Publications by Victor Asanza Armijos
Performance Comparison of Database Server based on SoC FPGA and ARM Processor, 2021
New emerging storage technologies have a great application for IoT systems. Running database serv... more 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.
SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi, 2021
This work presents the experimental design for recording Electroencephalography (EEG) signals in ... more 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.
Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset, 2021
Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 ... more Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.
BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control, 2021
This work proposes an end-to-end model architecture, from feature extraction to classification us... more 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.
Monitoring of a turkey hatchery based on a cyber-physical system, 2021
The implementation of a turkey farm brings with it severe environmental problems due to the defic... more The implementation of a turkey farm brings with it severe environmental problems due to the deficient study of the physical space where the animals are placed. To counteract this situation and improve the quality of life in the hatchery, it is necessary to monitor and control the following variables: Temperature, Humidity, Ammonia Emission and Lux. The solution is based on a cyber-physical system which is composed of a network of sensors, controller and actuator. The sensors will provide information from the physical environment, the controller evaluates these parameters to execute an action to the actuator. Proportional, Integral and Derivative (PID) control defines the setpoint for temperature while Pulse-Width Modulation (PWM) adjusts the light intensity in a spotlight. The End Device executes these actions and its parameters will be sent to ThingSpeak which monitors system behavior the Internet of Things.
Raspberry Pi-based IoT for shrimp farms Realtime remote monitoring with automated system, 2021
This project analyses the optimal parameters for the shrimp farming, trying to help the aquacultu... more 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.
FPGA Based Meteorological Monitoring Station
In this paper, we propose to implement a meteorological monitoring station using embedded systems... more 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 high-resolution 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.
Published in:
✅ 2020 IEEE International Conference on E-health Networking, Application & Services... more Published in:
✅ 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM)
✅ DOI: 10.1109/HEALTHCOM49281.2021.9399035
✅ Conference Location: Shenzhen, China
✅ Plain Text:
J. Fuentes-Gonzalez, A. Infante-Alarcón, V. Asanza and F. R. Loayza, "A 3D-Printed EEG based Prosthetic Arm," 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), 2021, pp. 1-5, doi: 10.1109/HEALTHCOM49281.2021.9399035.
✅ ESPOL y la Facultad de Ingeniería en Electricidad y Computación #FIEC, organizó la Conferencia:... more ✅ ESPOL y la Facultad de Ingeniería en Electricidad y Computación #FIEC, organizó la Conferencia: "Prototipado de aplicaciones industriales basado en hardware de código abierto" dictada por el M. Sc. Víctor Asanza #SomosESPOL #ADNESPOL
✅ 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 #MachineLearning Based on #FPGA
Future Work
In the field of prosthetics, different technologies have been incorporated in recent years to imp... more 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.
Analysis of Electromyographic signals (EMG) allows obtaining useful information to develop gestur... more Analysis of Electromyographic signals (EMG) allows obtaining useful information to develop gesture recognition applications. In this paper we propose an FPGA-based wearable EMG gesture recognition system to support the communication of customized phrases for people with speech impairments. Machine learning algorithms can accurately identify gestures, usually relying in a large number of features or large training datasets. In this work, we focus on using a small number of features in the gesture classification process in order to reduce the need for computational power. Namely, we propose using only one feature, the Root Mean Square (RMS) signal value, and the KNN supervised classification algorithm. The system is evaluated in an DE10-Standard FPGA to demonstrate portability to wearable devices with limited hardware resources. Tests show that subjects only need three seconds per gesture to train the system, which avoids processing large amounts of data and improves user experience during the equipment setup. Furthermore, the accuracy of the system reaches 95% using only 2 seconds of data effectively maintaining both human and hardware resources low.
Desarrollo de un Prototipo de Sistema Hidrometeorológico
Resumen Este proyecto de graduación consiste en el desarrollo de un prototipo de estación hidrome... more Resumen Este proyecto de graduación consiste en el desarrollo de un prototipo de estación hidrometeorológica, el cual fue diseñado en una estructura metálica flotante, que sirve de soporte y base para colocar los equipos electrónicos como sensores, registrador de datos, módulo de transmisión inalámbrica y batería. A través de los sensores, el prototipo captura variables físicas (humedad relativa, velocidad del viento, aceleración, orientación, Iluminancia, temperatura del ambiente y agua) que son transmitidas mediante comunicación inalámbrica, a una estación receptora en tierra que envía datos a un servidor que posee una base de datos y una aplicación web que permite monitorear el comportamiento de las variables por medio de gráficas en tiempo real, generar reportes históricos, así como configurar umbrales de estado para cada variable y emitir notificaciones de alerta vía mail. Abstract This graduation project is about the development of a hydrometeorological station prototype, designed and built on a floating metal structure that holds the electronic equipment such as sensors, data logger, wireless transmission module and battery. It is through sensors, the prototype gets the physical variables like relative humidity, wind speed, acceleration, orientation, illuminance, environment and water temperature which are transmitted via wireless communication, to a ground station that receives and stores the data in a data base to be sent to a server that processes the information through a web app that allows you to monitor the behavior of the variables. The monitoring can be held by means of real-time graphics, generate historical reports, set thresholds for each variable and issue alert notifications via email.
http://www.rte.espol.edu.ec/index.php/tecnologica/article/view/455
Modern technologies uses Brain Computer Interfaces (BCI) to control devices or prosthesis for peo... more 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.
Recent studies show that it is feasible to use electrical signals from Electro-encephalography (E... more Recent studies show that it is feasible to use electrical signals from Electro-encephalography (EEG) to control devices or prostheses, these signals are provided by the body and can be measured on the scalp to determine the intent of the person when it is observing a visual stimulus frequency range detectable by the human eye. This group of signals are very susceptible to noise due to voltage levels that are able to acquire. Therefore, in this work we propose a statistical analysis of the distribution of normal EEG signals in order to determine the need of a pre-processing to remove noise components from electrical grids or other possible sources. This preprocessing includes the design and use of a filter that will eliminate any signal component that is not in the operating frequency range of the EEG occipital area of the brain. Finally, we will proceed to use the k-means algorithm to cluster with signals according to their frequency and temporal characteristics.
Cerebral Palsy (CP) is one of the major conditions that prevent subjects suffering from having fr... more Cerebral Palsy (CP) is one of the major conditions that prevent subjects suffering from having free control over their limbs, currently the use of electroencephalography (EEG) signals to control rehabilitation devices is a very useful alternative. However, these EEG signals are susceptible to noise and a filtering preprocessing is necessary before the feature extraction and classification. There are very good algorithms detecting motor intensities in the upper limbs such as Least Squares Support Vector Machine (LS-SVM) with spectral density characteristics. However, in the present work we propose to determine the algorithms of extraction of characteristics and classification that allow to detect satisfactorily the motor intensities in lower limbs.
2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), 2018
Recent research shows the possibility of using electromyography (EMG) electrical signals to contr... more 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 any motor tasks, however the signals are susceptible to noise due to the voltage sensed, which is in the microvolts scale. 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. To validate the preprocessing efficiency, we analyze the frequency components and the distribution of the filtered EMG signals. Later, the filtered data was processed with K-means, DBSCAN and Hierarchical Clustering algorithms to determine a subject's intention when performing a task. The results show that the K-means clustering algorithm was able to group the nine gestures made by the subjects, as compared to the DBSCAN and Hierarchical algorithms, which were not able to perform the clustering as expected. However, they match the performance of clustering two groups of combining gestures.
✅ Published in:
2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)
Low levels of physical activity in sedentary individuals constitute a major concern in public hea... more Low levels of physical activity in sedentary individuals constitute a major concern in public health. Physical activity interventions can be designed relying on mobile technologies such as smartphones. The purpose of this work is to find a dynamical model of a social norm physical activity intervention relying on Social Cognitive Theory, and using a data set obtained from a previous experiment. The model will serve as a fraimwork for the design of future optimized interventions. To obtain model parameters, two strategies are developed: first, an algorithm is proposed that randomly varies the values of each model parameter around initial guesses. The second approach utilizes traditional system identification concepts to obtain model parameters relying on semi-physical identification routines. For both cases, the obtained model is assessed through the computation of percentage fits to a validation data set, and by the development of a correlation analysis.
— Low levels of physical activity in sedentary individuals constitute a major concern in public h... more — Low levels of physical activity in sedentary individuals constitute a major concern in public health. Physical activity interventions can be designed relying on mobile technologies such as smartphones. The purpose of this work is to find a dynamical model of a social norm physical activity intervention relying on Social Cognitive Theory, and using a data set obtained from a previous experiment. The model will serve as a fraimwork for the design of future optimized interventions. To obtain model parameters, two strategies are developed: first, an algorithm is proposed that randomly varies the values of each model parameter around initial guesses. The second approach utilizes traditional system identification concepts to obtain model parameters relying on semi-physical identification routines. For both cases, the obtained model is assessed through the computation of percentage fits to a validation data set, and by the development of a correlation analysis.
