The Global Positioning System (GPS) has become popular research and application interests in surveying and many other areas. Nowadays, the accuracy of the Differential GPS can easily reach the order of a few meters. Yet, there are still many ways to exploit the GPS system signal carrier to improve the accuracy to less than meter level. In this thesis, a new approach to improve the accuracy to less than meter level is presented while the observer is in the dynamic situation. In order to reach the sub-meter accuracy, we measure on the carrier phase difference (The L1 carrier frequency is 1575.42 Mhz, 1=19 em) between a reference point A and a primary point B. This actually means we work on the accuracy of centimeter. In this proposed method of the precision survey, first the Differential GPS is used to fix the position in the accuracy of meter level, and then by measuring the signal carrier relative difference we can work on the accuracy in the accuracy level of the wavelength (19 cm). The measuring on the relative carrier phase will introduce the problem of initial modulo 2π phase (integer wavelength) ambiguity. To solve the initial integer ambiguity, A Multiple Model Estimation Algorithm (MMEA) which was developed by D.T. Magill in 1965 is applied. The MMEA is composed of a bank of parallel Kalman filters, all operating on the input measurement sequence simultaneously. Each filter in the bank of filters is modeled around a different hypothesis. The number of the required parallel filters is the number of hypothesis of integer ambiguity which is determined by the error range of the differential phase measurement. And the error range of the differential phase measurement is related to the accuracy of the Differential GPS. The precision positioning by MMEA method has some advantage compares with other methods now being used . . It does not require continuous observation of the satellites initially. . Kalman filter is recursive technique. So it has the potential of on-line . . Kalman filter is widely used in navigation and approved to be very efficient and versatile. Computer simulation results are given for a hypothetical GPS system. They demonstrate that the MMEA can effectively solve the integer wavelength ambiguity problem in dynamic situation. The simulation results presented are especially encouraging with regard to the flexibility and efficiency in precision survey. A further improvement of precision surveying by GPS is also discussed in the last Chapter. By using Markov Model and Verterbi Algorithm, a more flexible and reliable precision surveying method could be available.

This thesis investigates the detection and classification of small boats using a passive sonar system. Noise radiated from a small boats consists of broadband noise and harmonically related tones that correspond to parameters in the boats engine and propeller. A novel signal processing method for detection and discrimination of noise radiated from small boats has been developed. There are two main components to the algorithm. The first component detects the presence of small boats by the harmonic tonals radiated from the boat propeller and engine. The second component was designed to extract the a signature from passive sonar data. The Harmonic Extraction and Analysis Tool (HEAT) was designed to estimate the fundamental frequency of the harmonic tones, track the fundamental frequency using a Kalman filter, and automatically extract the amplitudes of the harmonic tonals to generate a harmonic signature for the boat. The algorithm is shown to accurately extract theses signatures, and results show that the signatures are unique enough that the same boat passing by the hydrophone multiple times can be recognized.

Disposable filtering facepiece respirators (FFRs) include a wide range of products that may be certified or non-certified. Many of these respirators are being produced with activated carbon claiming nuisance level organic vapor (OV) relief. OV includes a wide range of volatile organic compounds (VOCs) which have been linked to major and minor health discomfort such as headaches, upper extremity discomfort, nausea, respiratory irritation, asthma nervous system complications, hearing loss, cancer, and death. Common industries that have been identified that may expose employees to nuisance level OV, resulting in minor symptoms, include beautician salons, dry cleaning operations, and pesticide applications. FFRs with activated carbon (FFR-AC) may provide a more convenient alternative for reusable respirators which could also protect employees from OV exposure. This study investigated the adsorption capabilities of one certified respirator (3M) and two alternatively designed respirators (RZ Hunting Mask, Surgical Mask) with activated carbon filtering media. The three FFRs were tested to determine the 50% breakthrough time for two hydrocarbons and one non-carbon-based vapor. 50% breakthrough was chosen because we felt that reducing nuisance level exposures by half would still be protective. Non-certified respirators were exposed to 15 parts per million (ppm) and 50 ppm for all three vapors. Concentrations of 15 ppm and 50 ppm were standardized to achieve similar mass per time exposures across all contaminants and because these values represented the range of nuisance level exposure documented in literature. The 3M respirator was exposed to 15 and 50 ppm of acetone and ammonia, and perchloroethylene was evaluated at 50 ppm. Perchloroethylene was not evaluated at 15 ppm because breakthrough was longer than 8 hours. 3M respirators were also evaluated at 95% relative humidity using 50 ppm of acetone, ammonia, and perchloroethylene. The total number of trials was 43 (n=43). These contaminants and concentrations were chosen based on published data on occupational exposures. The non-certified respirators, (RZ Hunting Mask and Surgical Mask) , were ineffective for all vapors and offered less than 10 minutes of protection before 50% breakthrough occurred. Respirators performed poorly, when exposed to ammonia, with breakthrough less than 5 mins at 50 ppm and 10 minutes at 15 ppm. The 3M respirator had the longest breakthrough times for all trials. Acetone breakthrough occurred at 121 minutes for 50 ppm and 233 minutes at 15 ppm. Perchloroethylene took over 400 minutes to achieve 50% breakthrough at 50 ppm. When acetone at 50 ppm and perchloroethylene at 50 ppm were evaluated with 95% R.H. breakthrough times decreased to 39 and 144 minutes respectively, a nearly 70% decrease in time for both vapors. The results of this study show that non-certified respirators advertised as nuisance level relief may not offer protection for OV. Certified respirators show much more promise, but their performance is highly dependable upon the characteristics of the vapor and environment the respirators are being used in. Additional research is needed to increase our understanding of FFR-ACs performance under more conditions.

Clustering large data sets recently has emerged as an important area of research. The ever-increasing size of data sets and poor scalability of clustering algorithms has drawn attention to distributed clustering for partitioning large data sets. Sometimes, centrally pooling the distributed data is also expensive. There might be also constraints on data sharing between different distributed locations due to privacy, security, or proprietary nature of the data. In this work we propose an algorithm to cluster large-scale data sets without centrally pooling the data. Data at distributed sites are clustered independently i.e. without any communication among them. After partitioning the local/distributed sites we send only the centroids of each site to a central location. Thus there is very little bandwidth cost in a wide area network scenario. The distributed sites/subsets neither exchange cluster labels nor individual data features thus providing the framework for privacy preserving distributive clustering. Centroids from each local site form an ensemble of centroids at the central site. Our assumption is that data in all distributed locations are from the same underlying distribution and the set of centroids obtained by partitioning the data in each subset/distributed location gives us partial information about the position of the cluster centroids in that distribution. Now, the problem of finding a global partition using the limited knowledge of the ensemble of centroids can be viewed as the problem of reaching a global consensus on the position of cluster centroids. A global consensus on the position of cluster centroids of the global data using only the very limited statistics of the position of centroids from each local site is reached by grouping the centroids into consensus chains and computing the weighted mean of centroids in a consensus chain to represent a global cluster centroid. We compute the Euclidean distance of each example from the global set of centroids, and assign it to the centroid nearest to it. Experimental results show that quality of clusters generated by our algorithm is similar to the quality of clusters generated by clustering all the data at a time. We have shown that the disputed examples between the clusters generated by our algorithm and clustering all the data at a time lay on the border of clusters as expected. We also proposed a centroid-filtering algorithm to make partitions formed by our algorithm better.

