Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits
Smart Electrical Vehicle
Development of Smart Electronic System to Implement Smart Home
Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment
Photographing a Black Hole
Development of an Intelligent Battery Charging System Based on PIC16F877A Microcontroller
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Fetal ECG Extraction from Maternal ECG using MATLAB
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
Wearable Health Monitoring Smart Gloves
The cardiovascular diseases such as Arrhythmia and Myocardial Infarction are becoming more alarming in causing heart attacks. The early detection of cardiac related deceases has become an essential activity to save a patient from death. For detecting these cardiovascular diseases useful information is hidden in the ECG waves, it have to be extracted from the ECG signal. In this paper the authors used the Hilbert Transform (HT), Principle Component Analysis (PCA) and Independent Component Analysis (ICA). The Hilbert Transform is useful in providing good resolutions to the ECG and it is able to easily interpret the unknown difficulties in the ECG. The Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were independently applied on Hilbert Transformed ECG signal to enhance the hidden complexities in ECG signal by eliminating non-Gaussian noise elements. Latter the suitable algorithms were applied to detect the fiducial points such as PQRST and perform statistical analysis on ST Interval variability. The authors noticed that the ICA has better performance than the PCA.
Heart attacks mostly occur in people who suffer from heart or heart-relate diseases if these diseases, are not detected early enough and treated problem will be occurred. There is a need for a reliable means of detecting these diseases to save the patients from these attacks which are increasing in proportion all over the world. Electrocardiography (ECG), which measures the electrical activity of the heart, generates a signal referred to as ECG signal or simply ECG and the shape of this signal tells much about the condition of the heart of a patient. Naturally the ECG signal gets distorted by different artifacts which must be removed otherwise it will convey an incorrect information regarding the patient's heart condition. One of the ways to eliminate ECG Artifacts is using Adjustable FIR Digital filters. The authors apply a specific filter which will allow only the desired signal to pass,. Thus the noise will be removed efficiently. In this proposed work the authors calculate the signal to noise ratio of ECG signal by different factors of Adjustable filters
Electrocardiogram (ECG) is a graphical illustration of the cardiac cycle as produced by an electrocardiograph. ECG recordings are indispensable when it comes to monitoring critical cardiac patients, astronauts etc. However, this around-the-clock surveillance results in voluminous ECG data, which becomes difficult to handle. Thus, the basic requisities of minimal usage of data storage space and speedy transmission over channels in tele-medicine fostered research in the field of ECG Data Compression. So far numerous techniques under Direct Data methods of compression like Turning Point (TP), Amplitude Zone Time Epoch Coding (AZTEC), Coordinate Reduction Time Encoding System (CORTES), Scan Along Polygonal Approximation (SAPA), Fan etc. have served the purpose fairly. This work tends to amalgamate the TP and modified AZTEC techniques, providing an efficient hybrid algorithm for compression.
Image steganography is the art of hiding information into a cover image. Steganography gained importance in the past few years due to the increasing need for providing secrecy in an open environment like the internet. The Least Significant Bit (LSB) substitution is the most commonly used spatial domain technique. In LSB substitution technique the least significant bit of each pixel of the cover is replaced by the secret message bits. In transform domain technique, the transform is applied on cover image and the secrete message bits are hidden inside the coefficients of the transformed cover image. Image steganography based on DWT (Discrete Wavelet Transform), is used to transform original image (cover image) from spatial domain to frequency domain. Two dimensional Discrete Wavelet Transform (2D DWT) is performed on a cover image of size performed on the secret messages before embedding. Then each bit of secret message is embedded using LSBMR algorithm in the selected frequency coefficients from Discrete Wavelet Transform. The experimental results show that the algorithm has a high capacity and a good invisibility. Moreover PSNR of cover image with stego-image shows the better results in comparison with other existing steganography approaches. Furthermore, satisfactory security is maintained since the secret message cannot be extracted without knowing rules.
Presently Electromyography (EMG) signals are widely utilized for clinical/biomedical applications, such as disease prognosis and advanced human machine interface. EMG signals are picked from muscles by invasive process or from surface of skin called surface EMG. However acquired from any of the technique it requires important aspect is how to extract useful information from the cached signal for understanding and relating the signal with its relative physical and biological aspects. The reason for this paper is to present analyze the behavior of EMG signal under different transform domains such as frequency and wavelet domain and to relate the coefficients of these domains with the physical and biological aspects of signals. Furthermore the authors point out how the unwanted signals such as noise and other interfering signals can be removed using the different transforms. This paper gives specialists a decent understanding of EMG signals and its investigation methods. This learning can be helpful for creating automated systems for prognosis and man machine interface development.
Diabetes mellitus is a major, and increasing, global problem. The existing method of blood glucose measurement is invasive which requires extraction of blood through a lancing device. This method is painful, potentiality dangerous and expensive to operate. Noninvasive glucose measurement eliminates the painful pricking expensive, risk of infection and damage to finger tissue. Many non-invasive methods for blood glucose monitoring is under study. Optical methods have been developed as the most powerful technique for non-invasive glucose measurement. The NIR spectroscopy method is one of the most promising optical approaches. The spectrum of the blood is obtained from the spectrometer which contains various interfering components. By application of statistical algorithm the interfering substances has to be removed and the peak glucose wavelength has to be determined. The Levenberg-Marquardt algorithm is used to make accurate short-term and long-term blood glucose predictions during the nocturnal period of the daily cycle.