Un microgyromètre 3 axes permet avec une structure unique de mesurer la vitesse de rotation d’un objet autour des trois axes de l’espace. Les micro-gyromètres 3 axes existants sont peu nombreux et typiquement résonants, fabriqués en technologie silicium par micro-usinage de surface, à transductions électro-statiques, et conçus pour des applications de fort volume ou la taille et le coût sont des critères majeurs. Dans cette thèse nous avons étudié la transduction et le procédé de fabrication d’un gyromètre résonant 3 axes à actionnement et détection piézoélectriques, fabriqué par micro-usinage de volume dans du GaAs semi-isolant, et dont les performances sont potentiellement très supérieures à l’état de l’art tout en conservant une taille et un coût limité. Ce microgyromètre nécessite une transduction piézoélectrique 3D et un routage des connexions électriques qui ont été modélisés et optimisés pour réduire les couplages parasites entre les modes de détection et le mode pilote. Un procédé original de fabrication collective du microgyromètre a été développé, modélisé et caractérisé. Ce procédé utilise notamment une gravure ionique réactive très profonde et traversante du GaAs dans un plasma BCl3-Cl2. Il est démontré pour la première fois qu’une gravure anisotrope traversante de tranchées de 450 μm de profondeur peut être réalisée grâce à une optimisation des paramètres de gravure et à l’utilisation d’un masque en résine. Un procédé original de dépôt et de délimitation d’électrodes Au/Cr sur les flancs verticaux d’une structure gravée par évaporation sous incidence oblique avec rotation du substrat et à travers un masque pochoir en film sec photosensible a aussi été étudié en détail. Une caractérisation fine de la structure cristalline, de la résistivité et des contraintes mécaniques avant, pendant et après recuit des couches Au/Cr poreuses évaporées sous incidence oblique a été menée. Des micro-gyromètres complets avec tout le système de transduction 3D ont été réalisés. Des premières caractérisations par vibrométrie optique hors du plan et dans le plan des gyromètres réalisés démontrent des résultats encourageants. Enfin, différentes voies d’amélioration de la conception et du procédé sont proposées. / A 3 axis gyroscope allows, with a single mechanical structure, the measurement of rotation rates of an object around 3 perpendicular spatial axes. Existing 3 axis microgyroscopes are scarce and typically resonating, made in silicon technology by surface micromachining, use electrostatic transductions and are designed for high volume applications where size and cost are major characteristics. In this thesis we investigated the transduction and fabrication process of a resonating 3 axis microgyroscope having piezoelectric actuation and detection, made in semi-insulating GaAs by bulk micromachining, and with performances potentially much higher than state of the art while limiting the size and cost. This microgyroscope requires a 3D piezoelectric transduction and circuitry which were modelled and optimized to reduce cross-talks effects. An original batch fabrication process was developed, modelled and characterized. This process notably makes use of very deep through wafer reactive ion etching of GaAs in a BCl3-Cl2 plasma. It is demonstrated for the first time that a through wafer highly anisotropic etching of 450 μm deep trenches can be realized owing to etching parameters optimization and the use of a resist masking layer. An original deposition and patterning process of Au/Cr electrodes on the vertical walls of an etched structure by oblique evaporation on rotated substrate through a dry film shadow mask has also been investigated in details. A fine characterization of the crystallographic structure, resistivity and mechanical stress before, during and after annealing of Au/Cr films evaporated under oblique incidence has been performed. Full microgyroscopes with the whole 3D tranduction system were realized. Preliminary characterizations of realized gyroscopes by out-of-plane and in-plane optical vibrometry demonstrated promising results. Finally, different ways to improve the design and fabrication process are proposed.

We consider two models which lead to the prediction of a weight change of gyroscopes depending on the rate of rotation: mass-energy equivalence and Weber's force for gravitation. We calculate the order of magnitude of this effect in both models and show that Weber's model predicts a weight change depending on the spin axis orientation resembling close similarities to observed Earth flyby anomalies. however, our predicted effect is much smaller than the observed effect, which could explain why flyby anomalies were not detected anymore in recent spracecraft trajectories.

