International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
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- 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.
The document discusses brain computer interfaces (BCI), which allow humans to control devices with their thoughts by detecting brain signals. It covers the history and principles of BCI, describing invasive and non-invasive techniques for acquiring brain signals like EEG, ECoG, and fMRI. Applications discussed include using BCI for gaming, robotics, medicine, and military purposes. Challenges include training users and improving signal acquisition, but proposed solutions involve selective attention strategies and signal preprocessing techniques. In conclusion, BCI technology continues to improve detection methods and provide new options for human-machine interaction.
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.
Neuro-technology aims to restore or improve human nervous system function through electronics. Neuromotor prostheses (NMPs) extract signals from the nervous system to control devices. The goal of NMPs is to convey motor control intent from the central nervous system to drive multi-degree of freedom prosthetic devices for amputees or paralyzed patients. Key challenges are developing neural interfaces that last a lifetime and providing dexterous, natural control of prosthetics. NMP systems involve neural implants to record brain signals, decoding software to translate signals into motor commands, and output devices like prosthetics. Technological advances now allow basic NMP control but further progress is still needed.
Neuroprosthetics involves using brain signals acquired from neurons for various purposes like restoring movement in paralyzed patients. Nanotechnology like nano multi-electrode arrays can be used to receive and transmit brain signals more effectively by increasing electrode conduction and reducing incorrect connections with neurons. Neuroprosthetics has applications in both in vivo and in vitro contexts and can help improve functions like movement, speech, and understanding of drug effects on animal behavior and emotions.
IRJET- Improving the DPCM based Compressor for Endoscopy Videos using Cellula...IRJET Journal
This document discusses improving compression for endoscopy videos using cellular automata. It aims to develop a low complexity, lossless image compression system for capsule endoscopy. The proposed system uses differential pulse code modulation (DPCM) along with a variable length predictive algorithm for lossless compression. It converts images to a low-cost YEF color space and aims to sufficiently compress images while maintaining quality for accurate medical diagnosis. The system is evaluated using MATLAB software to meet objectives of efficient compression that consumes low power while transmitting and receiving data without loss of significant information.
A brain-computer interface (BCI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain-computer interface in scientific literature.The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
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- 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.
The document discusses brain computer interfaces (BCI), which allow humans to control devices with their thoughts by detecting brain signals. It covers the history and principles of BCI, describing invasive and non-invasive techniques for acquiring brain signals like EEG, ECoG, and fMRI. Applications discussed include using BCI for gaming, robotics, medicine, and military purposes. Challenges include training users and improving signal acquisition, but proposed solutions involve selective attention strategies and signal preprocessing techniques. In conclusion, BCI technology continues to improve detection methods and provide new options for human-machine interaction.
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.
Neuro-technology aims to restore or improve human nervous system function through electronics. Neuromotor prostheses (NMPs) extract signals from the nervous system to control devices. The goal of NMPs is to convey motor control intent from the central nervous system to drive multi-degree of freedom prosthetic devices for amputees or paralyzed patients. Key challenges are developing neural interfaces that last a lifetime and providing dexterous, natural control of prosthetics. NMP systems involve neural implants to record brain signals, decoding software to translate signals into motor commands, and output devices like prosthetics. Technological advances now allow basic NMP control but further progress is still needed.
Neuroprosthetics involves using brain signals acquired from neurons for various purposes like restoring movement in paralyzed patients. Nanotechnology like nano multi-electrode arrays can be used to receive and transmit brain signals more effectively by increasing electrode conduction and reducing incorrect connections with neurons. Neuroprosthetics has applications in both in vivo and in vitro contexts and can help improve functions like movement, speech, and understanding of drug effects on animal behavior and emotions.
IRJET- Improving the DPCM based Compressor for Endoscopy Videos using Cellula...IRJET Journal
This document discusses improving compression for endoscopy videos using cellular automata. It aims to develop a low complexity, lossless image compression system for capsule endoscopy. The proposed system uses differential pulse code modulation (DPCM) along with a variable length predictive algorithm for lossless compression. It converts images to a low-cost YEF color space and aims to sufficiently compress images while maintaining quality for accurate medical diagnosis. The system is evaluated using MATLAB software to meet objectives of efficient compression that consumes low power while transmitting and receiving data without loss of significant information.
A brain-computer interface (BCI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain-computer interface in scientific literature.The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
IRJET- Precision of Lead-Point with Support Vector Machine based Microelectro...IRJET Journal
This document discusses using microelectrode recordings (MER) with support vector machine learning to study the function of subthalamic nucleus (STN) neurons in the human brain during deep brain stimulation for Parkinson's disease. The study aims to improve the signal-to-noise ratio of MER signals and precisely identify the location of the STN to ensure safety and efficacy of deep brain stimulation chip implantation. MER is found to confirm the presence of abnormal STN neurons and clear positioning of the microchip electrode in the target area. Access to functional data from neurons deep in the brain using MER may help further elucidate cryptic aspects of brain function.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
EEG Classification using Semi Supervised Learningijtsrd
The major challenge in the current brain–computer interface research is the accurate classification of time varying electroencephalographic EEG signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real applications. Semi supervised learning SSL methods can utilize both labeled and unlabeled data to improve performance over supervised approaches. However, it has been reported that the unlabeled data may undermine the performance of SSL in some cases. This study proposes a three stages technique for automatic detection of epileptic seizure in EEG signals. In practical application of pattern recognition, there are often diverse features extracted from raw data which needs to be recognized. Proposed method is based on time series signal, spectral analysis and recurrent neural networks RNNs . Decision making was performed in three stages i feature extraction using Welch method power spectrum density estimation PSD ii dimensionality reduction using statistics over extracted features and time series signal samples iii EEG classification using recurrent neural networks. This study shows that Welch method power spectrum density estimation is an appropriate feature which well represents EEG signals. We achieved higher classification accuracy specificity, sensitivity, classification accuracy in comparison with other researches to classify EEG signals exactly 100 in this study. To improve the safety of SSL, we proposed a new safety control mechanism by analyzing the differences between unlabeled data analysis in supervised and semi supervised learning. We then develop and implement a safe classification method based on the semi supervised extreme learning machine SS ELM . Following this approach, the Wasserstein distance is used to measure the similarities between the predictions obtained from ELM and SS ELM algorithms, and a different risk degree is thereby calculated for each unlabeled data instance. A risk based regularization term is then constructed and embedded into the objective function of the SS ELM. Extensive experiments were conducted using benchmark and EEG datasets to evaluate the effectiveness of the proposed method. Experimental results show that the performance of the new algorithm is comparable to SS ELM and superior to ELM on average. It is thereby shown that the proposed method is safe and efficient for the classification of EEG signals. Shivshankar Kumar Yadav | Veena S. ""EEG Classification using Semi Supervised Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23355.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23355/eeg-classification-using-semi-supervised-learning/shivshankar-kumar-yadav
A low cost SSVEP-EEG based human-computer-interaction system for completely l...journalBEEI
Human computer interaction (HCI) for completely locked-in patients is a very difficult task. Nowadays, information technology (IT) is becoming an essential part of human life. Patients with completely locked-in state are generally unable to facilitate themselves by these useful technological advancements. Hence, they cannot use modern IT gadgets and applications throughout the lifespan after disability. Advancements in brain computer interface (BCI) enable operating IT devices using brain signals specifically when a person is unable to interact with the devices in conventional manner due to cognitive motor disability. However, existing state-of-the-art application specific BCI devices are comparatively too expensive. This paper presents a research and development work that aims to design and develop a low-cost general purpose HCI system that can be used to operate computers and a general purpose control panel through brain signals. The system is based on steady state visual evoked potentials (SSVEP). In proposed system, these electrical signals are obtained in response of a number of different flickering lights of different frequencies through electroencephalogram (EEG) electrodes and an open source BCI hardware. Successful trails conducted on healthy participants suggest that severely paralyzed subjects can operate a computer or control panel as an alternative to conventional HCI device.
Brain-computer interfaces (BCI) allow communication between the brain and external devices using electroencephalography (EEG) to measure brain activity. BCIs can help patients with neuromuscular disorders by using remaining brain pathways to provide new channels for communication and control. Non-invasive BCIs use EEG electrodes placed on the scalp to detect patterns in frequency bands associated with events like movements to control devices. Invasive BCIs are implanted in the brain but non-invasive options avoid risks of surgery.
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.
This document describes a study that designed ECG and EEG hardware and implemented software using LabVIEW to analyze medical test data and identify abnormalities. The goal was to develop a robotic system to facilitate remote patient monitoring and doctor-patient interaction. Hardware was designed using Multisim to condition ECG and EEG signals. LabVIEW was used to analyze the signals, detect abnormalities in ECG and EEG reports, and calculate pulse rate. The system is intended to help address issues with limited doctor availability in India by allowing remote medical testing and consultation.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
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- 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.
The human brain is a complex system consisting of connections between different neurons and regions creating networks, known as Brain Networks. Particularly relevant in this context are Resting State Networks (RSNs), which are synchronous fluctuations between spatially distinct regions, occurring in the absence of a task or stimulus. Neuroscientists have identified modifications in RSNs in many neurodegenerative diseases, thus a long-term analysis of them is fundamental to monitor alterations in brain functional connectivity. Nonetheless, the statistical tools in charge of analyzing RSNs currently fail in reaching significant levels of throughput, due to the huge amount of data to process. For this reason, our work consists of a hardware acceleration on FPGA design of the Independent Component Analysis (ICA), a state-of-the-art statistical method for RSNs recognition, in order to accelerate the data analysis process. We evaluated and deployed our implementation on Amazon F1 instances. The experimental results show that our hardware implementation is able to outperform GIFT, one of the most commonly used tools to identify RSNs, by a factor of 5x.
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
A-Virtual-Electrode-through-Summation-of-Time-Offset-PulsesTrevor Davis
This paper presents a technique for creating virtual electrodes between physical electrodes in a retinal prosthesis. The technique involves stimulating two adjacent physical electrodes with identical pulses that have pulse widths too short to activate neurons on their own. However, one pulse is time-offset from the other. This results in a virtual electrode appearing in the center of the two physical electrodes, with a pulse width that is the sum of the two individual pulses. This combined pulse width is long enough to activate neurons. Experimental results show that when two electrodes 250 μm apart were stimulated with 1 ms pulses offset by 1 ms, a virtual electrode appeared between them with a pulse width of 2 ms, matching the expected results. This virtual electrode technique could help increase the effective resolution of
Giritharan Ravichandran proposes a system to provide artificial sight to visually impaired individuals through brain-to-brain visual transmission. The system uses electro-oculogram to record electrical signals from the eye, processes the signals to extract those corresponding to eye activity, and transmits the signals wirelessly to another brain. A transcranial magnetic stimulator induces equivalent electrical currents in the optic nerve of the recipient, allowing them to perceive the transmitted visual information. The proposed system aims to help those blinded due to eye or retinal defects by bypassing the eyes and providing artificial sight directly to the brain.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
This document summarizes a student project titled "Design and Implementation of Patient Monitoring System Using Steady State Visual Evoked Potential Signal(SSVEP) Based on Labview". The project involved:
1. Designing and implementing a brain-computer interface using SSVEP signals to allow communication for disabled individuals. Electrodes were placed on the visual cortex and subjects were shown visual stimuli at different frequencies.
2. Signal processing circuits including amplifiers and filters were designed and tested to extract the SSVEP frequencies from EEG signals.
3. A visual stimulation system was developed using flashing lights at different frequencies.
4. LabVIEW was used to analyze the SSVEP signals and detect the frequency the
Recent articles published in VLSI design & Communication SystemsVLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.
Motor Imagery based Brain Computer Interface for Windows Operating SystemIRJET Journal
This document summarizes a research paper that proposes a motor imagery-based brain computer interface (MI-BCI) to allow physically challenged individuals to control basic functions of a Windows operating system using only their brain activity. The MI-BCI system uses an 8-channel EEG device to capture brainwaves while a subject imagines moving their arm or blinking their eyes. A convolutional neural network (CNN) classifier is trained on the EEG data to identify 7 possible commands: left, right, up, down mouse movement or left/right click, or an idle state. The trained CNN achieved an average accuracy of 92.85% in identifying commands. A Python program integrates the EEG data stream, CNN classifier, and Windows mouse/
A nonlinearities inverse distance weighting spatial interpolation approach ap...IJECEIAES
Spatial interpolation of a surface electromyography (sEMG) signal from a set of signals recorded from a multi-electrode array is a challenge in biomedical signal processing. Consequently, it could be useful to increase the electrodes' density in detecting the skeletal muscles' motor units under detection's vacancy. This paper used two types of spatial interpolation methods for estimation: Inverse distance weighted (IDW) and Kriging. Furthermore, a new technique is proposed using a modified nonlinearity formula based on IDW. A set of EMG signals recorded from the noninvasive multi-electrode grid from different types of subjects, sex, age, and type of muscles have been studied when muscles are under regular tension activity. A goodness of fit measure (R2) is used to evaluate the proposed technique. The interpolated signals are compared with the actual signals; the Goodness of fit measure's value is almost 99%, with a processing time of 100msec. The resulting technique is shown to be of high accuracy and matching of spatial interpolated signals to actual signals compared with IDW and Kriging techniques.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
IRJET- Precision of Lead-Point with Support Vector Machine based Microelectro...IRJET Journal
This document discusses using microelectrode recordings (MER) with support vector machine learning to study the function of subthalamic nucleus (STN) neurons in the human brain during deep brain stimulation for Parkinson's disease. The study aims to improve the signal-to-noise ratio of MER signals and precisely identify the location of the STN to ensure safety and efficacy of deep brain stimulation chip implantation. MER is found to confirm the presence of abnormal STN neurons and clear positioning of the microchip electrode in the target area. Access to functional data from neurons deep in the brain using MER may help further elucidate cryptic aspects of brain function.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
EEG Classification using Semi Supervised Learningijtsrd
The major challenge in the current brain–computer interface research is the accurate classification of time varying electroencephalographic EEG signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real applications. Semi supervised learning SSL methods can utilize both labeled and unlabeled data to improve performance over supervised approaches. However, it has been reported that the unlabeled data may undermine the performance of SSL in some cases. This study proposes a three stages technique for automatic detection of epileptic seizure in EEG signals. In practical application of pattern recognition, there are often diverse features extracted from raw data which needs to be recognized. Proposed method is based on time series signal, spectral analysis and recurrent neural networks RNNs . Decision making was performed in three stages i feature extraction using Welch method power spectrum density estimation PSD ii dimensionality reduction using statistics over extracted features and time series signal samples iii EEG classification using recurrent neural networks. This study shows that Welch method power spectrum density estimation is an appropriate feature which well represents EEG signals. We achieved higher classification accuracy specificity, sensitivity, classification accuracy in comparison with other researches to classify EEG signals exactly 100 in this study. To improve the safety of SSL, we proposed a new safety control mechanism by analyzing the differences between unlabeled data analysis in supervised and semi supervised learning. We then develop and implement a safe classification method based on the semi supervised extreme learning machine SS ELM . Following this approach, the Wasserstein distance is used to measure the similarities between the predictions obtained from ELM and SS ELM algorithms, and a different risk degree is thereby calculated for each unlabeled data instance. A risk based regularization term is then constructed and embedded into the objective function of the SS ELM. Extensive experiments were conducted using benchmark and EEG datasets to evaluate the effectiveness of the proposed method. Experimental results show that the performance of the new algorithm is comparable to SS ELM and superior to ELM on average. It is thereby shown that the proposed method is safe and efficient for the classification of EEG signals. Shivshankar Kumar Yadav | Veena S. ""EEG Classification using Semi Supervised Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23355.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23355/eeg-classification-using-semi-supervised-learning/shivshankar-kumar-yadav
A low cost SSVEP-EEG based human-computer-interaction system for completely l...journalBEEI
Human computer interaction (HCI) for completely locked-in patients is a very difficult task. Nowadays, information technology (IT) is becoming an essential part of human life. Patients with completely locked-in state are generally unable to facilitate themselves by these useful technological advancements. Hence, they cannot use modern IT gadgets and applications throughout the lifespan after disability. Advancements in brain computer interface (BCI) enable operating IT devices using brain signals specifically when a person is unable to interact with the devices in conventional manner due to cognitive motor disability. However, existing state-of-the-art application specific BCI devices are comparatively too expensive. This paper presents a research and development work that aims to design and develop a low-cost general purpose HCI system that can be used to operate computers and a general purpose control panel through brain signals. The system is based on steady state visual evoked potentials (SSVEP). In proposed system, these electrical signals are obtained in response of a number of different flickering lights of different frequencies through electroencephalogram (EEG) electrodes and an open source BCI hardware. Successful trails conducted on healthy participants suggest that severely paralyzed subjects can operate a computer or control panel as an alternative to conventional HCI device.
Brain-computer interfaces (BCI) allow communication between the brain and external devices using electroencephalography (EEG) to measure brain activity. BCIs can help patients with neuromuscular disorders by using remaining brain pathways to provide new channels for communication and control. Non-invasive BCIs use EEG electrodes placed on the scalp to detect patterns in frequency bands associated with events like movements to control devices. Invasive BCIs are implanted in the brain but non-invasive options avoid risks of surgery.
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.
This document describes a study that designed ECG and EEG hardware and implemented software using LabVIEW to analyze medical test data and identify abnormalities. The goal was to develop a robotic system to facilitate remote patient monitoring and doctor-patient interaction. Hardware was designed using Multisim to condition ECG and EEG signals. LabVIEW was used to analyze the signals, detect abnormalities in ECG and EEG reports, and calculate pulse rate. The system is intended to help address issues with limited doctor availability in India by allowing remote medical testing and consultation.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
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- 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.
The human brain is a complex system consisting of connections between different neurons and regions creating networks, known as Brain Networks. Particularly relevant in this context are Resting State Networks (RSNs), which are synchronous fluctuations between spatially distinct regions, occurring in the absence of a task or stimulus. Neuroscientists have identified modifications in RSNs in many neurodegenerative diseases, thus a long-term analysis of them is fundamental to monitor alterations in brain functional connectivity. Nonetheless, the statistical tools in charge of analyzing RSNs currently fail in reaching significant levels of throughput, due to the huge amount of data to process. For this reason, our work consists of a hardware acceleration on FPGA design of the Independent Component Analysis (ICA), a state-of-the-art statistical method for RSNs recognition, in order to accelerate the data analysis process. We evaluated and deployed our implementation on Amazon F1 instances. The experimental results show that our hardware implementation is able to outperform GIFT, one of the most commonly used tools to identify RSNs, by a factor of 5x.
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
A-Virtual-Electrode-through-Summation-of-Time-Offset-PulsesTrevor Davis
This paper presents a technique for creating virtual electrodes between physical electrodes in a retinal prosthesis. The technique involves stimulating two adjacent physical electrodes with identical pulses that have pulse widths too short to activate neurons on their own. However, one pulse is time-offset from the other. This results in a virtual electrode appearing in the center of the two physical electrodes, with a pulse width that is the sum of the two individual pulses. This combined pulse width is long enough to activate neurons. Experimental results show that when two electrodes 250 μm apart were stimulated with 1 ms pulses offset by 1 ms, a virtual electrode appeared between them with a pulse width of 2 ms, matching the expected results. This virtual electrode technique could help increase the effective resolution of
Giritharan Ravichandran proposes a system to provide artificial sight to visually impaired individuals through brain-to-brain visual transmission. The system uses electro-oculogram to record electrical signals from the eye, processes the signals to extract those corresponding to eye activity, and transmits the signals wirelessly to another brain. A transcranial magnetic stimulator induces equivalent electrical currents in the optic nerve of the recipient, allowing them to perceive the transmitted visual information. The proposed system aims to help those blinded due to eye or retinal defects by bypassing the eyes and providing artificial sight directly to the brain.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
This document summarizes a student project titled "Design and Implementation of Patient Monitoring System Using Steady State Visual Evoked Potential Signal(SSVEP) Based on Labview". The project involved:
1. Designing and implementing a brain-computer interface using SSVEP signals to allow communication for disabled individuals. Electrodes were placed on the visual cortex and subjects were shown visual stimuli at different frequencies.
2. Signal processing circuits including amplifiers and filters were designed and tested to extract the SSVEP frequencies from EEG signals.
3. A visual stimulation system was developed using flashing lights at different frequencies.
4. LabVIEW was used to analyze the SSVEP signals and detect the frequency the
Recent articles published in VLSI design & Communication SystemsVLSICS Design
International Journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.
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Optimization techniques are important in analog circuit design to minimize circuit size and maximize performance under constraints. Available techniques for optimization include classical methods, nature-inspired algorithms like particle swarm optimization and ant colony optimization, and learning-based optimization. Single-objective optimization aims to optimize a single metric like power or area, while multi-objective optimization aims to optimize multiple conflicting objectives simultaneously, such as power and speed.
Class D Power Amplifier for Medical Applicationieijjournal
The objective of this research was to design a 2.4 GHz class AB Power Amplifier (PA), with 0.18um Semiconductor Manufacturing International Corporation (SMIC) CMOS technology by using Cadence software, for health care applications. The ultimate goal for such application is to minimize the trade-offs between performance and cost, and between performance and low power consumption design. This paper introduces the design of a 2.4GHz class D power amplifier which consists of two stage amplifiers. This power amplifier can transmit 15dBm output power to a 50Ω load. The power added efficiency was 50% and the total power consumption was 90.4 mW. The performance of the power amplifier meets the specification requirements of the desired.
CLASS D POWER AMPLIFIER FOR MEDICAL APPLICATIONieijjournal1
The objective of this research was to design a 2.4 GHz class AB Power Amplifier (PA), with 0.18um
Semiconductor Manufacturing International Corporation (SMIC) CMOS technology by using Cadence
software, for health care applications. The ultimate goal for such application is to minimize the trade-offs
between performance and cost, and between performance and low power consumption design. This paper
introduces the design of a 2.4GHz class D power amplifier which consists of two stage amplifiers. This
power amplifier can transmit 15dBm output power to a 50Ω load. The power added efficiency was 50%
and the total power consumption was 90.4 mW. The performance of the power amplifier meets the
specification requirements of the desired.
CLASS D POWER AMPLIFIER FOR MEDICAL APPLICATIONieijjournal
This document describes the design of a 2.4 GHz class D power amplifier for medical applications using 0.18um CMOS technology. A two-stage class D power amplifier was designed that can transmit 15dBm of output power to a 50Ω load with 50% power added efficiency and total power consumption of 90.4 mW. Simulation results showed the amplifier was stable with an S11 of less than -10 dB and meets requirements for wireless medical sensor networks. The goal was to minimize trade-offs between performance, cost and power consumption for healthcare applications.
June 2020: Top Read Articles in Control Theory and Computer Modellingijctcm
This summary provides the key details from the document in 3 sentences:
This document discusses two articles from the International Journal of Control Theory and Computer Modelling. The first article describes Zigbee, a low power wireless technology standard, its specifications, architecture and applications. The second article presents an artificial neural network approach using multilayer perceptrons to forecast weather parameters like temperature based on past data.
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IRJET- EOG based Human Machine Interface to Control Electric Devices using Ey...IRJET Journal
This document describes the development of an eye movement (EOG) based human-machine interface to control electric devices. EOG signals are acquired from electrodes placed around the eyes and processed to generate control signals for movements. Specifically, the document discusses:
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2. Acquiring and processing EOG signals to generate control signals for wheelchair movement depending on signal amplitude and duration.
3. Reviewing previous literature on EOG-based systems for controlling rehabilitation devices and assisting disabled individuals.
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.
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International journal of VLSI design & Communication Systems (VLSICS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of VLSI Design & Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & communication concepts a destablishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the VLSI design & Communications.
Electrical Engineering: An International Journal (EEIJ) is a Quarterly peer-reviewed and refered open access journal that publishes articles which contribute new results in all areas of Electrical Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of Electrical Engineering.
A Survey on Ultrasound Beamforming StrategiesIRJET Journal
This document summarizes different strategies for ultrasound beamforming. Beamforming is the crucial step in ultrasound imaging where sound waves are focused on a specific point or area. The strategies are different in aspects like the type of signals used, imaging region size, time and computational costs. Several strategies are discussed including plane wave beamforming using the Fourier transform, software-based beamforming using data compression techniques, and FPGA-based modular digital beamforming. Beamforming strategies also differ in image resolution, information loss, and ability to reduce clutter from unwanted signals. Strict timing architectures can guarantee timing coherence for applications like ultrasound beamforming.
IJCNC Top 10 Trending Articles in Academia !!!IJCNCJournal
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In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
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IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...IRJET Journal
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Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
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Trends in VLSI circuit in 2020 - International Journal of VLSI design & Communication Systems ( VLSICS )
1. Trends in VLSI Circuit in 2020
International Journal of VLSI design &
Communication Systems (VLSICS)
ISSN : 0976 - 1357 (Online); 0976 - 1527(print)
http://airccse.org/journal/vlsi/vlsics.html
2. EFFICIENT ABSOLUTE DIFFERENCE CIRCUIT FOR SAD COMPUTATION ON
FPGA
Jaya Koshta, Kavita Khare and M.K Gupta Maulana Azad
National Institute of Technology, Bhopal
ABSTRACT
Video Compression is very essential to meet the technological demands such as low power, less
memory and fast transfer rate for different range of devices and for various multimedia
applications. Video compression is primarily achieved by Motion Estimation (ME) process in
any video encoder which contributes to significant compression gain.Sum of Absolute
Difference (SAD) is used as distortion metric in ME process.In this paper, efficient Absolute
Difference(AD)circuit is proposed which uses Brent Kung Adder(BKA) and a comparator based
on modified 1’s complement principle and conditional sum adder scheme. Results shows that
proposed architecture reduces delay by 15% and number of slice LUTs by 42 % as compared to
conventional architecture. Simulation and synthesis are done on Xilinx ISE 14.2 using Virtex 7
FPGA.
KEYWORDS
HEVC, motion estimation, sum of absolute difference, parallel prefix adders, Brent Kung Adder.
Full Text: http://aircconline.com/vlsics/V10N2/10219vlsi01.pdf
REFERENCES:
[1] G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the high efficiency
video coding (HEVC)standard,” IEEE Trans. Circuits Syst. Video Technol., vol.22, no. 12, pp.
1649-1668, December 2012.
[2] I. Richardson, “HEVC: An introduction to high efficiency video coding,” 2001,
https://www.vcodex.com/h265.html
[3] N. Purnachand, L. N. Alves, and A. Navarro, “Fast Motion Estimation Algorithm for
HEVC”, IEEE International Conference on Consumer Electronics-Berlin (ICCE-Berlin),
September 2012.
[4] S.Wong, B. Stougie, and S. Cotofana, “Alternatives in FPGA-based SAD Implementations,”
in IEEE International Conference on Field-Programmable Technology (FPT),IEEE, 2002, pp.
449–452.
3. [5] S. Vassiliadis, E. A. Hakkennes, J. S. S. M. Wong, and G.G.Pechanek, “The Sum-
AbsoluteDifference Motion Estimation Accelerator,” in Euromicro Conference, 1998.
Proceedings, 24th. IEEE, 1998, pp. 559–566.
[6] Ahmed Medhat, Ahmed Shalaby, Mohammed S. Sayed, Maha Elsabrouty and Farhad
Mehdipour. “A Highly Parallel SAD Architecture for Motion Estimation in HEVC Encoder”,
Circuits and Systems (APCCAS), IEEE Asia Pacific Conference, 280 - 283, 2014.
[7] Stefania Perri , Paolo Zicari , Pasquale Corsonello, “Efficient Absolute Difference Circuits in
Virtex5 FPGAs”, IEEE, 2010.
[8] Martin Kumm, Marco Kleinlein and Peter Zipf, “Efficient Sum of Absolute Difference
Computation on FPGAs”,26thInternational Conference on Field Programmable Logic and
Applications.
[9] Joaquin Olivares, Ignacio Benavides and et.al., “Minimum Sum of Absolute Differences
implementation in a single FPGA device”, Dept. of Electro-technics and Electronics, University
of Cordoba, Spain.
[10] P.Jayakrishnan and Harish. M. Kittur, “Pipelined Arch.for Motion Estimation in HEVC
Video Coding”, Indian Journal of Science and Tech,August 2016.
[11] D. V Manjunatha, Pradeep Kumar and R. Karthik, “FPGA Implementation of Sum of
Absolute Difference (SAD) for video applications”, ARPN Journal of Engineering and Applied
Sciences, Vol. 12, No. 24, December 2017
[12] Geeta Rani and Sachin Kumar, “Delay analysis of parallel-prefix adders”, International
Journal of Science and Research (IJSR), 3(6):2339-2342, 2014.
[13] Nurdiani Zamhari, Peter Voon, Kuryati Kipli, Kho Lee Chin, Maimun Huja
Husin,“Comparison of Parallel Prefix Adder (PPA)”, Proceedings of the World Congress on
Engineering 2012,Vol II.
[14] R. P Brent & H. T. Kung, “A Regular Layout for Parallel Adders”, IEEE Trans. Computers,
Vol. C31, pp 260-264, 1982.
[15] Shun-Wen Cheng, “A High-Speed Magnitude Comparator with Small Transistor Count”, in
Proceedings of IEEE internationalconference ICECS, 1168 - 1171 Vol.3, Dec 2003.
[16] J.Sklansky, “Conditional-Sum Addition Logic,” IRE Transactions on Electronic Computers,
Vol. EC9, No. 2, pp. 226-231, June,1960
[17] S. Rehman; R. Young;C. Chatwin;P. Birch, “An FPGA Based Generic Framework for High
Speed Sum of Absolute Difference Implementation”, Europ. Jour Scient. Res., vol.33, no.1,
2009.
4. [18] Manjunatha, D. V., and G. Sainarayanan. “Low-Power Sum of Absolute Difference
Architecture for Video Coding”, Emerging Research in Electronics, Computer Science and
Technology. Springer India, 2014. 335-341.
[19] LiYufei,Feng Xiubo and Wang Q in, “A High-Performance Low Cost SAD Architecture
for Video Coding”, IEEE Transactions on Consumer Electronics, pp. 535-541, Vol. 53, No. 2,
May 2007.
[20] Jarno, Vanne, Eero Aho, Timo D. Hamalainen and Kimmo Kuusilinna, “A High-
Performance Sum of Absolute Difference Implementation Motion Estimation”, IEEE
Transactions on Circuits and Systems for Video Technology, pp. 876-883, Vol. 16, No. 7, 2006.
[21] Elhamzi W., Dubois J., Miteran J “An efficient low-cost FPGA implementation of a
configurable motion estimation for H.264 video coding”, Springer Journal of Real-Time
Processing,Vol:9, No:1, pp. 19–30,2014.
[22] Moorthy T., Ye A, “A scalable architecture for variable block size motion estimation on
fieldprogrammable gate arrays” , IEEE Canadian Conference of Electrical and Computer
Engineering (CCECE),Niagara Falls, May, pp.1303–1308,2008.
[23] Davis P., Sangeetha M. ,“Implementation of Motion Estimation Algorithm for
H.265/HEVC”, International Journal of Advanced Research in Electrical, Electronics and
Instrumentation Engineering. Vol:3,No:3, pp. 122–126,2014.
5. FPGA IMPLEMENTATION OF HUANG HILBERT TRANSFORM FOR
CLASSIFICATION OF EPILEPTIC SEIZURES USING ARTIFICIAL NEURAL
NETWORK
G.Deepika1
and K.S.Rao2
1
Research scholar at JNTU,Hyderabad , Asso.Prof at RRS college of Engg,
2
Director & Professor in ECE Dept,Anurag group of Institutions, Hyderabad
ABSTRACT
The most common brain disorders due to abnormal burst of electrical discharges are termed as
Epileptic seizures. This work proposes an efficient approach to extract the features of epileptic
seizures by decomposing EEG into band limited signals termed as IMF’s by empirical
decomposition EMD. Huang Hilbert Transform is applied on these IMF’s for calculating
Instantaneous frequencies and are classified using artificial neural network trained by Back
propagation algorithm. The results indicate an accuracy of 97.87%. The algorithm is
implemented using Verilog HDL on Zynq 7000 family FPGA evaluation board using Xilinx
vivado 2015.2 version.
KEYWORDS
EEG, IMF,EMD
Full Text: http://aircconline.com/vlsics/V10N3/10319vlsi02.pdf
REFERENCES
[1] J. Gotman., “Automatic recognition of epileptic seizures in the EEG,” Clinical
Neurophysiology, vol. 54, pp. 530–540, 1982
[2] J.Gotman., “Automatic seizure detection: improvements and evaluation,” Clinical
Neurophysiology, vol. 76, pp. 317–324, 1990
[3] N.E. Huang, Z. Shen, S.R. Long, M.L. Wu, H.H. Shih,Q. Zheng, N.C. Yen, C.C. Tung, and
H.H. Liu, “TheEmpirical Mode Decomposition and Hilbert Spectrumfor Nonlinear and
Nonstationary Time Series Analysis,” Proc. Roy. Soc., vol. 454, pp. 903 – 995, 1998.
[4] Y.U. Khan, J. Gotman, “Electroencephalogram Wavelet based automatic seizure detection
intra cerebral”, Clinical Neurophysiology, vol. 114, pp. 899-908, 2003
[5] Güler NF, Übeyli ED, Güler.”Recurrent neural networks employing Lyapunov xponents for
EEG signal classification”,Expert Syst Appl. 2005; 29(3):506-14
6. [6] Varun Bajaj, Ram Bilas Pachori “Epileptic Seizure Detection Based on the Instantaneous
Area of Analytic Intrinsic Mode Functions of EEG Signals,” Biomed Eng Lett, vol. 3, pp. 17-21,
2013
[7] EEG time time series (epilepticdata)(2005,Nov.) [Online],
http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html
[8] Hedi Khammari , Ashraf Anwar, “A Spectral Based Forecasting Tool of Epileptic Seizures ”
IJCSI International.Journal of Computer Science Issues, Vol. 9, Issue 3, No 3, May 2012
[9] Rami J Oweis and Enas W Abdulhay., “Seizure classification in EEG signals utilizing
Hilbert- Huang transform” BioMedical Engineering OnLine 2011, 10:38
[10] lajos losonczi, lászló bakó, sándor-tihamér ,Brassai and lászló-ferenc Márton., “Hilbert-
huang transform used for eeg signal analysis ,” The 6th edition of the Interdisciplinarity in
Engineering International Conference , “Petru Maior” University ofTîrgu Mure, Romania, 2012
7. DUTY CYCLE CORRECTOR USING PULSE WIDTH MODULATION
Meghana Patil1
, Dr. Kiran Bailey2
and Rajanikanth Anuvanahally3
1
Department of Electronics and Communication, BMSCE, Bengaluru, Karnataka,India
2
Department of Electronics and Communication, BMSCE, Bengaluru, Karnataka,India
3
Senior Member IEEE, Bengaluru, Karnataka, India
ABSTRACT
In circuits, clocks usually play a very important role. Whenever data needs to be sampled, it is
done with respect to clock signals. It uses the edges of the clock to sample the data. So, it
becomes very much necessary to see to it that the clock signals are properly received specially in
receiver circuits where data sampling is done, mainly in Double data rate(DDR) circuits. Due to
effects such as jitter, skew, interference, device mismatches etc., duty cycle gets affected. We
come up with duty cycle correctors that ensure 50% duty cycle of the clock signals. A duty cycle
corrector (DCC) with analog feedback is proposed and simulated in 45nm process technology
node. The duty cycle corrector operates for MHz frequency range covering the duty cycle from
35%-65%, with +/- 1.5% accuracy. The design is simple and the power consumption is 1.01mW.
KEYWORDS
DCC, Integrator, Control voltage generator, frequency range
Full Text: http://aircconline.com/vlsics/V10N3/10319vlsi01.pdf
REFERENCES
[1] Jayaprakash SR, Sujatha S. Hiremath,(2017) “Dual loop clock duty cycle corrector for high
speed serial interface”, IEEE, pp 935-939.
[2] Immanuel Raja, Gaurab Banerjee, Jacob A Abraham, (2016) “ A 0/1-3.5Ghz duty cycle
measurement and correction technique in 130nm CMOS”, IEEE transaction on VLSI, Vol. 24,
No.5, pp 1975-1983.
[3] Feng Lin, (2011) “All digital duty-cycle correction circuit design and its applications in high
performance DRAM”, IEEE.
[4] Yusong Qiu, Yun Zeng and Feng Zhang, (2014) “1-5Ghz duty-cycle corrector circuit with
wide correction range and high precision”, Electronics letters, Vol. 50, No.11, pp 792-794.
[5] Young Jae Min, Chan Hui Jeong, et.al.,(2012) “A 0.31-1 Ghz Fast corrected duty cycle
corrector with successive approximation register for DDR DRAM applications”, IEEE
transaction on VLSI, Vol. 20, No. 8, pp 1524-1528.
8. [6] Chan hui Jeong, Ammar Abdullah, (2016) “All digital duty cycle corrector with a wide duty
correction range for DRAM applications”, IEEE transaction on VLSI, Vol. 24, No.1, pp 363-
367.
[7] Poki Chen, Shi Wei Chen, Juan-shan Lai, (2007) “Low power wide range duty cycle
corrector based on pulse shrinking/stretching mechanism”, IEEE, pp 935-939.
[8] Behzad Razavi, :Design of analog CMOS integrated circuits”, McGraw Hill International
Edition.
[9] Ravi Mehta, Sumanthra set, et.al., (2012) “A programmable, Multi GHz, Wide range duty
cycle correction circuit in 45nm process”, IEEE, pp 257-260.
[10] Sotirios Tambouris, Texas Instruments Deutschland, (2009) “CMOS integrated circuit for
correction of duty cycle of clock signal”, US Patent 7586349.
[11] Chin – Wei Tsai, Yu – Lung Lo, Chia – Chen Chang, et.al., (2017) “ All digital duty cycle
corrector with synchronous and high accuracy output for double data rate synchronous dynamic
random access memory applications”, The Japan Society of Applied Physics, pp 04CF02-1 –
04CF02-6.
[12] Sharath Patil, S. B. Rudraswamy, (2009) “Duty cycle correction using negative feedback
loop”, MIXDES 16th International Conference on Mixed design of integrated circuits systems,
Poland, pp 424-426.
[13] Ji - Hoon Lim, Jun – Hyun Bae, et.al., (2016) “ A Delay- locked loop with a feedback edge
combiner of duty cycle corrector with 20-80% input duty cycle for SDRAMs”, IEEE transcation
on circuits and systems – II: express briefs, Vol. 63. No/ 2, pp 141-145.
[14] Kanak Agarwal, Robert Montoye, (2006) “ A duty cycle correction circuit for high
frequency clocks”, IEEE : Symposium on VLSI circuit digest of technical papers.
