Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería...Victor Asanza
EEG SIGNALS
Data Set
Methodology and Results
Analysis of Results
Trabajos en cursor
Resultados Obtenidos
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ 2020 #IEEE #IES #UPS #Cuenca: Clasificación de señales de Electroencefa...Victor Asanza
Agenda:
✅ Introducción
✅ Clustering of #EEG Occipital Signals using #K_means
Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.
✅ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
✅ Field Programmable Gate Arrays (#FPGAs)
✅ Implementation of a Classification System of EEG Signals Based on FPGA
✅ Otros proyectos con FPGA
C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry PiVictor Asanza
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.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGAVictor Asanza
In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.
⭐⭐⭐⭐⭐ #BCI System using a Novel Processing Technique Based on Electrodes Sele...Victor Asanza
This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Qadri et Al., en su trabajo “The Future of Healthcare Internet of Things (H-IoT): A Survey of Emerging Technologies” propone como uno de los desafíos del H-IoT:
Monitoreo de Desórdenes neurológicos
Ambient Assisted Living (AAL)
Fitness Tracking
Uso de técnicas de Big Data
Uso de Edge Computing
Internet of Nano-Things
⭐⭐⭐⭐⭐ #FPGA Based Meteorological Monitoring StationVictor Asanza
In this paper, we propose to implement a meteorological monitoring station using embedded systems. This model is possible thanks to different sensors that enable us to measure several environmental parameters, such as i) relative humidity, ii) average ambient temperature, iii) soil humidity, iv) rain occurrence, and v) light intensity. The proposed system is based on a field-programmable gate array device (FPGA). The proposed design aims at ensuring highresolution data acquisition and at predicting samples with precision and accuracy in real-time. To present the collected data, we develop also a web application with a simple and friendly user interface.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ EMG Signal Processing with Clustering Algorithms for Motor Gesture TasksVictor Asanza
✅ 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.
⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería...Victor Asanza
EEG SIGNALS
Data Set
Methodology and Results
Analysis of Results
Trabajos en cursor
Resultados Obtenidos
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ 2020 #IEEE #IES #UPS #Cuenca: Clasificación de señales de Electroencefa...Victor Asanza
Agenda:
✅ Introducción
✅ Clustering of #EEG Occipital Signals using #K_means
Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., & Peláez, E. (2016, October). Clustering of EEG occipital signals using k-means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5). IEEE.
✅ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal clustering for motor and imaginary motor tasks on hands and feet. In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
✅ Field Programmable Gate Arrays (#FPGAs)
✅ Implementation of a Classification System of EEG Signals Based on FPGA
✅ Otros proyectos con FPGA
C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)
✅ Preguntas
⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry PiVictor Asanza
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.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGAVictor Asanza
In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.
⭐⭐⭐⭐⭐ #BCI System using a Novel Processing Technique Based on Electrodes Sele...Victor Asanza
This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
Qadri et Al., en su trabajo “The Future of Healthcare Internet of Things (H-IoT): A Survey of Emerging Technologies” propone como uno de los desafíos del H-IoT:
Monitoreo de Desórdenes neurológicos
Ambient Assisted Living (AAL)
Fitness Tracking
Uso de técnicas de Big Data
Uso de Edge Computing
Internet of Nano-Things
⭐⭐⭐⭐⭐ #FPGA Based Meteorological Monitoring StationVictor Asanza
In this paper, we propose to implement a meteorological monitoring station using embedded systems. This model is possible thanks to different sensors that enable us to measure several environmental parameters, such as i) relative humidity, ii) average ambient temperature, iii) soil humidity, iv) rain occurrence, and v) light intensity. The proposed system is based on a field-programmable gate array device (FPGA). The proposed design aims at ensuring highresolution data acquisition and at predicting samples with precision and accuracy in real-time. To present the collected data, we develop also a web application with a simple and friendly user interface.
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
⭐⭐⭐⭐⭐ EMG Signal Processing with Clustering Algorithms for Motor Gesture TasksVictor Asanza
✅ 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.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...Md Kafiul Islam
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).
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...Md Kafiul Islam
This is a comprehensive review of article titled "Integrated device for combined optical neuromodulation and electrical recording for chronic in vivo applications" by Wang et al. 2012, appeared in JNE. The presentation was made during my Comprehensive Qualifying Examination (CQE) back in Jan, 2013
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.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
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/
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.
