This document describes a study on the design and development of a low-cost prosthetic arm controlled by brain waves. Researchers created an electroencephalography (EEG)-based system to read brain signals and control a prosthetic arm. The system detects different brain wave patterns like alpha, beta, gamma, delta and theta waves using EEG sensors. It then processes the signals with an Arduino microcontroller and uses servo motors to move the prosthetic arm. The goal is to make a prosthetic arm that can move similarly to a biological human arm by interpreting the user's thoughts detected through brain waves.
IRJET- Fundamental of Electroencephalogram (EEG) Review for Brain-Computer In...IRJET Journal
The document provides an overview of electroencephalography (EEG) and brain-computer interface (BCI) systems. It discusses EEG fundamentals including brainwaves, EEG definition, and commercial EEG devices. It also reviews the components of a BCI system including signal acquisition, preprocessing, feature extraction, and classification. The goal is to help understand EEG-based BCI systems for research purposes such as controlling robots.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
This document summarizes a research paper that compares the performance of different independent component analysis (ICA) algorithms and a new technique called Cycle Spinning Wavelet-ICA Merger (CTICA) for removing artifacts from electroencephalogram (EEG) signals. It finds that CTICA performs as well as other ICA algorithms like FastICA, JADE, and Radical at denoising EEG signals. The document provides background on EEG signals, common artifacts that contaminate EEG signals, existing techniques like ICA and wavelet transforms for removing artifacts, and prior research combining ICA and wavelets. It also describes the two datasets and methodology used to test CTICA's performance.
A Study of EEG Based MI BCI for Imaginary Vowels and Wordsijtsrd
Some people may have congenital or acquired biological or physical disabilities. For individuals in this situation, some positive developments are experienced with the advancement of technology. One of these developments can be considered today as BCI Brain Computer Interface .BCI systems can be divided into invasive and non invasive. Different techniques are used to obtain brain activities in non invasive BCI systems. Some of these techniques are EEG, ECoG, fNIRS, MEG, PET, MRI. Among these techniques, EEG and fNIRS are often preferred because of their easy applicability compared to other techniques. In this study, EEG based motor imagery BCI system is used. In this study, an experimental BCI system has been developed in which Turkish vowels that individuals say imaginatively are perceived over the EEG signal. In the methods section of this article, information is given about the experimental environment and techniques used in the study. The results obtained with the designed system are included in the results section. In the conclusion part, a general evaluation of the study is made and improvements that can be made to improve it are emphasized. Kadir Haltas | Atilla Ergüzen | Erdal Erdal "A Study of EEG Based MI-BCI for Imaginary Vowels and Words" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38165.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38165/a-study-of-eeg-based-mibci-for-imaginary-vowels-and-words/kadir-haltas
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
This document describes a brainwave-controlled robotic arm. The arm is designed to help disabled individuals express themselves. Brainwaves are detected by a Neurosky headset and transmitted via Bluetooth to an Arduino microcontroller. The microcontroller maps the brainwave signals to control servo motors that move the artificial arm. Specifically, different levels of attention and meditation detected in the brainwaves will trigger opening and closing of the hand or elbow movement of the arm. The system was tested on 10 people with promising but imperfect results, suggesting it needs further development to achieve full control of the arm's movements.
This document describes a smart home system designed to aid paralyzed individuals living alone. EEG signals are collected from 25 paralyzed subjects to study brain activity related to hunger, thirst, sleepiness, excitement and stress. The EEG data is preprocessed and classified using kNN classifiers to identify the individual's needs. An Internet of Things platform uses the classified EEG data to make logical decisions and control automated modules to meet the person's basic needs. These include modules for feeding, sleep, temperature control and more. Experimental results showed an overall 89.73% accuracy for automating units to fulfill a paralyzed person's basic needs. The system aims to help paralyzed individuals live more independently at home.
1) The document presents research on using EEG signals to predict reaching targets during two experiments.
2) It describes the components of a BCI system and the challenges of using EEG data, which can be contaminated by artifacts from eye and muscle movements.
3) The researchers used independent component analysis and other techniques to remove artifacts from the EEG data before extracting features and using classification algorithms to decode reaching targets.
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.
IRJET- Fundamental of Electroencephalogram (EEG) Review for Brain-Computer In...IRJET Journal
The document provides an overview of electroencephalography (EEG) and brain-computer interface (BCI) systems. It discusses EEG fundamentals including brainwaves, EEG definition, and commercial EEG devices. It also reviews the components of a BCI system including signal acquisition, preprocessing, feature extraction, and classification. The goal is to help understand EEG-based BCI systems for research purposes such as controlling robots.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
This document summarizes a research paper that compares the performance of different independent component analysis (ICA) algorithms and a new technique called Cycle Spinning Wavelet-ICA Merger (CTICA) for removing artifacts from electroencephalogram (EEG) signals. It finds that CTICA performs as well as other ICA algorithms like FastICA, JADE, and Radical at denoising EEG signals. The document provides background on EEG signals, common artifacts that contaminate EEG signals, existing techniques like ICA and wavelet transforms for removing artifacts, and prior research combining ICA and wavelets. It also describes the two datasets and methodology used to test CTICA's performance.
A Study of EEG Based MI BCI for Imaginary Vowels and Wordsijtsrd
Some people may have congenital or acquired biological or physical disabilities. For individuals in this situation, some positive developments are experienced with the advancement of technology. One of these developments can be considered today as BCI Brain Computer Interface .BCI systems can be divided into invasive and non invasive. Different techniques are used to obtain brain activities in non invasive BCI systems. Some of these techniques are EEG, ECoG, fNIRS, MEG, PET, MRI. Among these techniques, EEG and fNIRS are often preferred because of their easy applicability compared to other techniques. In this study, EEG based motor imagery BCI system is used. In this study, an experimental BCI system has been developed in which Turkish vowels that individuals say imaginatively are perceived over the EEG signal. In the methods section of this article, information is given about the experimental environment and techniques used in the study. The results obtained with the designed system are included in the results section. In the conclusion part, a general evaluation of the study is made and improvements that can be made to improve it are emphasized. Kadir Haltas | Atilla Ergüzen | Erdal Erdal "A Study of EEG Based MI-BCI for Imaginary Vowels and Words" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38165.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38165/a-study-of-eeg-based-mibci-for-imaginary-vowels-and-words/kadir-haltas
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
This document describes a brainwave-controlled robotic arm. The arm is designed to help disabled individuals express themselves. Brainwaves are detected by a Neurosky headset and transmitted via Bluetooth to an Arduino microcontroller. The microcontroller maps the brainwave signals to control servo motors that move the artificial arm. Specifically, different levels of attention and meditation detected in the brainwaves will trigger opening and closing of the hand or elbow movement of the arm. The system was tested on 10 people with promising but imperfect results, suggesting it needs further development to achieve full control of the arm's movements.
This document describes a smart home system designed to aid paralyzed individuals living alone. EEG signals are collected from 25 paralyzed subjects to study brain activity related to hunger, thirst, sleepiness, excitement and stress. The EEG data is preprocessed and classified using kNN classifiers to identify the individual's needs. An Internet of Things platform uses the classified EEG data to make logical decisions and control automated modules to meet the person's basic needs. These include modules for feeding, sleep, temperature control and more. Experimental results showed an overall 89.73% accuracy for automating units to fulfill a paralyzed person's basic needs. The system aims to help paralyzed individuals live more independently at home.
1) The document presents research on using EEG signals to predict reaching targets during two experiments.
2) It describes the components of a BCI system and the challenges of using EEG data, which can be contaminated by artifacts from eye and muscle movements.
3) The researchers used independent component analysis and other techniques to remove artifacts from the EEG data before extracting features and using classification algorithms to decode reaching targets.
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.
A Review on Motor Imagery Signal Classification for BCICSCJournals
Brain computer interface (BCI) is an evolving technology from past few years. Scalp recorded electroencephalogram (EEG) based BCI technologies are widely used because of safety, low cost and portability. Millions of people are suffering from stroke worldwide and become disabled. They may lose communication control and fall into the locked in state (LIS) or completely locked in state (CLIS). Motor imagery brain computer interface (MI-BCI) can provide non-muscular channel for communication to those who are suffering from neuronal disorders, only by imagination of different motor tasks e.g. left-right hand and foot movement imagination. EEG signals are time varying, non-stationary random signals which are changes in person to person and occurs at different frequencies. For real time application of such a system efficient classification of motor tasks is required. The biggest challenge in MI-BCI system design is extraction of robust, informative and discriminative features which can be converted into device commands. The main application of MI-BCI is neurorehabilitation and control of wheelchair or robotic limbs. The objective of this paper is to give brief information about different stages of EEG based MI-BCI system. It also includes the review on motor imagery signal classification.
This webinar is part of a 2-hour monthly series hosted by the Neurotechnology Innovation Network: https://ktn-uk.org/health/neurotechnology/
Each webinar features expert speakers and focusses on a new development in a different technology area.
The third topic in this series is Dementia treatment using a biodesign approach. Dementia can have enormous effects, not only to those suffering but also family members and others
caring for them, but there are currently no effective therapies available. Neurotechnology offers a new way of treating dementia.
There is growing evidence that technologies such as deep brain stimulation and transcranial magnetic stimulation could help treat some of the effects of dementia and brain-computer interfaces are now able to detect the first signs of dementia years before symptoms appear.
In collaboration with UK Dementia Research Institute this webinar explores novel neurotechnologies to treat dementia, discuss barriers to adoption and new opportunities in the field.
This document summarizes and compares algorithms for detecting and predicting epileptic seizures from electroencephalogram (EEG) signals. It begins by introducing the challenges of epilepsy and importance of automatic seizure detection and prediction. It then provides an overview of state-of-the-art algorithms operating in different transform domains, including time, frequency, wavelet, empirical mode decomposition, singular value decomposition, and principal/independent component analysis domains. The document concludes by comparing seizure detection and prediction methods and discussing future research directions.
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
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.
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewIRJET Journal
This document provides a review of techniques for denoising electroencephalogram (EEG) signals to remove noise and artifacts for improved analysis of brain disorders. It discusses how EEG signals are contaminated by various noise sources that can obscure important information. Several denoising techniques are examined, including independent component analysis (ICA), principal component analysis (PCA), wavelet-based denoising, and wavelet packet-based denoising. Wavelet transforms are highlighted as providing effective solutions for denoising non-stationary signals like EEG due to their ability to perform time-frequency analysis. The document concludes that wavelet methods, especially using wavelet packets, are useful for removing noise from EEG signals.
IRJET- An Efficient Approach for Removal of Ocular Artifacts in EEG-Brain Com...IRJET Journal
This document summarizes a research paper that proposes a method to remove ocular artifacts from electroencephalogram (EEG) signals. Ocular artifacts are contaminants in EEG signals caused by eye blinks and movements that can distort the brain activity being measured. The proposed method uses discrete wavelet transform (DWT) to isolate the ocular artifact components in the frequency domain. It then applies adaptive noise cancellation (ANC) to the wavelet coefficients to remove the artifact components without damaging the underlying brain activity signal. The method is intended to enable more effective analysis of EEG data for applications like diagnosing epilepsy and developing brain-computer interfaces.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
The document describes a brain-computer interface (BCI) system that uses electroencephalography (EEG) to classify motor imagery of the left or right arm and control an assistive device for paralyzed upper limbs. EEG signals are recorded over motor cortex areas during right and left arm imagery tasks. The mu and beta frequency bands are extracted and used to classify intended movement based on features like power and mean. If right arm imagery is classified, a stepper motor attached to the patient's forearm is activated to help lift their arm. The system was tested on 7 subjects with over 10 trials each, achieving classification of intended movement.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
IRJET- Investigation of Electroencephalogram Signals in Different Posture...IRJET Journal
This study investigated electroencephalogram (EEG) signals in radiologists observing clinical images in different postures during angiography procedures. Specifically, it examined attention levels and alpha and beta wave values in both standing and sitting positions. The study found no significant differences in attention levels or beta waves between postures, but did find significant differences in alpha wave values at several time points and in the ratio of beta to alpha waves. This suggests an image manipulation system using EEG signals could work in both standing and sitting postures by monitoring attention levels, which are not impacted by posture changes. The study demonstrated such a system developed previously could potentially be used in clinical situations.
IRJET-Analysis of EEG Signals and Biomedical Changes due to Meditation on Bra...IRJET Journal
This document reviews research on analyzing EEG signals and biomedical changes due to meditation on the brain. It begins with an abstract stating that meditation can significantly contribute to physical and mental relaxation and is gaining popularity as a stress reduction technique. The review examines the effects of meditation on the human brain using electroencephalography (EEG) signals and various signal processing methods. It summarizes several studies that have found increases in theta and alpha band power and decreases in overall frequency during meditation based on EEG analyses. The document provides background on EEG and different brainwave frequencies and then proposes a system model for analyzing meditation effects on brainwaves through feature extraction from EEG data and classification of meditational vs. non-meditational states.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
15 Trends In Neurotechnologies That Will Change The WorldNikita Lukianets
Below are technologies related to neuro and cognitive under three key areas of accelerating change: Machine Learning & Neural Network Computing, Extended Cognition and Neural Interfaces. Neural network computing will lead to improvements in computer vision and analysis, such as detecting emotions and moods, which may have safety and security applications. Extended cognition involves more direct connection to people's brains, allowing mood, thought patterns and information to be altered in the brain. Neural interfaces get information out of people's brains more efficiently, ultimately allowing a machine-enabled form of telepathy. This presentation covers Michell Zappa research from Policy Horizons Canada
IRJET- Automation of a Prosthetic Limb using Shared Control of Brain Mach...IRJET Journal
This document summarizes research into automating a prosthetic limb using shared control of a brain-machine interface (BMI) and vision-guided robotics. A BMI uses electroencephalogram (EEG) signals to allow subjects to control a robotic arm. However, grasping objects precisely is challenging with a BMI alone. The document proposes using computer vision and algorithms like Simultaneous Localization and Mapping (SLAM) to provide positional feedback to improve grasping accuracy. An experiment is described where a robotic arm is controlled through a shared-control approach combining BMI signals and vision guidance. The results demonstrate that combining BMI with computer vision improves prosthetic arm performance during grasping tasks.
IRJET- Survey on Home Automation System using Brain Computer Interface Pa...IRJET Journal
This document summarizes a research paper on using brain-computer interfaces for home automation. It discusses how EEG signals collected from the brain can be used to control external devices without physical movement. The proposed system uses an OpenBCI board and EEG headset to collect brain signals in response to auditory tones. These signals are processed to determine commands, which are then sent to control smart home appliances like lights and fans through voice commands to an Alexa device. The system aims to help people with disabilities control their home environment through thought.
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.
Prediction Model for Emotion Recognition Using EEGIRJET Journal
The document describes a study that compares different machine learning models for emotion recognition using EEG data. The study uses EEG data collected from patients with depression and healthy controls. It extracts features from the EEG data, including linear and nonlinear parameters from different frequency bands. It then uses classifiers like random forest, KNN, CNN, and LSTM to classify emotions as positive, negative or neutral. The random forest model achieved the best accuracy for identifying depression patients' emotions. The study provides a framework for EEG data collection, preprocessing, feature extraction and applying different machine learning models to optimize emotion recognition from EEG signals.
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.
This document describes a smart helmet system that embeds sensors to monitor the wearer's brain activity (EEG) and respiration. The system includes an Arduino microcontroller, GPS module, GSM module for emergency alerts, EEG sensor, respiratory sensor, and power supply. It monitors sensor values and compares them to normal tolerance levels. If any abnormal readings or accidents occur, the system sends an emergency alert message along with GPS location to recovery authorities. The wearer can also manually trigger an emergency message using a button. The document discusses recording EEG and ECG signals from inside the helmet and transmitting data to evaluate feasibility. It aims to monitor physiological signals during activities like sports or military engagements for safety.
