IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Characterization of transients and fault diagnosis in transformer by discreteIAEME Publication
This document discusses using discrete wavelet transform (DWT) and artificial neural networks (ANN) to characterize transients and diagnose faults in transformers. It begins with an introduction to the problem and background on using the second harmonic component for discrimination. It then discusses why time-frequency information is needed and the advantages of wavelet transforms over Fourier transforms. The document describes collecting data from a test transformer under normal and faulted conditions. It explains using DWT for feature extraction and visualizing the wavelet decomposition levels to characterize magnetizing inrush versus inter-turn faults. Finally, it proposes using ANN trained on the wavelet spectral energies for automated discrimination between fault cases.
IRJET- Design Simulation and Analysis of Efficient De-Noising of ECG Signals ...IRJET Journal
This document discusses techniques for removing noise from electrocardiogram (ECG) signals, including adaptive filtering algorithms and a patch-based method. It first provides background on ECG signals and sources of noise that can interfere with diagnosis. Adaptive filters like least mean square (LMS) and recursive least squares (RLS) are introduced to update filter coefficients based on the signal environment. Simulation results show an ECG signal contaminated with powerline noise can be effectively filtered using LMS. The document also explores a patch-based nonlocal means method previously used for image denoising and applies it to remove noise from ECG signals.
IRJET- Analysis of Electroencephalogram (EEG) SignalsIRJET Journal
The document analyzes EEG signals to classify emotions. EEG signals were collected from 30 subjects aged 18-25 using a Neurosky Mindwave sensor with electrodes on the forehead and ear. The analog EEG data was converted to digital by an Arduino UNO and sent to a computer. Feature extraction using continuous wavelet transform, probability distribution function, peak plot, and fast Fourier transform was performed on the EEG data in LabVIEW. The analysis classified emotions with 90.69% accuracy and 85.55% sensitivity.
Method to Measure Displacement and Velocityfrom Acceleration SignalsIJERA Editor
This paper discusses a methodology for measuring the displacement and velocity vibration of a
structure from a noisy acceleration signal. Digital FIR (finite impulse response)filters were used to remove the
noise present in the signal as well to eliminate numerical integration problems which produce offsets in velocity
and displacement amplitudes. The choice of this type of filter aims to prevent the signal delay that may cause
distortions in numerical integration process. To validate this methodology the defined procedure was applied to
determine the deflection history of a beam with random excitation base. The acceleration signals considered as
input data were obtained from two piezoelectric accelerometers used to measure the vibration of the base and
the beam response. The correlation was defined between the stress measured by a strain gage and the stress
calculated from its deflection. The result was great, enabling the application of this methodology to determine
the history of displacement and velocity of the structure's vibration, through signals measured with
accelerometers.
Computer Based Model to Filter Real Time Acquired Human Carotid PulseCSCJournals
This document describes a computer-based model developed in Simulink to filter real-time acquired human carotid pulse signals. The model uses digital filtering techniques like FIR filters, IIR notch filters, spectrum analysis, and convolution to filter noise from carotid pulses acquired non-invasively using a piezoelectric sensor. The techniques are tested on real-time carotid data and results show the designed filters and techniques accurately filter noise and provide an easy way to acquire and analyze bio-signals on a computer in real-time.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
This document summarizes research on using filtered acoustic emission signals to monitor the condition of rolling element bearings. The researchers collected acoustic emission data from both healthy and defective bearings. They applied three active noise cancellation techniques (LMS, EMD, wavelet) to filter the noisy acoustic signals and compared their performance based on SNR and MSE, finding that EMD provided the best filtering. Time, frequency, and time-frequency analyses were then used to analyze the filtered signals and diagnose bearing faults. The analyses clearly showed differences between healthy and defective bearings and could detect different types of defects. The research demonstrates that acoustic emission monitoring combined with noise filtering is effective for rolling element bearing condition monitoring and fault diagnosis.
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
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.
Characterization of transients and fault diagnosis in transformer by discreteIAEME Publication
This document discusses using discrete wavelet transform (DWT) and artificial neural networks (ANN) to characterize transients and diagnose faults in transformers. It begins with an introduction to the problem and background on using the second harmonic component for discrimination. It then discusses why time-frequency information is needed and the advantages of wavelet transforms over Fourier transforms. The document describes collecting data from a test transformer under normal and faulted conditions. It explains using DWT for feature extraction and visualizing the wavelet decomposition levels to characterize magnetizing inrush versus inter-turn faults. Finally, it proposes using ANN trained on the wavelet spectral energies for automated discrimination between fault cases.
IRJET- Design Simulation and Analysis of Efficient De-Noising of ECG Signals ...IRJET Journal
This document discusses techniques for removing noise from electrocardiogram (ECG) signals, including adaptive filtering algorithms and a patch-based method. It first provides background on ECG signals and sources of noise that can interfere with diagnosis. Adaptive filters like least mean square (LMS) and recursive least squares (RLS) are introduced to update filter coefficients based on the signal environment. Simulation results show an ECG signal contaminated with powerline noise can be effectively filtered using LMS. The document also explores a patch-based nonlocal means method previously used for image denoising and applies it to remove noise from ECG signals.
IRJET- Analysis of Electroencephalogram (EEG) SignalsIRJET Journal
The document analyzes EEG signals to classify emotions. EEG signals were collected from 30 subjects aged 18-25 using a Neurosky Mindwave sensor with electrodes on the forehead and ear. The analog EEG data was converted to digital by an Arduino UNO and sent to a computer. Feature extraction using continuous wavelet transform, probability distribution function, peak plot, and fast Fourier transform was performed on the EEG data in LabVIEW. The analysis classified emotions with 90.69% accuracy and 85.55% sensitivity.
Method to Measure Displacement and Velocityfrom Acceleration SignalsIJERA Editor
This paper discusses a methodology for measuring the displacement and velocity vibration of a
structure from a noisy acceleration signal. Digital FIR (finite impulse response)filters were used to remove the
noise present in the signal as well to eliminate numerical integration problems which produce offsets in velocity
and displacement amplitudes. The choice of this type of filter aims to prevent the signal delay that may cause
distortions in numerical integration process. To validate this methodology the defined procedure was applied to
determine the deflection history of a beam with random excitation base. The acceleration signals considered as
input data were obtained from two piezoelectric accelerometers used to measure the vibration of the base and
the beam response. The correlation was defined between the stress measured by a strain gage and the stress
calculated from its deflection. The result was great, enabling the application of this methodology to determine
the history of displacement and velocity of the structure's vibration, through signals measured with
accelerometers.
