable wiper motion, with strong agreement between simulation predictions and measured performance. The findings confirm that the proposed system is both structurally robust and functionally reliable, offering a cost-effective and manufacturable solution for enhanced windscreen cleaning. This work contributes to automotive engineering by demonstrating how simulation-driven design can be translated into a validated prototype, providing a practical foundation for future refinement and durability studies.

An Adaptive Ensemble Machine Learning Classifier for Sentiment Analysis on Twitter

Shakirah Mohd Sofi, Ali Selamat, Zatul Alwani Shaffiei — Thu, 15 Jan 2026 00:00:00 +0800

Social media platforms serve as ubiquitous channels for individuals to connect, communicate, and share information in real-time across the globe. The exponential growth of social media platforms, particularly Twitter, has led to a significant increase in textual data, shaping social discourse and sentiment analysis. This abundance of data presents challenges and opportunities for understanding the dynamics of social media interactions and sentiment expression. Sentiment analysis faces challenges: sparse data limits understanding, while topic coherence and interpretability demand improvement for clearer insights. The primary goal of this paper is to improve the accuracy and effectiveness of sentiment analysis through the application of advanced techniques and classifiers. Traditional machine learning techniques often struggle to effectively capture the nuanced sentiment expressed in tweets. To address this issue, we propose a novel ensemble learning framework that dynamically adapts to the evolving characteristics of Twitter data. We experiment with baseline classifiers such as Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), and Naive Bayes (NB) on Twitter data. Our approach combines these weak learners through ensemble methods like Voting, Bagging, XGBoost, and stacking, incorporating a meta-learner to optimize prediction performance. The experimental findings demonstrate that our innovative ensemble classifier achieves a remarkable accuracy rate, significantly surpassing that of individual classifiers. This paper contributes to the advancement of sentiment analysis techniques tailored for social media data, offering insights into the potential of adaptive ensemble learning in addressing the unique challenges posed by Twitter sentiment analysis.

Integrating PSO with Butterfly Optimization for Efficient Feature Selection: An IBFPSO Approach

Ali Abdulkadhim Taher, Manar Bashar Mortatha Alkorani — Tue, 12 Aug 2025 00:00:00 +0800

Feature selection is an effective way to decrease dataset dimensions and increase classification accuracy. But feature selection is a complex and challenging procedure that needs a highly efficient algorithm. The collective behavior of decentralized, self-organized natural or artificial systems is known as swarm intelligence (SI). The migration patterns of butterflies serve as the inspiration for the Butterfly Optimization algorithm, a type of swarm intelligence metaheuristic algorithms.. In this enhanced Butterfly Optimization algorithm (BOA-PSO), the issue of feature selection is initially conceptualized and subsequently transformed into a fitness function. Next, we proposed an IBFPSO to address the issue of feature selection. In order to enhance the BOA and expand its applicability to feature selection issues, we integrated PSO into the BOA. Ultimately The proposed algorithm IBFPSO is benchmarked against , binary PSO (BPSO), Binary dragonfly algorithm (BDA), Binary grey wolf optimization approach (BGWO), Binary bat algorithm (BBA) and enhanced binary bat algorithm (EBBA). To evaluate these algorithms, five datasets were sourced from the UC Irvine Machine Learning Repository. The experimental findings reveal that the IBFPSO algorithm outperforms other comparative algorithms across all datasets. In the Breastcancer dataset, the accuracy rate for IBFPSO was (0.9886) compared to the closest algorithm's (0.9786). In the BreastEW dataset, the accuracy rate for IBFPSO was (0.9843) compared to the closest algorithm's (0.9614). In the Congress dataset, the accuracy rate for IBFPSO was ( 0.9874), whereas it was (0.9793) for the nearest algorithm. In the SpectEW dataset, the accuracy rate for IBFPSOwas (0.8556) compared to the nearest algorithm where it was (0.7407). In the tic-tac-toe dataset, the accuracy rate was (0.9791), while the closest algorithm's was (0.8521).