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.
A Review on Motor Imagery Signal Classification for BCICSCJournals
Brain computer interface (BCI) is an evolving technology from past few years. Scalp recorded electroencephalogram (EEG) based BCI technologies are widely used because of safety, low cost and portability. Millions of people are suffering from stroke worldwide and become disabled. They may lose communication control and fall into the locked in state (LIS) or completely locked in state (CLIS). Motor imagery brain computer interface (MI-BCI) can provide non-muscular channel for communication to those who are suffering from neuronal disorders, only by imagination of different motor tasks e.g. left-right hand and foot movement imagination. EEG signals are time varying, non-stationary random signals which are changes in person to person and occurs at different frequencies. For real time application of such a system efficient classification of motor tasks is required. The biggest challenge in MI-BCI system design is extraction of robust, informative and discriminative features which can be converted into device commands. The main application of MI-BCI is neurorehabilitation and control of wheelchair or robotic limbs. The objective of this paper is to give brief information about different stages of EEG based MI-BCI system. It also includes the review on motor imagery signal classification.
This webinar is part of a 2-hour monthly series hosted by the Neurotechnology Innovation Network: https://ktn-uk.org/health/neurotechnology/
Each webinar features expert speakers and focusses on a new development in a different technology area.
The third topic in this series is Dementia treatment using a biodesign approach. Dementia can have enormous effects, not only to those suffering but also family members and others
caring for them, but there are currently no effective therapies available. Neurotechnology offers a new way of treating dementia.
There is growing evidence that technologies such as deep brain stimulation and transcranial magnetic stimulation could help treat some of the effects of dementia and brain-computer interfaces are now able to detect the first signs of dementia years before symptoms appear.
In collaboration with UK Dementia Research Institute this webinar explores novel neurotechnologies to treat dementia, discuss barriers to adoption and new opportunities in the field.
This document summarizes and compares algorithms for detecting and predicting epileptic seizures from electroencephalogram (EEG) signals. It begins by introducing the challenges of epilepsy and importance of automatic seizure detection and prediction. It then provides an overview of state-of-the-art algorithms operating in different transform domains, including time, frequency, wavelet, empirical mode decomposition, singular value decomposition, and principal/independent component analysis domains. The document concludes by comparing seizure detection and prediction methods and discussing future research directions.
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
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.
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewIRJET Journal
This document provides a review of techniques for denoising electroencephalogram (EEG) signals to remove noise and artifacts for improved analysis of brain disorders. It discusses how EEG signals are contaminated by various noise sources that can obscure important information. Several denoising techniques are examined, including independent component analysis (ICA), principal component analysis (PCA), wavelet-based denoising, and wavelet packet-based denoising. Wavelet transforms are highlighted as providing effective solutions for denoising non-stationary signals like EEG due to their ability to perform time-frequency analysis. The document concludes that wavelet methods, especially using wavelet packets, are useful for removing noise from EEG signals.
