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
Electrocardiogram (ECG) is an important diagnostic tool for the diagnosis of cardiac abnormalities. In this paper, the authors introduce a study on different types of noises, For example, Power Line Interference (PLI), Motion Artifacts, Electrode Contact Noise, Muscle Contraction, Base Line Drift, Electromyography/noise (EMG), Instrumentation Noise, etc. To eliminate the above mentioned noises, various algorithms of adaptive filter are used and authors also used Discrete Wavelet Transform (DWT) to remove Random Artifacts and filter with constant coefficients as because, hum manner is not accurate. To solve this problem, digital filters are used such as Adaptive filters as Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS), sign LMS, sign-sign LMS algorithms and Discrete Wavelet Transform (DWT). The performance of algorithms are evaluated by Signal to Noise Ratio (SNR), Mean Square Error (MSE), Percentage Root Mean Square (%PRD) and Normalized Mean Square (NMSE). In comparison to various adaptive algorithms, SSLMS gives better result for all parameters with MSE = 0.0262, NRMSE = 0.0033 , %PRD = 0.3331, RMSE = 0.331, and SNR = -4.3914 .
In this paper, the authors extracted features of ECG signal using LabVIEW software. The real time ECG signal the authors use, is taken from MIT BIH database in .edf format. The signal is then converted into suitable LabVIEW format using biomedical toolkit provided by NI. The converted signal is then filtered and pre-processed using wavelet transformation technique. ECG features is then extracted which includes P onset, P offset, QRS onset, QRS offset, T onset, T offset, R, P and T wave using the extracted features using which they calculate various parameters like heart rate.
ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection, arrhythmias can be done properly. In other words, we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). Feature extraction of ECG plays a vital role in manual as well as automatic analysis of ECG for the use in specially designed instruments like ECG monitors, Holter tape recorders and scanners, ambulatory ECG recorders and analyzers. In this paper, the study of pattern recognition of ECG is done. The ECG signal generated waveform gives almost all information about activity of the heart. The feature extraction of ECG is by Wavelet transform. This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.
As we know that the capacity limits of single mode fiber has almost reached its maxima, Space Division Multiplexing can be helpful for increasing the data rate requirement. This paper, inferred the transmission of 6 spatial and polarisation modes, each carrying the quadrature-phase-shift-keyed channels over few-mode fiber keeping lower differential group delay. The authors present a Multiple-Input Multiple-Output (MIMO) optical link based on coherent optics and its ability to exploit the inherent information capacity of a multimode fiber. A Coherent implementation differs from previous work in optical MIMO by allowing the system to tolerate smaller modal delay spread yet maintains the necessary diversity needed for MIMO operation. Here in this paper, the authors use Optiwave’s Optisystem software for carrying out required simulations. Optisystem provides Visualizer library consisting of spectrum analyzer, time domain visualizer, power meter, WDM analyzer, Oscilloscope visualizer, etc, which will be helpful in verifying whether the signal has been transmitted efficiently via Few-mode fiber from transmitter section to receiver one with significant flexibility.
The major cause of human loss in Cardiovascular Disease (CVD) is Cardiac problems, that are increasing day-by-day in the world. In order to achieve a great effort and to diagnose the cardiovascular disease, many people use different types of Mobile Electrocardiogram (ECG) in remote monitoring techniques. ECG Feature Extraction acts as an important role in diagnosing most part of the cardiac diseases. Now it has been comprehensively reviewed all way through for feature extraction of ECG signal analyzing, feature extracting, followed by classifying which has been planned a longtime ago. Here the authors have introduced soft computing techniques. To recognize the present situation of the heart, Electrocardiography and is an essential tool, but it is a time consuming process to analyze a continuous ECG signal as it may hold thousands of nonstop heart beats. At this point, the authors convert analog signal in to a digital one, vice versa, and it helps in accurately diagnosing the signal. Aim of this paper is to present a detection of some heat arrhythmias using soft computing techniques.