This document describes the development of a GUI-based simulator for electrical bio-impedance (EBI) signals. Six curve fitting models (polynomial, Fourier series, sum of sine waves, exponential, Gaussian, rational polynomial) were used to model impedance cardiography and impedance respirography signals. The models were evaluated based on sum of square errors, correlation between actual and modeled data, and execution time. The Fourier model provided the best fit for EBI signals. A GUI simulator was created that allows users to generate simulated EBI signals by controlling signal model parameters and adding noise/motion artifacts. The simulator could be useful for research and analysis of EBI signals.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
This document discusses extracting respiratory rate from photoplethysmography (PPG) signals using principal component analysis (PCA) and empirical mode decomposition (EMD). It begins with an introduction to PPG signals and how they contain respiratory information. It then discusses previous efforts to extract respiratory signals from PPG that used methods like filtering and wavelets. The document proposes using PCA and EMD to improve upon existing methods. It provides background on PCA, EMD, and reviews literature on extracting respiratory information from ECG and how respiration modulates PPG signals. The aim is to evaluate different signal processing techniques to extract respiratory information from commonly available biomedical signals like ECG and PPG to avoid using additional sensors.
Photoplethysmography (PPG) and Phonocardiography (PCG) are two important non-invasive techniques for monitoring physiological parameters of cardiovascular diagnostics. The PCG signal discloses information about cardiac function through vibrations caused by the working heart. PPG measures relative blood volume changes in the blood vessels close to the skin. This paper emphasizes on simultaneous acquisition of PCG and PPG signals from the same subject with the aid of NIELVIS II+ DAQ and the signals are imported to MATLAB for further processing. Heart rate is extracted from both the signals which are found to be distinctive. This analytical approach of processing these signals can abet for analysis of Heart rate variability (HRV) which is widely used for quantifying neural cardiac control and low variability is particularly predictive of death in patients after myocardial infarction.
Maximum power point tracking techniques for photovoltaic systems: a comparati...IJECEIAES
Photovoltaic (PV) systems are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Swarm algorithm based adaptive filter design to remove power line interferenc...eSAT Journals
Abstract
ECG signal is having wide importance in the biomedical field, but for proper diagnosis of ECG always a noise free ECG signal is needed. Many researchers have already developed filters for getting appropriate desirable ECG signal and till today many researchers are still developing different filters using different algorithms in order to get clearer ECG signal for proper diagnosis. Noises and Interferences get added in the ECG by different ways, at the time of ECG Acquisition or at the time of ECG signal recording.
In this paper newly adapted algorithm is used for the filtering of ECG signal that is a Swarm algorithm which is used for the Error signal optimization from the original corrupted ECG signal. This algorithm is implemented with Adaptive filter to removes Power Line Interference noise having Frequency component of 50 Hz. The ECG signal considered may be retrieved from ECG acquisition system or from MIT-BIH database.
Keywords: Adaptive Filter, SWARM Algorithm, MIT-BIH Database, Matlab, ECG Signal and Power line Noise Signal etc.
Motion artifacts reduction in cardiac pulse signal acquired from video imaging IJECEIAES
This study examines the possibility of remotely measuring the cardiac pulse activity of a patient, which could be an alternative technique to the classical method. This type of measurement is non-invasive. However, several limitations may deteriorate the accuracy of the results, including changes in ambient illumination, motion artifacts (MA) and other interferences that may occur through video recording. The paper in hand presents a new approach as a remedy for the aforementioned problem in cardiac pulse signals extracted from facial video recordings. Partitioning provides the basis for the presented MA reduction method; the acquired signals are partitioned into two sets for each second and every partition is shifted to the mean level and then all the partitions are recombined again into one signal, which is followed by low-pass filtering for enhancement. The proposed compared with ordinary pulse oximetry Photoplethysmographic (PPG) method. The resulted correlation coefficient was found (0.957) when calculated between the results of the proposed method and the ordinary one. Experiments were implemented using a common camera by creating a dataset from 11 subjects. The ease of implementation of this method with a simple that can be used to monitor the cardiac pulse rates in both home and the clinical environments.
