Algorithm-Based Efficient Approaches for Motion Estimation Systems Teahyung Lee 121 pages Directed by Dr. David V. Anderson This research addresses algorithms for efficient motion estimation systems. With the growth of wireless video system market, such as mobile imaging, digital still and video cameras, and video sensor network, low-power consumption is increasingly desirable for embedded video systems. Motion estimation typically needs considerable computations and is the basic block for many video applications. To implement low-power video systems using embedded devices and sensors, a CMOS imager has been developed that allows low-power computations on the focal plane. In this dissertation efficient motion estimation algorithms are presented to complement this platform. In the first part of dissertation we propose two algorithms regarding gradient-based optical flow estimation (OFE) to reduce computational complexity with high performance. The first is a checkerboard-type filtering (CBTF) algorithm for prefiltering and spatiotemporal derivative calculations. Another one is spatially recursive OFE frameworks using recursive LS (RLS) and/or matrix refinement to reduce the computational complexity for solving linear system of derivative values of image intensity in least-squares (LS)-OFE. From simulation results, CBTF and spatially recursive OFE show improved computational efficiency compared to conventional approaches with higher or similar performance. In the second part of dissertation we propose a new algorithm for video coding application to improve motion estimation and compensation performance in the wavelet domain. This new algorithm is for wavelet-based multi-resolution motion estimation (MRME) using temporal aliasing detection (TAD) to enhance rate-distortion (RD) performance under temporal aliasing noise. This technique gives competitive or better performance in terms of RD compared to conventional MRME and MRME with motion vector prediction through median filtering.

Direct sequence code division multiple access (DS-CDMA) technique is one of the significant multiplexing technologies used in wireless communication services. In the DS-CDMA framework, all users have been assigned distinct signature code sequence to achieve multiple accesses within the same frequency band, and allow signal separating at the receiver. Under multipath fading environment with near-far effect, the current CDMA systems employed the RAKE receiver, to enhance the system performance. It is known that if training data is available the minimum mean squares error (MMSE) multiuser receiver, in which the average power of the receiver output is minimized subject to appropriate constraints, could be obtained by solving directly by the constrained Wiener estimation solution. However, if this is not the case, the blind multiuser receiver is an alternative approach to achieve desired performance closed to the one with the MMSE approach. In this thesis, based on the max/min criterion, the blind multiuser receiver, with linear constraints, is devised. Here constraint equations are written in parametric forms, which depend on the multipath structure of the signal of interest. Constraint parameters are jointly optimized with the parameters of the linear receiver to obtain the optimal parameters. In consequence, the sliding window linearly constrained RLS (SW-LC-RLS) algorithm is employed to implement the optimal blind receiver, with max/min approach. This new proposed scheme can be used to deal with multiple access interference (MAI) suppression for the environments, in which the narrow band interference (NBI) due to other systems is joined suddenly to the DS-CDMA systems, and having serious near-far effect. Under such circumstance, the channel character due to the NBI and near-far effect will become violent time varying, such that the conventional LC-RLS algorithm as well as LC-LMS algorithms could not perform well. Via computer simulation it confirms that our proposed scheme has better capability for MAI suppression in DS-CDMA systems than other existing schemes, and is more robust against the NBI and near-far problems.

Resumo: O Sistema de Posicionamento Global oferece um poderoso e relativamente barato processo para se determinar órbitas de satélites artificiais da Terra. Este trabalho apresenta um método de determinação de órbita para satélites com um receptor GPS a bordo. Medidas de pseudo-distância são usadas para estimar o vetor de estado. O estimador considerado é o método dos mínimos quadrados recursivo, através de rotações ortogonais de Givens, com a finalidade de evitar problemas numéricos e de inversão de matrizes. É considerado a modelagem das forças devido ao geopotencial de alto grau e ordem. Resultados indicam que a precisão em posição melhor que 10 m foi obtido usando dados reais do satélite Topex (com um mínimo de duas horas de dados - aproximadamente um período orbital). O resíduo de pseudo-distância teve um desvio padrão cerca de 5 m. / Abstract: The Global Positioning System is a powerful and low cost process to compute orbits for some artificial Earth satellites. This work presents a method of orbit determination for satellites with an onboard GPS receiver. Pseudo-ranges are used in the measurements equations for the orbit estimator. The estimator considered is the recursive least squares method, numerically improved with orthogonal Givens rotations and thus avoiding problems concerning inversion of matrices. Up to high order geopotential perturbations are taken into account. Results indicate that precision better than 10 m is easily obtained using batches of one orbital period for the TOPEX satellite (two hours of orbital period). Standard deviation of about 5 m resulted for the residuals. / Orientador: Rodolpho Vilhena de Moraes / Coorientador: Hélio Koiti Kuga / Mestre

Master’s thesis is focused on the adaptive controllers. The first theoretic part mainly describes the parametric identification, which belongs to the most important part of the adaptive controller’s structure. Classical identification methods (the recursive least squares methods) are firstly mentioned and afterwards the identification methods based on the neural network (the Marquardt-Levenberg algorithm and the new identification algorithm NIA inspired by the neural networks) are described. At the conclusion of the theoretic part there are mentioned the algorithm of the adaptive controller’s tuning which uses the identification parameters (the modified Z-N method) and the tested types of adaptive controllers. Particular results, which were found out by verifying of the adaptive controllers on the simulation and real models, are contained in second, the practical, part of the thesis. Finally, achieved results are compared with the classical discrete PID controller and with the adaptive controller of the B&R company.

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Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.

Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.

Made available in DSpace on 2014-06-11T19:25:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2001Bitstream added on 2014-06-13T19:06:44Z : No. of bitstreams: 1 silva_aa_me_guara.pdf: 449677 bytes, checksum: 4be6e3535fba004c9b44ce9da9bf8c3a (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O Sistema de Posicionamento Global oferece um poderoso e relativamente barato processo para se determinar órbitas de satélites artificiais da Terra. Este trabalho apresenta um método de determinação de órbita para satélites com um receptor GPS a bordo. Medidas de pseudo-distância são usadas para estimar o vetor de estado. O estimador considerado é o método dos mínimos quadrados recursivo, através de rotações ortogonais de Givens, com a finalidade de evitar problemas numéricos e de inversão de matrizes. É considerado a modelagem das forças devido ao geopotencial de alto grau e ordem. Resultados indicam que a precisão em posição melhor que 10 m foi obtido usando dados reais do satélite Topex (com um mínimo de duas horas de dados - aproximadamente um período orbital). O resíduo de pseudo-distância teve um desvio padrão cerca de 5 m. / The Global Positioning System is a powerful and low cost process to compute orbits for some artificial Earth satellites. This work presents a method of orbit determination for satellites with an onboard GPS receiver. Pseudo-ranges are used in the measurements equations for the orbit estimator. The estimator considered is the recursive least squares method, numerically improved with orthogonal Givens rotations and thus avoiding problems concerning inversion of matrices. Up to high order geopotential perturbations are taken into account. Results indicate that precision better than 10 m is easily obtained using batches of one orbital period for the TOPEX satellite (two hours of orbital period). Standard deviation of about 5 m resulted for the residuals.

An accurate estimate of the mass of a passenger vehicle is important for several safety systems and environmental aspects. In this thesis, an algorithm for estimating the mass of a passenger vehicle using the recursive least squares methodis presented. The algorithm is based on a physical model of the vehicle and is designed to be able to run in real-time onboard a vehicle and uses the wheel torque signal calculated in the electrical control unit in the engine. Therefore no estimation of the powertrain is needed. This is one contribution that distinguishes this thesis from previous work on the same topic, which has used the engine torque. The benefit of this is that no estimation of the dynamics in the powertrain is needed. The drawback of using this method is that the algorithm is dependenton the accuracy of the estimation done in the engine electrical control unit. Two different versions of the recursive least squares method (RLS) have been developed - one with a single forgetting factor and one with two forgetting factors. The estimation performance of the two versions are compared on several different real-world driving scenarios, which include driving on country roads, highways, and city roads, and different loads in the vehicle. The algorithm with a single forgetting factor estimates the mass with an average error for all tests of 4.42% and the algorithm with multiple forgetting factors estimates the mass with an average error of 4.15 %, which is in line with state-of-the-art algorithms that are presented in other studies. In a sensitivity analysis, it is shown that the algorithms are robust to changes in the drag coefficient. The single forgetting factor algorithm is robust to changes in the rolling resistance coefficient whereas the multiple forgetting factor algorithm needs the rolling resistance coefficient to be estimated with fairly good accuracy. Both versions of the algorithm need to know the wheel radius with an accuracy of 90 %. The results show that the algorithms estimate the mass accurately for all three different driving scenarios and estimate highway roads best with an average error of 2.83 % and 2.69 % for the single forgetting factor algorithm and the multiple forgetting factor algorithm, respectively. The results indicate it is possible to use either algorithm in a real-world scenario, where the choice of which algorithm depends on sought-after robustness.

Master thesis is focused on the philosophy of design adaptive controller. In the theoretic part are described parts of the adaptive controller, which belongs parts as online identification by recursive least-squares method and PSD controller, which can set its parameters according to identified system (use Z-N method). The part of control system with transport delay is situated at the conclusion of the theoretic part, there are focused on Smith predictor. Practical part is focused on verification of all algorithms, which was performed on models and real systems.