Advancements in MEMS Gyroscopes: Piezoelectric Plate-Based Devices for Enhanced Precision and Stability in Microelectromechanical Systems
Weather monitoring system using Internet of Things
MULTILEVEL THRESHOLDING IMAGE SEGMENTATION USING MIXED STRATEGY IMPROVED CONVERGENCE BASED WHALE OPTIMIZATION ALGORITHM
AN IOT-BASED WEARABLE DEVICE FOR ENSURING SOCIAL DISTANCING USING RSSI TECHNOLOGY
Advanced Image Encryption Algorithm Integrating Chaotic Image Encryption and Convolutional Neural Networks
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
Dual Frequency Circular Shaped Two Port MIMO Antenna
In recent years, 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. We address current challenges in this 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, we propose solutions to enhance the performance of MEMS gyroscopes under varying operational conditions. Finally, we present a case study 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 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 likewise built to showcase and display the data to users. Various parameters are analyzed, including temperature, humidity, pressure, altitude, light intensity, carbon monoxide levels in the air and the detection of rain or snow. The technology gathers data and presents it in a visual style that can be accessed from any location worldwide.
In this work, a new multilevel image segmentation method based on an improved Whale Optimization Algorithm (WOA) is presented. Although WOA has demonstrated potential in a number of optimization problems, its efficacy may be hampered by its vulnerability to local optima. In order to overcome this, we suggest a Mixed-Strategy Improved Convergence WOA (MSICWOA) that strengthens its optimization capabilities by combining a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization approach. After that, the MSICWOA is used with Otsu cross variance and Kapur entropy as objective functions to identify the best thresholds for multilevel grayscale image segmentation. Results from experiments on benchmark functions show that MSICWOA outperforms other optimization methods in terms of search accuracy and convergence speed. Moreover, it successfully overcomes local optima. Experiments on image segmentation using typical datasets verify that the MSICWOA-Kapur technique is effective in precisely and quickly identifying multilevel thresholds.
In response to the COVID-19 pandemic, WHO issued guidelines emphasizing social distancing as a primary preventive measure. Despite recommendations to maintain a 4 to 6 feet distance, adherence remains inconsistent. This research proposes an IoT-based solution using RSSI (Received Signal Strength Indication) for target detection within 1.5 meters. 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 6-foot threshold. The proposed system significantly outperforms existing methods, achieving 57.7% higher accuracy, 26.7% greater sensitivity, and 48.1% improved specificity. It also reduces the false positive rate (FPR) by 89.7% and the false negative rate (FNR) by 89.6%, ensuring precise and reliable social distancing compliance.
With the rapid progress of information technology, ensuring the security of images has become a critical research focus. This study introduces a method that integrates 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. We start by outlining the basic principles of CNN and chaotic image encryption. 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 chaotic sequence generation and nonlinear transformations to scramble pixel values, ensuring secure encryption. These procedures consist of feature extraction, pixel value mapping, key management, and chaotic sequence production. Through the use of CNN to extract high-level image characteristics and dissimilarity operations between the chaotic sequences and image pixel values, the approach achieves high-strength encryption. Lastly, we test the approach experimentally by contrasting it with more conventional chaotic picture encryption techniques. The experimental findings show that the picture encryption technique offers advantages in computing performance and encryption/decryption speed, along with significant enhancements in encryption quality and security.
