This project examines the level of accuracy that can be achieved in precision positioning by using built-in sensors in an Android smartphone. The project is focused in estimating the position of the phone inside a building where the GPS signal is bad or unavailable. The approach is sensor-fusion: by using data from the device’s different sensors, such as accelerometer, gyroscope and wireless adapter, the position is determined. The results show that the technique is promising for future handheld indoor navigation systems that can be used in malls, museums, large office buildings, hospitals, etc.

GPS/INS (Global Positioning System / Inertial Navigation System) brings possibility of relaxing the demand for aerial triangulation in stereo model construction and rectification. In this thesis a differential rectification algorithm is programmed with Matlab software for aerial frame camera images. This program is tested using exterior orientation parameters obtained by GPS/INS and images are ortho-rectified. Ground Control Points (GCP) are measured in the orthorectified images and compared with other rectification methods according to RMSE and mean error. Besides, direct georeferencing accuracy is investigated by using GPS/INS data. Therefore, stereo models and ortho-images are constructed by using exterior orientation parameters obtained with both aerial triangulation and GPS/INS integration. GCPs are measured in both stereo models and ortho-images, compared with respect to their RMSE and mean error. In order to determine Digital Elevation Model (DEM) effect in ortho-rectification, different DEM data are used and the results are compared.

An Inertial Navigation System (INS) can individually produce the navigation data, i.e.position, velocity, of the aircraft without any help or aid. However, a large number of errors are ntroduced by sensors causing to an unacceptable drift in the output. Because of this reason, external aids are used to correct INS. Using these aids an integrated navigation structure is developed. In an integrated navigation system, INS output is used to alculate current navigation states / aid is used to supply external measurements and dierent algorithms are used to provide the most probable corrections to the state estimate using all data. One of the integrated navigation approaches is Terrain Aided Navigation (TAN). Terrain Aided Navigation is a technique to estimate the position of a moving vehicle by comparing the measured terrain profile under the vehicle to a stored map, DTED. This thesis describes the theoretical aspects implementation of a simulation environment, simulations of the implemented Kalman Filtering TAN algorithms with developed INS model. In order to perform the study, first a thorough survey of the literature on TAN navigation algorithms is performed. Then, we have developed a dynamics simulation environment. A flight profile generator is designed. Since, the main issue of this work is to correct INS, an Strapdown INS model developed using Matlab INS Toolbox. Therefore, to model a Strapdown INS, mathematical equations of INS system are derived and they are linearized to form linear error model. In addition, a radar altimeter simulator is also developed that provides measurement to the error dynamics. Then, a Kalman filter structure is designed and implemented using Matlab. The simulations are done with dierent linearization approaches using Kalman filter structure. Finally, the performance of the implemented algorithms are evaluated.

In order to overcome the various disadvantages of standalone INS and GPS, these systems are integrated using nonlinear estimation techniques and benefits of the two complementary systems are obtained at the same time. The standard and most widely used estimation algorithm in the INS/GPS integrated systems is Extended Kalman Filter (EKF). Linearization step involved in the EKF algorithm can lead to second order errors in the mean and covariance of the state estimate. Another nonlinear estimator, Unscented Kalman Filter (UKF) approaches this problem by carefully selecting deterministic sigma points from the Gaussian distribution and propagating these points through the nonlinear function itself leading third order errors for any nonlinearity. Scaled Unscented Transformation (SUT) is one of the sigma point selection methods which gives the opportunity to adjust the spread of sigma points and control the higher order errors by some design parameters. Determination of these parameters is problem specific. In this thesis, effects of the SUT parameters on integrated navigation solution are investigated and an &ldquo / Adaptive UKF&rdquo / is designed for a tightly-coupled INS/GPS integrated system. Besides adapting process and v measurement noises, SUT parameters are adaptively tuned. A realistic fighter flight trajectory is used to simulate IMU and GPS data within Monte Carlo analysis. Results of the proposed method are compared with standard EKF and UKF integration. It is observed that the adaptive scheme used in the sigma point selection improves the performance of the integrated navigation system especially at the end of GPS outage periods.

<p>At Flight and Systems test Saab AB, a post-processing software is used to process GPS data. A new software by the name GrafNav has been purchased and the purpose of this master thesis therefore became, partly to make a judgment regarding GrafNav’s ability to estimate position, velocity and accuracy, partly to if needed improve the estimate and finally find one or several methods to estimate the position and velocity accuracy. </p><p>The judgment of GrafNav was performed partly by a comparison to the former post-processing software (PNAV) and partly by a comparison to the airplane’s inertial navigation system (INS). The experiments showed that GrafNav’s ability to estimate the position is comparable with PNAV:s, but its capacity to estimate the velocity is considerably worse. The velocity estimate even showed a more noisy behavior than the original velocity from the receiver. More effort is needed to judge GrafNav’s ability to estimate the accuracy thru its quality signals. </p><p>A few trials were made to improve the velocity estimate thru Kalman filtering (Rauch-Tung-Striebel smoothing). The filtering was first made using only the position data from GrafNav as measurements and afterwards both position and velocity data from GrafNav was used. The outcome of the Kalman filtering showed that the best result is obtained when solely position data is used and that the estimate in general is comparable with PNAV:s estimate, but considerable big deviations is obtained in conjunction to interruptions in position data. More over, is more effort needed using both position and velocity data when performing the smoothing and also replacing the stationary Kalman filter with an adaptive filter. </p><p>Finally a method was brought out to estimate the position precision and a method to estimate the velocity accuracy. Both methods use the INS velocity to perform an estimation.</p>

Methodologies are proposed for combining carrier-phase differential GPS (CDGPS), visual simultaneous localization and mapping (SLAM), and inertial measurements to obtain precise and globally-referenced position and attitude estimates of a rigid structure connecting a GPS receiver, a camera, and an inertial measurement unit (IMU). As part of developing these methodologies, observability of globally-referenced attitude based solely on GPS-based position estimates and visual feature measurements is proven. Determination of attitude in this manner eliminates the need for attitude estimates based on magnetometer and accelerometer measurements, which are notoriously susceptible to magnetic disturbances. This combination of navigation techniques, if coupled properly, is capable of attaining centimeter-level or better absolute positioning and degree-level or better absolute attitude accuracies in any space, both indoors and out. Such a navigation system is ideally suited for application to augmented reality (AR), which often employs a GPS receiver, a camera, and an IMU, and would result in tight registration of virtual elements to the real world. A prototype AR system is presented that represents a first step towards coupling CDGPS, visual SLAM, and inertial navigation. While this prototype AR system does not couple CDGPS and visual SLAM tightly enough to obtain some of the benefit of the proposed methodologies, the system is capable of demonstrating an upper bound on the precision that such a combination of navigation techniques could attain. Test results for the prototype AR system are presented for a dynamic scenario that demonstrate sub-centimeter-level positioning precision and sub-degree-level attitude precision. This level of precision would enable convincing augmented visuals. / text

Vehicle navigation systems incorporate on-board sensors/signal receivers and provide necessary positioning and guidance information for land, marine, airborne and space vehicles. Among different navigation solutions, the Global Positioning System (GPS) and an Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characters in many aspects, a GPS/INS integrated navigation system has been a hot research topic in recent decades. Both advantages and disadvantages of each individual system and their combination are analysed in this thesis. The Micro Electrical Mechanical Sensors (MEMS) successfully solved the problems of price, size and weight with traditional INS, and hence are widely applied in GPS/INS integrated systems. The main problem of MEMS is the large sensor errors, which rapidly degrade the navigation performance in an exponential speed. By means of different methods, such as autoregressive model, Gauss-Markov process, Power Spectral Density and Allan Variance, we analyse the stochastic errors within the MEMS sensors. The test results show that different methods give similar estimates of stochastic error sources. An equivalent model of coloured noise components (random walk, bias instability and ramp noise) is given. Three levels of GPS/IMU integration structures, i.e. loose, tight and ultra-tight GPS/IMU navigation, are introduced with a brief analysis of each character. The loose integration principles are presented with detailed equations as well as the INS navigation principles. The Extended Kalman Filter (EKF) is introduced as the data fusion algorithm, which is the core of the whole navigation system. Based on the system model, we show the propagation of position standard errors with the tight integration structure under different scenarios. Even less than 4 observable GNSS satellites can contribute to the integrated system, especially for the orientation errors. A real test with loose integration is carried out, and the EKF performance is analysed in detail. Since the GPS receivers are normally working with a digital map, the map matching principle and its link-choosing problem are briefly introduced. This problem is proposed to be solved by the lane detection from real-time images. The procedures for the lane detection based on image processing are presented. The test on high ways, city streets and pathways are successfully carried out, and analyses with possible solutions are given for some special failure situations. To solve the large error drift of the IMU, we propose to support the IMU orientation with camera motion estimation from image pairs. First the estimation theory and computer vision principles are briefly introduced. Then both point and line matches algorithms are given. Finally the L1-norm estimator with balanced adjustment is proposed to deal with possible mismatches (outliers). Tests and comparisons with the RANSAC algorithm are also presented. For the latest trend of MEMS chip sensors, their industry and market are introduced. To evaluate the MEMS navigation performance, we augment the EKF with an equivalent coloured noise model, and the basic observability analysis is given. A realistic simulated navigation test is carried out with single and multiple MEMS sensors, and a sensor array of 5-10 sensors are recommended according to the test results and analysis. Finally some suggestions for future research are proposed. / <p>QC 20131016</p>

