Browsing by Keyword "Signal Processing"
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Item 5G MEC deployments: Low-latency services context synchronization in mobility(VDE Verlag GmbH, 2022) Regueiro, Cristina; Gutierrez-Agüero, Iván; Lage, Oscar; Zhang, Yu-Dong; CIBERSEC&DLT5G Multi Access Edge Computing (MEC) is a rising technology aimed to reduce latency by bringing services close to the users. However, current MEC based services coverage is geographically limited and will often require of MEC data centers synchronization to avoid operation handovers when moving to a different coverage area; this is especially relevant in mobility. This paper focuses on this situation, identifying the benefits and limitations of the current MEC-based deployments to maintain the service continuity. Additionally, it proposes a theoretical method for data synchronization in future 5G MEC-based deployments to allow applications context reallocation between several MEC data centers achieving to properly offer different services in mobility. This new method is generic and can be applied to any sector (industry, health, automation, etc.).Item Absolute position calculation for a desktop mobile rehabilitation robot based on three optical mouse sensors(2011) Zabaleta, Haritz; Valencia, David; Perry, Joel; Veneman, Jan; Keller, Thierry; Tecnalia Research & InnovationArmAssist is a wireless robot for post stroke upper limb rehabilitation. Knowing the position of the arm is essential for any rehabilitation device. In this paper, we describe a method based on an artificial landmark navigation system. The navigation system uses three optical mouse sensors. This enables the building of a cheap but reliable position sensor. Two of the sensors are the data source for odometry calculations, and the third optical mouse sensor takes very low resolution pictures of a custom designed mat. These pictures are processed by an optical symbol recognition algorithm which will estimate the orientation of the robot and recognize the landmarks placed on the mat. The data fusion strategy is described to detect the misclassifications of the landmarks in order to fuse only reliable information. The orientation given by the optical symbol recognition (OSR) algorithm is used to improve significantly the odometry and the recognition of the landmarks is used to reference the odometry to a absolute coordinate system. The system was tested using a 3D motion capture system. With the actual mat configuration, in a field of motion of 710 450 mm, the maximum error in position estimation was 49.61 mm with an average error of 36.70 22.50 mm. The average test duration was 36.5 seconds and the average path length was 4173 mm.Item Advancing cardiometabolic monitoring: A hybrid study on smart garments and wearables(Institute of Electrical and Electronics Engineers Inc., 2023) Mirkov, Marta; Kostić, Miloš; Kojić, Vladimir; Štrbać, Matija; SGWearable sensors have enabled continuous cardiometabolic monitoring, revolutionizing the field. In this paper, we review the current state of the art and introduce a system based on textile-integrated printed electrode array that enables multimodal measurements. We aim to capture ECG-derived parameters, electrodermal activity, skin temperature and key electrochemical analytes from sweat (sodium and potassium ions). Integrated into garments for comfort and wearability, our system aims to advance wearable healthcare technologies and improves long-term monitoring solutions.Item Age-Friendly Route Planner: Calculating Comfortable Routes for Senior Citizens(Springer Science and Business Media Deutschland GmbH, 2024) Aranguren, Andoni; Osaba, Eneko; Urra-Uriarte, Silvia; Molina-Costa, Patricia; Rocha, Alvaro; Adeli, Hojjat; Dzemyda, Gintautas; Moreira, Fernando; Colla, Valentina; IA; Quantum; LABORATORIO DE TRANSFORMACIÓN URBANA; GENERALThe application of routing algorithms to real-world situations is a widely studied research topic. Despite this, routing algorithms and applications are usually developed for a general purpose, meaning that certain groups, such as ageing people, are often marginalized due to the broad approach of the designed algorithms. This situation may pose a problem in cities which are suffering a slow but progressive ageing of their populations. With this motivation in mind, this paper focuses on describing our implemented Age-Friendly Route Planner, whose goal is to improve the experience in the city for senior citizens. In order to measure the age-friendliness of a route, several variables have been deemed, such as the number of amenities along the route, the amount of comfortable elements found, or the avoidance of sloppy sections. In