Capítols de llibrehttp://hdl.handle.net/2117/55542020-07-09T14:26:20Z2020-07-09T14:26:20ZElectrical impedance signal analysis for medical diagnosisVargas Luna, José MiguelBalleza Ordaz, José MarcoHuerta Franco, María-RaquelRiu Costa, Pere Joanhttp://hdl.handle.net/2117/1203822020-04-22T18:14:31Z2018-07-31T11:58:57ZElectrical impedance signal analysis for medical diagnosis
Vargas Luna, José Miguel; Balleza Ordaz, José Marco; Huerta Franco, María-Raquel; Riu Costa, Pere Joan
This book is based on the best contributions to the advancement of bioimpedance knowledge and use from the Latin American Congress series, CLABIO. Basic bioimpedance facts as well as promising and original contributions to bioimpedance theory and applications are presented, giving the reader stimulating material for reflection, decision making, and further experiments. Contributions come from a diverse international pool of experts and address topics on electrode and skin impedance modelling, tomography, spectroscopy, instrumentation, and clinical applications.
2018-07-31T11:58:57ZVargas Luna, José MiguelBalleza Ordaz, José MarcoHuerta Franco, María-RaquelRiu Costa, Pere JoanThis book is based on the best contributions to the advancement of bioimpedance knowledge and use from the Latin American Congress series, CLABIO. Basic bioimpedance facts as well as promising and original contributions to bioimpedance theory and applications are presented, giving the reader stimulating material for reflection, decision making, and further experiments. Contributions come from a diverse international pool of experts and address topics on electrode and skin impedance modelling, tomography, spectroscopy, instrumentation, and clinical applications.Numerical assessment in aeronautics for electromagnetic environmental effectsRuiz-Cabello Nuñez, Miguel DavidFernández Romero, SergioPous Solà, MarcSilva Martínez, FernandoFernández Chimeno, MireyaRiu Costa, Pere JoanJauregui Telleria, Ricardo Ignaciohttp://hdl.handle.net/2117/1172092020-06-16T04:17:08Z2018-05-14T16:14:27ZNumerical assessment in aeronautics for electromagnetic environmental effects
Ruiz-Cabello Nuñez, Miguel David; Fernández Romero, Sergio; Pous Solà, Marc; Silva Martínez, Fernando; Fernández Chimeno, Mireya; Riu Costa, Pere Joan; Jauregui Telleria, Ricardo Ignacio
Electromagnetic Compatibility for Space Systems Design is a critical scholarly resource that examines the applications of electromagnetic compatibility and electromagnetic interference in the space industry. Featuring coverage on a wide range of topics, such as magnetometers, electromagnetic environmental effects, and electromagnetic shielding, this book is geared toward managers, engineers, and researchers seeking current research on the applications of electromagnetic technologies in the aerospace field
2018-05-14T16:14:27ZRuiz-Cabello Nuñez, Miguel DavidFernández Romero, SergioPous Solà, MarcSilva Martínez, FernandoFernández Chimeno, MireyaRiu Costa, Pere JoanJauregui Telleria, Ricardo IgnacioElectromagnetic Compatibility for Space Systems Design is a critical scholarly resource that examines the applications of electromagnetic compatibility and electromagnetic interference in the space industry. Featuring coverage on a wide range of topics, such as magnetometers, electromagnetic environmental effects, and electromagnetic shielding, this book is geared toward managers, engineers, and researchers seeking current research on the applications of electromagnetic technologies in the aerospace fieldGold standard generation using electrooculogram signal for drowsiness detection in simulator conditionsRodríguez Ibáñez, NoeliaMeca-Calderón, PabloGarcía González, Miguel ÁngelRamos Castro, Juan JoséFernández Chimeno, Mireyahttp://hdl.handle.net/2117/845012020-02-12T22:02:22Z2016-03-16T14:17:58ZGold standard generation using electrooculogram signal for drowsiness detection in simulator conditions
Rodríguez Ibáñez, Noelia; Meca-Calderón, Pablo; García González, Miguel Ángel; Ramos Castro, Juan José; Fernández Chimeno, Mireya
2016-03-16T14:17:58ZRodríguez Ibáñez, NoeliaMeca-Calderón, PabloGarcía González, Miguel ÁngelRamos Castro, Juan JoséFernández Chimeno, MireyaMonitoring drivers' ventilation using an electrical bioimpedance systems: tests in a controlled enviornmentMacías Macías, RaúlGarcía González, Miguel ÁngelRamos Castro, Juan JoséBragós Bardia, RamonFernández Chimeno, Mireyahttp://hdl.handle.net/2117/250762020-02-12T22:17:53Z2014-12-17T18:17:31ZMonitoring drivers' ventilation using an electrical bioimpedance systems: tests in a controlled enviornment
Macías Macías, Raúl; García González, Miguel Ángel; Ramos Castro, Juan José; Bragós Bardia, Ramon; Fernández Chimeno, Mireya
As improving road safety is one of the first aims in the automotive
world, several new techniques and methods are being researched
in recent years. Some of them consist of monitoring the driver behavior
to detect non-appropriate states for driving, e.g. drowsy driving or
drunk driving. Usually, the appearance of these non-appropriate states
is related to changes in several physiological parameters. This work is
divided into two main parts. The first one presents an electrical bioimpedance
system capable of monitoring the ventilation using textile electrodes.
