Multilevel Thresholding Image Segmentation using Mixed Strategy Improved Convergence Based Whale Optimization Algorithm
Advanced Image Encryption Algorithm Integrating Chaotic Image Encryption and Convolutional Neural Networks
Weather Monitoring System using Internet of Things
Advancements in MEMS Gyroscopes: Piezoelectric Plate-Based Devices for Enhanced Precision and Stability in Microelectromechanical Systems
An IoT-Based Wearable Device for Ensuring Social Distancing using RSSI Technology
The Impact of Substrate Doping Concentration on Electrical Characteristics of 45nm Nmos Device
A Study on Globally Asynchronous and locally Synchronous System
Method of 2.5 V RGMII Interface I/O Duty Cycle and Delay Skew Enhancement
Performance Analysis of Modified Source Junctionless Fully Depleted Silicon-on-Insulator MOSFET
Automatic Accident Detection and Tracking of Vehicles by Using MEMS
Efficient Image Compression Algorithms Using Evolved Wavelets
Computer Modeling and Simulation of Ultrasonic Signal Processing and Measurements
Effect of Nano-Coatings on Waste-to-Energy (WTE) plant : A Review
ANFIS Controlled Solar Pumping System
Advanced Image Encryption Algorithm Integrating Chaotic Image Encryption and Convolutional Neural Networks
This paper presents a novel multilevel image segmentation method that leverages an enhanced Whale Optimization Algorithm (WOA). While WOA has shown promise in solving various optimization problems, its performance can be limited by susceptibility to local optima. To address this challenge, a Mixed-Strategy Improved Convergence WOA (MSICWOA) is proposed, which enhances the algorithm's optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization technique. The MSICWOA is then applied alongside Otsu's cross- variance and Kapur entropy as objective functions to determine optimal thresholds for multilevel grayscale image segmentation. Experimental results on benchmark optimization functions demonstrate that MSICWOA outperforms traditional optimization methods in terms of both search accuracy and convergence speed, effectively overcoming local optima. Furthermore, image segmentation experiments on standard datasets validate the effectiveness of the MSICWOA-Kapur method in quickly and accurately identifying multilevel thresholds.
ABSTRACT With the rapid growth of information technology, safeguarding the security of images has become a crucial area of study. This study introduces a method that combines chaotic image encryption with convolutional neural networks (CNNs) to enhance both security and efficiency. To create strong image encryption, the approach combines the sophisticated feature extraction capabilities of a CNN model with the randomness and nonlinear mapping of chaotic sequences. The basic principles of CNN and chaotic image encryption are outlined. A Convolutional Neural Network (CNN), a deep learning model characterized by weight sharing and a local perceptual field, effectively reconstructs high-level image features. Meanwhile, chaotic image encryption leverages nonlinear transformations and chaotic sequence generation are used to jumble pixel values, ensuring secure encryption. These procedures consist of feature extraction, pixel value mapping, key management, and chaotic sequence production. To accomplish high-strength encryption, CNN is used to extract high-level picture properties and perform difference actions among the chaotic patterns and image pixel values. Lastly, the approach is tested experimentally by contrasting it with more conventional chaotic picture encryption techniques. The experimental findings show that the picture encryption technique offers advantages in computational efficiency and the speed of encryption and decryption, along with significant enhancements in encryption quality and security.
In this paper a portable Weather Monitoring system using Internet of Things (IoT) along with cloud for data storage has been presented. The objective for this paper is to make an efficient, accurate and portable system that measures meteorological factors. The form of technology presented in this work is an advanced system for monitoring and detecting local meteorological conditions and making the data accessible from anywhere in the globe. The technology that is presented here is IoT which is a network of interconnected physical devices embedded with sensors, software and other technologies to collect along with exchanging data over the internet. The other technology is Wireless Network System (WSN) which are dynamic, distributed networks comprising physically separated autonomous sensors that collaboratively monitor the environment, objects and their interactions. The system uses an Arduino UNO board, sensors and a WIFI module to communicate data to cloud computing services. A web page is also developed to showcase and display the data to users. Various parameters are analyzed, including temperature, humidity, pressure, altitude, light intensity, carbon monoxide levels, and the detection of rain or snow. The technology collects this data and presents it in a visual format that can be accessed from anywhere in the world.
Micro-Electromechanical Systems (MEMS) gyroscopes have gained significant attention due to their compact size, low cost, and versatility in various applications, ranging from consumer electronics to aerospace and automotive systems. Among the different MEMS gyroscope designs, piezoelectric plate-based gyroscopes have emerged as a promising solution for achieving high sensitivity and precision in angular rate measurements. This paper explores the latest advancements in MEMS gyroscope technology, with a particular focus on the design, working principles, and potential applications of piezoelectric plate-based gyroscopes. This study addresses current challenges in the field, including accuracy, stability, and thermal performance, while presenting a detailed analysis of the mechanical and electrical characteristics of piezoelectric gyroscopes. Through a series of experiments and mathematical formulations, solutions are proposed to enhance the performance of MEMS gyroscopes under varying operational conditions. Finally, a case study is presented, demonstrating the application of piezoelectric MEMS gyroscopes in real-world scenarios, such as automotive safety systems and drone navigation, highlighting their impact on precision measurement technology.
In response to the COVID-19 pandemic, WHO issued guidelines emphasizing social distancing as a primary preventive measure. Despite recommendations to maintain a certain distance, adherence remains inconsistent. This study proposes an IoT-based solution using RSSI (Received Signal Strength Indication) for target detection within a specific range. If a breach occurs, an indicator alerts the individual. Unlike many studies focusing on contact tracing, this approach emphasizes proactive social distancing. The portable device continuously monitors and alerts individuals when they fall below the threshold. The proposed system outperforms existing methods, ensuring precise and reliable social distancing compliance.
