Recent Submissions
SmartSphere: Bridging AI-Powered Automation and Tesla Coil Wireless Energy for Smarter Living
(Institute of Electrical and Electronics Engineers Inc., 2025) Jurison Jenaraj, J.K; Umasuthan, A; Vigneswaran, V; Raveendran, K; Thiruthanigesan, K; Kasthuriarachchi, S
This paper presents SmartSphere, an intelligent, secure, and wire-free home automation system that synergizes artificial intelligence, multi-factor access control, and resonant wireless power transmission. Built on an edge-computing architecture using ESP32 and Arduino Mega 2560 microcontrollers, SmartSphere integrates facial recognition, fingerprint authentication (96.7% accuracy), and IR-based presence detection to reduce false activations by 75%. The system employs OpenCV and TensorFlow Lite for real-time anomaly detection and environmental personalization, including weather-responsive adjustments and emotion-aware lighting via facial expression analysis. A key innovation is the incorporation of a Tesla coil-based wireless power transmission module, which eliminates conventional wiring constraints and reduces installation cabling by 86%. Experimental validation demonstrates a 14% improvement in energy efficiency, 75% faster response time (0.3 s), and seamless compatibility with 92% of tested IoT devices. Through comprehensive testing and validation, this research establishes SmartSphere as a secure, intelligent, and sustainable solution for next-generation smart homes, addressing the limitations of traditional wired systems while enhancing the user experience through AI-driven personalization.
Adaptive AI-Based Enhancement of Critical External Sounds in Insulated Vehicle Cabins for Improved Safety
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Rathnayaka D.B; Wickramasuriya L.H.N.Y; Walpalage J.V; Rathnayake, S
The increasing acoustic insulation in modern and electric vehicles improves passenger comfort but unintentionally suppresses critical external sounds such as ambulance sirens, car horns, and train alarms, creating potential safety risks. While existing research has explored sound detection or localization in isolation, few systems integrate both capabilities in a unified framework for real-time vehicular deployment. This research proposes an adaptive AI-based system that detects, classifies, and selectively enhances these critical sounds in real time while providing directional awareness. Using a convolutional recurrent neural network (CRNN) trained on the UrbanSound8K dataset, the system processes incoming audio from external microphones, extracts Mel-frequency cepstral coefficients (MFCCs), and distinguishes safety-relevant cues from non-essential background noise. A dual-microphone setup enables the estimation of sound direction (left or right), providing additional spatial awareness to the driver. Detected signals are isolated through spectral filtering and relayed into the cabin with sub-30 ms latency, ensuring timely driver and passenger awareness without compromising comfort. Experimental results achieved 91.2% classification accuracy and 87.4% directional accuracy,confirming the system's feasibility for enhancing safety in insulated vehicle cabins and supporting future autonomous driving environments.
Fidelity-Driven Physically Constrained Adaptive Code Distance for Surface Codes under Biased Circuit-Level Noise
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Hettiarachchi, A; Dissanayaka, K
Surface codes are leading candidates for fault-tolerant quantum computing, but conventional implementations use fixed code distances that result in conservative resource allocation during varying noise conditions. We propose a physically-constrained, fidelity-driven adaptive surface code framework that dynamically adjusts code distance at scheduled intervals to maintain target logical fidelity while minimizing resource overhead. Our approach introduces a syndrome-based logical error rate estimation algorithm using rolling-window statistics, a scheduled adaptation policy operating within pre-allocated qubit pools, and detailed modeling of adaptation latency and reconfiguration-induced errors for superconducting qubit devices. Through large-scale Monte Carlo simulations under realistic biased circuit-level noise, we demonstrate that hardware-constrained adaptive codes achieve 43-62% success rate improvements over fixed-distance strategies, reaching 72-82% success rates compared to 5̃0% for fixed approaches. Oracle adaptive strategies with perfect error knowledge achieve 91-94% success rates, indicating substantial theoretical potential. Our results show that adaptive surface codes provide significant performance improvements even under realistic hardware constraints, with oracle strategies consuming only 37% of maximum resources while achieving 86% higher success rates for shorter circuits.
AI-Assisted Criminal Investigations: Enhancing Testimony Analysis and Case Correlation
(Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Karunarathna K.A.D; Ediriweera E.R.L.C; Fernando N.D.R.N; Abeywardana B.A.I.S; Abeywardhana, L
This paper presents Lexa AI, a holistic AIpowered solution for improving criminal investigative practices through four phases that target key drawbacks found in traditional practices. The first model provides an automated data/information collection stage that accepts legal documents in multiple formats and utilizes a three-stage processing pipeline based on Gemini 2.0 Flash model, which performs Optical Character Recognition (OCR) with a higher level of accuracy and speed compared to alternative approaches. The collected data will be forwarded to the next phase by leveraging dynamic question generation that uses Reinforcement Learning (RL), instantaneous multilingual capabilities, and real-time scoring of relevance. The third phase conducts Multimodal Behavioral and Physiological Analysis (MBPA), which includes facial signals, speech signals, and heart rate signals to create a combined Stress Index as an objective indicator and avoids subjective judgment. Finally, semantic similarity will be measured to correlate incidents, assess risk for victims, and provide explainable predictions
Generative AI Based Chatbot for Customer IT Support Automation
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Lakshan S.N; Gamage U.R; Fernando W.S.N; Kuruppu K.A.V.U.; Kodagoda, N; Abeywardhana, L
Enterprises are facing increasing challenges in delivering efficient, adaptive, and secure IT support, as traditional ticketing systems suffer from latency, rigidity, and limited scalability. Existing chatbot solutions built on commercial large language models (LLMs) often introduce high operational costs, token inefficiency, and data privacy concerns, while current retrieval and multi-agent systems lack personalization, multimodal understanding, and dynamic adaptability. This research presents a generative AI-based IT support assistant that integrates four core innovations: a multi-agent architecture enabling distributed and scalable task execution; a retrieval-augmented knowledge base enhanced with knowledge graphs and episodic memory for more accurate and personalized responses; conversational form automation using Rasa and transformer-based models to extract structured ticket information from natural queries; and an interactive 3D avatar interface with real-time speech-to-text, text-to-speech, and emotion-aware multimodal interaction to support more human-like communication. The system was evaluated using RAGAS metrics to measure accuracy, contextual recall, and response faithfulness. Results show improved domain-specific performance, reduced reliance on manual ticket handling, and lower computational overhead through caching and adaptive slot filling. This study proposes a privacy-preserving, scalable enterprise IT support framework, with future work targeting multilingual support and deeper ITSM integration.
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