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.
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
⭐⭐⭐⭐⭐ Charla FIEC: #SSVEP_EEG Signal Classification based on #Emotiv EPOC #BC...Victor Asanza
Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
1) The document describes the design of an embedded wireless ECG system using IEEE 802.11G for wireless transmission. It acquires ECG signals from electrodes, amplifies and filters the signals, digitizes them using a PIC microcontroller, and transmits the data wirelessly.
2) The received data is processed using MATLAB to remove power line interference through an EMI filter designed using a core algorithm. This allows specialists to remotely monitor patients' ECG signals.
3) The EMI filter effectively tracks variations in interference frequency and amplitude to extract the power line signal from the ECG, improving over existing techniques. This allows clean ECG signals to be obtained with minimal computational resources.
This document provides a summary of brain-computer interface (BCI) technology. It discusses how BCI allows direct communication between the brain and external devices, enabling thoughts to be translated into actions. The summary describes the main steps in BCI, including signal acquisition using invasive or non-invasive methods, preprocessing to remove noise, feature extraction to analyze patterns in brain signals, and classification to interpret user intentions and provide feedback/control of external devices. Examples of applications like controlling a robotic arm are also mentioned.
Design and Implementation of Brain Computer Interface for Wheelchair controlIRJET Journal
This document describes the design and implementation of a brain-computer interface (BCI) system to control a wheelchair using electroencephalography (EEG) signals. Specifically, it presents a BCI system that acquires EEG signals from electrodes on a user's scalp, processes the signals to classify intended movement commands (left, right), and uses these commands to control the direction of a wheelchair. The system achieves an average success rate of 83% for left commands and 80% for right commands across 6 test subjects. It provides a potential communication method for people with severe physical disabilities.
Feature extraction of electrocardiogram signal using machine learning classif...IJECEIAES
In the various field of life person identification is an essential and important task. This helps for the investigation of criminal activities and used in various type of forensic applications like surveillance. For biometric recognition iris, face, voice and fingerprint have a limited fabrication and from there the exact decision regarding liveliness of the subject can be drawn. The aim of the approach is to construct a biometric recognition system based on ECG which processes the raw ECG signal. The entire process is supported by different filters for noise elimination and ECG characteristics waves gone through time domain analysis. Based on the analysis an efficient feature extraction model is developed where several best P-QRS-T signal parts are taken and the positions of the fragmented signals are normalized depends on the priorities of their positions. The calculation of domain features done 72 times. It checks the data sets (train and test) and from feature vector matching to each of the individual signal, separately. The performance and utility of the system are analyzed and feature vectors are examined by different classification algorithms of machine learning. The leading algorithms like K-nearest neighbor, artificial neural network and support vector machine are used to classify different features of ECG, and it is tested using standard cardiac database i.e. the MIT-BIH ECG -ID database.
⭐⭐⭐⭐⭐ 2020 #IEEE #IES #UPS #Cuenca: Clasificación de señales de Electroencefa...Victor Asanza
Agenda:
✅ Introducción
✅ Clustering of #EEG Occipital Signals using #K_means
Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.
✅ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
✅ Field Programmable Gate Arrays (#FPGAs)
✅ Implementation of a Classification System of EEG Signals Based on FPGA
✅ Otros proyectos con FPGA
C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
⭐⭐⭐⭐⭐ Charla FIEC: #SSVEP_EEG Signal Classification based on #Emotiv EPOC #BC...Victor Asanza
Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
1) The document describes the design of an embedded wireless ECG system using IEEE 802.11G for wireless transmission. It acquires ECG signals from electrodes, amplifies and filters the signals, digitizes them using a PIC microcontroller, and transmits the data wirelessly.
2) The received data is processed using MATLAB to remove power line interference through an EMI filter designed using a core algorithm. This allows specialists to remotely monitor patients' ECG signals.
3) The EMI filter effectively tracks variations in interference frequency and amplitude to extract the power line signal from the ECG, improving over existing techniques. This allows clean ECG signals to be obtained with minimal computational resources.
This document provides a summary of brain-computer interface (BCI) technology. It discusses how BCI allows direct communication between the brain and external devices, enabling thoughts to be translated into actions. The summary describes the main steps in BCI, including signal acquisition using invasive or non-invasive methods, preprocessing to remove noise, feature extraction to analyze patterns in brain signals, and classification to interpret user intentions and provide feedback/control of external devices. Examples of applications like controlling a robotic arm are also mentioned.
Design and Implementation of Brain Computer Interface for Wheelchair controlIRJET Journal
This document describes the design and implementation of a brain-computer interface (BCI) system to control a wheelchair using electroencephalography (EEG) signals. Specifically, it presents a BCI system that acquires EEG signals from electrodes on a user's scalp, processes the signals to classify intended movement commands (left, right), and uses these commands to control the direction of a wheelchair. The system achieves an average success rate of 83% for left commands and 80% for right commands across 6 test subjects. It provides a potential communication method for people with severe physical disabilities.
Feature extraction of electrocardiogram signal using machine learning classif...IJECEIAES
In the various field of life person identification is an essential and important task. This helps for the investigation of criminal activities and used in various type of forensic applications like surveillance. For biometric recognition iris, face, voice and fingerprint have a limited fabrication and from there the exact decision regarding liveliness of the subject can be drawn. The aim of the approach is to construct a biometric recognition system based on ECG which processes the raw ECG signal. The entire process is supported by different filters for noise elimination and ECG characteristics waves gone through time domain analysis. Based on the analysis an efficient feature extraction model is developed where several best P-QRS-T signal parts are taken and the positions of the fragmented signals are normalized depends on the priorities of their positions. The calculation of domain features done 72 times. It checks the data sets (train and test) and from feature vector matching to each of the individual signal, separately. The performance and utility of the system are analyzed and feature vectors are examined by different classification algorithms of machine learning. The leading algorithms like K-nearest neighbor, artificial neural network and support vector machine are used to classify different features of ECG, and it is tested using standard cardiac database i.e. the MIT-BIH ECG -ID database.
⭐⭐⭐⭐⭐ 2020 #IEEE #IES #UPS #Cuenca: Clasificación de señales de Electroencefa...Victor Asanza
Agenda:
✅ Introducción
✅ Clustering of #EEG Occipital Signals using #K_means
Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.
✅ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
✅ Field Programmable Gate Arrays (#FPGAs)
✅ Implementation of a Classification System of EEG Signals Based on FPGA
✅ Otros proyectos con FPGA
C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
Robot Motion Control Using the Emotiv EPOC EEG SystemjournalBEEI
Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
IRJET- Development of Data Transmission using Smart Sensing Technology for St...IRJET Journal
This document describes a new method for data transmission using smart sensing technology for structural health monitoring. The proposed method includes two coding stages: 1) source coding to compress redundant information in structural health monitoring signals and 2) redundant coding to inject artificial redundancy to enhance transmission reliability over wireless sensors. The method is implemented on an Imote2 smart sensor platform and tested on a cable-stayed bridge, showing it can withstand up to 30% data loss while still reconstructing the original sensor data with high probability. This improves reliability of data transmission for wireless structural health monitoring systems.
IRJET- IoT Based Home Automation And Health Monitoring System for Physically ...IRJET Journal
This document proposes an IoT-based home automation and health monitoring system for physically challenged individuals using gesture recognition. The system uses MEMS sensors to detect hand gestures which are then used to control home appliances like fans and lights. It also includes health monitoring sensors to monitor the user's heartbeat and detect falls using a vibration sensor. If any abnormal health readings are detected, an SMS alert will be sent using GCM cloud messaging. The system is intended to make daily tasks easier for disabled users and provide remote health monitoring assistance when caregivers are not present.
Qadri et Al., en su trabajo “The Future of Healthcare Internet of Things (H-IoT): A Survey of Emerging Technologies” propone como uno de los desafíos del H-IoT:
Monitoreo de Desórdenes neurológicos
Ambient Assisted Living (AAL)
Fitness Tracking
Uso de técnicas de Big Data
Uso de Edge Computing
Internet of Nano-Things
⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGAVictor Asanza
In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.
IRJET- Intelligent Home Monitoring using IoT for Physically ChallengedIRJET Journal
This document proposes an intelligent home monitoring system using Internet of Things (IoT) technology to help physically challenged and elderly people. The system uses a Raspberry Pi microcontroller connected to sensors like a PIR motion sensor, gas leakage sensor, and Bluetooth module for voice control. It allows users to remotely monitor and control home devices like lights and appliances via an app. The system is low-cost, easy to use, improves safety and independence for users.
⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería...Victor Asanza
EEG SIGNALS
Data Set
Methodology and Results
Analysis of Results
Trabajos en cursor
Resultados Obtenidos
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
The data required in vehicle motor cash investigation is lost and also inputs taken from eye witness , victims and police reports may not be accurate to state the cause of an accident. The death rate due to an accident on roads can be reduced by minimizing the delay in rescuing operation. In order to claim insurance detailed information is a prerequisite that how accident took place.