Suppression of power line interference correction of baselinewanders andIAEME Publication
This document summarizes a research paper that proposes a new method for enhancing electrocardiogram (ECG) signals based on the Constrained Stability Least Mean Square (CSLMS) algorithm. The CSLMS algorithm is applied to an adaptive noise cancellation filter to remove two dominant artifacts from ECG signals: high-frequency noise and baseline wander. Simulation results on ECG data from the MIT-BIH database show that the CSLMS method provides better denoising and artifact removal compared to the conventional LMS algorithm, improving signal-to-noise ratio by 3-6 decibels. The CSLMS algorithm exhibits smaller excess mean squared error and faster convergence than LMS, resulting in less signal distortion in
Implementation and design of new low-cost foot pressure sensor module using p...IJECEIAES
Basically, human can sense the active body force trough the soles of their feet and can feel the position vector of zero moment point (ZMP) based on the center of pressure (CoP) from active body force. This behavior is adapted by T-FLoW humanoid robot using unique sensor which is piezoelectric sensor. Piezoelectric sensor has a characteristic which is non-continuous reading (record a data only a moment). Because of it, this sensor cannot be used to stream data such as flex sensor, loadcell sensor, and torque sensor like previous research. Therefore, the piezoelectric sensor still can be used to measure the position vector of ZMP. The idea is using this sensor in a special condition which is during landing condition. By utilizing 6 unit of piezoelectric sensor with a certain placement, the position vector of ZMP (X-Y-axis) and pressure value in Z-axis from action body force can be found. The force resultant method is used to find the position vector of ZMP from each piezoelectric sensor. Based on those final conclusions in each experiment, the implementation of foot pressure sensor modul using piezoelectric sensor has a good result (94%) as shown in final conclusions in each experiment. The advantages of this new foot pressure sensor modul is low-cost design and similar result with another sensor. The disadvantages of this sensor are because of the main characteristic of piezoelectric sensor (non-continuous read) sometimes the calculation has outlayer data.
Improving hemoglobin estimation accuracy through standardizing of light-emitt...IJECEIAES
This document describes a study that developed a non-invasive hemoglobin meter to estimate hemoglobin concentration using photoplethysmography (PPG). Five light-emitting diodes (LEDs) at different wavelengths were used to acquire the PPG signal. Initially, testing on 15 subjects achieved 96.51% accuracy and 0.57 gm/dL root mean square error (RMSE) without standardizing LED power. To improve accuracy, LED power was standardized to 1 mW for each wavelength. Re-acquiring PPG signals from the same subjects with standardized LED power improved accuracy to 98.29% and reduced RMSE to 0.36 gm/dL, demonstrating the effectiveness of LED power standardization for improving hemoglobin
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
This document discusses extracting respiratory rate from photoplethysmography (PPG) signals using principal component analysis (PCA) and empirical mode decomposition (EMD). It begins with an introduction to PPG signals and how they contain respiratory information. It then discusses previous efforts to extract respiratory signals from PPG that used methods like filtering and wavelets. The document proposes using PCA and EMD to improve upon existing methods. It provides background on PCA, EMD, and reviews literature on extracting respiratory information from ECG and how respiration modulates PPG signals. The aim is to evaluate different signal processing techniques to extract respiratory information from commonly available biomedical signals like ECG and PPG to avoid using additional sensors.
Photoplethysmography (PPG) and Phonocardiography (PCG) are two important non-invasive techniques for monitoring physiological parameters of cardiovascular diagnostics. The PCG signal discloses information about cardiac function through vibrations caused by the working heart. PPG measures relative blood volume changes in the blood vessels close to the skin. This paper emphasizes on simultaneous acquisition of PCG and PPG signals from the same subject with the aid of NIELVIS II+ DAQ and the signals are imported to MATLAB for further processing. Heart rate is extracted from both the signals which are found to be distinctive. This analytical approach of processing these signals can abet for analysis of Heart rate variability (HRV) which is widely used for quantifying neural cardiac control and low variability is particularly predictive of death in patients after myocardial infarction.
Maximum power point tracking techniques for photovoltaic systems: a comparati...IJECEIAES
Photovoltaic (PV) systems are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Swarm algorithm based adaptive filter design to remove power line interferenc...eSAT Journals
Abstract
ECG signal is having wide importance in the biomedical field, but for proper diagnosis of ECG always a noise free ECG signal is needed. Many researchers have already developed filters for getting appropriate desirable ECG signal and till today many researchers are still developing different filters using different algorithms in order to get clearer ECG signal for proper diagnosis. Noises and Interferences get added in the ECG by different ways, at the time of ECG Acquisition or at the time of ECG signal recording.
In this paper newly adapted algorithm is used for the filtering of ECG signal that is a Swarm algorithm which is used for the Error signal optimization from the original corrupted ECG signal. This algorithm is implemented with Adaptive filter to removes Power Line Interference noise having Frequency component of 50 Hz. The ECG signal considered may be retrieved from ECG acquisition system or from MIT-BIH database.
Keywords: Adaptive Filter, SWARM Algorithm, MIT-BIH Database, Matlab, ECG Signal and Power line Noise Signal etc.
