As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of <−10 dB. On top of that, the antenna shows an axial ratio of less than 3 dB at 4.23 GHz - 4.62 GHz (8.81%) and 7.11 GHz - 7.36 GHz (3.45%), whereas left hand circular polarization (LHCP) is observed in the first band and right hand circular polarization (RHCP) is observed in the second band. The overall dimensions of the antenna is $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

Maritime management is a crucial concern for movable bridge safety. Irregular management of water vehicles near movable bridges may lead to collision among ships and bridge infrastructures, causing massive losses of life and property. The paper presents a theoretical framework and simulation of an intelligent water vehicle management system for movable bridges corresponding to vehicle traffic responses. The water regime around the bridge is considered in virtually separated domains to estimate the desired safety actions based on the position of the approaching ships. An emergency clash avoidance control system is represented to prevent ship-infrastructure collision and ensure transportation safety. In addition, a simulation platform is developed specifically adaptable for movable bridge maritime and dynamic traffic management. The proposed theory is experimented using the simulation platform for different ship speeds and bridge-vehicle traffic volumes. Based on analyzing the velocity profile of approaching ships at different incidents, the bridge is found incapable of evacuating vehicles and unable to open promptly in case of speeding ships and high traffic density of vehicles on the bridge. Computational results show that the emergency control system is effective in reducing ship speed and prevent certain collisions. Lastly, the transportation policy for the newly proposed maritime management system is validated by real-world implementation in movable bridges across the world.

Road accident is a major problem in Bangladesh which claims many lives. The major reason behind these road accidents are due to the careless driving of the drivers. In this paper, an efficient driving monitoring system has been Proposed which subsumes Bangladeshi traffic sign, vehicle detection as well as driver alerting system. Twenty important road signs of Bangladesh have been used for such purpose. The traffic signs and vehicle images have been captured from roads during daytime and night time. A Convolutional Neural Network (CNN) is modeled by tuning its parameters to Figure out the most robust architecture to detect traffic signs and vehicles from images. Alongside the signs and vehicle detection, Aspect ratio based driver sleepiness detection is made to ensure road safety by avoiding such road accidents. This work is conducted using 4200 images which has gained an average recognition accuracy of 96.8%.

Accurate online state of health (SoH) estimation, especially for highly degraded second-life batteries (SLBs), is critical for the safe and efficient operation of any Battery Management System (BMS). However, existing data-driven estimation methods typically rely on complete charge/discharge cycles or assume a fixed starting state of charge (SoC) and fixed voltage windows. These assumptions are highly restrictive and rarely align with the random, partial charging behaviors of real-world electric vehicle (EV) operations and Battery Energy Storage System (BESS) applications. To overcome this limitation, this paper presents the following framework: an online SoH estimation approach utilizing long short-term-memory (LSTM)-ensembled learning using consecutive constant current (CC) charging segments. Instead of relying on a rigid voltage window, the presented method utilizes nested, expanding slices of highly flexible CC charging intervals to capture sequential degradation dynamics. Furthermore, this study maps suggestive ranges of optimal voltage intervals that dynamically adapt to different battery health conditions, ensuring high estimation accuracy despite the type of degradation. The framework is validated using four commercially available lithium-ion batteries (LIBs), with a moderate to low SoH down to ~40%. To ensure practical viability, the hybrid deep neural network (DNN) and LSTM architecture has been highly optimized, requiring only 474 trainable parameters. The framework has been evaluated utilizing multiple performance matrices, showing minimal discrepancy with excellent correlation to the true SoH throughout the lifespan of the testing cell.