Manual pavement condition surveys are gradually replaced by more comprehensive automated surveys conducted by multi-function highway evaluation vehicles. Highway evaluation vehicles are generally equipped with laser profiling, land navigation, and imaging sub-systems. The imaging system consists of three cameras; forward-view and side-view digital area-scan cameras for capturing images of traffic signs and right-of-way safety features, and a pavement digital line-scan or area-scan camera for capturing images of the pavement surface. In addition to the 3-laser and accelerometer-based profiling system, these vehicles are also equipped with differential global positioning equipment (DGPS) and an inertial measurement unit (IMU) for cross-slope, curvature and grade measurements. Digital imaging systems installed in automated highway evaluation vehicles are generally designed on modular basis where subsystems by different manufacturers are assembled to customize the system and fulfill the users' needs while minimizing the cost. In most such cases, manufacturers' specifications for a subsystem would not be reliable with respect to the eventual performance of that subsystem as part of the entire assembly. On the other hand, no guidelines are available for performance evaluation of imaging systems as assemblies of discrete subsystems. Moreover, images acquired by digital cameras can become contaminated by random noise affecting their quality and the ability of identifying important features. These issues have surfaced during the development and testing of the Florida Department of Transportation (FDOT) highway evaluation vehicle. This first phase of the work involved in this dissertation research concerns the study of basic criteria for evaluation of image quality through measurement of well-defined properties of images such as color reproduction, tone reproduction, detail reproduction, as well as the levels of noise, and optical distortion. Standard and reliable methods that can be adopted for evaluation of the above properties are discussed first. Then, by applying the above evaluation criteria to the imaging systems of the FDOT highway evaluation vehicle, it is shown how the sources of images sub-quality can be recognized and the optimum settings achieved. The second phase of the dissertation research is focused on the investigation of the sources of noise that can affect the digital line-scan distress images. As a result of this study, a novel technique was developed to filter out noise present in pavement distress images by using intensity measurement obtained from a standard grayscale target. In addition, a detailed experimental study was conducted to investigate the effect of the speed of evaluation and lighting conditions on the accuracy and repeatability of digital line-scan images in representing the actual distress condition of a pavement. The conclusions drawn from the second phase can be used to minimize the effect of noise on digital images of pavement distress and to improve the accuracy of evaluation of pavement cracks based on digital images. Hence the results of this study will certainly enhance the overall efficiency of the automated evaluation of pavement distress and highway features.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 128-136).

<p>The object of this thesis is to provide a MATLAB framework for nonlinear filtering in general, and particle filtering in particular. This is done by using the object-oriented programming paradigm, resulting in truly expandable code. Three types of discrete and nonlinear state-space models are supported by default, as well as three filter algorithms: the Extended Kalman Filter and the SIS and SIR particle filters. Symbolic expressions are differentiated automatically, which allows for comfortable EKF filtering. A graphical user interface is also provided to make the process of filtering even more convenient. By implementing a specified interface, programming new classes for use within the framework is easy and guidelines for this are presented.</p>

<p>The novel method of this thesis work is based on using quadrature filters to estimate an orientation tensor and to use the advantage of tensor information to control 3D adaptive filters. The adaptive filters are applied to enhance the Magnetic Resonance Angiography (MRA) images. The tubular structures are extracted from the volume dataset by using the quadrature filters. The idea of developing adaptive filtering in this thesis work is to enhance the volume dataset and suppress the image noise. Then the output of the adaptive filtering can be a clean dataset for segmentation of blood vessel structures to get appropriate volume visualization.</p><p>The local tensors are used to create the control tensor which is used to control adaptive filters. By evaluation of the tensor eigenvalues combination, the local structures like tubular structures and stenosis structures are extracted from the dataset. The method has been evaluated with synthetic objects, which are vessel models (for segmentation), and onion like synthetic object (for enhancement). The experimental results are shown on clinical images to validate the proposed method as well.</p>

Michael Triantafyllou, Marc Bodson, Michael Athans. / "July, 1982." / Bibliography: p. 118-120. / National Aeronautics and Space Administration and Langely Research Grant NGL-22-009-124

In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and effectively learn a near-optimal policy in a wide variety of settings. A sequence of increasingly complex empirical tests verifies the efficacy of this technique. / Singapore-MIT Alliance (SMA)