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Subject: search.filters.subject.context-aware

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    Context-Aware Behavior-Driven Pipeline Generation
    (Institute of Electrical and Electronics Engineers Inc., 2025) Gunathilaka, P; Senadheera, D; Perara, S; Gunawardana, C; Thelijjagoda, S; Krishara, J
    Enterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritization
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    PublicationOpen Access
    A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray, I
    Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users.