Motion artifacts reduction in cardiac pulse signal acquired from video imaging IJECEIAES
This study examines the possibility of remotely measuring the cardiac pulse activity of a patient, which could be an alternative technique to the classical method. This type of measurement is non-invasive. However, several limitations may deteriorate the accuracy of the results, including changes in ambient illumination, motion artifacts (MA) and other interferences that may occur through video recording. The paper in hand presents a new approach as a remedy for the aforementioned problem in cardiac pulse signals extracted from facial video recordings. Partitioning provides the basis for the presented MA reduction method; the acquired signals are partitioned into two sets for each second and every partition is shifted to the mean level and then all the partitions are recombined again into one signal, which is followed by low-pass filtering for enhancement. The proposed compared with ordinary pulse oximetry Photoplethysmographic (PPG) method. The resulted correlation coefficient was found (0.957) when calculated between the results of the proposed method and the ordinary one. Experiments were implemented using a common camera by creating a dataset from 11 subjects. The ease of implementation of this method with a simple that can be used to monitor the cardiac pulse rates in both home and the clinical environments.
Suppression of power line interference correction of baselinewanders andIAEME Publication
This document summarizes a research paper that proposes a new method for enhancing electrocardiogram (ECG) signals based on the Constrained Stability Least Mean Square (CSLMS) algorithm. The CSLMS algorithm is applied to an adaptive noise cancellation filter to remove two dominant artifacts from ECG signals: high-frequency noise and baseline wander. Simulation results on ECG data from the MIT-BIH database show that the CSLMS method provides better denoising and artifact removal compared to the conventional LMS algorithm, improving signal-to-noise ratio by 3-6 decibels. The CSLMS algorithm exhibits smaller excess mean squared error and faster convergence than LMS, resulting in less signal distortion in
Implementation and design of new low-cost foot pressure sensor module using p...IJECEIAES
Basically, human can sense the active body force trough the soles of their feet and can feel the position vector of zero moment point (ZMP) based on the center of pressure (CoP) from active body force. This behavior is adapted by T-FLoW humanoid robot using unique sensor which is piezoelectric sensor. Piezoelectric sensor has a characteristic which is non-continuous reading (record a data only a moment). Because of it, this sensor cannot be used to stream data such as flex sensor, loadcell sensor, and torque sensor like previous research. Therefore, the piezoelectric sensor still can be used to measure the position vector of ZMP. The idea is using this sensor in a special condition which is during landing condition. By utilizing 6 unit of piezoelectric sensor with a certain placement, the position vector of ZMP (X-Y-axis) and pressure value in Z-axis from action body force can be found. The force resultant method is used to find the position vector of ZMP from each piezoelectric sensor. Based on those final conclusions in each experiment, the implementation of foot pressure sensor modul using piezoelectric sensor has a good result (94%) as shown in final conclusions in each experiment. The advantages of this new foot pressure sensor modul is low-cost design and similar result with another sensor. The disadvantages of this sensor are because of the main characteristic of piezoelectric sensor (non-continuous read) sometimes the calculation has outlayer data.
Improving hemoglobin estimation accuracy through standardizing of light-emitt...IJECEIAES
This document describes a study that developed a non-invasive hemoglobin meter to estimate hemoglobin concentration using photoplethysmography (PPG). Five light-emitting diodes (LEDs) at different wavelengths were used to acquire the PPG signal. Initially, testing on 15 subjects achieved 96.51% accuracy and 0.57 gm/dL root mean square error (RMSE) without standardizing LED power. To improve accuracy, LED power was standardized to 1 mW for each wavelength. Re-acquiring PPG signals from the same subjects with standardized LED power improved accuracy to 98.29% and reduced RMSE to 0.36 gm/dL, demonstrating the effectiveness of LED power standardization for improving hemoglobin
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Novel method to find the parameter for noise removal from multi channel ecg w...eSAT Journals
This document presents a novel method for removing noise from multi-channel electrocardiogram (ECG) waveforms using a multi-swarm optimization (MSO) approach. The method involves extracting features from ECG data, using MSO to identify an optimal cutoff frequency parameter for a finite impulse response (FIR) filter, and applying the FIR filter using the identified parameter to remove noise from the ECG signals. The MSO approach divides particles into multiple swarms that each focus on a region of the search space, helping to overcome sensitivity to initial positions found in traditional particle swarm optimization. The resulting filtered ECG signals are evaluated against original clean signals to validate the noise removal performance of the MSO-identified cutoff frequency parameter and
Noise analysis & qrs detection in ecg signalsHarshal Ladhe
The document discusses removing noise from ECG signals using adaptive filtering techniques. It focuses on using an LMS algorithm to remove powerline interference at 50 Hz from ECG signals. The LMS algorithm is tested with different filter tap lengths and step sizes to determine the optimal parameters for noise cancellation. Additional filtering using notch filters is also explored to remove harmonics and high frequency noise. The results show that the LMS algorithm effectively removes powerline interference from ECG signals.