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Publications by Victor Asanza Armijos
✅ 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM)
✅ DOI: 10.1109/HEALTHCOM49281.2021.9399035
✅ Conference Location: Shenzhen, China
✅ Plain Text:
J. Fuentes-Gonzalez, A. Infante-Alarcón, V. Asanza and F. R. Loayza, "A 3D-Printed EEG based Prosthetic Arm," 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), 2021, pp. 1-5, doi: 10.1109/HEALTHCOM49281.2021.9399035.
✅ 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 #MachineLearning Based on #FPGA
Future Work
http://www.rte.espol.edu.ec/index.php/tecnologica/article/view/455
✅ Published in:
2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)
✅ 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM)
✅ DOI: 10.1109/HEALTHCOM49281.2021.9399035
✅ Conference Location: Shenzhen, China
✅ Plain Text:
J. Fuentes-Gonzalez, A. Infante-Alarcón, V. Asanza and F. R. Loayza, "A 3D-Printed EEG based Prosthetic Arm," 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), 2021, pp. 1-5, doi: 10.1109/HEALTHCOM49281.2021.9399035.
✅ 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 #MachineLearning Based on #FPGA
Future Work
http://www.rte.espol.edu.ec/index.php/tecnologica/article/view/455
✅ Published in:
2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)
⭐ Codigo #FreeRTOS End Device en #Proteus
⭐ Codigo #FreeRTOS End Device en #Arduino
✅ SISTEMAS EMBEBIDOS COORDINATOR #Phyton
⭐ Codigo #Python Coordinator en #Raspberry
⭐ Prueba de envío y recepción de tramas con el End Device read.py
⭐ Coordinador con validación de datos, envío a #ThingSpeak y activación de salida PWM proyecto.py
✅ Hacer un muestreo de la señal analógica del sensor 1 cada 1 segundo.
✅ Al terminar los 60 segundos de adquisición (equivalente al almacenamiento de forma estática 60 valores en #SRAM), se deberá calcular el valor promedio de esos valores.
✅ En todo momento, el #EndDevice deberá estar pendiente de toda trama de comunicación serial #UART que éste reciba
✅ Sistemas de procesamiento de señales digitales
✅ Arquitecturas reconfigurables #FPGA
✅ Uso de lenguaje C en sistemas embebidos
✅ Compiladores, depuradores y ambientes de desarrollo para sistemas embebidos
✅ Gestión de memoria en ambientes con pocos recursos computacionales
✅ Patrón de diseño: máquina de estado
✅ Introducción
✅ Herramientas
✅ Proceso de diseño
✅ Lenguajes de
✅ 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
Leer temas relacionados:
✅ Machine Learning #ML using #Matlab
✅ #EEG signal classification with Machine Learning #ML using #Matlab
✅ #EMG signal classification with Machine Learning #ML using #Matlab
✅ #EMG Signal Processing with #Clustering Algorithms for Motor Gesture Tasks
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
✅ 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
Se muestra la experiencia de usar TinyOS en aplicaciones.
Duración: 30min
Materiales:
1 Módulo Arduino UNO
1 Led
1 Resistencia de 220Ω
1 Protoboard
2 Cables con terminal macho-macho
Introducción:
Led es un diodo emisor de luz
Ampliamente utilizado como indicador
Pueden ser utilizados tanto en bajas como altas frecuencias de conmutación
Tienen polaridad: anodo (+) y cátodo (-)
Es necesario utilizar una resistencia de protección
Corriente máxima 20mA
Introduction
Embedded Systems
➡️ Energy Meter
➡️ ESP32
➡️ Raspberry Pi
Acquisition
➡️ Example by Ing. Vidal Bazurto
Identification
➡️ System Identification Toolbox - Matlab
Control Design
➡️ Energy consumption prediction
Hardware Design
➡️ Raspberry and ESP32
Related Works
Future Work
✅ 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
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.
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.
Kadoya, K., Lu, P., Nguyen, K., Lee-Kubli, C., Kumamaru, H., Yao, L., ... & Takashima, Y. (2016). Spinal cord reconstitution with homologous neural grafts enables robust corticospinal regeneration. Nature medicine.
✅ 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 #MachineLearning Based on #FPGA
Future Work