IRJET - Home Automation for Physically Challenged and Elder PeopleIRJET Journal
This document describes a home automation system designed to help physically challenged and elderly people control appliances in their homes remotely. The system uses an Arduino board connected to sensors like a passive infrared sensor for motion detection, an IR receiver for remote control, and relays to control electrical devices. An ESP8266 module enables wireless control via an Android app, allowing users to operate appliances from their phones. The system is intended to assist people who have difficulty with mobility or need help with daily tasks by automating electrical devices in their homes through simple remote access. Evaluation of the system found that it allows physically challenged users to fulfill their needs independently by controlling appliances on their own from a distance.
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.
IRJET- Power Monitoring with Time Controlling & Data LoggingIRJET Journal
This document describes a power monitoring system that measures and logs the power consumption of electrical devices. The system uses a current sensor to measure current, which is sent to an Arduino microcontroller. The Arduino calculates power consumption based on current and voltage. Data on power usage is sent wirelessly to a database on ThingSpeak using an ESP8266 WiFi module. The system allows setting timers to automatically turn devices off after a certain time period. This helps save energy. The system was tested on various loads and accurately measured and logged their power usage over time to the ThingSpeak database.
Implementation and demonstration of li fi technologyeSAT Journals
Abstract Li-Fi is a wireless communication system in which light is used as a carrier signal instead of traditional radio frequency as in Wi-Fi. Li-Fi is a technology that uses light emitting diodes to transmit data wirelessly. Li-Fi is a form of Visible Light Communication (VLC). VLC uses rapid pulses of light to transmit information wirelessly that cannot be detected by the human eye. This paper demonstrates the working of Li-Fi by simulating a simple circuit which gave us the required output. Li-Fi technology was first demonstrated by Harald Hass, a German Physicist from the University of Edinburgh Keywords—Li-Fi, VLC, Optical Communication, Wireless Communication, LED, Visible Light Spectrum.
IRJET - ATMEGA 328P based Smart Energy Meter Test Jig using PLX-DAXIRJET Journal
An Arduino-based smart energy meter test jig was developed to test energy meters. It uses an Arduino Uno microcontroller connected to a main board with 8 golden pins that act as testing points. A multiplexer is used to measure the voltage from the 8 testing points. The Arduino collects data from the testing points and sends it to a PC via serial communication. Data collection software displays the voltage readings from each testing point in an Excel sheet in real-time. This automated test jig eliminates errors from manual testing and improves testing efficiency by testing multiple meters simultaneously.
A machine learning algorithm for classification of mental tasks.pdfPravinKshirsagar11
In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised using two different approaches such as wavelet transforms at a discrete time (DWT) and support vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG) database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from a brain-computer interface (BCI). In the working model, signals from the database are wed out into different frequency sub-bands using DWT. Initially, updated statistical features are obtained from different frequency sub-bands. This type of representation defines the wavelet co-efficient which is introduced for reducing the measurement of data. Then, the projected method is realized using SVM for segregating both port and veracious hand movement. After segregation of EEG signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and 97.89% for B-alert machine.
IRJET - Detection of Depression Level using EEG SignalIRJET Journal
This document describes a system to detect depression levels using EEG signals. EEG signals are collected from electrodes placed on the scalp and analyzed to determine a person's mental state and depression level. If the depression level exceeds a threshold, a recorded audio will play to help relieve depression symptoms. Additionally, an SMS will be sent to alert a caretaker. EEG data is stored in the cloud for future reference. The system aims to help identify depression and provide relief in real-time using technologies like IoT and cloud computing.
Voice recognition based advance patients room automationeSAT Journals
Abstract This is a unique and most useful system for totally or partially handicapped patients who are unable do basic tasks. Patients who are unable do anything have to totally depend on nurses. Hospitals have to provide a round-the-clock 24 Hour an attendant for these patients and hence total fees of hospitals gets increased. The device that we proposed here can actually help these patients and hospitals without requiring 24 hour attendant. With this system, patient can call nurse or any attendant at any time whenever required by simply voice controlled commands. This system listen voice commands and can call nurse by simply ringing bell. It can also control basic switching on/off tasks of fan, light and any device by patient voice. Keywords Automation, hospital automation, nursing, voice recognition, Atmega328 applications, Easy VR.
