Dual Frequency Circular Shaped Two Port MIMO Antenna
Design and Development of Portable Oxygen Concentrator
Design and Simulation of Antenna for Foliage Penetration Application
Performance Enhancement of Microstrip Patch Antenna with Slots for 5G Communication
Ergonomic Wheelchair - Stretcher for Enhanced Patient Mobility
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
This paper focuses on a very pertinent issue in the field of cybersecurity, particularly concerning the increased use of public Wi-Fi and the proliferation of IoT devices. The scenario, where an attacker captures user details through a malicious access point set up through a NodeMCU, is a practical illustration of a Man-in-the-Middle (MitM) attack. These attacks are especially concerning in the context of smart homes and smart cities, where security breaches can lead to severe privacy invasions and disruptions of essential services. The NodeMCU, being a low-cost, open-source IoT platform, is accessible for creating such malicious access points. It can be programmed to mimic legitimate Wi-Fi hotspots to intercept the data of unsuspecting users. This type of attack can enable unauthorized access to sensitive information such as login credentials, personal data, and even control over connected IoT devices. This approach is user-friendly and can significantly reduce the risk of users unknowingly connecting to malicious websites. The contribution of this paper includes a demonstration and analysis of a common network and IoT attack and a novel method for detecting fraudulent URLs, particularly those used in phishing attacks. By addressing these aspects, the paper makes a significant contribution to the field of cybersecurity and helps foster a more secure and resilient network and IoT ecosystem.
Audio cryptography is the practice of encrypting audio data to prevent illegal access to and listening to it. This paper presents an innovative technique of audio cryptography based on the Python computer language. To ensure secrecy and integrity, the suggested system encrypts and decrypts audio signals using advanced cryptographic techniques. A crucial component of AES, the cryptographic key is dynamically created to improve security. Python's broad library support and ease of use make it an ideal platform for implementing the AES algorithm, which ensures dependable and effective audio data encryption. The system utilizes Python's cryptography library for seamless integration and ease of implementation. Simulation results demonstrate the efficacy of the AES algorithm in securely encrypting and decrypting audio data with reduced noise compared to traditional methods.
This paper presents the development and implementation of a Smart Rover, integrating robotics and IoT technologies to create a versatile and efficient robotic platform. The Smart Rover is a versatile robotic platform integrating Bluetooth, voice control, and IoT capabilities for seamless navigation and obstacle avoidance. Powered by an Arduino UNO microcontroller and programmed in C++, the Smart Rover provides a user-friendly interface for remote control through Bluetooth-enabled devices. Voice commands add convenience and accessibility, offering an alternative control method. The onboard sensors facilitate autonomous obstacle detection and avoidance, enabling the Smart Rover to navigate its environment intelligently. The prototype demonstrates successful integration and functionality, offering a compact and sturdy design suitable for various applications. IoT integration expands functionality, allowing the rover to communicate with other smart devices or cloud services, opening up possibilities for data collection, remote monitoring, and collaborative tasks.
In biomedical research, Electromyography (EMG) data play a crucial role as a bridge between human motions and machine interpretation, offering valuable insights into muscle activation. EMG signals give vital information on hand movements in the context of applications like gesture recognition, prosthetic control, and rehabilitation. This paper describes the classification of EMG signals based on muscle motions, which makes it simpler to identify distinct gestures or movements. A Linear Discriminant Analysis (LDA) classifier is used to differentiate between various classes of muscle activity. In order to record EMG signals during hand motions, surface electrodes are carefully positioned on pertinent muscles. Muscle activity may be tracked in real time with these non-invasive electrodes. In order to extract meaningful information from these signals, which are complex and frequently contaminated by noise, strong feature extraction techniques are needed. When working with noisy signals, denoising is a commonly used approach to restoring the original quality of the source data. It attempts to maintain relevant information by reducing noise in the raw EMG signals. In order to retrieve only the pertinent information from the original EMG signal data, any unnecessary noise must first be removed. Through the identification of key characteristics in the time, frequency, and time-frequency domains, it transforms unstructured EMG data. This procedure improves the next step of classification, which is the identification and classification of patterns in the EMG signals. Ultimately, the obtained information is employed to classify signals by the Linear Discriminant Analysis (LDA) classifier, demonstrating a distinction between various muscle motions with over 80% accuracy.
The demand for wireless wideband communications is rising as a result of the need to accommodate more users and deliver more data at faster data rates. Ultra-wideband (UWB) technology, which uses very brief pulses on the order of nanoseconds to cover a very large frequency spectrum, may be able to tackle this problem. UWB is a wireless technology with several potential applications that was created to transmit data at extremely high speeds over very short distances at very low power densities. This paper presents the UWB technology as it currently exists and its potential uses. The question of how to employ this technology without interfering with the current means of communication is also investigated. Comparisons with other wireless technologies highlight UWB's superiority in terms of accuracy, power consumption, and cost-effectiveness. The paper also explores modulation techniques and deployment scenarios, showcasing UWB's capability to revolutionize short-range communication networks and seamlessly integrate with existing wireless infrastructures.
The iris identification technology that is commercially available often uses segmentation to capture images of the eye within the 850-nm electromagnetic radiation spectrum. In this paper, the strongly pigmented iris image is taken with a camera at 12 different wavelengths, ranging from 420 to 940 nm. The goal is to identify the best wavelength band to expand the iris image wavelengths from 420 to 940 nm in NIR (near infrared) for the purpose of densely pigmented iris recognition. A system that obtains data, usually by digitizing analog channels and storing the data in digital form, is primarily anticipated for imaging the iris at narrow spectral bands in the range of 420–940 nm. This system takes pictures within particular wavelength ranges across the electromagnetic spectrum. Then, 200 human black iris that match the left and right eyes of 100 distinct people are obtained for the assessment. The most common wavelength for recognizing an iris with significant pigmentation is based on texture quality assurance, Equivalent Error Rate (EER), and False Rejection Rate (FRR) matching performance. The visual perception of heightened, detailed local iris texture information supports this effect.
