A Review of Range-based RSSI Algorithms for Indoor Wireless Sensor Network Localization

The secure localisation of unknown nodes in Wireless Sensor Networks (WSNs) is a crucial research topic due to the vast range of applications of WSNs. These applications drive the development of WSNs, as real-world obstacles typically motivate them. WSN technology is rapidly evolving, and this paper provides a brief overview of WSNs, including key research findings on energy conservation and node deployment. The paper discusses the applications of WSNs in medical health, environment and agriculture, intelligent home furnishing and construction, and military, space, and marine exploration. The paper focuses on the research of RSS-based locating algorithms in WSNs and is divided into two sections. Firstly, accurate location depends on the accurate RSSI received from nodes. This experiment analyses the distribution trend of RSSI and derives the loss model of signal propagation by processing experimental data. Secondly, Gaussian fitting calculates the distance between receiving and sending nodes by processing individual RSSI at different distances. The primary challenge in studying this RSSI range-based technique is the low positioning accuracy, low energy, and high error rate. To solve this problem, a recommended GA is used to find the optimal site by minimising error, providing the best feasible solution, and being energy-sensitive, with accuracy based on the least error inside the network. The proposed approach aims to optimise sensor placements for improved performance

A Survey on Layout Implementation and Analysis of Different SRAM Cell Topologies

Because powered widgets are frequently used, the primary goal of electronics is to design low-power devices. Because of its applications in low-energy computing, memory cell operation with low voltage consumption has become a major interest in memory cell design. Because of specification changes in scaled methodologies, the only critical method for the success of low-voltage SRAM design is the stable operation of SRAM. The traditional SRAM cell enables high-density and fast differential sensing but suffers from semi-selective and read-risk issues. The simulation results show that the proposed design provides the fastest read operation and overall power delay product optimization. Compared to the current topologies of 6T, 8T, and 10T, while a traditional SRAM cell solves the reading disruption problem, previous strategies for solving these problems have been ineffective due to low efficiency, data-dependent leakage, and high energy per connection. Our primary goal is to reduce power consumption, improve read performance, and reduce the area and power of the proposed design cell work. The proposed leakage reduction design circuit has been implemented on the micro-wind tool. Delay and power consumption are important factors in memory cell performance. The primary goal of this project is to create a low-power SRAM cell

Machine Learning Methods for detection of bystanders: A Survey

The number of users on social media networks is increasing day by day as their popularity increases. The users are sharing their photos, videos, daily life, experiences, views, and status updates on different social networking sites. Social networking sites give great possibilities for young people to interact with others, but they also make them more subject to unpleasant phenomena such as online harassment and abusive language, which leads to cyberbullying. Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behavior, and suicide. To minimize the impact of Cyberbullying, the Bystander role is very important. In this paper, a review of the cyberbullying content on the Internet, the classification of cyberbullying categories, classifying author roles (harasser, victim, bystander-defender, bystander-assistant), data sources containing cyberbullying data for research, and machine learning techniques for cyberbullying detection are overviewed.&nbsp

A Review on K-means Clustering Based on Quantum Particle Swarm Optimisation Algorithm

Unsupervised learning clustering techniques play a vital role in data mining, with a wide range of applications in unsupervised classification. Clustering is a method used to categorise data into meaningful groups. The k-means algorithm is a well-known clustering algorithm that aims to minimise the squared distance between feature values of points within the same cluster. In many applications, using an evolutionary computation technique called Quantum Particle Swarm Optimization (QPSO) in conjunction with the k-means algorithm has proven effective in finding suboptimal solutions. In this algorithm, the cluster centres are simulated as particles, allowing for the identification of suitable and stable cluster centres. This paper discusses the current improvement in the QPSO-k-means clustering algorithm, focusing on swarm initialisation and algorithm parameter optimisation. We validate the algorithm using the UCI healthcare dataset and demonstrate its ability to address suboptimal clustering by optimising parameters such as the number of iterations, error rate, and optimal solution for cluster centres. The minimisation factor of the validation parameter indicates the compactness and validity of the clustering algorithm

Enhancing SRAM Cell Circuitry through PDLPDC Optimization

This study focuses on improving static random-access memory (SRAM) cell circuit design by leveraging the Power Dissipation Low Power Dissipation Circuit (PDLPDC). The PDLPDC, a low-power dissipation circuit, has gained widespread use in designing cells for read operations, write operations, and idle modes, contributing to power optimisation in submicron or nano-range Very Large Scale Integration (VLSI) designs. While various SRAM cells, including 6T and 10T configurations, have been developed, they often exhibit higher power consumption. In contrast, our PDLPDC-based approach operates at lower power levels. With the increasing integration of portable devices into everyday life, power optimisation has emerged as a critical challenge in modern VLSI technology. Many contemporary gadgets and systems rely on very Large-scale Integration (VLSI) technology, where static random-access memory (SRAM) blocks occupy substantial chip space and represent a significant source of leakage power in current systems. However, a common practice, scaling the supply voltage of SRAM macros can lead to elevated power dissipation. This research addresses the challenge by efficiently scaling the supply voltage of SRAM macros, resulting in an overall reduction in power dissipation. The study introduces 6T and 10T SRAM circuits that minimise power dissipation during read and write operations while maintaining reasonable performance and stability. The impact of process parameter variations on various design metrics, including read and write power, leakage power, leakage current, and latency, becomes a critical consideration in SRAM cell design with increased integration scale. The proposed circuit, optimised for the minimum power-delay product during read, write, and idle modes, is compared with traditional SRAM cells (6T and 10T) and demonstrates superior performance, reliability, and power efficiency. This research contributes to advancing the understanding of SRAM circuit design, especially in the context of power optimisation and process variations