The locomotive disabled people and elderly people cannot control the wheelchair manually. The key
objective of this paper is to help the locomotive disabled and old people to easily manoeuvre without any social
aid through a brainwave-controlled wheelchair. There are various types of wheelchair available in the market
such as Voice controlled wheelchair, Joystick control wheelchair, Smart phone controlled wheelchair, Eye
controlled wheelchair, Mechanical wheelchair. These wheelchairs hold certain limitations for e.g. if the user is
dumb; user cannot access voice controlled wheelchair, etc. Brain-computer interface (BCI) is a new method used
to interface between the human mind and a digital signal processor. An Electroencephalogram (EEG) based BCI
is connected with an artificial reality system to control the movement and direction of a wheelchair. This paper
proposes brainwave controlled wheelchair, which uses the captured EEG signals from the brain. This EEG
signals are then passed to Arduino. It converts into control signals which will in turn help to move the wheelchair
in different direction.
This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
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.
IRJET- Survey on EEG Based Brainwave Controlled Home AutomationIRJET Journal
This document summarizes research on using electroencephalography (EEG) brainwave signals to control home automation devices. It discusses several previous studies that used EEG signals to control robots, anticipate finger movements, control environmental devices for paralyzed patients, and classify gestures for home automation. The document then outlines a study that analyzed brainwave signals to detect patterns related to thoughts and emotions. These signals were sensed by a brainwave sensor, transmitted via Bluetooth, processed using Matlab, and used to send commands to control home devices. The goal was to develop a brain-computer interface for controlling home automation systems using EEG brainwave signals.
IRJET- Human Emotions Detection using Brain Wave SignalsIRJET Journal
This document discusses detecting human emotions using brain wave signals captured through electroencephalography (EEG). It aims to provide a mobile system that can analyze EEG signals captured wirelessly from an EEG headset to classify emotions. The system architecture involves collecting raw EEG data, performing noise filtering, feature extraction using techniques like discrete wavelet transform and K-nearest neighbors, then classifying emotions using algorithms like support vector machines. The goal is to identify emotions in a cost-effective and mobile way to enable applications in healthcare, games, education, and more. Key challenges include designing stimuli to elicit single emotions, removing noise from EEG signals, and selecting the best machine learning techniques for emotion classification.
IRJET- Arduino based Smart Grid Power Monitoring and Control by using IoTIRJET Journal
This document describes a smart grid power monitoring and control system using IoT. The system uses sensors to monitor the voltage, current and temperature of a 3-phase power system. It can detect faults by comparing the measured voltage to a reference value. When a fault is detected, an alarm is triggered and the fault information is sent to a webpage via WiFi. The system aims to quickly detect faults, protect equipment, and maintain a stable power supply. It is controlled using an Arduino board and sensors, and allows remote monitoring through an IoT connection.
Robot Motion Control Using the Emotiv EPOC EEG SystemjournalBEEI
Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
IRJET- Development of Data Transmission using Smart Sensing Technology for St...IRJET Journal
This document describes a new method for data transmission using smart sensing technology for structural health monitoring. The proposed method includes two coding stages: 1) source coding to compress redundant information in structural health monitoring signals and 2) redundant coding to inject artificial redundancy to enhance transmission reliability over wireless sensors. The method is implemented on an Imote2 smart sensor platform and tested on a cable-stayed bridge, showing it can withstand up to 30% data loss while still reconstructing the original sensor data with high probability. This improves reliability of data transmission for wireless structural health monitoring systems.
IRJET- IoT Based Home Automation And Health Monitoring System for Physically ...IRJET Journal
This document proposes an IoT-based home automation and health monitoring system for physically challenged individuals using gesture recognition. The system uses MEMS sensors to detect hand gestures which are then used to control home appliances like fans and lights. It also includes health monitoring sensors to monitor the user's heartbeat and detect falls using a vibration sensor. If any abnormal health readings are detected, an SMS alert will be sent using GCM cloud messaging. The system is intended to make daily tasks easier for disabled users and provide remote health monitoring assistance when caregivers are not present.
Qadri et Al., en su trabajo “The Future of Healthcare Internet of Things (H-IoT): A Survey of Emerging Technologies” propone como uno de los desafíos del H-IoT:
Monitoreo de Desórdenes neurológicos
Ambient Assisted Living (AAL)
Fitness Tracking
Uso de técnicas de Big Data
Uso de Edge Computing
Internet of Nano-Things
⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGAVictor Asanza
In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.
IRJET- Intelligent Home Monitoring using IoT for Physically ChallengedIRJET Journal
This document proposes an intelligent home monitoring system using Internet of Things (IoT) technology to help physically challenged and elderly people. The system uses a Raspberry Pi microcontroller connected to sensors like a PIR motion sensor, gas leakage sensor, and Bluetooth module for voice control. It allows users to remotely monitor and control home devices like lights and appliances via an app. The system is low-cost, easy to use, improves safety and independence for users.
⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería...Victor Asanza
EEG SIGNALS
Data Set
Methodology and Results
Analysis of Results
Trabajos en cursor
Resultados Obtenidos
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
The data required in vehicle motor cash investigation is lost and also inputs taken from eye witness , victims and police reports may not be accurate to state the cause of an accident. The death rate due to an accident on roads can be reduced by minimizing the delay in rescuing operation. In order to claim insurance detailed information is a prerequisite that how accident took place.
IRJET - Home Automation for Physically Challenged and Elder PeopleIRJET Journal
This document describes a home automation system designed to help physically challenged and elderly people control appliances in their homes remotely. The system uses an Arduino board connected to sensors like a passive infrared sensor for motion detection, an IR receiver for remote control, and relays to control electrical devices. An ESP8266 module enables wireless control via an Android app, allowing users to operate appliances from their phones. The system is intended to assist people who have difficulty with mobility or need help with daily tasks by automating electrical devices in their homes through simple remote access. Evaluation of the system found that it allows physically challenged users to fulfill their needs independently by controlling appliances on their own from a distance.
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.
IRJET- Power Monitoring with Time Controlling & Data LoggingIRJET Journal
This document describes a power monitoring system that measures and logs the power consumption of electrical devices. The system uses a current sensor to measure current, which is sent to an Arduino microcontroller. The Arduino calculates power consumption based on current and voltage. Data on power usage is sent wirelessly to a database on ThingSpeak using an ESP8266 WiFi module. The system allows setting timers to automatically turn devices off after a certain time period. This helps save energy. The system was tested on various loads and accurately measured and logged their power usage over time to the ThingSpeak database.
Implementation and demonstration of li fi technologyeSAT Journals
Abstract Li-Fi is a wireless communication system in which light is used as a carrier signal instead of traditional radio frequency as in Wi-Fi. Li-Fi is a technology that uses light emitting diodes to transmit data wirelessly. Li-Fi is a form of Visible Light Communication (VLC). VLC uses rapid pulses of light to transmit information wirelessly that cannot be detected by the human eye. This paper demonstrates the working of Li-Fi by simulating a simple circuit which gave us the required output. Li-Fi technology was first demonstrated by Harald Hass, a German Physicist from the University of Edinburgh Keywords—Li-Fi, VLC, Optical Communication, Wireless Communication, LED, Visible Light Spectrum.
IRJET - ATMEGA 328P based Smart Energy Meter Test Jig using PLX-DAXIRJET Journal
An Arduino-based smart energy meter test jig was developed to test energy meters. It uses an Arduino Uno microcontroller connected to a main board with 8 golden pins that act as testing points. A multiplexer is used to measure the voltage from the 8 testing points. The Arduino collects data from the testing points and sends it to a PC via serial communication. Data collection software displays the voltage readings from each testing point in an Excel sheet in real-time. This automated test jig eliminates errors from manual testing and improves testing efficiency by testing multiple meters simultaneously.
A machine learning algorithm for classification of mental tasks.pdfPravinKshirsagar11
In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised using two different approaches such as wavelet transforms at a discrete time (DWT) and support vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG) database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from a brain-computer interface (BCI). In the working model, signals from the database are wed out into different frequency sub-bands using DWT. Initially, updated statistical features are obtained from different frequency sub-bands. This type of representation defines the wavelet co-efficient which is introduced for reducing the measurement of data. Then, the projected method is realized using SVM for segregating both port and veracious hand movement. After segregation of EEG signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and 97.89% for B-alert machine.
IRJET - Detection of Depression Level using EEG SignalIRJET Journal
This document describes a system to detect depression levels using EEG signals. EEG signals are collected from electrodes placed on the scalp and analyzed to determine a person's mental state and depression level. If the depression level exceeds a threshold, a recorded audio will play to help relieve depression symptoms. Additionally, an SMS will be sent to alert a caretaker. EEG data is stored in the cloud for future reference. The system aims to help identify depression and provide relief in real-time using technologies like IoT and cloud computing.
Voice recognition based advance patients room automationeSAT Journals
Abstract This is a unique and most useful system for totally or partially handicapped patients who are unable do basic tasks. Patients who are unable do anything have to totally depend on nurses. Hospitals have to provide a round-the-clock 24 Hour an attendant for these patients and hence total fees of hospitals gets increased. The device that we proposed here can actually help these patients and hospitals without requiring 24 hour attendant. With this system, patient can call nurse or any attendant at any time whenever required by simply voice controlled commands. This system listen voice commands and can call nurse by simply ringing bell. It can also control basic switching on/off tasks of fan, light and any device by patient voice. Keywords Automation, hospital automation, nursing, voice recognition, Atmega328 applications, Easy VR.
