Examinando por Autor "Morgado, Eduardo"

Mostrando 1 - 16 de 16

A practical method for vibration frequency response characterization of handheld probes using Bootstrap in building acoustics
(Elsevier, 2019-02) San Millán-Castillo, Roberto; Goya-Esteban, Rebeca; Morgado, Eduardo
Vibration measurement in building acoustics can help understand and estimate different physical phenomena for both researchers and practitioners. Sound insulation and flanking sound transmission are just some of these phenomena and interesting information can be obtained from wall vibration. Different approaches are available in terms of instruments and techniques, ranging from laser interferometry to single axis accelerometers. The latter are simple and cost-effective solutions because they allow many practitioners to use them in an affordable way. In order to deal with the problem in a more efficient way, there is a need to employ a less intrusive mounting technique and we therefore performed a study of the handheld probe solution in detail. Calibration and theoretical data on probe tips attached to different sensors is extremely difficult to find in relation to frequency response, resonance or repeatability. A new and simple sensor characterization procedure is presented to study deviations in probes, depending on the mounting technique and its comparison to a more robust wax fixing method. Handheld probes modify accelerometer response, mainly due to the probe length and the material. Sensor size, weight and connector location were also observed as influencing variables, in addition to others, such as operator hand tremor and the way the sensor is held. Nevertheless, a study of all these variables would provide a very complex model and we therefore used a statistical approach to simplify the characterization tasks. In building acoustic vibration, a Gaussian probability distribution is usually assumed in the collected data, although not being true in all cases. An innovative Bootstrap approach was thus employed in this study without any assumptions on data probability distribution. Bootstrap is a non-parametric method that provides further information than typical average values on a particular experimental population, when the real population is unknown and difficult to estimate. Bootstrap statistical mean and its confidence interval are used as performance indexes. Ninety probe types and sensor set-ups were characterized according to their frequency response and repeatability in a real environment, as compared to regular Wax fixing. Probes show less repeatability than wax or simply handheld broadband techniques, but 95% Bootstrap statistical mean confidence intervals were less than 0.5 dB in a low frequency range, up to a maximum of 3.8 dB at higher frequency bands of interest. Higher deviations are found in system resonance. Nevertheless, uncertainty values on repeatability in building acoustics are not far from these values. A good similarity is found in a probe useful bandwidth ranging from 50 Hz to 800 Hz–1 kHz, depending on the probe’s features. Bootstrap statistical mean is useful to correct measurements of deviations in frequency response. This handheld vibration probe data approach can provide more efficient resource management in real test situations.
An Exhaustive Variable Selection Study for Linear Models of Soundscape Emotions: Rankings and Gibbs Analysis
(IEEE, 2022-07-20) San Millán-Castillo, Roberto; Martino, Luca; Morgado, Eduardo; LLorente, Fernando
In the last decade, soundscapes have become one of the most active topics in Acoustics, providing a holistic approach to the acoustic environment, which involves human perception and context. Soundscapes-elicited emotions are central and substantially subtle and unnoticed (compared to speech or music). Currently, soundscape emotion recognition is a very active topic in the literature. We provide an exhaustive variable selection study (i.e., a selection of the soundscapes indicators) to a well-known dataset (emo-soundscapes). We consider linear soundscape emotion models for two soundscapes descriptors: arousal and valence. Several ranking schemes and procedures for selecting the number of variables are applied. We have also performed an alternating optimization scheme for obtaining the best sequences keeping fixed a certain number of features. Furthermore, we have designed a novel technique based on Gibbs sampling, which provides a more complete and clear view of the relevance of each variable. Finally, we have also compared our results with the analysis obtained by the classical methods based on p-values. As a result of our study, we suggest two simple and parsimonious linear models of only 7 and 16 variables (within the 122 possible features) for the two outputs (arousal and valence), respectively. The suggested linear models provide very good and competitive performance, with R2 > 0.86 and R2 > 0.63 (values obtained after a cross-validation procedure), respectively.
An index of effective number of variables for uncertainty and reliability analysis in model selection problems
(Elsevier, 2024-10-16) Martino, Luca; Morgado, Eduardo; San Millán Castillo, Roberto
An index of an effective number of variables (ENV) is introduced for model selection in nested models. This is the case, for instance, when we have to decide the order of a polynomial function or the number of bases in a nonlinear regression, choose the number of clusters in a clustering problem, or the number of features in a variable selection application (to name few examples). It is inspired by the idea of the maximum area under the curve (AUC). The interpretation of the ENV index is identical to the effective sample size (ESS) indices concerning a set of samples. The ENV index improves drawbacks of the elbow detectors described in the literature and introduces different confidence measures of the proposed solution. These novel measures can be also employed jointly with the use of different information criteria, such as the well-known AIC and BIC, or any other model selection procedures. Comparisons with classical and recent schemes are provided in different experiments involving real datasets. Related Matlab code is given
Assessing IEEE 802.11 and IEEE 802.16 as Backhauling Technologies for 3G Small Cells in Rural Areas of Developing Countries
(Hindawi, 2019-01-17) Simó Reigadas, Francisco Javier; Figuera, Carlos; Morgado, Eduardo; Municio, Esteban; Martínez Fernández, Andrés
Mobile networks are experiencing a great development in urban areas worldwide, and developing countries are not an exception. However, sparsely populated rural areas in developing regions usually do not have any access to terrestrial communications networks because operators cannot ensure enough revenues to justify the required investments. Therefore, alternative low-cost solutions are needed for both the access network and the backhaul network. In this sense, in order to provide rural 3G coverage in small villages, state-of-the-art approaches propose to use Small Cells in access networks and inexpensive multihop wireless networks based on WiFi for long distances (WiLD) or WiMAX for backhauling them. These technologies provide most of the required capabilities; however, there is no complete knowledge about the performance of WiFi and WiMAX in long-distance links under quality of service constraints. The aim of this work is to provide a detailed overview of the different alternatives for building rural wireless backhaul networks. We compare both IEEE 802.11n and IEEE 802.16 distance-aware analytical models and validate them by extensive simulations and field experiments. Also WiFi-based TDMA proprietary solutions are evaluated experimentally and compared. Finally, results are used to model a real study case in the Peruvian Amazon in order to illustrate that the performance provided by these technologies may be sufficient for the backhaul network of a rural 3G access network based on Small Cells.
Deep Neural Network: An Alternative to Traditional Channel Estimators in Massive MIMO Systems
(IEEE, 2022-04-05) Melgar, Antonio; de la Fuente, Alejandro; Carro-Calvo, Leopoldo; Barquero-Pérez, Óscar; Morgado, Eduardo
Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations: LS exhibits a poor estimation when inadequate channel conditions (i.e., low- signal-to-noise ratio (SNR) region) and MMSE requires prior statistical knowledge of the channel and noise (complex to implement in practice). We present a deep learning framework based on deep neural networks (DNNs) for fifth-generation (5G) MaMIMO channel estimation. After a first preliminary scheme with which we verify the good estimation capacity of our DNN-based approach, we propose two different models, which differ in the information processed by the DNN and benefit from lower computational complexity or greater flexibility for any reference signal pattern, respectively. The results show that, compared to the LS-based channel estimation, the DNN approach decreases the mean square error (MSE) and the system’s spectral efficiency (SE) increases, especially in the low- SNR region. Our approach provides results close to optimal MMSE estimation but benefits from not requiring any prior channel statistics information.