Analyzing Cryptocurrency Volatility Data for Breaks, Trend Breaks and Outliers using the Indicator Saturation Method

Wed, 05 Nov 2025 00:00:00 +0800

Blockchain technology sank a volatile market. Extracting volatility insights from real-time cryptocurrency price data using advanced methods is a problem that needs to be addressed. The cryptocurrency market is volatile and subject to enormous swings in value. Thus, this study examines the daily volatility of five cryptocurrency markets, including Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Tether (USDT), and Ripple (XRP), for the last ten years. The volatility data obtained from the GARCH (1,1) model was used to identify daily breaks, trend breaks, and outliers using the indicator saturation (IS) method. The IS method identified a total of 24 outliers, 91 structural breaks, and 305 trend breaks across the cryptocurrency volatility data over the last decade. Of which BTC had 111 daily breaks, trend breaks, and outliers, compared to LTC's 101, ETH's 74, XRP's 71, and USDT's 63. Fluctuations occurred each year, mostly in 2017, 2018, 2020, and 2021, due to legal uncertainty, security issues, and initial coin offers (ICOs). The COVID-19 pandemic in 2020 also caused economic uncertainty and increased volatility. These findings can aid in improving volatility models by comprehending how various events affect financial markets. Authorities and decision-makers may also ensure financial stability. This study sheds light on how future money and virtual currencies may affect financial technology. Our analysis shows that the market has been risky for a decade.

Immersive Cinematic Experience Through Sound Technology in Film: A Systematic Review

Tue, 11 Nov 2025 00:00:00 +0800

Sound technology has a significant influence on audience engagement, emotional resonance, and overall storytelling impact, yet it is often underappreciated and overlooked. As cinema progresses, advances in audio technologies have become crucial to narrative development, providing a dynamic sound backdrop that complements visual storytelling. The objective of this systematic review is to investigate the importance of sound technologies in shaping the immersive film experience and to thoroughly analyze and synthesize current literature on the impact of sound technology in film, including spatial audio, surround sound, and binaural recording techniques. This research seeks to identify patterns, trends, and gaps in current knowledge. The significance of this study lies in its potential to enlighten filmmakers, sound designers, and researchers about the profound impact of sound technology on audience engagement and emotional resonance. This study contributes to the broader discussion on the intersection of technology and storytelling, offering insights that can help shape the future of audiovisual experiences. The findings of this systematic review can inspire future innovations in sound technology, not only in the realm of film but also in industries beyond entertainment, such as virtual reality and augmented reality, by fostering a deeper appreciation for the auditory dimension in the cinematic realm. Lastly, this analysis paves the way for a more holistic understanding of cinematic art, emphasizing the symbiotic relationship between technology and storytelling in the audiovisual landscape.

Hybrid Feature Selection Method using Novel Pasi-Luukka and Genetic Algorithm Method for Microarray Cancer Classification

Mon, 21 Jul 2025 00:00:00 +0800

Deoxyribonucleic acid (DNA) microarray technology enables the simultaneous measurement of the expression level of numerous genes, thus enabling the identification of patterns in gene expression that may cause a disease or a particular biological process. The DNA microarray technology can identify cancer cells by analysing the gene expression difference between normal cells and cancer cells. However, due to the vast number of features in the DNA microarray, the feature selection method is required to identify the most relevant subset of microarray features for subsequent analysis. In this research paper, a novel hybrid feature selection method called Pasi Luukka-Genetic Algorithm + Support Vector Machine is introduced. This approach combines the strengths of filter and wrapper methods to effectively select features from eight (8) cancer datasets. The Pasi Luukka algorithm filters irrelevant features and reduces dimensionality. A metaheuristic-based feature selection, which is the Genetic Algorithm (GA), selects the optimum features from the filtered features. This paper evaluates the performance of the proposed method, and a comparison work is conducted against other existing hybrid feature selection methods in the literature. The evaluation considers accuracy metrics and the number of selected features using the same microarray datasets.

Development of Cockleshell Filter as Adsorbent in Palm Oil Mill Effluents

Mon, 21 Jul 2025 00:00:00 +0800

The Malaysian palm oil industry has experienced significant growth, contributing to a substantial increase in processed waste, particularly palm oil mill effluent (POME). POME contains highly concentrated compositions that contribute to its brownish colour, posing environmental and health risks. The adsorption method, particularly using cockleshells (Anadara granosa), has emerged as a promising and environmentally friendly approach. The study aims to assess the performance of cockleshells in treating POME, focusing on reducing pollutants and colour to ensure compliance with effluent discharge standards for crude palm oil mills.

