The security and efficiency of Internet of Things (IoT) Closed-Circuit Television (CCTV) devices become crucial with the increased popularity of CCTV surveillance systems. Security is provided by traditional encryption techniques but with the large computational overhead that makes it infeasible and impractical for resource constrained IoT devices. Therefore, to secure critical video frames and ensure the authenticity of the video data, a selective encryption algorithm has been proposed in this study that uses an Advanced Encryption Standard (AES) in Galois Counter Mode (GCM) method. For optimizing storage, H.264 compression is integrated with the encryption without compromising the security. The method selectively encrypts every nth frame with authentication, and reducing computational overhead without significantly compromising the security. Experiments were conducted on multiple CCTV videos with varying resolutions, bitrates, and lighting conditions to validate the effectiveness of the proposed approach. Standard security metrics such as entropy, correlation coefficient, Number of Pixels Change Rate (NPCR), and Unified Average Changing Intensity (UACI), along with encoding/decoding frame rates, are evaluated to verify real-time feasibility. Experimental results demonstrate that the proposed scheme achieves approximately 60

This study aims to improve the communication channels and also effectively exploit the existent green energy sources by observing the heat and power performance of this so-called decimal number converter to (binary, octal, and hexadecimal) integrated and fabricated on Zynq FPGA board. Here, we study the conversion paradigms and evaluate their geometric configurations concerning the temperature and power performance characteristics. Additional research confirms an evolution in the design of architectures that enhances the integration of components, thus optimizing power dissipation and thermal management. This shows that consideration should be given to more eco-friendly designs for FPGA-based systems as these systems are more energy-efficient for use in digital components. The power consumption of the Zynq board is $\mathbf{1 2. 0 8 5}$ W, and the thermal operating junction and case temperatures are 36.8° C and 63.8° C respectively. These findings imply that it is possible to address the ‘green’ issues of computing technologies without diminishing the performance or speed of operation of such systems. Within appropriate conditions which is optimization, FPGA technology will spearhead the development of systems of the next generation with very low power consumption.

The Internet of Medical Things (IoMT) is changing the way modern healthcare works through real-time monitoring, remote diagnostics, and data-driven decision-making. Increased interconnectivity, however, also raises substantial security issues concerning data privacy, authentication, access control, interoperability, and scalability. The use of blockchain technology stands out as a possible fix as it provides decentralized tamper-proof, and cryptographic security features. This paper sheds light on the main security issues in IoMT and examines how blockchain-based answers can tackle risks with unauthorized access, data breach, and system weak points. A comprehensive review of existing literature has brought to light blockchain frameworks tailored to IoMT security concerns. These frameworks focus on authentication methods, privacy protection through encryption, and safe data transfer. Even with these steps forward several factors hold back the widespread use of blockchain in IoMT. These include problems with scaling up high computing needs following regulations, and working with other systems. The study also points out some gaps and possible future paths such as streamlined blockchain models improved consensus methods, and advanced cryptographic techniques to boost privacy. To overcome these hurdles will help blockchain grow into a more effective and scalable security framework for future IoMT environments.

Urbanization and population expansion have accelerated, highlighting the critical need for thoughtful, long‐term solutions to today's most important environmental and socioeconomic issues. In order to realize the vision of smart cities, this research investigates the revolutionary potential of incorporating blockchain technology and the Internet of Things (IoT) into numerous facets of urban life. Smart city programs seek to enhance resource efficiency, foster sustainability in critical domains, including healthcare, education, transportation, energy, and building management by utilizing sensor‐driven data and advanced analytics. In order to build blockchain‐enabled Internet of Things applications in smart cities, this paper lays out crucial goals, procedures, and elements. It highlights the importance of decentralized systems in lowering single points of failure and improving data integrity, security, and privacy. Personalized learning paths in education, real‐time patient monitoring in healthcare, effective transit networks, and dependable energy grids using renewable energy sources are a few examples of specific uses. The report also emphasizes how robust service delivery, smart contracts for process automation, and safe data interchange are made possible by blockchain and IoT integration. This study shows how decentralized blockchain solutions may greatly improve governance and operational resilience while also substantially contributing to the sustainable development of smart cities by resolving security and privacy.

Early Clinical Exposure (ECE) is a pedagogy to develop confidence and competence in Medical and Allied Health students. Challenges in conventional teaching include less clinical access, varied patient cases, and potential risks to students and patients. This study explores Augmented Reality (AR) as a tool for ECE. It highlights AR’s potential to enhance students’ understanding, skills acquisition, learning outcomes, motivation, and confidence. Though AR is beneficial in improving learning outcomes, it can only be an additive tool, without replacing traditional teaching. Further research is essential to formulate standardized policies, and implementation guidelines, reduce costs, and evaluate long-term effects on clinical expertise.

In the contemporary era, a vast array of applications employs encryption techniques to ensure the safeguarding and privacy of data. Quantum computers are expected to threaten conventional security methods and two existing approaches, namely Shor's and Grover's algorithms, are expediting the process of breaking both asymmetric and symmetric key classical algorithms. The objective of this article is to explore the possibilities of creating a new polynomial based encryption algorithm that can be both classically and quantum safe. Polynomial reconstruction problem is considered as a nondeterministic polynomial time hard problem (NP hard), and the degree of the polynomials provide the usage of scalable key lengths. The primary contribution of this study is the proposal of a novel encryption and decryption technique that employs polynomials and various polynomial interpolations, specifically designed for optimal performance in the context of a block cipher. This study also explores various root convergence techniques and provides algorithmic insights, working principles and the implementation of these techniques, which can potentially be utilized in the design of a proposed block-cipher symmetric cryptography algorithm. From the implementation, comparison and analysis of Durand Kernal, Laguerre and Aberth Ehrlich methods, it is evident that Laguerre method is performing better than other root finding approaches. The present study introduces a novel approach in the field of polynomial-based cryptography algorithms within the floating-point domain, thereby offering a promising solution for enhancing the security of future communication systems.

Data security and privacy are critical concerns when integrating Closed-Circuit Television (CCTV) cameras with the Internet of Things (IoT). To enhance security, IoT data must be encrypted before transmission and storage. However, to minimize overheads related to storage space, computational time, and transmission energy, data can be compressed prior to encryption. H.264/AVC (Advanced Video Coding) offers a balanced solution for video compression by addressing processing demands, video quality, and compression efficiency. Encryption is vital for safeguarding data security, yet the integrity of IoT data may sometimes be compromised. Ineffective data selection can lead to inefficiencies and potential security risks, highlighting the importance of addressing CCTV video data security carefully. This study proposes an algorithm that integrates compression with selective encryption techniques to reduce computational overhead while ensuring access to critical information for real-time analysis. By employing frame intervals, the algorithm enhances efficiency without compromising security. The execution details and merits of the proposed approach are analyzed, demonstrating its effectiveness in safeguarding the privacy and integrity of IoT CCTV video data. Results reveal superior performance in terms of compression efficiency and encryption/decryption times, with an average encryption time of 0.00171 seconds for a 128-bit key, enabling fast processing suitable for real-time applications. The decryption time matches the encryption time, confirming the method's viability for practical IoT CCTV implementations. Metrics such as correlation coefficient, bitrate overhead, and histogram analysis further validate the approach's robustness against statistical attacks.

Wheat, a critical staple crop, faces significant yield losses due to diseases such as wheat rusts, powder-like mildew, and Fusarium-induced scab. However, traditional diagnostic methods, like visual inspection and laboratory assays, are subject to these problems, and are usually not suitable for huge scale deployment. To better understand the potential of deep-learning (DL) in helping to detect wheat diseases, this review reviews the literature of 58 seminal studies synthesizing their findings. The controlled datasets are worked on by DL models, particularly Convolutional neural networks (CNNs) such as EfficientNet and C-DenseNet with attention mechanisms, and over 95% accuracy is reached in terms of accuracy. In addition, DL performance can be improved with hybrid approaches and advanced technologies for data acquisition, like transfer learning, with UAVs with hyperspectral sensors. Unfortunately, field deployment is difficult for many reasons, including environmental variability, a small amount of data (e.g., class imbalance), and interpretability. Federated learning provides solutions for privacy-preserving collaboration, edge computing helps to deploy it at the least cost, and explainable AI (XAI) is added for transparency. Robust preprocessing, multimodal sensor fusion, and farmer-centric design are key for fostering trust and adoption, and are the subjects of the review. With low cost, scalability, and interpretability of a DL system bridging the gap between research and practice, future research should aim to create a DL system for sustainable development in wheat production and global food security.

To improve communication efficiency and minimize the energy issue affecting digital devices, this work offers an experimental FPGA-based digital circuit decoder. By using FPGA boards and putting clever resource allocation algorithms into practice, the decoder maximizes energy efficiency. The decoder saves a lot of power without compromising functionality by incorporating energy-saving techniques and looking into renewable energy sources to power digital gadgets. A more environmentally friendly method of powering digital gadgets is made possible by these green energy alternatives. This paper establishes the foundation for future sustainable developments in the realm of green communication and digital technology through an analysis of the potential of FPGA technology.

