Automatical electrochemical measurements are sources of large data sets intended for further analysis. This work deals with classification, evaluation and processing of electrochemical signals using artificial neural networks. Due to high dimensionality of input data, an autoassociative neural network (AANN) is used in this work. This type of network performs dimensionality reduction via filtering the input data into relatively small number of principal parameters at the bottleneck output. These extracted parameters can be used for classification, evaluation and additional modelling of analyzed data trough the reconstructive part of this network. Furthermore, this work deals with implementation of a feedforward neural network in OpenCL language.

The goal of this master’s thesis was to design antenna working in frequency range from 30 MHz to 1 GHz. Designed biconical antenna requires balanced feed to work properly, therefore designing the balancing unit based on discrete components was necessary. Designed antenna was also constructed and its return loss, gain and radiation patterns were verified by measurement. This master’s thesis also introduces readers into principles of antenna measurements of disturbing signals in the area of EMC and compares different types of antennas for bands of meter and decimeter waves.

Dans cette thèse, nous nous intéressons à deux méthodes permettant de reconstruire un signal parcimonieux largement sous-échantillonné : l’échantillonnage de signaux à taux d’innovation fini et l’acquisition comprimée.Il a été montré récemment qu’en utilisant un noyau de pré-filtrage adapté, les signaux impulsionnels peuvent être parfaitement reconstruits bien qu’ils soient à bande non-limitée. En présence de bruit, la reconstruction est réalisée par une procédure d’estimation de tous les paramètres du signal d’intérêt. Dans cette thèse, nous considérons premièrement l’estimation des amplitudes et retards paramétrisant une somme finie d'impulsions de Dirac filtrée par un noyau quelconque et deuxièmement l’estimation d’une somme d’impulsions de forme quelconque filtrée par un noyau en somme de sinus cardinaux (SoS). Le noyau SoS est intéressant car il est paramétrable par un jeu de paramètres à valeurs complexes et vérifie les conditions nécessaires à la reconstruction. En se basant sur l’information de Fisher Bayésienne relative aux paramètres d’amplitudes et de retards et sur des outils d’optimisation convexe, nous proposons un nouveau noyau d’échantillonnage.L’acquisition comprimée permet d’échantillonner un signal en-dessous de la fréquence d’échantillonnage de Shannon, si le vecteur à échantillonner peut être approximé comme une combinaison linéaire d’un nombre réduit de vecteurs extraits d’un dictionnaire sur-complet. Malheureusement, dans des conditions réalistes, le dictionnaire (ou base) n’est souvent pas parfaitement connu, et est donc entaché d’une erreur (DB). L’estimation par dictionnaire, se basant sur les mêmes principes, permet d’estimer des paramètres à valeurs continues en les associant selon une grille partitionnant l’espace des paramètres. Généralement, les paramètres ne se trouvent pas sur la grille, ce qui induit un erreur d’estimation même à haut rapport signal sur bruit (RSB). C’est le problème de l’erreur de grille (EG). Dans cette thèse nous étudions les conséquences des modèles d’erreur DB et EG en terme de performances bayésiennes et montrons qu’un biais est introduit même avec une estimation parfaite du support et à haut RSB. La BCRB est dérivée pour les modèles DB et EG non structurés, qui bien qu’ils soient très proches, ne sont pas équivalents en terme de performances. Nous donnons également la borne de Cramér-Rao moyennée (BCRM) dans le cas d’une petite erreur de grille et étudions l’expression analytique de l’erreur quadratique moyenne bayésienne (BEQM) sur l’estimation de l’erreur de grille à haut RSB. Cette dernière est confirmée en pratique dans le contexte de l’estimation de fréquence pour différents algorithmes de reconstruction parcimonieuse.Nous proposons deux nouveaux estimateurs : le Bias-Correction Estimator (BiCE) et l’Off-Grid Error Correction (OGEC) permettant de corriger l'erreur de modèle induite par les erreurs DB et EG, respectivement. Ces deux estimateurs principalement basés sur une projection oblique des mesures sont conçus comme des post-traitements, destinés à réduire le biais d’estimation suite à une pré-estimation effectuée par n’importe quel algorithme de reconstruction parcimonieuse. Les biais et variances théoriques du BiCE et du OGEC sont dérivés afin de caractériser leurs efficacités statistiques.Nous montrons, dans le contexte difficile de l’échantillonnage des signaux impulsionnels à bande non-limitée que ces deux estimateurs permettent de réduire considérablement l’effet de l'erreur de modèle sur les performances d’estimation. Les estimateurs BiCE et OGEC sont tout deux des schémas (i) génériques, car ils peuvent être associés à tout estimateur parcimonieux de la littérature, (ii) rapides, car leur coût de calcul reste faible comparativement au coût des estimateurs parcimonieux, et (iii) ont de bonnes propriétés statistiques. / In this thesis, we are interested in two different low rate sampling schemes that challenge Shannon’s theory: the sampling of finite rate of innovation signals and compressed sensing.Recently it has been shown that using appropriate sampling kernel, finite rate of innovation signals can be perfectly sampled even though they are non-bandlimited. In the presence of noise, reconstruction is achieved by a model-based estimation procedure. In this thesis, we consider the estimation of the amplitudes and delays of a finite stream of Dirac pulses using an arbitrary kernel and the estimation of a finite stream of arbitrary pulses using the Sum of Sincs (SoS) kernel. In both scenarios, we derive the Bayesian Cramér-Rao Bound (BCRB) for the parameters of interest. The SoS kernel is an interesting kernel since it is totally configurable by a vector of weights. In the first scenario, based on convex optimization tools, we propose a new kernel minimizing the BCRB on the delays, while in the second scenario we propose a family of kernels which maximizes the Bayesian Fisher Information, i.e., the total amount of information about each of the parameter in the measures. The advantage of the proposed family is that it can be user-adjusted to favor either of the estimated parameters.Compressed sensing is a promising emerging domain which outperforms the classical limit of the Shannon sampling theory if the measurement vector can be approximated as the linear combination of few basis vectors extracted from a redundant dictionary matrix. Unfortunately, in realistic scenario, the knowledge of this basis or equivalently of the entire dictionary is often uncertain, i.e. corrupted by a Basis Mismatch (BM) error. The related estimation problem is based on the matching of continuous parameters of interest to a discretized parameter set over a regular grid. Generally, the parameters of interest do not lie in this grid and there exists an estimation error even at high Signal to Noise Ratio (SNR). This is the off-grid (OG) problem. The consequence of the BM and the OG mismatch problems is that the estimation accuracy in terms of Bayesian Mean Square Error (BMSE) of popular sparse-based estimators collapses even if the support is perfectly estimated and in the high Signal to Noise Ratio (SNR) regime. This saturation effect considerably limits the effective viability of these estimation schemes.In this thesis, the BCRB is derived for CS model with unstructured BM and OG. We show that even though both problems share a very close formalism, they lead to different performances. In the biased dictionary based estimation context, we propose and study analytical expressions of the Bayesian Mean Square Error (BMSE) on the estimation of the grid error at high SNR. We also show that this class of estimators is efficient and thus reaches the Bayesian Cramér-Rao Bound (BCRB) at high SNR. The proposed results are illustrated in the context of line spectra analysis for several popular sparse estimator. We also study the Expected Cramér-Rao Bound (ECRB) on the estimation of the amplitude for a small OG error and show that it follows well the behavior of practical estimators in a wide SNR range.In the context of BM and OG errors, we propose two new estimation schemes called Bias-Correction Estimator (BiCE) and Off-Grid Error Correction (OGEC) respectively and study their statistical properties in terms of theoretical bias and variances. Both estimators are essentially based on an oblique projection of the measurement vector and act as a post-processing estimation layer for any sparse-based estimator and mitigate considerably the BM (OG respectively) degradation. The proposed estimators are generic since they can be associated to any sparse-based estimator, fast, and have good statistical properties. To illustrate our results and propositions, they are applied in the challenging context of the compressive sampling of finite rate of innovation signals.

Aposematic animals advertise their defensive mechanisms to potential predators using warning signals. Signalling through more than one sensory pathway is called multimodal warning display. Most experimental studies of aposematism have been focused on the effect of a particular warning signal rather than on importance of multimodal signalling. Focusing on the multimodal signalling of real prey is the best way how to understand its effect in nature. The present study is focused on comparing the effect of multimodal warning display of insect prey with its particular warning signals on two species of bird predators. Multimodal warning signalisation of the burrowing bug, Tritomegas sexmaculatus consists of visual (black and white coloration), chemical (odour, possibly taste) and acoustic (stridulation) signals. We compared reactions of wild-caught great tit (Parus major) and blue tit (Cyanistes caeruleus) to three types of bugs with different warning displays. The non-manipulated bugs displayed multimodally, the brown painted bugs had their warning coloration manipulated and the dealatized bugs couldn't emit acoustic signals. The wild-caught birds of both species avoided all types of bugs. In an experiment with naive hand reared great tits we compared their reactions to non-manipulated and dealatized bugs. Naive...

Varje år går ca 6 000 företag i konkurs och det påverkar intressenter i form av kreditgivare, leverantörer, anställda och staten i form av inställda amorteringar, betalningar, skatteskulder samt löneutbetalningar. Därav är intresset stort att kunna predicera samt identifierav arningssignaler för konkurs. Denna uppsats kommer att undersöka skillnader mellan de finansiella nyckeltalen mellan konkursade och icke konkursade företag i de två mest konkursutsatta branscherna i svenskt näringsliv. Syfte Syftet med studien är att få bättre inblick i finansiella nyckeltalens betydelse för företag som går i konkurs genom att studera sambandet mellan konkurs och nyckeltal på mindre aktiebolag i Sverige inom byggnad och detaljbranschen. Metod Vi har använt oss av en kvantitativ metod där vi analyserat data genom deskriptiv statistik, oberoende t-test, korrelationsanalys och en logistisk regressionsanalys. Urvalet har varit påmindre företag i Sverige som upprättar årsredovisning i enlighet med K2-regelverket. Urvalet på 45 konkursade företag och 90 icke konkursade företag per bransch gjordes genom ett systematiskt urval av varje bransch. Ur varje grupp gjordes ett systematiskt urval på var femte företag som matchade våra kriterier. Slutsats Vår studie visar att det i den univariata och bivariata analysen finns tydliga skillnader mellan konkursade och icke konkursade företag. Studien visar också att nyckeltalen har en negativ trend från en treårsperiod innan konkursen fram tills konkursförfarandet. Ett år innan konkurs var differensen mellan konkursade och icke konkursade företag i byggbranschen signifikant på samtliga nyckeltal medan detaljhandeln endast hade sex av åtta nyckeltal signifikanta. / Every year, about 6,000 companies go bankrupt and this affects stakeholders in the form of creditors, suppliers, employees and governments in the form of canceled repayments, payments, tax liabilities and salary payments. As a result, there is great interest in being ableto predict and identify warning signals for bankruptcy. This thesis will examine differences between the financial key figures between bankrupt and non-bankrupt companies in the two most bankrupt industries in Swedish business. Purpose The purpose of the study is to gain a better insight into the significance of financial ratios for companies that go bankrupt by studying the relationship between bankruptcy and financialratios on smaller limited companies in Sweden in the construction and retail industry. Method We have used a quantitative method where we analyzed the data through descriptive statistics, independent t-tests, correlation analysis and a logistic regression analysis. The sample has been smaller companies in Sweden that prepare annual reports in accordance with the K2 regulations. The selection of 45 bankrupt companies and 90 non-bankrupt companies per industry was made through a systematic selection. From each group, a systematic selection was made on every fifth company that matched our criteria. Conclusion Our study shows that in the univariate and bivariate analysis there are clear differences between bankrupt and non-bankrupt companies. The study also shows that the key figures have a negative trend from a three-year period in bankruptcy until the bankruptcy proceedings. One year before bankruptcy, the difference between bankrupt and non-bankrupt companies in the construction industry was significant on all key figures, while the retail tradehad only six out of eight key figures significant.

According to previous research physical attractiveness plays an important role in our everyday life. People are treated differently on the basis of their physical appearance and from an evolutionary point of view physical attractiveness is a key factor in mate selection, being a cue of an individual's mate value and genetic qualities. Research shows that the highest attractiveness ratings tend to be given to physiques with a higher level of development of lean muscle mass and a V shaped upper body. Such a physique body constitution could indicates how a high level of physical fitness and a man's health of man and be a cue of the man's qualities as a mating partner. However, results of the previous studies are inconclusive due to the methods and stimuli employed. To address the shortcomings exhibited by the previous studies investigations, we ran two online studies. In the first study, women rated the attractiveness of a new and more extensive set of black and gray silhouettes derived from photos of the somatotypes. In the second study, we tested whether physical attractiveness of men can be increased by a voluntary change of their upper body shape. In both studies we were testing how interindividual differences of the female raters modulate the ratings of attractiveness. Results of the first study...

Gegenstand dieser Arbeit ist die Klassifikation von funktionellen Elektromyographie-Signalen des Nervus facialis, die bei Parotidektomien und sanierenden Ohr-Operationen aufgezeichnet wurden. Hierfür wurde eine detaillierte Analyse der intraoperativ auftretenden Aktionen Stimulation, Koagulation, Einsatz der Fräse und Spülung an Hand von geeigneten Signalparametern (Amplitude, Dauer, Fläche/Symmetrie, Leistung und Frequenz) durchgeführt. Darüber hinaus erfolgte eine Gegenüberstellung der EMG-Daten der zwei durchgeführten operativen Eingriffe und ein Vergleich der zwei untersuchten Erfolgsorgane des Nervus facialis (Mm. orbiculares). Dabei zeigten sich in allen Parametern relevante Unterschiede zwischen den verschiedenen Kategorien. Auf Grund dieser Ergebnisse lässt sich schlussfolgern, dass es möglich ist, ein Klassifikationsschema für die intraoperativen EMG Signale des Nervus facialis zu entwickeln. Dieses ist unabhängig von der Art des durchgeführten Eingriffs und unabhängig vom beobachteten Fazialisast. Als weiterführendes Ziel soll diese Klassifikation der Kontrolle kraftgetriebener Instrumente nach dem Prinzip „Navigated Control“ dienen.

Cleavage and polyadenylation of a precursor mRNA is important for transcription termination, mRNA stability, and regulation of gene expression. This process is directed by a multitude of protein factors and cis elements in the pre-mRNA sequence surrounding the cleavage and polyadenylation site. Importantly, the location of the cleavage and polyadenylation site helps define the 3’ untranslated region of a transcript, which is important for regulation by microRNAs and RNA binding proteins. Additionally, these sites have generally been poorly annotated. To identify 3’ ends, many techniques utilize an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Previously, simple heuristic filters relying on the number of adenines in the genomic sequence downstream of a putative polyadenylation site have been used to remove these sites of internal priming. However, these simple filters may not remove all sites of internal priming and may also exclude true polyadenylation sites. Therefore, I developed a naïve Bayes classifier to identify putative sites from oligo-dT primed 3’ end deep sequencing as true or false/internally primed. Notably, this algorithm uses a combination of sequence elements to distinguish between true and false sites. Finally, the resulting algorithm is highly accurate in multiple model systems and facilitates identification of novel polyadenylation sites.