The locomotive disabled people and elderly people cannot control the wheelchair manually. The key
objective of this paper is to help the locomotive disabled and old people to easily manoeuvre without any social
aid through a brainwave-controlled wheelchair. There are various types of wheelchair available in the market
such as Voice controlled wheelchair, Joystick control wheelchair, Smart phone controlled wheelchair, Eye
controlled wheelchair, Mechanical wheelchair. These wheelchairs hold certain limitations for e.g. if the user is
dumb; user cannot access voice controlled wheelchair, etc. Brain-computer interface (BCI) is a new method used
to interface between the human mind and a digital signal processor. An Electroencephalogram (EEG) based BCI
is connected with an artificial reality system to control the movement and direction of a wheelchair. This paper
proposes brainwave controlled wheelchair, which uses the captured EEG signals from the brain. This EEG
signals are then passed to Arduino. It converts into control signals which will in turn help to move the wheelchair
in different direction.
This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
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.
IRJET- Survey on EEG Based Brainwave Controlled Home AutomationIRJET Journal
This document summarizes research on using electroencephalography (EEG) brainwave signals to control home automation devices. It discusses several previous studies that used EEG signals to control robots, anticipate finger movements, control environmental devices for paralyzed patients, and classify gestures for home automation. The document then outlines a study that analyzed brainwave signals to detect patterns related to thoughts and emotions. These signals were sensed by a brainwave sensor, transmitted via Bluetooth, processed using Matlab, and used to send commands to control home devices. The goal was to develop a brain-computer interface for controlling home automation systems using EEG brainwave signals.
IRJET- Human Emotions Detection using Brain Wave SignalsIRJET Journal
This document discusses detecting human emotions using brain wave signals captured through electroencephalography (EEG). It aims to provide a mobile system that can analyze EEG signals captured wirelessly from an EEG headset to classify emotions. The system architecture involves collecting raw EEG data, performing noise filtering, feature extraction using techniques like discrete wavelet transform and K-nearest neighbors, then classifying emotions using algorithms like support vector machines. The goal is to identify emotions in a cost-effective and mobile way to enable applications in healthcare, games, education, and more. Key challenges include designing stimuli to elicit single emotions, removing noise from EEG signals, and selecting the best machine learning techniques for emotion classification.
IRJET- Arduino based Smart Grid Power Monitoring and Control by using IoTIRJET Journal
This document describes a smart grid power monitoring and control system using IoT. The system uses sensors to monitor the voltage, current and temperature of a 3-phase power system. It can detect faults by comparing the measured voltage to a reference value. When a fault is detected, an alarm is triggered and the fault information is sent to a webpage via WiFi. The system aims to quickly detect faults, protect equipment, and maintain a stable power supply. It is controlled using an Arduino board and sensors, and allows remote monitoring through an IoT connection.
IRJET- Disentangling Brain Activity from EEG Data using Logistic Regression, ...IRJET Journal
This document discusses using machine learning algorithms to analyze EEG data and predict a person's learning capabilities. It extracts features from raw EEG data, including delta, theta, alpha, beta, and gamma waves. It then applies logistic regression, XGBoost, RNN, and decision trees to classify if a student is confused while learning from videos. The highest accuracy was achieved using XGBoost. Overall, the study aims to develop a system to monitor learning using EEG and analyze the correlation between brain activity and learning capability.
Tuning of Proportional Integral Derivative Controller Using Artificial Neural...IRJET Journal
This document discusses tuning a proportional-integral-derivative (PID) controller using an artificial neural network (ANN). Specifically:
1. A PID controller is used to control various process variables like pressure, temperature, and speed. The PID controller gains (KP, KI, KD) are tuned by training an ANN to optimize the controller response.
2. An ANN is trained using the Levenberg-Marquardt algorithm to determine the optimal PID gains. The tuned PID controller results in reduced overshoot, peak value, and settling time compared to the untuned controller.
3. Simulation results show that with ANN tuning, overshoot is reduced from 27.1% to 7
IRJET- IoT based Industrial Level Sensor Data Acquisition & MonitoringIRJET Journal
This document discusses an IoT-based system for industrial level sensor data acquisition and remote monitoring. Key aspects include:
1. An industrial level sensor uses radar principles to measure liquid or solid levels and transmits this data via a 4-20mA current loop signal.
