Les moyens mis en œuvre pour surveiller la surface de la Terre, notamment les zones urbaines, en cas de catastrophes naturelles telles que les inondations ou les tremblements de terre, et pour évaluer l’impact de ces événements, jouent un rôle primordial du point de vue sociétal, économique et humain. Dans ce cadre, des méthodes de classification précises et efficaces sont des outils particulièrement importants pour aider à l’évaluation rapide et fiable des changements au sol et des dommages provoqués. Étant données l’énorme quantité et la variété des données Haute Résolution (HR) disponibles grâce aux missions satellitaires de dernière génération et de différents types, telles que Pléiades, COSMO-SkyMed ou RadarSat-2 la principale difficulté est de trouver un classifieur qui puisse prendre en compte des données multi-bande, multi-résolution, multi-date et éventuellement multi-capteur tout en gardant un temps de calcul acceptable. Les approches de classification multi-date/multi-capteur et multi-résolution sont fondées sur une modélisation statistique explicite. En fait, le modèle développé consiste en un classifieur bayésien supervisé qui combine un modèle statistique conditionnel par classe intégrant des informations pixel par pixel à la même résolution et un champ de Markov hiérarchique fusionnant l’information spatio-temporelle et multi-résolution, en se basant sur le critère des Modes Marginales a Posteriori (MPM en anglais), qui vise à affecter à chaque pixel l’étiquette optimale en maximisant récursivement la probabilité marginale a posteriori, étant donné l’ensemble des observations multi-temporelles ou multi-capteur / The capabilities to monitor the Earth's surface, notably in urban and built-up areas, for example in the framework of the protection from environmental disasters such as floods or earthquakes, play important roles in multiple social, economic, and human viewpoints. In this framework, accurate and time-efficient classification methods are important tools required to support the rapid and reliable assessment of ground changes and damages induced by a disaster, in particular when an extensive area has been affected. Given the substantial amount and variety of data available currently from last generation very-high resolution (VHR) satellite missions such as Pléiades, COSMO-SkyMed, or RadarSat-2, the main methodological difficulty is to develop classifiers that are powerful and flexible enough to utilize the benefits of multiband, multiresolution, multi-date, and possibly multi-sensor input imagery. With the proposed approaches, multi-date/multi-sensor and multi-resolution fusion are based on explicit statistical modeling. The method combines a joint statistical model of multi-sensor and multi-temporal images through hierarchical Markov random field (MRF) modeling, leading to statistical supervised classification approaches. We have developed novel hierarchical Markov random field models, based on the marginal posterior modes (MPM) criterion, that support information extraction from multi-temporal and/or multi-sensor information and allow the joint supervised classification of multiple images taken over the same area at different times, from different sensors, and/or at different spatial resolutions. The developed methods have been experimentally validated with complex optical multispectral (Pléiades), X-band SAR (COSMO-Skymed), and C-band SAR (RadarSat-2) imagery taken from the Haiti site

Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring. The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data. In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest). Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster. Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.

Home-based exercise is a popular physical activity of maintaining fitness, health andwellness in general. However, without proper supervision and basic knowledge of theexercises in the workout plan, there is an increased risk of injury. Considering that noteveryone is willing to attend crowded gyms or schedule professional personal trainingsessions, in this study, a novel feedback system is proposed, in the form of a mobileapplication. Accelerometer and gyroscope data were collected from 10 volunteersperforming 3 exercises, squats, lunges and bridges, with inertial sensors attachedto their back lumbar region, on both shanks and on both thighs. Each participantperformed 5 repetitions of the correct technique and 5 repetitions of 4 mistakes foreach exercise. The accuracies of 3 classifiers, a SVM, a RF and DT were comparedwith the SVM performing the best across all 3 exercises. The best location and numberof sensors was determined by examining the accuracy of a SVM model for 15 uniquemulti-sensor configurations. The best performing setup, being the configuration with 2sensors, one at the lumbar area and one at the shank, was used in exploring the efficacyof different data processing techniques. Time-domain statistical features, sensor angletimeseries and the filtered signal timeseries were evaluated as input to a NN. The timedomainfeatures performed the best achieving the highest accuracy in all 3 exercises,with an accuracy of 67% for the squats, 87% for the lunges and 75% for the hip bridges.Overall, the final model demonstrated promising capabilities of classifying exercisetechnique of basic lower-body exercises, with a real-time feedback implementationbeing a feasible solution for self-efficient fitness. / Hemmaträning är en populär typ av fysisk aktivitet för att upprätthålla kondition,hälsa och välbefinnande. Dock utan övervakning och basal kunskap om hur olikaövningar bör utföras så finns det en ökad risk för skador. Alla människor går intefrivilligt till trånga och fullsatta gym eller bokar in pass med personlig tränare. Därförföreslås i denna studie ett nytt återkopplingssytem vid träning som kan användas via enmobilapp. Data från en accelerometer och ett gyroskop har samlats in från tio frivilligapersoner. De har utfört tre olika styrkeövningar; knäböj, utfallssteg och höftlyft medtröghetssensorer placerade på deras ländrygg, på underbenen och på låren. Varjedeltagare utförde fem repetitioner med korrekt teknik och sedan fem repetitionermed fyra olika typer av felaktig teknik för varje styrkeövning. Noggrannheten förtre klassificerare, SVM, RF och DT jämfördes sedan med det SVM som presteradebäst i alla tre styrkeövningarna. Det optimala antalet sensorer tillsammans med bästplacering av dessa räknades ut genom att undersöka en SVM modell med 15 unikamultisensorkonfigurationer. Det visade sig att kombinationen med två sensorer, envid ländryggen och en på underbenet var den bästa och därför användes den föratt undersöka effektiviteten av olika databehandlingstekniker. Tidsdomänsstatistiskafunktioner, sensorvinkeltidsserier och filtrerade signaltidsserier utvärderades sominmatning till ett NN. Tidsdomänsfunktionerna presterade bäst och uppnådde högstnoggrannhet i alla tre övningarna. Detta med ett korrekt utfall av 67% för knöböj,87% för utfallsteg och 75% för höftlyft. Sammantaget visade den slutliga modellenen lovande förmåga att klassificera träningsteknik för basala styrkeövningar för nedredelen av kroppen. Samtidigt som användaren får feedback i realtid vilket gör detmöjligt att utföra effektiv träning själv hemma.

Spectrally-based fiber optic sensors are a rapidly maturing technology capable of sensing several environmental parameters in environments that are unfitting to electrical sensors. However, the sensor interrogation systems for this type of sensors are not yet fit to replace conventional sensor systems. They lack the speed, compact size, and usability necessary to move into mainstream test and measurement. The Fiber Sensor Integrated Monitor (FSIM) technology leverages rapid optical components and parallel hardware architecture to move these sensors across the research threshold into greater mainstream use. By dramatically increasing speed, shrinking size, and targeting an interface that can be used in large-scale industrial interrogation systems, spectrally-based fiber optic sensors can now find more widespread use in both research labs and industrial applications. The technology developed in this thesis was demonstrated by producing two advanced interrogators: one that was one half the size of commercially available systems, and one that accelerated live spectral capture by one thousand times – both of which were operated by non-developers with little training.

No description available.

En imagerie par résonance magnétique (IRM) interventionnelle, des interventions percutanées minimalement-invasives (biopsies, ablations de tumeurs,...) sont réalisées sous guidage IRM. Lors de l’intervention, les plans de coupe acquis sont alignés sur l’outil chirurgical et les régions anatomiques d’intérêt afin de surveiller la progression de l’outil dans le corps du patient en temps réel. Le suivi d’objets dans l’IRM facilite et accélère les interventions guidées par IRM en permettant d’aligner automatiquement les plans de coupe avec l’outil chirurgical. Dans cette thèse, un système d’alignement automatique des plans de coupe établi sur une séquence IRM clinique est développé. Celui-ci réalise automatiquement la détection et le suivi d’un marqueur passif directement dans les images IRM tout en minimisant le temps d’imagerie dédié à la détection. L’inconvénient principal de cette approche est sa dépendance au temps d’acquisition de la séquence IRM clinique utilisée. Dans un premier temps, les performances du suivi ont pu être améliorées grâce à l’estimation et la prédiction du mouvement suivi par un filtre de Kalman. Puis un capteur optique complémentaire a été ajouté pour réaliser un suivi multi-capteurs, découplant ainsi la fréquence de rafraichissement du suivi de la fréquence de rafraichissement des images IRM. La performance du système développé a été évaluée par des simulations et des expériences utilisant un banc d’essai compatible IRM. Les résultats montrent une bonne robustesse du suivi multi-capteurs pour l’alignement des plans de coupe grâce à la combinaison des qualités individuelles de chaque capteur. / Interventional magnetic resonance imaging (MRI) aims at performing minimally invasive percutaneous interventions, such as tumor ablations and biopsies, under MRI guidance. During such interventions, the acquired MR image planes are typically aligned to the surgical instrument (needle) axis and to surrounding anatomical structures of interest in order to efficiently monitor the advancement in real-time of the instrument inside the patient’s body. Object tracking inside the MRI is expected to facilitate and accelerate MR-guided interventions by allowing to automatically align the image planes to the surgical instrument. In this PhD thesis, an image-based workflow is proposed and refined for automatic image plane alignment. An automatic tracking workflow was developed, performing detection and tracking of a passive marker directly in clinical real-time images. This tracking workflow is designed for fully automated image plane alignment, with minimization of tracking-dedicated time. Its main drawback is its inherent dependence on the slow clinical MRI update rate. First, the addition of motion estimation and prediction with a Kalman filter was investigated and improved the workflow tracking performance. Second, a complementary optical sensor was used for multi-sensor tracking in order to decouple the tracking update rate from the MR image acquisition rate. Performance of the workflow was evaluated with both computer simulations and experiments using an MR compatible testbed. Results show a high robustness of the multi-sensor tracking approach for dynamic image plane alignment, due to the combination of the individual strengths of each sensor.

Abstract The last decade was marked by a set of harmful events ranging from economical crises to organized crime, acts of terror and natural catastrophes. This has led to a paradigm transformation concerning security. Millions of surveillance cameras have been deployed, which led to new challenges, as the systems and operations behind those cameras could not cope with the rapid growth in number of video cameras and systems. Looking at today’s control rooms, often hundreds or even thousands of cameras are displayed, overloading security officers with irrelevant information. The purpose of this research was the creation of a novel video surveillance system with automated analysis mechanisms which enable security authorities and their operators to cope with this information flood. By automating the process, video surveillance was transformed into a proactive information system. The progress in technology as well as the ever increasing demand in security have proven to be an enormous driver for security technology research, such as this study. This work shall contribute to the protection of our personal freedom, our lives, our property and our society by aiding the prevention of crime and terrorist attacks that diminish our personal freedom. In this study, design science research methodology was utilized in order to ensure scientific rigor while constructing and evaluating artifacts. The requirements for this research were sought in close cooperation with high-level security authorities and prior research was studied in detail. The created construct, the “Intelligent Video Surveillance System”, is a distributed, highly-scalable software framework, that can function as a basis for any kind of high-performance video surveillance system, from installations focusing on high-availability to flexible cloud-based installation that scale across multiple locations and tens of thousands of cameras. First, in order to provide a strong foundation, a modular, distributed system architecture was created, which was then augmented by a multi-sensor analysis process. Thus, the analysis of data from multiple sources, combining video and other sensors in order to automatically detect critical events, was enabled. Further, an intelligent mobile client, the video surveillance local control, which addressed remote access applications, was created. Finally, a wireless self-contained surveillance system was introduced, a novel smart camera concept that enabled ad hoc and mobile surveillance. The value of the created artifacts was proven by evaluation at two real-world sites: An international airport, which has a large-scale installation with high-security requirements, and a security service provider, offering a multitude of video-based services by operating a video control center with thousands of cameras connected. / Tiivistelmä Viime vuosikymmen tunnetaan vahingollisista tapahtumista alkaen talouskriiseistä ja ulottuen järjestelmälliseen rikollisuuteen, terrori-iskuihin ja luonnonkatastrofeihin. Tämä tilanne on muuttanut suhtautumista turvallisuuteen. Miljoonia valvontakameroita on otettu käyttöön, mikä on johtanut uusiin haasteisiin, koska kameroihin liittyvät järjestelmät ja toiminnot eivät pysty toimimaan yhdessä lukuisien uusien videokameroiden ja järjestelmien kanssa. Nykyajan valvontahuoneissa voidaan nähdä satojen tai tuhansien kameroiden tuottavan kuvaa ja samalla runsaasti tarpeetonta informaatiota turvallisuusvirkailijoiden katsottavaksi. Tämän tutkimuksen tarkoitus oli luoda uusi videovalvontajärjestelmä, jossa on automaattiset analyysimekanismit, jotka mahdollistavat turva-alan toimijoiden ja niiden operaattoreiden suoriutuvan informaatiotulvasta. Automaattisen videovalvontaprosessin avulla videovalvonta muokattiin proaktiiviseksi tietojärjestelmäksi. Teknologian kehitys ja kasvanut turvallisuusvaatimus osoittautuivat olevan merkittävä ajuri turvallisuusteknologian tutkimukselle, kuten tämä tutkimus oli. Tämä tutkimus hyödyttää yksittäisen ihmisen henkilökohtaista vapautta, elämää ja omaisuutta sekä yhteisöä estämällä rikoksia ja terroristihyökkäyksiä. Tässä tutkimuksessa suunnittelutiedettä sovellettiin varmistamaan tieteellinen kurinalaisuus, kun artefakteja luotiin ja arvioitiin. Tutkimuksen vaatimukset perustuivat läheiseen yhteistyöhön korkeatasoisten turva-alan viranomaisten kanssa, ja lisäksi aiempi tutkimus analysoitiin yksityiskohtaisesti. Luotu artefakti - ’älykäs videovalvontajärjestelmä’ - on hajautettu, skaalautuva ohjelmistoviitekehys, joka voi toimia perustana monenlaiselle huipputehokkaalle videovalvontajärjestelmälle alkaen toteutuksista, jotka keskittyvät saatavuuteen, ja päättyen joustaviin pilviperustaisiin toteutuksiin, jotka skaalautuvat useisiin sijainteihin ja kymmeniin tuhansiin kameroihin. Järjestelmän tukevaksi perustaksi luotiin hajautettu järjestelmäarkkitehtuuri, jota laajennettiin monisensorianalyysiprosessilla. Siten mahdollistettiin monista lähteistä peräisin olevan datan analysointi, videokuvan ja muiden sensorien datan yhdistäminen ja automaattinen kriittisten tapahtumien tunnistaminen. Lisäksi tässä työssä luotiin älykäs kännykkäsovellus, videovalvonnan paikallinen kontrolloija, joka ohjaa sovelluksen etäkäyttöä. Viimeksi tuotettiin langaton itsenäinen valvontajärjestelmä – uudenlainen älykäs kamerakonsepti – joka mahdollistaa ad hoc -tyyppisen ja mobiilin valvonnan. Luotujen artefaktien arvo voitiin todentaa arvioimalla ne kahdessa reaalimaailman ympäristössä: kansainvälinen lentokenttä, jonka laajamittaisessa toteutuksessa on korkeat turvavaatimukset, ja turvallisuuspalveluntuottaja, joka tarjoaa moninaisia videopohjaisia palveluja videovalvontakeskuksen avulla käyttäen tuhansia kameroita.

With the advancement of technology and the development of more sophisticated remote sensing sensor systems, the use of satellite imagery has opened up various fields of exploration and application. There has been an increased interest in analysis of multi-temporal satellite image in the past few years because of the wide variety of possible applications of in both short-term and long-term image analysis. The type of changes that might be of interest can range from short-term phenomena such as flood assessment and crop growth stage, to long-term phenomena such as urban fringe development. This thesis studies flood assessment and land cover mapping of satellite images, and proposes nature inspired algorithms that can be easily implemented in realistic scenarios. Disaster monitoring using space technology is one of the key areas of research with vast potential; particularly flood based disasters are more challenging. Every year floods occur in many regions of the world and cause great losses. In order to monitor and assess such situations, decision-makers need accurate near real-time knowledge of the field situation. How to provide actual information to decision-makers for effective flood monitoring and mitigation is an important task, from the point of view of public welfare. Over-estimation of the flooded area leads to over-compensation to people, while under-estimation results in production loss and negative impacts on the population. Hence it is essential to assess the flood damage accurately, both in qualitative and quantitative terms. In such situations, land cover maps play a very critical role. Updating land cover maps is a time consuming and costlier operation when it is performed using traditional or manual methods. Hence, there is a need to find solutions for such problem through automation. Design of automatic systems dedicated to satellite image processing which involves change detection to discriminate areas of land cover change between imaging dates. The system integrates the spectral and spatial information with the techniques of image registration and pattern classification using nature inspired techniques. In the literature, various works have been carried out for solving the problem of image registration and pattern classification using conventional methods. Many researchers have proved, for different situations, that nature inspired techniques are promising in comparison with that of conventional methods. The main advantage of nature inspired technique over any other conventional methods is its stochastic nature, which converges to optimal solution for any dynamic variation in a given satellite image. Results are given in such terms as to delineate change in multi-date imagery using change-versus-no-change information to guide multi-date data analysis. The main objective of this study is to analyze spatio-temporal satellite data to bring out significant changes in the land cover map through automated image processing methods. In this study, for satellite image analysis of flood assessment and land cover mapping, the study areas and images considered are: Multi-temporal MODerate-resolution Imaging Spectroradiometer (MODIS) image around Krishna river basin in Andhra Pradesh India; Linear Imaging Self Scanning Sensor III (LISS III)and Synthetic Aperture Radar(SAR)image around Kosi river basin in Bihar, India; Landsat7thematicmapperimage from the southern part of India; Quick-Bird image of the central Bangalore, India; Hyperion image around Meerut city, Uttar Pradesh, India; and Indian pines hyperspectral image. In order to develop a flood assessment framework for this study, a database was created from remotely sensed images (optical and/or Synthetic Aperture Radar data), covering a period of time. The nature inspired techniques are used to find solutions to problems of image registration and pattern classification of a multi-sensor and multi-temporal satellite image. Results obtained are used to localize and estimate accurately the flood extent and also to identify the type of the inundated area based on land cover mapping. The nature inspired techniques used for satellite image processing are Artificial Neural Network (ANN), Genetic Algorithm (GA),Particle Swarm Optimization (PSO), Firefly Algorithm(FA),Glowworm Swarm Optimization(GSO)and Artificial Immune System (AIS). From the obtained results, we evaluate the performance of the methods used for image registration and pattern classification to compare the accuracy of satellite image processing using nature inspired techniques. In summary, the main contributions of this thesis include (a) analysis of flood assessment and land cover mapping using satellite images and (b) efficient image registration and pattern classification using nature inspired algorithms, which are more popular than conventional optimization methods because of their simplicity, parallelism and convergence of the population towards the optimal solution in a given search space.

The Wadden Sea off the coast of the southern North Sea is the largest coherent area of tidal flats worldwide. As a highly productive ecosystem it is of global importance, e.g. as nursery for fish and as a feeding and resting area for 10 – 12 million migratory birds following the East Atlantic Flyway. The outstanding ecological significance of this region corresponds to a high level of protection by EU directives and national law and by inscription as UNESCO World Heritage Site, all of which requires regular monitoring and assessment. Apart from the ecological aspects, the Wadden Sea is also of great importance for coastal protection. To survey the extensive, often inaccessible tidal area, remote sensing is essential and while mainly airborne techniques have been carried out for decades, now high-resolution satellite-borne sensors open up new possibilities relevant for monitoring and long-term ecological research. Especially satellite synthetic aperture radar (SAR) sensors offer a high availability of acquisitions as they operate largely independently of daylight and weather. The aim of the studies presented here was to explore the use of data from the TerraSAR-X satellite to record geomorphological structures and habitats for Wadden Sea Monitoring. More than 100 TerraSAR-X acquisitions from 2009 to 2016 were analyzed to distinguish various and variable surface types continuously influenced by tidal dynamics in the main study area, the tidal flats near the island of Norderney. Visual image interpretation supported by extensive in-situ verification proved to be a suitable and unsophisticated approach which is unspecific enough to identify mussel beds, fields of shell-detritus, gully structures, mud fields, and intertidal bedforms in the upper flats of the East Frisian Islands. The method proved to be robust against changes in geometry of acquisition and environmental influences. Several time series of TerraSAR-X data enabled to follow inter-annual and seasonal dynamics as well as event effects (Adolph et al. 2018). The high-frequency TerraSAR-X data revealed novel evidence of an intertidal bedform shift in an easterly direction during the study period. To this aim, an unsupervised ISODATA classification of textural parameters was developed to vectorize and compare the bedforms positions in a GIS (Adolph et al. 2017a). The same intertidal bedform area was chosen as test-site for comparison of different remote sensing methods, namely airborne lidar, satellite-based radar (TerraSAR-X) and electro-optical sensors (RapidEye) (Adolph et al. 2017b). High-resolution SAR data offer a relevant component for Wadden Sea Monitoring and Research, as they provide reliable, regular data with a high repetition rate. In particular, habitats with noticeable surface roughness, specific structures and textures are well reflected. Geomorphic Structures and their dynamics can be observed indirectly via detection of residual water trapped within. A comprehensive concept for Wadden Sea Monitoring however, requires automatized classifications and an integrative, multi-sensor approach (SAR, LIDAR, multi-spectral data, drones) in which different and complementary information, coverage and resolutions (spatial and temporal) contribute to an overall picture. The studies were carried out as part of the joint research project “Scientific monitoring concepts for the German Bight” (WIMO), jointly funded by the Ministry of Environment, Energy and Climate Protection (NMU) and the Ministry of Science and Culture (NMWK) of the Federal State of Lower Saxony. The findings have been published in Geo-Marine Letters 37/2 (2017) and in Remote Sensing 10/7 (2018).

For developing the available hydrocarbon reserves and for exploring new reservoirs, deeper and more complex wells are drilled. Drilling such deeper and complex wells requires a constant monitoring and controlling of the well paths. Therefore, the bottom hole assembly, the lower section of the drill string above the drill bit, is equipped with numerous measuring sensors for collecting geological and directional data while drilling. The collected data have to be transmitted to the surface in real time. Prior to transmit the data measured downhole to the surface, they are processed and translated into a binary code. Accordingly, the data will be represented as a series of zeroes and ones. The most common method for data transmission in boreholes is the so called mud pulse telemetry which sends the information through the drilling mud inside the drill string by means of coded pressure pulses. There are two types of devices available for downhole pressure pulses generation. The first type is the (positive or negative) pressure pulser which transmits the data by quasi-static variations of the pressure level inside the drill string. The second type is the (rotating or oscillating) mud siren which transmits the data by generating continuous pressure waves at specific frequencies. The main disadvantage of the mud pulse telemetry is its low data transmission rate which is about 10 bps. This data rate is very low compared to the measured amount of raw data. Therefore, the efficiency of the mud pulse telemetry must be improved, so that the data could be transmitted at higher rates. The present research work presents different developed and tested concepts for increasing the efficiency and the data transmission rate of the mud pulse telemetry. Both, the transmitter and the receiver end, were taken into consideration by developing the new concepts. Different hardware and software tools were used for performing the present research work. The available flow loop test facility and the experimental prototypes of the mud siren and positive pulser were used. The test facility was extended in order to enable the investigation of the new concepts. The available 3D numerical model (ANSYS CFX) was modified and extended in order to study the new concepts. At the transmitter end, a novel concept for a hybrid mud pulse telemetry system was developed and successfully tested. Here, two different types of mud pulse telemetry could be used in a combination, such as a mud siren and a pressure pulser. The developed concept was registered at the German Patent and Trade Mark Office for a patent in 2018. Two concepts for a multi-frequency mud siren were developed for simultaneous generation of two frequencies. In the first approach, two sets of stator/rotor were installed in a row connection, while they were installed in a parallel connection in the second approach. The two concepts were registered at the German Patent and Trade Mark Office for patents in 2015. An experimental multi-frequency generator was built and used for testing of several new ideas, such as transmitting the data using several carrier frequencies at the same time, transmitting the data with different wave forms (sine, sawtooth, triangle and rectangle), or transmitting the data using the chirp modulation. The innovative design of the experimental multi-frequency generator was registered at the German Patent and Trade Mark Office for patents in 2016. At the receiver end, two different methods for processing and analyzing the received multi-frequency signals using the Wavelet and Fourier analysis were drafted and tested. A novel concept for the use of a multi-sensor receiver was developed and successfully tested. The use of a multi-sensor receiver could strongly improve the detection of the received signals.:Table of Contents Declaration ii Abstract iii Acknowledgements v Table of Contents vi List of Abbreviations x List of Symbols xii CHAPTER 1 Introduction 1 CHAPTER 2 Modern Drilling Technology and Low Data Transmission Rate as a Limitation 5 2.1 Introduction to the modern drilling technology 5 2.1.1 Directional drilling technology 5 2.1.2 Steering technology 6 2.1.3 Measuring technology 8 2.1.4 Technology of data transmission in boreholes 9 2.2 Low data transmission rate as a problem with respect to the whole drilling process 13 CHAPTER 3 Fundamentals of Communication Technology 16 3.1 Modulation techniques for data transmission in baseband 16 3.2 Modulation techniques for data transmission in passband 17 3.3 Multiple frequency and chirp spread spectrum modulation techniques 19 3.4 Digital signal processing 21 3.4.1 Fourier transformation 21 3.4.2 Continuous wavelet transformation 23 3.4.3 Filtering 24 CHAPTER 4 State of the Art for Mud Pulse Telemetry Systems 26 4.1 Historical development of mud pulse telemetry including latest improvements applied for increasing its data transmission rate 26 4.2 Available types of mud pulse telemetry devices 30 4.2.1 Negative pulser 31 4.2.2 Positive pulser 32 4.2.3 Mud siren 32 4.2.4 Oscillating shear valve 33 4.3 Limitations of data transmission via mud pulse telemetry 34 4.3.1 Effect of noise sources in the mud channel on the transmission signal 34 4.3.2 Effect of attenuation in the mud channel on the transmission signal 36 4.3.3 Effect of reflections and their interference with the main transmission signal 37 4.3.4 Pass and stop bands 38 4.4.5 Minimum transmission time slot 38 CHAPTER 5 Novel Concepts and Tools for Increased Data Transmission Rates of Mud Pulse Telemetry 40 5.1 Transmitter end 41 5.1.1 Hybrid mud pulse telemetry (HMPT) 41 5.1.2 Multi-frequency generator 43 5.2 Receiver end 45 5.2.1 Investigation of the Wavelet analysis suitability for multi-frequency signal detection 45 5.2.2 Flexible placement of multi-sensor receiver 46 CHAPTER 6 Laboratory Test Facility and Used Hard and Soft Tools 49 6.1 Laboratory test facility for hydraulic data transmission in boreholes 49 6.2 Experimental prototypes of the pressure pulsers and mud siren 53 6.3 3D numerical simulation model for the test facility and mud siren 55 6.4 MATLAB software 58 CHAPTER 7 Hybrid Mud Pulse Telemetry (HMPT) System 59 7.1 Combination of mud siren and negative pressure pulser 60 7.2 Combination of mud siren and positive pressure pulser 63 7.3 Evaluating the laboratory investigations of the hybrid mud pulse telemetry (HMPT) system 66 CHAPTER 8 Mathematical and Numerical Investigation of the Concept of the Multi-Frequency Mud Siren 68 8.1 Preliminary considerations for the concept of the multi-frequency mud siren 69 8.2 Mathematical model investigation of different approaches for the multi-frequency mud siren concept 71 8.2.1 Multi-frequency mud siren with stators and rotors in a row 72 8.2.2 Multi-frequency mud siren with parallel connection of stators and rotors 74 8.3 Numerical model investigation of multi-frequency mud siren with two sets of stator/rotor in a row 77 8.3.1 Numerical simulations for data transmission with a multi-frequency mud siren using two carrier frequencies 79 8.3.2 Evaluation of the simulation results 81 8.3.3 Increasing the transmission reach of the mud siren for deep drilling operations 83 CHAPTER 9 Laboratory Investigations of Multi-Carrier Hydraulic Data Transmission Using an Experimental Multi-Frequency Generator 85 9.1 Laboratory multi-carrier frequency transmission tests 87 9.2 Investigation of the Wavelet analysis suitability for the detection of multi-frequency signal transmitted in boreholes 95 9.3 Initial investigations of hydraulic data transmission using chirp modulation and different pressure wave forms 100 9.3.1 Data transmission using chirp modulation (Chirp Spread Spectrum, CSS) 100 9.3.2 Data transmission using different wave forms 101 CHAPTER 10 Investigation of the Use of a Multi-Sensor Receiver for Improving the Hydraulic Data Transmission in Boreholes 104 10.1 Numerical model investigation of the use of a multi-sensor receiver 104 10.1.1 Data transmission using single-input and multiple-output (SIMO) 104 10.1.2 Data transmission using multiple-input and multiple-output (MIMO) 107 10.2 Laboratory investigations of the use of a multi-sensor receiver 108 10.3 Evaluating the use of a multi-sensor receiver for improving the hydraulic data transmission in boreholes 112 CHAPTER 11 Conclusion and Outlook 116 11.1 Conclusion 116 11.2 Outlook 120 References 122 List of Figures 129 List of Tables 136 List of Publications 137 List of Patents 138 Appendix- Chapter 7 139 Appendix- Chapter 8 141 Appendix- Chapter 9 142 Appendix- Chapter 10 146