Photovoltaic (PV) arrays and their electronic converters are subject to various environmental disturbances and component-related faults that affect their normal operations and result in a considerable energy loss. Therefore, it is ever demanding to design such closed-loop operating algorithms that tolerate faults, present acceptable performance, and avoid wear and tear in the systems. In this work, the core objective is to extract maximum power from a PV array subject to environmental disturbances and plant uncertainties. The system is considered under input channel uncertainties (i.e., faults) along with variable resistive load and charging stations. A neuro-fuzzy network (NFN)-based reference voltage is generated to extract maximum power while considering variable temperature and irradiance as inputs. Furthermore, the estimated reference is tracked by the actual PV voltage under two types of controllers: certainty-equivalence-based robust sliding mode (CERSMC) and certainty-equivalence-based robust integral sliding mode (CERISMC). These strategies benefit from improving the robustness against faults (disturbances). The proposed methods use the inductor current, which is recovered via the velocity observer and the flatness property of nonlinear systems. The system’s stability is proven in the form of very appealing theorems. These claims are validated by the simulation results, which are carried out in a MATLAB environment.

Vehicular Internet of Things (V-IoT) networks need intelligent and adaptive spectrum access methods for ensuring ultra-reliable and low-latency communication (URLLC) in highly dynamic environments. Traditional reinforcement learning (RL)-based algorithms, such as Q-Learning and Double Q-Learning, are often characterized by unstable convergence and inefficient exploration in the presence of stochastic vehicular traffic and interference. This paper proposes Adaptive Reinforcement Q-learning with Upper Confidence Bound (ARQ-UCB), a lightweight and reliability-aware RL framework, which explicitly reduces interruption and blocking probabilities while improving throughput and delay across diverse vehicular traffic conditions. This proposed ARQ-UCB algorithm extends the basic Q-updates with an exploration confidence term able to dynamically balance exploration and exploitation based on uncertainty estimates, hence allowing faster convergence in case of bursty vehicular traffic. A comprehensive simulation framework evaluates throughput, delay, fairness, energy efficiency, and computational complexity in several V-IoT scenarios. Obtained results indicate that ARQ–UCB attains substantial gains in terms of throughput, fairness, and blocking/delay probabilities while retaining sub-20 μs decision latency and 𝒪(1) complexity per decision, thus validating real-time feasibility for reliable spectrum access in 5G and beyond V-IoT networks.

Background: Accurate segmentation of skin lesions in dermoscopic images is essential for the detection of skin cancers, particularly melanoma, as precise boundary identification enables timely and effective diagnosis. Cancerous skin lesions, particularly malignant melanoma, contribute significantly to the mortality rate, underscoring the need for early detection and precise diagnostic techniques. However, achieving this precision poses challenges owing to indistinct lesion borders, asymmetrical shapes, and common obstructions such as hair, markings, and occlusions. Method: This study addresses these challenges by introducing an end-to-end trainable network incorporating focal modulation to enhance the feature refinement for pixel-level classification. Focal modulation effectively extracts fine-grained, multi-scale features while preserving contextual information, thereby improving the performance of lesion segmentation. The decoder part of the proposed method utilizes transposed convolution for up-sampling, which preserves the spatial detail necessary for high-resolution segmentation. Results and Conclusion: The proposed FM-Net was validated on several benchmark datasets and demonstrated state-of-the-art (SOTA) performance in skin lesion segmentation. The FM-Net’s ability to provide SOTA performance without data augmentation is one of its significant strengths. Performance on the International Skin Imaging Collaboration (ISIC) datasets demonstrated FM-Net’s adaptability, with Jaccard index scores of 89.60% on ISIC 2016, 82.34% on ISIC 2017, and 87.71% on ISIC 2018. The comprehensive performance of FM-Net highlights its ability to accurately segment lesions and its potential for assisting in timely diagnosis. The code used to reproduce the results is available on GitHub.

This research addresses the challenge of controlling the air delivery system of Proton Exchange Membrane Fuel Cells (PEMFC) to ensure smooth operation and prolong device lifespan. The study is based on PEMFC model developed by Pukrushpan et al. for the Ford P2000 fuel cell vehicle. The stack temperature has been incorporated in the state-space model to improve its accuracy. Robust non-linear control strategies are designed and compared, including first-order Sliding Mode Control (SMC), second-order SMC, Integral SMC (ISMC), and Fast Terminal Integral SMC (FTISMC) with Super Twisting Algorithm (STA). In addition, a Uniform Robust Exact Differentiator (URED) based observer is proposed for estimating supply manifold pressure, mitigating measurement noise effects by eliminating the need of its direct measurement. Moreover, a three-layered feedforward neural network (TLFFNN) using FTISMC has been employed to estimate the continuous part of the control, trained to enhance robustness against noise. Extensive software simulations are conducted to validate the effectiveness of the proposed controllers. The simulation results demonstrate superior performance in terms of closed-loop stability, tracking accuracy, and robustness against measurement noise and system uncertainties, confirming the capability of the proposed control strategies to achieve reliable and accurate PEMFC air supply control. The results demonstrate that the proposed observer and all the control approaches achieve stable operation. However, FTISMC exhibits fastest convergence, superior tracking accuracy, and greater robustness to parameter uncertainties, external disturbances, and measurement noise. The proposed framework, thus, contributes towards the development of efficient and environmentally sustainable fuel cell technologies. Future research avenues may explore ways to enhance PEMFC robustness using online NN methods.