Computer Based Model to Filter Real Time Acquired Human Carotid PulseCSCJournals
This document describes a computer-based model developed in Simulink to filter real-time acquired human carotid pulse signals. The model uses digital filtering techniques like FIR filters, IIR notch filters, spectrum analysis, and convolution to filter noise from carotid pulses acquired non-invasively using a piezoelectric sensor. The techniques are tested on real-time carotid data and results show the designed filters and techniques accurately filter noise and provide an easy way to acquire and analyze bio-signals on a computer in real-time.
Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission IJECEIAES
This document summarizes research on using filtered acoustic emission signals to monitor the condition of rolling element bearings. The researchers collected acoustic emission data from both healthy and defective bearings. They applied three active noise cancellation techniques (LMS, EMD, wavelet) to filter the noisy acoustic signals and compared their performance based on SNR and MSE, finding that EMD provided the best filtering. Time, frequency, and time-frequency analyses were then used to analyze the filtered signals and diagnose bearing faults. The analyses clearly showed differences between healthy and defective bearings and could detect different types of defects. The research demonstrates that acoustic emission monitoring combined with noise filtering is effective for rolling element bearing condition monitoring and fault diagnosis.
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
Standards define measurement units and allow for comparison of measurements. Standards organizations establish standards through consensus. International standards are defined at the international level, while primary standards are maintained at the national level in national laboratories. Secondary standards are used in industry labs to calibrate working standards, which are used daily to calibrate instruments. Standards organizations exist at the international, regional, and national levels. The largest international standards organizations are ISO, IEC, and ITU. National standards bodies represent their countries within international standards organizations. Quality assurance ensures a high quality of products and services by confirming that quality requirements are met through planning, fulfilling, and monitoring activities.
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.
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.
Design and Implementation of PCB Using CNCIRJET Journal
This document summarizes the design and implementation of an automatic PCB drilling machine using Arduino Uno. The machine extracts drill coordinates from an Eagle PCB design software file. It uses stepper motors controlled by an Arduino Uno microcontroller to move the drill head according to the coordinates. This allows the machine to accurately drill holes and cut traces on a PCB based on the digital design file. The document reviews previous work optimizing PCB drilling parameters and describes the hardware and software design of the proposed machine. It explains how the Arduino controls the stepper motors to position the drill based on G-code generated from the Eagle file. This automatic process reduces time and effort for PCB fabrication compared to manual methods.
This document summarizes a study that models and compares fuzzy PID and PSD controllers for regulating temperature in a discrete thermodynamic system. It describes the design of the thermodynamic system and measurement chain used, which includes temperature and humidity sensors connected to control software. Transient characteristics of the system were determined and fitted to a first-order model. The PSD controller coefficients were then calculated using Kuhn's method for a first-order system. The fuzzy PID controller structure and use of fuzzy logic for control is also discussed.
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.
Analysis of intelligent system design by neuro adaptive control no restrictioniaemedu
This document discusses using neuro-adaptive control to analyze the design of intelligent systems. It begins by introducing the topic and noting that conventional adaptive control techniques assume explicit system models or dynamic structures based on linear models, which may not be valid for complex nonlinear systems. Neural networks and other intelligent control approaches that do not require explicit mathematical modeling are presented as alternatives. The paper then focuses on using time-delay neural networks for system identification and control of nonlinear dynamic systems. Various neural network architectures and learning algorithms for system modeling and control are described.
Analysis of intelligent system design by neuro adaptive controliaemedu
This document summarizes the analysis of intelligent system design using neuro-adaptive control methods. It discusses using neural networks for system identification through series-parallel and parallel models. It also discusses supervised control using a neural network trained by an expert operator, inverse control using a neural network trained on the inverse system model, and neuro-adaptive control using two neural networks - one for system identification and one for control. Neuro-adaptive control allows handling nonlinear system behavior without linear approximations.
Performance analysis of ecg qrs complex detection using morphological operatorsIAEME Publication
The QRS complex detection is one of the most essential tasks in ECG analysis. This paper
presents an algorithm of QRS complex detection using morphological operators. The proposed
algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background
noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal
and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH
arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high
speed.
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.
The document discusses distributing a magazine through different media institutions. It considers whether an independent distributor or a large global publisher would be better. While an independent distributor would allow the magazine to succeed on its own, a large institution could provide more publicity and promotion. The document concludes that for the hobby-focused content of the magazine, distribution in stores that sell brands like Nike, Adidas, and Puma would be most beneficial to reach the target audience.
The document discusses a proposed market research study to understand factors that college students consider when buying tennis shoes. It will survey students at the mall about the importance of cost, comfort, and appearance/style. The study aims to determine which factor is most influential for different demographic groups. Results will help Nike understand consumer priorities for tennis shoe purchases.
Design and MATLAB Simulation of a Fuel Cell Based Interleaved Buck Converter ...IOSR Journals
1. The document describes the design and MATLAB simulation of an interleaved buck converter with low switching losses and an improved conversion ratio that is powered by a fuel cell.
2. A new interleaved buck converter topology is proposed that connects two active switches in series and employs a coupling capacitor. This reduces the voltage stress across the switches and improves efficiency by lowering switching losses.
3. The dynamics of a polymer electrolyte membrane fuel cell stack are modeled in MATLAB/Simulink to serve as the DC input source. Both the proposed and conventional interleaved buck converters are simulated and their outputs are compared.
This document contains a custom hotkey configuration file for Warcraft III DotA Allstars 6.73. It assigns hotkeys to common actions like move, attack, and stop. It also assigns hotkeys to hero abilities and spells for different neutral creeps. The file was created with a custom key generator tool and includes instructions for downloading an extension to enable the hotkeys.
A New Theoretical Approach to Location Based Power Aware RoutingIOSR Journals
This document proposes a new theoretical approach to location based power aware routing in mobile ad hoc networks (MANETs). It aims to extend the network lifetime by improving power utilization during routing. The approach uses nodes' location information, remaining battery power, and bandwidth status to assign link stability and select routes with lower "uptime values" and minimum bandwidth over time. This is hypothesized to better utilize nodes' power sources and bandwidth. The document outlines calculating a root up time factor for each node based on its power backup and required power, and only using nodes with maximum backup. It concludes future work will design and validate a new protocol based on this approach.
Heart Attack Prediction System Using Fuzzy C Means ClassifierIOSR Journals
This document presents a heart attack prediction system using a fuzzy C-means classifier. The system utilizes 13 patient attributes as inputs to the fuzzy C-means classifier to determine the risk of a heart attack. The classifier was tested on medical records from 270 patients and achieved a classification accuracy of 92%. Fuzzy C-means clustering allows data points to belong to multiple clusters, providing a more efficient and cost-effective way to predict the likelihood of patients experiencing a heart attack compared to other algorithms.
