The design of a low-cost driver assistance system (DAS) using monocular camera input and artificial intelligence to enhance road awareness consists of using low-cost sensors instead of costly configurable sensors used in typical systems. The hybrid perception architecture of this system incorporates deep learning (via optimized YOLOv8) and traditional computer vision techniques to achieve high accuracy in detecting vehicles and pedestrians, which is consistent regardless of traffic conditions. Additionally, the hybrid lane detection algorithm combines edge-filtering techniques with geometric models to allow for lane detection in low-light or poorly marked lane conditions. Also, the development of a modular processing pipeline allows for real-time video preprocessing, feature extraction and risk assessment, therefore requiring less computational resources than standard DAS systems. Finally, testing showed that this DAS system consistently performs in real-time and achieves an acceptable degree of accuracy, irrespective of environmental conditions. The DAS system provides a common structure for a variety of vision techniques and can be scaled and constructed for a lower cost than most current DAS solutions, thereby facilitating the development of intelligent transportation systems and increasing access to transportation technology.
Poola Joshika, C. Dharshana, Shreya Sridharan et al.· 2026 6th International Confe...· 0 citations
Quantum Key Distribution (QKD) using the BB84 protocol offers the potential for information-theoretic security based upon the principles of quantum mechanics; however, eavesdropping mechanisms such as intercept-resend attacks and ambient noise sources that degrade quantum channel fidelity may render such systems exploitable. We present a oneclass machine learning approach to detect two categories of threats present within BB84 quantum channels: (i) interceptresend eavesdropping committed by an adversarial actor (Eve), and (ii) six types of channel noise. We have trained three unsupervised anomaly detectors (One-Class Support Vector Machine (OC-SVM), Isolation Forest (iForest), and Extended Isolation Forest (Ex-iForest)) on a dataset consisting of 1000 instances (940 clean and 60 anomalous) and tested each of them on separate test sets of 100 instances. With respect to the detection of adversarial attacks, Ex-iForest outperformed both OC-SVM and iForest; it achieved 95.00% accuracy, precision, recall, and F1-score, and when detecting channel noise, it achieved an overall accuracy, precision, recall, and F1-score of 92.00%. Feature importance analysis confirmed that the Quantum Bit Error Rate (QBER) and the fidelity of polarization measurement were the two most discriminative features for adversarial detection while fluctuations of photon detection counts and timing jitter were the two most informative features for channel noise discrimination. These results suggest that ExiForest is a viable, robust, and efficient solution for real-time anomaly detection systems in BB84-based quantum communication systems.
H. A. Sharath, J. Vrindavanam· 2026 Third International Con...· 0 citations