⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGA
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In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.
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⭐⭐⭐⭐⭐ Charla FIEC: #SSVEP_EEG Signal Classification based on #Emotiv EPOC #BC...
Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC.
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Agenda:
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✅ Clustering of #EEG Occipital Signals using #K_means
Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.
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C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
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https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ #BCI System using a Novel Processing Technique Based on Electrodes Sele...
This document summarizes a study that developed a brain-computer interface (BCI) system using electroencephalography (EEG) for controlling a hand prosthesis. The system uses an unsupervised learning technique with k-means clustering and principal component analysis to select relevant electrodes and signals. This reduces processing costs and allows for real-time classification on an FPGA. The system achieved 95.1% accuracy in classifying motor intentions to open/close fists and flex/extend ankles using EEG data from selective electrodes near the motor and somatosensory cortices. The electrode selection technique enables processing EEG data efficiently for prosthesis control using affordable off-the-shelf hardware.
⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi
This work presents the experimental design for recording Electroencephalography (EEG) signals in 20 test subjects submitted to Steady-state visually evoked potential (SSVEP). The stimuli were performed with frequencies of 7, 9, 11 and 13 Hz. Furthermore, the implementation of a classification system based on SSVEP-EEG signals from the occipital region of the brain obtained with the Emotiv EPOC device is presented. These data were used to train algorithms based on artificial intelligence in a Raspberry Pi 4 Model B. Finally, this work demonstrates the possibility of classifying with times of up to 1.8 ms in embedded systems with low computational capacity.
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⭐⭐⭐⭐⭐ CHARLA FIEC: Monitoring of system memory usage embedded in #FPGA
Introduction:
Field Programmable Gate Array #FPGA
System on Chip #SoC
#Nios_II_Processor
Hard Processor System #HPS
Advanced RISC Machine #ARM
Logical bridges
Share physical resources
Related Works:
Renovell et Al., testing #RAM modules in #FPGA
Focus on functional tests RAM of the FPGA
Wei et Al., RAM memory monitoring
Embedded System from the #HardProcessor
Wang et Al., Real-time applications
Use memory optimized way during the execution of tasks based on SoC architecture
real-time Electrocardiogram #ECG
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Results:
As shown in Fig 12, the SRAM is working in the logical part executing several tasks and it is validated that as time passes the memory consumption increases. In addition, the writing times will depend on the amount of memory to be written and this varies according to the task that is being executed by the user or those that he has programmed in the Nios II.
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Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
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C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
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✅ Preguntas
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Methodology and Results
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⭐ For more information visit our blog:
https://vasanza.blogspot.com/
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⭐ For more information visit our blog:
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✅ Introduction
✅ Embedded Systems
➡️ Energy Meter
➡️ ESP32
➡️ Raspberry Pi
✅ Acquisition
➡️ Example
✅ Identification
➡️ System Identification Toolbox - Matlab
✅ Control Design
➡️ Energy consumption prediction
✅ Hardware Design
➡️ Raspberry and ESP32
✅ Related Works
✅ Future Work
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ CHARLA FIEC: Monitoring of system memory usage embedded in #FPGA
Introduction:
Field Programmable Gate Array #FPGA
System on Chip #SoC
#Nios_II_Processor
Hard Processor System #HPS
Advanced RISC Machine #ARM
Logical bridges
Share physical resources
Related Works:
Renovell et Al., testing #RAM modules in #FPGA
Focus on functional tests RAM of the FPGA
Wei et Al., RAM memory monitoring
Embedded System from the #HardProcessor
Wang et Al., Real-time applications
Use memory optimized way during the execution of tasks based on SoC architecture
real-time Electrocardiogram #ECG
FPGA with two 8GB Dual Data Rate Synchronous Dynamic Random Access Memories #DDR3 #SDRAM
Results:
As shown in Fig 12, the SRAM is working in the logical part executing several tasks and it is validated that as time passes the memory consumption increases. In addition, the writing times will depend on the amount of memory to be written and this varies according to the task that is being executed by the user or those that he has programmed in the Nios II.
As for the DD3, it is executing the Linux OS as a basis and additionally, a size proportional to the size of the SRAM is reserved for the respective comparisons, so it is observed that it has a higher consumption and longer response times. It should be considered in this comparison that the DD3 in addition to running the OS, also has the web server implemented which consumption varies according to the clients that are connecting to the webpage where it can be seen the memory monitoring of the embedded system. Also, thanks to the part of the HPS it is possible to monitor the memory of the embedded system without affecting its consumption.
As shown in Fig. 13, the SRAM is not under the same workload since it is only responsible for storing what Nios II needs for the execution of the tasks.
Finally, it was consider that the HPS portion to be very important for a clean monitoring not only of the SRAM but also of any core that is implemented in the FPGA portion, since if this application is implemented on a chip that only has FPGA the application would affect the consumption and performance of it, therefore you could not have completely reliable results.
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✅ In this work, the preprocessing of the EMG signals includes the design and test of a filter.
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✅ Later, the filtered data was processed with K-means, DBSCAN and Hierarchical Clustering algorithms.
✅ The results show that the K-means clustering algorithm was able to group the nine gestures made by the subjects, while the DBSCAN and Hierarchical algorithms were not able to perform the nine clusters, otherwise they match the performance of clustering two groups combining gestures.
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⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía ...
IV Jornada de Telecomunicaciones - #TELTEC_2020
Agenda:
✅ Introducción
✅ Clustering of #EEG Occipital Signals using #K_means
⇨ Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.
⇨ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
✅ Field Programmable Gate Arrays (#FPGAs)
✅ Implementation of a Classification System of EEG Signals Based on FPGA
✅ Otros proyectos con FPGA
⇨ C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
⇨ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
⭐⭐⭐⭐⭐ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and ...
✅ Modern technologies uses Brain Computer Interfaces (BCI) to control devices or prosthesis for people with physical impairments. In some cases, EEG data are used to determine the intentionality of the subject when performing motor and imaginary motor tasks.
✅ However, EEG signals are very susceptible to noise due to the lower voltage levels that are acquired. We used a data set of 64 EEG recordings of 25 subjects while they were doing motor and imaginary motor movements of hands and feets. Data were preprocessing, including the design of a filter for noise reduction outside the expected frequency spectral that operate the EEG signals.
✅ Then, we used features extraction based on spectral density. Finally, the application of five clustering algorithms to detect motor and imaginary motor tasks.
✅ Results showed that the k-means, k-medoids and hierarchical clustering algorithms were better in detecting motor activity, and hierarchical clustering for imaginary tasks of hands.
✅ Finally, the results show that k-means, k-medoids and Hierarchical clustering algorithms have a better performance detecting motor activity of both hands, but the spectral clustering algorithm has a better performance in the detection of motor tasks of both feet.
More Related Content
What's hot
⭐⭐⭐⭐⭐ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
✅ Recent research shows the possibility of using electromyography #EMG electrical signals to control devices or prosthesis.
✅ The EMG signals are measured in muscles, such as the forearm. These signals can lead to determine the intentionality of the patient when performing some motor tasks, however the signals are susceptible to noise due to the voltage levels in microvolts that are acquired.
✅ In this work, the preprocessing of the EMG signals includes the design and test of a filter.
✅ Our designed filter allows eliminating any signal components from the electrical network or any other sources that are not EMG signals. Then, analyzing the frequency components and the distribution of the filtered EMG signals validated the preprocessing efficiency.
✅ Later, the filtered data was processed with K-means, DBSCAN and Hierarchical Clustering algorithms.
✅ The results show that the K-means clustering algorithm was able to group the nine gestures made by the subjects, while the DBSCAN and Hierarchical algorithms were not able to perform the nine clusters, otherwise they match the performance of clustering two groups combining gestures.
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...
This research presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 - 30 Hz) into account to separate artifacts from seizures. It requires a reference seizure epoch of N-sec which can either be generated from a patient-specific
seizure database (if available) or can be simulated by a simple mathematical model of seizure. The purpose of the algorithm is to reduce as much artifacts as possible without distorting the desired seizure events to be detected/diagnosed. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data:
fully simulated, semi-simulated and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for detection of seizures from non-seizure epochs have been found to be easily distinguishable after artifacts are removed and consequently reduces the false alarms in seizure detection. Results from an extensive experiment with these datasets prove the efficacy of
the proposed algorithm and hence this algorithm (with some modifications) is expected to be a future candidate for artifact removal not only in epilepsy diagnosis applications but also in other applications (e.g. BCI or other neuroscience studies).
Artificial neural networks
This is a presentation created by our group at UCSC referring the research paper written by James Cannady .
Artificial neural network for misuse detection
Manoj Kumar Gantayat presented on using artificial neural networks for intrusion detection. He discussed how neural networks can be applied to misuse detection in intrusion detection systems. Specifically, he covered three neural network models (Perceptron, Backpropagation, hybrid), components of intrusion detection systems, advantages of neural networks in analyzing incomplete or distorted data, and challenges like needing accurate training data. He concluded the early results of neural network intrusion detection systems show promise for refinement and full-scale demonstration systems.
EEG based Motor Imagery Classification using SVM and MLP
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...
This summarizes a document reviewing an integrated device for combined optical neuromodulation and electrical recording for chronic in-vivo applications. The device consists of an optical fiber integrated with a microelectrode array (MEA), called an optrode-MEA, that allows for simultaneous optical stimulation and electrophysiological recording. The summary is:
[1] The optrode-MEA was developed to allow for optical stimulation at one cortical site while recording neural activity from surrounding neurons to study local circuit dynamics. [2] Experiments in freely moving rats demonstrated the device could successfully record neural signals over months, mapping spatially and temporally resolved neuronal responses. [3] Analysis of recorded signals under different stimulation conditions proved the
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
This document reviews techniques for extracting features and classifying EEG signals to detect human stress levels. It discusses EEG signals and how they can provide information about mental states. It also reviews common feature extraction methods like DCT and DWT that can preprocess EEG data by transforming it from the time to frequency domain. Classification algorithms like KNN, LDA, and Naive Bayes that can classify EEG data are also examined. The document proposes a system to use a Neurosky Mindwave EEG headset to record raw EEG signals, preprocess them with DWT, and classify stress levels using a combination of classifiers.
FPGA Debug Using Incremental Trace Buffer
Abstract: Design verification is an essential step in the development of any product. It ensures that the product as designed is the same as the product as intended. Software simulation is the common approach for validating hardware design unfortunately, it will take hours together to execute. Difficulties in validation arise due to the complexity of the design and also due to the lack of on chip observability. One common solution to this problem is to instrument the prototype using trace-buffers to record a subset of internal signals into on-chip memory for subsequent analysis. In the proposed system, an example circuit is implemented to perform the tracing operation and various trace buffers are designed to record the different stages of internal signal states. The resulting signal states are to be stored, like a error outputs. Low power methodologies are also implemented to achieve low power consumption. Thus the errors are separately stored in the memory for analyzing the signals. This might be used for changes in the logic wherever needed. Thus this tracing is performed to monitor signal states of an FPGA.
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...
This document summarizes an oral defense presentation for a PhD dissertation on artifact characterization, detection, and removal from neural signals. The presentation outlines the background on in-vivo neural signals and EEG, problems and motivation regarding artifacts corrupting signals, thesis objectives, literature review on existing artifact removal methods, contributions of the dissertation including artifact study and proposed removal algorithms, and plans for future work. The presentation aims to investigate artifacts in neural data, develop automated detection and removal without distorting signals, evaluate methods, and improve applications like epilepsy detection and brain-computer interfaces.
Classification of EEG Signals for Brain-Computer Interface
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Adaptive Monitoring and Localization of Faulty Node in a Wireless Sensor Netw...
This document summarizes a study that developed an adaptive monitoring system using a wireless sensor network to detect faulty nodes. Sensor nodes were deployed to monitor temperature and carbon monoxide levels in an indoor environment. An algorithm was developed to detect faulty nodes based on a received signal strength threshold of -100 dBm. Data transmitted from the sensor nodes was visualized using a C-sharp interface. The results showed the sensor nodes could capture a temperature range of 25-51°C and carbon monoxide levels of 0.01-30 g/m3. Comparing data at the source and destination showed a 93.25% correlation, validating the integrity of the data received.
Impact of GPS Signal Loss and Spoofing on Power System Synchrophasor Applicat...
This presentation shows an experimental assessesment of the impact of time synchronization spoofing attacks (TSSA) on synchrophasor-based Wide-Area Monitoring, Protection and Control applications. Phase Angle Monitoring (PAM), anti-islanding protection and power oscillation damping applications are investigated. TSSA are created using a real-time IRIG-B signal generator and power system models are executed using a real-time simulator with commercial phasor measurement units (PMUs) coupled to them as hardware-in-the-loop. Because PMUs utilize time synchronization signals to compute synchrophasors, an error in the PMUs’ time input introduces a proportional phase error in the voltage or current phase measurements provided by the PMU. The experiments conclude that a phase angle monitoring application will show erroneous power transfers, whereas the anti-islanding protection mal-operates and the damping controller introduces negative damping in the system as a result of the time synchronization error incurred in the PMUs due to TSSA.
The proposed test-bench and TSSA approach can be used to investigate the impact of TSSA on any WAMPAC application and to determine the time synchronization error threshold that can be tolerated by these WAMPAC applications.
Wearable Gait Classification Using STM Sensortile
- Successful and efficient classification of gait behavior.
- Automated real-time discrimination.
- Used STM Sensortile in a dual sensor data acquisition module and a Beaglebone for processing.
- Utilized the FANN neural network library to train and test the system.
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...
This document presents a design for analyzing electrocardiogram (ECG) signals using an FPGA. It employs a least-square linear phase finite impulse response filter to remove noise from the ECG signal. It then uses discrete wavelet transform for feature extraction and a backpropagation neural network classifier to classify the ECG signal as normal or abnormal. If abnormal, a support vector machine is used to detect the type of heart disease. The system is implemented on a Xilinx FPGA using MATLAB.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger
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.
Interoperable data management OBSEA
Interoperable Data Management - Environmental Monitoring - Pollution - Instrument - Calibration - 24/7 - Big Data
Model-Simulation-and-Measurement-Based Systems Engineering of Power System Sy...
The document discusses model-simulation-and-measurement-based systems engineering of power system synchrophasor systems. It outlines the speaker's background and research interests in modeling and simulation technologies for cyber-physical power systems. The talk motivates the need for these technologies to enable applications like wide-area control systems using synchronized phasor measurements. It also discusses challenges in developing smart grids as complex cyber-physical systems and the roles that modeling and simulation can play in addressing these challenges.
Wireless Body Area Networking
This document summarizes security issues in wireless body area networks and possible solutions. It discusses how wireless body area networks integrate sensor nodes on or around the human body to monitor health parameters. Key challenges include low energy devices, security threats, and interference. Identity-based encryption is proposed to provide data confidentiality using bilinear pairings like Weil pairing and Tate pairing. Weil pairing requires more computation time than Tate pairing. Tate pairing also consumes less power, making it more suitable for low-energy sensor nodes. Performance analysis of the schemes can examine memory usage and runtime.
Epileptic seizure detection using modern clp and dwt rrca design
Abstract Epileptic seizure detection with modern designs are becoming more popular in EEG signals because it consumes low power and area. Reducing power consumption and area in these modern design for the development of better, cheaper products and also minimizes failure in power related chip.CLP with Fourier Transform based seizure detection consumed more power and area. To overcome these drawbacks and in order to improve the application performance of these designs, various advanced blocks are needed for reducing the area which will be occupied by the whole epileptic seizure detection design. The CLP and DWT-RRCA design is necessary for the accurate low power epileptic seizure detection application. The low power and smaller area occupied in the coastline parameter and DWT-RRCA Design was estimated using the Xilinx ISE tool, that preferable for designing modern epileptic seizure devices. Key Words: Low power, CLP, DWT-RRCA, Minimum size architectural design
EEG based security
This document presents the development of an EEG-based biometric authentication and security system. It involves acquiring EEG signals from the scalp, preprocessing the raw signals by filtering and artifact removal, extracting features from the signals in the time, frequency and time-frequency domains, classifying the features using support vector machines or neural networks, and authenticating individuals based on their unique EEG patterns. The system aims to provide a more secure alternative to conventional authentication methods like passwords, RFID, and facial recognition.
What's hot (20)
⭐⭐⭐⭐⭐ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
⭐⭐⭐⭐⭐ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...
EEG based Motor Imagery Classification using SVM and MLP
EEG based Motor Imagery Classification using SVM and MLP
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...
Classification of EEG Signals for Brain-Computer Interface
Classification of EEG Signals for Brain-Computer Interface
Adaptive Monitoring and Localization of Faulty Node in a Wireless Sensor Netw...
Adaptive Monitoring and Localization of Faulty Node in a Wireless Sensor Netw...
Impact of GPS Signal Loss and Spoofing on Power System Synchrophasor Applicat...
Impact of GPS Signal Loss and Spoofing on Power System Synchrophasor Applicat...
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...
IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger
Model-Simulation-and-Measurement-Based Systems Engineering of Power System Sy...
Model-Simulation-and-Measurement-Based Systems Engineering of Power System Sy...
Epileptic seizure detection using modern clp and dwt rrca design
Epileptic seizure detection using modern clp and dwt rrca design
Similar to ⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGA
⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía ...
IV Jornada de Telecomunicaciones - #TELTEC_2020
Agenda:
✅ Introducción
✅ Clustering of #EEG Occipital Signals using #K_means
⇨ Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.
⇨ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
✅ Field Programmable Gate Arrays (#FPGAs)
✅ Implementation of a Classification System of EEG Signals Based on FPGA
✅ Otros proyectos con FPGA
⇨ C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
⇨ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
⭐⭐⭐⭐⭐ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and ...
✅ Modern technologies uses Brain Computer Interfaces (BCI) to control devices or prosthesis for people with physical impairments. In some cases, EEG data are used to determine the intentionality of the subject when performing motor and imaginary motor tasks.
✅ However, EEG signals are very susceptible to noise due to the lower voltage levels that are acquired. We used a data set of 64 EEG recordings of 25 subjects while they were doing motor and imaginary motor movements of hands and feets. Data were preprocessing, including the design of a filter for noise reduction outside the expected frequency spectral that operate the EEG signals.
✅ Then, we used features extraction based on spectral density. Finally, the application of five clustering algorithms to detect motor and imaginary motor tasks.
✅ Results showed that the k-means, k-medoids and hierarchical clustering algorithms were better in detecting motor activity, and hierarchical clustering for imaginary tasks of hands.
✅ Finally, the results show that k-means, k-medoids and Hierarchical clustering algorithms have a better performance detecting motor activity of both hands, but the spectral clustering algorithm has a better performance in the detection of motor tasks of both feet.
Matthew Gray Summer 2015 Presentation
This document discusses using EEG signals to monitor crew state and improve aviation safety. It involves the following:
1. Collecting physiological data like EEG, eye tracking, and EKG from pilots during flight simulation to measure cognitive engagement.
2. Processing the EEG data to remove artifacts and extract features like percentage of different brain wave types to calculate an engagement index.
3. Using the engagement index and other features to train machine learning models to classify crew state in real-time and provide feedback to pilots and instructors on cognitive engagement levels.
The goal is to help improve pilot performance and safety by monitoring and increasing awareness of cognitive engagement during flight operations. Future work involves expanding EEG sensors, optimizing data processing
⭐⭐⭐⭐⭐ 2020 WEBINAR: TECNOLOGÍA Y SALUD (Abril 2020)
Procesamiento de Señales Biomédicas de Electroencefalografía y Electromiografía
Agenda:
✅ Introducción
⇨ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
⇨ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks
Asanza, V., Peláez, E., Loayza, F., Mesa, I., Díaz, J., & Valarezo, E. (2018, October).
⇨ EMG Signal Processing with Clustering Algorithms for motor gesture Tasks. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.
✅ Resultados Obtenidos
⇨ C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
⇨ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
The second seminar
This document discusses developing a generic EEG classification system using brain signals for brain-computer interface applications. The system architecture includes EEG signal acquisition, preprocessing to remove noise and artifacts, feature extraction using independent component analysis and power spectral density, dimensionality reduction, classification using convolutional neural networks, and postprocessing. The goals are to extract spatial and temporal information from EEG signals to classify different brain states and movements like hand movement, tongue movement, walking, eye blinks, and more. This will help build a robust EEG classification system to be used in various BCI applications.
Acquisition & analysis of neurologic data
This document describes a wireless telemetry system called rodentPACK2 that is used to acquire EEG and other biopotential and physiological data from rodents. It consists of a permanently implanted skull connector, a transmitter connected during recordings, and a receiver that can monitor up to 4 animals simultaneously. The system transmits up to 9 signals including EEG, ECG, temperature and activity data. Accompanying software allows sleep scoring, seizure detection, and power spectral analysis of the EEG data. Video can also be integrated to observe associated behaviors.
40120130405007
This document discusses a method for detecting epilepsy in EEG signals using discrete wavelet transforms and neural networks. It first preprocesses EEG data using wavelet decomposition to extract features in different frequency subbands (delta, theta, alpha, beta, gamma). Features like covariance, energy, minimum/maximum power spectral density, and entropy are then calculated from the subband signals. These features are input to a neural network for training to classify EEG patterns as epileptic or non-epileptic. The method aims to automatically detect seizures in a more time-efficient manner compared to visual inspection of long EEG recordings. It presents wavelet decomposition and feature extraction techniques, and uses neural networks for classification of epileptic vs. non-epileptic
Biomedical Signals Classification With Transformer Based Model.pptx
The document summarizes research using a transformer model to classify biomedical signals, specifically EEG signals. Key points:
- A transformer model was used to classify EEG signals from epilepsy patients using statistical features extracted from wavelet decompositions of the signals.
- The model was tested on the Bonn and CHB-MIT EEG datasets, achieving accuracy above 97% on binary, tertiary and 5-class problems from the Bonn data and above 95% accuracy for individual patients in the CHB-MIT data.
- The transformer model outperformed previous methods like MLP, SVM, ANN and CNN in classifying the EEG signals, demonstrating its effectiveness for biomedical signal classification tasks.
Poster Presentation on "Artifact Characterization and Removal for In-Vivo Neu...
Background: In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest.
Proposed method: The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform (SWT) with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed.
Results: Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio.
Comparison with existing methods: Both real and synthesized data have been used for testing the pro-posed algorithm in comparison with other artifact removal algorithms (e.g. ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms.
Conclusion: Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments.
Electroencephalography-based brain-computer interface using neural networks
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Denoising Techniques for EEG Signals: A Review
The document reviews techniques for denoising EEG signals contaminated with artifacts. It discusses regression, blind source separation (BSS) including principal component analysis (PCA) and independent component analysis (ICA), wavelet transform, and empirical mode decomposition (EMD). Each method has benefits and limitations. Combining multiple current approaches can address individual constraints and provide superior outcomes than single algorithms alone by overcoming each other's limitations.
Robot Motion Control Using the Emotiv EPOC EEG System
Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
A hybrid classification model for eeg signals
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
Ffeature extraction of epilepsy eeg using discrete wavelet transform
This document summarizes a presentation on feature extraction of epilepsy EEG signals using discrete wavelet transforms. The presentation discusses EEG data acquisition from public datasets containing healthy and epileptic patient recordings. It then describes using discrete wavelet transforms to decompose EEG signals into different frequency sub-bands, and extracting statistical features from each sub-band like maximum, minimum, mean, standard deviation, and entropy. These extracted features are used to classify EEG signals as normal or epileptic. The approach decomposes signals into 5 sub-bands corresponding to delta, theta, alpha, beta, and gamma frequency ranges to capture characteristics of different brain states for epilepsy identification.
⭐⭐⭐⭐⭐ #IEEE #PRC #YP Puerto Rico and Caribbean (Virtual Summit 2020): Clasifi...
Summary
✅ Introduction
✅ Field Programmable Gate Arrays (FPGAs)
✅ Implementation of a Classification System of EEG Signals Based on FPGA
✅ More FPGA projects
✅ On going Jobs
✅ Questions
Recognition of Epilepsy from Non Seizure Electroencephalogram using combinati...
IC3: International IEEE Conference on Contemporary Computing
Noida India
Presented on 10th August 2017.
Topic : Recognition of Epilepsy from Non Seizure Electroencephalogram using combination of Linear SVM and Time Domain Attributes.
Brain-computer interface of focus and motor imagery using wavelet and recurre...
Brain-computer interface is a technology that allows operating a device without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model.
Sensory system for implementing a human—computer interface based
This document describes a human-computer interface system based on electrooculography (EOG) signals. The system uses eyewear with electrodes to capture EOG signals, which are transmitted to a processing module. The processing module applies algorithms like the continuous wavelet transform and neural networks to detect the direction of the user's gaze in real time. It then projects a user interface onto the eyewear and transmits commands to external devices based on the user's selections.
EEG signal background and real-time processing
This document discusses EEG signal background and real-time processing. It begins by describing different methods of measuring brain activity, including EEG. It then discusses the source of EEG signals and how they are generated by synaptic activity and summed across electrodes. The document outlines controlling alpha oscillations and using them in brain-computer interfaces. Finally, it discusses real-time processing and closed-loop systems, including buffering data, connecting different recording devices, and creating online analysis pipelines to generate control signals.
Differential data processing for energy efficiency of wireless
Wireless sensor networks consist of many types of wireless sensors. Sensor nodes are used for event detection, event ID location detection, and motion control. Sensor nodes are also used to collect data and act as a gateway or router.
However, the location in which the sensor node is used may be an environment where charging or maintaining power is difficult. Given that the lifetime of the sensor nodes has a great effect on the service life of the wireless sensor network, research has been conducted to maximize the senor nodes lifetime by efficiently using their energy.
The differential data processing for the energy efficiency of the wireless sensor network proposed in this paper provides energy efficient communication by reducing the data size. The reduction is achieved by transmitting the difference values between the previously collected data and the currently collected data.
Similar to ⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGA (20)
⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía ...
⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía ...
⭐⭐⭐⭐⭐ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and ...
⭐⭐⭐⭐⭐ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and ...
⭐⭐⭐⭐⭐ 2020 WEBINAR: TECNOLOGÍA Y SALUD (Abril 2020)
⭐⭐⭐⭐⭐ 2020 WEBINAR: TECNOLOGÍA Y SALUD (Abril 2020)
Biomedical Signals Classification With Transformer Based Model.pptx
Biomedical Signals Classification With Transformer Based Model.pptx
Poster Presentation on "Artifact Characterization and Removal for In-Vivo Neu...
Poster Presentation on "Artifact Characterization and Removal for In-Vivo Neu...
Electroencephalography-based brain-computer interface using neural networks
Electroencephalography-based brain-computer interface using neural networks
Robot Motion Control Using the Emotiv EPOC EEG System
Robot Motion Control Using the Emotiv EPOC EEG System
Ffeature extraction of epilepsy eeg using discrete wavelet transform
Ffeature extraction of epilepsy eeg using discrete wavelet transform
⭐⭐⭐⭐⭐ #IEEE #PRC #YP Puerto Rico and Caribbean (Virtual Summit 2020): Clasifi...
⭐⭐⭐⭐⭐ #IEEE #PRC #YP Puerto Rico and Caribbean (Virtual Summit 2020): Clasifi...
Recognition of Epilepsy from Non Seizure Electroencephalogram using combinati...
Recognition of Epilepsy from Non Seizure Electroencephalogram using combinati...
Brain-computer interface of focus and motor imagery using wavelet and recurre...
Brain-computer interface of focus and motor imagery using wavelet and recurre...
Sensory system for implementing a human—computer interface based
Sensory system for implementing a human—computer interface based
Differential data processing for energy efficiency of wireless
Differential data processing for energy efficiency of wireless
More from Victor Asanza
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...
This document discusses device-free indoor localization using machine learning techniques at 28 GHz. The methodology uses ray tracing to generate fingerprint data and selects features from received power values. A random forest algorithm is used for classification and regression training on global and combined classifiers. Results show that combining independent classifiers from one or two transmitters reduces positioning error by at least 16-19% compared to global classification, and by at least 36-37% when combining two transmitters with classification-regression. The size and number of partition classes impacts error, and additional small improvements are achieved through classification-regression combination.
⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2022PAO2)
Este documento describe un sistema digital que incluye una máquina de estado secuencial síncrona y tres registros. El sistema permite el ingreso de datos a los registros y encuentra el valor máximo y mínimo ingresado. Se pide completar la partición funcional del sistema, elaborar el diagrama de estados de la máquina de estado y proveer la descripción VHDL de la máquina de estado.
⭐⭐⭐⭐⭐ CV Victor Asanza
Researcher in fields like Digital Systems Design based on FPGA, Embedded Systems, Open-Source Hardware, Artificial Intelligence and Biomedical Signal Processing with a major research interest in Brain-Computer Interface.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ Trilateration-based Indoor Location using Supervised Learning Algorithms
The indoor positioning system (IPS) has a wide range of applications, due to the advantages it has over Global Positioning Systems (GPS) in indoor environments. Due to the biosecurity measures established by the World Health Organization (WHO), where the social distancing is provided, being stricter in indoor environments. This work proposes the design of a positioning system based on trilateration. The main objective is to predict the positioning in both the ‘x’ and ‘y’ axis in an area of 8 square meters. For this purpose, 3 Access Points (AP) and a Mobile Device (DM), which works as a raster, have been used. The Received Signal Strength Indication (RSSI) values measured at each AP are the variables used in regression algorithms that predict the x and y position. In this work, 24 regression algorithms have been evaluated, of which the lowest errors obtained are 70.322 [cm] and 30.1508 [cm], for the x and y axes, respectively.
Published in: 2022 International Conference on Applied Electronics (AE)
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ Learning-based Energy Consumption Prediction
✅ Published in: https://doi.org/10.1016/j.procs.2022.07.035
As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL,
which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days.
⭐ The matlab code used for data processing are available in: https://github.com/vasanza/Matlab_Code/tree/EnergyConsumptionPredictionDatacenter
⭐ The dataset used for data processing are available in:https://ieee-dataport.org/open-access/data-server-energy-consumption-dataset
✅ Read more related topics:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ Raspberry Pi-based IoT for Shrimp Farms Real-time Remote Monitoring wit...
This project analyses the optimal parameters for the shrimp farming, trying to help the aquaculture of Ecuador, using a cyberphysical system, which includes temperature, salinity, dissolved oxygen, and pH sensors to monitor the water conditions and an embedded system to control it using an XBee andATMega328p microcontrollers to remotely activate and deactivate aerators to maintain the quality of each pool in neat conditions.
⭐⭐⭐⭐⭐Classification of Subjects with Parkinson's Disease using Finger Tapping...
La enfermedad de Parkinson es el segundo trastorno neurodegenerativo más común y afecta a más de 7 millones de personas en todo el mundo. En este trabajo, clasificamos a los sujetos con la enfermedad de Parkinson utilizando datos de la pulsación de los dedos en un teclado. Utilizamos una base de datos gratuita de Physionet con más de 9 millones de registros, preprocesada para eliminar los datos atípicos. En la etapa de extracción de características, obtuvimos 48 características. Utilizamos Google Colaboratory para entrenar, validar y probar nueve algoritmos de aprendizaje supervisado que detectan la enfermedad. Como resultado, conseguimos un grado de precisión superior al 98 %.
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS EMBEBIDOS, 1er Parcial (2022 PAO1)
Examen 1er parcial que incluye temas de los capítulos:
Capítulo 1, historia de los sistemas IoT y sistemas ciberfísicos.
Capítulo 2, tipos de arquitecturas incluyendo las multiprocessor y multicore.
Capítulo 3, donde se estudia las memorias FLASH, RAM, EEPROM.
Capítulo 4, registros de configuraciones del ADC, PWM, comunicacion serial, I2C y SPI.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ CHARLA #PUCESE Arduino Week: Hardware de Código Abierto TSC-LAB
✅ #PUCESE, organizó el webinar: "ARDUINO WEEK 2022 PUCESE"
✅ Arduino Week PUCE Esmeraldas- Charla con Expertos
➡️ This is an initiative developed by FIEC-ESPOL professors. Temperature and Speed Control Lab (TSC-LAB) is an open-source hardware development.
➡️ Topics
1- Introducción
2- Hardware de Código Abierto
3- Temperature and Speed Control Lab (TSC-LAB)
4- Códigos de ejemplo
5- Datasets
6- Publicaciones científicas
7- Proyectos
8- Cursos
⭐ Para más contenido visita nuestro blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2...
Problema 1A: (10%) Dado la siguiente expresión booleana que define el comportamiento de la señal de salida F sin minimizar, reducir dicha expresión usando mapas de Karnaugh (A, B, C, D) agrupando unos. Luego, seleccionar cuál de las siguientes opciones es la correcta.
Problema 2: (10%) Dado la siguiente expresión booleana que define el comportamiento de la señal de salida F sin minimizar, reducir dicha expresión usando mapas de Karnaugh (A, B, C, D) agrupando unos. Luego, seleccionar cuál de las siguientes opciones es la correcta.
Problema 3: (25%) Se desea diseñar un Sistemas Digital que capaz de controlar dos actuadores tipo bomba (A y B) en función del nivel de agua presente en un tanque. Este nivel de agua se monitorea con dos sensores (S0 y S1). El Sistemas Digital se muestra en la siguiente gráfica.
Problema 5: (15%): Dado el siguiente circuito digital, primero obtener la expresión resultante y luego seleccionar el mapa que corresponde al funcionamiento de dicha expresión.
Problema 6: (15%): Dado el siguiente circuito, encontrar la expresión booleana que define el comportamiento de la señal de salida F sin minimizar, luego reducir la expresión booleana usando mapas de Karnaugh (A, B, C, D) agrupando unos.
Problema 7: (20%). En la siguiente gráfica se puede observar el registro de un electrodo de Electromiografía (EMG) durante la ejecución de una tarea motora en extremidad superior. La señal EMG tiene una amplitud en el orden de los microvoltio - milivoltios y es susceptible a ruido debido a la adherencia del electrodo utilizado, frecuencia cardiaca, red eléctrica, tejido adiposo, etc. Como se muestra en la Fig. 1 el análisis post adquisición en el dominio de la frecuencia de la señal EMG indica que existe ruido de baja frecuencia menores a 5Hz debido a ruidos relacionados a movimientos relativos y en 50 Hz debido a la red eléctrica. Las señales EMG tienen información en el rango de 7 a 20Hz, por lo cual se sugiere diseñar un filtro RC paso banda que permita eliminar el ruido de la señal EMG.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN SISTEMAS DIGITALES 2, 2do Parcial (2021PAO2) C6
Problema #1 (50%) Dado el siguiente diagrama de un microprocesador genérico de 32 bits por instrucción de hasta 1023 instrucciones visto completamente en clase, que utiliza datos almacenados en memoria RAM (Register Files), como se muestra a continuación.
Problema #2: (10%) ¿Cuáles de las siguientes afirmaciones referentes a las memorias de Instrucciones de un microprocesador son ciertas?
Problema #3: (10%) ¿Cuáles de las siguientes afirmaciones referentes a las memorias EEPROM son ciertas?
Problema #4: (10%) ¿Cuáles de las siguientes afirmaciones referentes a las memorias de datos (Register File) son ciertas?
Problema #5: (20%) Shen et Al., escribió el paper titulado “An FPGA-based Distributed Computing System with Power and Thermal Management Capabilities” en donde desarrolla una plataforma computacional distribuida compuesta de múltiples FPGAs conectadas via Ethernet y cada FPGA está configurada como un sistema multi-core. Los núcleos en el mismo FPGA se comunican a través de la memoria compartida, mientras que diferentes FPGA se comunican a través de enlaces Ethernet, como se muestra en la siguiente gráfica.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ Performance Comparison of Database Server based on #SoC #FPGA and #ARM ...
New emerging storage technologies have a great application for IoT systems. Running database servers on development boards, such as Raspberry or FPGA, has a great impact on effective performance when using large amounts of data while serving requests from many clients at the same time. In this paper, we designed and implemented an embedded system to monitor the access of a database using MySql database server installed on Linux in a standard FPGA DE10 with HPS resources. The database is designed to keep the information of an IoT system in charge of monitoring and controlling the temperature inside greenhouses. For comparison purposes, we carried out a performance analysis of the database service running on the FPGA and in a Raspberry Pi 4 B to determine the efficiency of the database server in both development cards. The performance metrics analyzed were response time, memory and CPU usage taking into account scenarios with one or more requests from clients simultaneously.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2021PAO2)
La siguiente partición funcional que incluye una Maquina Secuencial Sincrónica (MSS) y tres registros de sostenimiento, debe realizar el ingreso de datos a cada uno de los registros y luego permitirá encontrar el valor máximo y mínimo ingresado. Además, cada uno de los registros indicados es de 8 bits para mostrar los valores encontrados de máximo (Qmax) y mínimo (Qmin) serán de 8 bits cada uno. El sistema digital funciona con una MSS modelo Moore de la siguiente forma:
1. La MSS luego de ser reiniciado empieza en el estado inicial.
2. El Sistema Digital en el estado inicial, esperará que el usuario presione y suelte la tecla Start dos veces, luego de lo cual esperará el ingreso de datos.
3. El ingreso de datos se lo hará presentando un byte en la entrada Datos, presionando y soltando la tecla Load (el usuario deberá realizar este paso tres veces, uno por cada registro).
4. Luego de ingresar los 3 datos, el usuario deberá presionar y soltar la tecla Find. Esta señal es la que le indica a la MSS del Sistema Digital, que es momento de realizar la búsqueda del valor máximo y mínimo.
5. Una vez finalizado el proceso de búsqueda de los valores máximo y mínimo, se activará la salida Done. El valor máximo se guardará en el RegistroMax y se presentará en su salida Qmax, por otro lado, el valor mínimo se guardará en el RegistroMin y se presentará en su salida Qmin.
6. La señal Done, las salidas Qmax y Qmin se presentarán hasta que el usuario presione y suelte la tecla Start una vez, luego de lo cual la MSS regresará al estado inicial.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2do ...
Problema #1,2,3: (10%) El siguiente circuito es de un filtro paso banda. Los datos del circuito son los siguientes, R1 = 1K[Ω] y R2 = 1K[Ω]. ¿cuáles de las siguientes afirmaciones son correctas?
Problema #4,5,6: (10%) El siguiente bloque convertidor analógico digital (ADC) de 8 bits de resolución, se tiene un voltaje de referencia de 5Vcc. ¿cuáles de las siguientes afirmaciones son correctas?
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS DIGITALES 1, 1er Parcial (2021 PAO1)
Este documento presenta las instrucciones para un examen parcial de un curso de Sistemas Digitales. Detalla los criterios de calificación, incluyendo el requisito de presentar desarrollos a mano claros y correctos. Incluye dos problemas de circuitos lógicos, el primero con una entrada de un bit y salida de un bit, y el segundo con entradas de dos bits y salida de dos bits. Se pide simplificar las expresiones booleanas usando mapas de Karnaugh y describir el funcionamiento del segundo circuito.
⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 2, PROYECTOS PROPUESTOS (2021 PAO1)
Propuesta 1: BÚSQUEDA DE DATOS
Propuesta 2-3: ORDENAMIENTO DE DATOS
Propuesta 4: Microprocessor Architecture.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 1, PROYECTOS PROPUESTOS (2021 PAE)
Proyectos propuestos basados en MSS:
VALOR MÍNIMO DE 3 NÚMEROS
VALOR MÁXIMO DE 3 NÚMEROS
VALOR PROMEDIO DE 4 NÚMEROS
CONTADOR UP EN GRAY
CONTADOR DOWN EN BCD
VALIDADOR DE CLAVE DE 3 DIGITOS
SUMADOR DE 3 NUMEROS BCD
VALIDADOR DE 3 NÚMEROS ASCENDENTES
VALIDADOR DE 3 NÚMEROS DESCENDENTE
VALIDADOR DE 3 NÚMEROS MULTIPLOS DE BASE 2
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ Localización en ambiente de interiores basado en Machine Learning con r...
Diseño de un método pasivo de ubicación de una persona en ambiente de interiores basado en aprendizaje automático con datos obtenidos a partir de enlaces de comunicaciones en la banda de 28 GHz
➡️ #DigitalSystems #DigitalElectronic #DigitalCircuits #HDL #VHDL #FPGA
⭐ Para más contenido visita nuestro blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS DIGITALES 1, 2do Parcial (2021 PAE)
Problema #1 (10%). El siguiente Sistema Digital permite resolver Mapas de Karnaugh de dos variables. Este sistema Digital tiene como entrada los cuatro valores presentes dentro del mapa y como salida tres Displays de 7 segmentos cátodo común que indiquen la resolución del mapa con la cantidad de grupos de uno, de dos y de cuatro. Este circuito siempre determina la resolución más eficiente, bajo la condición de que únicamente se tendrán valores de Ceros y Unos dentro del mapa;
Código VHDL de la pregunta: https://github.com/vasanza/MSI-VHDL/tree/2021PAE/ExamenFinal
Problema #2 (20%). El siguiente Sistema Digital funciona como una maquina secuencial modelo moore. Este sistema Digital tiene como entrada las señales: X0, X1, X2 y X3; y como salidas las señales: Q0 y Q1; tal como se presenta a continuación:
Hay que recordar que las maquinas secuenciales sincrónicas están conformadas por tres bloques principales: Decodificador de estados siguientes, memoria de estados y decodificador de salidas. El decodificador de estados siguientes se representa con los siguientes multiplexores:
Problema #3 (40%). El siguiente Sistema Digital funciona como una maquina secuencial modelo mealy. Este sistema Digital tiene como entrada la señal: A; y como salidas las señales: Q y P; tal como se presenta a continuación:
Hay que recordar que las maquinas secuenciales sincrónicas están conformadas por tres bloques principales: Decodificador de estados siguientes, memoria de estados y decodificador de salidas. La memoria de estados implementado con Flip-Flops tipo D, el decodificador de estados siguientes y de salidas implementado con multiplexores se representa a continuación:
La asignación de códigos de estado que deberá emplear es el siguiente:
Se le pide:
a) Completar los siguientes mapas de Karnaugh que describen el comportamiento de los decodificadores de salidas y de estados siguientes (20p).
b) Realizar el diagrama de estados completo que describe el funcionamiento de la maquina secuencial sincrónica, utilizando el siguiente formato: A / Q, P. (20p).
Problema #4 (30%). Utilizando el el registro universal 74194 en modo carga paralelo (S1=1 y S0=1), realizar el circuito que permita generar la siguiente secuencia:
Se le pide:
a) Completar la siguiente tabla de estados presentes y siguiente del registro universal 74194 (10P).
b) Determinar la expresión booleana de las entradas en paralelo A, B, C y D (10P).
c) Dibujar el circuito resultante utilizando puertas lógicas (no usar multiplexores) (10P).
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ CHARLA #PUCESE: Industrial Automation and Internet of Things Based on O...
✅ Contenido:
Introduction
AVR Architecture
➡️ Acquisition
➡️ Identification
➡️ Control Design
ARM Architecture
➡️ GPIO Control
Automation Solutions
➡️ Industrial Shields
FPGA Architecture vs Hardware Design
➡️ Behavioral Signal Processing with Machine Learning Based on FPGA
➡️ More FPGA projects
➡️ On going jobs
Future Work
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
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⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...
⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2022PAO2)
⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2022PAO2)
⭐⭐⭐⭐⭐ Trilateration-based Indoor Location using Supervised Learning Algorithms
⭐⭐⭐⭐⭐ Trilateration-based Indoor Location using Supervised Learning Algorithms
⭐⭐⭐⭐⭐ Learning-based Energy Consumption Prediction
⭐⭐⭐⭐⭐ Learning-based Energy Consumption Prediction
⭐⭐⭐⭐⭐ Raspberry Pi-based IoT for Shrimp Farms Real-time Remote Monitoring wit...
⭐⭐⭐⭐⭐ Raspberry Pi-based IoT for Shrimp Farms Real-time Remote Monitoring wit...
⭐⭐⭐⭐⭐Classification of Subjects with Parkinson's Disease using Finger Tapping...
⭐⭐⭐⭐⭐Classification of Subjects with Parkinson's Disease using Finger Tapping...
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS EMBEBIDOS, 1er Parcial (2022 PAO1)
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS EMBEBIDOS, 1er Parcial (2022 PAO1)
⭐⭐⭐⭐⭐ CHARLA #PUCESE Arduino Week: Hardware de Código Abierto TSC-LAB
⭐⭐⭐⭐⭐ CHARLA #PUCESE Arduino Week: Hardware de Código Abierto TSC-LAB
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2...
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2...
⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN SISTEMAS DIGITALES 2, 2do Parcial (2021PAO2) C6
⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN SISTEMAS DIGITALES 2, 2do Parcial (2021PAO2) C6
⭐⭐⭐⭐⭐ Performance Comparison of Database Server based on #SoC #FPGA and #ARM ...
⭐⭐⭐⭐⭐ Performance Comparison of Database Server based on #SoC #FPGA and #ARM ...
⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2021PAO2)
⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2021PAO2)
⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2do ...
⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2do ...
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS DIGITALES 1, 1er Parcial (2021 PAO1)
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS DIGITALES 1, 1er Parcial (2021 PAO1)
⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 2, PROYECTOS PROPUESTOS (2021 PAO1)
⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 2, PROYECTOS PROPUESTOS (2021 PAO1)
⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 1, PROYECTOS PROPUESTOS (2021 PAE)
⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 1, PROYECTOS PROPUESTOS (2021 PAE)
⭐⭐⭐⭐⭐ Localización en ambiente de interiores basado en Machine Learning con r...
⭐⭐⭐⭐⭐ Localización en ambiente de interiores basado en Machine Learning con r...
⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS DIGITALES 1, 2do Parcial (2021 PAE)
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⭐⭐⭐⭐⭐ CHARLA #PUCESE: Industrial Automation and Internet of Things Based on O...
⭐⭐⭐⭐⭐ CHARLA #PUCESE: Industrial Automation and Internet of Things Based on O...
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGA
- 1. Implementation of a Classification System of EEG Signals Based on FPGA Víctor Asanza , Alisson Constantine, Stephany Valarezo, and Enrique Peláez Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador April 22 2020 to April 24 2020 Buenos Aires, Argentina
- 2. Implementation of a Classification System of EEG Signals Based on FPGA 64 surface EEG Electrodes International System 10-10 DC artifact present on the 64 electrodes of the EEG signal EEG-BCI (0,1 - 100)Hz, (10uV - 10mV) EEG Signals Data Set Signals Preprocessing 7-30 Hz Features Extraction Classification
- 3. Implementation of a Classification System of EEG Signals Based on FPGA 0.5 –4 Hz • Delta waves • Sleep REM 4 –7 Hz • Theta waves • Meditation 8 –12 Hz • Alpha waves • Relax • μ waves (7,5- 12,5Hz) • Imaginary Motor 16 –31 Hz • Beta waves • Alert • Concentration 32 –110 Hz • Gamma • High brain activity
- 4. Implementation of a Classification System of EEG Signals Based on FPGA RMS(PSD(1:64)) 64 surface EEG Electrodes Periodogram estimation of the Power Spectral Density (PSD) Time-domain representation of the EEG signal from motor activity of both feet (ME2) EEG Signals Data Set Signals Preprocessing 7-30 Hz Features Extraction Classification
- 5. Implementation of a Classification System of EEG Signals Based on FPGA 656 samples (4,1s / Fs 160Hz) x 64 surface EEG Electrodes Electrodes S A M P L E S 1 …. 64 0x01 0x32 . . . . 0x25 0x21 64 656 • EEG Signals Data Set • https://physionet.org/ • 8 healthy subjects ⁓7 motor activity of both feet (ME2) ⁓9 imaginary motor activity of both feet (IE2) Features 64 x 1 Label S A M P L E S [1 0] [1 0] [1 0] [1 0] [1 0] 128 65 EEG Signals Data Set Signals Preprocessing 7-30 Hz Features Extraction Classification
- 6. Implementation of a Classification System of EEG Signals Based on FPGA Block diagram of data processing in FPGA NIOS II processor Diagram of the pattern recognition function of neural networks in Simulink Multi-Layer Perceptron (MLP) EEG Signals Data Set Signals Preprocessing 7-30 Hz Features Extraction Classification
- 7. Implementation of a Classification System of EEG Signals Based on FPGA Confusion Matrix of the classification of all events Logic utilization (Cyclone V) 1,303 / 41,910 (4%) Total block memory bits 47,360 /5,662,720 (<1%) Total pins 45/499 (9%) Resources used by the fpga Analysis of Results crossval(trainedClassifier.Classification, 'KFold', 5); • ME2 events with 92,1% accuracy • IE2 events with 93,8% accuracy Time to look for the file in the SD 21,26 [us] Time to open the file in the SD 22,30 [us] Processing time of the neural network 27,36 [us]
- 8. For citations • V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020, pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752. • Published in: https://ieeexplore.ieee.org/document/9096752 • Proceedings: https://www.computer.org/csdl/proceedings/icedeg/2020/1jZacXR14xa
- 9. For more information Víctor Asanza Mail: vasanza@espol.edu.ec Facultad de Ingeniería en Electricidad y Computación, FIEC Escuela Superior Politécnica del Litoral, ESPOL Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863 090150 Guayaquil, Ecuador Personal Blog: Orcid: ResearchGate: Google Schoolar:
- 10. Thank you!