The thesis is focused on physical principles of signal transmission by optical fiber and effects that may have influence on such transmission. This knowledge is acquired with regard to future usage of optical fiber as a sensor for detection and localization of mechanical vibration. In the thesis, mentioned knowledge is taken in account and also there are described methods, which allow mechanical vibration for dozens of km. At the conclusion the laboratory sollution is suggested, allowing detection and localization in vast distance with possibility of real test in practice.The testing curcuits are used for measurement and results are processed for purpose of detection and localization of source. The measurement itself was made by testing curcuits and results were processed for purpose of detection and localization of source.

Within industrial systems, dependability is critical to keep the system reliable and available. Faults leading to failures of the whole system must be mitigated by a laborious design, testing, and verification process. This thesis aims to build a Network Management Agent (NMA), capable of fault detection, localization, and recovery in an Ethernet environment. The chosen NMA environment was a distributed industrial system using a redundant controller pair, where the controllers must determine when the roles should switch in case of primary failure. In the industrial context, this role-switching must be bounded and robust enough to withstand mixed traffic classes competing for network resources. An NMA was built to monitor the network with the help of Media Redundancy Protocol (MRP), heartbeats, and industrial switch queue status queries. The NMA could distinguish between node and link failure by localizing the fault while adjusting the network's Quality of Service (QoS). The controllers could safely switch roles after an average difference of 29.7065 ms from the moment the primary failed, and the secondary took over. Link failure was detected in three possible locations within 31.297 ms and the location was found within 373.419 ms. To the authors' best knowledge, other solutions mainly target L3 networks or require specialized supporting technology, whereas MRP was found in the majority of the investigated industrial switches. The proposed solution requires any heartbeat-like function sent from the switch, which MRP offers, and can be generalized to any environment where distinguishing a link from a node failure is important. QoS anomaly detection however requires capable switches and configuration of rules to prioritize the traffic accordingly.

La déformation des signaux au long de leur trajet de propagation est un des plus importants facteurs qui doivent être considérés à la réception. Ces effets sont dus à des phénomènes comme l’atténuation, la réflexion, la dispersion et le bruit. Alors que les premiers deux phénomènes sont assez facile à surveiller, parce qu’elles affectent l’amplitude, respectivement le retard des signaux, les deux derniers phénomènes sont plus difficiles à contrôler, parce qu’elles changent les paramètres du signal (amplitude, fréquence et phase) de manière totalement dépendante de l’environnement.Dans cette thèse, l’objectif principal est de contribuer à l’analyse des signaux liés aux différents phénomènes physiques, en visant une meilleure compréhension de ces phénomènes, ainsi que l’estimation de leurs paramètres qui sont intéressants de point de vue applicatif. Plusieurs contextes applicatifs ont été investigués dans deux configurations de : active et passive.Pour la configuration active, le premier contexte applicatif consiste en l’étude du phénomène de cavitation dans le domaine de la surveillance de systèmes hydrauliques. La deuxième application de la configuration active est la détection et le suivi des objets immergés sans synchronisation entre les capteurs d’émission et de réception.Pour la configuration passive, nous nous concentrons sur l’analyse des transitoires de pression dans les conduites d’eau en utilisant une méthode non-intrusive ainsi que sur la surveillance des réseaux d’énergie électrique en présence des phénomènes transitoires comme les arcs électriques.Malgré les différences entre les considérations physiques spécifiques à ces applications, nous proposons un modèle mathématique unique pour les signaux issus des deux types de configurations. Le modèle est basé sur l’analyse des récurrences. Avec ce concept, nous proposons une nouvelle approche pour les ondes basées sur l’espace des phases. Cette technique de construction des formes d’ondes présente l’intérêt de conduire à des méthodes de d’investigation active à haut cadence, très utiles pour la surveillance des phénomènes dynamiques.En plus, nous proposons des approches nouvelles pour l’investigation des caractéristiques des signaux. La première est la mesure TDR* (Time Distributed Recurrences) qui quantifie la matrice des récurrences/ distances et qui est utilisée pour la détection des signaux transitoires. La deuxième approche est l’analyse des phases à plusieurs retards et elle est utilisée pour la discrimination entre des signaux avec des paramètres très proches. Finalement, la quantification des lignes diagonales de la matrice des récurrences est proposée comme alternative pour l’analyse des signaux modulés en fréquence.Les travaux présentent les résultats expérimentaux en utilisant les méthodes théorétiques proposées dans cette thèse. Les résultats sont comparés avec des techniques classiques.Des perspectives de ces travaux, tant dans les domaines théorique et qu’applicatif, sont discutés à la fin du mémoire. / Signals’ deformation along their propagation path is among the most important aspect which has to be taken into account at reception. These effects are caused by phenomena like attenuation, reflection, dispersion and noise. Whereas the first two are rather easy to monitor, because they affect the amplitude, respectively the delay, the latter two are more difficult to control, because they change signals’ parameters (amplitude, frequency and phase) in an environment-dependent manner.In this thesis, the main objective is to contribute to the analysis of signals related to different physical phenomena, aiming to better understand them as well as to estimate their parameters that are interesting from application point of view. Different applicative contexts have been investigated in active and passive sensing configurations. For the active part, we mention the monitoring of cavitation phenomena and its characterization for hydraulic system surveillance. The second application of the active sensing is the underwater object detection and tracking without synchronization between sensors. For the passive configuration, we focus on the pressure transient analysis in water pipes investigation with a non-intrusive method and on the surveillance of electrical power systems in the presence of transient phenomena such as electrical arcs.Despite the differences between the physical considerations, we propose a unique mathematical model of the signals issued from the active/passive sensing system used to analyze the considered phenomena. This model is based on the Recurrence Plot Analysis (RPA) method. With this concept, we propose the phase-space based waveform design. This waveform design technique presents the interest to conduct to a high speed sensing methods, very useful to monitor dynamic phenomena.Moreover, we propose new tools for the investigation of the signals characteristics. The first one is the TDR* measure (Time Distributed Recurrences) that quantifies the recurrence/ distance matrix and it is used for the detection of transient signals. The second one is the multi-lag phase analysis using multiple lags and it is successfully used to discriminate between signals with close parameters. Finally, the diagonal lines quantification of RPA matrix is proposed as an alternative for the analysis of modulated signals.Our work presents the experimental results using the proposed theoretical methods introduced by this thesis. The results are compared with classical techniques.The perspectives of this thesis are presented at the end of this paper.

L'objectif de ce travail est de mettre au point des méthodes pour détecter, localiser, quantifier et suivre l'évolution de l'endommagement dans les câbles courts, tels que les suspentes des ponts suspendus, à partir de leurs réponses vibratoires. Afin de modéliser ces câbles, un modèle linéaire 1D de poutre d'Euler Bernoulli avec tension est utilisé. Ce modèle permet de modéliser une large gamme de structures, allant de la corde vibrante à la poutre sans tension. Pour le câble, l'endommagement est introduit dans l'équation vibratoire par des modifications locales de la masse linéique et de la rigidité en flexion et par un changement global de la tension. De plus, pour introduire une "fissure" dans l'équation vibratoire d'une poutre, la modification de la rigidité peut être remplacée par un ressort de rotation au niveau de la fissure. Pour ces deux modèles d'introduction d'endommagements, une estimation analytique au premier ordre des variations des paramètres modaux en fonction des modifications est établie. Grâce aux estimations analytiques obtenues pour la variation relative des fréquences en fonctions des modifications physiques, nous développons des techniques de localisation pour deux cas d'étude : deux essais seuls correspondants à deux états (sain et endommagé) et une série d'essais (plusieurs essais de l'état sain à l'état endommagé). Pour ce second cas, une autre méthode de détection et de localisation utilisant cette fois la SVD est proposée. Les méthodes proposées sont testées sur des données numériques et sur des données expérimentales existant dans la littérature ou effectuées pendant la thèse / The objective of this work is to develop methods to detect, localize, quantify and follow the evolution of the damage in short cables, such as suspenders of the suspension bridges, using their vibratory responses. To simulate these cables, a 1D Euler Bernoulli beam linear model with tension is used. This model allows to study a wide range of structures from the vibrating string to the beam without tension. For cables, damage is introduced into the vibratory equation by local changes of the linear density and the bending stiffness and a global change in the tension. To introduce a crack in the vibrating beam equation, the change in the rigidity may be replaced by a pinned joint at the location ofthe crack. For both these models, a first order analytical estimation of the variation of modal parameters due to theses changes is established. Using these analytical estimations of the relative frequency variations in functions of the physical changes, we develop methods of localization for two cases : only two tests corresponding to two states (healthy and damaged) and a series of tests (several tests on the healthy state and several tests on the damaged state). For the second case, we propose another method of detection and localization which uses the SVD tool . These methods are tested on numerical data and experimental data from literature or from tests performed during the phD.

No description available.

Since February 2020, the world is in a COVID-19 pandemic [1]. Researchers around the globe are pitching in to develop a fast reliable, non-invasive testing methodology to solve this problem and one of the key directions of research is to utilize coughs and their corresponding vocal biomarkers for diagnosis of COVID-19. In this thesis, we propose a fast, real-time cough detection pipeline that can be used to detect and localize coughs from audio samples. The core of the pipeline utilizes the yolo-v3 model [2] from vision domain to localize coughs in the audio spectrograms by treating them as objects. This outcome is transformed to localize the boundaries of cough utterances in the input signal. The system to detect coughs from CoughVid dataset [3] is then evaluated. Furthermore, the pipeline is compared with other existing algorithms like tinyyolo-v3 to test for better localization and classification. Average precision(AP@0.5) of yolo-v3 and tinyyolo-v3 model are 0.67 and 0.78 respectively. Based on the AP values, tinyyolo-v3 performs better than yolo-v3 by atleast 10% and based on its computational advantage, its inference time was also found to be 2.4 times faster than yolo-v3 model in our experiments. This work is considered to be novel and significant in detection and localization of cough in an audio stream. In the end, the resulting cough events are used to extract MFCC features from it and classifiers were trained to predict whether a cough has COVID-19 or not. The performance of different classifiers were compared and it was observed that random forest outperformed other models with a precision of 83.04%. It can also be inferred from the results that the classifier looks promising, however, in future this model has to be trained using clinically approved dataset and tested for its reliability in using this model in a clinical setup. / Sedan februari 2020 är världen inne i en COVID-19-pandemi [1]. Forskare runt om i världen satsar på att utveckla en snabb tillförlitlig, icke-invasiv testmetodik för att lösa detta problem och en av de viktigaste forskningsriktningarna är att använda hosta och deras motsvarande vokala biomarkörer för diagnos av COVID-19. I denna avhandling föreslår vi en snabb pipeline för hostdetektering i realtid som kan användas för att upptäcka och lokalisera hosta från ljudprover. Kärnan i rörledningen använder yolo-v3-modellen [2] från syndomänen för att lokalisera hosta i ljudspektrogrammen genom att behandla dem som objekt. Detta resultat transformeras för att lokalisera gränserna för hosta yttranden i insignalen. Systemet för att upptäcka hosta från CoughVid dataset [3] utvärderas sedan. Dessutom jämförs rörledningen med andra befintliga algoritmer som tinyyolo-v3 för att testa för bättre lokalisering och klassificering. Genomsnittlig precision (AP@0.5) för modellen yolo-v3 och tinyyolo-v3 är 0,67 respektive 0,78. Baserat på AP-värdena fungerar tinyyolo-v3 bättre än yolo-v3 med minst 10% och baserat på dess beräkningsfördel befanns dess inferenstid också vara 2,4 gånger snabbare än yolo-v3- modellen i våra experiment. Detta arbete anses vara nytt och viktigt för att upptäcka och lokalisera hosta i en ljudström. I slutändan används de resulterande hosthändel-serna för att extrahera MFCC-funktioner från det och klassificerare utbildades för att förutsäga om en hosta har COVID-19 eller inte. Prestanda för olika klassificerare jämfördes och det observerades att slumpmässig skog överträffade andra modeller med en precision på 83.04%. Av resultaten kan man också dra slutsatsen att klassificeraren ser lovande ut, men i framtiden måste denna modell utbildas med hjälp av kliniskt godkänd dataset och testas med avseende på dess tillförlitlighet vid användning av denna modell i ett kliniskt upplägg.

Visual place recognition is the task of finding same places in a set of database images for a given set of query images. This becomes particularly challenging for long-term applications when the environmental condition changes between or within the database and query set, e.g., from day to night. Visual place recognition in changing environments can be used if global position data like GPS is not available or very inaccurate, or for redundancy. It is required for tasks like loop closure detection in SLAM, candidate selection for global localization, or multi-robot/multi-session mapping and map merging. In contrast to pure image retrieval, visual place recognition can often build upon additional information and data for improvements in performance, runtime, or memory usage. This includes additional data-inherent knowledge about information that is contained in the image sets themselves because of the way they were recorded. Using data-inherent knowledge avoids the dependency on other sensors, which increases the generality of methods for an integration into many existing place recognition pipelines. This thesis focuses on the usage of additional data-inherent knowledge. After the discussion of basics about visual place recognition, the thesis gives a systematic overview of existing data-inherent knowledge and corresponding methods. Subsequently, the thesis concentrates on a deeper consideration and exploitation of four different types of additional data-inherent knowledge. This includes 1) sequences, i.e., the database and query set are recorded as spatio-temporal sequences so that consecutive images are also adjacent in the world, 2) knowledge of whether the environmental conditions within the database and query set are constant or continuously changing, 3) intra-database similarities between the database images, and 4) intra-query similarities between the query images. Except for sequences, all types have received only little attention in the literature so far. For the exploitation of knowledge about constant conditions within the database and query set (e.g., database: summer, query: winter), the thesis evaluates different descriptor standardization techniques. For the alternative scenario of continuous condition changes (e.g., database: sunny to rainy, query: sunny to cloudy), the thesis first investigates the qualitative and quantitative impact on the performance of image descriptors. It then proposes and evaluates four unsupervised learning methods, including our novel clustering-based descriptor standardization method K-STD and three PCA-based methods from the literature. To address the high computational effort of descriptor comparisons during place recognition, our novel method EPR for efficient place recognition is proposed. Given a query descriptor, EPR uses sequence information and intra-database similarities to identify nearly all matching descriptors in the database. For a structured combination of several sources of additional knowledge in a single graph, the thesis presents our novel graphical framework for place recognition. After the minimization of the graph's error with our proposed ICM-based optimization, the place recognition performance can be significantly improved. For an extensive experimental evaluation of all methods in this thesis and beyond, a benchmark for visual place recognition in changing environments is presented, which is composed of six datasets with thirty sequence combinations.

The widespread availability of image editing tools and improvements in image processing techniques make image manipulation feasible for the general population. Oftentimes, easy-to-use yet sophisticated image editing tools produce results that contain modifications imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, such as inserting objects into an image to hide existing scenes and structures. In this thesis, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives.