Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program

Salsa20 based lightweight security scheme for smart meter communication in smart grid

The traditional power gird is altering dramatically to a smart power grid with the escalating development of information and communication technology (ICT). Among thousands of electronic devices connected to the grid through communication network, smart meter (SM) is the core networking device. The consolidation of ICT to the electronic devices centered on SM open loophole for the adversaries to launch cyber-attack. Therefore, for protecting the network from the adversaries it is required to design lightweight security mechanism for SM, as conventional cryptography schemes poses extensive computational cost, processing delay and overhead which is not suitable to be used in SM. In this paper, we have proposed a security mechanism consolidating elliptic curve cryptography (ECC) and Salsa20 stream cipher algorithm to ensure security of the network as well as addressing the problem of energy efficiency and lightweight security solution. We have numerically analyzed the performance of our proposed scheme in case of energy efficiency and processing time which reveals that the suggested mechanism is suitable to be used in SM as it consumes less power and requires less processing time to encrypt or decrypt

Support Vector Machines Study on English Isolated-Word-Error Classification and Regression

Abstract: A better understanding on word classification and regression could lead to a better detection and correction technique. We used different features or attributes to represent a machine-printed English word and support vector machines is used to evaluate those features into two class types of word: correct and wrong word. Our proposed support vectors model classified the words by using fewer words during the training process because those training words are to be considered as personalized words. Those wrong words could be replaced by correct words predicted by the regression process. Our results are very encouraging when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight

Towards Wearable Augmented Reality in Healthcare: A Comparative Survey and Analysis of Head-Mounted Displays

Head-mounted displays (HMDs) have the potential to greatly impact the surgical field by maintaining sterile conditions in healthcare environments. Google Glass (GG) and Microsoft HoloLens (MH) are examples of optical HMDs. In this comparative survey related to wearable augmented reality (AR) technology in the medical field, we examine the current developments in wearable AR technology, as well as the medical aspects, with a specific emphasis on smart glasses and HoloLens. The authors searched recent articles (between 2017 and 2022) in the PubMed, Web of Science, Scopus, and ScienceDirect databases and a total of 37 relevant studies were considered for this analysis. The selected studies were divided into two main groups; 15 of the studies (around 41%) focused on smart glasses (e.g., Google Glass) and 22 (59%) focused on Microsoft HoloLens. Google Glass was used in various surgical specialities and preoperative settings, namely dermatology visits and nursing skill training. Moreover, Microsoft HoloLens was used in telepresence applications and holographic navigation of shoulder and gait impairment rehabilitation, among others. However, some limitations were associated with their use, such as low battery life, limited memory size, and possible ocular pain. Promising results were obtained by different studies regarding the feasibility, usability, and acceptability of using both Google Glass and Microsoft HoloLens in patient-centric settings as well as medical education and training. Further work and development of rigorous research designs are required to evaluate the efficacy and cost-effectiveness of wearable AR devices in the future

Path optimization using genetic algorithm in laser scanning system

Laser marking is superior in quality and flexibility to conventional marking techniques such as ink stamping marking. The beam deflected scanning has been commonly applied in the industries. In this paper, the genetic algorithm (GA) has been proposed to optimize the scanning sequence, thus shortening the required laser scanning path. The GA would base on the real-number representation, namely Real-Coded Genetic Algorithm (RCGA). It employs the Dynamic Variable Length Two Point Crossover (DVL-2PC). The simulation results indicating that the proposed algorithm has good convergent speed and manage to solve the sequencing problem efficiently

An Innovative Approach for Environmental Monitoring by Solar Powered Kite

This study investigates the design of a solar powered kite equipped with sensors for any environmental data monitoring, such as, temperature, pressure and so on towards the elevated environment. The developed prototype transforms a traditional kite with a unique design approach that involves the upward height measurement, energy harvesting by solar cells as well as data transmission via wireless network. However, the results from initial monitoring shows only the vertical mapping of ambient temperature as the test case. The developed system can successfully sense and display the temperature data from various height within a certain range as found in the initial investigation. Therefore, upon monitoring various environmental parameters at any cases or during emergency situations using the solar-kite as the simple tool, decision can be made to take appropriate measures against any detrimental changes of the environment by other means

The approach of value innovation towards superior performance, competitive advantage, and sustainable growth: A systematic literature review

The value innovation strategy of pursuing differentiation and low cost has diverged and correlated with various notions and perspectives, which adds complexity and ambiguity to the current knowledge of value innovation. Thus, this study uses a systematic literature review methodology to identify key scientific contributions to the field of value innovation by providing a structured reliable overview of the current knowledge. This study aims to integrate the findings of previous research on value innovation to identify where conclusions converge and diverge and highlight emerging trends and gaps in the literature. This study seeks to answer the research question, “How can value innovation be an approach for superior performance, competitive advantage, or sustainable growth?” In this context, results are achieved through analyzing and synthesizing 73 empirical articles on value innovation literature published from 1997 to January 2021. Particularly, this study contributes to the extant literature by providing an integrative framework that summarizes the literature findings and addressing thematic classifications of the value innovation process. This study also helps further improve research on value innovation by identifying gaps and suggesting a conceptual model to mitigate those gaps

A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions

Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies