Sound source localization with microphone arrays has received considerable attention as a means for the automated tracking of individuals in an enclosed space and as a necessary component of any general-purpose speech capture and automated camera pointing system. A novel computationally efficient method compared to traditional source localization techniques is proposed and is both theoretically and experimentally investigated in this research. This thesis first reviews the previous work in this area. The evolution of a new localization algorithm accompanied by an array structure for audio signal localization in three dimensional space is then presented. This method, which has similarities to the structure of the eye, consists of a novel hemispherical microphone array with microphones on the shell and one microphone in the center of the sphere. The hemispherical array provides such benefits as 3D coverage, simple signal processing and low computational complexity. The signal processing scheme utilizes parallel computation of a special and novel closeness function for each microphone direction on the shell. The closeness functions have output values that are linearly proportional to the spatial angular difference between the sound source direction and each of the shell microphone directions. Finally by choosing directions corresponding to the highest closeness function values and implementing linear weighted spatial averaging in those directions we estimate the sound source direction. The experimental tests validate the method with less than 3.10 of error in a small office room. Contrary to traditional algorithmic sound source localization techniques, the proposed method is based on parallel mathematical calculations in the time domain. Consequently, it can be easily implemented on a custom designed integrated circuit.

One of the most fundamental problems in mobile robotics is localization. The solution to most problems requires that the robot first determine its location in the environment. Even if the absolute position is not necessary, the robot must know where it is in relation to other objects. Virtually all activities require this preliminary knowledge. Another part of the localization problem is mapping, the robot’s position depends on its representation of the environment. An object’s position cannot be known in isolation, but must be determined in relation to the other objects. A map gives the robot’s understanding of the world around it, allowing localization to provide a position within that representation. The quality of localization thus depends directly on the quality of mapping. When a robot is moving in an unknown environment these problems must be solved simultaneously in a problem called SLAM (Simultaneous Localization and Mapping). Some of the best current techniques for localization and SLAM are based on particle filters which approximate the belief state. Monte Carlo Localization (MCL) is a solution to basic localization, while FastSLAM is used to solve the SLAM problem. Although these techniques are powerful, certain assumptions reduce their effectiveness. In particular, both techniques assume an underlying static environment, as well as certain basic sensor models. Also, MCL applies to the case where the map is entirely known while FastSLAM solves an entirely unknown map. In the case of partial knowledge, MCL cannot succeed while FastSLAM must discard the additional information. My research provides improvements to particle based localization and mapping which overcome some of the problems with these techniques, without reducing the original capabilities of the algorithms. I also extend their application to additional situations and make them more robust to several types of error. The improved solutions allow more accurate localization to be performed, so that robots can be used in additional situations.

One of the most fundamental problems in mobile robotics is localization. The solution to most problems requires that the robot first determine its location in the environment. Even if the absolute position is not necessary, the robot must know where it is in relation to other objects. Virtually all activities require this preliminary knowledge. Another part of the localization problem is mapping, the robot’s position depends on its representation of the environment. An object’s position cannot be known in isolation, but must be determined in relation to the other objects. A map gives the robot’s understanding of the world around it, allowing localization to provide a position within that representation. The quality of localization thus depends directly on the quality of mapping. When a robot is moving in an unknown environment these problems must be solved simultaneously in a problem called SLAM (Simultaneous Localization and Mapping). Some of the best current techniques for localization and SLAM are based on particle filters which approximate the belief state. Monte Carlo Localization (MCL) is a solution to basic localization, while FastSLAM is used to solve the SLAM problem. Although these techniques are powerful, certain assumptions reduce their effectiveness. In particular, both techniques assume an underlying static environment, as well as certain basic sensor models. Also, MCL applies to the case where the map is entirely known while FastSLAM solves an entirely unknown map. In the case of partial knowledge, MCL cannot succeed while FastSLAM must discard the additional information. My research provides improvements to particle based localization and mapping which overcome some of the problems with these techniques, without reducing the original capabilities of the algorithms. I also extend their application to additional situations and make them more robust to several types of error. The improved solutions allow more accurate localization to be performed, so that robots can be used in additional situations.

