A Critical Review of Various Peak Detection Techniques of ECG Signals

Vol. 4  Issue 3
Year:2016
Issue:Jul-Sep
Title:A Critical Review of Various Peak Detection Techniques of ECG Signals
Author Name:Desh Deepak Gautam and V.K.Giri 
Synopsis:
ECG is an important signal which is most commonly used for the diagnosing of various heart diseases. The analysis of an ECG signal includes preprocessing and feature extraction. Signal processing of an ECG wave, which includes noise reduction and R-Peak detection of the signal, is one of the most important part for its analysis. The presented paper discusses several techniques of noise reduction and R-Peak detection which were proved effective in last few decades. Efficiency of various methods can be defined in terms of detection error rate. Latest research has shown very effective results with error rate less than 0.3%.

Design of 2D-DCT and Quantization Using Dadda and Vedic Multipliers

Vol. 4  Issue 3
Year:2016
Issue:Jul-Sep
Title:Design of 2D-DCT and Quantization Using Dadda and Vedic Multipliers
Author Name:P.Vinay Mallik and G.Hemachandra
Synopsis:
In this generation of Internet of Things (IoT), a lot of image processing algorithms are applied on high resolution displays which are used in mobile devices of various sizes. It becomes vital to design a high speed and low-power image processing algorithm for high speed transmission and processing of data. This paper proposes a progressive design of 2D-DCT and quantization which is one of the abundantly used image processing algorithm and is realized using Dadda and Vedic multipliers which work in real time exceptionally in both parallel and pipelined process for calculating 2D-DCT. The high speed, accuracy and less hardware complexity of the proposed systems outclass with those of other presentday systems. The frequency of proposed system is increased to 185.048 which is 19% when compared to the prevailing systems. The proposed system architecture can be easily modified to compute 2D-IDCT which decompress the coefficients to get the image value.

Melanoma Region Detection from Dermoscopy Images with Hybrid Technique using Gaussian Mixtures and Fuzzy Clustering

Vol. 4  Issue 3
Year:2016
Issue:Jul-Sep
Title:Melanoma Region Detection from Dermoscopy Images with Hybrid Technique using Gaussian Mixtures and Fuzzy Clustering
Author Name:Ruchika Sharma, Pankaj Mohindru, and Pooja
Synopsis:
The malignant melanoma is a deadly form of the skin cancer in humans. It develops quickly, and effortlessly metastasizes. Late identification of the dangerous melanoma is in charge of 75% of deaths connected with skin growths. Early diagnosis is an important factor that increases chances of successful cure as there is a rapid course of the disease. Computer analysis and image processing are efficient tools supporting quantitative medical diagnosis. Therefore it is relevant to develop computer based methods for dermatological images. So, in order to get the effective results and information about distinctive stages of the infected portion, one needs the corresponding features of that particular area in order to decide the stage. So, the feature extraction phase is hugely dependent on the region detected which has the disease. So appropriate segmentation algorithm is required which can affectivity detect the skin melanoma pixels in the information image. In this work, an algorithm is presented which can adequately detect the pixels having melanoma region and ordinary skin. The proposed work uses a hybrid technique in which space complexity of intensity values is reduced by taking pre-segmentation results from Gaussian mixtures posterior algorithm. The algorithm first chooses some candidates from different regions of the images having distinctive intensity values and then Gaussian models are built from the chosen places by taking their neighborhood pixels. After this, posterior testing is carried out to get pre-segmented results. In the end neural network based training and testing is implemented to get final segmentation results. Experimental results show that the proposed algorithm gives 98% accuracy results on the tested database images.

Smart Antennas for Next Generation Mobile Communication

Vol. 4  Issue 3
Year:2016
Issue:Jul-Sep
Title:Smart Antennas for Next Generation Mobile Communication
Author Name:Veerendra and Md. Bakhar 
Synopsis:
In this paper, the authors analyzed the performances of well known array signal processing algorithms for adaptive beam forming namely, Sample Matrix Inversion (SMI), and Least Mean Square (LMS) algorithms. From the simulation results, it is observed that, for SMI as the number of user increases, the main lobe spreads in all the directions. As a result of this, beam forming in the user direction may not be accurate. To overcome this problem, LMS algorithm can be used. The LMS algorithm is a best choice in most of the commercial wireless applications due to its low complexity. 5G and beyond 5G networks require beam forming algorithms with high directivity and fast converengence rate. Hence, in this work, they have modified the LMS algorithm to improve the converengence rate and then they applied improved LMS algorithms to horizontal-vertical Uniform Linear Array (ULA). This is also called as two dimensional ULA (2D-ULA). Hence these algorithms are called as 2D-LMS1 and 2D-LMS2. Simulation results show that the proposed algorithms have improved convergence rate, directivity as compared to conventional LMS algorithm. Hence these algorithms are best suited for 5G and beyond mobile communications.

Design of An Adaptive Noise Cancellation System in A Time Varying, Non-Linear Automobile Environment

Vol. 4  Issue 3
Year:2016
Issue:Jul-Sep
Title:Design of An Adaptive Noise Cancellation System in A Time Varying, Non-Linear Automobile Environment
Author Name:S. Thilagam and P. Karthigaikumar
Synopsis:
There are various types of noises in an automobile. It includes tire noises, belt noises, noises due to braking systems, and engine. The engine noise reduction techniques play a major role to improve the life and efficiency of the engine. However, these noises need to be reduced and cancelled in a real-time environment using statistical Digital Processing techniques. This paper proposes the use of adaptive filters using LMS and RLS algorithms to cancel the engine noise. The performance analysis is done using MATLAB through proven simulation results and by comparing the parameters like Mean Square Error, Convergence speed, and Stability of the system.

Few-Mode Fibers Supporting 6 Spatial and Polarisation Modes for Mode-Division-Multiplexed Transmission with MIMO DSP

Vol. 4  Issue 2
Year:2016
Issue:Apr-Jun
Title:Few-Mode Fibers Supporting 6 Spatial and Polarisation Modes for Mode-Division-Multiplexed Transmission with MIMO DSP
Author Name:Anuja Mishra and Sharad Mohan Shrivastava
Synopsis:
Next-generation communication evolution towards 4G promises to meet the demands for ubiquitous communication. MIMO technology convolved with Optical fiber has become a mandatory requirement to achieve the goals of future communications. This paper describes the System design methodology for MIMO DSP platform. Software simulations provide flexibility, hence the authors simulate results using a software named “Optiwave's Optisystem”. The authors propose a transmission fiber for mode-division multiplexed transmission with multiple-input multiple-output (MIMO) digital signal processing supporting six spatial and polarisation modes. To increase the transmission capacity of fewmode fiber (FMF) drastically, Wavelength-Division Multiplexing Mode-Division Multiplexing (WDM-MDM), an optical MIMO technique, can be applied. With WDM-MDM, different mode groups, propagating in FMF are used to carry different information.

Image Edge Preserving Filter with Impulse Noise Detection and Removal

Vol. 4  Issue 2
Year:2016
Issue:Apr-Jun
Title:Image Edge Preserving Filter with Impulse Noise Detection and Removal
Author Name:Neelima, Pankaj Mohindru and Pooja Mohindru
Synopsis:
This paper illustrates a new non-linear image edge-preserving filter, used for the detection and elimination of immense frequency impulse noise from digital images. Schemed method subsists of two stages i.e., pixel position detection and filtering. Simulation analysis has been performed on distinct images and results have been demonstrated in comparison to some previously designed filters. The proposed scheme works in a satisfactory way even upon noise densities in a high range of 95% to 97% in both visual presentation and quantitative measures. This designed filter preserves the edges of images with high degree of accuracy. Performance parameters such as PSNR, MAE, RMSE, and SSIM are better than most of the schemes currently in use.

A New Proposed Window for Designing FIR Low Pass Filter Using MATLAB

Vol. 4  Issue 2
Year:2016
Issue:Apr-Jun
Title:A New Proposed Window for Designing FIR Low Pass Filter Using MATLAB
Author Name:Manjinder Kaur and Sangeet Pal Kaur
Synopsis:
This paper presents a new window for designing FIR low pass filter. While designing a FIR filter, there are two important parameters which must be taken care of which are transition width and ripples. Transition width should be as small as possible and ripples should be less both in pass band as well as stop band. There are different windows like Rectangular, Triangular, Hanning, Hamming, Blackman, etc., which are used for filter designing. The proposed window which is a modified form of Blackman window contains four cosine parameters and the simulation result shows that this window has better response when compared to Blackman Window.

An Approach of ECG Analysis for Diagnosis of Heart Diseases

Vol. 4  Issue 2
Year:2016
Issue:Apr-Jun
Title:An Approach of ECG Analysis for Diagnosis of Heart Diseases
Author Name:Ekta Gajendra and Jitendra Kumar
Synopsis:
In Today's World, heart problems are the major health concern people are facing. In order to prolong life, one must be fit and should keep a check on their health. Cardiac Arrhythmia is one such heart condition, which can be diagnosed from the persons ECG (Electrocardiogram). ECG is the graphical representation of the heart's electrical activity. Any change in the waveform of the ECG depicts change in the functioning of it, which can be used as a diagnostic measure. ECG Classification is done in 3 stages, first is de-noising of the ECG signal, in the second step, the authors perform feature Extraction and finally Classifies it. The authors use FIR Filter for signal de-noising and then, they have tried to do the analysis using DOM (Difference Operation Method) for feature extraction and LDA (Linear Discriminant Analysis) for the classification of ECG signal to predict if he/she is vulnerable to any such heart condition or not. On using the above named methods the authors achieve an accuracy of 98.077% and a sensitivity of 98.009%.

Fetal ECG Extraction Using Wavelet and Adaptive Filtering Techniques

Vol. 4  Issue 2
Year:2016
Issue:Apr-Jun
Title:Fetal ECG Extraction Using Wavelet and Adaptive Filtering Techniques
Author Name:K. Purushotham Prasad and B. Anuradha
Synopsis:
The electrocardiogram (ECG) signal is a graphical recording of the electrical potentials generated in association with heart activity, which is one of the many important physiological signals commonly used in clinical practice. The wellbeing and status of a fetus can be accessed from a fetal ECG signal. Detecting and analysing the fetal ECG is the primary objective of electronic fetal monitoring. This paper presents a method to separate fetal ECG signals from the maternal abdomen. The method is general and is able to separate the fetal ECG signals using any number of recording electrodes, including the difficult case of single channel. This approach is based on some simple mathematical model using convolution matrix and with the help of maternal ECG, the fetal ECG signals can be extracted from the maternal abdomen. Further, the fetal ECG signals are passed through wavelets and adaptive filter to reduce the noise in the fetal ECG. The results show that the proposed method has a promising performance.

Forecast and Explication of ECG Signal Ongoings Using Soft Computing Techniques

Vol. 4  Issue 1
Year:2016
Issue:Jan-Mar
Title:Forecast and Explication of ECG Signal Ongoings Using Soft Computing Techniques
Author Name:Santosh Kumar Suman,Mayank Kumar Gautham and Vinod Kumar Giri 
Synopsis:
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.

Mode-Division Multiplexing over Few-Mode Fiber Using Coherent MIMO Digital Signal Processing

Vol. 4  Issue 1
Year:2016
Issue:Jan-Mar
Title:Mode-Division Multiplexing over Few-Mode Fiber Using Coherent MIMO Digital Signal Processing
Author Name:Anuja Mishra, Sharad Mohan Shrivastava, Pooja Sharma, Prachi Agrawal and Rahul Parganiha
Synopsis:
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.

A Comprehensive Analysis for ECG Classification Using Wavelet Transform

Vol. 4  Issue 1
Year:2016
Issue:Jan-Mar
Title:A Comprehensive Analysis for ECG Classification Using Wavelet Transform
Author Name:Mayank Kumar Gautham and Vinod Kumar Giri 
Synopsis:
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.

Feature Extraction of ECG Signal Using Labview

Vol. 4  Issue 1
Year:2016
Issue:Jan-Mar
Title:Feature Extraction of ECG Signal Using Labview
Author Name:Shubham Mishra,Shreyash Pandey,Khemraj Deshmukh and Jitendra Kumar
Synopsis:
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.

Filtering of ECG Signal Using Adaptive and Non Adaptive Filters

Vol. 4  Issue 1
Year:2016
Issue:Jan-Mar
Title:Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Author Name:Abhishek Sahu and Jitendra Kumar
Synopsis:
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 .