HOME APPLIANCE IDENTIFICATION FOR NILM SYSTEMS BASED ON DEEP NEURAL NETWORKSijaia
This paper presents the proposal for the identification of residential equipment in non-intrusive load
monitoring systems. The system is based on a Convolutional Neural Network to classify residential
equipment. As inputs to the system, transient power signal data obtained at the time an equipment is
connected in a residence is used. The methodology was developed using data from a public database
(REED) that presents data collected at a low frequency (1 Hz). The results obtained in the test database
indicate that the proposed system is able to carry out the identification task, and presented satisfactory
results when compared with the results already presented in the literature for the problem in question.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain.
Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some
activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information
that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human
stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms
(LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of
classifiers, will detect the human stress levels.
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...Md Kafiul Islam
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).
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...Md Kafiul Islam
This is a comprehensive review of article titled "Integrated device for combined optical neuromodulation and electrical recording for chronic in vivo applications" by Wang et al. 2012, appeared in JNE. The presentation was made during my Comprehensive Qualifying Examination (CQE) back in Jan, 2013
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.
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
Electroencephalogram (EEG) is useful for biological research and clinical diagnosis. These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by using Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique which utilizes wavelet transform. Comparing these ICA algorithms, it is observed that our new technique performs as well as these algorithms at denoising EEG signals.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
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/
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.
HOME APPLIANCE IDENTIFICATION FOR NILM SYSTEMS BASED ON DEEP NEURAL NETWORKSijaia
This paper presents the proposal for the identification of residential equipment in non-intrusive load
monitoring systems. The system is based on a Convolutional Neural Network to classify residential
equipment. As inputs to the system, transient power signal data obtained at the time an equipment is
connected in a residence is used. The methodology was developed using data from a public database
(REED) that presents data collected at a low frequency (1 Hz). The results obtained in the test database
indicate that the proposed system is able to carry out the identification task, and presented satisfactory
results when compared with the results already presented in the literature for the problem in question.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
Electrophysiological imaging for advanced pharmacological screening3Brain AG
We at 3Brain are committed to advancing scientific research and boosting drug discovery. Like our technology, our product lines are always evolving to accommodate high-resolution recording of in vitro cultures. Discover our HD-MEA technology and soon-to-be-released devices and see how they are furthering research in brain diseases, drug discovery, retinal organoids, etc..
For more information, visit our website at https://www.3brain.com
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.
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
The Future of Metabolic Phenotyping Using data bandwidth to maximize N, analy...InsideScientific
Methods matter. In metabolic measurement, confidence in reproducible results relies heavily on the design of the system used to acquire data. In the field of translational metabolic and behavioral phenotyping there is critical demand for more – throughput, standardization, synchronization of diverse data streams, temporal resolution, efficiency of workflow, and verification of results. We compare continuous and switched metabolic measurement methodologies and explore applications that benefit most from continuous measurement.
In this exclusive webinar sponsored by Sable Systems International, experts contrast methodologies and discuss how to improve best practices in metabolic phenotyping. We show how advances in high-bandwidth metabolic measurement, as implemented in Promethion metabolic phenotyping systems, leverage a 60- to 1200-fold increase in temporal resolution and achieve synchrony with intake and other behavioral data.
Key Topics:
* Time-saving methodologies for increasing throughput in multiplexed or continuous metabolic phenotyping
* Evaluation criteria for selecting a metabolic measurement system
* How the home-cage advantage of a pull-mode system reliably increases animal safety while dramatically reducing stress on both the animal and the researcher
* How to improve the resolution, accuracy and versatility of metabolic data using water vapor measurement
* The importance of raw data retention in metabolic phenotyping
* How deep data field format leads to greater traceability, improved reliability and far greater data extraction versatility to address research objectives
* How exact metabolic costs can be assigned to transient activities, with important implications for studies of energy balance, obesity, drug kinetics and metabolic diseases
Biomedical Signals Classification With Transformer Based Model.pptxSandeep Kumar
Seizures are caused by abnormal neuronal activity and are a common chronic brain disease. It is estimated that around 60 million people have epileptic seizures worldwide. Having an epileptic seizure can have serious consequences for the patient. Surface electroencephalogram (EEG) is a non-intrusive method frequently used to detect epileptic brain action. Nevertheless, graphic inspection of the EEG is biased, time-taken, and tedious for the neurologist. This paper proposes an automatic epileptic seizure classification technique using transformer based deep learning. Fast Fourier Transform (FFT) is first used to extract features from EEG data, and features are then used as inputs to a classifier for selection and classification. The suggested technique successfully gave 100% accuracy in the tested EEG reading. The effectiveness of the proposed method could vary across different EEG databases.
Survey analysis for optimization algorithms applied to electroencephalogramIJECEIAES
This paper presents a survey for optimization approaches that analyze and classify electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.
⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...Victor Asanza
By exploiting the received power change in a communication link produced by the presence of a human body in an otherwise empty room, this work evaluates indoor free device localization methods in the 28 GHz band using machine learning techniques. For this objective, a database is built using results from ray tracing simulations of a system comprised of 4 receivers and up to 2 transmitters, while a person is standing within the room. Transmitters are equipped with uniform linear arrays that switch their main beams sequentially at 21 angles, whereas the receivers operate with omnidirectional antennas. Statistical localization error reduction of at least 16% over a global-based classification technique can be obtained through the combination of two independent classifiers using one transmitter and a reduction of at least 19% for 2 transmitters. An additional improvement is achieved by combining each independent classifier with a regression algorithm. Results also suggest that the number of examples per class and size of the blocks (strips) in which the study area is partitioned play a role in the localization error.
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.
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 AlgorithmsVictor Asanza
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 PredictionVictor Asanza
✅ 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/
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...Victor Asanza
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 %.
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 Victor Asanza
✅ #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...Victor Asanza
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/
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 ...Victor Asanza
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/
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 ...Victor Asanza
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/
Problema #1 (x%). El siguiente es un Sistema Digital que tiene las señales ‘A’,’ B’, ‘C’ y ‘D’ como entradas de un bit; por otro lado, la señal ‘Y’ es una salida de un bit tal como se muestra en la siguiente imagen:
El comportamiento de la señal de salida ‘Y’ en función de las señales de entrada, es descrito con el siguiente código VHDL:
Código GitHub:
https://github.com/vasanza/MSI-VHDL/blob/2021PAO1/ExamenParcial/ExamSD1_1.vhd
Realizar los siguientes desarrollos:
a) Usando mapas de karnaught y agrupamiento de minterms (SOP), simplificar la expresión booleana hasta obtener su minima expresión (x/2 %).
b) Utilizando puertas lógicas, graficar el circuito que represente a la ecuación simplificada en el literal anterior (x/2 %).
Problema #2 (x%). El siguiente es un Sistema Digital que tiene las señales ‘A’ y ‘B’ como entradas de dos bits; por otro lado, la señal ‘Y’ es una salida de dos bits tal como se muestra en la siguiente imagen:
El comportamiento de la señal de salida ‘Y’ en función de las señales de entrada, es descrito con el siguiente código VHDL:
Código GitHub:
https://github.com/vasanza/MSI-VHDL/blob/2021PAO1/ExamenParcial/ExamSD1_2.vhd
Realizar los siguientes desarrollos:
a) Usando mapas de karnaught y agrupamiento de minterms (SOP), simplificar la expresión booleana hasta obtener su minima expresión de Y(1) = f(A(1),A(0),B(1),B(0)) y Y(0) = f(A(1),A(0),B(1),B(0)) (x/2 %).
b) Indicar con sus propias palabras el funcioamiento que realiza el sistemas digital propuesto (x/2 %).
⭐ For more information visit our blog:
https://vasanza.blogspot.com/
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/
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...Victor Asanza
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/
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...Victor Asanza
✅ 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/
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
⭐⭐⭐⭐⭐ Charla FIEC: #SSVEP_EEG Signal Classification based on #Emotiv EPOC #BCI and #RaspberryPi
1. SSVEP-EEG Signal Classification based on
Emotiv EPOC BCI and Raspberry Pi
Karla Avilés-Mendoza , Víctor Asanza , Hector Trivino-Gonzalez, Félix Rosales-Uribe,
Jamil Torres-Brunes, Francis R. Loayza, Enrique Peláez, Ricardo Cajo and Raquel Tinoco-
Egas
Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
Facultad de Ingeniería en Electricidad y Computación, FIEC
4. 1 billion
According to OMS over
people live with some form of disability, this is about 15%
of the world’s population.
5. ● First approach for a low cost
device capable of processing EEG
signals in real-time to control an
external actuator.
● More affordability for people with
low economic respurces.
● Improve response time.
● Visual stimuli comming from a
mobile device, eg: to control a
wheelchair, a robotic arm, among
others.
● To achieve a high accuracy with
low computational cost.
● Accesible and easy to use.
● To achieve a aceptable accuracy
with a faster pre-processing and
classification time.
● The light of a mobile device is not
that intense in comparison to
light bulbs.
Motivation Challenges
6. ● Zhang et al., 2015 proposed to analize beta wave for a range of [5-20] Hz in the
front parietal and occipital regions. This requires more computational capacity
for the data processing device.
● Khosla et al., 2020 proposed feature selection methods in order to choose
relevant features that contribute to a successful classification of the user’s
intentions, resulting in an increase in the quality of the later results.
● Han et al., 2018 proposed to focus on the activity of the occipital and parietal
regions of the subject, in order to obtain a high classification rate.