2. An I-V converter changes the current signal to a 1-5V voltage signal readable by a data acquisition system.
3. An ESP8266 microcontroller connects to WiFi and transmits level data to an IoT cloud server.
4. Remote users can access the cloud server to monitor level readings in real-time from any location over the internet.
This document describes a smart blind walking stick that uses ultrasonic sensors and other components to help blind people navigate independently. The stick detects nearby objects using ultrasonic sensors and notifies the user via voice commands from a playback module. It determines distances to nearby objects and warns the user if objects are close through different voice alerts. The stick is powered by an Arduino microcontroller and includes additional sensors, displays, and circuits to accurately detect obstacles and assist blind users in walking confidently without needing assistance from others. The goal is to help overcome challenges faced by blind people through an improved walking stick that provides object detection and distance information.
IRJET- Classification of Arrhythmic ECG Data using Artificial Neural NetworkIRJET Journal
This document presents a study on classifying arrhythmic electrocardiogram (ECG) data using an artificial neural network. Researchers first extracted features from ECG signals, including R-R intervals and amplitudes. They then used these features to train a neural network classifier to categorize ECG data as normal or abnormal. The network was tested on the MIT-BIH arrhythmia database and showed ability to deal with ambiguous ECG signals. Future work could involve applying this approach to real-time patient data classification.
Epileptic Seizure Detection using An EEG SensorIRJET Journal
This document presents a method for detecting epileptic seizures using an EEG sensor and signal processing techniques. It involves using an EEG headset to record raw brain wave data, filtering the signals to remove noise, applying discrete wavelet transform to extract features from different frequency bands, and using a support vector machine classifier to classify segments as normal, interictal, or ictal based on the extracted features. The proposed method aims to help doctors more accurately diagnose and monitor epilepsy in patients by objectively detecting seizures from EEG data in near real-time.
Detection of EEG Spikes Using Machine Learning ClassifierIRJET Journal
This document discusses a study on detecting epileptic seizures from EEG data using machine learning classifiers. It begins with an introduction to epilepsy and EEG signals. Feature extraction is identified as an important step, as is understanding the statistical properties of the data. Previous studies that used time domain, frequency domain, and time-frequency domain features are summarized. Commonly used machine learning classifiers like SVMs, ANNs, and random forests are also mentioned. The methodology of the presented study involved recording EEG data from rats injected with penicillin to induce seizures, extracting time and frequency domain features, and using an SVM classifier to classify signals as epileptic or non-epileptic. The goal of the study was to analyze and identify features to classify EEG
IRJET - Real Time Muscle Fatigue Monitoring using IoT Cloud ComputingIRJET Journal
This document describes a real-time muscle fatigue monitoring system using IoT cloud computing. Surface electromyography is used to acquire electromyography signals from muscles during isotonic contraction using a sensor. The signals are preprocessed on a Wemos D1 mini board and sent to an IoT cloud for further processing. In the cloud, time-frequency analysis is performed to extract features like median frequency and mean frequency over time. A decrease in these frequencies indicates muscle fatigue. The results are displayed on a mobile app interface for users and healthcare professionals to monitor fatigue in real-time. The system aims to provide a low-cost, non-invasive way to monitor muscle fatigue using IoT technologies.
IRJET-Estimation of Meditation Effect on Attention Level using EEGIRJET Journal
This document discusses a study that investigates the effect of meditation on attention level using EEG data analysis. EEG data was collected from subjects during meditation and non-meditation states. The data was preprocessed to remove noise and artifacts. Statistical features were then extracted from the EEG data, including standard deviation, relative power, average power spectral density, and entropy. A random forest classification method was used to analyze the data and detect attention states, achieving 90% accuracy. The study aims to objectively measure attention levels and the impact of meditation using EEG analysis to better understand cognitive disorders like ADHD.
Wavelet-Based Approach for Automatic Seizure Detection Using EEG SignalsIRJET Journal
This document presents a wavelet-based approach for automatically detecting seizures using EEG signals. EEG data is decomposed into detailed and approximate coefficients using discrete wavelet transform up to the fourth level. Statistical features are extracted from the wavelet coefficients and the most significant features are selected using the Wilcoxon rank-sum test. Three classifiers - SVM, kNN, and ensemble subspace kNN - are used to classify EEG segments as pre-ictal, inter-ictal, or ictal. The proposed method achieves 100% classification accuracy when discriminating between healthy and epileptic EEG signals on the neurology and sleep centre EEG database.
IRJET- Wireless Data Monitoring and Fault Identification by using IoT in Ther...IRJET Journal
This document describes a wireless IoT-based system for monitoring key parameters like temperature, vibration, pressure, and level in a thermal power plant. The system uses sensors to monitor these parameters, an Arduino microcontroller to process the sensor data, and an IoT module to transmit the data remotely via SMS. This allows plant operators to continuously monitor the parameters anywhere, increasing reliability while reducing maintenance and operating costs compared to manual monitoring. If any parameter exceeds a threshold, workers are alerted to potential issues via on-site displays and remote notifications, allowing faults to be identified early and equipment damage to be prevented.