ECG signal denoising using a novel approach of adaptive filters for real-time...IJECEIAES
Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.
Electrocardiogram (ECG) flag is the electrical action of the human heart. The ECG contains imperative data about the general execution of the human heart framework. In this way, exact examination of the ECG flag is extremely critical however difficult undertaking. ECG flag is regularly low adequacy and polluted with various kinds of commotions due to its estimation procedure e.g. control line obstruction, amplifier clamor and standard meander. Benchmark meander is a sort of organic commotion caused by the arbitrary development of patient amid ECG estimation and misshapes the ST fragment of the ECG waveform. In this paper, we present a far reaching near investigation of five generally utilized versatile filtering calculations for the evacuation of low recurrence clamor. We perform broad investigations on the Physionet MIT BIH ECG database and contrast the flag with commotion proportion (SNR), combination rate, and time many-sided quality of these calculations. It is discovered that modified LMS has better execution than others regarding SNR and assembly rate.
Hybrid neural networks in cyber physical system interface control systemsjournalBEEI
The calculation and results of simulation of the magnetic control system for the spacecraft momentum are presented in the paper. The simulation includes an assessment of the reliability of calculating the Earth's magnetic field parameters, as well as an assessment of the quality of object stabilization by resetting the total momentum with the aid of the system under review. The outcome of a comparative analysis of resource efficiency and energy efficiency are demonstrated in the implementation of the proposed hardware models of controllers on FPGA. The strengths and weaknesses of the programming models are shown. The developed models will allow to be modified and perform more complex operations in the future.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document presents an adaptive algorithm for removing baseline wander (artifacts) from radial bioimpedance signals using wavelet packet transform. Bioimpedance signals are affected by respiratory and motion artifacts that cause baseline drift. Existing artifact removal methods are not fully effective due to spectral overlap between the signal and artifacts. The proposed method adaptively decomposes the signal using wavelet packets to distinguish the signal from the artifacts based on their energies at different scales. Simulation results on bioimpedance signals from 5 subjects show the method reduces variance by 71-94% and increases SNR, outperforming other wavelet functions. The algorithm effectively removes artifacts while preserving the bioimpedance signal characteristics.
IRJET- Prominent MPPT Techniques for PV CellIRJET Journal
This document discusses prominent maximum power point tracking (MPPT) techniques for photovoltaic (PV) cells. It begins with an introduction to the need for MPPT to improve PV cell efficiency given varying solar irradiance and temperature. It then reviews several common MPPT techniques - perturb and observe, incremental conductance, fractional short circuit current, fuzzy logic control, and artificial neural networks. It compares the techniques based on tracking speed, algorithm complexity, ability to handle changing conditions, hardware requirements, and costs. Overall, the document provides an overview of key MPPT techniques used to optimize power extraction from PV systems under varying environmental factors.
Surface Electromyography (SEMG) Based Fuzzy Logic Controller for Footballer b...IRJET Journal
This document describes a study that uses surface electromyography (SEMG) signals from the gastrocnemius muscle and a fuzzy logic controller to classify different football player foot actions (dorsiflexion, plantarflexion, no action). SEMG signals were collected using electrodes and processing hardware, then analyzed in MATLAB. Three statistical parameters (root mean square, median, standard deviation) of the SEMG signals were calculated and used as inputs for the fuzzy logic controller. Results showed that standard deviation was the best parameter for distinguishing between the different foot actions. The system has applications in human-computer interfaces that recognize different foot motions.
Study and Analysis of ECG Signal using ADS1298RECG-FE Analog Front End KitIRJET Journal
This document discusses analyzing ECG signals using MATLAB. It describes simulating ECG waveforms, acquiring real ECG data, preprocessing signals to remove noise, and extracting features. MATLAB and LabVIEW tools are used to simulate ECG signals, filter noise, detect heartbeats, and analyze signals in real-time or after simulation for diagnostics. The document focuses on processing ECG signals with MATLAB and LabVIEW for research applications.