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There are more and more multinational companies adopting localization strategy in china, including all kinds of functions. Comparing with the reality, the related reaserchs about localization issues are very fewer. In order to understand more about the content of ¡§localizatin¡¨ (or call ¡§local responsiceness¡¨), the study divided localization into three kinds of sub-parts, including physical business activities, soft management machanism and human resources. Futhermore, the study propses and examines various determinants of localization. The findings of this reaserch are following: (1) Factors including culture distance, product demand heterogenity and Taiwan experience will influence the localization of physical business activities. (2) Factors of affecting localization of soft management machanism contain product demand heterogenity, industry cluster, headquarter commitment and Taiwan experience. (3) Factors of affecting localization of human resource contains product demand heterogenity, headquarter commitment and the dependence of subsidiary on headquarter. Above all, we also found the influencial factors for three different kinds of localization are different.

The operation of a complete license plate recognition system includes three parts: license plate localization, character segmentation, and character identification. Among these three parts, license plate localization is relatively more difficult and complicated. Until now, differentiating background and real license plate images in real and random traffic conditions remains to be a very difficult task. Via a VQ coding technique, this study introduces a method resolve this problem. As a preprocessing step, this method first converts an image to be classified into binary form by using statistics generated from a license plate image database. The next step of the proposed approach is to use a VQ method to represent the image by a series of codewords. By computing the probability of these codewords used by the license plate and background images, these codewords are renumbered. By using neural networks to classify such images, our experimental results show that the proposed approach can differentiate background and real license plate images with a very high successful rate.

Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator.

NTPDases (Apyrases) (EC 3.6.1.5) require divalent cations (Mg2+, Ca2+) for hydrolysis of di- and triphosphate nucleotides, but do not hydrolyze monophosphate nucleotides. They are insensitive to inhibitors of F-type, P-type, and V-type ATPases and are categorized as E-type ATPases. They are grouped in the GDA_CD39 superfamily. Seven NTPDases (AtAPY1-7) have been cloned from Arabidopsis. In this work, AtAPY1 or AtAPY2 tagged with C-terminal green fluorescence protein (GFP) and driven by their respective native promoter displayed Golgi apparatus localization. These GFP constructs can rescue the apy1 apy2 double knockout (apy1 apy2 dKO) successfully, which indicates their accuracy and functionality in localization studies. Furthermore, both AtAPY1 and AtAPY2 can complement the Saccharomyces cerevisiae Golgi-localized GDA1 mutant by rescuing its aberrant protein glycosylation phenotype. The GFP tagged AtAPY1 or AtAPY2 constructs in the apy1 apy2 dKO plants can restore microsomal UDP/GDPase activity in the mutants confirming that they both also have functional competency. Loss-of-function apy1, apy2 and APY1RNAi apy2 mutants showed higher levels of galactose in the cell wall monosaccharide analysis. However, the efficiency of the galactose transport was not altered APY1RNAi apy2 mutants. AtAPY3 through 7 all displayed intracellular localization by transiently expressed C-terminal tagged YFP in the onion epidermal cells. AtAPY3 showed a subcellular localization distinct from the others. Biochemical analyses showed that AtAPY3 prefers to hydrolyze NTP more than NDP. AtAPY4 resides in the cis-Golgi. It has fairly weak NTPDase activity but can still rescue some part of the phenotypic defects in Golgi luminal NTPDases mutants. AtAPY5 is a strong NDPase and has a broad spectrum of substrate preferences. It can fully restore phenotypic defects in Golgi luminal NTPDases in yeast. AtAPY6 and AtAPY7 are ER and Golgi associated. However, the expression of these two enzymes cannot be detected in the Saccharomyces cerevisiae host, which prevents further analysis. Taken together these results reveal that the current seven APYRASE members are intracellulary associated with Golgi/ER or unknown vesicles. They all display typical NTPDase enzyme activities that can hydrolyze di- or triphosphate nucleotides in the cells. / text

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Open-loop position estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity with which an estimated position is determined. However, these methods can lead to inaccurate or unreliable estimates. Two methods are developed in this thesis. The first uses a single optical sensor and can accurately estimate position under ideal conditions and when wheel slip perpendicular to the axis of the wheel occurs. A second method which uses two optical sensors is developed which can accurately estimate position even when wheel slip parallel to the axis of the wheel occurs. Location of the optical sensors is investigated in order to minimize errors caused by inaccurate sensor readings. Finally, the method is implemented and tested using a potential field based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where kinematic violations occur.