Standards define measurement units and allow for comparison of measurements. Standards organizations establish standards through consensus. International standards are defined at the international level, while primary standards are maintained at the national level in national laboratories. Secondary standards are used in industry labs to calibrate working standards, which are used daily to calibrate instruments. Standards organizations exist at the international, regional, and national levels. The largest international standards organizations are ISO, IEC, and ITU. National standards bodies represent their countries within international standards organizations. Quality assurance ensures a high quality of products and services by confirming that quality requirements are met through planning, fulfilling, and monitoring activities.
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.
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.
Design and Implementation of PCB Using CNCIRJET Journal
This document summarizes the design and implementation of an automatic PCB drilling machine using Arduino Uno. The machine extracts drill coordinates from an Eagle PCB design software file. It uses stepper motors controlled by an Arduino Uno microcontroller to move the drill head according to the coordinates. This allows the machine to accurately drill holes and cut traces on a PCB based on the digital design file. The document reviews previous work optimizing PCB drilling parameters and describes the hardware and software design of the proposed machine. It explains how the Arduino controls the stepper motors to position the drill based on G-code generated from the Eagle file. This automatic process reduces time and effort for PCB fabrication compared to manual methods.
This document summarizes a study that models and compares fuzzy PID and PSD controllers for regulating temperature in a discrete thermodynamic system. It describes the design of the thermodynamic system and measurement chain used, which includes temperature and humidity sensors connected to control software. Transient characteristics of the system were determined and fitted to a first-order model. The PSD controller coefficients were then calculated using Kuhn's method for a first-order system. The fuzzy PID controller structure and use of fuzzy logic for control is also discussed.
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.
Analysis of intelligent system design by neuro adaptive control no restrictioniaemedu
This document discusses using neuro-adaptive control to analyze the design of intelligent systems. It begins by introducing the topic and noting that conventional adaptive control techniques assume explicit system models or dynamic structures based on linear models, which may not be valid for complex nonlinear systems. Neural networks and other intelligent control approaches that do not require explicit mathematical modeling are presented as alternatives. The paper then focuses on using time-delay neural networks for system identification and control of nonlinear dynamic systems. Various neural network architectures and learning algorithms for system modeling and control are described.
Analysis of intelligent system design by neuro adaptive controliaemedu
This document summarizes the analysis of intelligent system design using neuro-adaptive control methods. It discusses using neural networks for system identification through series-parallel and parallel models. It also discusses supervised control using a neural network trained by an expert operator, inverse control using a neural network trained on the inverse system model, and neuro-adaptive control using two neural networks - one for system identification and one for control. Neuro-adaptive control allows handling nonlinear system behavior without linear approximations.
Performance analysis of ecg qrs complex detection using morphological operatorsIAEME Publication
The QRS complex detection is one of the most essential tasks in ECG analysis. This paper
presents an algorithm of QRS complex detection using morphological operators. The proposed
algorithm utilizes the dilation-erosion mathematical morphology filtering to suppress the background
noise and remove the baseline drift. Then the modulus accumulation is used to enhance the signal
and improve signal-to-noise ratio. The performance of the algorithm is evaluated with MIT-BIH
arrhythmia database and wearable ECG Data. The algorithm gets the high detection rate and high
speed.
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.
The document discusses distributing a magazine through different media institutions. It considers whether an independent distributor or a large global publisher would be better. While an independent distributor would allow the magazine to succeed on its own, a large institution could provide more publicity and promotion. The document concludes that for the hobby-focused content of the magazine, distribution in stores that sell brands like Nike, Adidas, and Puma would be most beneficial to reach the target audience.
The document discusses a proposed market research study to understand factors that college students consider when buying tennis shoes. It will survey students at the mall about the importance of cost, comfort, and appearance/style. The study aims to determine which factor is most influential for different demographic groups. Results will help Nike understand consumer priorities for tennis shoe purchases.
Design and MATLAB Simulation of a Fuel Cell Based Interleaved Buck Converter ...IOSR Journals
1. The document describes the design and MATLAB simulation of an interleaved buck converter with low switching losses and an improved conversion ratio that is powered by a fuel cell.
2. A new interleaved buck converter topology is proposed that connects two active switches in series and employs a coupling capacitor. This reduces the voltage stress across the switches and improves efficiency by lowering switching losses.
3. The dynamics of a polymer electrolyte membrane fuel cell stack are modeled in MATLAB/Simulink to serve as the DC input source. Both the proposed and conventional interleaved buck converters are simulated and their outputs are compared.
This document contains a custom hotkey configuration file for Warcraft III DotA Allstars 6.73. It assigns hotkeys to common actions like move, attack, and stop. It also assigns hotkeys to hero abilities and spells for different neutral creeps. The file was created with a custom key generator tool and includes instructions for downloading an extension to enable the hotkeys.
A New Theoretical Approach to Location Based Power Aware RoutingIOSR Journals
This document proposes a new theoretical approach to location based power aware routing in mobile ad hoc networks (MANETs). It aims to extend the network lifetime by improving power utilization during routing. The approach uses nodes' location information, remaining battery power, and bandwidth status to assign link stability and select routes with lower "uptime values" and minimum bandwidth over time. This is hypothesized to better utilize nodes' power sources and bandwidth. The document outlines calculating a root up time factor for each node based on its power backup and required power, and only using nodes with maximum backup. It concludes future work will design and validate a new protocol based on this approach.
Heart Attack Prediction System Using Fuzzy C Means ClassifierIOSR Journals
This document presents a heart attack prediction system using a fuzzy C-means classifier. The system utilizes 13 patient attributes as inputs to the fuzzy C-means classifier to determine the risk of a heart attack. The classifier was tested on medical records from 270 patients and achieved a classification accuracy of 92%. Fuzzy C-means clustering allows data points to belong to multiple clusters, providing a more efficient and cost-effective way to predict the likelihood of patients experiencing a heart attack compared to other algorithms.
A Novel PSNR-B Approach for Evaluating the Quality of De-blocked Images IOSR Journals
This document discusses evaluating the quality of deblocked images using different quality assessment metrics. It proposes a new metric called PSNR-B that includes a blocking effect factor in PSNR calculations. The document compares PSNR-B to PSNR and SSIM metrics. It studies the effect of quantization step size on measured image quality and analyzes how deblocking algorithms like lowpass filtering can reduce blocking artifacts but also introduce new distortions. Simulation results show PSNR-B correlates better than PSNR with subjective quality judgments of deblocked images.
Comparative study of IPv4 & IPv6 Point to Point Architecture on various OS pl...IOSR Journals
This document provides a summary of a comparative study on the performance of IPv4 and IPv6 protocols under different operating systems. The study analyzed bandwidth utilization, round trip time, and overhead for IPv4 and IPv6 in point-to-point configurations under Windows 2007, Mac OS, and Red Hat Linux. Experiments were conducted between 3 PCs configured for IPv4 and IPv6 communications over an unloaded network with 3 routers and 3 workstations. Key differences between IPv4 and IPv6 such as address length, header fields, and transition mechanisms are also outlined.