this paper, we describe one of the main features of the Age-Friendly Route Planner: the preference-based routes, and we also demonstrate how it can contribute to the creation of adapted friendly routes.Item Audio classification techniques in home environments for elderly/dependant people(2010) Lozano, Héctor; Hernáez, Inmaculada; Picón, Artzai; Camarena, Javier; Navas, Eva; Medical Technologies; COMPUTER_VISION; Tecnalia Research & InnovationThe study of techniques and algorithms for "non-speech" sounds analysis and processing can be foreseen as a technological progress bringing important benefits to disadvantaged groups. This article focuses on finding solutions which allow the detection and classification of household sounds with two clearly defined objectives: 1) Helping people with hearing impairment to overcome daily situations which they have to deal with, such as warnings/alarms, door bells or telephones ringing, alarm clocks, etc. 2) To collect information from the environment which may be used to track the behavior pattern of an individual.Item Automatic 3D model-based method for liver segmentation in MRI based on Active Contours and Total Variation minimization(2015-07-01) Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro M.; COMPUTER_VISION; Tecnalia Research & InnovationLiver cancer is one of the leading causes of cancer-related mortality worldwide. Non-invasive techniques of medical imaging such as Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) are often used by radiologists for diagnosis and surgery planning. With the aim of assuring the most reliable intervention planning to surgeons, new accurate methods and tools must be provided to locate and segment the regions of interest. Automated liver segmentation is a challenging problem for which promising results have been achieved mostly for CT. However, MRI is required by radiologists, since it offers better information for diagnosis purposes. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise, low contrast and poorly defined edges of the liver in relation to adjacent organs. In this paper, we present a method for MRI automatic 3D liver segmentation by means of an Active Contour model extended to 3D and minimized by Total Variation dual approach that has also been extended to 3D. A new approach to enhance the contrast in the input MRI image is proposed and it allows more accurate segmentation. The proposed methodology allows replacing the input image by a probability map obtained by means of a previously generated statistical model of the liver. An Accuracy of 98.89 and Dice Similarity Coefficient of 90.19 are in line with other state-of-the-art methodologies.Item Automatic determination of parameters for multipad functional electrical stimulation: Application to hand opening and closing(2012) Hoffmann, Ulrich; Deinhofer, Martin; Keller, Thierry; Tecnalia Research & InnovationTranscutaneous functional electrical stimulation (FES) is a method used for rehabilitation of patients having suffered a stroke or spinal cord injury. When applying FES a common problem is that stimulation electrodes have to be placed with great care in order to avoid activation of muscles close to the target muscles. A promising approach to circumvent this problem is to employ multipad FES, i.e. to employ electrode arrays containing many small electrodes allowing selective activation of muscles. In this work an algorithm is presented which automatically determines subsets of active electrodes and stimulation currents such that movements with user-specified amplitudes are induced. Using a recently developed portable multipad FES system and a virtual reality dataglove, the algorithm was tested with seven able-bodied subjects. Stimulation with parameters determined by the algorithm led to movements with a median deviation of between 0° and 5° from the specified wrist angle and between 0% and 12% from the specified degree of finger flexion.Item Automatic Red-Channel underwater image restoration(2015-01) Galdran, Adrian; Pardo, David; Picon, Artzai; Alvarez-Gila, Aitor; Tecnalia Research & Innovation; COMPUTER_VISION; VISUALUnderwater images typically exhibit color distortion and low contrast as a result of the exponential decay that light suffers as it travels. Moreover, colors associated to different wavelengths have different attenuation rates, being the red wavelength the one that attenuates the fastest. To restore underwater images, we propose a Red Channel method, where colors associated to short wavelengths are recovered, as expected for underwater images, leading to a recovery of the lost contrast. The Red Channel method can be interpreted as a variant of the Dark Channel method used for images degraded by the atmosphere when exposed to haze. Experimental results show that our technique handles gracefully artificially illuminated areas, and achieves a natural color correction and superior or equivalent visibility improvement when compared to other state-of-the-art methods.Item Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations(2023-10-01) Fernandez-Navamuel, Ana; Pardo, David; Magalhães, Filipe; Zamora-Sánchez, Diego; Omella, Ángel J.; Garcia-Sanchez, David; E&I SEGURAS Y RESILIENTESThis work proposes a novel supervised learning approach to identify damage in operating bridge structures. We propose a method to introduce the effect of environmental and operational conditions into the synthetic damage scenarios employed for training a Deep Neural Network, which is applicable to large-scale complex structures. We apply a clustering technique based on Gaussian Mixtures to effectively select Q representative measurements from a long-term monitoring dataset. We employ these measurements as the target response to solve various Finite Element Model Updating problems before generating different damage scenarios. The synthetic and experimental measurements feed two Deep Neural Networks that assess the structural health condition in terms of damage severity and location. We demonstrate the applicability of the proposed method with a real full-scale case study: the Infante Dom Henrique bridge in Porto. A comparative study reveals that neglecting different environmental and operational conditions during training detracts the damage identification task. By contrast, our method provides successful results during a synthetic validation.Item Collision module integration in a specific graphic engine for terrain visualization(2004) López, Susana; Fernández, Borja; De Arrieta, Arkaitz Glz; De Segura, José Daniel Gómez; Garcia-Alonso, Alex; Infraestructuras y Servicios Corporativos; WEATHER AND CLIMATE INTELLIGENCE FOR BUSINESSThis paper presents the design and implementation of a collision management module. This module is part of a specific graphic engine that manages large size terrain data. This engine has been used for example in a Basque Country terrain demo with a database up to 2.5Gb and texture resolution of 1024×1024 (3mts/pixel). As data textures are increasing resolution it seems convenient to allow near-terrain navigations. This behavior makes more complex the collision problem while navigating a large database. The main purpose of this project is the integration of a new collision module without any remarkable decrease in the performance of the engine. This work comprises the following research topics: preprocessed collision data structure definition, collision data management algorithm in execution time, collision detection algorithms and collision response algorithm. It has provided more flexibility in the interaction of the engine with the user, and more realism overflying terrains.Item Comparing metrics to evaluate performance of regression methods for decoding of neural signals(Institute of Electrical and Electronics Engineers Inc., 2015-11-04) Spuler, Martin; Sarasola-Sanz, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang; Ramos-Murguialday, Ander; Medical TechnologiesThe use of regression methods for decoding of neural signals has become popular, with its main applications in the field of Brain-Machine Interfaces (BMIs) for control of prosthetic devices or in the area of Brain-Computer Interfaces (BCIs) for cursor control. When new methods for decoding are being developed or the parameters for existing methods should be optimized to increase performance, a metric is needed that gives an accurate estimate of the prediction error. In this paper, we evaluate different performance metrics regarding their robustness for assessing prediction errors. Using simulated data, we show that different kinds of prediction error (noise, scaling error, bias) have different effects on the different metrics and evaluate which methods are best to assess the overall prediction error, as well as the individual types of error. Based on the obtained results we can conclude that the most commonly used metrics correlation coefficient (CC) and normalized root-mean-squared error (NRMSE) are well suited for evaluation of cross-validated results, but should not be used as sole criterion for cross-subject or cross-session evaluations.Item Computer-mediated emotional regulation: Detection of emotional changes using non-parametric cumulative sum(2010) Leon, Enrique; Montalban, Iraitz; Schlatter, Sarah; Dorronsoro, Iñigo; Tecnalia Research & InnovationIt has been demonstrated that negative emotions have adverse effects on the immune system of a person. This contributes to increased morbidity and mortality in the elderly population and has a direct impact on quality of life. Positive emotions on the other hand may not only undo the harmful effects of negative emotions but also protect against certain diseases. Hence the use of technology to facilitate emotional regulation that reduces negative emotions may be a good way to promote self-care and support well-being. In this paper we present the early design stages of an emotion detection system that aims to support remote support and self-regulation in situations of intense emotional distress. We provide evidence of the suitability of non-parametric cumulative sum (CUSUM) to indentify emotional changes from neutral to non-neutral and vice versa in real time.Item Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection(2021-06-12) Ciani, Lorenzo; Guidi, Giulia; Patrizi, Gabriele; Galar, Diego; Tecnalia Research & InnovationReliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.Item Continuous decoding of intention to move from contralesional hemisphere brain oscillations in severely affected chronic stroke patients(2012) Antelis, Javier M.; Montesano, Luis; Ramos-Murguialday, Ander; Birbaumer, Niels; Minguez, Javier; Medical TechnologiesDecoding motor information directly from brain activity is essential in robot-assisted rehabilitation systems to promote motor relearning. However, patients who suffered a stroke in the motor cortex have lost brain activity in the injured area, and consequently, mobility in contralateral limbs. Such a loss eliminates the possibility of extracting motor information from brain activity while the patient is undergoing therapy for the affected limb. This work proposes to decode motor information from EEG activity of the contralesional hemisphere in patients who suffered a hemiparetic stroke. Four stroke patients participated in this study and the results proved the feasibility of decoding motor information while patients attempted to move the affected limb.Item Convolutional Neural Networks for Structured Industrial Data(Springer Science and Business Media Deutschland GmbH, 2023) Moles, Luis; Boto, Fernando; Echegaray, Goretti; Torre, Iván G.; García Bringas, Pablo; Pérez García, Hilde; Martinez-de-Pison, Francisco Javier; Villar Flecha, José Ramón; Troncoso Lora, Alicia; de la Cal, Enrique A.; Herrero, Alvaro; Martínez Álvarez, Francisco; Psaila, Giuseppe; Quintián, Héctor; Corchado Rodriguez, Emilio S.; Tecnalia Research & Innovation; FACTORYRegression methods aim to predict a numerical value of a target variable given some input variables by building a function f: Rn→ R. In Industry 4.0 regression tasks, tabular data-sets are especially frequent. Decision Trees, ensemble methods such as Gradient Boosting and Random Forest, or Support Vector Machines are widely used for regression tasks with tabular data. However, Deep Learning approaches are rarely used with this type of data, due to, among others, the lack of spatial correlation between features. Therefore, in this research, we propose two Deep Learning approaches for working with tabular data. Specifically, two Convolutional Neural Networks architectures are tested against different state of the art regression methods. We perform an hyper-parameter tuning of all the techniques and compare the model performance in different industrial tabular data-sets. Experimental results show that both Convolutional Neural Network approaches can outperform the commonly used methods for regression tasks.Item Convolutional Recurrent Neural Networks to Characterize the Circulation Component in the Thoracic Impedance during Out-of-Hospital Cardiac Arrest(Institute of Electrical and Electronics Engineers Inc., 2019-07) Elola, Andoni; Aramendi, Elisabete; Irusta, Unai; Picon, Artzai; Alonso, Erik; Isasi, Iraia; Idris, Ahamed; COMPUTER_VISIONPulse detection during out-of-hospital cardiac arrest remains challenging for both novel and expert rescuers because current methods are inaccurate and time-consuming. There is still a need to develop automatic methods for pulse detection, where the most challenging scenario is the discrimination between pulsed rhythms (PR, pulse) and pulseless electrical activity (PEA, no pulse). Thoracic impedance (TI) acquired through defibrillation pads has been proven useful for detecting pulse as it shows small fluctuations with every heart beat. In this study we analyse the use of deep learning techniques to detect pulse using only the TI signal. The proposed neural network, composed by convolutional and recurrent layers, outperformed state of the art methods, and achieved a balanced accuracy of 90% for