IMPROVEMENT OF DATA ANALYSIS BASED ON K-MEANS ALGORITHM AND AKMCA

Data analysis is improved using the k-means algorithm and AKMCA. Data mining aims to extract information from a large data set and transform it into a functional structure. Exploratory data analysis and data mining applications rely heavily on clustering. Clustering is grouping a set of objects so that those in the same group (called a cluster) are more similar to those in other groups (clusters). There are various types of cluster models, such as connectivity models, distribution models, centroid models, and density models. Clustering is a technique in data mining in which the set of objects is classified as clusters. Clustering is the most important aspect of data mining. The algorithm makes use of the density number concept. The high-density number point set is extracted from the original data set as a new training set, and the point in the high-density number point set is chosen as the initial cluster centre point. The basic clustering technique and the most widely used algorithm is K-means clustering. K-Means, a partition-based clustering algorithm, is widely used in many fields due to its efficiency and simplicity. However, it is well known that the K-Means algorithm can produce suboptimal results depending on the initial cluster centre chosen. It is also referred to as Looking for the nearest neighbours. It simply divides the datasets into a specified number of clusters. Numerous efforts have been made to improve the K-means clustering algorithm’s performance. Advanced k-mean clustering algorithm (AKMCA) is used in data analysis to obtain useful knowledge of various optimisation and classification problems that can be used for processing massive amounts of raw and unstructured data. Knowledge discovery provides the tools needed to automate the entire data analysis and error reduction process, where their efficacy is investigated using experimental analysis of various datasets. The detailed experimental analysis and a comparison of proposed work with existing k-means clustering algorithms. Furthermore, it provides a clear and comprehensive understanding of the k-means algorithm and its various research directions

STABILITY ANALYSIS OF DFIG WIND POWER SYSTEM USING PI CONTROLLER WITH STATIC FEEDBACK

When wind electricity is associated with an electric grid affects power quality. The consequences of the power quality like active & reactive power, change in voltage, flicker, harmonics, and electric behaviour of switching operations has to measure. The doubly-fed induction generator (DFIG) is used in most wind energy conversion systems (WECS) due to its advantage of ensuring a variable speed and running above the synchronous speed. This characteristic avoids damage to the wind turbine mechanism, especially when the measured wind speed is above the rated speed. In this paper, we present the enhancement behaviour of a DFIG by the PI regulator

Hybrid Approach for Food Recognition Using Various Filters

Food image recognition system has various applications now a day. In this paper we have used machine learning supervised approach and Support Vector Machine to classify different food images. SVM has being classified to detect and recognize food images with least modification. By applying various filters like texture filter, segmentation method, clustering and SVM approach we have achieved more accuracy then other machine learning approaches with manually extract features. Sustenance is an indivisible piece of people groups lives. we tend to apply an convolution neural network(CNN) to the undertakings of analyst work and perceiving sustenance pictures. Be clarification for the wide decent variety of styles of nourishment, picture acknowledgment of sustenance things is typically unpleasantly difficulties. Nevertheless, profound learning has been demonstrated starting late to be a genuinely extreme picture acknowledgment framework, and CNN could be a dynamic approach to manage profound learning. CNN showed on a very basic level higher precision than did old-fashioned help vector-machine-based courses with carefully assembled decisions. For sustenance picture disclosure, CNN likewise demonstrated fundamentally count higher precision than a standard technique. Generally higher precision than standard techniques.Keywords: CNN, texture filter, k-mean clustering, segmentatio

Adaptive-time Synchronization Algorithm for Semiconductor Superlattice Key Distribution

This paper presents a synchronization algorithm for semiconductor superlattice key distribution, which is a symmetric encryption solution, by optimizing the Euclidian distance between the two chaotic waveforms generated in the receiver and the sender, respectively. This algorithm based on time synchronization is capable of reconstructing the generated waveforms in the receiver and the sender within the error of 5% to 6% (given the fact that the waveforms were not perfectly congruent when they were originally created)

A Framework for Enhancing the Energy Efficiency of IoT Devices in 5G Network

A wide range of services, such as improved mobile broadband, extensive machine-type communication, ultra-reliability, and low latency, are anticipated to be delivered via the 5G network. The 5G network has developed as a multi-layer network that uses numerous technological advancements to provide a wide array of wireless services to fulfil such a diversified set of requirements. Several technologies, including software-defined networking, network function virtualization, edge computing, cloud computing, and tiny cells, are being integrated into the 5G networks to meet the needs of various requirements. Due to the higher power consumption that will arise from such a complicated network design, energy efficiency becomes crucial. The network machine learning technique has attracted a lot of interest from the scientific community because it has the potential to play a crucial role in helping to achieve energy efficiency. Utilization factor, access latency, arrival rate, and other metrics are used to study the proposed scheme. It is determined that our system outperforms the present scheme after comparing the suggested scheme to these parameters