Legislative Council FTU HCMC received the Best Finance Award for Term 13-14. They conducted legislative and auditing meetings to improve financial policies, procedures, and internal processes. Nearly 50% of monthly budget reports were submitted to an online Dropbox and key financial metrics like DSO, asset turnover, debt ratio, and returns indicated strong financial sustainability. Initiatives were also taken to implement online accounting and contribute to national pricing projects.
El documento parece ser un título o encabezado que incluye los meses de Noviembre y Diciembre del año 2012, pero no proporciona ningún otro contenido o detalles sobre el tema.
Medial Axis Transformation based Skeletonzation of Image Patterns using Image...IOSR Journals
1) The document discusses extracting the medial axis transform (MAT) of an image pattern using the Euclidean distance transform. The image is first converted to binary, then the Euclidean distance transform is used to compute the distance of each non-zero pixel to the closest zero pixel.
2) The medial axis transform represents the core or skeleton of an image pattern. There are different algorithms for extracting the skeleton or medial axis, including sequential and parallel algorithms. The skeleton provides a simple representation that preserves topological and size characteristics of the original shape.
3) The document provides background on medial axis transforms and different skeletonization algorithms. It then describes preparing the binary image and applying the Euclidean distance transform to extract the MAT and skeleton
Mobile Networking and Mobile Ad Hoc Routing Protocol ModelingIOSR Journals
This document summarizes a research paper about modeling mobile ad hoc networks and routing protocols. It discusses modeling the network topology and connectivity between nodes. It also describes modeling the routing protocol instances running on each node. The document outlines different approaches to modeling the network, including explicitly modeling topology and transitions, parameterizing based on network properties, and developing a universal quantification model. It discusses techniques for coping with state explosion when modeling mobile ad hoc networks, such as symbolic representation, partial order reduction, and abstraction.
The document presents a compartmental model for characterizing the spread of malware in peer-to-peer (P2P) networks like Gnutella. The model partitions peers into compartments based on their state - those wishing to download (S), currently downloading (E), having downloaded (I), and no longer interested (R). Differential equations track changes between compartments over time. Simulation results show the model effectively captures the impact of parameters like peer online/offline switching rates and quarantine strategies on malware intensity. The model improves on prior work by incorporating user behavior dynamics and limiting malware spread to a node's time-to-live range.
In the heart of the French Alps Eider
has been working since 1962 to
bring together a passion for mountain
sports, a knowledge of textiles
and a constant search for innovation
in a style that is unique and modern.
BREATHE, IT IS SUMMERTIME !
No matter if you are mountain climbing, running or hiking
Eider has been working with the most technical fabrics so that your body can breathe while active.
The 2012 summer key-word: BREATHABILITY. When your body is working hard, your
clothes are breathing.
This document provides information about transportation in Lübeck, Germany. It discusses how students travel to school, including data showing most use bicycles. It also addresses air and noise pollution levels in Lübeck. Specifically, it notes that particulate limits were exceeded on some days in 2011-2012. The document outlines plans to expand transportation infrastructure and makes comparisons between sustainable and unsustainable transportation modes in the region.
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Development and Validation of prediction for estimating resting energy expend...IOSR Journals
This document describes a study conducted to develop prediction equations for estimating resting energy expenditure (REE) in Indian subjects. Researchers measured body composition parameters of 100 Indian subjects using bioelectrical impedance analysis at frequencies of 5 kHz, 50 kHz, 100 kHz, and 200 kHz. Multiple regression analysis was used to develop two sets of REE prediction equations: 1) equations estimating REE at each frequency based on sex, age, weight, and impedance index, and 2) an equation estimating overall REE based on sex, age, fat-free mass, and fat mass. The predicted REE values from the equations closely matched measured REE values from the instrument, validating the developed prediction equations as the first such equations for Indian subjects
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
A Study of Ball Bearing’s Crack Using Acoustic Signal / Vibration Signal and ...INFOGAIN PUBLICATION
The field of fault diagnostic in rotating machinery is vast, including the diagnosis of items such as rotating shafts, rolling element bearings, couplings, gears and so on. Vibration analysis is the main condition monitoring technique for machinery maintenance. The different types of faults that are observed in these areas and the methods of their diagnosis are accordingly great, including vibration analysis, model-based techniques, and statistical analysis and artificial intelligence techniques. However, they have difficulties with certain applications whose behavior is non-stationary and transient nature.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
An Algorithm Based On Discrete Wavelet Transform For Faults Detection, Locati...paperpublications3
Abstract: An electric power distribution system is the final stage in the delivery of electric power; it carries electricity from the transmission system to individual consumers. Fault classification and location is very important in power system engineering in order to clear fault quickly and restore power supply as soon as possible with minimum interruption. Hence, ensuring its efficient and reliable operation is an extremely important and challenging task. With availability of inadequate system information, locating faults in a distribution system pose a major challenge to the utility operators. In this paper, a faults detection, location and classification technique using discrete wavelet multi-resolution approach for radial distribution systems are proposed. In this distribution network, the current measurement at the substation have been utilized and is demonstrated on 9-bus distribution system. Also in this work distribution system model was developed and simulated using power system block set of MATLAB to obtain fault current waveforms. The waveforms were analyzed using the Discrete Wavelet Transform (DWT) toolbox by selecting suitable wavelet family. It was estimated and achieved using Daubechies ‘db5’ discrete wavelet transform.
A machine learning algorithm for classification of mental tasks.pdfPravinKshirsagar11
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Implementation of adaptive stft algorithm for lfm signalseSAT Journals
Abstract
Normally Time-Frequency analysis is done by sliding a window through the time domain data and computing the Fourier
Transform of the data within the window. The choice of the window length determines whether specular or resonant information
will be emphasized. A narrow window will isolate specular reflections but will not be wide enough to accommodate the slowly
varying global resonances; a wide window cannot temporally separate resonance and specular information. So we will adapt
window length according to changes in frequencies. In this case we are realizing the specifications of Linear Frequency
Modulation (LFM) signal.
Index Terms—LFM, FFT, DFT, STFT and ASTFT.
Comparison of signal smoothing techniques for use in embedded system for moni...Dalton Valadares
Paper about the comparison between some signal smoothing techniques for use in an embedded system responsible for monitoring the biofuels quality, specificaly the oxidative stability.
This document describes algorithms for detecting single radio pulses in real-time using graphics processing units (GPUs). It presents two new algorithms that use incomplete sets of boxcar filters to detect pulses at accelerated speeds with minimal signal loss. The algorithms were tested on simulated data and were found to process data 266-500 times faster than real-time on GPUs, detecting pulses with a mean 7% reduction in signal power.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document presents a study on using acoustic signal analysis to detect faults in bearings. The study develops an experimental setup to acquire acoustic signals from bearings under different conditions, including with and without defects. The acoustic signals are processed using techniques like fast Fourier transforms and wavelet transforms to extract information about faults. Signals are analyzed from bearings with no defects, misalignment, looseness, missing balls, and combinations of defects. Results show the acoustic signal energy at different frequencies for healthy and faulty bearings. This acoustic signal analysis technique can be used to detect bearing faults and failures.