IRJET- Wireless Data Monitoring and Fault Identification by using IoT in ...IRJET Journal
This document describes a wireless IoT-based system for monitoring key parameters like temperature, vibration, pressure, and level in a thermal power plant. The system uses sensors to monitor these parameters, an Arduino microcontroller to process the sensor data, and an IoT module to transmit the data remotely via SMS. This allows plant operators to continuously monitor the parameters anywhere, increasing reliability while reducing maintenance and operating costs compared to manual monitoring. If any parameter exceeds a threshold, workers are alerted to potential issues via on-site displays and remote notifications, allowing faults to be identified early.
IRJET- Implementation of Continues Body Monitoring System with Wireless B...IRJET Journal
This document describes the implementation of a continuous body monitoring system using wireless body sensor networks and IoT. It uses sensors like ECG, temperature, and pulse attached to the body to monitor vital signs. The sensor data is sent wirelessly to a cloud platform via an Arduino and WiFi module. Doctors can access the data remotely to monitor and diagnose patients from anywhere. This overcomes limitations of traditional wired systems and allows for continuous remote patient monitoring.
Design and Implementation of Real Time Remote Supervisory SystemIJERA Editor
In today’s fast growing communication environment and rapid exchange of data in networking field has triggered us to develop a home based remote supervisory monitoring system. In the present paper the physiological parameters of the patient such as body temperature, ECG, Pulse rate and Oxygen Saturation is displayed in MATLAB graphical user interface which is processed using ARM7 LPC2138. In case any emergency persist and parameters goes abnormal over the optimum level then a buzzer will ring to alert the caretaker. And the vital parameters will be displayed on the patient side computer and an automatic SMS will be sent to the doctor using GSM interface.
IRJET- Low Powered Radio Frequency and PIR Sensor based Security DeviceIRJET Journal
This document describes the design of a low-powered wireless security device using a radio frequency (RF) transmitter and passive infrared (PIR) sensor. The system is divided into two hardware units - a sensor unit with PIR sensors interfaced to an RF transmitter, and a computational unit with a microcontroller interfaced to an RF receiver and alarm system. The microcontroller-based system detects intruders via the PIR sensors and wirelessly transmits a signal to trigger an alarm. It aims to provide security with low power consumption to operate on batteries for longer. The system was tested and able to detect movement at distances up to 7 meters with components operating between 4-12 volts and power consumption as low as 1.
This document proposes an automatic license issuing system that uses sensors to monitor a driver's behavior during a driving test. The system aims to replace the current manual process of evaluating driving tests. Sensors like force sensors, piezo sensors, MEMS accelerometers, smoke sensors, and an LCD display are mounted on a test vehicle. The sensors collect data on the driver's control of the vehicle and emissions. This data is sent wirelessly via a ZigBee module to a remote server for analysis. The server compares the test data to reference data to evaluate the driver's performance and determine if a license should be issued. The goal is to objectively assess driving ability and prevent illegal or unsafe licenses from being issued.
IRJET- A Cloud based Virtual Brain Connectivity with EEG Sensor using Interne...IRJET Journal
This document describes a cloud-based virtual brain connectivity system using an EEG sensor and the Internet of Things. It uses an EEG sensor to measure brain activity, an Arduino microcontroller to process the EEG signals, an ESP8266 WiFi module to connect to the cloud, and a touchscreen display. The system can determine if the brain is alive or dead by analyzing the EEG signals. If any abnormal activity is detected, it will send alerts by SMS and email. The goal is to monitor brain activity and store the data in the cloud for analysis. This could help with conditions like autism or detect forgery. The system aims to scale up processing of large brain datasets in the future.
IRJET- Measurement of Temperature and Humidity by using Arduino Tool and DHT11IRJET Journal
This document describes a system to measure temperature and humidity using an Arduino microcontroller board and a DHT11 sensor. It consists of 3 sections: 1) the DHT11 sensor measures temperature and humidity, 2) the Arduino reads the sensor output and extracts temperature and humidity values, and 3) an LCD display shows the measured values. The DHT11 sensor communicates with the Arduino using a single-wire serial protocol. The system provides low-cost, real-time monitoring of temperature and humidity that can benefit various industries.
Similar to IRJET- Analysis of Electroencephalogram (EEG) Signals (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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