In recent years, Component models have become common for desktop and server-side applications. But it has not obtained such importance in case of embedded real-time systems.  Therefore, there has been a lot of research undergoing for introducing such component models for embedded real-time systems.  This thesis work proposes an alternative approach for doing this, by the generation of proxies.   The idea is to provide an extension to an existing binary component and modify it to adapt to the targeted real-time operating system. Rather than modifying the existing component, a new component is generated, which is called as the proxy component.  This newly generated proxy component provides the same method implementation as the original component and also provides some additional services.  These services enable these components to meet the needs of targeted embedded real-time systems.  In order to achieve this, a Visual Studio 2008 add-in has been created. This add-in is capable of inspecting an existing Smart Device Component and visualizes the Classes, Interfaces and Methods in the original component in its UI.  In addition to this, the add-in also shows the available services to be included in the proxy component.  The UI of the add-in is designed in such a way that, the user is able to select the services, which should be included in the proxy component.  Based on the user’s selection, the add-in generates the proxy component with the additional services.

No description available.

Die Mitglieder der Insulinrezeptorfamilie spielen eine wichtige Rolle in der Funktion von Zellen. Die Hauptangriffspunkte liegen hierbei im Bereich der Glucosehomöostase, sowie weiterhin bei der Proteinbiosynthese, dem Fettstoffwechsel, Elektrolyttransport, Zellwachstum, Differenzierung und Apoptose. Die beiden Hauptvertreter der Insulinrezeptorfamilie, Insulinrezeptor (InsR) und Insulin-like Growth Factor 1 Rezeptor (IGF1R) sind am besten untersucht und viele ihrer Funktionen aufgeklärt. Ein weiteres Mitglied der Familie stellt hier noch eine Ausnahme dar, der Insulin receptor-related receptor (IRR). Obwohl er hohe Sequenzhomologien zum InsR und IGF1R aufweist und in Untersuchungen gezeigt werden konnte, dass er die Möglichkeit besitzt wichtige Punkte der Insulinsignalkaskade zu aktivieren, bleibt seine Funktion unverstanden. Auch ein Ligand wurde bisher nicht identifiziert. Untersuchungen an aktivierbaren IRR-Rezeptorkonstrukten zeigten einen möglichen Einfluss auf Differenzierung, Proliferation und Apoptose von Zellen. Der IRR wird gewebespezifisch exprimiert und v.a. in neuronalen Zellen und β-Zellen des Pankreas nachgewiesen. In der vorliegenden Doktorarbeit wurde der mögliche Einfluss eines stimulierbaren IRR-Rezeptorkonstruktes auf die Proliferation und Apoptose von β-Zellen untersucht. Weiterhin wurde ermittelt, in wie fern dabei zwei Hauptsignalwege der Insulinrezeptorkaskade, der AKT/PKB und der MAPK/ERK Signalweg, aktiviert werden. Hierzu wurde ein bereits beschriebenes Colony stimulating factor 1 receptor/Insulin receptor-related receptor- (CSF1R/IRR-) Konstrukt (Dandekar et al.1998) in INS-1E Zellen überexprimiert und anschließend mit Macrophage colony stimulating factor (MCSF), dem Liganden des CSF1R, stimuliert. Dieses CSF1R/IRR-Konstrukt besteht aus dem extrazellulären Teil des CSF1R, der Transmembrandomäne des InsR und dem intrazellulären Teil des IRR. Es zeigte sich, dass das Konstrukt zu einer transienten Aktivierung des ERK-Signalweges fähig ist, ein Einfluss auf den AKT-Weg allerdings ausblieb. Ferner konnte kein Einfluss auf Proliferation oder Apoptose gezeigt werden. Dies lässt vermuten, dass die mögliche Funktion über alternative Wege verwirklicht wird, wie z.Bsp. eine Hybridrezeptorbildung zwischen IRR und IGF1R. Die angefertigte Arbeit liefert somit einen weiteren Beitrag zum Verständnis der Rolle des IRR in der Zelle.