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.
EFFICIENCY ENHANCEMENT BASED ON ALLOCATING BIZARRE PEAKSijwmn
A new work has been proposed in this paper in order to overcome one of the main drawbacks that found in
the Orthogonal Frequency Division Multiplex (OFDM) systems, namely Peak to Average Power Ratio
(PAPR). Furthermore, this work will be compared with a previously published work that uses the neural
network (NN) as a solution to remedy this deficiency.
The proposed work could be considered as a special averaging technique (SAT), which consists of wavelet
transformation in its first stage, a globally statistical adaptive detecting algorithm as a second stage; and
in the third stage it replaces the affected peaks by making use of moving average filter process. In the NN
work, the learning process makes use of a previously published work that is based on three linear coding
techniques.
In order to check the proposed work validity, a MATLAB simulation has been run and has two main
variables to compare with; namely BER and CCDF curves. This is true under the same bandwidth
occupancy and channel characteristics. Two types of tested data have been used; randomly generated data
and a practical data that have been extracted from a funded project entitled by ECEM. From the achieved
simulation results, the work that is based on SAT shows promising results in reducing the PAPR effect
reached up to 80% over the work in the literature and our previously published work. This means that this
work gives an extra reduction up to 15% of our previously published work. However, this achievement will
be under the cost of complexity. This penalty could be optimized by imposing the NN to the SAT work in
order to enhance the wireless systems performance.
EFFICIENCY ENHANCEMENT BASED ON ALLOCATING BIZARRE PEAKSijwmn
A new work has been proposed in this paper in order to overcome one of the main drawbacks that found in
the Orthogonal Frequency Division Multiplex (OFDM) systems, namely Peak to Average Power Ratio
(PAPR). Furthermore, this work will be compared with a previously published work that uses the neural
network (NN) as a solution to remedy this deficiency.
The proposed work could be considered as a special averaging technique (SAT), which consists of wavelet
transformation in its first stage, a globally statistical adaptive detecting algorithm as a second stage; and
in the third stage it replaces the affected peaks by making use of moving average filter process. In the NN
work, the learning process makes use of a previously published work that is based on three linear coding
techniques.
In order to check the proposed work validity, a MATLAB simulation has been run and has two main
variables to compare with; namely BER and CCDF curves. This is true under the same bandwidth
occupancy and channel characteristics. Two types of tested data have been used; randomly generated data
and a practical data that have been extracted from a funded project entitled by ECEM. From the achieved
simulation results, the work that is based on SAT shows promising results in reducing the PAPR effect
reached up to 80% over the work in the literature and our previously published work. This means that this
work gives an extra reduction up to 15% of our previously published work. However, this achievement will
be under the cost of complexity. This penalty could be optimized by imposing the NN to the SAT work in
order to enhance the wireless systems performance. Orthogonal Frequency Division Multiplexing
Fault Diagnosis of a High Voltage Transmission Line Using Waveform Matching A...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Design of Scalable FFT architecture for Advanced Wireless Communication Stand...IOSRJECE
Now a day’s numerous wireless communication standards have raised additional stringent requirements on each throughput and flexibility for FFT computation. Advanced wireless systems support multiple standards to satisfy the demands of user application necessities. A wireless system whereas supporting multiple standards should also satisfy performance necessities of these supported standards. Meeting performance requirements of multiple standards is a challenge while designing a system. Fast Fourier transformations, a kernel processing task in communication systems, are studied intensively for efficient software and hardware implementations. To design an efficient system, it's necessary to efficiently design its performance critical component. each system must meet stringent design parameters like high speed, low power, low area, low cost, high flexibility and high scalability, designing FFT processor to support multiple wireless standards whereas meeting the above such performance necessities is a difficult task. This paper proposed a highly efficient scalable architecture, software tools design, and design implementation. The reconstruction of the FFT computation flow is design into a scalable structure. The FFT can be easily expanded for any-point FFT computation. The various parameters satisfied the conditions, gives proper and efficient outputs as compare to other platforms.
Performance analysis of ecg qrs complex detection using morphological operatorsIAEME Publication
The document summarizes a research paper that proposes a new algorithm for detecting QRS complexes in electrocardiogram (ECG) signals using mathematical morphology operators. The algorithm utilizes dilation-erosion filtering to suppress noise and remove baseline drift. It then applies modulus accumulation to enhance the signal and improve the signal-to-noise ratio. The algorithm is evaluated using standard ECG databases and achieves high detection rates with high speed.
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Implementation Of Grigoryan FFT For Its Performance Case Study Over Cooley-Tu...ijma
This document discusses the implementation and performance comparison of two fast Fourier transform (FFT) algorithms - the Cooley-Tukey FFT and the Grigoryan FFT - on three Xilinx FPGAs. The Grigoryan FFT uses a decomposition based on paired transforms, while the Cooley-Tukey FFT uses a radix-2 decomposition. Both algorithms were implemented on Virtex-II Pro, Virtex-5, and Virtex-4 FPGAs. The results showed that the Grigoryan FFT operated at higher sampling rates and was faster than the Cooley-Tukey FFT. Additionally, the Virtex-5 FPGA provided the highest speed for implementing the Grigoryan FFT compared
Signal Processing and Soft Computing Techniques for Single and Multiple Power...idescitation
This paper reviews various techniques used for detection and classification of power quality events. It divides the techniques into those for single events versus combined events. For single events, techniques like wavelet transforms, statistical estimators, and intelligent methods are discussed. For combined events, papers addressing harmonic disturbances combined with others are summarized. The paper also includes a table providing a comparative analysis of several references based on aspects like classification accuracy, noise tolerance, and computation time. It concludes that the field is growing and future work could address techniques for large data and detection of both single and combined disturbances simultaneously.
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
This document describes modeling an adaptive controller for an aircraft roll control system using PID, fuzzy-PID, and genetic algorithm. It begins by introducing the aircraft roll control system and motivation for developing an adaptive controller to minimize errors from noisy analog sensor signals. It then provides the mathematical model of aircraft roll dynamics and describes modeling the real-time flight control system in MATLAB/Simulink. The document evaluates PID, fuzzy-PID, and PID-GA (genetic algorithm) controllers for aircraft roll control and finds that the PID-GA controller delivers the best performance.
1. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735. Volume 5, Issue 4 (Mar. - Apr. 2013), PP 21-30
www.iosrjournals.org
Computational Time Analysis of Signal Processing Algorithm-An
Analysis
Gokul P*, Karthikeyan T* , KrishnaKumar R.* Malini S.**
* Final Year ECE students, Department of Electronics and Communication Engineering,
**Assistant Professor, Department of Electronics and Communication Engineering, SVS College Of
Engineering, Coimbatore
Abstract: Current manufacturing systems, including dedicated transfer lines and flexible manufacturing
systems, do not provide adequate and most economical solutions for products with variable market demands.
One of the crucial problems in manufacturing is the machine tool failure due to loss of surface material in
cutting operations like drilling and milling. Proceeding with a damaged tool may result in unfit fabricated work
piece. On the other hand, it is unnecessary to change the cutting tool if it is still able to continue cutting
operation. Hence, an effective diagnosis mechanism is necessary for the automation of machining processes so
that production loss and downtime can be avoided. This study concerns with the development of a tool wear
condition-monitoring technique. Tool Wearing Monitoring technique can be established based on fuzzy logic.
Fuzzy logic scheme explains Tool wearing Monitoring based on Signal processing by obtaining statistical
parameters derived from thrust force, machine sound and vibration signals were used as inputs to fuzzy process
and their output values serves as the input parameters to the second stage. The second stage with a remarkable
threshold function, then assess the condition of the tool. Index Terms- FFT, Singular Spectrum Analysis, Tool
Wearing Monitoring.
I. Introduction
Most natural phenomena occur continuously in time, such as the variations of temperature of the
human body, forces exerted by muscles, or electrical potentials generated on the surface of the scalp. These are
analogue signals, being able to take on any value (though usually limited to a finite range). They are also
continuous in time, i.e. at all instants in time is their value available. However, analogue, continuous-time
signals are not suitable for processing on the now ubiquitous computer-type processors (or other digital
machines), which are built to deal with sequential computations involving numbers. These require digital
signals, which are formed by sampling the original analogue data.
The theory of sampling was developed in the early twentieth century by Nyquist and others, and
revolutionized signal processing and analysis especially from the 1960s onwards, when the appropriate
computer technology became widely available. The rapid development of high-speed digital integrated circuit
technology in the last three decades has made digital signal processing the technique of choice for many most
applications, including multimedia, speech analysis and synthesis, mobile radio, sonar, radar, seismology and
biomedical engineering. Digital signal processing presents many advantages over analog approaches: digital
machines are flexible, reliable, easily reproduced and relatively cheap. As a consequence, many signal
processing tasks originally performed in the analog domain are now routinely implemented in the digital
domain, and others can only feasibly be implemented in digital form. In most cases, a digital signal processing
systems is implemented using software on a general-purpose digital computer or digital signal processor (DSP).
Signal processing applications includes medical applications, where signal analysis has been widely
applied in patient monitoring, diagnosis and prognosis, as well as physiological investigation and in some
therapeutic settings (e.g. muscle and sensory stimulation, hearing aids). Early work on tool wear monitoring
focused on time series methods and frequency domain analysis. With these methods, a threshold value needs to
be set between the normal and abnormal tool states. However, the threshold value varies with cutting conditions.
To improve the performance of tool failure sensing, more advanced methods, such as pattern recognition
analysis and statistical methods have been developed. These methods have gained various degrees of success in
practical applications for monitoring cutting processes. More recently, artificial neural networks (ANN) and
their combination with fuzzy logic models have been extensively applied to the area of tool wear estimation.
ANN has the advantages of superior learning, noise suppression and parallel computation abilities. However,
successful application of an ANN-based monitoring system is strongly dependent on the proper selection of the
type of network structure and needs adequate training data, which is not always available, especially for the
abnormal tool state. The traditional signal processing, statistical and pattern recognition approaches generally
assume that the sensor signals are stationary. However, due to the nature of manufacturing processes, the sensor
signals are usually non stationary. The non-stationary nature of signals is due to nonlinearity and/or time
www.iosrjournals.org 21 | Page
2. Computational Time Analysis of Signal Processing Algorithm-An Analysis
dependency of the process. Thus, the approaches that deal with non-stationary signals are more appropriate for
process monitoring. Many signals, including most from a mechanical based machine like lathe origin, can be
classified as random: repeated recordings result in signals that are all different from each other, but share the
same statistical characteristics. The power spectrum reflects some of the most interesting of these
characteristics. The interpretation and estimation of the power spectral density will be addressed in the final part
of this chapter.The signal processing approach in this paper is carried out with a basic Fast Fourier Transform on
different tools and in identifying the best and worst tool based on sound processing. Further this approach is to
be carried out with Singular Spectrum Analysis and the suitable algorithm is analyzed. This paper is organized
as follows. The principle of the Fourier transform is briefly described in section 2. Following the experimental
setup in section 3, Analysis of the data using the Fourier transform and SSA method are discussed in section 4.
Section 7 presents some results. The conclusions are given in section 8.
II. Signal Processing Algorithms
2.1 Fast Fourier Transform:
A fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier
transform (DFT) and its inverse. An FFT computes the DFT and produces exactly the same result as evaluating
the DFT definition directly; the only difference is that an FFT is much faster. A discrete Fourier transform can
be computed using an FFT by means of the Danielson-Lanczos lemma if the number of points is a power of
two. If the number of points is not a power of two, a transform can be performed on sets of points
corresponding to the prime factors of which is slightly degraded in speed. An efficient real Fourier transform
algorithm or a fast Hartley transform (Brace well 1999) gives a further increase in speed by approximately a
factor of two. Base-4 and base-8 fast Fourier transforms use optimized code, and can be 20-30% faster than
base-2 fast Fourier transforms. prime factorization is slow when the factors are large, but discrete Fourier
transforms can be made fast for N= 2 , 3, 4, 5, 7, 8, 11, 13, and 16 using the Winograd transform algorithm.
Fast Fourier transform algorithms generally fall into two classes: decimation in time, and decimation in
frequency. The Cooley-Tukey FFT algorithm first rearranges the input elements in bit-reversed order, then
builds the output transform (decimation in time). The basic idea is to break up a transform of length into two
transforms of length N/2.
The most commonly used FFT is the Cooley–Tukey algorithm. This is a divide and conquer
algorithm that recursively breaks down a DFT of any composite size N = N1N2 into many smaller DFTs of
sizes N1 and N2, along with O(N) multiplications by complex roots of unity traditionally called twiddle factors.
The most well-known use of the Cooley–Tukey algorithm is to divide the transform into two pieces of size N/2
at each step, and is therefore limited to power-of-two sizes, but any factorization can be used in general (as was
known to both Gauss and Cooley/Tukey). These are called the radix-2 and mixed-radix cases, respectively (and
other variants such as the split-radix FFT have their own names as well). Although the basic idea is recursive,
most traditional implementations rearrange the algorithm to avoid explicit recursion. Also, because the Cooley–
Tukey algorithm breaks the DFT into smaller DFTs, it can be combined arbitrarily with any other algorithm for
the DFT.
