Towards 5G-enabled vehicular communication systems, message dissemination in a mobile, low-latency environment, like public safety, is critical to the timely and reliable delivery of emergency messages. It is well-known that classical cryptographic primitives such as RSA and ECC are no longer secure (feasible) in the future when QC is practical. Proposition 1 In the spirit of such observations, this paper provides a lightweight post-quantum authentication scheme for emergency message propagation in vehicular fog networks. The proposed technique relies on CRYSTALSDilithium, a NIST-standard post-quantum lattice-based digital signature scheme, to achieve quantum-safe authentication at low cost. The protocol has four phases: system initialization, vehicle emergency token generation, fog-level validation, and distributed revocation. It adopts a semi-trusted architecture consisting of certified On-Board Units (OBUs), roadside fog nodes, and a central authority. Empirical results show a 2.65 ms signing time, 1.43 ms verification time, and 3.2 KB token size, all leading to prototyping in 5G network real-time applications. Experimental results reveal >30% reduction of computational delay with lower communication overhead compared with other existing schemes and without sacrificing security. The results demonstrate that the proposed scheme achieves post-quantum security, scalable revocation, and the preservation of privacy in fog computing environments that are characterised by limited computing capability. This paper presents a practical, future-proof authentication model well-suited for emergency response in next-generation vehicular networks.
Yaw stability control systems are one of the approaches implement for lateral dynamics motion control of road vehicles. An important variable that we need to control in order to achieve vehicle lateral stability is the vehicle yaw rate by proposing an upper controller that is based on the Sliding Mode Control (SMC) technique. The objective for the controller is to control the vehicle yaw rate to a reference trajectory coming from the nonlinear single-track vehicle model assuming that the desired side slip angle is equal to zero. The proposed SMC-based upper controller will adjust the steering angle of the front wheel through the steer-by-wire system for yaw motion stabilization control to account for the system's nonlinearities and external disturbances. Simulation results show robustness of stability and performance using the SMC logic with the ability to react to the dynamic behavior experienced in different driving situations including a lane change and emergency maneuver.
Background: Coordinating differential-drive mobile robots for landmark coverage is challenging due to non-holonomic dynamics, clutter, and sparse rewards. Standard multi-agent RL pipelines often show unstable learning and inconsistent completion in this setting. Methodology: We adopt a centralized-training, decentralized-execution actor-critic without inter-agent commu nication. Our replay-centric design combines a tagged buffer that up-samples goal-reaching transitions and an offline replay initialization that seeds early learning with curated trajectories. Dynamic task assignment uses the Hungarian algorithm during training and evaluation, and we benchmark against uniform replay and established variants. Results: In a cluttered six-robot arena, the approach improves training stability relative to uniform replay. Convergence is faster and requires fewer updates to reach consistent success. Coverage efficiency increases as landmarks are reached earlier across runs. Collisions per episode decrease without adding communication or architectural changes. Multi seed evaluations show gains that persist with narrow confidence intervals. Train-evaluation gaps shrink on unseen maps, indicating improved generalization. Ablations attribute complementary ben efits to the tagged and offline components. Performance remains competitive with prioritized and hindsight replay baselines under matched budgets. Computational overhead is small because sam pling logic changes while network sizes do not. Conclusions: Focus ing on replay design substantially stabilizes multi-agent learning for differential-drive coordination. The pipeline integrates cleanly with standard CTDE implementations and supports practical deployment in coverage tasks.
This paper presents a regularized Extreme Learning Machine (ELM) framework for identifying nonlinear dynamic systems affected by multicollinearity, with application to a Hammerstein-structured model of a Continuous Stirred Tank Reactor (CSTR). The model architecture employs a single-hidden-layer feedforward network (SLFN) for the static nonlinear block, and an autoregressive linear dynamic block whose order is determined using a Lipschitz quotient criterion. Traditional ELM models are known to suffer from instability when lagged input variables are highly correlated, a common occurrence in block-oriented system identification. To address this, the study investigates enhanced variants of ELM incorporating regularization, namely Ridge-ELM and Liu-ELM, which introduce biasing parameters to improve numerical stability and generalization. The proposed regularized ELM variants are evaluated against traditional ELM using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results show that Ridge-ELM and Liu-ELM significantly reduce parameter variance and improve predictive performance on datasets. Additionally, confidence intervals and condition number analysis demonstrate improved robustness in the presence of multicollinearity. Cross-validation is used to tune hyperparameters, and the Diebold-Mariano test confirms that the improvements are statistically significant. This approach offers a computationally efficient, scalable solution for robust nonlinear system identification in multivariate chemical processes and beyond.
Accurate prediction of maize seed vigor is essential for achieving uniform germination, stable crop yields, and resilience in farming systems; however, traditional laboratory methods, such as germination assays and staining, are destructive, slow, and unsuitable for large-scale deployment. Hyperspectral imaging has shown promise as a noninvasive tool, but existing approaches often rely on spectral data alone and overlook the influence of environmental factors, limiting their usefulness in real agricultural settings. This study introduced HiveFusion, a biologically inspired framework designed to integrate hyperspectral reflectance data with environmental parameters, specifically temperature, humidity, and soil moisture, to improve the reliability of vigor prediction. The proposed system follows a structured process: denoising and correcting spectral signals, normalizing them using an environmentally aware scaling method, selecting informative spectral bands, compressing redundant features, and generating compact latent representations. These spectral features are then combined with environmental input through a mid-level attention-fusion mechanism that assigns importance to each modality, enabling the model to adapt dynamically to different conditions. A dual network, consisting of a lightweight convolutional branch for hyperspectral data and a multilayer perceptron for environmental data, performs final classification into three categories: high, moderate, and low. Experiments on a dataset of 17,724 seed images from three Ghanaian maize varieties demonstrated that HiveFusion achieved 91% accuracy, 0.90 F1-score, and 0.93 ROC-AUC, outperforming both unimodal and simple fusion baselines. Although environmental features were synthetically generated, the results highlight the value of incorporating contextual factors. Future work will validate the model with real sensor data to ensure broader applicability.