IRJET- Detection of Abnormal ECG Signal using DWT Feature Extraction and CNNIRJET Journal
This document discusses a study that uses discrete wavelet transform (DWT) to extract features from electrocardiogram (ECG) signals and then uses a convolutional neural network (CNN) to classify the signals as normal or abnormal. DWT is used to represent the ECG signals at different resolutions, which allows numerical features to be extracted. A CNN is then trained on the extracted features to predict whether signals indicate normal or abnormal heart conditions. The goal is to develop an efficient early detection system for cardiovascular disease by combining DWT feature extraction and CNN classification of ECG signals.
IRJET- R Peak Detection with Diagnosis of Arrhythmia using Adaptive Filte...IRJET Journal
The document presents a method for detecting R peaks in electrocardiogram (ECG) signals with high accuracy by combining adaptive filtering and Hilbert transform. Adaptive filtering reduces noise and estimates the fundamental signal, while Hilbert transform eliminates signal distortion and shows time dependency. Features are then extracted from the ECG, including RR interval, heart rate, QRS width, and PR interval. These features can be used to diagnose arrhythmias based on irregular heart rhythms. A graphical user interface was also developed to conveniently display the output waveform, features, and type of arrhythmia diagnosis. When tested on data from the MIT-BIH arrhythmia database, the proposed method achieved a sensitivity of 99.22% and positive predict
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
CLASSIFICATION OF ECG ARRHYTHMIAS USING /DISCRETE WAVELET TRANSFORM AND NEURA...IJCSEA Journal
Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalies. Several algorithms have been proposed to classify ECG arrhythmias; however, they cannot perform very well. Therefore, in this paper, an expert system for ElectroCardioGram (ECG) arrhythmia classification is proposed. Discrete wavelet transform is used for processing ECG recordings, and extracting some features, and the Multi-Layer Perceptron (MLP) neural network performs the classification task. Two types of arrhythmias can be detected by the proposed system. Some recordings of the MIT-BIH arrhythmias database have been used for training and testing our neural network based classifier. The simulation results show that the classification accuracy of our algorithm is 96.5% using 10 files including normal and two arrhythmias.
IRJET- Performance Analysis of Trade Off in SE-EE in Cooperative Spectrum Sen...IRJET Journal
This document analyzes the trade-off between energy efficiency (EE) and spectral efficiency (SE) in cooperative spectrum sensing for cognitive radio networks. It finds that EE and SE are inversely related, so improving one typically decreases the other. Through simulation, it analyzes how EE and SE are affected by signal-to-noise ratio (SNR) and sensing time. The simulations match theoretical predictions and find optimal trade-off points between EE and SE for different parameters. Understanding this trade-off is important for designing efficient cognitive radio systems.
This document summarizes a research paper that uses a backward propagation neural network with the Levenberg Marquardt algorithm to classify electrocardiogram (ECG) signals as normal or abnormal. The network was trained on the MIT-BIH arrhythmia database and achieved 99.9% accuracy at classifying heartbeats, outperforming other methods. Features extracted from the ECG signals like standard deviation and wavelet coefficients were used as input to the neural network. The results demonstrate that neural networks can accurately analyze ECG signals and detect cardiac abnormalities.
This document summarizes a research paper that uses a backward propagation neural network with the Levenberg Marquardt algorithm to classify electrocardiogram (ECG) signals as normal or abnormal. The network was trained on the MIT-BIH arrhythmia database and achieved 99.9% accuracy at classifying heartbeats, outperforming other methods. Features extracted from the ECG signals like standard deviation and wavelet coefficients were used as input to the neural network. The results demonstrate that neural networks can accurately analyze ECG signals and detect cardiac abnormalities.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
Development of a Condition Monitoring Algorithm for Industrial Robots based o...IJECEIAES
Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.
Detection of EEG Spikes Using Machine Learning ClassifierIRJET Journal
This document discusses a study on detecting epileptic seizures from EEG data using machine learning classifiers. It begins with an introduction to epilepsy and EEG signals. Feature extraction is identified as an important step, as is understanding the statistical properties of the data. Previous studies that used time domain, frequency domain, and time-frequency domain features are summarized. Commonly used machine learning classifiers like SVMs, ANNs, and random forests are also mentioned. The methodology of the presented study involved recording EEG data from rats injected with penicillin to induce seizures, extracting time and frequency domain features, and using an SVM classifier to classify signals as epileptic or non-epileptic. The goal of the study was to analyze and identify features to classify EEG
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Novel method to find the parameter for noise removal from multi channel ecg w...eSAT Journals
This document presents a novel method for removing noise from multi-channel electrocardiogram (ECG) waveforms using a multi-swarm optimization (MSO) approach. The method involves extracting features from ECG data, using MSO to identify an optimal cutoff frequency parameter for a finite impulse response (FIR) filter, and applying the FIR filter using the identified parameter to remove noise from the ECG signals. The MSO approach divides particles into multiple swarms that each focus on a region of the search space, helping to overcome sensitivity to initial positions found in traditional particle swarm optimization. The resulting filtered ECG signals are evaluated against original clean signals to validate the noise removal performance of the MSO-identified cutoff frequency parameter and
Noise analysis & qrs detection in ecg signalsHarshal Ladhe
The document discusses removing noise from ECG signals using adaptive filtering techniques. It focuses on using an LMS algorithm to remove powerline interference at 50 Hz from ECG signals. The LMS algorithm is tested with different filter tap lengths and step sizes to determine the optimal parameters for noise cancellation. Additional filtering using notch filters is also explored to remove harmonics and high frequency noise. The results show that the LMS algorithm effectively removes powerline interference from ECG signals.
ECG signal denoising using a novel approach of adaptive filters for real-time...IJECEIAES
Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.
Electrocardiogram (ECG) flag is the electrical action of the human heart. The ECG contains imperative data about the general execution of the human heart framework. In this way, exact examination of the ECG flag is extremely critical however difficult undertaking. ECG flag is regularly low adequacy and polluted with various kinds of commotions due to its estimation procedure e.g. control line obstruction, amplifier clamor and standard meander. Benchmark meander is a sort of organic commotion caused by the arbitrary development of patient amid ECG estimation and misshapes the ST fragment of the ECG waveform. In this paper, we present a far reaching near investigation of five generally utilized versatile filtering calculations for the evacuation of low recurrence clamor. We perform broad investigations on the Physionet MIT BIH ECG database and contrast the flag with commotion proportion (SNR), combination rate, and time many-sided quality of these calculations. It is discovered that modified LMS has better execution than others regarding SNR and assembly rate.
Hybrid neural networks in cyber physical system interface control systemsjournalBEEI
The calculation and results of simulation of the magnetic control system for the spacecraft momentum are presented in the paper. The simulation includes an assessment of the reliability of calculating the Earth's magnetic field parameters, as well as an assessment of the quality of object stabilization by resetting the total momentum with the aid of the system under review. The outcome of a comparative analysis of resource efficiency and energy efficiency are demonstrated in the implementation of the proposed hardware models of controllers on FPGA. The strengths and weaknesses of the programming models are shown. The developed models will allow to be modified and perform more complex operations in the future.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document presents an adaptive algorithm for removing baseline wander (artifacts) from radial bioimpedance signals using wavelet packet transform. Bioimpedance signals are affected by respiratory and motion artifacts that cause baseline drift. Existing artifact removal methods are not fully effective due to spectral overlap between the signal and artifacts. The proposed method adaptively decomposes the signal using wavelet packets to distinguish the signal from the artifacts based on their energies at different scales. Simulation results on bioimpedance signals from 5 subjects show the method reduces variance by 71-94% and increases SNR, outperforming other wavelet functions. The algorithm effectively removes artifacts while preserving the bioimpedance signal characteristics.
IRJET- Prominent MPPT Techniques for PV CellIRJET Journal
This document discusses prominent maximum power point tracking (MPPT) techniques for photovoltaic (PV) cells. It begins with an introduction to the need for MPPT to improve PV cell efficiency given varying solar irradiance and temperature. It then reviews several common MPPT techniques - perturb and observe, incremental conductance, fractional short circuit current, fuzzy logic control, and artificial neural networks. It compares the techniques based on tracking speed, algorithm complexity, ability to handle changing conditions, hardware requirements, and costs. Overall, the document provides an overview of key MPPT techniques used to optimize power extraction from PV systems under varying environmental factors.
Surface Electromyography (SEMG) Based Fuzzy Logic Controller for Footballer b...IRJET Journal
This document describes a study that uses surface electromyography (SEMG) signals from the gastrocnemius muscle and a fuzzy logic controller to classify different football player foot actions (dorsiflexion, plantarflexion, no action). SEMG signals were collected using electrodes and processing hardware, then analyzed in MATLAB. Three statistical parameters (root mean square, median, standard deviation) of the SEMG signals were calculated and used as inputs for the fuzzy logic controller. Results showed that standard deviation was the best parameter for distinguishing between the different foot actions. The system has applications in human-computer interfaces that recognize different foot motions.
Study and Analysis of ECG Signal using ADS1298RECG-FE Analog Front End KitIRJET Journal
This document discusses analyzing ECG signals using MATLAB. It describes simulating ECG waveforms, acquiring real ECG data, preprocessing signals to remove noise, and extracting features. MATLAB and LabVIEW tools are used to simulate ECG signals, filter noise, detect heartbeats, and analyze signals in real-time or after simulation for diagnostics. The document focuses on processing ECG signals with MATLAB and LabVIEW for research applications.
