Construction of Substitution Boxes Using Finite Fields

Substitution boxes (S-boxes) play a crucial role in modern cryptographic algorithms, providingnon-linear transformations that enhance the security of data encryption. S-boxes are essentialcomponents of symmetric key ciphers, such as Advanced Encryption Standard (AES) and DataEncryption Standard (DES). Non-linearity is a fundamental property that resists linear attacksand enhances the cryptographic strength of S-boxes. Constructing secure and efficient S-boxeswith high non-linearity is a critical challenge in cryptography. The problem of this research isto develop a systematic approach for constructingsubstitution boxes (S-boxes) using finite fields,with a particular focus on achieving optimal non-linearity to enhance the security of cryptographicalgorithms

Analysis of Coverage and Area Spectral Efficiency under Various Design Parameters of Heterogeneous Cellular Network

As day by day the population is increasing, the use of mobile phones and different applications is increasing which requires high data rate for transmission. Homogeneous cellular network cannot fulfill the demand of mobile users, so creating a heterogeneous cellular network (HCN) is a better choice for higher coverage and capacity to fulfil the increasing demand of upcoming 5G and ultra-dense cellular networks. In this research, the impact of antenna heights and gains under varying pico to macro base stations density ratio from 2G to 5G and beyond on two-tier heterogeneous cellular network has been analyzed for obtaining optimum results of coverage and area spectral efficiency. Furthermore, how the association of UEs affects the coverage and ASE while changing the BSs antenna heights and gains has been explored for the two-tier HCN network model. The simulation results show that by considering the maximum macro BS antenna height, pico BS antenna height equal to user equipment (UE) antenna height and unity gains for both macro and pico tiers, the optimum coverage and area spectral efficiency (ASE) for a two-tier fully loaded heterogeneous cellular network can be obtained

Advanced Vehicle Safety: A Prototype Circuit for Accident Prevention and Emergency Response

In contemporary society, the pursuit of robust accident prevention, detection, and reporting systems is paramount, particularly within the context of vehicular safety. This study introduces a meticulously designed prototype circuit, adaptable for deployment across diverse vehicle types. This intelligent circuitry endeavors to mitigate accidents and safeguard human lives by employing a multifaceted approach.The system's primary functions encompass the verification of seat belt usage and the assessment of alcohol consumption by the driver. Additionally, it incorporates a vibration sensor to detect accidents promptly. Complementing these features, the integration of GPS and GSM modules facilitates the rapid notification of emergency services, ensuring prompt assistance in the event of an accident.The core of this system is an Arduino microcontroller, orchestrating the interconnected components to process data and trigger actions based on predefined conditions. The circuit's performance has been rigorously tested, initially through simulation in Proteus software, and subsequently via real-world hardware implementation. Comparative analysis of software and hardware results lends insights into the system's functionality and reliability.The overarching objective of this study is to curtail accidents arising from intoxicated driving, unforeseen driver fatigue, and road obstructions. In instances of accidents, the electronic apparatus employed has the capacity to dispatch spontaneous and precise distress signals to law enforcement and medical personnel, thereby expediting casualty recovery and potentially saving lives. This research advances the fusion of technology and safety measures to augment road safety comprehensively.[6] Upadhyay, V., Gupta, S., Chaturvedi, S. and Singh, D. [2020], ‘Integrated accident prevention detectionand response system (iapdrs)’,International journal of engineering and advanced technology9(3), 2086–2089.[7] Vitkar, S. P., Banare, A. and Nadar, J. [2022], ‘Conceptual framework for accident detection and pre-vention’,Journal of Pharmaceutical Negative Resultspp. 7449–7455.[8] Wu, C., Zhang, P., Zhang, Z., Zheng, W., Xu, B., Wang, W., Yu, Z., Dai, X., Zhang, B. and Zang, K. [2023],‘Slip partitioning and crustal deformation patterns in the tianshan orogenic belt derived from gpsmeasurements and their tectonic implications’,Earth-Science Reviewsp. 104362.[9] Xu, X., Hu, X., Zhao, Y., Lü, X. and Aapaoja, A. [2023], ‘Urban short-term traffic speed prediction withcomplicated information fusion on accidents’,Expert Systems with Applicationsp. 119887.  

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms