Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Dallas, 2006. / Includes vita. Includes bibliographical references (leaves 166-175).

This study focuses on the mechanism of transmembrane signaling by Tar, the aspartate chemoreceptor of Escherichia coli. Like other bacterial chemoreceptors, Tar localizes to the cell membrane and relays information about the external chemical environment through the membrane to a cytoplasmic signaling domain. The output of the signaling domain controls the directional bias of the rotary flagellar motors of the cell. Net movement of a cell in a chemical gradient involves temporal comparison of the current concentration with the concentration in the recent (a few seconds) past. The current concentration is measured as the percent occupancy of the extracellular ligand-binding domain of the receptor, and the past is represented by the extent of covalent methylation of four conserved glutamyl residues in the cytoplasmic domain. Under steady-state conditions, the methylation level corresponds to ligand occupancy. Tar is a dimer, and much evidence suggests that dimers associate into trimers of dimers. Higher-order arrays of receptors form in the presence of the cytoplasmic proteins CheA and CheW. The conformational change generated by ligand binding is transmitted through the membrane by one subunit of a dimer. To examine whether this initially asymmetric signal becomes symmetric within the cytoplasmic domain, I examined aspartate-induced adaptive methylation of the two subunits of mutant Tar receptor heterodimers. In the presence of CheA and CheW, adaptive methylation after addition of aspartate was symmetric, but in their absence, although the level of methylation increased, the rates were different for the two subunits. I also found that cross-talk, at the level of adaptive methylation, occurs between different receptor types even in the absence of CheA and CheW. These results provide support for the idea that a tight association of receptor dimers within trimers of dimers allows for an actively signaling receptor to affect the methylation state, and thus presumably the signaling state, of receptors within a trimer that are not bound to an attractant ligand.

SH2-containing inositol polyphosphate 5-phosphatases, SHIP2, has been established as a regulator of the insulin cascade, of cell adhesion and spreading, actin structures, remodelling and cytoskeletal organization. However, the molecular mechanisms underlying these processes still needed additional investigations. Among different regulatory mechanisms, protein-protein interaction play an essential role. To better understand the molecular mechanism of SHIP2 in signalling pathway as well as to reveal novel roles of SHIP2, a two-hybrid was performed to search for SHIP2 protein interactors. JNK-interacting protein 1 (JIP1) and intersectin 1 (ITSN1) were two of the newly identified protein partners of SHIP2. In this thesis, we characterized the associations of SHIP2 with JIP1 and ITSN1 in different aspects as identifying the interacting domain involved, biochemical function regulations and cellular biological roles. The JIP scaffold family of proteins associate with MAPK, MAPKK and MAPKKK creating functional signaling modules to control the specificity of signal transduction. JIP1 is characterized as a scaffold protein assembling JNK, MAPK kinase 7 (MKK7), mixed lineage kinase (MLK), dual leucine zipper-bearing kinase (DLK). It thus enhances the selectivity and effectiveness of kinase activation during JNK signaling. In this thesis, the SHIP2-JIP1 interaction has been confirmed both in overexpression system in COS-7 and CHO-IR cells, and in native cells of COS-7. Both the proline-rich (PR) domain (residues 359-487) and PTB domain of JIP1 participated in this interaction. Overexpression of SHIP2 in COS-7 cells up-regulated JIP1-mediated JNK activation and the tyrosine phosphorylations of both JIP1 and MLK3. These effects were independent of SHIP2 catalytic activity. By the use of kinase inhibitors, we showed that Abl and Src family tyrosine kinases might be implicated in the regulation of JIP1 tyrosine phosphorylation. The residue Y270 of JIP1, a potential target of Abl tyrosine kinase, was shown to be involved in SHIP2-increased JIP1 tyrosine phosphorylation. In an in vitro assay, JIP1 negatively regulated the catalytic activity of SHIP2. In addition, upon the stimulation of okadaic acid, the overexpression of SHIP2 caused less viability of COS-7 cells. These data provide a new molecular link between SHIP2 and JIP1-mediated JNK pathway, and may help explain the biochemical mechanisms of SHIP2 in cellular apoptosis, as well as in insulin pathway. Another protein partner, ITSN1, is a multi-domain protein which plays a role in endocytosis, MAPK signalling and actin cytoskeleton. The interaction between SHIP2 and ITSN1 was confirmed in overexpression systems in COS-7 cells, as well as at the physiological concentration with the endogenously expressed proteins in C2C12 and COS-7 cells. EGF stimulation did not modulate the association of SHIP2 and ITSN1. ITSN1-SH3D, A, C and E domains interacted with the C-terminal part of SHIP2 with the binding affinity as SH3D>SH3A>SH3C>SH3E. Upon the stimulation of EGF, the expression of SHIP2 may recruit ITSN1 short form (ITSN1-S) to cell membrane. The ITSN-mediated ERK1/2 and JNK activations in response to EGF were not modulated when SHIP2 or catalytic mutant of SHIP2 or TSHIP2 was overexpressed. The link between SHIP2 and ITSN may provide one of the molecular mechanisms used by SHIP2 to participate in receptor endocytosis regulation. In conclusion, our data of the associations of SHIP2 with JIP1 and ITSN1 provide evidence for potential novel biochemical mechanisms of SHIP2 to be implicated in JNK pathway as well as EGF receptor endocytosis. JIP1 and ITSN1, which are both implicated in the JNK pathway, may also have a link through the common protein partner SHIP2, giving rise to potential interesting study goal.