Space-Division Multiplexed Transmission over Few-Mode Fiber Based On Coherent MIMO Digital Signal Processing: A Review

Vol. 3  Issue 4
Year:2015
Issue:Oct-Dec 
Title:Space-Division Multiplexed Transmission over Few-Mode Fiber Based On Coherent MIMO Digital Signal Processing: A Review
Author Name:Anuja Mishra, Sharad Mohan Shrivastava, Pooja Sharma, Prachi Agrawal and Rahul Parganiha 
Synopsis:
The objective of this review paper is to make readers understand the key terms related to optical fiber specifically few mode fiber to help them carry out further research work. With the increasing demand for faster transmission systems, optical fiber communication system requirement is increasing day-by-day. As we know that the capacity limits of single mode fiber is almost reached its maxima, Space division multiplexing can be helpful for increasing the data rate requirement. This review 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 detection of these channels is being carried out using coherent detection namely MIMO DSP. The 66 impulse response matrix representation of few-mode fiber is presented, revealing the coupling characteristics between the modes.

Feature Extraction and Classification of ECG Signal Using Neuro-Wavelet Approach

Vol. 3  Issue 4
Year:2015
Issue:Oct-Dec 
Title:Feature Extraction and Classification of ECG Signal Using Neuro-Wavelet Approach
Author Name:Mayank Kumar Gautam and Vinod Kumar Giri 
Synopsis:
The real wellspring of human misfortune in Cardiovascular Diseases (CVD) is Cardiac issues that are expanding step-bystep in the world. Incredible exertion is done to analyze the cardiovascular disease, where numerous individuals are utilized to the diverse sort of portable Electrocardiogram (ECG) using remote observing method. ECG Feature Extraction act as a critical part in diagnosing generally of the heart sicknesses. Presently a complete inspection has been done for highlighting the extraction of ECG sign dissecting, and extricating and finally characterizing have been arranged amid the long-prior time, and here the authors have presented delicate processing procedures. To perceive the current circumstance of the heart, Electrocardiography is a fundamental device however it is a period expending procedure to break down a persistent ECG signal as it might hold a huge number of relentless heart pulsates. Right now a simple sign can be converted in to a computerized one which helps in precisely diagnosing the sign. Point of this paper is to show an identification of some warmth arrhythmias utilizing the emerging neuro-wavelet approach.

Enhanced Fingerprint Image De-Noising Using Bi-Directional Recurrent Neural Network

Vol. 3  Issue 4
Year:2015
Issue:Oct-Dec 
Title:Enhanced Fingerprint Image De-Noising Using Bi-Directional Recurrent Neural Network
Author Name:Deepika Bancchor and Siddharth Choubey 
Synopsis:
Fingerprint Images (FPI) are always prone to be corrupted by various sources of noise during the capture of an image, i.e., acquisition period. This paper studies the implementation of Pixel Component Analysis (PCA) algorithm with Bi- Directional Recurrent Neural Network (BRNN) which will effectively de-noise the FPI images. BRNN enables compression of non-reusable fingerprint image data points during PCA execution and can transform vector co-ordinates in a rational manner. The duration of execution of the operation is also significantly reduced. The output of the proposed model has showed an optimized performance for de-noising of FPI images.

Estimation of ECG Features Using Wavelet Analysis

Vol. 3  Issue 4
Year:2015
Issue:Oct-Dec 
Title:Estimation of ECG Features Using Wavelet Analysis
Author Name:Y. Dileep Kumar and A.M. Prasad 
Synopsis:
In previous days, acquiring and analysis of ECG signals can be done using different softwares. But in this work with the help of Wavelet analysis in LabVIEW software (Graphical programming software), it is easy to understand and use when compared to other softwares like MATLAB, C etc. To be in advance, they focused not only on acquiring and analysis of ECG signal, but also on identification of cardiac disorders. This system can be executed in three stages. In the first stage, the signal is preprocessed to remove the noise and onsets and the offsets are extracted. In the second stage, detection of peaks and in the third stage, cardiac disorders were estimated.