The Fast Fourier Transform (FFT) is an algorithm* for transforming data from the time
domain to the frequency domain. Since this is exactly what we want a spectrum analyzer to do, it would seem
easy to implement a Dynamic Signal Analyzer based on the FFT. However, we will see that there are many
factors which complicate this seemingly straightforward task. First, because of the many calculations involved
in transforming domains, the transform must be implemented on a digital computer if the results are to be
sufficiently accurate. Fortunately, with the advent of microprocessors, it is easy and inexpensive to incorporate
all the needed computing power in a small instrument package. Note, however, that we cannot now transform to
the frequency domain in a continuous manner, but instead must sample and digitize the time domain input.
2.1.1 Resolution and Range
FFT takes N sample points and returns N/2 frquency lines.
Some higher-frequency lines are lost because of the anti-aliasing filter
Highest frequency controlled by the sampling rate.
Lowest frequency controlled by block size.
Highest frequency Fa = Fs/2 (Nyquist theorem)
Lowest frequency = 1/T (block fundamental frequency)
Lines evenly spaced in between. Spacing = 1/T.
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3. Computational Time Analysis of Signal Processing Algorithm-An Analysis
III. Experimental Model
3.1 SOUND SIGNAL IN A CNC LATHE:
Cutting tests were performed on a 30hp CNC lathe. An accelerometer was mounted on the cutting tool
holder attached to the turret, as shown in Figure 1, to measure vibration in the feed direction. This is because
preliminary results have shown the vibration signal in the feed direction to
be more sensitive than those in the cutting and radial directions in detecting tool wear. The vibration signals
were first amplified using a charge amplifier and low-pass filtered with cut-off frequency of 6 kHz, and then
sampled at 100 kHz using a 12-bit data acquisition card. Every data set was 0.1s in length, equivalent to 50 k
data points. Cutting started with a sharp insert and was stopped after every minute for tool wear measurement
using a toolmaker‘s microscope. When the cutting edge develops an average flank wear height of at least 0.3mm
or the maximum wear height of 0.6mm, it is considered to be a worn out edge. This limit was chosen in
accordance with the criteria recommended by ISO 3685 to define effective tool life for carbide tools [ISO,1993].
Figure 1.1 :Experimental Setup for measuring Sound Signal from a CNC Lathe
The experiments were conducted to detect two tool states, namely a sharp tool and a worn tool. The work
piece material and cutting conditions are shown below
Fig (a) Good tool
Fig (b) 20% Worn tool
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4. Computational Time Analysis of Signal Processing Algorithm-An Analysis
Fig (c) 40% Worn tool
IV. Methodology:
This chapter explains about the principles and process involving in the analysis. The sound is extracted
by the microphone and it is recorded as a wave sound file. The amplitude of the signal determines the condition
of the cutting tool. Hence it is necessary to convert into frequency domain and for this fast Fourier transform is
taken to obtain the signal in frequency domain.
From the report of FFT of the given input signal is varied in amplitude with other. The sound signal is
recorded at the spindle speed of 350 RPM and feed rate is 1mm.
The Flowchart is given below
V. Data Analysis:
This section explains the extraction of distinct features of sensor signal using Fourier transform
analysis. The effectiveness of the approach focussed are shown by the simulated results.
5.1 Fast Fourier Transform Analysis:
The first stage is the extraction of usefulfeatures from the bulk original data signals. Thisprocess is
performed by computing the discretewavelet coefficients of the framed data signal. Todistinguish between a
worn tool and a sharp tool, itis necessary to visually examine the time frequencyplanes generated by fast fourier
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5. Computational Time Analysis of Signal Processing Algorithm-An Analysis
transformfrom different tool states. In Figure 2, shows the tool selected for our experimental analysis. variations
ofthe coefficients of the wavelet transformations are presented for a normal and a worn tool. The horizontal axis
represents time. The vertical axis represents the amplitude of coefficients. Scales 1-6are equivalent to frequency
bandwidths from low to high. It can be seen from Figure 2 (a) that the turning vibration signal from the sharp
tool can be mainly characterized by lower frequency coefficients (lower scales 1-3). The higher scalecoefficients
are very small, and can be neglected. Thus the characteristic of sharp tool signals can be represented by a small
number of coefficients.This makes the wavelet transforms very efficient.The same observation can be made for
a worn tool from Figure 2 (b).Referring to the same figures, it can be seen that the distribution of intensities is
different for a sharp and a worn tool. The highest energy for a sharp tool concentrates on the lowest scale.
However, this shifts to the next higher frequency region (higher scale) when the tool is worn out. The results
presented in this paper are typical experimental results obtained so far. The work is underway to verify
consistency, repeatability and reliability of the approach, and the results will be reported in the future.
VI. Tool wear estimation
The relationship between the AE signal and tool wear is not simple. Kim et al. [18] observed the purely
progressive Tools wear in turning operations. As a result, they found that in most experimental results the
refined mean level (RML) of the averaged AE signal increases at first with an increase of flank wear, and then
stays at an approximately constant level even with further increase of flank wear while the fluctuation of the
RML across the constant level becomes rather high. Clearly, the relationship between the AE signal and tool
wear condition is nonlinear, so the general mathematical relation cannot be used to map this relation. If we can
look for an effective mathematical model to map the relationship between the AE signal (some features) and
tool wear, the AE signal could be used to monitor tool wear condition in real time for turning.
Some models have been presented, such as a linear regression model developed to relate the flank
wear of a carbide turning insert with the cumulative count of AE, and the limiting value of the cumulative AE
count for the limiting flank wear was predicted using this model. The AE count rate has been found to be a
reliable parameter for predicting the flank wear of a cutting tool in real time. A linear regression model has been
developed to relate the flank wear of a carbide turning insert with the cumulative count of AE. The correlation
between intrinsic frequencies and AE sources is identified by examining the RMS, dominant amplitude, type,
and count rate of the AE signals. The tool life estimated from the RMS of the AE signal is shown to be in good
agreement with that determined from measurements of the maximum wears and width on the tool nose. The
results obtained demonstrate that AE is an effective technique for in-process wear monitoring and wear
mechanism identification of multilayer ceramic-coated tools. To effectively monitor different tool wear
conditions, while avoiding the effect of other factors such as cutting parameters, some methodologies have been
presented, as follows.
6.1. Fuzzy classifier
Fuzzy c-means algorithm is one of the most popular methods in the fuzzy classifier. In this approach,
the aim in clustering is to determine the cluster centers, which are representative values of features
corresponding to the classified categories. Once clustering centers are determined at the learning stage, then the
classification is made by the comparison of the incoming pattern and each clustering center. Let
X{X1,X2,…,Xn}_R, where each Xi(xi1,xi2,…,xis _R is a feature vector; xij is the jth feature of individual xi.