Direct Link Aware Cooperative Relaying
(Wireless Communication Systems, 2006. ISWCS '06. 3rd International Symposium on, 2006-09-06) Figuera Pozuelo, Carlos; Morgado, Eduardo; Caamaño, Antonio J.; Cano, Alfonso
Cooperative relaying strategies enable spatial diversity gains. Using a proper forwarding strategy, these techniques can achieve diversity order as high as the number of diverse paths. We derive a strategy named Direct Link-Aware Relaying (DLA), which uses the source-destination link status as a condition to forward or not the signal at the relay. DLA is shown to achieve full diversity and to save energy compared to decodeand-forward strategies. Compared to selective-forwarding, DLA overcomes the use of error detection codes. Extension to the Multi-Branch case is developed. Simulations corroborate our analytical claims.
Distributed Double-Differential Modulation for Cooperative Communications Under CFO
(Global Telecommunications Conference, 2007. GLOBECOM '07. IEEE, 2007-11-26) Cano, Alfonso; Morgado, Eduardo; Caamaño, Antonio J.; Ramos, Javier
When a terminal is recruited to cooperate with other neighboring terminals, its channel state and carrier frequency offset (CFO) may be unknown to the destination. Under these circumstances, this paper considers the use of distributed double-differential (DD) modulation, which simplifies receiver implementation because it by-passes channel and CFO estimation. Two double-differential codecs are proposed transmitting: i) across orthogonal channels using time-division multiplexing, achieving rate and error performance similar to that of co-located multi-antenna DD systems; or ii) simultaneously, benefiting from the distinct CFOs across terminals and bypassing the need of ordering protocols. Both (i)-(ii) approaches are considered in adaptive- and selective-relaying cooperation protocols demonstrating that maximum spatial diversity is achievable. Simulations corroborate the theoretical error performance claims.
End-to-End Average BER in Multihop Wireless Networks over Fading Channels
(2011-03-10T15:48:29Z) Morgado, Eduardo; Mora Jiménez, Inmaculada; Vinagre, Juan José; Ramos, Javier; Caamaño, Antonio
This paper addresses the problem of finding an analytical expression for the end-to-end Average Bit Error Rate (ABER) in multihop Decode-and-Forward (DAF) routes within the context of wireless networks. We provide an analytical recursive expression for the most generic case of any number of hops and any single-hop ABER for every hop in the route. Then, we solve the recursive relationship in two scenarios to obtain simple expressions for the end-to-end ABER, namely: (a) The simplest case, where all the relay channels have identical statistical behaviour; (b) The most general case, where every relay channel has a different statistical behaviour. Along with the theoretical proofs, we test our results against simulations. We then use the previous results to obtain closed analytical expressions for the end-to-end ABER considering DAF relays over Nakagami-m fading channels and with various modulation schemes. We compare these results with the corresponding expressions for Amplify-and-Forward (AAF) and, after corroborating the theoretical results with simulations, we conclude that DAF strategy is more advantageous than the AAF over Nakagami-m fading channels as both the number of relays and m-index increase.