IRJET- An Efficient Approach for Removal of Ocular Artifacts in EEG-Brain Com...IRJET Journal
This document summarizes a research paper that proposes a method to remove ocular artifacts from electroencephalogram (EEG) signals. Ocular artifacts are contaminants in EEG signals caused by eye blinks and movements that can distort the brain activity being measured. The proposed method uses discrete wavelet transform (DWT) to isolate the ocular artifact components in the frequency domain. It then applies adaptive noise cancellation (ANC) to the wavelet coefficients to remove the artifact components without damaging the underlying brain activity signal. The method is intended to enable more effective analysis of EEG data for applications like diagnosing epilepsy and developing brain-computer interfaces.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
The document describes a brain-computer interface (BCI) system that uses electroencephalography (EEG) to classify motor imagery of the left or right arm and control an assistive device for paralyzed upper limbs. EEG signals are recorded over motor cortex areas during right and left arm imagery tasks. The mu and beta frequency bands are extracted and used to classify intended movement based on features like power and mean. If right arm imagery is classified, a stepper motor attached to the patient's forearm is activated to help lift their arm. The system was tested on 7 subjects with over 10 trials each, achieving classification of intended movement.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
IRJET- Investigation of Electroencephalogram Signals in Different Posture...IRJET Journal
This study investigated electroencephalogram (EEG) signals in radiologists observing clinical images in different postures during angiography procedures. Specifically, it examined attention levels and alpha and beta wave values in both standing and sitting positions. The study found no significant differences in attention levels or beta waves between postures, but did find significant differences in alpha wave values at several time points and in the ratio of beta to alpha waves. This suggests an image manipulation system using EEG signals could work in both standing and sitting postures by monitoring attention levels, which are not impacted by posture changes. The study demonstrated such a system developed previously could potentially be used in clinical situations.
IRJET-Analysis of EEG Signals and Biomedical Changes due to Meditation on Bra...IRJET Journal
This document reviews research on analyzing EEG signals and biomedical changes due to meditation on the brain. It begins with an abstract stating that meditation can significantly contribute to physical and mental relaxation and is gaining popularity as a stress reduction technique. The review examines the effects of meditation on the human brain using electroencephalography (EEG) signals and various signal processing methods. It summarizes several studies that have found increases in theta and alpha band power and decreases in overall frequency during meditation based on EEG analyses. The document provides background on EEG and different brainwave frequencies and then proposes a system model for analyzing meditation effects on brainwaves through feature extraction from EEG data and classification of meditational vs. non-meditational states.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
15 Trends In Neurotechnologies That Will Change The WorldNikita Lukianets
Below are technologies related to neuro and cognitive under three key areas of accelerating change: Machine Learning & Neural Network Computing, Extended Cognition and Neural Interfaces. Neural network computing will lead to improvements in computer vision and analysis, such as detecting emotions and moods, which may have safety and security applications. Extended cognition involves more direct connection to people's brains, allowing mood, thought patterns and information to be altered in the brain. Neural interfaces get information out of people's brains more efficiently, ultimately allowing a machine-enabled form of telepathy. This presentation covers Michell Zappa research from Policy Horizons Canada
IRJET- Automation of a Prosthetic Limb using Shared Control of Brain Mach...IRJET Journal
This document summarizes research into automating a prosthetic limb using shared control of a brain-machine interface (BMI) and vision-guided robotics. A BMI uses electroencephalogram (EEG) signals to allow subjects to control a robotic arm. However, grasping objects precisely is challenging with a BMI alone. The document proposes using computer vision and algorithms like Simultaneous Localization and Mapping (SLAM) to provide positional feedback to improve grasping accuracy. An experiment is described where a robotic arm is controlled through a shared-control approach combining BMI signals and vision guidance. The results demonstrate that combining BMI with computer vision improves prosthetic arm performance during grasping tasks.
IRJET- Survey on Home Automation System using Brain Computer Interface Pa...IRJET Journal
This document summarizes a research paper on using brain-computer interfaces for home automation. It discusses how EEG signals collected from the brain can be used to control external devices without physical movement. The proposed system uses an OpenBCI board and EEG headset to collect brain signals in response to auditory tones. These signals are processed to determine commands, which are then sent to control smart home appliances like lights and fans through voice commands to an Alexa device. The system aims to help people with disabilities control their home environment through thought.
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.
Prediction Model for Emotion Recognition Using EEGIRJET Journal
The document describes a study that compares different machine learning models for emotion recognition using EEG data. The study uses EEG data collected from patients with depression and healthy controls. It extracts features from the EEG data, including linear and nonlinear parameters from different frequency bands. It then uses classifiers like random forest, KNN, CNN, and LSTM to classify emotions as positive, negative or neutral. The random forest model achieved the best accuracy for identifying depression patients' emotions. The study provides a framework for EEG data collection, preprocessing, feature extraction and applying different machine learning models to optimize emotion recognition from EEG signals.