In this thesis a terrain positioning method for underwater vehicles called the correlation method is presented. Using the method the vehicle can determine its absolute position with the help of a sonar and a map of the bottom topography. The thesis is focused towards underwater positioning but most of the material is directly applicable to flying vehicles as well. The positioning of surface vehicles has been revolutionized by the global positioning system (GPS). However, since the GPS signal does not penetrate into the sea water volume, underwater vehicles still have to use the inertial navigation system (INS) for navigation. Terrain positioning is therefore a serious alternative to GPS for underwater vehicles for zeroing out the INS error in military applications. The thesis begins with a review of different estimation methods as Bayesian and extended Kalman filter methods that have been used for terrain navigation. Some other methods that may be used as the unscented Kalman filter or solving the Fokker-Planck equation using finite element methods are also discussed. The correlation method is then described and the well known problem with multiple terrain positions is discussed. It is shown that the risk of false positions decreases exponentially with the number of measurement beams. A simple hypothesis test of false peaks is presented. It is also shown that the likelihood function for the position under weak assumptions converges to a Gaussian probability density function when the number of measuring beams tends to infinity. The Cramér-Rao lower bound on the position error covariance is determined and it is shown that the proposed method achieves this bound asymptotically. The problem with measurement bias causing position bias is discussed and a simple method for removing the measurement bias is presented. By adjusting the footprint of the measuring sonar beams to the bottom topography a large increase in accuracy and robustness can be achieved in many bottom areas. This matter is discussed and a systematic theory about how to choose way-points is developed. Three sea-trials have been conducted to verify the characteristics of the method and some results from the last one in October 2002 are presented. The sea-trials verify to a very high degree the theory presented. Finally the method is briefly discussed under the assumption that the bottom topography can be described by an autoregressive stochastic process. / <p>QC 20101014</p>

The overall purpose with this thesis is to investigate and provide computationally efficient methods for estimation and detection. The focus is on airborne applications, and we seek estimation and detection methods which are accurate and reliable yet effective with respect to computational load. In particular, the methods shall be optimized for terrain-aided navigation andcollision avoidance respectively. The estimation part focuses on particle filtering and the in general much more efficient marginalized particle filter. The detection part focuses on finding efficient methods for evaluating the probability of extreme values. This is achieved by considering the, in general, much easier task to compute the probability of level-crossings. The concept of aircraft navigation using terrain height information is attractive because of the independence of external information sources. Typicallyterrain-aided navigation consists of an inertial navigation unit supported by position estimates from a terrain-aided positioning (TAP) system. TAP integrated with an inertial navigation system is challenging due to its highly nonlinear nature. Today, the particle filter is an accepted method for estimation of more or less nonlinear systems. At least when the requirements on computational load are not rigorous. In many on-line processing applications the requirements are such that they prevent the use of theparticle filter. We need more efficient estimation methods to overcome this issue, and the marginalized particle filter constitutes a possible solution. The basic principle for the marginalized particle filter is to utilize linear and discrete substructures within the overall nonlinear system. These substructures are used for efficient estimation by applying optimal filters such as the Kalman filter. The computationally demanding particle filter can then be concentrated on a smaller part of the estimation problem. The concept of an aircraft collision avoidance system is to assist or ultimately replace the pilot in order to to minimize the resulting collision risk. Detection is needed in aircraft collision avoidance because of the stochastic nature of thesensor readings, here we use information from video cameras. Conflict is declared if the minimum distance between two aircraft is less than a level. The level is given by the radius of a safety sphere surrounding the aircraft.We use the fact that the probability of conflict, for the process studied here, is identical to the probability for a down-crossing of the surface of the sphere. In general, it is easier to compute the probability of down-crossings compared to extremes. The Monte Carlo method provides a way forward to compute the probability of conflict. However, to provide a computationally tractable solution we approximate the crossing of the safety sphere with the crossing of a circular disc. The approximate method yields a result which is as accurate as the Monte Carlo method but the computational load is decreased significantly.

In this thesis we present the concept of concatenated multilevel codes. These codes are a combination of generalized concatenated codes with multilevel coding. The structure of these codes is simple and relies on the concatenation of two or more codes of shorter length. These codes can be designed to have large diversity which makes them attractive for use in fading channels. We also present an iterative decoding algorithm taylored to fit the properties of the proposed codes. The iterative decoding algorithm we present has a complexity comparable to the complexity of GMD decoding of the same codes. However, The gain obtained by using the iterative decoder as compared to GMD decdoing of these codes is quite high for Rayleigh fading channels at bit error rates of interest. Some bounds on the performance of these codes are given in this thesis. Some of the bounds are information theoretic bounds which can be used regardless of the code under study. Other bounds are on the error probability of concatenated multilevel codes. Finally we give examples on the implementation of these codes in adaptive coding of OFDM channels and MIMO channels. / QC 20100629

This thesis deals with the problem of classifying automotive vehicle occupants and estimating their position. This information is critical in designing future smart airbag systems providing maximum protection for passengers. According to the American National Highway Traffic Safety Administration (NHTSA), since 1990, in the USA, 227 deaths have been attributed to airbags deployed in low-speed crashes which included 119 children, and 22 infants. In these cases, intelligent deployment of the airbag, based on the type and position of occupant could have avoided these fatalities. Current commercial classification systems based on traditional sensors, such as pressure sensors are not able to detect the position of occupants. Vision-based systems are advantageous over pressure sensor based systems, as they can provide additional functionalities like dynamic occupant position analysis or child seat orientation detection. On the other hand, vision-based systems have to cope with several challenges, such as, illumination conditions, temperature, humidity, large variation of scenes, cost, and computational aspects. This thesis presents new pattern recognition techniques for classifying, localizing and tracking vehicle occupants using a low-resolution 3-D optical time-of-flight range camera. This sensor is capable of providing directly a dense range image, independent of the illumination conditions and object textures. Based on this technology, IEE S.A. is presently developing a camera system for the application of occupant classification. A prototype of this camera has been the basis for this study. The first part of the thesis presents the problem of occupant classification. Herein, we investigate geometric feature extraction methods to discriminate between different occupant types. We develop features that are invariant under rotation and translation. A method for reducing the size of the feature set is analyzed with emphasis on robustness and low computational complexity while maintaining highly discriminative information. In addition, several classification methods are studied including Bayes quadratic classifier, Gaussian Mixture Model (GMM) classifier and polynomial classifier. We propose the use of a cluster based linear regression classifier using a polynomial kernel which is particularly well suited to coping with large variations within each class. Full scale experiments have been conducted which demonstrate that a classification reliability of almost 100\% can be achieved with the reduced feature set in combination with a cluster based classifier. In this safety critical application, it is equally important to address the problem of reliability estimation for the system. State-of-the-art methods to estimate the reliability of the classification are based either on classification output or based on density estimation. The second part of the thesis treats estimation of the reliability of the pattern classification system. Herein, a novel reliability measure is proposed for classification output which takes into account the local density of training data. Experiments verify that this reliability measure outperforms state-of-the-art methods in many cases. Lastly, the problem of dynamically detecting out-of-position occupants is addressed in the third part of the thesis. This task requires detecting and localizing the position of the occupant's head. Traditional head detection methods, such as detecting head-like objects in the image by analyzing the local shapes are not robust with the current sensor. Many regions in a scene such as the shoulder or the elbow of the occupant can be incorrectly detected as the head. In order to cope with these challenges, we exploit topology information in the range image. A modified Reeb graph technique has been developed that extracts a topological skeleton of the 3D contour that is invariant under rotation and translations. Results verify that the Reeb graph detects successfully the head i.e., the head always corresponds to one of the end points of the skeleton. Subsequently, a data association algorithm to select the correct head candidate out of the Reeb graph candidates is presented. Results show that the resulting head detection algorithm based on Reeb graphs is robust under scene changes. / QC 20100714