Massive MIMO Channel Estimation With Convolutional Neural Network Structures
(Institute of Electrical and Electronics Engineers, 2024-07-29) Carro-Calvo, Leopoldo; de la Fuente, Alejandro; Melgar, Antonio; Morgado, Eduardo
Massive multiple-input-multiple-output (mMIMO) enables a significant increase in capacity in fifth-generation (5G) communications systems, both in beamforming and spatial multiplexing scenarios, demanding highly accurate channel estimates. We present two models based on convolutional neural networks (CNNs) for 5G mMIMO channel estimation that differ in complexity and flexibility. The results achieved with both models are competitive compared to traditional methods, such as least squares (LS) which presents a poor estimate in the low signal-to-noise ratio (SNR) region, or minimum mean square error (MMSE) which requires prior statistical knowledge of the channel and noise estimation. Furthermore, the proposed CNN models outperform estimation structures based on conventional deep neural networks (DNNs). Our approach achieves results close to the MMSE estimates, improving them in the low SNR regime, and enabling them to a wide range of channel conditions, i.e., variability in time, frequency, and SNR, not requiring any prior channel statistics information. Furthermore, we present a deep analysis of the computational and cost complexity, demonstrating the suitability of the proposed models for real hardware structure implementation
On the Use of Decision Tree Regression for Predicting Vibration Frequency Response of Handheld Probes
(IEEE, 2020-04-15) San Millán-Castillo, Roberto; Morgado, Eduardo; Goya-Esteban, Rebeca
This article focuses on the prediction of the vibration frequency response of handheld probes. A novel approach that involves machine learning and readily available data from probes was explored. Vibration probes are efficient and affordable devices that provide information about testing airborne sound insulation in building acoustics. However, fixing a probe to a vibrating surface downshifts sensor resonancesi and underestimates levels. Therefore, the calibration response of the sensor included in a probe differs from the frequency response of that same probe. Simulation techniques of complex mechanical systems may describe this issue, but they include hardly obtainable parameters, ultimately restricting the model. Thus, this study discusses an alternativemethod, which comprises different parts. Firstly, the vibration frequency responses of 85 probes were measured and labelled according to six features. Then, Linear Regression, Decision Tree Regression and Artificial NeuralNetworks algorithmswere analysed. Itwas revealed that decision tree regression is themore appropriate technique for this data. The best decision tree models, in terms of scores and model structure, were fine-tuned. Eventually, the final suggested model employs only four out of the six original features. A trade-off solution that involved a simple structure, an interpretable model and accurate predictions was accomplished. It showed a maximum average deviation from test measurements ranging from 0.6 dB in low- frequency to 3 dB in high-frequency while remaining at a low computational load. This research developed an original and reliable prediction tool that provides the vibration frequency response of handheld probes.
Patrones Eficientes de Pilotos en Sistemas OFDM para Canales Inalámbricos Selectivos en Tiempo y Frecuencia
(2005-07-01T07:45:41Z) Garcia Marques, Antonio; Morgado, Eduardo; Cano, Alfonso; Caamaño, Antonio J.; Ramos, Javier
In coherent systems, when the channel is not known at the receiver, pilot-assisted techniques are needed to estimate the channel. Using OFDM, this paper overcomes the problem of designing such optimum pilot patterns that efficiently estimate doubly selective (in time and frequency)fading channels. We show that,decoupling time- and frequency- selectivity, the challenging process of estimating such channels can be seen as a two-dimensional sampling problem. We further propose efficient sampling patterns depending on the spreading (multipath and Doppler) function of the channel.
Self-Organized Distributed Compressive Projection in Large Scale Wireless Sensor Networks
(2013-09) Chidean, Mihaela I.; Morgado, Eduardo; Ramiro Bargueño, Julio; Caamaño, Antonio J.
The optimal configuration for a Large Scale Wireless Sensor Networks (LS-WSN) is the one that minimizes the sampling rate, the CPU time and the channel accesses (thus maximizing the network lifetime), with a controlled distortion in the recovered data. Initial deployments of LS-WSN are usually not able to adapt to changing environments and rarely take into account either the spatial or temporal nature of the sensed variables, both techniques that optimize the network operation. In this work we propose the use of Self-Organized Distributed Compressive Projection (SODCP) in order to let the nodes to form clusters in a distributed and data-driven way, exploiting the spatial correlation of the sensed data. We compare the performance of this innovative technique, using actual data from the LUCE LS-WSN, with two different baselines: Centralized Compressive Projection (CCP) and Distributed Compressive Projection (DCP). The former uses no clustering, whereas the latter makes use of an a priori clustering that favors proximity and balances the number of nodes in each cluster. We show that SODCP outperforms DCP (in terms of Signal-to-Noise vs. Compression Rate). We also show that the performance of SODCP converges to that of CCP for relatively high compression rates of 55%.
