Algorithms and performance analysis for indoor location tracking systems

Ph.DDOCTOR OF PHILOSOPH

Precision autonomous underwater navigation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003.Includes bibliographical references (p. 175-185).Deep-sea archaeology, an emerging application of autonomous underwater vehicle (AUV) technology, requires precise navigation and guidance. As science requirements and engineering capabilities converge, navigating in the sensor-limited ocean remains a fundamental challenge. Despite the logistical cost, the standards of archaeological survey necessitate using fixed acoustic transponders - an instrumented navigation environment. This thesis focuses on the problems particular to operating precisely within such an environment by developing a design method and a navigation algorithm. Responsible documentation, through remote sensing images, distinguishes archaeology from salvage, and fine-resolution imaging demands precision navigation. This thesis presents a design process for making component and algorithm level tradeoffs to achieve system-level performance satisfying the archaeological standard. A specification connects the functional requirements of archaeological survey with the design parameters of precision navigation. Tools based on estimation fundamentals - the Cram6r-Rao lower bound and the extended Kalman filter - predict the system-level precision of candidate designs. Non-dimensional performance metrics generalize the analysis results. Analyzing a variety of factors and levels articulates the key tradeoffs: sensor selection, acoustic beacon configuration, algorithm selection, etc. The abstract analysis is made concrete by designing a survey and navigation system for an expedition to image the USS Monitor. Hypothesis grid (Hgrid) is both a representation of the sensed environment and an algorithm for building the representation. Range observations measuring the line-of-sight distance between two acoustic transducers are subject to multipath errors and spurious returns.The quality of this measurement is dependent on the location of the estimator. Hgrids characterize the measurement quality by generating a priori association probabilities - the belief that subsequent measurements will correspond to the direct-path, a multipath, or an outlier - as a function of the estimated location. The algorithm has three main components: the mixed-density sensor model using Gaussian and uniform probability distributions, the measurement classification and multipath model identification using expectation-maximization (EM), and the grid-based spatial representation. Application to data from an autonomous benthic explorer (ABE) dive illustrates the algorithm and shows the feasibility of the approach.by Brian Steven Bingham.Ph.D

Change-point Problem and Regression: An Annotated Bibliography

The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as the change-point problem or, in the Eastern literature, as disorder . The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis. Numerous methodological approaches have been implemented in examining change-point models. Maximum-likelihood estimation, Bayesian estimation, isotonic regression, piecewise regression, quasi-likelihood and non-parametric regression are among the methods which have been applied to resolving challenges in change-point problems. Grid-searching approaches have also been used to examine the change-point problem. Statistical analysis of change-point problems depends on the method of data collection. If the data collection is ongoing until some random time, then the appropriate statistical procedure is called sequential. If, however, a large finite set of data is collected with the purpose of determining if at least one change-point occurred, then this may be referred to as non-sequential. Not surprisingly, both the former and the latter have a rich literature with much of the earlier work focusing on sequential methods inspired by applications in quality control for industrial processes. In the regression literature, the change-point model is also referred to as two- or multiple-phase regression, switching regression, segmented regression, two-stage least squares (Shaban, 1980), or broken-line regression. The area of the change-point problem has been the subject of intensive research in the past half-century. The subject has evolved considerably and found applications in many different areas. It seems rather impossible to summarize all of the research carried out over the past 50 years on the change-point problem. We have therefore confined ourselves to those articles on change-point problems which pertain to regression. The important branch of sequential procedures in change-point problems has been left out entirely. We refer the readers to the seminal review papers by Lai (1995, 2001). The so called structural change models, which occupy a considerable portion of the research in the area of change-point, particularly among econometricians, have not been fully considered. We refer the reader to Perron (2005) for an updated review in this area. Articles on change-point in time series are considered only if the methodologies presented in the paper pertain to regression analysis

Energy Technology and Management

The civilization of present age is predominantly dependent on energy resources and their utilization. Almost every human activity in today's life needs one or other form of energy. As world's energy resources are not unlimited, it is extremely important to use energy efficiently. Both energy related technological issues and policy and planning paradigms are highly needed to effectively exploit and utilize energy resources. This book covers topics, ranging from technology to policy, relevant to efficient energy utilization. Those academic and practitioners who have background knowledge of energy issues can take benefit from this book

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Towards real-world clinical colonoscopy deep learning models for video-based bowel preparation and generalisable polyp segmentation

Colorectal cancer is the most prevalence type of cancers within the digestive system. Early screening and removal of precancerous growths in the colon decrease mortality rate. The golden standard screening type for colon is colonoscopy which is conducted by a medical expert (i.e., colonoscopist). Nevertheless, due to human biases, fatigue, and experience level of the colonoscopist, colorectal cancer missing rate is negatively affected. Artificial intelligence (AI) methods hold immense promise not just in automating colonoscopy tasks but also enhancing the performance of colonoscopy screening in general. The recent development of intense computational GPUs enabled a computational-demanding AI method (i.e., deep learning) to be utilised in various medical applications. However, given the gap between the clinical-practice and the proposed deep learning models in the literature, the actual effectiveness of such methods is questionable. Hence, this thesis highlights such gaps that arises from the separation between the theoretical and practical aspect of deep learning methods applied to colonoscopy. The aim is to evaluate the current state of deep learning models applied in colonoscopy from a clinical angle, and accordingly propose better evaluation strategies and deep learning models. The aim is translated into three distinct objectives. The first objective is to develop a systematic evaluation method to assess deep learning models from a clinical perspective. The second objective is to develop a novel deep learning architecture that leverages spatial information within colonoscopy videos to enhance the effectiveness of deep learning models on real-clinical environments. The third objective is to enhance the generalisability of deep learning models on unseen test images by developing a novel deep learning framework. To translate these objectives into practice, two critical colonoscopy tasks, namely, automatic bowel preparation and polyp segmentation are attacked. In both tasks, subtle overestimations are found in the literature and discussed in the thesis theoretically and demonstrated empirically. These overestimations are induced by improper validation sets that would not appear or represent the real-world clinical environment. Arbitrary dividing colonoscopy datasets to do deep learning evaluation can result in producing similar distributions, hence, achieving unrealistic results. Accordingly, these factors are considered in the thesis to avoid such subtle overestimation. For the automatic bowel preparation task, colonoscopy videos that closely resemble clinical settings are considered as input and accordingly it necessitates the design of the proposed model as well as evaluation experiments. The proposed model’s architecture is designed to utilise both temporal and spatial information within colonoscopy videos using Gated Recurrent Unit (GRU) and a proposed Multiplexer unit, respectively. Meanwhile for the polyp segmentation task, the efficiency of current deep learning models is tested in terms of their generalisation capabilities using unseen test sets from different medical centres. The proposed framework consists of two connected models. The first model is responsible for gradually transforming textures of input images and arbitrary change their colours. Meanwhile the second model is a segmentation model that outlines polyp regions. Exposing the segmentation model to such transformed images acquires the segmentation model texture/colour invariant properties, hence, enhances the generalisability of the segmentation model. In this thesis, rigorous experiments are conducted to evaluate the proposed models against the state-of-the-art models. The yielded results indicate that the proposed models outperformed the state-of-the-art models under different settings

Towards real-world clinical colonoscopy deep learning models for video-based bowel preparation and generalisable polyp segmentation

Colorectal cancer is the most prevalence type of cancers within the digestive system. Early screening and removal of precancerous growths in the colon decrease mortality rate. The golden standard screening type for colon is colonoscopy which is conducted by a medical expert (i.e., colonoscopist). Nevertheless, due to human biases, fatigue, and experience level of the colonoscopist, colorectal cancer missing rate is negatively affected. Artificial intelligence (AI) methods hold immense promise not just in automating colonoscopy tasks but also enhancing the performance of colonoscopy screening in general. The recent development of intense computational GPUs enabled a computational-demanding AI method (i.e., deep learning) to be utilised in various medical applications. However, given the gap between the clinical-practice and the proposed deep learning models in the literature, the actual effectiveness of such methods is questionable. Hence, this thesis highlights such gaps that arises from the separation between the theoretical and practical aspect of deep learning methods applied to colonoscopy. The aim is to evaluate the current state of deep learning models applied in colonoscopy from a clinical angle, and accordingly propose better evaluation strategies and deep learning models. The aim is translated into three distinct objectives. The first objective is to develop a systematic evaluation method to assess deep learning models from a clinical perspective. The second objective is to develop a novel deep learning architecture that leverages spatial information within colonoscopy videos to enhance the effectiveness of deep learning models on real-clinical environments. The third objective is to enhance the generalisability of deep learning models on unseen test images by developing a novel deep learning framework. To translate these objectives into practice, two critical colonoscopy tasks, namely, automatic bowel preparation and polyp segmentation are attacked. In both tasks, subtle overestimations are found in the literature and discussed in the thesis theoretically and demonstrated empirically. These overestimations are induced by improper validation sets that would not appear or represent the real-world clinical environment. Arbitrary dividing colonoscopy datasets to do deep learning evaluation can result in producing similar distributions, hence, achieving unrealistic results. Accordingly, these factors are considered in the thesis to avoid such subtle overestimation. For the automatic bowel preparation task, colonoscopy videos that closely resemble clinical settings are considered as input and accordingly it necessitates the design of the proposed model as well as evaluation experiments. The proposed model’s architecture is designed to utilise both temporal and spatial information within colonoscopy videos using Gated Recurrent Unit (GRU) and a proposed Multiplexer unit, respectively. Meanwhile for the polyp segmentation task, the efficiency of current deep learning models is tested in terms of their generalisation capabilities using unseen test sets from different medical centres. The proposed framework consists of two connected models. The first model is responsible for gradually transforming textures of input images and arbitrary change their colours. Meanwhile the second model is a segmentation model that outlines polyp regions. Exposing the segmentation model to such transformed images acquires the segmentation model texture/colour invariant properties, hence, enhances the generalisability of the segmentation model. In this thesis, rigorous experiments are conducted to evaluate the proposed models against the state-of-the-art models. The yielded results indicate that the proposed models outperformed the state-of-the-art models under different settings

Aeronautical Engineering, a continuing bibliography with indexes, supplement 173

This bibliography lists 704 reports, articles and other documents introduced into the NASA scientific and technical information system in March 1984

Energy: A continuing bibliography with indexes (Issue 29)

This bibliography lists 1360 reports, articles, and other documents introduced into the NASA scientific and technical information system from January 1, 1981 through March 31, 1981