Complexity lies halfway between stochasticity and determinism, suggesting that brain activity is neither fully random nor fully predictable but lives by the rules of nonlinear high- and low-complexity dynamics. One important aspect of brain function is noise-like activity (NLA), defined as background, electrical potential fluctuations in the nervous system distinct from spiking rhythms in the foreground. The objective of this thesis was to investigate the neurodynamical complexity of NLA recorded at the cellular and local network scales in in vitro preparations of mouse and human hippocampal tissue, under healthy and epileptiform conditions. In particular, it was found that neuronal NLA arises out of the physiological contributions of gap junctions and chemical synaptic channels and is characterized by a spectrum of complexity, ranging from high- to low-complexity, that was measured using methods from nonlinear dynamical systems theory. Importantly, the complexity of background, neuronal NLA was shown to depend on the degree of cellular interconnectivity to the surrounding local network. In addition, the complexity and multifractality of NLA was further studied at the cellular and local network scales in epileptiform transitions to seizure-like events, identifying emergent low-complexity and reduced multifractality (bordering on monofractal-type dynamics) in the pathological ictal state. Finally, dual intracellular recordings of hippocampal epileptiform activity were analyzed to measure NLA synchronicity, showing evidence for increased same- and cross-frequency correlations and increased phase synchronization in the pathological ictal state. Convergence towards increased phase synchrony manifested in lower frequency regions including theta (4-10 Hz) and beta (12-30 Hz), but also in higher frequency bands (gamma, 30-80 Hz). In summary, there is evidence to suggest that background NLA captures important neurodynamical information pertinent to the classification and identification of brain state transitions in healthy and epileptiform hippocampal dynamics, using sophisticated neuroengineering analyses of these physiological signals.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Η λειτουργία του εγκεφάλου βασίζεται στο σύνολο της ηλεκτροχημικής δραστηριότητας των νευρώνων, που αποτελούν το δομικό λίθο του νευρικού συστήματος. Η καταγραφή των βιολογικών σημάτων, ή βιοσημάτων, τα οποία προκύπτουν από την ηλεκτρική δραστηριότητα των νευρώνων του εγκεφάλου, αποτελούν το αντικείμενο της Ηλεκτροεγκεφαλογραφίας, η οποία είναι μια μη επεμβατική μέθοδος καταγραφής του παραγόμενου, από τον εγκέφαλο, ηλεκτρικού πεδίου. Η πρώτη επιτυχημένη καταγραφή βιοδυναμικών του ανθρώπινου εγκεφάλου αποδίδεται στο Γερμανό φυσιολόγο Hans Berger, ο οποίος στα τέλη της δεκαετίας του 1930, πραγματοποίησε για πρώτη φορά τη μέτρηση διαφορών δυναμικού από την επιφάνεια του κεφαλιού. Σήμερα, το Ηλεκτροεγκεφαλογράφημα (ΗΕΓ) αποτελεί μια ευρέως διαδεδομένη τεχνική κλινικής εξέτασης που χρησιμοποιείται με κύριο σκοπό τη διάγνωση. Στην παρούσα εργασία περιγράφεται η λειτουργία του νευρικού συστήματος, ενώ παρατίθενται στοιχεία ανατομίας του εγκεφάλου. Ακόμα, αναλύεται η φύση και τα χαρακτηριστικά των βιοσημάτων, που λαμβάνονται από την εξωτερική δερματική επιφάνεια του κεφαλιού, ενώ παρουσιάζονται οι βασικές αρχές της Ηλεκτροεγκεφαλογραφίας, ο τρόπος καταγραφής και τα κυριότερα χαρακτηριστικά του σήματος του ΗΕΓ. Εν συνεχεία, περιγράφεται η καταγραφή Ηλεκτροεγκεφαλογραφήματος, που έγινε με τη χρήση του συστήματος MP150 της Biopac. Ο MP150, σε συνδυασμό με το γραφικό περιβάλλον του Acqknowledge 3.8.2 αποτελεί ένα πλήρες σύγχρονο σύστημα καταγραφής πολλών ειδών βιοδυναμικών από διαφορετικές περιοχές του ανθρώπινου σώματος. Αξιοποιώντας τις δυνατότητες του συστήματος αυτού, καταφέραμε να καταγράψουμε και να παρατηρήσουμε τη ρυθμική δραστηριότητα του εγκεφάλου. Επίσης, γίνεται αναφορά στα σημαντικότερα πακέτα εργαλείων του Matlab, που έχουν σαν αντικείμενο την ανάλυση του Ηλεκτροεγκεφαλογραφήματος, ενώ επιχειρείται η περαιτέρω επεξεργασία του σήματος που καταγράφηκε, με τη χρήση ενός από αυτά (eeglab). Επιπλέον, στην εργασία αυτή παρουσιάζονται τα κυριότερα χαρακτηριστικά κάποιων από τις σημαντικότερες μεθόδους ανάλυσης του σήματος που προκύπτει από το ηλεκτροεγκεφαλογράφημα, ενώ παράλληλα γίνεται μια προσπάθεια σύγκρισης των δυνατοτήτων τους, με σκοπό την εξαγωγή χρήσιμων συμπερασμάτων που μπορούν να αξιοποιηθούν σε επόμενες μελέτες. / The entire function of the human brain is based on the electrochemical activity of the neurons, which form the whole nervous system. The recording of the biological signals resulting from the electrical activity of the brain forms the content of Electroencephalography, which is a non invasive method of recording the electric field produced in the human brain. The first successful recording of human brain’s biodynamics was made by German physiologist Hans Berger, who was the first to measure the electrical potential difference on the human head. Nowadays, Electroencephalography (EEG) is a very common clinical examination technique, which is used for the purpose of diagnosis. One of the objects of the present thesis is the description of the nervous system function, while there are some basic elements of the human brain’s anatomy. There is a brief analysis of the nature and features of the biological signals produced by the brain, as well as a presentation of the basic principles of the Electroencephalography. In addition, there is a description of the recording of the EEG signal, which was made with the use of Biopac MP150 system. MP150 and its graphic interface Acqknowledge 3.8.2 form a complete modern data acquisition system, which offers the possibility to record biodynamics from different parts of the human body. Making use of the Biopac system, we were able to record and analyze the brain rhythmic activity. Moreover, the most common Matlab toolboxes that are used for EEG signal processing are presented, while a further analysis of our EEG signal was attempted through eeglab. Furthermore, in the present thesis, the basic features of some of the most important methods of EEG signal analysis are presented. This quite extended presentation is made in order to be used as information for future study and research on EEG signal analysis. Key words: nervous system, biological signals, Electroencephalography, MP150, Acqknowledge, Matlab toolboxes, eeglab, signal analysis

Neste trabalho simulou-se o escoamento do fluxo de ar em um tubo, com um anteparo de formato triangular com arestas cortantes, posicionado no centro do tubo. O objetivo do estudo é a análise do comportamento do número de Strouhal em função do número de Reynolds. Para isto, foi utilizada a técnica da análise tempo-freqüência, baseada na transformada de Fourier e na transformada de Gabor. Os ensaios foram realizados com o fluxo com velocidades médias de escoamento de 3 a 10 m/s, sendo utilizado um sensor de pressão tipo piezo-resistivo para a detecção da flutuação de pressão ocasionada pelo desprendimento e formação dos vórtices. Os ensaios foram realizados em cinco etapas com o objetivo de se verificar a influência dos seguintes parâmetros na coleta de sinais e no fenômeno: ruído da rede elétrica; influência do anteparo e do ruído proveniente do escoamento do fluxo de ar; número de pontos da amostragem na coleta dos dados; do comprimento da tubulação; e posicionamento do sensor. Pode-se observar, a sensibilidade do sistema de medição através do ensaio realizado sem o anteparo, sendo verificada a influência do ruído do escoamento de ar pelo tubo; pode-se observar também uma pequena interferência do ruído da rede elétrica predominantemente para velocidades abaixo de 3 m/s. Apesar das influências citadas, e utilizando a transformada de Gabor para análise dos sinais, observou-se um sinal mais intenso na freqüência dos vórtices para as velocidades de escoamento, podendo-se comprovar que o número de Strouhal permanece quase constante e é independente do número de Reynolds, devendo-se ressaltar que esta conclusão é valida para números de Reynolds compreendidos na faixa de 3000 a 100000. No experimento obteve-se um fator de sensibilidade (freqüência vórtices/velocidade média) de 8,2 Hz/m/s, e número de Strouhal médio de 0,196. / This work concerns the simulation of an air flux through a pipe with a triangular bluff body positioned inside it. In order to study the behavior of the