Nov 2026· Engineering and Technology Journal· 0 citations
Abstract
Microstrip antennas are very important in modern wireless communication systems due to their small size, low cost of manufacturing and easy integration with electronic devices. With the continuous development of wireless technologies, the demand for antennas with better bandwidth, gain, radiation efficiency, miniaturization and intelligent design capability is growing. This paper provides a detailed narrative review on the recent developments in microstrip antenna technology in three complementary research areas: antenna design techniques, advanced substrate materials, and intelligent design and optimization methods. The recent studies were systematically gathered and analyzed, and compared critically based on the design methodologies, material characteristics, antenna performance, optimization approaches, practical applications, and reported limitations. As revealed by the review, innovative structural designs, engineered substrate materials and artificial intelligence based optimization techniques have been able to enhance the performance of the antennas while minimizing the complexity of the designs and the development time. Important research challenges are also identified, such as the need for multi-objective optimization, standardized evaluation methods, wider experimental validation, and better incorporation of advanced materials with intelligent optimization techniques. In general, the review shows that the integration of structural innovation, substrate engineering and intelligent computational methods is an effective way for the development of next generation microstrip antennas. The results provide a single reference for researchers and engineers to design compact, efficient, and intelligent antennas for future applications such as 5G and 6G, the Internet of Things, satellite communications, wearable electronics, and millimeter-wave wireless systems.
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
An adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs is developed that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model.
B. Kapusuzoglu, S. Mahadevan, Shunsaku Matsumoto et al.· Structural And Multidiscipli...· 17 citations
An adaptive jailbreak attack framework for systematic evaluation of both cascaded pipelines and end-to-end large audio-language models under a unified experimental setting that achieves consistently higher attack success rates across diverse audio-based LLM systems.
Linghan Huang, Bo Li, Huaming Chen et al.· 12 citations· ⚡2
This review provides a systematic literature review of LLM-based Verilog code generation, analyzing 102 papers (70 published and 32 high-quality preprints) from SE, AI, and EDA venues and outlines a roadmap highlighting potential opportunities in LLM-assisted hardware design.
This work introduces Behavior-Outcome Freedom (F), a pre-synthesis diagnostic of signed behavior-outcome rank mismatch, and formalizes its candidate-conditional role through Signed Anchor-Rank Transfer, which preserves validated capability resources, removes runtime orchestration, and conditionally inherits pipeline guidance using a calibrated rule over F.
Binyan Xu, Dong Fang, Haitao Li et al.· arXiv.org· 10 citations
Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL), and explore the integration of DMs with RL and DT.