IRJET- Detection of Abnormal ECG Signal using DWT Feature Extraction and CNNIRJET Journal
This document discusses a study that uses discrete wavelet transform (DWT) to extract features from electrocardiogram (ECG) signals and then uses a convolutional neural network (CNN) to classify the signals as normal or abnormal. DWT is used to represent the ECG signals at different resolutions, which allows numerical features to be extracted. A CNN is then trained on the extracted features to predict whether signals indicate normal or abnormal heart conditions. The goal is to develop an efficient early detection system for cardiovascular disease by combining DWT feature extraction and CNN classification of ECG signals.
IRJET- R Peak Detection with Diagnosis of Arrhythmia using Adaptive Filte...IRJET Journal
The document presents a method for detecting R peaks in electrocardiogram (ECG) signals with high accuracy by combining adaptive filtering and Hilbert transform. Adaptive filtering reduces noise and estimates the fundamental signal, while Hilbert transform eliminates signal distortion and shows time dependency. Features are then extracted from the ECG, including RR interval, heart rate, QRS width, and PR interval. These features can be used to diagnose arrhythmias based on irregular heart rhythms. A graphical user interface was also developed to conveniently display the output waveform, features, and type of arrhythmia diagnosis. When tested on data from the MIT-BIH arrhythmia database, the proposed method achieved a sensitivity of 99.22% and positive predict
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
CLASSIFICATION OF ECG ARRHYTHMIAS USING /DISCRETE WAVELET TRANSFORM AND NEURA...IJCSEA Journal
Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalies. Several algorithms have been proposed to classify ECG arrhythmias; however, they cannot perform very well. Therefore, in this paper, an expert system for ElectroCardioGram (ECG) arrhythmia classification is proposed. Discrete wavelet transform is used for processing ECG recordings, and extracting some features, and the Multi-Layer Perceptron (MLP) neural network performs the classification task. Two types of arrhythmias can be detected by the proposed system. Some recordings of the MIT-BIH arrhythmias database have been used for training and testing our neural network based classifier. The simulation results show that the classification accuracy of our algorithm is 96.5% using 10 files including normal and two arrhythmias.
IRJET- Performance Analysis of Trade Off in SE-EE in Cooperative Spectrum Sen...IRJET Journal
This document analyzes the trade-off between energy efficiency (EE) and spectral efficiency (SE) in cooperative spectrum sensing for cognitive radio networks. It finds that EE and SE are inversely related, so improving one typically decreases the other. Through simulation, it analyzes how EE and SE are affected by signal-to-noise ratio (SNR) and sensing time. The simulations match theoretical predictions and find optimal trade-off points between EE and SE for different parameters. Understanding this trade-off is important for designing efficient cognitive radio systems.
This document summarizes a research paper that uses a backward propagation neural network with the Levenberg Marquardt algorithm to classify electrocardiogram (ECG) signals as normal or abnormal. The network was trained on the MIT-BIH arrhythmia database and achieved 99.9% accuracy at classifying heartbeats, outperforming other methods. Features extracted from the ECG signals like standard deviation and wavelet coefficients were used as input to the neural network. The results demonstrate that neural networks can accurately analyze ECG signals and detect cardiac abnormalities.
This document summarizes a research paper that uses a backward propagation neural network with the Levenberg Marquardt algorithm to classify electrocardiogram (ECG) signals as normal or abnormal. The network was trained on the MIT-BIH arrhythmia database and achieved 99.9% accuracy at classifying heartbeats, outperforming other methods. Features extracted from the ECG signals like standard deviation and wavelet coefficients were used as input to the neural network. The results demonstrate that neural networks can accurately analyze ECG signals and detect cardiac abnormalities.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
Development of a Condition Monitoring Algorithm for Industrial Robots based o...IJECEIAES
Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.
Detection of EEG Spikes Using Machine Learning ClassifierIRJET Journal
This document discusses a study on detecting epileptic seizures from EEG data using machine learning classifiers. It begins with an introduction to epilepsy and EEG signals. Feature extraction is identified as an important step, as is understanding the statistical properties of the data. Previous studies that used time domain, frequency domain, and time-frequency domain features are summarized. Commonly used machine learning classifiers like SVMs, ANNs, and random forests are also mentioned. The methodology of the presented study involved recording EEG data from rats injected with penicillin to induce seizures, extracting time and frequency domain features, and using an SVM classifier to classify signals as epileptic or non-epileptic. The goal of the study was to analyze and identify features to classify EEG
IRJET- Facial Gesture Recognition using Surface EMG and Multiclass Support Ve...IRJET Journal
This document presents a study on facial gesture recognition using surface electromyography (EMG) signals and a multiclass support vector machine (SVM) classifier. EMG signals were collected from facial muscles for four expressions: frowning, puckering, smiling, and chewing. Time-domain features like root mean square, variance, and standard deviation were extracted from the EMG signals. These features were classified using SVM and k-nearest neighbors algorithms. SVM achieved the highest accuracy of 92.76% for recognizing the four facial expressions based on the EMG signals. The results indicate this EMG-based approach can efficiently predict different facial expressions.
