AUTOMATED SCOUR DETECTION ARRAYS USING BIO-INSPIRED MAGNETOSTRICTIVE FLOW SENSORS

Scour is the most common cause of catastrophic bridge failures worldwide. Approximately over 60% of bridge failures reported in the United States from 1966 to 2005 are scour related. To ensure the continued safe operation of bridges, monitoring bridge scour is of paramount importance. Most monitoring regimes that are widely used are based on expensive underwater instrumentation. This research focuses on scour detection using automated remote flow detection arrays based on bio-inspired flow sensors. This study employs an array of bio inspired flow sensors that are inexpensive and robust versions of buried-rod scour sensor arrays, coupled with low-power wireless sensor network utilizing civil-engineering domain wireless sensing units to detect scour around bridge piers and abutments. Sensors within the network that report dynamic flow signals are considered to be waterborne or located above the sediment and sensors reporting static signals are characterized as buried or as being located in sediment. The a priori information of sensor depth will help to establish the sediment level in real time. An automated data interrogation system collects data, processes the raw sensor data using in-network data interrogation methods, then and communicates the results to the on-site base station. The relative directness of this data interrogation adds to the robustness of the system. The main purpose of the scour detection system is to provide remote scour information to bridge owners in a format that is easy to comprehend as an aid in decision making. In this project, only processed results, not raw data, are transmitted to the user. The system under study utilizes a cellular data link to relay simplified data to the bridge owner to aid in decision making. A robust program of validation has been conducted to define the limits of the approach in the laboratory and the results of the laboratory validation experiments have been presented in this thesis. This thesis also illustrates the ongoing initial field installation of scour monitoring system on local bridges with some scour concern

SOIL MOISTURE DETECTION USING ELECTRICAL CAPACITANCE TOMOGRAPHY SENSOR (ECT)

Soil moisture content determination is a common process in agriculture which needs sensors with high accuracy and compatibility with the environment. The available methods are described with attention given to the gravimetry measurement, lysimeters, neutron scattering, gamma absorption, time domain reflectometer, electrical resistance blocks, and electrical tomography sensors. Current technique used is time domain reflectometer which is convenient and reliable. However, this device is quite expensive and cannot provide clear view of moisture percentage distribution in soil. The proposed sensor which is using tomographic method, can visualize data using permittivity distribution. By using an array of sensors that are positioned around the pipe, it is possible to visualize the percentage of soil moisture. Electrical Capacitance Tomography (ECT) is comparatively low cost and capable to make measurements rapidly. The mechanism used in ECT is non-invasive, inert, and non-ionizing. The report consists of an introduction, problem statement, objectives, literature review and methodology used to solve the problem. It further looks into the obtained results with consistent discussion

Towards CMOS integrated microfluidics using dielectrophoretic immobilization

Dielectrophoresis (DEP) is a nondestructive and noninvasive method which is favorable for point-of-care medical diagnostic tests. This technique exhibits prominent relevance in a wide range of medical applications wherein the miniaturized platform for manipulation (immobilization, separation or rotation), and detection of biological particles (cells or molecules) can be conducted. DEP can be performed using advanced planar technologies, such as complementary metal-oxide-semiconductor (CMOS) through interdigitated capacitive biosensors. The dielectrophoretically immobilization of micron and submicron size particles using interdigitated electrode (IDE) arrays is studied by finite element simulations. The CMOS compatible IDEs have been placed into the silicon microfluidic channel. A rigorous study of the DEP force actuation, the IDE’s geometrical structure, and the fluid dynamics are crucial for enabling the complete platform for CMOS integrated microfluidics and detection of micron and submicron-sized particle ranges. The design of the IDEs is performed by robust finite element analyses to avoid time-consuming and costly fabrication processes. To analyze the preliminary microfluidic test vehicle, simulations were first performed with non-biological particles. To produce DEP force, an AC field in the range of 1 to 5 V (peak-to-peak) is applied to the IDE. The impact of the effective external and internal properties, such as actuating DEP frequency and voltage, fluid flow velocity, and IDE’s geometrical parameters are investigated. The IDE based system will be used to immobilize and sense particles simultaneously while flowing through the microfluidic channel. The sensed particles will be detected using the capacitive sensing feature of the biosensor. The sensing and detecting of the particles are not in the scope of this paper and will be described in details elsewhere. However, to provide a complete overview of this system, the working principles of the sensor, the readout detection circuit, and the integration process of the silicon microfluidic channel are briefly discussed. © 2019 by the authors

Hardware architectures for compact microwave and millimeter wave cameras

Millimeter wave SAR imaging has shown promise as an inspection tool for human skin for characterizing burns and skin cancers. However, the current state-of-the-art in microwave camera technology is not yet suited for developing a millimeter wave camera for human skin inspection. Consequently, the objective of this dissertation has been to build the necessary foundation of research to achieve such a millimeter wave camera. First, frequency uncertainty in signals generated by a practical microwave source, which is prone to drift in output frequency, was studied to determine its effect on SAR-generated images. A direct relationship was found between the level of image distortions caused by frequency uncertainty and the product of frequency uncertainty and distance between the imaging measurement grid and sample under test. The second investigation involved the development of a millimeter wave imaging system that forms the basic building block for a millimeter wave camera. The imaging system, composed of two system-on-chip transmitters and receivers and an antipodal Vivaldi-style antenna, operated in the 58-64 GHz frequency range and employed the ω-k SAR algorithm. Imaging tests on burnt pigskin showed its potential for imaging and characterizing flaws in skin. The final investigation involved the development of a new microwave imaging methodology, named Chaotic Excitation Synthetic Aperture Radar (CESAR), for designing microwave and millimeter wave cameras at a fraction of the size and hardware complexity of previous systems. CESAR is based on transmitting and receiving from all antennas in a planar array simultaneously. A small microwave camera operating in the 23-25 GHz frequency was designed and fabricated based on CESAR. Imaging results with the camera showed it was capable of basic feature detection for various applications --Abstract, page iv

Evaluating the benefit of structural health monitoring for improving bridge resilience against scour. Deliverable D1 – Report on critical review of alternative techniques for bridge scour monitoring

No abstract available

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

A rapid-acquisition electrical time-domain reflectometer for analysis of time-variant impedance discontinuities

A distributed crack sensor has been developed for the measurement of cracks in concrete structures. The sensor is measured using a distributed measurement technique known as electrical time-domain reflectometry (ETDR). ETDR has traditionally been used to measure time-invariant (i.e. unchanging with time) impedance discontinuities, however applications of the sensor in structural failure analysis require measurement of time-variant (i.e. changing with time) impedance discontinuities at rates as high as 10 k measurements per second. ETDR is a suitable measurement technique for these applications since a time-domain reflectometer (TDR) acquisition can be performed in less than 100 µs. Employment of ETDR in these applications, however, requires a TDR that supports measurement rates as high as 10 k measurements per second. Commercial TDRs are not suitable for these applications since their measurement rates are typically less than 10 measurements per second. In order to satisfy the high measurement rates required for these applications, a rapid-acquisition TDR was developed that supports measurement rates as high as 10.1725 k measurements per second. The acquisition rate of the TDR was evaluated by modulating the voltage reflected from a short termination with a voltage variable attenuator. The TDR was able to monitor the reflected voltage at modulation frequencies as high as 1 kHz. The TDR was applied in the monitor of a crack sensor embedded in a bridge column during a shake-table experiment. The TDR was able to monitor the evolution of a crack which formed in the column during the experiment. The operation, design, evaluation, and application of the TDR are discussed herein --Abstract, page iii

Phase-sensitive optical time domain reflectometer assisted by first-order raman amplification for distributed vibration sensing over >100 km

In this study, the authors present an experimental and theoretical description of the use of first order Raman amplification to improve the performance of a Phase-sensitive optical time domain reflectometer (φ OTDR) when used for vibration measurements over very long distances. A special emphasis is given to the noise which is carefully characterized and minimized along the setup. A semiconductor optical amplifier and an optical switch are used to greatly decrease the intra-band coherent noise of the setup and balanced detection is used to minimize the effects of RIN transferred from the Raman pumps. The sensor was able to detect vibrations of up to 250 Hz (close to the limits set by the time of flight of light pulses) with a resolution of 10 m in a range of 125 km. To achieve the above performance, no post-processing was required in the φOTDR signal. The evolution of the φOTDR signal along the fiber is also shown to have a good agreement with the theoretical model.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

Grasp-sensitive surfaces

Grasping objects with our hands allows us to skillfully move and manipulate them. Hand-held tools further extend our capabilities by adapting precision, power, and shape of our hands to the task at hand. Some of these tools, such as mobile phones or computer mice, already incorporate information processing capabilities. Many other tools may be augmented with small, energy-efficient digital sensors and processors. This allows for graspable objects to learn about the user grasping them - and supporting the user's goals. For example, the way we grasp a mobile phone might indicate whether we want to take a photo or call a friend with it - and thus serve as a shortcut to that action. A power drill might sense whether the user is grasping it firmly enough and refuse to turn on if this is not the case. And a computer mouse could distinguish between intentional and unintentional movement and ignore the latter. This dissertation gives an overview of grasp sensing for human-computer interaction, focusing on technologies for building grasp-sensitive surfaces and challenges in designing grasp-sensitive user interfaces. It comprises three major contributions: a comprehensive review of existing research on human grasping and grasp sensing, a detailed description of three novel prototyping tools for grasp-sensitive surfaces, and a framework for analyzing and designing grasp interaction: For nearly a century, scientists have analyzed human grasping. My literature review gives an overview of definitions, classifications, and models of human grasping. A small number of studies have investigated grasping in everyday situations. They found a much greater diversity of grasps than described by existing taxonomies. This diversity makes it difficult to directly associate certain grasps with users' goals. In order to structure related work and own research, I formalize a generic workflow for grasp sensing. It comprises *capturing* of sensor values, *identifying* the associated grasp, and *interpreting* the meaning of the grasp. A comprehensive overview of related work shows that implementation of grasp-sensitive surfaces is still hard, researchers often are not aware of related work from other disciplines, and intuitive grasp interaction has not yet received much attention. In order to address the first issue, I developed three novel sensor technologies designed for grasp-sensitive surfaces. These mitigate one or more limitations of traditional sensing techniques: **HandSense** uses four strategically positioned capacitive sensors for detecting and classifying grasp patterns on mobile phones. The use of custom-built high-resolution sensors allows detecting proximity and avoids the need to cover the whole device surface with sensors. User tests showed a recognition rate of 81%, comparable to that of a system with 72 binary sensors. **FlyEye** uses optical fiber bundles connected to a camera for detecting touch and proximity on arbitrarily shaped surfaces. It allows rapid prototyping of touch- and grasp-sensitive objects and requires only very limited electronics knowledge. For FlyEye I developed a *relative calibration* algorithm that allows determining the locations of groups of sensors whose arrangement is not known. **TDRtouch** extends Time Domain Reflectometry (TDR), a technique traditionally used for inspecting cable faults, for touch and grasp sensing. TDRtouch is able to locate touches along a wire, allowing designers to rapidly prototype and implement modular, extremely thin, and flexible grasp-sensitive surfaces. I summarize how these technologies cater to different requirements and significantly expand the design space for grasp-sensitive objects. Furthermore, I discuss challenges for making sense of raw grasp information and categorize interactions. Traditional application scenarios for grasp sensing use only the grasp sensor's data, and only for mode-switching. I argue that data from grasp sensors is part of the general usage context and should be only used in combination with other context information. For analyzing and discussing the possible meanings of grasp types, I created the GRASP model. It describes five categories of influencing factors that determine how we grasp an object: *Goal* -- what we want to do with the object, *Relationship* -- what we know and feel about the object we want to grasp, *Anatomy* -- hand shape and learned movement patterns, *Setting* -- surrounding and environmental conditions, and *Properties* -- texture, shape, weight, and other intrinsics of the object I conclude the dissertation with a discussion of upcoming challenges in grasp sensing and grasp interaction, and provide suggestions for implementing robust and usable grasp interaction.Die Fähigkeit, Gegenstände mit unseren Händen zu greifen, erlaubt uns, diese vielfältig zu manipulieren. Werkzeuge erweitern unsere Fähigkeiten noch, indem sie Genauigkeit, Kraft und Form unserer Hände an die Aufgabe anpassen. Digitale Werkzeuge, beispielsweise Mobiltelefone oder Computermäuse, erlauben uns auch, die Fähigkeiten unseres Gehirns und unserer Sinnesorgane zu erweitern. Diese Geräte verfügen bereits über Sensoren und Recheneinheiten. Aber auch viele andere Werkzeuge und Objekte lassen sich mit winzigen, effizienten Sensoren und Recheneinheiten erweitern. Dies erlaubt greifbaren Objekten, mehr über den Benutzer zu erfahren, der sie greift - und ermöglicht es, ihn bei der Erreichung seines Ziels zu unterstützen. Zum Beispiel könnte die Art und Weise, in der wir ein Mobiltelefon halten, verraten, ob wir ein Foto aufnehmen oder einen Freund anrufen wollen - und damit als Shortcut für diese Aktionen dienen. Eine Bohrmaschine könnte erkennen, ob der Benutzer sie auch wirklich sicher hält und den Dienst verweigern, falls dem nicht so ist. Und eine Computermaus könnte zwischen absichtlichen und unabsichtlichen Mausbewegungen unterscheiden und letztere ignorieren. Diese Dissertation gibt einen Überblick über Grifferkennung (*grasp sensing*) für die Mensch-Maschine-Interaktion, mit einem Fokus auf Technologien zur Implementierung griffempfindlicher Oberflächen und auf Herausforderungen beim Design griffempfindlicher Benutzerschnittstellen. Sie umfasst drei primäre Beiträge zum wissenschaftlichen Forschungsstand: einen umfassenden Überblick über die bisherige Forschung zu menschlichem Greifen und Grifferkennung, eine detaillierte Beschreibung dreier neuer Prototyping-Werkzeuge für griffempfindliche Oberflächen und ein Framework für Analyse und Design von griff-basierter Interaktion (*grasp interaction*). Seit nahezu einem Jahrhundert erforschen Wissenschaftler menschliches Greifen. Mein Überblick über den Forschungsstand beschreibt Definitionen, Klassifikationen und Modelle menschlichen Greifens. In einigen wenigen Studien wurde bisher Greifen in alltäglichen Situationen untersucht. Diese fanden eine deutlich größere Diversität in den Griffmuster als in existierenden Taxonomien beschreibbar. Diese Diversität erschwert es, bestimmten Griffmustern eine Absicht des Benutzers zuzuordnen. Um verwandte Arbeiten und eigene Forschungsergebnisse zu strukturieren, formalisiere ich einen allgemeinen Ablauf der Grifferkennung. Dieser besteht aus dem *Erfassen* von Sensorwerten, der *Identifizierung* der damit verknüpften Griffe und der *Interpretation* der Bedeutung des Griffes. In einem umfassenden Überblick über verwandte Arbeiten zeige ich, dass die Implementierung von griffempfindlichen Oberflächen immer noch ein herausforderndes Problem ist, dass Forscher regelmäßig keine Ahnung von verwandten Arbeiten in benachbarten Forschungsfeldern haben, und dass intuitive Griffinteraktion bislang wenig Aufmerksamkeit erhalten hat. Um das erstgenannte Problem zu lösen, habe ich drei neuartige Sensortechniken für griffempfindliche Oberflächen entwickelt. Diese mindern jeweils eine oder mehrere Schwächen traditioneller Sensortechniken: **HandSense** verwendet vier strategisch positionierte kapazitive Sensoren um Griffmuster zu erkennen. Durch die Verwendung von selbst entwickelten, hochauflösenden Sensoren ist es möglich, schon die Annäherung an das Objekt zu erkennen. Außerdem muss nicht die komplette Oberfläche des Objekts mit Sensoren bedeckt werden. Benutzertests ergaben eine Erkennungsrate, die vergleichbar mit einem System mit 72 binären Sensoren ist. **FlyEye** verwendet Lichtwellenleiterbündel, die an eine Kamera angeschlossen werden, um Annäherung und Berührung auf beliebig geformten Oberflächen zu erkennen. Es ermöglicht auch Designern mit begrenzter Elektronikerfahrung das Rapid Prototyping von berührungs- und griffempfindlichen Objekten. Für FlyEye entwickelte ich einen *relative-calibration*-Algorithmus, der verwendet werden kann um Gruppen von Sensoren, deren Anordnung unbekannt ist, semi-automatisch anzuordnen. **TDRtouch** erweitert Time Domain Reflectometry (TDR), eine Technik die üblicherweise zur Analyse von Kabelbeschädigungen eingesetzt wird. TDRtouch erlaubt es, Berührungen entlang eines Drahtes zu lokalisieren. Dies ermöglicht es, schnell modulare, extrem dünne und flexible griffempfindliche Oberflächen zu entwickeln. Ich beschreibe, wie diese Techniken verschiedene Anforderungen erfüllen und den *design space* für griffempfindliche Objekte deutlich erweitern. Desweiteren bespreche ich die Herausforderungen beim Verstehen von Griffinformationen und stelle eine Einteilung von Interaktionsmöglichkeiten vor. Bisherige Anwendungsbeispiele für die Grifferkennung nutzen nur Daten der Griffsensoren und beschränken sich auf Moduswechsel. Ich argumentiere, dass diese Sensordaten Teil des allgemeinen Benutzungskontexts sind und nur in Kombination mit anderer Kontextinformation verwendet werden sollten. Um die möglichen Bedeutungen von Griffarten analysieren und diskutieren zu können, entwickelte ich das GRASP-Modell. Dieses beschreibt fünf Kategorien von Einflussfaktoren, die bestimmen wie wir ein Objekt greifen: *Goal* -- das Ziel, das wir mit dem Griff erreichen wollen, *Relationship* -- das Verhältnis zum Objekt, *Anatomy* -- Handform und Bewegungsmuster, *Setting* -- Umgebungsfaktoren und *Properties* -- Eigenschaften des Objekts, wie Oberflächenbeschaffenheit, Form oder Gewicht. Ich schließe mit einer Besprechung neuer Herausforderungen bei der Grifferkennung und Griffinteraktion und mache Vorschläge zur Entwicklung von zuverlässiger und benutzbarer Griffinteraktion