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
In this paper, efficient least mean squares , normalized least mean squares and sign least mean squares algorithm are proposed for removing artifacts preserving the low frequency components and tiny features of the ECG. The proposed implementations are suitable for applications requiring large signal to noise ratios with fast convergence rate. The sign least mean squares algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS and NLMS algorithm. Finally, we have applied this algorithm on ECG signals and compared its performance with the LMS and NLMS algorithms. The results show that the performance of the sign least mean squares algorithm is superior to the LMS and NLMS algorithms.
The Fractional Fourier transform (FRFT) is the generalization of the classical Fourier transform (FT). The FRFT was introduced about seven decades ago as literature reveals. It appears that is was remained largely unknown to the signal image processing community to which it may be potentially useful. Here, introduces a novel derivation for FRFT to extract the spectral parameters like Maximum Side Lobe level Attenuation(MSLA), Half Band Width(HBW),Side Lobe Fall Of Ratio(SLFOR) of Dirichlet and Blackman window functions. And also an attempt is made to get the spectral characteristics of all existing windows like Dirichlet, Bartlett, Hanning, Bohman etc., with generalized equation which consists of FRFT of Dirichlet, Blackman window functions using Alaloui operator.
ECG feature extraction stage is a significant job in diagnosing most of the cardiac diseases after the preprocessing of the ECG signal. Features extracted from ECG are extremely useful in diagnosis. In the previous work to detect the QRS complex wavelet multi-resolution analysis, threshold consideration is used. There has been proposed a structure for detection of the QRS complexes of the ECG signals with the help of Virtual Instruments (VI) of LabVIEW for the standard MIT- BIH arrhythmia database. This structure detects the various features of QRS complex. This paper deals with a resourceful composite method which has been proposed for detrending, denoising and feature extraction of the ECG signals. The proposed structure first employed a wavelet-based detrending and denoising of the ECG signal. Then execute a novel ECG feature extractor. The proposed feature extractor consists of virtual instrument of LabVIEW like Read Biosignal VI, Extraction Portion of signal express VI, Waveform Max-Min VI etc. The Waveform Max-Min VI and Extraction Portion of signal express VI is the alternative of the Peak detector VI without any threshold calculation. Various features like QR level, RS level, QR slope and RS slope etc has been detected by proposed structure. LabVIEW 2013 version has been used here to design the feature extractor.
To solve the conflicts between spectrum scarcity and spectrum under-utilization, cognitive radio (CR) technology has been recently proposed. It can improve the spectrum utilization by allowing secondary networks (users) to make use of unused radio spectrum from primary licensed networks (users) or to share the spectrum with the primary networks (users). As an intelligent wireless communication system, a cognitive radio is sentient of the radio frequency environment. It selects the communication parameters such as carrier frequency, bandwidth, and transmission power to optimize the spectrum usage and adapts its transmission and reception accordingly. Cooperative spectrum sensing is considered where multiple cognitive radios detect the spectrum holes collaboratively through energy detection and examine the optimality of cooperative spectrum sensing. The aim is to optimize the detection per-formance in an efficient way. The optimal voting rule is derived for any detector functional to cooperative spectrum sensing. Also optimize the detection threshold when energy detection is in use. To conclude, we propose a spectrum sensing algorithm for the network which requires smaller quantity than the total number of cognitive radios in cooperative spectrum sensing while fulfilling a given error hurdle.
The Design of Infinite Impulse Response (IIR), basically are of two type. In literature ,one is Butterworth and second one is Chebyshev. Digital IIR Filters are derived from analog IIR Filters by different Laplace(S) to Z –Transformation techniques, out of which the most efficient techniques are differentiation approximation, Al-Alaoui and bilinear transformations. In our proposals an attempt is made to develop digital IIR Lowpass, high Pass and band pass filters from novel S-Z transformations .Which will be derived from Finite Impulse Response (FIR) windows. The performance of the proposed S-Z Transformation is verified by the comparison of differentiators.
One of the most significant problems in many application areas is to extract the required signal from background noise. Background noise is random, the occurrence of signal and the behaviour of signal are also random. Therefore, it is practical to deal with the signal extraction problem using methods based on probability theory and statistical estimation. That is to say that signal detection and parameter estimation problems are statistical hypothesis testing problems in mathematical statistics. In this paper, signal and noise environment is encountered in active sonar system for target detection. The optimum receiver is presented for range-Doppler-shift processing in a background-noise-limited environment using linear frequency modulation pulse .FFT based implementation for target detection of active sonar system using MATLAB has been shown here.