This document proposes an automatic license issuing system that uses sensors to monitor a driver's behavior during a driving test. The system aims to replace the current manual process of evaluating driving tests. Sensors like force sensors, piezo sensors, MEMS accelerometers, smoke sensors, and an LCD display are mounted on a test vehicle. The sensors collect data on the driver's control of the vehicle and emissions. This data is sent wirelessly via a ZigBee module to a remote server for analysis. The server compares the test data to reference data to evaluate the driver's performance and determine if a license should be issued. The goal is to objectively assess driving ability and prevent illegal or unsafe licenses from being issued.
IRJET- Arrhythmia Detection using One Dimensional Convolutional Neural NetworkIRJET Journal
1) The document discusses using a 1D convolutional neural network to detect different types of arrhythmias from electrocardiogram (ECG) signals.
2) It proposes a novel wavelet domain multiresolution convolutional neural network approach that avoids complicated heartbeat detection techniques and heavy manual feature engineering.
3) The approach segments ECG signals, applies a discrete cosine transform to select coefficients, and uses a CNN for classification and arrhythmia monitoring. It detects five types of arrhythmias from one-lead ECG signals.
Power Factor Detection and Data AnalyticsIRJET Journal
This document discusses a project that aims to provide a secure and efficient medium for transferring power factor and other power parameter data from a server to clients via an Android application. The project involves using smart meters to detect power factor values and other metrics through a zero-crossing method. An Android app is developed to allow users to access this encrypted data for analysis and comparison of load efficiency. Hardware is implemented using current and voltage sensors connected to a microcontroller. Calculations are performed to determine power, power factor, penalty costs, and consumption. Data is sent securely to a database and decrypted for viewing on the app. The goal is to allow real-time analytics of industrial power usage to improve efficiency.
Short term load forecasting using hybrid neuro wavelet modelIAEME Publication
The document presents a hybrid neuro-wavelet model for short term load forecasting. The model uses a cascaded feedforward backpropagation neural network approach. It first applies discrete wavelet transform to decompose historical electricity load data into wavelet coefficients. These coefficients are then used as input to train multiple neural networks. The neural network outputs are recombined using wavelet reconstruction to produce the final forecast. The model is tested on half-hourly electricity load data from Queensland. It achieves a mean square error of 2.07x10-3, indicating an accurate forecast.
Performance Evaluation of Percent Root Mean Square Difference for ECG Signals...CSCJournals
Electrocardiogram (ECG) signal compression is playing a vital role in biomedical applications. The signal compression is meant for detection and removing the redundant information from the ECG signal. Wavelet transform methods are very powerful tools for signal and image compression and decompression. This paper deals with the comparative study of ECG signal compression using preprocessing and without preprocessing approach on the ECG data. The performance and efficiency results are presented in terms of percent root mean square difference (PRD). Finally, the new PRD technique has been proposed for performance measurement and compared with the existing PRD technique; which has shown that proposed new PRD technique achieved minimum value of PRD with improved results.
This document describes a study that designed ECG and EEG hardware and implemented software using LabVIEW to analyze medical test data and identify abnormalities. The goal was to develop a robotic system to facilitate remote patient monitoring and doctor-patient interaction. Hardware was designed using Multisim to condition ECG and EEG signals. LabVIEW was used to analyze the signals, detect abnormalities in ECG and EEG reports, and calculate pulse rate. The system is intended to help address issues with limited doctor availability in India by allowing remote medical testing and consultation.
IRJET- Driver Drowsiness Detection & Identification of Alcoholic System using...IRJET Journal
This document summarizes a research paper on detecting driver drowsiness and identifying drunk driving using computer vision techniques. The paper proposes using a camera and sensors placed in a Raspberry Pi to monitor the driver. If the system detects the driver is sleeping based on eye closure analysis or detects alcohol on the driver's breath, it will capture an image, send alerts and location data to a server, and cut off the vehicle's motor. The system is intended to help prevent accidents caused by drowsy or intoxicated driving.
Similar to IRJET- Development of GUI based Simulator for the EBI Signal (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.