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.
This document describes a smart helmet system that embeds sensors to monitor the wearer's brain activity (EEG) and respiration. The system includes an Arduino microcontroller, GPS module, GSM module for emergency alerts, EEG sensor, respiratory sensor, and power supply. It monitors sensor values and compares them to normal tolerance levels. If any abnormal readings or accidents occur, the system sends an emergency alert message along with GPS location to recovery authorities. The wearer can also manually trigger an emergency message using a button. The document discusses recording EEG and ECG signals from inside the helmet and transmitting data to evaluate feasibility. It aims to monitor physiological signals during activities like sports or military engagements for safety.
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.
Brain computer interfacing for controlling wheelchair movementIRJET Journal
This document describes research on developing a brain-computer interface (BCI) system to control a wheelchair using EEG brain wave signals. Specifically, it focuses on using alpha waves detected during a relaxed state to allow users to control wheelchair movement and direction. The system is intended to help people with disabilities who cannot move themselves. The document provides background on BCI and previous related work, then describes the proposed system which uses EEG signals from a low-cost headset to classify motor imagery and control a wheelchair wirelessly. It discusses the algorithms and experimental results, showing the system can accurately detect different movement intentions based on alpha wave detection with minimal training.
This document summarizes an approach to embedding a human brain with smart devices using depreciated brain-computer interface (BCI) technology. It discusses how BCI systems work by acquiring EEG signals from the brain, preprocessing the signals, classifying them, and using them to control external applications. Specifically, it proposes controlling a tablet through a 1-channel EEG amplifier and non-invasive electrode placement. The document outlines the basic components and applications of BCI systems and describes implementing a basic prototype to test controlling a media player on a tablet using EEG signals processed in MATLAB.
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.
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.
CLASSIFICATION OF ELECTROENCEPHALOGRAM SIGNALS USING XGBOOST ALGORITHM AND SU...IRJET Journal
This document presents a study that uses the XGBoost algorithm and support vector machine to classify electroencephalogram (EEG) signals. The study acquires EEG data from healthy subjects and subjects with epilepsy during seizure and non-seizure periods. It preprocesses the data, extracts features using linear discriminant analysis, and feeds the extracted features into XGBoost and SVM classifiers. The results indicate that XGBoost exhibited superior classification performance compared to SVM for analyzing and classifying EEG signals.
Design & Implementation of Brain Controlled WheelchairIRJET Journal
This document describes a proposed design for a brain-controlled wheelchair. It uses an electroencephalography (EEG) technique with an electrode cap placed on the user's scalp to capture brain wave signals. The EEG signals are processed and translated into movement commands for the wheelchair by an Arduino microcontroller. Specifically, the system analyzes brain waves for alpha, beta, and gamma waves and uses the attention level measured from these waves to control the wheelchair's forward and stopping movements. The goal is to provide independent mobility for people with severe motor disabilities.
A robotic arm is a Programmable mechanical arm which copies the functions of the human arm. They
are widely used in industries. Human robot-controlled interfaces mainly focus on providing rehabilitation to
amputees in order to overcome their amputation or disability leading them to live a normal life. The major
objective of this project is to develop a movable robotic arm controlled by EMG signals from the muscles of the
upper limb. In this system, our main aim is on providing a low 2-dimensional input derived from emg to move the
arm. This project involves creating a prosthesis system that allows signals recorded directly from the human body.
The arm is mainly divided into 2 parts, control part and moving part. Movable part contains the servo motor
which is connected to the Arduino Uno board, and it helps in developing a motion in accordance with the EMG
signals acquired from the body. The control part is the part that is controlled by the operation according to the
movement of the amputee. Mainly the initiation of the movement for the threshold fixed in the coding. The major
aim of the project is to provide an affordable and easily operable device that helps even the poor sections of the
amputated society to lead a happier and normal life by mimicking the functions of the human arm in terms of both
the physical, structural as well as functional aspects.