In the development of digitally connected solutions that require the use of personal data, the issue of data privacy is an important factor that must be taken into account. Simply informing users about how data is used and getting their consent with a simple click is not enough to create awareness of the issue of data privacy and let them make a conscious decision about the use of their personal data. Furthermore, there is a big gap in knowledge about what personal data is and what is considered sensitive data. Especially the knowledge about what biometric identifiers that they are used in a variety of everyday life applications and in which sense the handling can be problematic is unknown.This thesis project explores how the use of critical play in form of an activist game can create awareness of the issue of data privacy, inform about the value of biometric data and foster self-reflection of handling one’s own personal data. Through the simulation of dependencies between personal data, the motivation to share them and the aggregation of personal data in combination with real and prospective use cases, players are empowered to reflect on their behaviour and to critically deal with the topic of data privacy.

En raison de la grande quantité d’EEG acquise sur plusieurs journées, une technique de compression efficace est nécessaire. Le manque des experts et la courte durée des crises encouragent la détection automatique des convulsions. Un affichage uniforme est obligatoire pour assurer l’interopérabilité et la lecture des examens EEG transmis. Le codeur certifié médical WAAVES fournit des CR élevés et assure une qualité de diagnostic d’image. Durant nos travaux, trois défis sont révélés : adapter WAAVES à la compression des signaux, détecter automatiquement les crises épileptiques et assurer l’interopérabilité des afficheurs EEG. L’étude du codeur montre qu’il est incapable de supprimer la corrélation spatiale et de compresser des signaux monodimensionnels. Par conséquent, nous avons appliqué l’ICA pour décorréler les signaux, la mise en échelle pour redimensionner les valeurs décimales et la construction d’image. Pour garder une qualité de diagnostic avec un PDR inférieur à 7%, nous avons codé le résidu. L’algorithme de compression EEGWaaves proposé a atteint des CR de l’ordre de 56. Ensuite, nous avons proposé une méthode d’extraction des caractéristiques des signaux EEG basée sur un nouveau modèle de calcul de la prédiction énergétique (EAM) des signaux. Ensuite, des paramètres statistiques ont été calculés et les Réseaux de Neurones ont été appliqués pour détecter les crises épileptiques. Cette méthode nous a permis d’atteindre de meilleure sensibilité allant jusqu’à 100% et une précision de 99.44%. Le dernier chapitre détaille le déploiement de notre afficheur multi-plateforme des signaux physiologiques. Il assure l’interopérabilité des examens EEG entre les hôpitaux. / Due to the large amount of EEG acquired over several days, an efficient compression technique is necessary. The lack of experts and the short duration of epileptic seizures require the automatic detection of these seizures. Furthermore, a uniform viewer is mandatory to ensure interoperability and a correct reading of transmitted EEG exams. The certified medical image WAAVES coder provides high compression ratios CR while ensuring image quality. During our thesis, three challenges are revealed : adapting WAAVES coder to the compression of the EEG signals, detecting automatically epileptic seizures in an EEG signal and ensure the interoperability of the displays of EEG exams. The study of WAAVES shows that this coder is unable to remove spatial correlation and to compress directly monodimensional signals. Therefore, we applied ICA to decorrelate signals, a scaling to resize decimal values, and image construction. To keep a diagnostic quality with a PDR less than 7%, we coded the residue. The proposed compression algorithm EEGWaaves has achieved CR equal to 56. Subsequently, we proposed a new method of EEG feature extraction based on a new calculation model of the energy expected measurement (EAM) of EEG signals. Then, statistical parameters were calculated and Neural Networks were applied to classify and detect epileptic seizures. Our method allowed to achieve a better sensitivity up to 100% and an accuracy of 99.44%. The last chapter details the deployment of our multiplatform display of physiological signals by meeting the specifications established by doctors. The main role of this software is to ensure the interoperability of EEG exams between healthcare centers.