Fabrication of hemispherical structures for application in hemispherical resonator gyro-scopes (HRG) is an integral part of modern sensing systems, especially in relation to space navigation. First, it is important for these structures to be as symmetric as possible in order to accurately track both in-plane and out-of-plane acceleration that occurs in fast moving satellites and space crafts. Next, they need to be larger for easier application in current mm scale systems and to maintain a lower noise floor and high quality factor. The work in this paper introduces a methodology for the analyzation of the micromachining process for larger symmetric hemispherical shell resonators (HSR). This is in order to increase their size while maintaining symmetry through isotropic etching using HNA and the pop-up ring mask design. The implementation of the pop-up ring mask allows for symmetric etching of<111> silicon and larger MEMS structures at a low cost while giving more design control to the user in comparison to alternative designs such as the pinhole. The investigation of how hemispheric structures are affected based on the adjustment of the pop-up ring design serves to both create larger symmetric HSRs and create a better model for future designs and applications. During this investigation, a range of design tests were done to create the hemispherical resonator molds in order to gauge the effectiveness of the pop-up ring changes. These results were then used to develop a method for achieving the desired larger symmetric HSRs.

Because of climate change renewable energy field is becoming more and more relevant. Renewable energy can be gained from the sun, from wind but also from ocean waves. To support the research and development in this field reliably measured wave data is collected through a measurement system that shows the exact position of a buoy. The project consists of the design, development and implementation of such a measurement system. It is divided into three subtasks: The power supply based on a solar panel and a battery The measurement part including a sensor and its implementation to the circuit The deployment of a module for data transmission and communication between the measurement system and the on-shore computer The power supply is capable to power the system and to maintain the battery voltage. A suggested stronger power supply will be used in a later state of the project to increase the reliability. The results of the charging test are good. The power supply system is connected to the circuit and the battery charges. For the measurement system the altitude and heading reference system (AHRS) “Ellipse2-D” from SBG Systems has been selected and connected to an Arduino Mega 2560. The AHRS provides raw data and values processed by a Kalman filter. Both data sets are picked up by the microcontroller. The raw data is backed up on a secure digital memory card (SD-card). For the evaluation of the sensor unit, static and dynamic tests are applied to the sensor. In the end it can be seen that the measurement series are aligned with each other. The information from the Kalman model of the AHRS is transmitted with the Adeunis ARF868 ultra-long range (ULR) modem. The transmitter is linked to the Arduino Mega 2560 and the sensor data is transmitted to the receiver. The first field test already shows the reliability of the system for a range of about 3 km. The results of the tests are as expected and in the future this system will be implemented on a buoy.

Klassificering av dagliga aktiviteter (ADL) kan användas i system som bevakar människors aktiviteter i olika syften. T.ex., i nödsituationssystem. Med machine learning och bärbara sensor som samlar in data kan ADL klassificeras med hög noggrannhet. I detta arbete, ett proof-of-concept system med tre olika machine learning algoritmer utvärderas och jämförs mellan tre olika dataset, ett som är allmänt tillgängligt på (Ugulino, et al., 2012), och två som har samlats in i rapporten med hjälp av en android enhet. Algoritmerna som har använts är: Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Neural Network. Sensorerna som har använts är en accelerometer och ett gyroskop. Resultatet visar hur ett konceptuellt system kan byggas i Azure Machine Learning Studio, och hur tre olika algoritmer presterar vid klassificering av tre olika dataset. En algoritm visar högre precision vid klassning av Ugolino’s dataset, jämfört med machine learning modellen som ursprungligen används i rapporten. / Classifying Activities of daily life (ADL) can be used in a system that monitor people’s activities for different purposes. For example, in emergency systems. Machine learning is a way to classify ADL with high accuracy, using wearable sensors as an input. In this paper, a proof-of-concept system consisting of three different machine learning algorithms is evaluated and compared between tree different datasets, one publicly available at (Ugulino, et al., 2012), and two collected in this paper using an android device’s accelerometer and gyroscope sensor. The algorithms are: Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Neural Network. The two sensors used are an accelerometer and a gyroscope. The result shows how a system can be implemented using Azure Machine Learning Studio, and how three different algorithms performs when classifying three different datasets. One algorithm achieves a higher accuracy compared to the machine learning model initially used with the Ugolino data set.

This dissertation presents a novel approach to develop a system for real-time tracking of the position and orientation of the human hand in three-dimensional space, using MEMS inertial measurement units (IMUs) and infrared cameras. This research focuses on the study and implementation of an algorithm to correct the gyroscope drift, which is a major problem in orientation tracking using commercial-grade IMUs. An algorithm to improve the orientation estimation is proposed. It consists of: 1.) Prediction of the bias offset error while the sensor is static, 2.) Estimation of a quaternion orientation from the unbiased angular velocity, 3.) Correction of the orientation quaternion utilizing the gravity vector and the magnetic North vector, and 4.) Adaptive quaternion interpolation, which determines the final quaternion estimate based upon the current conditions of the sensor. The results verified that the implementation of the orientation correction algorithm using the gravity vector and the magnetic North vector is able to reduce the amount of drift in orientation tracking and is compatible with position tracking using infrared cameras for real-time human hand motion tracking. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively. The proposed orientation correction algorithm represents a contribution to the emerging approaches to obtain reliable orientation estimates from MEMS IMUs. The development of a hand motion tracking system using IMUs and infrared cameras in this dissertation enables future improvements in natural human-computer interactions within a 3D virtual environment.

The whiteboard is a tool being used in several different facilities around the world. However, erasing the ink off the board can be unnecessarily time consuming and distracting. The purpose of the project was therefore to create a demonstrator to automate the process with a few restrictions. The demonstrator was required to be light enough and small enough to be handheld. In addition, the demonstrator needed to function and work properly on a magnetic surface. To conceptualize the purpose a demonstrator was built with an iterative process and thereafter programmed to get a systematic movement. It utilized continuous servo motors, IR-sensors and a 3-axis gyroscope and an accelerometer in combination with 3D-printed components. The final prototype turned out to function properly but could be improved as its erasing properties are not working as one could expect from a finished prototype. / Whiteboard är ett verktyg som används i flera olika fastigheter runt om i världen. Att ta bort bläcket från whiteboarden kan dock vara onödigt tidskrävande och distraherande. Syftet med projektet var därför att automatisera processen med några begränsningar. Två begränsningar var att prototypen skulle väga lite och vara tillräckligt liten för att vara handhållen. En annan begränsning var att den skulle fungera korrekt på en magnetisk yta. För att konceptualisera syftet byggdes en prototyp med en iterativ process och därefter programmerades den för att få en systematisk rörelse. Den utnyttjade kontinuerliga servomotorer, IR-sensorer och ett 3-axligt gyroskop i kombination med 3D-printade komponenter. Den slutgiltiga prototypen visade sig fungera korrekt men kan förbättras eftersom dess raderingsegenskaper inte fungerar som man kunde ha förväntat sig från en färdig prototyp.

The construction industry has lagged behind other industries in productivity growth rate. Earth-moving sites, and other practices where dumpers are used, are no exceptions. Such projects lack convenient and accurate solutions for utilization mapping and tracking of mass flows, which both currently and mainly rely on manual activity tracking. This study intends to provide insights of how autonomous systems for activity tracking of dumpers can contribute to the productivity at earthmoving sites. Autonomous systems available on the market are not implementable to dumper fleets of various manufacturers and model year, whereas this study examines the possibilities of using activity recognition by machine learning for a system based on smartphones mounted in the driver’s cabin. Three machine learning algorithms (naive Bayes, random forest and feed-forward backpropagation neural network) are trained and tested on data collected by smartphone sensors. Conclusions are that machine learning models, and particularly the neural network and random forest algorithms, trained on data from a standard smartphone, are able to estimate a dumper’s activities at a high degree of certainty. Finally, a market analysis is presented, identifying the innovation opportunity for a potential end-product as high. / Byggnadsbranschen har halkat efter andra branscher i produktivitetsökning. Markarbetesprojekt och andra arbeten där dumprar används är inga undantag. Sådana projekt saknar användarvänliga system för att kartlägga maskinutnyttjande och massaflöde. Nuvarande lösningar bygger framförallt på manuellt arbete. Denna studie syftar skapa kännedom kring hur autonoma system för aktivitetsspårning av dumprar kan öka produktiviteten på markarbetesprojekt. Befintliga autonoma lösningar är inte implementerbara på maskinparker med olika fabrikat eller äldre årsmodeller. Denna studie undersöker möjligheten att applicera aktivitetsigenkänning genom maskininlärning baserad på smartphones placerade i förarhytten för en sådan autonom lösning. Tre maskininlärningsalgoritmer (naive Bayes, random forest och backpropagation neuralt nätverk) tränas och testas på data från sensorer tillgängliga i vanliga smartphones. Studiens slutsatser är att maskininlärningsmodeller, i synnerhet neuralt nätverk och random forest-algoritmerna, tränade på data från vanliga smartphones, till hög grad kan känna igen en dumpers aktiviteter. Avslutningsvis presenteras en marknadsanalys som bedömer innovationsmöjligheten för en eventuell slutprodukt som hög.

Research in Unmanned Air Vehicles (UAV's) continues to push the limitations of size and weight. As technical advances have made UAV's smaller and less expensive, they have become more flexible and extensive in their roles. To continue using smaller and less expensive components while retaining and even enhancing performance requires more sophisticated processing of sensor data in order for the UAV to accurately determine its state and thereby allow the use of feedback in controlling the aircraft automatically. This work presents a three-stage state-estimation scheme for the class of UAV's know as Miniature Air Vehicles (MAV's). The first stage estimates pitch and roll, the second stage estimates heading, and the third stage produces a position estimate and an estimate of wind speed and direction. All three stages make use of the extended Kalman filter, a framework for using a system dynamic model to predict future states and to update the predictions using weighted sensor measurements as they become available, where the weighting is based on the relative uncertainty of the dynamic model and the sensors. Using the three-stage state esti-mation scheme, significant improvements in the estimation of pitch, roll and heading have been achieved in simulation and flight testing. Performance of the navigation (position and wind) stage is comparable to an existing baseline algorithms for position and wind, and shows additional promise for use in dead reckoning when GPS updates become unavailable.

For over a decade, Brigham Young University's Microwave Earth Remote Sensing (MERS) team has been developing SAR systems and SAR processing algorithms. In order to create the most accurate image reconstruction algorithms, detailed aircraft motion data is essential. In 2008, the MERS team purchased a costly inertial measurement unit (IMU) coupled with a high precision global positioning system (GPS) from NovAtel, Inc. In order to lower the cost of obtaining detailed motion measurements, the MERS group decided to build a system that mimics the capability the NovAtel system as closely as possible for a much lower cost. As a first step, the same sensors and a simplified set of flight dynamics are used. This thesis presents a standalone motion sensor recording system (MOTRON), and outlines a method of utilizing the square-root Unscented Kalman filter (SR-UKF) to estimate aircraft flight dynamics, based on recorded flight data, as an alternative to the extended Kalman filter. While the results of the SR-UKF are not as precise as the NovAtel results, they approach the accuracy of the NovAtel system despite the simplified dynamics model.