Morphological Descriptors as Tool for Characterization of Nuclear Pleomorphism in Breast Cancer

Mon, 21 Jul 2025 00:00:00 +0800

In recent years, the advancement in molecular pathology and genetic analysis of cancerous tissues has significantly remarked an increase in objective and measurable data. However, the traditional approach of morphological analysis in pathology diagnosis remains subjective in comparison, despite the introduction of digital pathology aiding computer-aided diagnosis. Certain pathological grading features, such as nuclear pleomorphism in breast cancer, still depend on a pathologist's expertise and are largely non-quantitative. This study aimed to investigate morphological descriptors as key elements to characterize the qualitative description of nuclear pleomorphism in breast cancer, in line with the Nottingham Histopathology Grading (NHG) system. Four morphological descriptors were extracted from segmented nuclear cells, including area, minimum ferret diameter, minor axis length and perimeter and used to assign scores of 1 to 3 to characterize pleomorphic nuclei. The proposed method was validated using the support vector machine (SVM), achieving promising results with 95.0% and 92.0% in accuracy (Acc) and F1 score (F1), respectively. This study serves as a pilot investigation for the quantitative measurement of nuclear pleomorphism in breast cancer.

Optimizing AMI Control Centres through Machine Learning: A Review

Mon, 21 Jul 2025 00:00:00 +0800

Modernizing electrical grids requires advanced metering infrastructure (AMI), which gives users and suppliers energy usage data and boosts grid efficiency. Utility smart meter operation centres monitor and analyse these benefits to maintain them. However, the massive data quantities make manual data management impractical. This paper discusses how data analytics and machine learning (ML) can automate and optimize this process to improve control centre decision-making. Our research examines global smart meter implementation and how ML helps operators with identifying problems, preventive maintenance, network selection and cybersecurity. These applications decrease manual labour, enhance accuracy and boost productivity. We also discuss recent AMI trends to help utilities, governments and regulators plan energy. This article shows ML's disruptive potential in smart meter management by focusing on network dependability, operational safety, maintenance optimization and cybersecurity. Our findings show how ML permits utilities to provide a seamless, robust and customer-centred experience, bolstering AMI as a modern electric grid basis.

Experimental Study of High Availability Cloud Learning Management System and Monitoring System Based on Grafana, Prometheus, and Telegram

Eko Marpanaji, Muhammad Fauzan Rafi, Salma Kusumawardhani — Mon, 21 Jul 2025 00:00:00 +0800

This paper discusses the optimization of server management for digital-based education services, especially Learning Management System (LMS) services in the learning process. The research was conducted at the data center of SMK Negeri 2 Yogyakarta which has successfully adopted the Learning Management System (LMS), but has not been optimal in its application. The results of the experimental study resulted in performance optimization efforts through the application of High Availability using the load balancing method with the Round Robin algorithm as a load balancing method using HAProxy as a load balancer on the web server, as well as the cluster method through Galera Cluster on the database to minimize failures on the LMS. In addition, this research successfully developed a monitoring system using Grafana and Prometheus to provide comprehensive visibility into the performance of the LMS cloud server. The system is equipped with real-time notifications via Telegram that allow server managers to respond quickly to any disruptions, thereby reducing downtime and increasing system reliability. Evaluation of the results of the implementation of cloud-based LMS with high availability and monitoring system based on Grafana, Prometheus, and Telegram at SMK Negeri 2 Yogyakarta proved to be effective in supporting the digital learning process which is expected to be a reference for other educational institutions that will adopt similar technology to improve the quality of learning services.

Review on the Stressors and the Psychosocial Factors Affecting Workers in Heavy Industries

Hanan Obaid Saeed Aljahdami, Ahmed Rasdan Ismail, Lucy Semerjian, Vorathin Epin — Mon, 21 Jul 2025 00:00:00 +0800

The issue addressed in this review is the significant impact of psychosocial factors on mental health within the global industrial sector, with a focus on mining, construction and the oil and gas industry. It delves into the pervasive influence of stressors, ranging from organizational functioning to physical conditions and how they affect workers in these industries. Specifically, workers in mining encounter challenges that affect both efficiency and safety, while the construction sector grapples with issues such as poor organizational structure. In the oil and gas industry, hazardous conditions and social isolation contribute significantly to worker stress. The study emphasizes the necessity for tailored interventions that take into account factors such as age, education and health conditions to address industry-specific stressors effectively. Furthermore, it highlights the global nature of workplace stressors and underscores the importance of holistic approaches for enhancing employee well-being. This review emphasizes the urgency of addressing these industry-specific stressors to promote mental health and improve overall well-being in the evolving landscape of global industrial work. Additionally, it aims to compare the listed factors and stressors among the three heavy industries: mining, construction and oil and gas.