Related work
7. AI techniques
• Reduce the
complexity of noisy
data.
• Increase data
classification accuracy.
• Feature extraction
using temporary
windows.
• Reduce costs and
improves chances of
usage.
BCI based on EEG
• Brain waves are use to
communicate the user’s
intentions to external
actuators.
• Non-invasive
electroencephalography
(EEG) techniques.
• Low – cost devices, more
accesible
8. Challenges
• High error rate.
• Multiclass EEG data
classification techniques.
• Data acquisition is highly
noise susceptible: a blink,
a small movement of the
scalp, hair, adipose
tisssue, among others
can cause noise.
SSVEP
• Evoked potential
produced by a visual
stimuli flashing at a
specific frecuency.
• Between 6-75 Hz
9. Dataset
02
Dataset Citation:
Raquel Tinoco-Egas, Karla Aviles, Jamil
Torres-Brunes, Hector Trivino-Gonzalez,
Víctor Asanza, Félix Rosales-Uribe, Francis
R. Loayza, Enrique Peláez, April 27, 2021,
"SSVEP-EEG data collection using Emotiv
EPOC", IEEE Dataport, doi:
https://dx.doi.org/10.21227/0j42-qd38.
10. Experiment organization
● 20 adult subjects were recruited between the age of 20 – 35.
● Staff avoided wearing brightly colored clothes that could distract the subjects.
● Staff respected COVID biosecurity measures.
● Temperature: 25 degrees Celsius.
● Noise: 30 – 55 decibels (air conditioning and car passing through)
● Participants signed an informed consent.
11. Acquisition device
● Emotiv EPOCx
● Sampling frecuency: 128 Hz
● 14 electrodes (2 ground references) - International 10-10 eeg system
● Conductive gel to reduce impedance between the electrodes and the scalp
13. ● Tasks duration: 3.5 seconds
● Frecuency tasks were shown 40
times each.
● When a frecuency task was being
shown, the other squares turned
opaque by 80%.
Experimental methodology
16. Data pre-processing
● Occipital region – electrodes: O1 and O2.
● The single output files were divided into
several files containing a temporary
window.
● Files were splitted into folders
representing their respective frecuency.
● A Butterworth filter of order 20 was
applied. Frecuency limit: 5 Hz – 30 Hz.
● Outliers were extracted.
17. Data pre-processing – Data augmentation
● Data augmentation was used by applying White noise of different amplitudes to the data.
● White noise: randomly generated array of values added to the signal, so it doesn’t lose the general
behavior but the values do change.
Small amplitude white noise Big amplitude white noise
18. Feature extraction
● Extracted 21 features.
• Mean
• Mean - weight I
• Mean - weight II
• Log Detector
• Median
• Variance
• Mean absolute difference
• Mean frecuency
• Peak frecuency
• Variance central frecuency
• Maximum PSD
• Amplitude Histogram (10 ranges)
19. Data normalization
● Sklearn MinMaxScaler.
1. Normalize training data and save minimum and maximum values
2. Normalize validation and test data using minimum and maximum values from the training data.
25. ● Without data augmentation RF and MLP whose accuracy values were 54% and
52%, respectively.
● With data augmentation RF and XGBoost whose accuracy values ware 58%
and 57%, respectively.
● Over-adjustment in the classification algorithms, due to the limited number of
examples.
● Real-time responses, shorter classification time were MLP and XGBoost with
times of 1.8 and 7.12 milliseconds, respectively.
● Adequate experimental design, because cleaner data will help to improve the
classification.
● Recruit more subjects to eliminate over-adjustment in the algorithms.
● Deep Learning techniques (DL) and spectral images.
26. 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
kpaviles@espol.edu.ec
karla-aviles-mendoza
For more information
Víctor Asanza
vasanza@espol.edu.ec
vasanza
Karla Avilés
29. BCI System using a Novel Processing Technique
Based on Electrodes Selection for Hand Prosthesis
Control
64 electrodes: International 10-10 system
Frequency 160Hz
30. BCI System using a Novel Processing Technique
Based on Electrodes Selection for Hand Prosthesis
Control
31.
32. Classification of Subjects with Parkinson's Disease
using Finger Tapping Dataset
• 162 were patients with PD
• 55 were healthy controls.
36. FPGA Based Meteorological Monitoring Station
Dataset Citation:
Juan Cadena, Steven Santillan, Víctor Asanza,
Rebeca Estrada, July 11, 2021, "Weather Monitoring
Station For Farms And Agriculture", IEEE Dataport,
doi: https://dx.doi.org/10.21227/mdfs-ya42.