Wireless communication is rapidly evolving, with 5G systems already in use and 6G being actively researched. In this progress, antennas are no longer just passive elements but play a central role in deciding the overall performance of a system. This paper reviews the latest trends in antenna design that are shaping next-generation applications. It highlights the shift from conventional metallic antennas to more advanced structures. Key developments covered include: compact Microstrip and Substrate Integrated Waveguide (SIW) antennas, miniaturized and multi-band Metamaterial and Fractal antennas, adaptable Smart and Reconfigurable antennas, and high-frequency Millimeter-Wave (mmWave) and Terahertz (THz) antennas. For each category, the working principles, recent improvements, and practical uses are discussed. Applications are explored across 5G/6G networks, the Internet of Things (IoT), satellite communication, biomedical devices, and defense systems. The paper also identifies current challenges and future directions, such as applying Artificial Intelligence (AI) in antenna design, using eco-friendly materials, and integrating antennas for terahertz technologies. Overall, this review provides a clear picture of the present state and future scope of antenna technology.

Mechatronics, which represents the integration of mechanical engineering, electronics, control systems, and computer science, has grown from being a specialized field into a central approach for modern technology development. This paper reviews the latest advances in mechatronics, showing how the close combination of these disciplines is enabling next-generation innovations. We begin by outlining the evolution of mechatronics, starting from its early use in robotics to its present role as a core engineering philosophy. The review then highlights major technological pillars such as advanced sensors and actuators, embedded electronic systems, and intelligent control strategies. The discussion further explores real-world applications in areas including industrial automation (Industry 4.0), autonomous machines, smart healthcare devices, and modern automotive technologies like electric and self-driving vehicles. In addition, recent trends such as the use of Artificial Intelligence (AI) and Machine Learning (ML) for predictive control, as well as the rise of cyber-physical systems, are examined. Finally, the paper addresses challenges in integrating multiple disciplines and outlines possible future directions. It concludes that the continued merging of mechanical and electronic systems, strengthened by intelligent technologies, will be key to addressing the complex engineering problems of the future.

Modern power systems are becoming increasingly complex due to high penetrations of variable renewables, evolving electricity markets, and rising expectations for reliability and resilience. Traditional, model-driven methods struggle to keep pace with this dynamic, data-rich environment. This review examines how Artificial Intelligence (AI) and Machine Learning (ML) are being applied across four core domains of grid operations: (1) forecasting of load and renewable generation with models such as LSTMs and Transformers; (2) fault detection and predictive maintenance for assets like transformers and lines; (3) security and stability control, including anomaly detection, cyber-threat mitigation, and voltage stability assessment; and (4) optimization and control for markets, demand response, and optimal power flow. We compare supervised, unsupervised, and reinforcement learning approaches, noting their data needs, effectiveness, and deployment practicality. Key barriers—data quality, interpretability, computational cost, and AI-driven cyber risks are assessed, and emerging directions such as federated learning and physics-informed neural networks are highlighted as paths toward more transparent, robust, and trustworthy AI-enabled power systems.

In the era of smart automation and enhanced security systems, facial recognition technology has emerged as a reliable and efficient solution for access control applications. Despite the availability of traditional security mechanisms such as keys, RFID tags, and biometric fingerprint systems, these approaches often face limitations including susceptibility to theft, duplication, unhygienic contact-based authentication, and higher costs. To address these challenges, this project presents the design and implementation of a Face Recognition-Based Automatic Door Open and Close System using the ESP32-CAM microcontroller.

The proposed system offers a cost-effective, compact, and efficient alternative by leveraging the capabilities of the ESP32-CAM module, which integrates a high-resolution camera, Wi-Fi, and Bluetooth connectivity on a single platform. The system is programmed to capture the face of a person at the door and employs built-in real-time face detection and recognition algorithms to authenticate users. The detected face is compared against a pre-stored dataset of authorized individuals. Upon successful authentication, the ESP32-CAM activates a servo motor or electronic lock to open the door, which subsequently closes automatically after a preset delay, thereby improving both convenience and security.

In addition to on-device processing, the system supports remote access and updates through Wi-Fi connectivity, allowing administrators to seamlessly add or remove users via a secure web interface. This enhances adaptability in dynamic environments such as residential complexes, corporate offices, and institutional campuses. The design emphasizes low power consumption, minimal hardware requirements, and efficient data processing, making it practical for real-world deployment.

The novelty of this work lies in its integration of affordable embedded hardware with intelligent access control, eliminating the dependency on physical keys, RFID cards, or third-party biometric systems. Moreover, the system provides a contactless and hygienic access solution, particularly relevant in the post-pandemic era where touchless authentication has become increasingly important. This project demonstrates the feasibility of combining embedded programming, image processing, IoT communication, and automation control into a unified prototype for secure access management. Future enhancements may include cloud-based user databases, mobile app notifications, integration with AI-driven anomaly detection, and blockchain-enabled authentication to strengthen system robustness, scalability, and data privacy. The proposed system contributes toward the vision of smart, sustainable, and user-friendly access control infrastructures for modern society.

The developed prototype has been successfully designed, implemented, and tested under real-time conditions, where it consistently demonstrated accurate face recognition, quick response times, and reliable door operation. Unlike many conceptual models that remain limited to simulation or theoretical frameworks, this system has been physically realized and proven to function effectively in practice. Its ability to operate continuously with low latency and minimal hardware requirements ensures that it can be directly integrated into everyday use without significant modifications. Thus, this project not only establishes a proof of concept but also validates the feasibility of real-time application, bridging the gap between academic research and practical implementation. The prototype demonstrates that smart, secure, and contactless access management can be achieved using open-source hardware and software, paving the way for widespread adoption of IoT-enabled security systems in modern society.

As global efforts intensify to combat climate change and address diminishing fossil fuel reserves, the convergence of electric vehicles (EVs), renewable energy systems (RES), and Internet of Things (IoT) technologies presents a transformative pathway toward sustainable transportation and energy management. This comprehensive review examines how EVs can significantly reduce carbon emissions, enhance energy efficiency, and mitigate urban air pollution, while addressing the inherent challenges in their widespread adoption for transportation applications. Critical enablers for EV integration include advanced battery technologies, robust charging infrastructure, and vehicle-to-grid (V2G) capabilities that position electric vehicles as distributed energy resources within the power grid ecosystem. The review analyzes the rapid growth trajectory of electric vehicles alongside renewable energy penetration in the electricity sector, highlighting the synergistic potential of clean energy sources including solar, wind, hydroelectric, and biomass systems.

The integration of IoT technologies emerges as a key differentiator, enabling smart charging stations, vehicle-to-infrastructure (V2I) connectivity, and intelligent energy management systems that fundamentally reshape energy consumption and distribution patterns. Through IoT-enabled real-time data analytics and automated control systems, this technological convergence facilitates optimized EV charging strategies, dynamic load balancing, and enhanced grid stability while minimizing overall carbon footprint. The review demonstrates how this integrated approach creates a sustainable ecosystem that maximizes renewable energy utilization while ensuring reliable and efficient electric transportation infrastructure.