Compressive Strength and Density in Fresh and Dried Cube Analysis of Autoclaved Aerated Concrete in Cooperating Glass Industrial Waste

Mon, 21 Jul 2025 00:00:00 +0800

In the pursuit of sustainable construction materials, cooperating industrial waste into innovative building solutions has emerged as a promising way. Autoclaved Aerated Concrete (AAC) stands out as one such eco-friendly material renowned for its lightweight and insulating properties. The research highlighted that glass Industrial waste (GIW) had the potential to improve the strength and fire resistance of AAC due to its excellent properties of GIW in terms of physical, mechanical and thermal performances. The possibility of GIW was explored by including it in AAC concrete and analysing the effect of varying additions on the properties of AAC. This fundamental research proposed different percentages of GIW composition (5%, 10%, 15%, 20%, 25% and 35%) as a partial replacement for 70% (Sand+ Gypsum) mixed with different amounts (0.1% and 0.065%) of Aluminium paste. Then, the same percentages of the GIW composition (5%, 10%, 15%, 20%, 25% and 35%) were used as a partial replacement to reduce the silica content of 68% (Sand+ Gypsum) mixed with 0.1% Aluminium paste. All specimens underwent a steam curing process for 12 hours at a temperature of 180°C and a steam pressure of 13 bars in an autoclave machine to produce Autoclaved Aerated Concrete based on Glass Industrial Waste (AAC-GIW). These aspects were crucial in determining the optimum composition of glass waste that could influence the physical, mechanical and thermal properties of AAC-GIW. The optimum composition of GIW, sand, gypsum, lime, cement and aluminium paste as an expanding agent in AAC-GIW were the factors contributing to high strength. The compressive results were analysed in 3 different ratios (Ratio A, Ratio B and Ratio C) for fresh cubes and dried cubes, the work density and dry density. It is revealed that 15% GIW for ratio C is an optimum compressive strength at 2.43 MPa work density of 677 kg/m3 for fresh cubes as a sand replacement, in dried cube condition, 15% produces the highest compressive strength at 6.64 MPa of 661 kg/m3 for Ratio B. At different ratios, the most effective GIW content differed, emphasizing the requirement for modifications in AAC-GIW formulations specific to the AAC.

Revolutionizing Education: Exploring Technological Innovations, Opportunities and Challenges Across I0T, AR, VR, AI, LMS, ML, Gamification and Emerging

Asep bayu dani Nandiyanto, Nor Azwadi Che Sidik — Mon, 21 Jul 2025 00:00:00 +0800

Technological advancements are reshaping science and engineering education by fostering interactive, hands-on, and personalized learning experiences. This review examines the role of key technologies, including the Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), Artificial Intelligence (AI), Learning Management Systems (LMS), Gamification, 3D Printing, Blockchain, Cloud Computing, Robotics, Big Data, and machine learning (ML), in making a modern education. Bibliometric analysis was employed to identify patterns in publication growth, disciplinary focus, and geographical distribution, providing quantitative insights into the evolution of research on educational technologies. Each technology offers unique contributions, such as IoT-enabled smart classrooms, AR and VR for immersive visualizations, AI-driven analytics for personalized learning, and 3D printing for prototyping. The findings highlight the benefits of these innovations, including enhanced engagement, deeper understanding, and global access to resources. Challenges such as high costs, digital divides, inadequate infrastructure, and the need for educator training persist. Using a qualitative review approach, the study synthesizes recent research to provide insights for educators, policymakers, and researchers. It underscores the need for collaborative efforts to overcome barriers and maximize the potential of these technologies in science and engineering education. The results point to future directions, emphasizing the importance of equitable access, teacher training, and scalable solutions to ensure that technology continues to enhance educational outcomes.