A big component of the exhilarating but difficult car-buying process is colour. Buying a vehicle is being changed by AR technology, which gives additional alternatives. This abstract suggests the use of AR to modify automotive hues. Car dealerships utilised booklets and colour swatches to offer consumers what hues to choose. These approaches are restricted and don't effectively reflect a colour on a small automobile. Using AR technology, customers may realistically determine the hue of their desired car. With AR, clients may view a broad array of hues on a tablet, smartphone, or showroom display. In order to more correctly show the automobile type and colour, this technology provides a 360-degree image and simulates real-life illumination. You may rapidly share personalised sets with friends and family, compare alternatives, and adjust the hues. Beyond looks, AR colour customization is available. Find out more about colour availability, upkeep, and market worth. The increased data supports customers in making well-informed selections that correspond with their requirements and preferences. For a more individualised experience, vehicle firms may enable augmented reality colour customization. This technology makes choice easier and minimises regret after a purchase, as it allows consumers to be certain of their options before completing a purchase.

There is a severe shortage of energy in many developing countries these days. The creation of an energy-efficient full subtractor that utilizes multiple FPGA (Field Programmable Logic Array) families and the Xilinx Vivado software framework has led to the evolution of environmentally conscious communication. With a focus on temperature, power dissipation, and energy consumption control, the study uses Xilinx Vivado to develop an energy-efficient design for communication devices. By integrating power-saving methods with effective hardware utilization, the suggested design optimizes energy economy. The study also looks at the effects of temperature on circuit performance and reliability to demonstrate the benefits and applicability of energy-efficient design for communication hardware circuits. We have compared the Zynq and Kintex boards to find out the best energy-saving family.

Security cameras are widely used for surveillance and monitoring purposes, but they often require human intervention to analyze the captured images and videos. In this paper, we review a project that aims to develop a smart security camera system that can automatically detect and track objects of interest using computer vision techniques. The project uses the following python libraries: cv2 for image processing and object detection, winsound for sound alerts, tkinter for graphical user interface, threading for concurrent execution, and PIL for image manipulation. The system runs on a python IDE such as pycharm and uses the built-in camera of the device. We describe the main features of the system, as well as the difficulties and constraints faced throughout the designing procedure. We also discuss the potential applications and future improvements of the system.

The growing demand for space-efficient antennas compatible with portable electronic gadgets inspired the integration of recursively iterated fractal geometries (characterized by self-similarity, space-filling, and multiband/wideband capabilities) in antenna designs. Therefore, this article conducts an extensive survey on fractal-based single-port and multi-port patch antenna designs with multiband and ultrawideband characteristics for their implementation in 5G/IoT (Internet of Things) devices. The survey begins with a basic overview of fractal geometries and the iterated function system (IFS) technique employed for their generation. Further, it highlights the limitation of multipath fading prevalent in single-port antenna arrays that can be overcome by adopting multiple-input multiple-output (MIMO) technology. The MIMO antenna systems are vital for 5G networks to enhance data transmission rate, network capacity, and system reliability. Apart from these admirable attributes, the closely packed antenna elements in the MIMO configuration result in a severe mutual coupling. To address this limitation, various isolation enhancement techniques developed by distinguished researchers are thoroughly discussed in this article, detailing their merits and demerits. At last, this article portrays diverse band-notch structures integrated into the antenna designs to eradicate the existing interfering narrow bands in the ultrawideband (UWB) spectrum. The underlying reason for conducting this survey is to aid antenna designers and academicians with a thorough knowledge of distinct fractal geometries, various isolation improvement approaches, and band-notch techniques on a single platform, desired for designing 5G/IoT-based antenna systems.

Unreal Engine and Unity stand out as two of the used game engines in today’s gaming landscape. Game developers often find themselves weighing the pros and cons of each seeking the engine, for their projects. Unreal Engine is a favored choice among game creators, known for its developer community and compatibility across platforms and devices. On the hand Unity appeals to developers for its flexibility, scalability and user-friendly interface. Its strong community support and broad platform compatibility make it a top contender, in the game development realm. This article offers an analysis of Unreal Engine and Unity focusing on their features, performance capabilities, user friendliness and community backing.

In light of the worldwide energy crisis, this research investigates methods for reducing the power consumption of digital circuits, focusing specifically on multiplexer design using FPGA families. In order to lower power consumption without compromising quality, this article looks at FPGA layouts and synthesis methods. Through the use of technology mapping significant power savings are accomplished without sacrificing function. In order to address the energy limitation challenges, this article offers practical methods for lowering power consumption in FPGA-based devices.