Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of −31.27 dB and the deepest null of −101.60 dB. Also, for sparse 20-element LAA, an SLL of −18.85 dB was attained alongside the deepest null of −87.37 dB. MA obtained an SLL of −35.73 dB and −23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

The aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack optimization algorithm based on the quantum theory. In the new method, wolves are coded by Bloch spherical coordinates of quantum bits, updated by quantum revolving gates, and selectively adaptively mutated when performing poorly. Because of the three-coordinate characteristics of the sphere, the number of global optimum solutions is greatly expanded and ultimately can be searched with a higher probability. Selective mutation enhances the robustness of the algorithm and improves the search speed. Furthermore, because the size of each dimension of Bloch spherical coordinates is always [−1, 1], the variables transformed by solution space must satisfy the constraints of the aperture of arrays and the minimum distance between adjacent elements, which effectively avoids infallible solutions in the process of updating and mutating the position of the wolf group, reduces the judgment steps, and improves the efficiency of optimization. The validity and robustness of the proposed method are verified by the simulation of two typical examples, and the optimization efficiency of the proposed method is higher than the existing methods.

Antenna array pattern synthesis technology plays a vital role in the field of smart antennas. It is well known that the pattern synthesis of homogeneous array is the key topic of pattern synthesis technology. But this technology needs plenty of homogeneous array elements to meet the antenna requirements. So, a novel pattern synthesis technology for sparse array based on the compressed sensing (CS) and low-rank matrix recovery (LRMR) methods is proposed. The proposed technology predominantly includes the design of sparse array, the recovery of homogeneous array, and the synthesis of antenna array pattern. The simulation result shows that an antenna array with low gain and strong directivity can be arbitrarily built by the use of a small amount of sparse array elements and it is useful for the miniaturization and economical efficiency of the antenna system.

Adverse weather conditions, including fog, heavy rain, and snow, pose significant challenges for vision-based systems in road safety and autonomous driving. This review synthesizes recent advances in deep learning for road scene analysis under degraded weather conditions, presenting a taxonomy organized by task (classification, detection, segmentation), data sources (real versus synthetic), and model architectures (CNNs, GANs, transformers). The methodology integrates both real-world and synthetic datasets, assesses robustness using standard benchmarks such as mIoU, PSNR, and FID, and compares architectures for accuracy, inference efficiency, and generalization capacity. Findings indicate that GANs significantly enhance training diversity through synthetic augmentation, transformers improve global context modeling, and hybrid designs offer promising robustness, albeit at the expense of computational efficiency. Despite these advances, current approaches remain limited by the availability of meteorologically diverse datasets and weak geographic transferability. Future research should prioritize the development of multimodal and geographically balanced benchmarks, lightweight hybrid architectures, and sensor fusion strategies that ensure real-time adaptability. This review contributes both a comparative synthesis and a roadmap toward resilient perception systems capable of reliable operation in adverse weather.

This paper presents the design, implementation, and comprehensive evaluation of an intelligent virtual simulation platform deeply integrated with a Case-Based Learning (CBL) pedagogy, specifically developed for automotive electrical fault diagnosis training. The research addresses three critical challenges in traditional vocational training: high equipment costs, safety risks associated with physical fault insertion, and the limited diversity of fault scenarios available for practice. We developed a novel, data-driven virtual simulation system featuring three core modules: 1) a high-fidelity 3D interactive model library enabling immersive disassembly and assembly of vehicle components, 2) an AI-assisted intelligent fault case generation and management system (IFCGMS) that supports configurable compound faults through a rule-based reasoning and fault tree analysis model, and 3) a dynamic principle visualization and intelligent assessment module (DPVIAM) that tracks, evaluates, and provides real-time feedback on student operations. A quasi-experimental study was conducted, comparing an experimental group (N=33) using the integrated “virtual simulation-CBL” model against a control group (N=30) receiving traditional instruction. The results demonstrate a significant superiority of the experimental group, with a +12.2 point improvement in real-vehicle troubleshooting scores and a +9.6 point gain in comprehensive course performance. Furthermore, in-depth analysis of platform log data using formal computational models—including a Diagnostic Sequence Similarity Score (DSSS) to evaluate diagnostic procedures and Pearson correlation analysis (r = 0.82) to link virtual and real performance—revealed that diagnostic efficiency and error correction patterns in the virtual environment are strong predictors of final practical competency. This work contributes a scalable, computer-aided instructional framework that effectively leverages virtual simulation, pedagogical design, and learning analytics, offering a replicable solution for enhancing skill acquisition in high-risk, high-cost technical training domains such as automotive diagnostics.