Strouhal number in function of the Reynolds number. For this, the time-frequency analysis technique was used, based on Fourier transform and the Gabor transform. The experiments were carried out with an air flux velocity ranging from 3 to 10 m/s and using a piezoresistive pressure sensor to detect pressure fluctuations caused by the shedding and vortex formation. The experimental procedures were divided in five stages to make it possible to verify the influence of the following parameters in the signal data acquisition: electric network noise, the bluff body presence and the noise generated due to its presence, number of sampling data points, tubing length and sensor positioning. The sensitivity of the experiment could be observed testing the air flowing with no bluff body inside the pipe. Thus, it was possible to investigate the influence of the noise generated due to this flux limiting body. It could be also observed, mainly at 3 m/s or less, the noise generated due to the electric network. Despite the listed influences, and with the use of the Gabor transform, a more intense signal on the vortex frequency for the flow velocity was observed, showing that the Strouhal number remains almost constant and is independent of the Reynolds number. It is important to recall that this conclusion is valid for Reynolds numbers between 3000 and 100000. In the experiments the factor of sensitivity (vortex frequency/mean velocity) obtained was 8,2 Hz/m/s and the mean Strouhal number 0,196.

Pour une meilleure gestion de l’eau, la Directive Cadre sur l’Eau requiert la prise en compte des relations entre les masses d’eau superficielles et souterraines. Dans ce cadre, la dynamique des échanges entre eaux de surface et eaux souterraines est étudiée, pour un aquifère alluvial du Rhône. Deux approches sont utilisées: une analyse des séries temporelles de niveaux de nappes et de rivières, à l’aide de techniques de traitement du signal, et des modèles d’écoulements numériques, déterministes, à base physique. Ces techniques sont mises en œuvre sur un secteur (Péage-de-Roussillon) à forts enjeux socio-économiques pour l’usage de la ressource en eau. Les résultats sont analysés du point de vue de leurs complémentarités.Une analyse en composantes principales, à partir des signaux piézométriques, a montré que les fluctuations de niveaux du Rhône expliquent la majeure partie des variations de niveau de la nappe. Les analyses corrélatoires et spectrales, ont permis de caractériser la relation existant entre les niveaux du Rhône et de la nappe. Des comportements particuliers de l’hydrosystème ont été identifiés : colmatage du fond des cours d’eau, écoulements transverses aux cours d’eau,…. Ces comportements ont ensuite pu être étudiés, plus en détail, à l’aide d’un modèle hydrodynamique de la nappe qui intègre un module de calcul des écoulements surfaciques. Le modèle permet également de quantifier les flux échangés entre la nappe et le cours d’eau. / For better water management, the Water Framework Directive requires to take into account the relationships between surface water and groundwater bodies.In this frame, the exchanges dynamic between surface water and groundwater is studied, for a Rhône alluvial aquifer. Two sets of tools are employed: a time series analysis of groundwater and rivers levels, using signal processing techniques, and numerical flow models, deterministic and physically based. These techniques are implemented on an area (Péage-de-Roussillon) with high socio-economic stakes regarding water resources. The complementarities among the results are analysed.A principal component analysis, based on piezometric head signals, showed that the fluctuations of the Rhône water level explain most of the groundwater variations. Correlative and spectral analysis were used to characterise the relationship between the Rhône and the groundwater level. Specific behaviours of the hydrosystem were identified: clogging of river beds, transversal flows below river beds,… These behaviours were then studied, in details, using a hydrodynamic model of the aquifer, which incorporates a surface runoff calculation module. The model also allows quantifying the exchange rates between rivers and groundwater.

This master’s thesis deals with the concept of passive multistatic radar. The radar system exploits WiFi or WiMAX transmitters as the source of radiolocation signal. The transmitters are considered non-cooperative. The master’s thesis evaluates limitations arising from utilization of WiFi or WiMAX signals. A Matlab simulator was created as a part of the thesis, which was used to verify the basic idea behind this concept. Based on the results of real-life simulations, conclusions are suggested towards a possible application of WiFi/WiMAX radar.

Today’s bridge design work in many cases demands a trustworthy dynamic analysis instead of using the traditional dynamic amplification factors. In this thesis a reliable 3D Bernoulli-Euler beam finite element model of the New Årsta Railway Bridge was prepared for thorough dynamic analysis using in situ bridge monitoring for correlation. The bridge is of the concrete box girder type with a heavily reinforced and prestressed bridge deck. The monitoring system was designed for long term monitoring with strain transducers embedded in the concrete and accelerometers mounted inside the edge beams and at the lower edge of the track slab. The global finite element model used the exact bridge geometry but was simplified regarding prestressing cables and the two railway tracks. The prestressing cables and the tracks were consequently not included and an equivalent pure concrete model was identified. A static macadam train load was eccentrically placed on one of the bridge’s two tracks. By using Vlasov’s torsional theory and thereby including constrained warping a realistic modulus of elasticity for the concrete without prestressing cables and stiffness contribution from the railway tracks was found. This was allowed by comparing measured strain from strain transducers with the linear elastic finite element model’s axial stresses. Mainly three monitoring bridge sections were used, each of which was modelled with plane strain finite elements subjected to sectional forces/moments from a static macadam train load and a separately calculated torsional curvature. From the identified modulus of elasticity the global finite element model was updated for Poisson’s ratio and material density (mass) to correspond with natural frequencies from the performed signal analysis of accelerometer signals. The influence of warping on the natural frequencies of the global finite element model was assumed small and the bridge’s torsional behaviour was modelled to follow Saint-Venant’s torsional theory. A first preliminary estimation of modal damping ratios was included. The results indicated that natural frequencies were in accordance between modelling and signal analysis results, especially concerning high energy modes. Estimated damping ratios for the first vibration modes far exceeded the lower limit value specified in bridge design codes and railway bridge dynamic analysis recommendations. / QC 20101124

V práci je popsán princip synchronní detekce (SD), který byl uplatněn při měření s biosenzory. Metoda SD umožňuje dosažení výrazně lepšího poměru signálu k šumu, vyššího limitu detekce a celkové zlepšení robustnosti měření. Uplatnění SD při měření s biosenzory umožní zlepšit analýzu jeho odezvy a umožní odstranění nežádoucích interferencí nebo šumů, které mohou být způsobeny například mícháním roztoku, elektromagnetickými vlivy nebo parazitními proudy. SD také umožňuje rozložit získaný signál na odezvu stimulace a na dlouhodobý signál jiného procesu, a dále také identifikovat jevy druhého řádu. Pro identifikaci stimulačního signálu ve výstupním signálu měření byl na základě lineárního statistického modelu vyvinut specializovaný software. SD byla ověřena na modelovém případu výstupního signálu biosenzoru s aplikovaným komplexem fotosystému II (PSII) a jeho odezvě na stimulaci světlem. Odezva PSII se řídí kinetikou prvního řádu a může být také ovlivněna inhibitory. Kinetické konstanty vazby herbicidu na PSII závisí lineárně na koncentraci herbicidu. To umožňuje jejich měření také při nízkých koncentracích herbicidu.