DASBE: Decision-Aided Semi-Blind Equalization for MIMO Systems with Linear Precoding

[Abstract] Multiple-Input Multiple-Output (MIMO) digital communications standards usually acquire Channel State Information (CSI) by means of supervised algorithms, which implies a loss of performance since pilot symbols do not convey information. We propose to obtain this CSI by using the so-called semi-blind techniques, which combine both supervised and unsupervised (blind) methods. The key idea consists in introducing a decision criterion to determine when the channel has suffered a significant change. In such a case, transmission of pilot symbols is required. The use of this criterion also allows us to determine the time instants in which CSI has to be sent to the transmitter from the receiver through a low-cost feedback channel.Ministerio de Ciencia e Innovación; 09TIC008105PRMinisterio de Ciencia e Innovación; TEC2007-68020-C04-01Ministerio de Ciencia e Innovación; CSD2008-0001

A Novel Hybrid Approach to Improve Performance on Frequency Division Duplex Systems with Linear Precoding

The final publication is available http://dx.doi.org/DOI: 10.1007/978-3-642-13803-4_31[Abstrtact] Linear precoding is an attractive technique to combat interference in multiple-input multiple-output systems because it reduces costs and power consumption in the receiver equipment. Most of the frequency division duplex systems with linear precoding acquire the channel state information at the receiver by using supervised algorithms. Such algorithms make use of pilot symbols periodically sent by the transmitter. In a later step, the channel state information is sent to the transmitter side through a limited feedback channel. In order to reduce the overhead inherent to the periodical transmission of training data, we propose to acquire the channel state information by combining supervised and unsupervised algorithms, leading to a hybrid and more efficient approach. Simulation results show that the performance achieved with the proposed scheme is clearly better than that with standard algorithms.Consellería de Economía e Industria; 09TIC008105PRMinisterio de Educación y Ciencia; TEC2007-68020-C04-0

EHEA Adaptation of the Exercises of Electricity and Electronics at the University of A Coruña Using a Design Based on Competences

[Summary] The adaptation to the European Higher Education Area (EHEA) of the subject of Electricity and Electronics at the Escuela Técnica Superior de Náutica y Máquinas of the University of A Coruña requires important modifications not only in the methodology to be used but also in the evaluation system. The objective is that the students acquire the specific competences and cross-competences identified for this subject. As a consequence of the process of adaptation to the new EHEA, a great improvement in the quality of the learning process has been obtained. Moreover, the necessity of such improvements have been confirmed by the results presented in this work, based on the teaching experience acquired during the last three years. However, the required improvements in the learning process also demand important changes in the relationship between professor and students.[Resumen] La adaptación al Espacio Europeo de Educación Superior (EEES) de la asignatura de Electricidad y Electrónica impartida en la E.T.S. de Náutica y Máquinas de la Universidade da Coruña requiere importantes modificaciones tanto en la metodología a usar como en el sistema de evaluación. El objetivo es que los estudiantes adquieran las competencias tanto específicas como transversales que han sido identificadas para esta asignatura. Como consecuencia del proceso de adaptación al EEES, se obtiene una importante mejora en la calidad del proceso de aprendizaje. Además, la necesidad de dichas mejoras queda claramente demostrada por los resultados presentados en este trabajo, basados en la experiencia adquirida a lo largo de los últimos tres años. Sin embargo, las mencionadas modificaciones en el proceso de aprendizaje también implican cambios importantes en la relación existente entre el profesor y los alumnos

Channel estimation techniques for linear precoded systems: Supervised, unsupervised, and hybrid approaches

The final publication is available at Springer via http://dx.doi.org/10.1016/j.sigpro.2011.01.001[Abstract] Linear precoding is an attractive technique to combat interference in multiple-input multiple-output systems because it reduces cost and power consumption at the receiver. Frequency division duplex systems with linear precoding acquire the channel state information at the receiver side by using supervised algorithms. Such methods make use of pilot symbols periodically provided by the transmitter. Next, this channel state information is sent to the transmitter side through a low-cost feedback channel. Thus, the available channel information allows the transmitter to adapt signals to the channel conditions. Given that pilot symbols do not convey user data, they penalize throughput, spectral efficiency, and transmission energy consumption of the system. In this work, we propose to mitigate the aforementioned limitations by combining both supervised and unsupervised algorithms to acquire the channel state information needed by the transmitter. The key idea consists in introducing a simple criterion to determine whether the channel has suffered a significant variation which requires the transmission of pilot symbols. Otherwise, when small fluctuations happen, an unsupervised method is used to track these channel variations instead. This criterion will be evaluated by considering two types of strategies for the design of the linear precoders: Zero-Forcing and Wiener criteria.Consellería de Economía e Industria; 10TIC105003PR.Consellería de Economía e Industria; 09TIC008105PRMinisterio de Ciencia e Innovación; TEC2010-19545-C04-01Ministerio de Ciencia e Innovación; CSD2008-00010