segments as short as 3 s.Item Coordination of Two Robots for Manipulating Heavy and Large Payloads Collaboratively: SOFOCLES Project Case Use(Springer Science and Business Media Deutschland GmbH, 2022-05-26) Bengoa, Pablo; González-Ojeda, Itzel de Jesus; Ibarguren, Aitor; Goenaga, Borja; Martínez-De-Lahidalga, Sandra; Gkournelos, Christos; Lotsaris, Konstantinos; Angelakis, Panagiotis; Makris, Sotiris; Antolín-Urbaneja, Juan Carlos; Garcia, Marcelo V.; Fernández-Peña, Félix; Gordón-Gallegos, Carlos; ROBOTICA_AUTOMA; Tecnalia Research & InnovationDuring the last two decades a great impulse in the development of applications to improve the collaboration between human and robots has been achieved in several sectors. This paper presents a new research carried out for coordinating two robots with the aim to manipulate heavy and large payloads up to 250 kg. This process is performed with the collaboration of the final user who clamps each robot to one point of the part by means of a flexible and reconfigurable tool. The architecture of the system and the followed methodology is envisaged. Also, information about the generation of the trajectory is explained. The work is focused on the coordination problem of two collaboratives robots used in a pick and place application, synchronized with a second process (e.g., sealing, painting, welding). The test has been performed using an 80 kg payload, handling the part synchronously using both collaborative robots, showing good articular coordinates tracking. This research has been done within SOFOCLES project, funded by the European Institute of Innovation and Technology (EIT), a body of the European Union.Item Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0(2019-10) Diez-Olivan, Alberto; Del Ser, Javier; Galar, Diego; Sierra, Basilio; Tecnalia Research & Innovation; IAThe so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.Item Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions: A state-of-the-art systematic review, meta-analysis and future research directions(2022-06) Nan, Yang; Ser, Javier Del; Walsh, Simon; Schönlieb, Carola; Roberts, Michael; Selby, Ian; Howard, Kit; Owen, John; Neville, Jon; Guiot, Julien; Ernst, Benoit; Pastor, Ana; Alberich-Bayarri, Angel; Menzel, Marion I.; Walsh, Sean; Vos, Wim; Flerin, Nina; Charbonnier, Jean-Paul; van Rikxoort, Eva; Chatterjee, Avishek; Woodruff, Henry; Lambin, Philippe; Cerdá-Alberich, Leonor; Martí-Bonmatí, Luis; Herrera, Francisco; Yang, Guang; IARemoving the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.Item Demonstration of Converter Control Interactions in MMC-HVDC Systems(2022-01-06) Chen, Jinlei; Wang, Sheng; Ugalde-Loo, Carlos E.; Ming, Wenlong; Adeuyi, Oluwole D.; D’Arco, Salvatore; Ceballos, Salvador; Parker, Max; Finney, Stephen; Pitto, Andrea; Cirio, Diego; Azpiri, Iñigo; POWER ELECTRONICS AND SYSTEM EQUIPMENTAlthough the control of modular multi-level converters (MMCs) in high-voltage direct-current (HVDC) networks has become a mature subject these days, the potential for adverse interactions between different converter controls remains an under-researched challenge attracting the attention from both academia and industry. Even for point-to-point HVDC links (i.e., simple HVDC systems), converter control interactions may result in the shifting of system operating voltages, increased power losses, and unintended power imbalances at converter stations. To bridge this research gap, the risk of multiple cross-over of control characteristics of MMCs is assessed in this paper through mathematical analysis, computational simulation, and experimental validation. Specifically, the following point-to-point HVDC link configurations are examined: (1) one MMC station equipped with a current versus voltage droop control and the other station equipped with a constant power control; and (2) one MMC station equipped with a power versus voltage droop control and the other station equipped with a constant current control. Design guidelines for droop coefficients are provided to prevent adverse control interactions. A 60-kW MMC test-rig is used to experimentally verify the impact of multiple crossing of control characteristics of the DC system configurations, with results verified through software simulation in MATLAB/Simulink using an open access toolbox. Results show that in operating conditions of 650 V and 50 A (DC voltage and DC current), drifts of 7.7% in the DC voltage and of 10% in the DC current occur due to adverse control interactions under the current versus voltage droop and power control scheme. Similarly, drifts of 7.7% both in the DC voltage and power occur under the power versus voltage droop and current control scheme.