Apart from describing the system, in this part some tests done
in a controlled environment are also shown. In the second part of this
paper, an enhancement of the system is described and checked using a
patient simulator.
2014-12-17T18:17:31ZMacías Macías, RaúlGarcía González, Miguel ÁngelRamos Castro, Juan JoséBragós Bardia, RamonFernández Chimeno, MireyaAs improving road safety is one of the first aims in the automotive
world, several new techniques and methods are being researched
in recent years. Some of them consist of monitoring the driver behavior
to detect non-appropriate states for driving, e.g. drowsy driving or
drunk driving. Usually, the appearance of these non-appropriate states
is related to changes in several physiological parameters. This work is
divided into two main parts. The first one presents an electrical bioimpedance
system capable of monitoring the ventilation using textile electrodes.
Apart from describing the system, in this part some tests done
in a controlled environment are also shown. In the second part of this
paper, an enhancement of the system is described and checked using a
patient simulator.Numerical validation methodsJauregui Tellería, RicardoSilva Martínez, Fernandohttp://hdl.handle.net/2117/134042020-02-12T22:53:08Z2011-10-03T10:33:34ZNumerical validation methods
Jauregui Tellería, Ricardo; Silva Martínez, Fernando
In the last years, numerical simulation has seen a great development thanks to costs reduction and speed increases of the computational systems. With these improvements, the mathematical algorithms are able to work properly with more realistic problems.
Nowadays, the solution of a problem using numerical simulation is not just finding a result, but also to ensure the quality. However, can we say that the model results are correct regarding the behaviour of the system? In other words, how could we quantify the similarity between reality and simulations? To answer these questions, it is necessary to establish a validation criterion that allows an objective quantification of the difference between the results and the reality. Another way to say this is, how “true” our results are.
In the case of numerical methods, the main objective is to replicate as closely as possible the behaviour of the "real" world through numbers. Normally, the results of the numerical methods are expressed in terms of graphics, pictures, etc. These results represent the view of reality that the chosen method provides. In order to affirm that the result of a numerical solution is fully consistent with the reality, it must be satisfied that:
a. The mathematical model must incorporate all aspects of the real world.
b. The numerical method has to solve exactly the equations of the mathematical modelling.
The problem starts with these two conditions that guarantee the "truth" of the results, since none of them are fully accomplished and it must be admitted that the numerical prediction never completely matches the "real" world behaviour. Then you can only be sure that the numerical solution is a good approximation of the reality. Now, new questions arise: How much does the result obtained by a numerical method resemble the reality? How can we objectively quantify this similarity? The answers to these questions are those that give rise to the validation methods.
2011-10-03T10:33:34ZJauregui Tellería, RicardoSilva Martínez, FernandoIn the last years, numerical simulation has seen a great development thanks to costs reduction and speed increases of the computational systems. With these improvements, the mathematical algorithms are able to work properly with more realistic problems.
Nowadays, the solution of a problem using numerical simulation is not just finding a result, but also to ensure the quality. However, can we say that the model results are correct regarding the behaviour of the system? In other words, how could we quantify the similarity between reality and simulations? To answer these questions, it is necessary to establish a validation criterion that allows an objective quantification of the difference between the results and the reality. Another way to say this is, how “true” our results are.
In the case of numerical methods, the main objective is to replicate as closely as possible the behaviour of the "real" world through numbers. Normally, the results of the numerical methods are expressed in terms of graphics, pictures, etc. These results represent the view of reality that the chosen method provides. In order to affirm that the result of a numerical solution is fully consistent with the reality, it must be satisfied that:
a. The mathematical model must incorporate all aspects of the real world.
b. The numerical method has to solve exactly the equations of the mathematical modelling.
The problem starts with these two conditions that guarantee the "truth" of the results, since none of them are fully accomplished and it must be admitted that the numerical prediction never completely matches the "real" world behaviour. Then you can only be sure that the numerical solution is a good approximation of the reality. Now, new questions arise: How much does the result obtained by a numerical method resemble the reality? How can we objectively quantify this similarity? The answers to these questions are those that give rise to the validation methods.