For each integer c, 2_c_n, let Vcn be the vector space of cn matrices with entries in [0,1], and let uij denote the
ijth element of any U_Vcn. The function ui: X→[0,1] becomes a membership function and is called a fuzzy
subset in X. Here uijui(xj) is called the grade of membership of xj in the fuzzy set ui. In the space of samples,
we suppose that there are n samples, which can be divided into c classes. Consider the following subset of Vcn:
Mfc={U€ Vcn| uij € [0,1] Ɐ i,j ; Σ uij=1 Ɐ j ;Σ uij > 0 Ɐ i }
Each U_Mfc is called a fuzzy c-partition of X; Mfc is the fuzzy c-partition space associated with X. For any real
number m_[1,5], define the real-valued functional J: MfcLc→R by
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6. Computational Time Analysis of Signal Processing Algorithm-An Analysis
1_m__, and usually m2. where U{uik} is the membership function, with uik_[0,1], which denotes the degree
of embership of the kth pattern and ith cluster centers; V{v1,v2,…,vc} is a vector of c clusters. These vi are
interpreted as clusters defined by their companion U matrix, and play a fundamental role in our development.
The functional J is a weight, least squares objective function. In order to obtain the optimum fuzzy partition, this
objective function must be minimized, i.e. minimize {J(U,V)} The optimal solution to the above equation is that
Suppose that under a given cutting condition, the features of training data sets determine a clustering center.
Then all subsequent observations can be classified by using above equation. That is
where uk0 is the fuzzy grade of the current observation being assigned to the kth wear state category and X0 is
the current observation. A wavelet packet transform is used to capture the features of the AE signal, which are
sensitive to the changes in tool wear condition, but are insensitive to the variation in process working conditions
and various noises. The extracted features are classified by using the fuzzy ISODATA algorithm. As a result,
the tool wear condition can be estimated over a wide range of cutting conditions for boring.
6.2 Neural networks
Neural networks are organized in layers each consisting of neurons or processing elements that are
interconnected. There are a number of learning methods to train neural nets but the back-propagation
(back-prop) Paradigm has emerged as the most popular training Mechanism. The back-prop method works
by measuring the difference between output and the observed output value. The values being calculated at
the output layer are propagated to the previous layers and used for Adjusting the connection weights. Fig. 5
shows a typical Multi layered feed forward neural network.
A class of polynomial learning network (PLN) models is used to identify cutting tool conditions; these
multilayered networks have a self-organizing control structure based on the mechanisms of exhibition and
Inhibition.
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7. Computational Time Analysis of Signal Processing Algorithm-An Analysis
Fig. 5. Typical multilayered feedforward neural network.
AE signals from an interrupted face-turning operation are modeled, and tool evolution from normal,
cracked and finally broken tools are discriminated through network connectivity and node weights.
However, feed forward neural networks require expensive training information and cannot remain adaptive
after training. An unsupervised ART2 neural network is used for the fusion of AE and force information
and decision making of the tool flank wear state. In order to overcome neural network drawbacks, a
hybrid model of neural network and fuzzy logic (fuzzy neural network) is presented. There are many
possible combinations of the two systems. A fuzzy neural network k is used to describe the relation
between the monitoring features, derived from wavelet-based AE signals, and the tool wear condition.
VII. Performance Analysis:
In this section, computer simulated results is provided to illustrate the performance of sound signal
obtained through FFT over the different tools selected.
Table: SIMULATION PARAMETERS
No. of Transmit Antennas 2
No. of receive Antennas 2
Modulation Scheme BPSK
Channel Model AWGN
Equalizer ZF,ML,MMSE,MMSE-
SIC
7.1 The performance of the 20% worn tool:
The Zero forcing equalizer is not much suitable. To say, it achieves diversity but doubles the data
rate.It is clear from the following zero forcing equalization, the channel for symbol transmitted from each spatial
antenna is similar to a 1×1 Rayleigh fading channel. Hence it is not much suitable equalizer though very much
simpler than all other equalizers.
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8. Computational Time Analysis of Signal Processing Algorithm-An Analysis
Fig.5.1: Performance of 20 % worn tool
7.2 The performance of 40% worn tool:
Fig.5.2: Performance of 40 % worn tool
The results for 2×2 MIMO with Maximum Likelihood (ML) equalization obtained is closely matching the
1X2 antenna of Maximum Ratio Combining(MRC) type.Of all the equalizer types Maximum Likelihood offers
better BER. The ML equalizer is optimal since it minimizes the probability of a sequence error. Further, this
equalizer requires knowledge of the channel characteristics and statistical distribution of noise corrupting the
signal to compute the metrics for decision making.
7.3 The performance of Good tool:
The results for 2×2 MIMO MMSE equalization shows a 3 dB improvement when compared to zero
forcing equalizer.When noise term is zero, MMSE equalizer reduces to ZF equalizer.
Fig.5.3: Performance of Good tool
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9. Computational Time Analysis of Signal Processing Algorithm-An Analysis
Fig.5.4: Performance without tool
The amplitude and the Condition of the tool is tabulated below
Tool Spindle Feed Amplitude (dB)
Speed rate
(RPM) (mm)
Good tool 350 1 17500
20% Worn tool 350 1 21000
40% Worn tool 350 1 31000
Without tool 350 1 7500
The above mentioned information‘s are the average of no of experimental values. At the specified
spindle speed, there are 5 experiments are taken with same tool for analyzing.
From the tabulation clearly shows that without tool the lathe machine exhibits very low amplitude and
the good tool of lathe machine exhibits slight increase in amplitude. If the tool is worn then its amplitude crosses
above 20000dB at initial frequency and as like 40% worn tool exhibits amplitude increased to 31000dB. By this
we can say the condition of tool clearly.
For estimation so many techniques like Neural network or Fuzzy control logics are used.
VIII. CONCLUSION:
In this paper, a combined approach for the acoustic signal processing for identifying tool wearing
condition is attempted. Here we considered the basic FFT algorithm of signal processing , whose performance is
evaluated based on computational time analysis . As it is known,Signal processing is an important measure in
many applications including medicine, multimedia compression, machineries. We had chosen machineries in
which tool wearing is to be monitored for seemless profitable outcome of an industry. The purpose of
monitoring the computational time is to prevent the damage of product as soon as possible. The result
performance of FFT with two transmit and receive antennas, the MMSE equalization results with improvement
in BER of around 10−3 . The performance of these different equalizers for two transmit and two receive antennas
has been tested with simulations. It was observed that among the different equalizers, MMSE equalizer when
combined with Successive Inference Canceller was able to provide unambiguous tracking after applying the
temporal filter and enhance the signal quality. The effectiveness of this paper work to provide maximum signal
gain in the presence of several interference sources was shown using simulated data.
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