Multi-UAV path planning in complex 3D urban environments is affected by large search spaces, obstacle constraints, redundant waypoints, and inter-UAV spatiotemporal conflicts. To address these issues, this paper proposes ViT-CoRRT*, a planning framework that integrates start-goal-conditioned ViT-guided RRT* search, dynamic programmingbased path compression, and sequential dispatching. The 3D Vision Transformer generates a task-dependent guidance map from the voxelized environment and uses it as a probabilistic sampling prior for RRT*. The generated paths are then compressed by selecting collision-checked shortcuts between non-adjacent waypoints, after which UAV departure times are adjusted according to spatiotemporal conflict checks. Across 50 baseline trials, ViT-CoRRT* reduced the mean total path length from 113.72 to 92.99 and the mean number of retained waypoints from 46.38 to 13.80 compared with standard RRT*. The mean task makespan also decreased from 35.00 to 28.33. The differences in these three metrics are statistically significant (p < 0.001). Additional experiments under different obstacle configurations and UAV fleet sizes achieved solution success rates of 98%–100%. No post-dispatch conflicts were detected in successful trials under the adopted discrete-time checking resolution.
Cloud-edge collaborative computing integrates the powerful computing capacity of cloud centers with the low-latency response of edge nodes, posing substantial challenges to resource scheduling under highly dynamic task loads. Balancing system operating costs and processing capacity constitutes a critical issue that urgently needs to be addressed. To tackle this problem, this paper models the cloud-edge collaborative system as a multi-server queuing system. It establishes a Cost-Performance Ratio (CPR) objective that jointly considers system capacity, operating costs, and delay penalties. To address the rigid, linearly decreasing convergence factor of the standard Grey Wolf Optimizer (GWO), a Q-learning-based Grey Wolf Optimizer (QGWO) is proposed that integrates an adaptive convergence factor, hierarchical weights, stagnation-duration jumping, periodic replacement of inferior wolves, and a greedy-strategy-based position acceptance mechanism. To evaluate online reconfiguration capability, a dynamic step-changing task-arrival-rate schedule is used. Extensive simulations show that our proposed QGWO achieves a cumulative normalized regret 27% lower than that of the standard GWO, providing an effective scheme for on-demand dynamic resource scheduling in cloud-edge environments.
Approximate computing has emerged as a viable paradigm to balance accuracy and hardware efficiency, as the demand for edge computing increases. While multipliers are inherently powerintensive components, their approximation can lead to significant power savings. Although approximate compressors are effective for unsigned arithmetic, they often result in substantial accuracy degradation in signed multipliers. To mitigate this issue, a multiplier was developed to optimally utilize approximate compressors. Additionally, two novel approximate 4-2 compressors are introduced. The error metrics of the proposed multiplier were compared with state-of-the-art designs, and all hardware designs were synthesized and implemented using Cadence Innovus with a 90 nm ASIC technology node. The hybrid utilization of the two proposed compressors in the designed multiplier achieved reductions of 39.26% in area, 49% in power, 18.63% in delay, and 74.8% in the power-delay area product (PDAP) compared to the exact Baugh-Wooley multiplier. The efficacy of the proposed multiplier was validated using neural network inference and Sobel edge detection. The results indicated that the proposed multiplier maintains the baseline accuracy, with only a 0.06% deviation in classification accuracy for Multilayer Perceptron (MLP) and a 0.14% deviation for ResNet-50, and no discernible deterioration in edge-detection quality.
Stiff delayed systems seek to improve the stability and performance of complicated systems with large time lags and uncertainty. In the context of a fluid catalytic cracking unit, this technique aimsto increase operational efficiency and reliability by resolving system delays and variabilities, resulting in consistent output and optimal performance. The challenge is to improve the stability and efficiency of a fluid catalytic cracking unit by rigorously improving its performance underlarge time delays and system uncertainty. In this manuscript,the proposed Optimizing Stability and Performance in Fluid Catalytic Cracking Unit Using a Brown-Bear Optimization Algorithm for Adaptive Neuro Sliding Mode Control(OSP-FCC-BBOA) proposes a strong steady state Brown-bear Optimization Algorithm (BBOA) technique. The BBOA approach is utilizedto maximize the gain parameters of the Adaptive Neuro Sliding Mode Control (ANSMC) controller. The objective of the manuscript is to improve the stability and performance of a Fluid Catalytic Cracking (FCC) unit. Delays are caused by catalyst hold-ups in the standpipes that link the riser and regenerator. Demonstrate that delays and stiffness can be handled by the proposed BBOA technique. Furthermore, the proposedapproach has the ability to adjust controller parameters and optimize process parameters at the same time. The proposed technique is implemented on MATLAB. The simulation results display that the proposed framework can effectively handle the delay and stiffness properties of the FCC process and improve the closed-loop control performance. Comparative analysis shows that the proposed method obtains the minimum error rate of 0.10%, mean error rate of 0.14%, which is better than the optimization of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey wolf optimizer (GWO) and Harris Hawks Optimization (HHO) methods. The results show the effectiveness of BBOA in improving the stability, disturbance rejection ability and controller tuning performance of stiff delayed FCC systems.
Schizophrenia, a complex and debilitating mental disease, is characterized by disturbances in behaviour, perceptions, and mental processes. Identification at early instances and critical diagnosis are crucial for improving treatment outcomes with regard to enhance the likelihood of patient care and quality. Electroencephalography (EEG) signals serves as a cornerstone in identification of abnormalities associated with schizophrenia. In this manuscript, Optimized 3D CNN with Vision Transformer for Schizophrenia Diagnosis using EEG Signals (3DCNN-ViT-DS-EEG) is proposed. Initially, input data is taken from EEG schizophrenia Dataset. Subsequently, the input data is preprocessing using Multi-window Savitzky-Golay Filter (MSGF) to remove unwanted noise from the input data. As feature extraction plays a major role in medical diagnosis, pre-processed data is input to feature extraction. High-order Time-reassigned Synchro squeezing Transform (HTST) technique is exploited to extract the features like Instantaneous Frequency, Time-Frequency Energy Distribution, Amplitude Modulation and Phase Information .Furthermore, the extracted features are systematically processed through3D CNN with Vision Transformer (3DCNN-ViT),enabling the classification of subjects to ascertain whether the subject is afflicted with schizophrenia or not. Finally, Wolf Bird Optimization (WBO) algorithm is employed to optimize the weight parameter of 3DCNN-ViT classifier, which detects the schizophrenia disease accurately. The proposed 3DCNNViT- DS-EEG is analyzed with performance metrics, like Accuracy, F1-Score, Precision, Computation Time and ROC. The proposed model attains 31.97%, 24.29%, 17.88%, 26.84%, 18.65% and 32.17% higher accuracy,23.80%, 18.64%, 31.79%, 16.24%, 30.89% and 23.16%higher precision and 18.49%, 25.89%, 30.66%, 20.15%, 26.14% and 31.30%lower computational time compared with existing methods respectively.