9. DESIGN AND IMPLEMENTATION OF COMBINED PIPELINING AND PARALLEL
PROCESSING ARCHITECTURE FOR FIR AND IIR FILTERS USING VHDL
Jacinta Potsangbam1
and Manoj Kumar2
1
M. Tech VLSI Design, Dept. of ECE, National Institute of Technology, Manipur, India
2
Assistant Professor, Dept. of ECE, National Institute of Technology, Manipur, India
ABSTRACT
Along with the advancement in VLSI (Very Large Scale Integration) technology, the
implementation of Finite impulse response (FIR) filters and Infinite impulse response (IIR)
filters with enhanced speed has become more demanding. This paper aims at designing and
implementing a combined pipelining and parallel processing architecture for FIR and IIR filter
using VHDL (Very High Speed Integrated Circuit Hardware Descriptive Language) to reduce
the power consumption and delay of the filter. The proposed architecture is compared with the
original FIR and IIR filter respectively in terms of speed, area, and power. Also, the proposed
architecture is compared with existing architectures in terms of delay. The implementation is
done by using VHDL codes. FIR and IIR filters structures are implemented at 1200 KHz clock
frequency. Synthesis and simulation have been accomplished on Artix-7 series FPGA, target
device (xc7a200tfbg676) (speed grade -1) using VIVADO 2016.3.
KEYWORDS
DSP, FIR, FPGA, IIR, MIMO.
Full Text: http://aircconline.com/vlsics/V10N4/10419vlsi01.pdf
REFERENCES
[1]. K. K. Parhi, VLSI Digital Signal Processing Systems: Design and Implementation. New
York: Wiley, 1999.
[2]. S. M. Rabiul Islam, R. Sarker, S. Saha and A. F. M. Nokib Uddin, “Design of a
Programmable digital IIR filter based on FPGA” 2012 International Conference on Informatics,
Electronics & Vision (ICIEV), Dhaka, pp. 716-72, 2012.
[3]. Suresh Gawande and SnehaBhujbal “High Speed IIR Notch Filter Using Pipelined
Technique” International Journal of Advanced Research in Electrical, Electronics and
Instrumentation Engineering Vol. 6, Issue 2, February 2017.
[4]. Yu-Chi Tsao and Ken Choi“Area-Efficient VLSI Implementation for Parallel Linear- Phase
FIR Digital Filters of Odd Length Based on Fast FIR Algorithm” IEEE Transactions On Circuits
And Systems—ii: Express Briefs, Vol. 59, No. 6, June 2012.
10. [5]. Ravinder Kaur and Ashish Raman “Design and Implementation of High Speed IIR and FIR
Filter using Pipelining”
[6]. B. K. Mohanty and P. K. Meher, "A High-Performance FIR Filter Architecture for Fixed and
Reconfigurable Applications," IEEE Transactions on Very Large Scale Integration (VLSI)
Systems, vol. 24, no. 2, pp. 444-452, Feb. 2016.
[7]. https://en.wikipedia.org/wiki/Infinite_impulse_responsepage was last edited on 17 January
2019, at 20:29 (UTC). International Journal of VLSI design & Communication Systems
(VLSICS) Vol 10, No 4, August 2019 16
[8]. Aarti Sharma and Sanjay Kumar “VLSI Implementation of Pipelined FIR Filter”
International Journal of Innovative Research In Electrical, Electronics, InstrumentationAnd
Control Engineering Vol. 1, Issue 5, August 2013.
[9]. S. Khorbotly, J. E. Carletta and R. J. Veillette, “A methodology for implementing pipelined
fixedpoint infinite impulse response filters,” 41st Southeastern Symposium on System Theory,
Tullahoma, TN, 2009, pp. 280-284, 2009.
[10]. Keshab K. Parhi and David G. Messerschmitt, “Pipeline Interleaving and Parallelism in
Recursive Digital Filters-Part I: Pipelining Using Scattered Look-Ahead and Decomposition”
IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 31. No. 7, July 1989.
[11]. David A. Parker and Keshab K. Parhi, “Low-Area/Power Parallel FIR Digital Filter
Implementations,” Journal of VLSI Signal Processing 17, 75–92 (1997).
[12]. Shaila Khan and Uma Sharma, “Implementation of Low Power Area Efficient Parallel FIR
Digital Filter Structures of Odd Length Based on Common Sub expression Algorithm”
International Journal of Advanced Research in Electronics and Communication Engineering
(IJARECE) Volume 5, Issue 1, January 2016.
[13]. S. Balasubramaniam and R. Bharathi, “Performance Analysis of Parallel FIR Digital Filter
using VHDL” International Journal of Computer Applications Volume 39– No.9, February 2012.
[14]. Keshab K. Parhi and David G. Messerschmitt, “Pipeline Interleaving and Parallelism in
Recursive Digital Filters-Part II: Pipelined Incremental Block Filtering” IEEE Transactions on
Acoustics, Speech, and Signal Processing. Vol 37. No.7. July1989.
[15]. KanuPriya and Rajesh Mehra “Area Efficient Design of Fir Filter using Symmetric
Structure” International Journal of Advanced Research in Computer and Communication
Engineering Vol. 1, Issue 10, December 2012.
[16]. L KholeePhimu and Manojkumar“VLSI Implementation of Area Efficient 2-parallel FIR
Digital Filter” International Journal of VLSI design & Communication Systems (VLSICS)
Vol.7, No.5/6, December 2016.
11. [17]. Saranya R, Pradeep C, Neena Baby and R Radhakrishnan “FPGA Synthesis of
Reconfigurable Modules for FIR Filter” International Journal of Reconfigurable and Embedded
Systems (IJRES) Vol. 4, No. 2, pp. 63-70, 2015.
[18]. Mahesh Kadam, KishorSawarkar and SudhakarMande “Comparative Analysis and
Efficient VLSI Implementation of FIR Filter” International Journal of Advanced Research in
Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 7, July 2014.
[19]. TamliDhanrajSawarkar, Prof.LokeshChawle and Prof. N.G. Narole, “Implementation of 4-
Tap Sequential and Parallel Micro-programmed Based Digital FIR Filter Architecture using
VHDL” International Journal of Innovative Research in Computer and Communication
Engineering Vol. 4, Issue 4, April 2016.
[20]. G. Deepak, P. K. Meher and A. Sluzek, "Performance Characteristics of Parallel and
Pipelined Implementation of FIR Filters in FPGA Platform," 2007 International Symposium on
Signals, Circuits and Systems, Iasi, 2007, pp. 1-4.
[21]. Manoj Kumar, “Design of IIR systolic array architecture by using linear mapping
technique”, International Journal of Computer Applications, vol.182, no.39, pp.14-19, 2019.
12. DESIGN AND ANALYSIS OF A 32-BIT PIPELINED MIPS RISC PROCESSOR
P. Indira1
, M. Kamaraju2
and Ved Vyas Dwivedi3
1,3
Department of Electronics and Communication Engineering, CU Shah University, Wadhwan,
Gujarat, India
2
Department of Electronics and Communication Engineering,Gudlavalleru Engineering College,
JNT University, Kakinada, Andhra Pradesh, India
ABSTRACT
Pipelining is a technique that exploits parallelism, among the instructions in a sequential
instruction stream to get increased throughput, and it lessens the total time to complete the work.
. The major objective of this architecture is to design a low power high performance structure
which fulfils all the requirements of the design. The critical factors like power, frequency, area,
propagation delay are analysed using Spartan 3E XC3E 1600e device with Xilinx tool. In this
paper, the 32-bit MIPS RISC processor is used in 6-stage pipelining to optimize the critical
performance factors. The fundamental functional blocks of the processor include Input/Output
blocks, configurable logic blocks, Block RAM, and Digital clock Manager and each block
permits to connect to multiple sources for the routing. The Auxiliary units enhance the
performance of the processor. The comparative study elevates the designed model in terms of
Area, Power and Frequency. MATLAB2D/3D graphs represents the relationship among various
parameters of this pipelining. In this pipeline model, it consumes very less power (0.129 W),path
delay (11.180 ns) and low LUT utilization (421). Similarly, the proposed model achieves better
frequency increase (285.583 Mhz.), which obtained better results compared to other models.
KEYWORDS
MATLAB, SPARTAN3E, MIPS RISC processor, Xilinx, Digital Clock Manager
Full Text: http://aircconline.com/vlsics/V10N5/10519vlsi01.pdf
REFERENCES
[1] Rashid F. Olanrewaju, Fawwaj E Fajingbesi, S.B. Junaid, Ridzwan Alahudin, Farhat Anwar
& Bisma Rasool Pampori (2017) “Design and Implementation of a Five Stage Pipelining
Architecture Simulator for RiSC-16 Instruction Set”, Indian Journal of Science and Technology,
Vol. 10, No. 3, pp 1-9.
[2] Vijaykumar J, Nagaraju B, Swapna C & Ramanujappa T (2014 April) “Design and
Development of FGPA based Low Power pipelined 64-bit RISC processor with Double
precession Floating point Unit”, International Conference on Communication and Signal
processing.
13. [3] Saranya Krishnamurthy, Ramani Kannan, Erman Azwan Yahya & Kishore Bingi (2017) “
Design of FIR Filter using Novel pipelined Bypass Multiplier”,IEEE 3rd International
Symposium on Robotics and Manufacturing Automation, pp1-6.
[4] Sneha Mangalwedhe, Roopa Kulkarni & S. Y. Kulkarni (2017) “Low Power Implementation
of 32-bit RISC Processor with pipelining. 2nd International Conference on Microelectronics”,
Computing & Communication Systems (MCCS-2017), at Bangalore.
[5] Husainali S Bhimani, Hitesh N. Patel & Abhishek A Davda (2016) “Design of 32-bit 3-stage
pipelined processor based on MIPS in Verilog HDL and implementation on FPGA
Virtex7”,International Journal of Applied Information Systems, Vol. 10, No. 9.
[6] Rakesh M.R. (2014 April) “RISC Processor Design in VLSI Technology Using the Pipeline
Technique”, International journal of innovative research in Electrical, Electronics,
Instrumentation and control Engineering, Vol. 2, No. 4.
[7] Indu M& Arun Kumar M. (2013 August) “Design of Low Power Pipelined RISC
Processor”,International Journal of Advanced Research in Electrical Electronics and
Instrumentation Engineering, Vol. 2, No. 8.
[8] Priyanka Trivedi &Rajan Prasad Tripathi (2015) “Design & Analysis of 16 bit RISC
Processor Using Low Power Pipelining”,International Conference on Computing,
Communication and Automation, pp 1294-1297.
[9] Charu Sharma & Gurupreet Singh Saini (2017 June) “Design and Analysis of High
Performance RISC Processor using Hyperpipelining Technique”,IJASRE, Vol. 3, No. 5, pp 200-
206.
[10] Meera S & Umamaheshwari D “Genetic Algorithm for Leakage Reduction through IVC
using Verilog” International Journal of Microelectronics Engineering, Vol. 1, No. 1, pp 51-62.
[11] Zulkifli.M, Yudhanto.Y.P , Soetharyo N.A, and Adiono.T (2009, August), “Reduced Stall
MIPS Architecture using Pre-Fetching Accelerator”,International Conference onElectrical
Engineering and Informatics, IEEE.
[12] Md. Ashraful Islam, Md. Yeasin Arafath, Md. Jahid Hasan (2014, December) “Design of
DDR4 SDRAM Controller”,8 th International Conference on Electrical and Computer
Engineering, Dhaka, Bangladesh.
[13] Liang Geng, Ji-zhong Shen, Cong-yuan Xu (2016) “Power-efficient dual-edge implicit
pulse-triggered flip-flop with an embedded clock-gating scheme”, Frontiers of Information
Technology and Electrical Engineering,Vol. 17, No. 9. PP 962-972.
14. [14] Aruljothi K, Prajitha PB & Rajaprabha R (2014) “Leakage Power reduction using Power
gating and Multi-vt technique”, International Journal of Advanced research in Computer
Engineering and Technology, Vol. 3, No. 1.
[15] Narender Kumar & Munish Rattan. (2015 December) “Implementation of Embedded RISC
processor with Dynamic Power Management for Low-Power Embedded system on SOC”, IEEE
Proceedings of 2015 RAECS.
[16] Nishant Kumar & Ekta Aggrawal (2013, September), “General Purpose Six-Stage Pipelined
Processor”,International Journal of Scientific & Engineering Research, Vol. 4, No.9.
[17] Mamum Bin IbneReaz, Shabiul Islam & Mohd. S. Sulaiman (2002 December) “A single
Clock Cycle MIPS RISC Processor Design using VHDL”,In proceedings of IEEE International
Conference on Semiconductor Electronics, Penang, Malaysia, pp 199-203.
[18] Gautham P, Parthasarathy R. & KarthiBalasubramanian (2009 December) “Low Power
Pipelined MIPS Processor Design”, In proceedings of IEEE International Conference on
Integrated circuits, pp 462- 465.
[19] Koji Nakano, Kensuke Kawakami, Koji Shigemoto, Yuki Kamada & Yasuaki Ito (2008) “A
Tiny Processing System for Education and Small embedded Systems on the FPGAs”, In IEEE
International Conference on Embedded and Ubiquitous Computing.