Omar Shalash

This preprint has been withdrawn at the request of the authors due to an authorship attribution dispute.

Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but rely on fixed rules, whereas multi-agent reinforcement learning (MARL) can learn adaptive behaviors but often struggles with coordination under partial observability. To address these limitations, this paper proposes a Hybrid stigmergy–MARL framework that introduces directional pheromone-gradient observations, enabling each drone to infer the direction of likely victims and unexplored regions using locally available information. The proposed framework combines reinforcement learning with four virtual pheromone layers representing coverage history, victim likelihood, environmental risk, and communication quality. Victim detection is modeled through an abstract short-range thermal/visual sensing mechanism, while environmental information is shared through pheromone-based environmental memory to reduce dependence on direct communication. The simulated environment consists of a 40 × 40 grid, where each grid cell represents a discrete two-dimensional location. Victims occupy a single grid cell, and obstacles are modeled as static two-dimensional impassable cells. Experimental results show that the proposed approach achieved 98.9% area coverage and 93.3% victim detection, compared with 81.8% coverage and 71.7% victim detection for the RL-only baseline. Ablation experiments confirmed that directional gradient observations are the primary contributor to these improvements, while communication-loss experiments demonstrated robust performance even under complete communication outage. These findings indicate that directional pheromone-gradient observations provide an effective and communication-efficient mechanism for decentralized swarm coordination, improving search effectiveness and operational robustness in post-disaster SAR scenarios.

The YOLO (You Only Look Once) object detection models have undergone rapid evolution, with each version introducing architectural enhancements aiming to improve speed, accuracy, and deployment. Simultaneously, single-board computers (SBCs) have advanced to support increasingly complex AI models in edge environments. This study presents a comprehensive benchmarking of YOLO versions 8 through 12 across a range of SBCs, including Raspberry Pi4/5, NVIDIA Jetson Nano, Jetson Orin, and LattePanda, under different power modes. Key performance metrics, including inference speed (FPS), detection accuracy (mAP), RAM usage, and computational complexity (FLOPs), are evaluated. These findings offer practical insights for developers and researchers to select optimal YOLO variants and SBC configurations for real-time edge deployment.

Autonomous driving systems rely on a sophisticated pipeline of artificial intelligence models to perceive, predict, and plan in dynamic environments. This review presents a systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks (CNNs) for object detection and semantic segmentation to recurrent and Transformer-based architectures for trajectory prediction and motion planning. It also provides a critical examination of the autonomous vehicle sensor stack, including cameras, LiDAR, radar, ultrasonics, and GNSS/IMU as data acquisition systems, highlighting modality-specific AI challenges such as monocular depth estimation, 3D point cloud processing, and radar Doppler interpretation. The evolution of perception and decision-making pipelines is reviewed, contrasting modular architectures with end-to-end learning paradigms that directly map raw sensor data to control commands, and discussing their trade-offs in interpretability, safety assurance, and robustness to rare edge cases. We further survey specialized hardware accelerators and heterogeneous automotive SoCs designed to meet stringent real-time and power constraints. Industrial strategies are compared, including multi-modal sensor fusion and vision-centric approaches based on large-scale imitation learning. Finally, we identify open challenges related to robustness under adverse conditions, domain shift, causal ambiguity, and the need for interpretable and certifiable AI in safety-critical autonomous driving systems.

Search and rescue (SAR) for disaster response involves quick and efficient exploration of large, uncertain, and dangerous spaces. In this paper, we introduce a swarm-drone SAR approach with a key innovation in the form of directional pheromone gradient observation, where each agent’s RL policy is informed not only by local pheromone levels but also by the directional gradients in victim likelihood and coverage in four spatial cones, along with neighbor density information. These attributes are based on four virtual pheromone layers, each evolving independently and representing coverage history, victim likelihood, environment risk, and communication quality. The system uses a centralized training with decentralized execution (CTDE) MARL approach. Experiments were conducted on a custom-designed 40×40 grid-world environment involving 10 drones and 20 victims whose locations are unknown to all agents at episode start, with 180 static obstacles and 50 static hazard zones, over 5,000 training episodes and 30 independent evaluation runs. The hybrid agent achieved 98.9% area coverage and 93.3% victim detection, exceeding the RL-only baseline by 17.1 and 21.6 percentage points, respectively, across five independent training seeds (pooled n=150 evaluation runs). Welch’s t-test confirmed statistically significant improvements over RL-only for both area coverage (t=18.88, df=168, p<0.001, Cohen’s d=2.18) and victim detection (t=16.49, df=218, p<0.001, Cohen’s d=1.90). Ablation confirms that excluding directional gradient features reduces coverage and victim detection by 16.7 and 21.6 percentage points, respectively, identifying them as the dominant contributors to hybrid performance.