Spectral information criterion for automatic elbow detection
(Elsevier, 2023) Martino, Luca; San Millán-Castillo, Roberto; Morgado, Eduardo
We introduce a generalized information criterion that contains other well-known information criteria, such as Bayesian information Criterion (BIC) and Akaike information criterion (AIC), as special cases. Furthermore, the proposed spectral information criterion (SIC) is also more general than the other information criteria, e.g., since the knowledge of a likelihood function is not strictly required. SIC extracts geometric features of the error curve and, as a consequence, it can be considered an automatic elbow detector. SIC provides a subset of all possible models, with a cardinality that often is much smaller than the total number of possible models. The elements of this subset are ‘‘elbows’’ of the error curve. A practical rule for selecting a unique model within the sets of elbows is suggested as well. Theoretical invariance properties of SIC are analyzed. Moreover, we test SIC in ideal scenarios where provides always the optimal expected results. We also test SIC in several numerical experiments: some involving synthetic data, and two experiments involving real datasets. They are all real-world applications such as clustering, variable selection, or polynomial order selection, to name a few. The results show the benefits of the proposed scheme. Matlab code related to the experiments is also provided. Possible future research lines are finally discussed.
Universal and automatic elbow detection for learning the effective number of components in model selection problems
(Elsevier, 2023) Morgado, Eduardo; Martino, Luca; San Millán-Castillo, Roberto
We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria widely employed in the literature is also discussed. The proposed UAED does not require the knowledge of a likelihood function and can be easily applied in diverse applications, such as regression and classification, feature and/or order selection, clustering, and dimension reduction. Several experiments involving synthetic and real data show the advantages of the proposed scheme with benchmark techniques in the literature.
Wireless Sensor Networr for Low-Complexity Entropy Determination of Human Gait
(2013-09) Chidean, Mihaela I.; Pastor, Giancarlo; Morgado, Eduardo; Ramiro Bargueño, Julio; Caamaño, Antonio J.
In this work we present a Wireless Sensor Network (WSN) system designed for the on-board determination of human gait entropy. The usage of nonlinear entropy-based metrics has proven to be a useful tool for analyzing the complexity of biological systems. The final goal of entropy calculation in this type of biological system is to identify possible causes of future injuries (in order to improve aging) and the early injury detection (ideal for elite athletes). Existing systems for human gait analysis are limited to traditional data gathering, e.g. continuous measurement and wireless transmission to a Data Fusion Center (DFC), due to the computational burden of entropy calculation. In addition, actual systems are likely to interfere the natural movement due to their cumbersome nature. The WSN presented here uses four sensor nodes, located in both ankles and hip sides, and are equipped with triaxial accelerometers. We propose the use of low-complexity algorithms in order to perform on- board entropy determination prior to wireless transmission. The proposed system can be used to reliably determine long-term human gait entropy.
Wireless4x4: an integrating learning experience for Telecommunications students
(2011-03-10T15:41:19Z) Figuera, Carlos; Morgado, Eduardo; Gutiérrez Pérez, David; Alonso Atienza, Felipe; Arco Fernández-Cano, Eduardo del
The Telecommunications Engineering degree contains the study and understanding of a wide variety of knowledge areas, like signal theory and communications, computer networks or radio propagation. This diversity of fields makes it hard for the students to integrate all these knowledge, which in turns results essential to tackle real and practical problems that involve different subjects. As a response to this need of integration, in University Rey Juan Carlos an educational project based on Problem Based Learning (PBL), called the Wireles4x4 Project, has been carried out. In this project, groups of students build a complete system that is able to autonomously drive a radio controlled car, involving different technologies such as wireless communications, positioning systems, power management or system integration. The objectives of this educational project are: (1) The development of an active learning methodology, by which the students acquire integrated knowledge and skills on a variety of subjects; (2) The acquisition of professional skills like teamwork capabilities, oral and written communication, and long term task scheduling; (3) The participation of the students in an interdisciplinary engineering project with time and budgetary constraints. The results show that the participating students improve not only their specific knowledge on the involved issues, but also their capability of integrating different subjects of the degree and the skills for autonomous learning.)