IRJET-Advanced Method of Epileptic detection using EEG by Wavelet DecompositionIRJET Journal
This document proposes a method to detect epileptic seizures from EEG signals using wavelet decomposition and entropy-based feature extraction. EEG data is decomposed using wavelets and features like entropy measures and power ratios in different frequency bands are extracted. These features are then used as inputs to a k-nearest neighbors classifier to classify signals as normal, ictal or inter-ictal. The method is tested on two benchmark EEG databases and aims to increase prediction accuracy of seizure onset to help localize epileptic foci. Statistical, spectral and nonlinear features are commonly used in existing methods. The proposed method uses entropy measures like Shannon, Renyi, approximate and sample entropy along with power ratios in frequency bands as features for classification.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
IRJET- Human Hand Movement Training with Exoskeleton ARMIRJET Journal
This document describes a proposed system to develop a humanoid robotic hand that can mimic the motions of a human hand in real-time. The system uses a MEMS sensor to capture motions of the human hand. This data is sent to an Arduino microcontroller via wireless sensor networks. The microcontroller then sends commands to the actuators in the robotic hand to replicate the motions. The goal is to create an inexpensive, automatically controlled robotic hand that is wirelessly interfaced to mimic natural human finger and hand motions for applications such as assistance for stroke patients. Hardware components include the MEMS sensor, Arduino, wireless modules, motors, and a robotic hand model while software includes programs for the Arduino.
IRJET- Review on Depression Prediction using Different MethodsIRJET Journal
This document summarizes various methods that have been used to predict depression. It discusses using questionnaires and psychometric tests administered by psychiatrists, analyzing EEG signals through signal processing techniques, and using artificial intelligence and machine learning algorithms to analyze text, audio, and visual inputs. Specifically, it describes using standardized tests like the Hospital Anxiety and Depression Scale and Beck's Depression Inventory, extracting features from EEG frequency bands to classify subjects, and employing sentiment analysis and other text analysis on speech, facial expressions, and head movements to predict mental states. The document provides background on relevant concepts in artificial intelligence, machine learning, deep learning, and neural networks.
IRJET- Neurotechnology for Superior BrainIRJET Journal
This document discusses neurotechnology and active implantable medical devices (AIMDs). It summarizes that AIMDs are designed to treat neurological disorders and are implanted in the brain or nervous system. For these devices to function properly in the human body, they must be hermetic, biocompatible, biostable, sterile, and clean. Various sensing methods like EEG and fMRI are used to record neural activity in the brain. Stimulation methods like transcranial direct current stimulation are also used to stimulate brain function. Overall, neurotechnology has advanced to allow for wireless and portable brain-computer interfaces, but challenges remain around device miniaturization and long-term implantation in the complex environment of the human body and brain.
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- Low – Cost Human Hand Prosthetic using EMG Signal with the Help of Mic...IRJET Journal
The document describes a study on developing a low-cost human hand prosthetic using EMG signals and an Arduino microcontroller. EMG signals from the user's residual limb muscles are measured using surface electrodes and processed to control servo motors in the prosthetic. The prosthetic was designed and built using inexpensive aluminum pipes and 3D printed parts. Initial tests controlling a single servo motor were successful, but integrating the EMG sensors with the Arduino platform posed challenges. Future work could focus on improving dexterity and reducing weight through optimized designs and materials like carbon fiber. The overall aim is to create a more affordable prosthetic option.
Epilepsy Prediction using Machine LearningIRJET Journal
This document discusses various machine learning models for predicting epilepsy using EEG signals. It evaluates models like KNN, logistic regression, SVM, naive Bayes, LSTM, decision trees, random trees, and random forests on an epilepsy dataset containing 11,500 samples. Feature selection is used to select the 25 most important features. The models are trained and validated, with metrics like accuracy, AUC, recall, and precision calculated. Random forest achieved the best performance with a training accuracy of 96.4% and validation accuracy of 95.8%, outperforming other models at predicting epilepsy from EEG data.
Similar to IRJET- Design and Development of Electroencephalography based Cost Effective Prosthetic ARM Controlled by Brain Waves (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.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.