Unmanned aerial vehicle (UAV) detection in real-time is a challenging task despite the advances in computer vision and deep learning techniques. The increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. Although vision-based UAV detection methods have been proposed in recent years, a standing open challenge and overlooked issue is that of adverse weather. This work is the first, to the best of our knowledge, to investigate the impact of adverse weather conditions and image distortions on vision-based UAV detection methods. To achieve this, a custom training dataset was curated with images containing a variety of UAVs in diverse complex backgrounds. In addition, this work develops a first-of-its-kind dataset, to the best of our knowledge, with UAV-containing images affected by adverse conditions. Based on the proposed datasets, a comprehensive benchmarking study is conducted to evaluate the impact of adverse weather and image distortions on the performance of popular object detection methods such as YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS. The experimental results reveal the weaknesses of the studied models and the performance degradation due to adverse weather, highlighting avenues for future improvement. The results show that even the best UAV detection model’s performance degrades in mean average precision (mAP) by $-50.62$ points in torrential rain conditions, by $-52.40$ points in high noise conditions, and by $-77.0$ points in high motion blur conditions. To increase the selected models’ resilience, we propose and evaluate a strategy to enhance the training of the selected models by introducing weather effects in the training images. For example, the YOLOv5 model with the proposed enhancement strategy gained $+35.4$, $+39.3$, and $+44.9$ points higher mAP in severe rain, noise, and motion blur conditions respectively. The findings presented in this work highlight the advantages of considering adverse weather conditions during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure more reliable UAV detection under extreme weather conditions and image distortions.

Nanodevices-based circuit design will be based on the acceptance that a high percentage of devices in the design will be defective. This study investigates a defect-tolerant technique that adds redundancy at the transistor level and provides built-in immunity to permanent defects (stuck-open, stuck-short and bridges). The proposed technique is based on replacing each transistor by N²-transistor structure (N≥2) that guarantees defect tolerance of all N−1 defects as validated by theoretical analysis and simulation. As demonstrated by extensive simulation results using ISCAS 85 and 89 benchmark circuits, the investigated technique achieves significantly higher defect tolerance than recently reported nanoelectronics defect-tolerant techniques (even with up to 4–5 times more transistor defect probability) and at reduced area overhead. For example, the quadded-transistor structure technique requires nearly half the area of the quadded-logic technique.

State assignment (SA) for finite state machines (FSMs) is one of the crucial synthesis steps in the design and optimisation of sequential circuits. In this study, we propose a majority‐based evolution (MBE) SA algorithm that can be considered a variant of the well known differential evolution algorithm. Each individual is evolved based on selecting three random individuals, one of which is selected to be the best individual with a 50% probability. Then, for each state in the individual a selection is made with a 50% probability between keeping the current state or replacing it with a newly computed state. The bit values of the new state are determined based on the majority values of the state of the three selected individuals under a randomly generated probability within a predetermined range. The proposed algorithm is used for FSM state encoding targeting the optimisation of both area and power. Experimental results demonstrate the effectiveness of the proposed MBE SA algorithm in comparison with other evolutionary algorithms including genetic algorithm, binary particle swarm optimisation, Tabu search and simulated evolution.