Optimization of Handover Delay during Authentication

Bhavna Ambudkar, Mushtaq Ahmed, Saif Al-Deen H. Hassan, Moumal Al-Saady — Mon, 21 Jul 2025 00:00:00 +0800

In this work, the significant problem of optimal handover delay in NGN authentication was considered. This study reports an innovative Authentication, Authorisation and Accounting (AAA) architecture together with a far-reaching authentication efficiency enhancement across various networks based on the Universal Seamless Network Access Protocol (USNAP). This research employs a cloud-based AAA server and introduces the concept of a validity certificate (VC) to achieve fast user authentication. The performance of the proposed system is analysed via extensive simulations performed in NS-2. The results show a significant reduction in handover latency, with an average service time of 1.13 ms and a total handover delay of 77.47 ms, demonstrating that it is able to reduce the latency of previous approaches by roughly 13%. With this approach, the USNAP protocol can encode the type of network faster than current protocols and perform account verification. It is capable of scaling massively in our system with high service probability as the user needs grow. This work is helpful for securing light-weight and high-quality network signal authentication methods in NGNs and can be used seamlessly across various types of networking technologies.

Examining the Impact of Negative Bias Temperature Instability on the Performance of Domino Logic Circuits

Mon, 21 Jul 2025 00:00:00 +0800

Negative Bias Temperature Instability (NBTI) poses a notable reliability concern in Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs), causing an aging effect that alters threshold voltage and reduces drain current. This effect holds particular significance in sub-micrometre CMOS circuitry. This study focuses on assessing NBTI's impact on domino logic circuits, exploring various NBTI defect mechanisms like interface trap (Nit) and oxide trap (Not). Evaluations extend to NOR and NAND domino logic circuits, analysing delay and average power to gauge NBTI effects. The study employs the Predictive Technology Model (PTM) based on 32nm technology, coupled with the MOSRA model, to illustrate circuit dependability. Simulations involve varied stress temperatures, revealing a proportional degradation in delay with increasing temperature. Specifically, when Nit serves as the sole defect mechanism, the time exponent stands at 0.25, whereas Nit and Not together reduce this exponent to 0.167. Higher stress temperatures correlate with increased delay, reduced average power, and a shift in threshold voltage towards higher values over prolonged stress durations.

Design of Prototype for Metal Detecting Arduino Mobile Robot using Wireless Sensor Network Powered by Solar Energy

Mon, 21 Jul 2025 00:00:00 +0800

In today’s modern technology world, there are several types of mobile robots which use different types of renewable energy systems. The advancement of modern technologies is increasing energy demand volume and is aimed at adopting alternative sources of energy. Renewable energy systems, such as solar power are the better choice to use in moving mobile robots. Robots are the fast-growing technology and communication mode to human in daily lives. Therefore, researchers have integrated solar power as an alternative energy source on mobile robots, which will solve the depletion of fossil fuels. Thus, a four wheels’ metal detection solar powered mobile robot was designed in these studies. The mobile robot is implemented using solar panels, Arduino Uno, metal detector sensor and solar power. The Android Uno was implemented in the development of this mobile robot as its primary brain and to communicate with all its components. In these studies, solar power metal detecting mobile robot is developed to detect the metal and the mobile robot is powered by solar energy and the robot control via wireless sensors network controller. Studies were also done on solar panel testing with various atmospheres and temperatures to accustom the product. The automated robot and the proposed design are compared in terms of the mobile robot's accessibility, metal detecting accuracy and stability of power source. The findings demonstrate that the mobile robot's proposed design can deliver faster metal detection with a more precise, easier in turning and highly dependable power supply.

Spine Tumor Segmentation using Deep Learning: A Review

Mon, 21 Jul 2025 00:00:00 +0800

he field of medicine has been significantly impacted by technological advancements, particularly in digital imaging and image processing. These advancements have revolutionized early disease detection, computer-aided diagnosis, minimally invasive procedures, and image-guided surgeries. However, medical images, including spine tumors, often face challenges such as low contrast, noise, and artefacts, which impede accurate diagnosis. This paper reviews spine tumor image segmentation techniques utilizing Deep Learning (DL). It explores the crucial role of image segmentation in isolating specific anatomical structures, such as spine tumors, for precise diagnosis. DL has shown great potential in medical image segmentation, learning hierarchical features directly from raw data without manual feature engineering. The review highlights the significance of early spine tumor detection, classifies tumor types, and examines features of benign and malignant tumors. It emphasizes the role of accurate segmentation in improving surgical outcomes and advancing computer-aided diagnostic systems. Additionally, challenges in standard MRI protocols for distinguishing intradural from extradural tumor compartments are addressed, proposing advanced imaging techniques and DL models as solutions.