Identity verification across pre-operative and post-operative facial images remains a challenging task, particularly following eyelid surgery, where localized periocular changes can disrupt conventional face recognition systems. This research introduces a novel verification framework using an ensemble-based autoencoder-initialized Siamese eye-region periocular verification network designed to remain resilient to surgically induced appearance variation. The proposed approach integrates anatomy-guided periocular normalization with a Siamese deep metric learning architecture, initialized via unsupervised autoencoder pretraining, enabling the model to acquire periocular-specific representations before supervised learning. Robustness in this data-limited clinical setting is enhanced through a combination of constrained periocular augmentation, dropout-based regularization, L2 weight decay, validation-guided checkpoint selection, staged hard-negative mining, validation-weighted multi-seed ensemble learning, and bootstrap-based threshold calibration. Experimental evaluation demonstrates recognition rates of 96.08% on the test set. These results indicate that the proposed framework maintains discriminative periocular identity representations under post-surgical appearance variation while remaining robust in a limited-data clinical setting.

The YOLO (You Only Look Once) object detection models have undergone rapid evolution, with each version introducing architectural enhancements aiming to improve speed, accuracy, and deployment. Simultaneously, Single-Board Computers (SBCs) have advanced to support increasingly complex AI models in edge environments. This study presents a comprehensive benchmarking of YOLO versions 8 through 12 across a range of SBCs, including Raspberry Pi4/5, NVIDIA Jetson Nano, Jetson Orin, and LattePanda, under different power modes. Key performance metrics, including inference speed (FPS), detection accuracy (mAP), RAM usage, and computational complexity (FLOPs), are evaluated. These findings offer practical insights for developers and researchers to select optimal YOLO variants and SBC configurations for real-time edge deployment.

Autonomous driving systems rely on a sophisticated pipeline of artificial intelligence models to perceive, predict, and plan in dynamic environments. This review presents a systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks (CNNs) for object detection and semantic segmentation, to recurrent and Transformer-based architectures for trajectory prediction and motion planning. In this review, a critical examination of the autonomous vehicle sensor stack—including cameras, LiDAR, radar, ultrasonics, and GNSS/IMU as data acquisition systems, highlighting modality-specific AI challenges such as monocular depth estimation, 3D point cloud processing, and radar Doppler interpretation. The evolution of perception and decision-making pipelines is reviewed, contrasting modular architectures with end-to-end learning paradigms that directly map raw sensor data to control commands, and discussing their trade-offs in interpretability, safety assurance, and robustness to rare edge cases. We further survey specialized hardware accelerators and heterogeneous automotive SoCs designed to meet stringent real-time and power constraints. Industrial strategies are compared, including multi-modal sensor fusion and vision-centric approaches based on large-scale imitation learning. Finally, we identify open challenges related to robustness under adverse conditions, domain shift, causal ambiguity, and the need for interpretable and certifiable AI in safety-critical autonomous driving systems.

Identity verification across pre-operative and post-operative facial images remains a challenging task, particularly following eyelid surgery, where localized periocular changes can disrupt conventional face recognition systems. This research introduces a novel verification framework using an ensemble-based autoencoder-initialized siamese eye-region periocular verification network designed to remain resilient to surgically induced appearance variation. The proposed approach integrates anatomy-guided periocular normalization with a Siamese deep metric learning architecture initialized through unsupervised autoencoder pretraining, allowing the model to acquire periocular-specific representations prior to supervised learning. Robustness in this data-limited clinical setting is further enhanced through staged hard-negative mining, validation-weighted multi-seed ensemble learning, and bootstrap-based threshold calibration. Ensemble Grad-CAM is employed to provide visual explanations that support clinical interpretability. Experimental evaluation demonstrates strong and consistent performance, achieving recognition rates of 94.71% on training data, 96.77% on validation, and 96.08% on the test set, with an overall recognition rate of 95.24%. Compared to previously reported periocular verification methods which reported an overall recognition rate of only 91.8% under similar conditions. These results highlight the effectiveness and stability of the proposed framework for post-surgical periocular identity verification in clinical and forensic applications.