Advanced Ration Distribution System using RFID and Two Step Authentication
Performance Comparison of ECG Signal Compression using EMD and DCT
Exploring Diverse Wavelet Approaches and Multi-Level Strategies for Image Compression
Motion Activated Security Camera System with Live Feed using Raspberry Pi
Comprehensive Respiratory Monitor with Exhalation Temperature Sensor
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Fetal ECG Extraction from Maternal ECG using MATLAB
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
Advanced Ration Distribution System using RFID and Two Step Authentication
The motivation behind this paper stems from the persistent issues of inefficiency, fraud, and lack of transparency in traditional ration distribution systems. This research is significant as it addresses a critical gap in the Public Distribution System (PDS) by introducing a secure, automated solution that minimizes human intervention. By integrating technologies such as RFID-based identification and OTP verification, the system ensures that only verified beneficiaries receive their rations, thereby curbing unauthorized access and reducing manual errors. The core problem tackled is the misallocation and misuse of subsidized goods due to outdated manual processes. To bridge this gap, the system leverages an Arduino Uno microcontroller, GSM communication, and precise load measurement to provide a streamlined and accountable distribution process. While the methodology involves IoT-based automation and real-time communication, it is designed to function reliably with minimal complexity. Results indicate a significant improvement in authentication accuracy and operational transparency, demonstrating the system's potential to modernize ration delivery and enhance trust in public welfare services.
This paper compares the two different types of decomposition of ECG signal. The two types of decomposition used are Empirical Mode Decomposition (EMD) and Discrete Cosine Transform (DCT) based decomposition. The performance evaluation of these two decomposition is done on the basis of Compression Ratio (CR) and Percent Root Mean Square difference (PRD).
This paper evaluates various wavelet-based techniques and multi-level decomposition strategies for efficient image compression, emphasizing the performance of four prominent wavelet families: Haar, Daubechies, Biorthogonal, and Meyer. Image compression aims to reduce the data required to represent an image while maintaining its visual quality, a crucial aspect of digital imaging. Wavelet-based compression algorithms are widely recognized for achieving high compression ratios with minimal loss of information, making them ideal for this purpose. The paper systematically applies each wavelet type to a set of standard images, performing compression at different decomposition levels to analyze the impact on both compression efficiency and image quality. Haar wavelets, noted for their simplicity and computational efficiency, are used as a baseline. Daubechies and Biorthogonal wavelets are evaluated for their ability to provide more detailed reconstructions, while Meyer wavelets are examined for their smoothness and compact support, which contribute to image fidelity preservation during compression. The study assesses performance using metrics such as Mean Square Error (MSE), Maximum Error (ME), L2 Norm ratio, Peak Signal-to-Noise Ratio (PSNR), Bits Per Pixel (BPP), and Compression Ratio (CR). The results indicate that multi-level decomposition, combined with appropriate wavelet selection, significantly enhances image compression performance. These findings offer valuable insights for selecting optimal wavelet approaches in image processing applications.
This study focuses on building a motion- activated security camera system using the Raspberry Pi 4 Model B as the main controller. The system combines a USB camera, buzzer, LED, and a light bulb to detect motion and respond in real-time. When movement is noticed, the camera records video, while alerts are sent visually and audibly. Users also receive an email with details of the event. The setup is cost- effective, easy to maintain, and suitable for both homes and small businesses. Future improvements like AI-based threat detection and smart responses are also considered.
This paper outlines the creation and assessment of a detailed monitoring system that includes exhalation temperature, aiming to improve precision of respiratory evaluations. Conventional respiratory monitoring systems typically depend on metrics like respiratory rate and tidal volume; however, these figures may not offer a complete understanding of respiratory health, particularly for individuals with chronic respiratory ailments. The exhalation sensor serves as a proxy for airflow and the thermal regulation of the respiratory system, allowing for real-time measurement of the temperature of exhaled air. This device continuously tracks exhalation temperature alongside standard respiratory metrics, resulting in a more comprehensive perspective on lung function. Clinical trials involving patients with asthma and COPD discovered that shifts in exhalation temperature corresponded with variations in respiratory effort, particularly during flare-ups. The capability to detect respiratory distress in real-time enabled swift medical interventions. The technology demonstrated minimal delays in alerting healthcare providers and achieved over 95% accuracy in recording respiratory metrics and exhalation temperature. Furthermore, the application of machine learning techniques improved the system's predictive capabilities, allowing it to anticipate respiratory episodes based on previous data. This paper emphasizes the promise of monitoring exhalation temperature as a holistic approach to managing respiratory health, ultimately improving both diagnostic precision and patient outcomes.
