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
Electrocardiography (ECG), which is the measure of the electrical activity of the heart, the shape of this signal tells much about the condition of the heart of the 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. Several simple and efficient LMS and Normalized LMS adaptive filters that are computationally superior having multiplier free weight update loops are used for cancellation of noise in ECG signals. Implementing Hybrid algorithm on ANC provides better performance than adaptive techniques used to enhance the ECG signal. In this work, fidelity parameters like Signal to Noise Ratio (SNR), MSE, and LSE have to be computed.
The objective of this paper is to detect the microcalcifications from the digitized mammograms using support vector machine, based on effective wavelet feature analysis. Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The identification of cancer tissue is prohibited by the poor contrast level of mammograms. In this paper, new approach helps to identify cancer tissue with better accuracy. Microcalcifications are extracted by using wavelet based feature extraction and compared with other feature extraction like Gabor filter based extraction. The results from the feature extraction are classified using support vector machine classifier that provides better performance than other classifiers on wavelet based feature extraction.
One of the most powerful tools for the representation of the classical signal processing methods is algebraic structure. Algebraic Signal Processing (ASP) provides a whole frame for representing the classical signal processing concepts. In ASP, the signal model is defined as a triple (A, M, Φ), where A is a chosen algebra filters, M is an associated A -module of signals, and Φ generalizes the idea of a Z-transform. By using Nearest-Neighbor shift, a new signal model can be developed, i.e., Nearest-Neighbor signal model. The main aim is to represent the Nearest-Neighbor signal in timefrequency domain and to analyze the spectrum. For studying the stationary signals, that is their properties are statistically invariant over time, Fourier analysis can be used, but for a non-stationary signal, it requires both time-frequency representation of the signal for complete analysis. In the context of signal analysis, Wigner-Ville distribution can be used effectively to analyze the time-frequency structures of a non-stationary signal. So Wigner-Ville distribution is used to represent the Nearest-Neighbor signal in time-frequency domain. At last, Wigner-Ville distribution of Nearest-Neighbor signal is simulated and absolute and Relative errors of Nearest-Neighbor signal and Wigner-Ville distribution of Nearest- Neighbor signal are calculated.
Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. EEG recording is highly susceptible to various forms and sources of noise, which present significant difficulties and challenges in analysis and interpretation of EEG data. Noise sources may consist of power line interference, base line noise, random body movements or respiration. A number of strategies are available to deal with noise effectively both at the time of EEG recording as well as during pre-processing of recorded data . In this work, the authors have proposed FrFT based Barlett window to enhance the quality of EEG signal and the fidelity parameters like Signal to Noise Ratio (SNR), MSE, LSE, and sensitivity have to be computed and analyzed in a Matlab environment.
This paper presents the designing of Dual-Band Bandpass Filter (DBBPF). The designed filter consists of two Stepped Impedance Resonators (SIR) of different impedance ratios. These two resonators are coupled parallel to source and load end. Due to source load coupling, several transmission zeros are generated. These transmission zeros will help in the suppression of harmonic responses. Location of transmission zeros can be adjusted by varying the gap between the input-output feed lines. The center frequencies of designed filter are 3.05 GHz and 6.2 GHz, with 3 dB bandwidth of 370 MHz and 1.9 GHz, respectively. In the designed filter, the fractional bandwidth and the center frequencies of both the pass bands can be controlled individually by controlling the impedance ratio. The filter shows better isolation between the pass bands with better out of band rejection without the spurious response. The designed filter has a very simple structure.
ECG analysis continues to play a vital role in the primary diagnosis and prognosis of cardiac ailments. This paper presents a new approach to classification of ECG signals based on feature extraction and Artificial Neural Network (ANN) using Discrete Wavelet Transform (DWT). Nineteen ECG signals from MIT-BIH database were used to test the performance of proposed method. A 97.12% of sensitivity and 94.37% of positive predictivity were reported in this test for QRS complex detection. Arrhythmias detected were bradycardia, tachycardia, premature ventricular contraction, supraventricular tachycardia, and myocardial infarction.
For better design of the FIR filter, it is necessary for the designer to know the drawbacks of all the design methods. In this paper, the authors have compared two algorithms to get a better solution to design an optimum FIR filter. A lot of works have been already done in the design of FIR filter. So the methods and analysis of the algorithms help in the design of the filter, which at least removes all the drawbacks of both the filter design algorithms, which has been discussed in this paper. The papers based on the Parks McClellan algorithm, Particle Swarm Optimization method (PSO), Dynamic and Adjustable Particle Swarm Optimization (DAPSO), Particle Swarm Optimization with Variable Acceleration Factor (PSOVAF) in Linear Phase Digital Low Pass FIR Filter, planned Hybrid algorithm are quick and economical evolutionary algorithms, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Differential Evolution (DE) based algorithms are used to compare them to obtain a solution. Therefore an effective and efficient optimized FIR filter can be designed.