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.
This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning techniques to ASD, highlighting key challenges and opportunities, particularly the need for models that can integrate complex data to improve diagnostic accuracy and treatment outcomes.
Rafael Muñoz-Terol, Jesús Peral, Sandra Amador et al.· Heliyon· 4 citations· ⚡1
This paper identifies two different routes through which models can acquire geometrically separable features: they can learn them from complementary co-occurrence signals in general language data, including text-number co-occurrence and cross-number interaction, or from multi-token addition problems.
This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
Eldad Haber, Shadab Ahamed, Md Shahriar Rahim Siddiqui et al.· SIAM Journal on Scientific C...· 3 citations
Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as tensor trains (TTs), a class of tensor networks. Our approach operates entirely in the compressed domain, enabling efficient computation on exponentially large datasets while maintaining a controlled computational cost. We demonstrate its power in two key areas: (I) evaluating highly nonlinear elementary and filtering functions on a 3D reactive flow field, enabling high-fidelity reaction rate computation and region filtering, and (II) finding extrema in complex optimization problems, such as solving Max-SAT instances on spaces up to $2^{70}$ configurations. These results establish ITNT as a foundational tool that provides tensor network methods with the capability for general-purpose data science and large-scale optimization.
Xiao Wang, Tomohiro Hashizume, Pia Siegl et al.· 2 citations