Within this thesis, the satellite broadcast scenario of geostationary satellites is reviewed. The densely crowded geostationary arc in the common broadcast frequencies may create significant interference from adjacent satellites (ASI). The possible use of multiple-input receivers and of interference processing techniques is analyzed in this specific context. In addition the synchronization problem is studied under interference limited conditions for broadcast as well as broadband satellite systems.We address fixed satellite broadcast reception with the goal of decreasing the aperture of the receiving antenna. The front-end antenna size is commonly defined by the presence of interference from adjacent satellites. A small antenna aperture leads to interference from neighboring satellites utilizing the same frequency bands. We propose a multi-input reception system with subsequent joint detection which provides reliable communication in the presence of multiple interfering signals. An iterative least square technique is adopted combining spatial and temporal processing. This approach achieves robustness against pointing errors and against changing interference scenarios. Different temporal interference processing methods are evaluated, including Minimum Mean Square Error (MMSE) based iterative soft-decision interference cancellation as well as Iterative Least Square with Projection (ILSP) based approaches, which include spatial and temporal iterations. Furthermore the potential of an additional convolutional channel decoding step in the interference cancellation mechanism is verified.Also, we demonstrate how to accurately synchronize the signals as part of the detection procedure. The technique is evaluated in a realistic simulation study representing the conditions encountered in typical broadcast scenarios.In a second part of the thesis the problem of synchronization is reviewed in the context of interference limited scenarios for broadband satellite return channels. Spectral efficiency is of great concern in the return channel of satellite based broadband systems. In recent work the feasibility of increased efficiency by reducing channel spacing below the Symbol Rate was demonstrated using joint detection and decoding for a synchronized system. We extend this work by addressing the critical synchronization problem in the presence of adjacent channel interference (ACI) which limits performance as carrier spacing is reduced.A pilot sequence aided joint synchronization scheme for a multi-frequency time division multiple access (MF-TDMA) system is proposed. Based on a maximum likelihood (ML) criterion, the channel parameters, including frequency, time and phase are jointly estimated for the channel of interest and the adjacent channels. The impact of ACI on the synchronization and detection performance is investigated. It is shown that joint channel parameter estimation outperforms single carrier synchronization with reasonable additional computational complexity in the receiver. Based on the proposed synchronization scheme in conjunction with an appropriate joint detection mechanism the carrier spacing can be reduced significantly compared to current systems providing a substantial increase in spectral efficiency / QC 20100727

This thesis deals with three estimation problems motivated by spatial signal processing using arrays of sensors. All three problems are approached using tools from estimation theory, including asymptotical analysis of performance and Cramér-Rao lower bound; Monte Carlo methods are used to evaluate small sample performance. The first part of this thesis treats direction of arrival estimation for narrowband signals. Most algorithms require the noise covariance matrix to be known or to possess a known structure. In many cases, the noise covariance is estimated from a separate batch of signal-free samples; in a non-stationary environment this sample set can be small. By deriving the Cramér-Rao bound in a form that can be compared to well-known results, we investigate the combined effects of finite sample sizes, both in the estimated noise covariance matrix and in the data with signals present. Under the same data model, we derive the asymptotical covariance of weighted subspace fitting, where the signal-free samples are used for whitening. The obtained expression suggests optimal weights that improve performance compared to the standard choice and that result in an asymptotically efficient estimate. In addition, we propose a new, asymptotically efficient, method based on the likelihood function. If the array is uniform and linear, then an iterative search can be avoided. We propose two such algorithms, based on the two general, iterative, algorithms discussed. We also treat the detection problem, and provide results that are useful in a joint detection and estimation algorithm based on the proposed estimators. Parameter estimation for the reduced rank linear regression is the second estimation problem treated in the thesis. It appears in, for example, system identification and signal processing for communications. We propose a new method based on instrumental variable principles and we analyze its asymptotical performance. The new method is asymptotically efficient if the noise is temporally white, and outperforms previously suggested algorithms when the noise is temporally correlated. As part of the estimation algorithm, the closest low rank approximation of a matrix, as measured under a weighted norm, has to be calculated. This problem lacks solution in the general case. We propose two new methods that can be computed in fixed time; both methods are approximate but asymptotically optimal as part of the estimation procedure in question. We also propose a new algorithm for the related rank detection problem. The third problem is that of estimating the covariance matrix of a multivariate stochastic process. In some applications, the structure of the problem suggests that the underlying, true, covariance matrix is the Kronecker product of two matrix factors. The covariance matrix of the channel realizations in multiple input multiple output (MIMO) communications systems can, under certain assumptions, have such Kronecker product structure. Moreover, the factor matrices can sometimes, in turn, be assumed to possess additional structure. We propose two asymptotically efficient estimators for the case where the channel realizations can be assumed known. Both estimators can be computed in fixed time; they differ in their small sample performance and in their ability to incorporate extra structure in the Kronecker factors. In a practical MIMO system, the channel realizations have to be estimated from training data. If the amount of training data is limited, then it is better to treat the training data, rather than the channel estimates, as inputs to the channel covariance estimator. We derive and analyze an estimator based on this new data model. This estimate can be computed in fixed time and the estimator is also able to optimally use extra structure in the factor matrices / QC 20100820