Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Kiran Raj, K.M.

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    A Survey on Vehicle Collision Avoidance Systems: Innovations, Challenges, and Future Prospects
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Kiran Raj, K.M.; Abhishek; Devadiga, M.T.; Manohara, M.; Boloor, S.; Sowjanya, N.
    Vehicle Collision Avoidance Systems (VCAS) enhance road safety by enabling vehicles to autonomously detect and respond to potential hazards using technologies such as radar, LiDAR, cameras, V2X communication, and machine learning algorithms. Key features like Adaptive Cruise Control, Autonomous Emergency Braking, and Lane Departure Warning help prevent accidents and improve driver assistance. Despite challenges like sensor limitations in adverse conditions, communication delays, and cybersecurity risks, advancements in sensor accuracy, decision-making algorithms, and edge computing continue to drive innovation. This paper highlights the importance of technological improvements, regulatory frameworks, and system interoperability in advancing VCAS adoption and achieving safer, autonomous transportation. © 2025 IEEE.
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    A Comprehensive Review on Scaling Machine Learning Workflows Using Cloud Technologies and DevOps
    (Institute of Electrical and Electronics Engineers Inc., 2025) Ramesh, G.; Vaikunta Pai, T.; Birǎu, R.; Poojary, K.K.; Abhay; Shingad, A.R.; Sowjanya, N.; Popescu, V.; Mitroi, A.T.; Nioata, R.M.; Kiran Raj, K.M.
    Scaling Machine Learning (ML) workflows in cloud environments presents critical challenges in ensuring reproducibility, low-latency inference, infrastructure reliability, and regulatory compliance. This review addresses the lack of a comprehensive synthesis of how integrated DevOps practices and cloud-native technologies enable scalable, production-grade ML systems. We analyze the convergence of MLOps with tools such as Kubernetes, Jenkins, and Terraform, detailing their role in automating CI/CD pipelines, infrastructure provisioning, and model lifecycle management. The main highlights strategies for optimizing resource utilization, minimizing inference latency, and managing data versioning across hybrid and multi-cloud architectures (AWS, Azure, GCP). We also examine serverless computing, container orchestration, and monitoring practices to enhance scalability and governance. By categorizing challenges chronologically and evaluating emerging practices such as federated learning and security-by-design, this work bridges a key gap in existing literature. It offers a unified perspective on building reliable, reproducible, and compliant ML workflows, thereby advancing the state of scalable AI system engineering. © IEEE. 2013 IEEE.