Auditory brainstem response (ABR) evaluation has been one of the most reliable methods for evaluating hearing loss. Clinically available methods for ABR tests require averaging for a large number of sweeps (~1000-2000) in order to obtain a meaningful ABR signal, which is time consuming.  This study proposes a faster new method for ABR filtering based on wavelet-Kalman filter that is able to produce a meaningful ABR signal with less than 500 sweeps. The method is validated against ABR data acquired from 7 normal hearing subjects with different stimulus intensity levels, the lowest being 30 dB NHL. The proposed method was able to filter and produce a readable ABR signal using 400 sweeps; other ABR signal criteria were also presented to validate the performance of the proposed method.

<p>The first objective of this research has always been carry out a study of visual techniques SLAM (Simultaneous localization and mapping), specifically the type monovisual, less studied than the stereo. These techniques have been well studied in the world of robotics. These techniques are focused on reconstruct a map of the robot enviroment while maintaining its position information in that map. We chose to investigate a method to encode the points by the inverse of its depth, from the first time that the feature was observed. This method permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF).At first, the study mentioned it should be consolidated developing an application that implements this method. After suffering various difficulties, it was decided to make use of a platform developed by the same author of Slam method mentioned in MATLAB. Until then it had developed the tasks of calibration, feature extraction and matching. From that point, that application was adapted to the characteristics of our camera and our video to work. We recorded a video with our camera following a known trajectory to check the calculated path shown in the application. Corroborating works and studying the limitations and advantages of this method.</p>

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The first objective of this research has always been carry out a study of visual techniques SLAM (Simultaneous localization and mapping), specifically the type monovisual, less studied than the stereo. These techniques have been well studied in the world of robotics. These techniques are focused on reconstruct a map of the robot enviroment while maintaining its position information in that map. We chose to investigate a method to encode the points by the inverse of its depth, from the first time that the feature was observed. This method permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF).At first, the study mentioned it should be consolidated developing an application that implements this method. After suffering various difficulties, it was decided to make use of a platform developed by the same author of Slam method mentioned in MATLAB. Until then it had developed the tasks of calibration, feature extraction and matching. From that point, that application was adapted to the characteristics of our camera and our video to work. We recorded a video with our camera following a known trajectory to check the calculated path shown in the application. Corroborating works and studying the limitations and advantages of this method.

Today, a rapid development towards fuel efficient technological aids for vehicles is in progress. One step towards this is the development of platooning systems. The main concept of platooning is to let several heavy duty vehicles (HDVs) drive in a convoy and share important information with each other via wireless communication. This thesis describes one out of three subsystems in a project developed to handle the process from raw sensor data to control signal. The goal of the project is to achieve a safe and smooth control with the main purpose of reduced fuel consumption. This subsystem processes the raw sensor data received from the different HDVs. The purpose is to estimate the positions and velocities of the vehicles in a platoon, taking into account that packet-loss, out of sequence measurements and irrelevant information can occur. This is achieved by filtering the information from different sensors in an Extended Kalman Filter and converting it into a local coordinate system with the origin in the ego vehicle. Moreover, the estimates are sorted and categorized into classes with respect to the status of the vehicles. The result of the thesis is useful estimates that are independent of outer effects in a local reference system with origin in the host vehicle. This information can then be used for further sensor fusion and implementation of a Model Predictive Controller (MPC) in two other subsystems. These three subsystems result in a smooth and safe control with an average reduced fuel consumption of approxi- mately 11.1% when the vehicles drive with a distance of 0.5 seconds in a simulated environment. / Dagens utveckling inom fordonsindustrin fokuserar mer och mer påutveckling av bränsleeffektiva hjälpmedel. Ett steg i denna riktning är utvecklingen av platooningsystem. Huvudkonceptet med platooning är att låta flera tunga fordon köra i följd i en konvoj och dela viktig information med varandra via trådlös kommuni- kation och en automatiserad styrstrategi. Detta examensarbete beskriver ett utav tre delsystem i ett projekt som är utvecklat för att hantera en process från rå sensordata till styrsignaler för fordonen. Målet är att uppnå en säker och mjuk reglering med huvudsyftet att reducera bränsleförbrukningen. Det här delsystemet behandlar mottagen sensordata från de olika fordonen. Målet med delsystemet är att skatta positioner och hastigheter för fordonen i konvojen med hänsyn till att förlorad, försenad eller irrelevant information från det trådlösa nätverket kan förekomma. Detta uppnås genom filtrering i ett Extended Kalman Filter och konvertering till ett lokalt referenssystem med origo i det egna fordo- net. Utöver detta sorteras informationen och kategoriseras in i olika klasser efter fordonens status. Examensarbetet resulterade i användbara skattningar oberoende av yttre om- ständigheter i ett lokalt referenssystem med origo i det egna fordonet. Denna information kan användas vidare för ytterligare sensorfusion och implementering av en modellbaserad prediktionsregulator (MPC) i två andra delsystem. De tre delsystemen resulterade i en mjuk och säker reglering och en reducerad bränsleför- brukning med i genomsnitt 11.1% då fordonen körde med 0.5 sekunders avstånd i en simulerad miljö.

In this thesis, a modified Kalman filter is proposed for time varying channel estimation in orthogonal frequency division multiplexing (OFDM). The proposed scheme adopts pre-coding scheme and minimum mean squared error (MMSE) equalizer to improve system performance. By using pre-coding schemes, information can be protected by signal diversities, which prevent Kalman filter to disperse due to erroneous data signals. In this investigation, the proposed system architecture is verified by using simulation experiments. Simulation results demonstrate that the proposed schemes substantially improve system performances under verious channel conditions.

In this paper, we propose a Kalman filtering method to restore signal when both the digital and analog signal are available. The digital video signal is coded by method of MPEG. The error can be introduced in the quantization process of the block DCT transformation. So the quality of the image from the digital video signal needs to be improved. Considering the analog video signal is corrupted by the Gauss White Noise. We can apply the Kalman filter to these two signals at the same time to restore the image for a better quality. The image structure is defined to be the linear relationship between pixels with their upper and left neighbors. So we can determinate the image structure property by the linear equations of the pixel gray level. Generally, the image segmentation takes the gray values as the property. In our case we take the linear equations as our property function. This property implies an abstract concept and can¡¦t measure directly. We determine the unity of the image structure by measuring the error from merging the pixel into one region. We achieve a recursive formula for computing the error by the sequential least square error method. In the signal processing, Kalman filter is used for optimal estimation of the signal corrupted by additive noise. We segment the image by its local property. By our segmentation technique every region has its specific image structure. The structures are system parameters of Kalman filter. We first utilize the method of segmentation on the image recovered from the MPEG signal to find the local parameters. The results of experiments show that we can improve the images quality when the MPEG signal is not very good.

The need addressed in this thesis is to provide an Autonomous Ground Vehicle (AGV) with accurate information regarding its position, velocity, and orientation. The system chosen to meet these needs incorporates (1) a differential Global Positioning System, (2) an Inertial Measurement Unit consisting of accelerometers and angular-rate sensors, and (3) a Kalman Filter (KF) to fuse the sensor data. The obstacle avoidance software requires position and orientation to build a global map of obstacles based on the returns of a scanning laser rangefinder. The path control software requires position and velocity. The development of the KF is the major contribution of this thesis. This technology can either be purchased or developed, and, for educational and financial reasons, it was decided to develop instead of purchasing the KF software. This thesis analyzes three different cases of navigation: one-dimensional, two dimensional and three-dimensional (general). Each becomes more complex, and separating them allows a three step progression to reach the general motion solution. Three tests were conducted at the Texas A&M University Riverside campus that demonstrated the accuracy of the solution. Starting from a designated origin, the AGV traveled along the runway and then returned to the same origin within 11 cm along the North axis, 19 cm along the East axis and 8 cm along the Down axis. Also, the vehicle traveled along runway 35R which runs North-South within 0.1°, with the yaw solution consistently within 1° of North or South. The final test was mapping a box onto the origin of the global map, which requires accurate linear and angular position estimates and a correct mapping transformation.

This master’s thesis describes a way to represent vehicles when tracking them through an image sequence. Vehicles are described with a state containing their position, velocity, size, etc.. The thesis highlights the properties of homographies due to their suitability for estimation of projective transformations. The idea is to approximatively represent vehicles with planes based on feature points found on the vehicles. The purpose with this approach is to estimate the displacement of a vehicle by estimating the transformation of these planes. Thus, when avehicle is observed from behind, one plane approximates features found on the back and one plane approximates features found on the side, if the side of the vehicle is visible. The projective transformations of the planes are obtained by measuring the displacement of feature points. The approach presented in this thesis builds on the prerequisites that a camera placed on a vehicle provides an image of its field of view. It does not cover how to find vehicles in an image and thus it requires that the patch which contains the vehicle is provided. Even though this thesis covers large parts of image processing functionalities, the focus is on how to represent vehicles and how to design an appropriate filter for improving estimates of vehicle displacement. Due to noisy features points, approximation of planes, and estimated homographies, the obtained measurements are likely to be noisy. This requires a filter that can handle corrupt measurements and still use those that are not. An unscented Kalman filter, UKF, is utilized in this implementation. The UKF is an approximate solution to nonlinear filtering problems and is here used to update the vehicle’s states by using measurements obtained from homographies. The choice of the unscented Kalman filter was made because of its ease of implementation and its potentially good performance. The result is not a finished implementation for tracking of vehicles, but rather a first attempt for this approach. The result is not better than the existing approach, which might depend on one or several factors such as poorly estimated homographies, unreliable feature points and bad performance of the UKF.

Stuttgart, Univ., Diss., 2000.