Dead Time Correction of Residence Time Distribution through Digital Signal Processing

Vol. 3  Issue 4
Year:2015
Issue:Oct-Dec 
Title:Dead Time Correction of Residence Time Distribution through Digital Signal Processing
Author Name:Mohamed S. El_Tokhy, Ibrahim M. Fayed, Mouldi A. Bedda and H. Kasban 
Synopsis:
This paper discusses the signal preprocessing of the acquired radiation signal for Residence Time Distribution (RTD). Radiation signals of Molybdenum-99 (Mo99 ) were acquired through system setup. This system begins with a scintillator detector, channel counter and a Personal Computer (PC). Different forms of noise are accompanied with the RTD radiation signal. Consequently, an algorithm was proposed based on signal processing. This algorithm depends on background correction, base line restoration, statistical error computation, radioactive decay correction and dead time correction methods. Therefore, background correction was performed using two independent methods. These methods are the minimum value of the RTD radiation signal method and the subtraction method. Then, base line restoration was performed. Statistical error of the RTD radiation signal was computed. However, two different methods were studied for radioactive decay correction. Moreover, a dead time correction method is proposed. Therefore, dead time percent is obtained. Consequently, the number of lost pulses is investigated. The accuracy of the considered algorithm is determined based on statistical measurements of the acquired RTD signal. A remarkable accuracy of the dead time measurements is observed.

A Combined Noise Filtration Approach for EMG Signals Using Classical Filters with Independent Component Analysis (ICA)

Vol. 3  Issue 3
Year:2015
Issue:Jul-Sep
Title:A Combined Noise Filtration Approach for EMG Signals Using Classical Filters with Independent Component Analysis (ICA)
Author Name:Pradeep Kumar Jaisal, R. N. Patel 
Synopsis:
Noise can limit the extraction of some basic and vital peculiarities from biomedical signals and thus makes it impossible to perform exact analysis of these signals. EMG (Electromyography) signals is one such case, which can be affected by number of factors. For example, power line noises, noises caused by electrical and electronic equipments, inherent semiconductor devices noises, etc. Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. This paper presents an independent component analysis approach for removing noise from raw EMG signals. As the base of the presented systems is independent component analysis, but the technique also uses a multistep approach of filtering and combining the signals to recover the lost components also. The simulation results show that the proposed algorithm removes the noise without compromising the useful information of signal.

System Identification and Echo Canceller with Adaptive Filtering Algorithms

Vol. 3  Issue 3
Year:2015
Issue:Jul-Sep
Title:System Identification and Echo Canceller with Adaptive Filtering Algorithms
Author Name:B. Anitha, Srinivas Bachu and C. Sailaja
Synopsis:
The primary objective of this paper is to present a simulation scheme to simulate an adaptive filter using Least Mean Square, and Normalized Least Mean Square adaptive filtering algorithms for system identification and echo cancellation. The objective of echo cancellation is to estimate the unknown system response that is system identification. With the help of system identification and adaptive filtering algorithms Mean Square Error (MSE) can be minimized and hence echo free signal can be obtained. This method uses a primary input signal that contains speech signal and a reference input signal containing noise. The estimated signal is obtained by subtracting adaptively filtered reference input signal from the primary input signal. In this method, the desired signal corrupted by an additive echo can be recovered by an adaptive echo canceller using LMS, and NLMS algorithms. This adaptive echo canceller is useful in minimizing the MSE and to improve the SNR. Here the estimation of the adaptive filtering is done using MATLAB environment.

Comparative Analysis and Automatic Segmentation of PCG Signal Using Wavelets Transform

Vol. 3  Issue 3
Year:2015
Issue:Jul-Sep
Title:Comparative Analysis and Automatic Segmentation of PCG Signal Using Wavelets Transform
Author Name:Lekram Bahekar, Deepali Shende and Simran Kaur Digwa
Synopsis:
All around the world there are various diseases acquired by human beings. These diseases are of various kinds and it affects almost all parts of the human body. Heart diseases are nowaday's becoming very painstaking part that needs to be taken much care. The major part of solving such problems involves a considerable amount of work to identify the disease. As the heart is the most complex and delicate structure of the human body, it is very difficult to deal with it physically. The area of biomedical signal processing is vast and very useful to accurately analyze and detect the disease. We calculate the normalised root mean square of the detailed coefficients at each level and threshold it in order to detect the murmur of heart sound signals. The result of this method clearly illustrates the detection of the main components S1, S2, S3, S4 Pathological murmurs and the identification of the disease.

Detection and Estimation of Range, Velocity and Direction of Arrival of Multiple Targets

Vol. 3  Issue 3
Year:2015
Issue:Jul-Sep
Title:Detection and Estimation of Range, Velocity and Direction of Arrival of Multiple Targets
Author Name:N. Padmaja
Synopsis:
A novel method for Multiple target localization based on linear canonical transform is presented in this paper. It involves a modal preprocessing step to transform the signals received at the sensor array into signals at different modes, where narrowband techniques for DOA estimation can be applied. The incorporation of the fractional Fourier transform in the proposed method makes it possible to estimate the parameters of multiple targets even in challenging scenarios such as low SNR and closely spaced targets. Detection and estimation of Range, Velocity and Direction Of Arrival (DOA) of multiple far field targets using wideband chirp signals using ROOT-MUSIC algorithm reduces the error for DOA, Range and Velocity. The proposed method is better than the existing method. While comparing Raleigh channel with Root- MUSIC algorithm where errors like PAPR, CFO and STO are minimized. Proposed method gives more accurate results with low Root-Mean-Square Errors in the parameters estimated under complex conditions such as closely spaced targets and low Signal-to-Noise-Ratio.

Removal of High Frequency Noise from the ECG Signal Using Averaging Filters

Vol. 3  Issue 3
Year:2015
Issue:Jul-Sep
Title:Removal of High Frequency Noise from the ECG Signal Using Averaging Filters
Author Name:Vishakha Pandey and V. K. Giri 
Synopsis:
The Electro-CardioGram (ECG) is a graphical representation of electro-mechanical activities of the heart. It reflects the state of heart, and is very much useful in disease diagnosis. Since, the ECG signal contains high frequency noise, is well known as power line interference. Hence, it must be removed for further processing. This paper presents an algorithm, developed for denoising high frequency noise from ECG signal which is based on a simple averaging and a moving averaging filter. The filtering process is followed by an algorithm for smoothing the ECG signal using polynomial curve fitting. It’s denoising performance is implemented, smoothened, and compared in the C++ environment. The proposed algorithm does require redundant preprocessing steps, thus allowing a simple architecture for its implementation as well as low computational cost.

Active Sonar Signal Processing in Noise Background Environment

Vol. 3  Issue 3
Year:2015
Issue:Jul-Sep
Title:Active Sonar Signal Processing in Noise Background Environment
Author Name:M. Rajeswari and S. Koteswara Rao
Synopsis:
One of the most important problems in many application areas is to extract the signal of interest from background noise. In background noise, the occurrence of signal and the behaviour of signal are random. Therefore, it is reasonable 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, we examine signal and noise environments encountered in active sonar using CW and LFM pulses. The optimum receiver is presented for range-Doppler-shift processing in a background-noise-limited environment. FFT based implementation for detection of CW and LFM active sonar target has been shown here. By following this method both range and Doppler resolution can be achieved.

Non-Invasive Glucose Detection Using PLS And LM Algorithm

Vol. 3  Issue 2
Year:2015
Issue:Apr-Jun
Title:Non-Invasive Glucose Detection Using PLS And LM Algorithm
Author Name:T.R.Jaya Chandra Lekha and C.Saravanakumar
Synopsis:
Diabetes mellitus is a major, and increasing, global problem. The existing method of blood glucose measurement[13] 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[11] 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.

Transform Domain Analysis Of EMG Signal For Efficient And Useful Feature Extraction Technique

Vol. 3  Issue 2
Year:2015
Issue:Apr-Jun
Title:Transform Domain Analysis Of EMG Signal For Efficient And Useful Feature Extraction Technique
Author Name:Pradeep Kumar Jaisal and R.N.Patel
Synopsis:
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.

Data Hiding In Image By LSBMR Algorithm With Wavelet Transform

Vol. 3  Issue 2
Year:2015
Issue:Apr-Jun
Title:Data Hiding In Image By LSBMR Algorithm With Wavelet Transform
Author Name:Shanthakumari.R and M.Arul
Synopsis:
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.