Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial operations.Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization.Network infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node distribution.This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and design.By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation.Through implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network operation.We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world problems.Being the most efficient in the optimization process,Biruni displays the lowest error rate at 0.00032.The two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific problems.Due to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made.Through delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

Graphics Evolutionary Computations in Higher Order Parametric Bezier Curves

This work demonstrates in practical terms the evolutionary concepts and computational applications of Parametric Curves.Specific cases were drawn from higher order parametric Bezier curves of degrees 2 and above.Bezier curves find real life applications in diverse areas of Engineering and Computer Science,such as computer graphics,robotics,animations,virtual reality,among others.Some of the evolutionary issues explored in this work are in the areas of parametric equations derivations,proof of related theorems,first and second order calculus related computations,among others.A Practical case is demonstrated using a graphical design,physical hand sketching,and programmatic implementation of two opposite-faced handless cups,all evolved using quadratic Bezier curves.The actual drawing was realized using web graphics canvas programming based on HTML 5 and JavaScript.This work will no doubt find relevance in computational researches in the areas of graphics,web programming,automated theorem proofs,robotic motions,among others.

Analytical Load Balancing Model in Distributed Open Flow Controller System

The Software Defined Network (SDN) is a concept based on a decoupling between the control plan and the data plan of a network. Thus, the network becomes programmable and can be coupled to the business applications of the users. The study that is discussed in this article looks at load planning and balancing in distributed controllers. To do this, a model and theoretical methods of performance evaluation related to appropriate software tools, to predict and control the quality of service offered to users is exposed. This paper exposed also a distributed architecture of controllers and then a module based on an adaptive load balancing algorithm that is fault tolerant and fluctuates controller loads. The experiments show a significant gain in efficiency of our solution.

A review of artificial intelligence for EEG-based brain-computer interfaces and applications

The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment,making brain–computer interface(BCI)top interdisciplinary research.Furthermore,with the modern technology advancement in artificial intelligence(AI),including machine learning(ML)and deep learning(DL)methods,there is vast growing interest in the electroencephalogram(EEG)-based BCIs for AI-related visual,literal,and motion applications.In this review study,the literature on mainstreams of AI for the EEG-based BCI applications is investigated to fill gaps in the interdisciplinary BCI field.Specifically,the EEG signals and their main applications in BCI are first briefly introduced.Next,the latest AI technologies,including the ML and DL models,are presented to monitor and feedback human cognitive states.Finally,some BCI-inspired AI applications,including computer vision,natural language processing,and robotic control applications,are presented.The future research directions of the EEG-based BCI are highlighted in line with the AI technologies and applications.

Design and simulation of a standing wave oscillator based PLL

A standing wave oscillator(SWO) is a perfect clock source which can be used to produce a high frequency clock signal with a low skew and high reliability. However, it is difficult to tune the SWO in a wide range of frequencies. We introduce a frequency tunable SWO which uses an inversion mode metal-oxide-semiconductor(IMOS) field-effect transistor as a varactor, and give the simulation results of the frequency tuning range and power dissipation. Based on the frequency tunable SWO, a new phase locked loop(PLL) architecture is presented. This PLL can be used not only as a clock source, but also as a clock distribution network to provide high quality clock signals. The PLL achieves an approximately 50% frequency tuning range when designed in Global Foundry 65 nm 1P9 M complementary metal-oxide-semiconductor(CMOS) technology, and can be used directly in a high performance multi-core microprocessor.

GPSPiChain-Blockchain and AI based Self-Contained Anomaly Detection Family Security System in Smart Home

With advancements in technology, personal computing devices are better adapted for and further integrated into people’s lives and homes. The integration of technology into society also results in an increasing desire to control who and what has access to sensitive information, especially for vulnerable people including children and the elderly. With blockchain rise as a technology that can revolutionize the world, it is now possible to have an immutable audit trail of locational data over time. By controlling the process through inexpensive equipment in the home, it is possible to control whom has access to such personal data. This paper presents a block-chain based family security system for outdoor tracking and in-house monitoring of users’ activities via sensors to detect anomalies in users’ daily activities with the integration of Artificial Intelligence (AI). For outdoor tracking the locations of the consenting family members’ smart phones are logged and stored in a private blockchain which can be accessed through a node installed in the family home on a computer. The data for the whereabouts and daily activities of family members stays securely within the family unit and does not go to any third-party organizations. A Self-Organizing Maps (SOM) based smart contract is used for anomaly detection in users’ daily activities in a smart home, which notifies emergency contact or other family members in case of anomaly detection. The approach described in this paper contributes to the development of in-house data processing for outdoor tracking, and daily activities monitoring and prediction without any third-party hardware or software. The system is implemented at a small scale with one miner, two user nodes and several device nodes, as a proof of concept;the technical feasibility is discussed along with the limitations of the system. Further research will cover the integration of the system into a smart-home environment with additional sensors and multiple users, and ethical implementations of tracking, especially of vulnerable people, via the immutability of blockchain.

Editorial Special Issue on AI-enabled System Simulation and Modelling

Al-enabled system simulation and modelling has become useful tools to facilitate humans to understand systems in different do-mains,such as physics,astrophysics,chemistry,biology,economics,engineering and social science.A complex system is featured with a large number of interacting components(agents,processes,etc.),whose aggregate activities are nonlinear and self-organized.Complex systems are hard to be simulated or mod-elled by using traditional computational approaches due to complex relationships among system components,distributed features of resources,and dynamics of environments.Meanwhile,agent and multi-agent systems have demonstrated advantages and great potentials in modelling and simulating complex systems.