International Journal of Computing and Engineering最新文献

{"title":"A Deep Reinforcement Learning Strategy for MEC Enabled Virtual Reality in Telecommunication Networks","authors":"Kodanda Rami Reddy Manukonda","doi":"10.47941/ijce.1820","DOIUrl":"https://doi.org/10.47941/ijce.1820","url":null,"abstract":"One of the most anticipated features of 5G and subsequent networks is wireless virtual reality (VR), which promises to transform human interaction via its immersive experiences and game-changing capabilities. Wireless virtual reality systems, and VR games in particular, are notoriously slow due to rendering issues. But most academics don't care about data correlation or real-time rendering. Using mobile edge computing (MEC) and mmWave-enabled wireless networks, we provide an adaptive VR system that enables high-quality wireless VR. By using this architecture, VR rendering operations may be adaptively offloaded to MEC servers in real-time, resulting in even greater performance advantages via caching.The limited processing power of VR devices, the need for a high quality of experience (QoE), and the small latency in VR activities make it difficult to connect wireless VR consumers to high-quality VR content in real-time. To solve these problems, we provide a wireless VR network that is enabled by MEC. This network makes use of recurrent neural networks (RNNs) to provide real-time predictions about each user's field of vision (FoV). It is feasible to simultaneously move the rendering of virtual reality material to the memory of the MEC server. To improve the long-term VR users' quality of experience (QoE) while staying within the VR interaction latency limitation, we provide decoupling deep reinforcement learning algorithms that are both centrally and distributedly run, taking into consideration the connection between requests' fields of vision and their locations. When compared with rendering on VR headsets, our proposed MEC rendering techniques and DRL algorithms considerably improve VR users' long-term experience quality and reduce VR interaction latency, according to the simulation results.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Artificial Intelligence Techniques for Quality Control in Semiconductor Production","authors":"Rajat Suvra Das","doi":"10.47941/ijce.1815","DOIUrl":"https://doi.org/10.47941/ijce.1815","url":null,"abstract":"Purpose: Exploring AI techniques to improve the quality control of semiconductor production brings numerous advantages, such as enhanced precision, heightened efficiency, and early detection of issues, cost reduction, continuous enhancement, and a competitive edge. These benefits establish this area of research and its practical application in the semiconductor industry as valuable and worthwhile. \u0000Methodology: It aims to highlight the advancements, methodologies employed, and outcomes obtained thus far. By scrutinizing the current state of research, the primary objective of this paper is to identify significant challenges and issues associated with AI approaches in this domain. These challenges encompass data quality and availability, selecting appropriate algorithms, interpreting AI models, and integrating them with existing production systems. It is vital for researchers and industry professionals to understand these challenges to effectively address them and devise effective solutions. Moreover, it aims to lay the groundwork for future researchers, offering them a theoretical framework to devise potential solutions for enhancing quality control in semiconductor production. This review aims to drive a research on the semi-conductor production with the AI techniques to enhance the Quality control. \u0000Findings: The main findings to offer research is more efficient and accurate approach compared to traditional manual methods, leading to improved product quality, reduced costs, and increased productivity. Armed with this knowledge, future researchers can design and implement innovative AI-driven solutions to enhance quality control in semiconductor production. \u0000Unique contribution to theory, policy and practice: Overall, the theoretical foundation presented in this paper will aid researchers in developing novel solutions to improve quality control in the semiconductor industry, ultimately leading to enhanced product reliability and customer satisfaction.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":" 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140684948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Literature Review on Graphics Processing Unit Accelerated Realm of High-Performance Computing","authors":"Rajat Suvra Das, Vikas Gupta","doi":"10.47941/ijce.1813","DOIUrl":"https://doi.org/10.47941/ijce.1813","url":null,"abstract":"GPUs (Graphics Processing Units) are widely used due to their impressive computational power and parallel computing ability.It have shown significant potential in improving the performance of HPC applications. This is due to their highly parallel architecture, which allows for the execution of multiple tasks simultaneously. However, GPU computing is synonymous with CUDA in providing applications for GPU devices. This offers enhanced development tools and comprehensive documentation to increase performance, while AMD’s ROCm platform features an application programming interface compatible with CUDA. Hence, the main objective of the systematic literature review is to thoroughly analyze and compute the performance characteristics of two prominent GPU computing frameworks, namely NVIDIA's CUDA and AMD's ROCm (Radeon Open Compute). By meticulously examining the strengths, weaknesses, and overall performance capabilities of CUDA and ROCm, a deeper understanding of these concepts is gained and will benefit researchers. The purpose of the research on GPU accelerated HPC is to provide a comprehensive and unbiased overview of the current state of research and development in this area. It can help researchers, practitioners, and policymakers understand the role of GPUs in HPC and facilitate evidence-based decision making. In addition, different real-time applications of CUDA and ROCm platforms are also discussed to explore potential performance benefits and trade-offs in leveraging these techniques. The insights provided by the study will empower the way to make well-informed decisions when choosing between CUDA and ROCm approaches that apply to real-world software.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":" October","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TensorFlow: Revolutionizing Large-Scale Machine Learning in Complex Semiconductor Design","authors":"Rajat Suvra Das","doi":"10.47941/ijce.1812","DOIUrl":"https://doi.org/10.47941/ijce.1812","url":null,"abstract":"The development of semiconductor manufacturing processes is becoming more intricate in order to meet the constantly growing need for affordable and speedy computing devices with greater memory capacity. This calls for the inclusion of innovative manufacturing techniques hardware components, advanced intricate assemblies and. Tensorflow emerges as a powerful technology that comprehensively addresses these aspects of ML systems. With its rapid growth, TensorFlow finds application in various domains, including the design of intricate semiconductors. While TensorFlow is primarily known for ML, it can also be utilized for numerical computations involving data flow graphs in semiconductor design tasks. Consequently, this SLR (Systematic Literature Review) focuses on assessing research papers about the intersection of ML, TensorFlow, and the design of complex semiconductors. The SLR sheds light on different methodologies for gathering relevant papers, emphasizing inclusion and exclusion criteria as key strategies. Additionally, it provides an overview of the Tensorflow technology itself and its applications in semiconductor design. In future, the semiconductors may be designed in order to enhance the performance, and the scalability and size can be increased. Furthermore, the compatibility of the tensor flow can be increased in order to leverage the potential in semiconductor technology.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":" May","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Semiconductor Functional Verification with Deep Learning with Innovation and Challenges","authors":"Rajat Suvra Das, Arjun Pal Chowdhury","doi":"10.47941/ijce.1814","DOIUrl":"https://doi.org/10.47941/ijce.1814","url":null,"abstract":"Purpose: Universally, the semiconductor is the foundation of electronic technology used in an extensive range of applications such as computers, televisions, smartphones, etc. It is utilized to create ICs (Integrated Circuits), one of the vital electronic device components. The Functional verification of semiconductors is significant to analyze the correctness of an IC for appropriate applications. Besides, Functional verification supports the manufacturers in various factors such as quality assurance, performance optimization, etc. Traditionally, semiconductor Functional verification is carried out manually with the support of expertise. However, it is prone to human error, inaccurate, expensive and time-consuming. To resolve the problem, DL (Deep Learning) based technologies have revolutionized the functional verification of semiconductor device. The utilization of various DL algorithms automates the semiconductor Functional verification to improve the semiconductor quality and performance. Therefore, the focus of this study is to explore the advancements in the functional verification process within the semiconductor industry. \u0000Methodology: It begins by examining research techniques used to analyse existing studies on semiconductors. Additionally, it highlights the manual limitations of semiconductor functional verification and the need for DL-based solutions. \u0000Findings: The study also identifies and discusses the challenges of integrating DL into semiconductor functional verification. Furthermore, it outlines future directions to improve the effectiveness of semiconductor functional verification and support research efforts in this area. The analysis reveals that there is a limited amount of research on deep learning-based functional verification, which necessitates further enhancement to improve the efficiency of functional verification. \u0000Unique contribution to theory, policy and practice: The presented review is intended to support the research in enhancing the efficiency of the semiconductor functional verification. Furthermore, it is envisioned to assist the semiconductor manufacturers in the field of functional verification regarding efficient verifications, yield enhancement, improved accuracy, etc.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140684346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Community-Led Development and Participatory Design in Open Source: Empowering Collaboration for Sustainable Solutions","authors":"Savitha Raghunathan","doi":"10.47941/ijce.1803","DOIUrl":"https://doi.org/10.47941/ijce.1803","url":null,"abstract":"This whitepaper delves into the active role of community-led development (CLD) and participatory design (PD) in open source software, highlighting how these complementary approaches bring stakeholders from various backgrounds together to create a cooperative atmosphere for developing stable solutions. It emphasizes the importance of these methodologies in enabling communities to tackle real-world issues effectively and robustly, thus influencing the expansion of open-source development. Integrating CLD and PD within open-source projects fosters a more inclusive collaborative development environment, driving innovation and user-centric solutions. Through case studies like Kubernetes and Konveyor, it is evident that these methodologies significantly contribute to project success by enhancing adaptability, ensuring broad community engagement, and addressing diverse user needs. The findings underscore the vital role of these strategies in creating sustainable and resilient software solutions, highlighting their potential to transform the technology development landscape.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":"10 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140696116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security in Machine Learning (ML) Workflows","authors":"Dinesh Reddy Chittibala, Srujan Reddy Jabbireddy","doi":"10.47941/ijce.1714","DOIUrl":"https://doi.org/10.47941/ijce.1714","url":null,"abstract":"Purpose: This paper addresses the comprehensive security challenges inherent in the lifecycle of machine learning (ML) systems, including data collection, processing, model training, evaluation, and deployment. The imperative for robust security mechanisms within ML workflows has become increasingly paramount in the rapidly advancing field of ML, as these challenges encompass data privacy breaches, unauthorized access, model theft, adversarial attacks, and vulnerabilities within the computational infrastructure. \u0000Methodology: To counteract these threats, we propose a holistic suite of strategies designed to enhance the security of ML workflows. These strategies include advanced data protection techniques like anonymization and encryption, model security enhancements through adversarial training and hardening, and the fortification of infrastructure security via secure computing environments and continuous monitoring. \u0000Findings: The multifaceted nature of security challenges in ML workflows poses significant risks to the confidentiality, integrity, and availability of ML systems, potentially leading to severe consequences such as financial loss, erosion of trust, and misuse of sensitive information. \u0000Unique Contribution to Theory, Policy and Practice: Additionally, this paper advocates for the integration of legal and ethical considerations into a proactive and layered security approach, aiming to mitigate the risks associated with ML workflows effectively. By implementing these comprehensive security measures, stakeholders can significantly reinforce the trustworthiness and efficacy of ML applications across sensitive and critical sectors, ensuring their resilience against an evolving landscape of threats.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":"29 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140081635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proactive Edge Computing for Smart City: A Novel Case for ML-Powered IoT","authors":"Rohan Singh Rajput, Sarthik Shah, Shantanu Neema","doi":"10.47941/ijce.1605","DOIUrl":"https://doi.org/10.47941/ijce.1605","url":null,"abstract":"Purpose: In response to the challenges posed by traditional cloud-centric IoT architectures, this research explores the integration of Proactive Edge Computing (PEC) in context of smart cities. The purpose addresses privacy concerns, enhance system capabilities, and introduce machine learning powered anticipation to revolutionize urban city management. \u0000Methodology: The research employs a comprehensive methodology that includes a thorough review of existing literature on use of IoT devices, edge computing and machine learning in context of smart cities. It introduces the concept of PEC to advocate for a shift from cloud-centric to on-chip computing. The methodology is based on several case studies in various domains of smart city management focusing on the improvement of public life. \u0000Findings: This research reveal that the integration of PEC in various smart city domains leads to a significant improvement. Real time data analysis, and machine learning predictions contributes to reduced congestion, enhance public safety, sustainable energy practices, efficient waste management, and personalized healthcare. \u0000Unique Contribution to Theory, Policy and Practice: The research makes a unique contribution to the field of theory, policy and practice by proposing a paradigm shift associated with IoT for smart cities. The suggested shift not only ensures data security but also offers a more efficient and proactive approach to urban challenges. The case studies provide actionable insights for policymakers and practitioners, fostering a holistic understanding of the complexities associated with deploying IoT devices in smart cities. The research lays the foundation for a more secure, efficient, and anticipatory ecosystem, aligning technological advancements with societal needs in the dynamic landscape of smart cities.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":"9 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139380289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Network Reliability Following Emergency (E911) Calls","authors":"Riteshkumar S. Patel, Jigarkumar Patel","doi":"10.47941/ijce.1600","DOIUrl":"https://doi.org/10.47941/ijce.1600","url":null,"abstract":"Purpose: In this research, the purpose is to explore E911 call reliability requirements, study real-world issues related to telecommunication networks transitioning from LTE to 5G (NR) and WCDMA, and present network optimization solutions. The primary objective is to ensure the continuous supply of emergency services and improve the dependability of Enhanced 911 (E911) calls. \u0000Methodology: The research methodology involves an examination of the transition from LTE to 5G (NR) and WCDMA in telecommunication networks. The study delves into government-mandated E911 call reliability requirements and conducts a detailed analysis of two real-world issues affecting tight connectivity for E911 calls. Additionally, the research proposes network optimization solutions to address these challenges and enhance the overall reliability of emergency services. \u0000Findings: The findings of this research reveal insights into government-mandated E911 call reliability requirements and identify two practical issues affecting the continuity of emergency services during the transition from LTE to 5G (NR) and WCDMA. \u0000Unique contributor to theory, policy and practice: The study presents network optimization solutions aimed at overcoming these challenges, with the ultimate goal of improving the dependability of E911 calls and enhancing public safety.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":"47 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139382224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demystifying AI: Navigating the Balance between Precision and Comprehensibility with Explainable Artificial Intelligence","authors":"Narayana Challa","doi":"10.47941/ijce.1603","DOIUrl":"https://doi.org/10.47941/ijce.1603","url":null,"abstract":"Integrating Artificial Intelligence (AI) into daily life has brought transformative changes, ranging from personalized recommendations on streaming platforms to advancements in medical diagnostics. However, concerns about the transparency and interpretability of AI models, intense neural networks, have become prominent. This paper explores the emerging paradigm of Explainable Artificial Intelligence (XAI) as a crucial response to address these concerns. Delving into the multifaceted challenges posed by AI complexity, the study emphasizes the critical significance of interpretability. It examines how XAI is fundamentally reshaping the landscape of artificial intelligence, seeking to reconcile precision with the transparency necessary for widespread acceptance.","PeriodicalId":198033,"journal":{"name":"International Journal of Computing and Engineering","volume":"119 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}