A Comparative Study of Low Cost Open Source EEG Devices

[Abstract] A comparison of two open source electroencephalography devices designed to acquire signals associated to the brain activity is presented in this work. The experiments are developed considering the task of determining the user eye state i.e., open eyes or closed eyes, applying an algorithm based on computing the sliding Fourier Transform of the captured signals.Xunta de Galicia; ED481A-2018/15

Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces

[Abstract] Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user’s intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms—one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED431G2019/01Agencia Estatal de Investigación de España; RED2018-102668-TAgencia Estatal de Investigación de España; PID2019-104958RB-C42Xunta de Galicia; ED481A-2018/156This work has been funded by the Xunta de Galicia (by grant ED431C 2020/15, and grant ED431G2019/01 to support the Centro de Investigación de Galicia “CITIC”), the Agencia Estatal de Investigación of Spain (by grants RED2018-102668-T and PID2019-104958RB-C42) and ERDF funds of the EU (FEDER Galicia & AEI/FEDER, UE); and the predoctoral Grant No. ED481A-2018/156 (Francisco Laport

A Prototype of EEG System for IoT

[Abstract] In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simulation results show both classifier accuracies and their corresponding system delays.Xunta de Galicia; ED431G2019/01)Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-RXunta de Galicia; ED481A-2018/156Xunta de Galicia; ED431G 2019/01This work has been funded by the Xunta de Galicia (ED431G2019/01), the Agencia Estatal de Investigacion of Spain (TEC2016-75067-C4-1-R) and ERDF funds of the EU (AEI/FEDER, UE), and the predoctoral Grant No. ED481A-2018/156 (Francisco Laport). CITIC as a Research Centre of the Galician University System is financed by the Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) through the ERDF (80%), Operational Programme ERDF Galicia 2014–2020 and the remaining 20% by the Secretaria Xeral de Universidades (Ref. ED431G 2019/01

Selection of sEMG-based Configuration for a Hand Gesture Recognition System

[Abstract]: In recent decades, extensive research has been conducted on the analysis of Electromyography (EMG) signals, aiming to establish a novel communication pathway that utilizes the electrical activity generated by muscle contractions to control external devices. However, determining the optimal configuration for such systems in a given scenario remains as a challenging task. The challenges arise from two main factors: the growing number of available feature extraction methods and classification algorithms, and the necessity of designing control systems that prioritize user comfort, with considerations such as a reduced number of electrodes and fast reaction times. In this paper we propose a method to determine the most suitable configuration for an EMG system by considering three crucial parameters in control systems: reaction time, accuracy, and the required number of channels.Xunta de Galicia (grants ED431C 2020/15 and ED481B 2022/012), MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR (grant TED2021-130240B-I00 (IVRY)). CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01).Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED481B 2022/012Xunta de Galicia; ED431G 2023/0

Eye State Detection Using Frequency Features from 1 or 2-Channel EEG

© The Author(s) This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.[Abstract]: Brain–computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.This work has been supported by Grant No. ED431C 2020/15 funded by Xunta de Galicia and ERDF Galicia 2014–2020; by Grant No. PID2019-104958RB-C42 (ADELE) funded by MCIN/AEI/10.13039/501100 011033; and by project TED2021-130240B-I00 (IVRY) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGeneration EU/PRTR and by the postdoctoral Grant No. ED481B 2022/012 funded by Xunta de Galicia.Xunta de Galicia; ED431C 2020/15Xunta de Galicia; ED481B 2022/01

Study of Machine Learning Techniques for EEG Eye State Detection

[Abstract] A comparison of different machine learning techniques for eye state identification through Electroencephalography (EEG) signals is presented in this paper. (1) Background: We extend our previous work by studying several techniques for the extraction of the features corresponding to the mental states of open and closed eyes and their subsequent classification; (2) Methods: A prototype developed by the authors is used to capture the brain signals. We consider the Discrete Fourier Transform (DFT) and the Discrete Wavelet Transform (DWT) for feature extraction; Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) for state classification; and Independent Component Analysis (ICA) for preprocessing the data; (3) Results: The results obtained from some subjects show the good performance of the proposed methods; and (4) Conclusion: The combination of several techniques allows us to obtain a high accuracy of eye identification.Xunta de Galicia; ED431G2019/01Xunta de Galicia; ED481A-2018/156Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-RThis work has been funded by the Xunta de Galicia (ED431G2019/01), the Agencia Estatal de Investigación of Spain (TEC2016-75067-C4-1-R) and ERDF funds of the EU (AEI/FEDER, UE), and the predoctoral Grant No. ED481A-2018/156 (Francisco Laport