Non-local methods for InSAR parameters estimation

In the thesis work the nonlocal paradigm has been investigated in the framework of Multitemporal SAR Interferometry, e.g. Differential Interferometry, Tomography, etc., and single InSAR pair, e.g. DEM generation. In the former, Adaptive Multi-Looking methods have been developed for the generation of interferometric data-stacks. Following the nonlocal approach, the proposed methods rely only on similar pixels according to a suitable similarity measure that exploits the stack's temporal information. An hybrid approach that jointly uses the nonlocal paradigm and transform domain filtering has been investigated for InSAR pair phase estimation. On the track of the BM3D and SARBM3D algorithms, different approaches to the filtering in the transform domain are investigated. Furthermore, a novel approach to the similarity computation and filtering, based on a relative-topography content of the interferometric phase rather than its absolute value, is proposed

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Thickness estimation, automated classification and novelty detection in ultrasound images of the plantar fascia tissues

The plantar fascia (PF) tissue plays an important role in the movement and the stability of the foot during walking and running. Thus it is possible for the overuse and the associated medical problems to cause injuries and some severe common diseases. Ultrasound (US) imaging offers significant potential in diagnosis of PF injuries and monitoring treatments. Despite the advantages of US, the generated PF images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. This limits the use of US in clinical practice and therefore impacts on patient services for what is a common problem and a major cause of foot pain and discomfort. It is therefore a requirement to devise an automated system that allows better and easier interpretation of PF US images during diagnosis. This study is concerned with developing a computer-based system using a combination of medical image processing techniques whereby different PF US images can be visually improved, segmented, analysed and classified as normal or abnormal, so as to provide more information to the doctors and the clinical treatment department for early diagnosis and the detection of the PF associated medical problems. More specifically, this study is required to investigate the possibility of a proposed model for localizing and estimating the PF thickness a cross three different sections (rearfoot, midfoot and forefoot) using a supervised ANN segmentation technique. The segmentation method uses RBF artificial neural network module in order to classify small overlapping patches into PF and non-PF tissue. Feature selection technique was performed as a post-processing step for feature extraction to reduce the number of the extracted features. Then the trained RBF-ANN is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and a proposed area-length calculation algorithm. Additionally, different machine learning approaches were investigated and applied to the segmented PF region in order to distinguish between symptomatic and asymptomatic PF subjects using the best normalized and selected feature set. This aims to facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Finally, a novelty detection framework for detecting the symptomatic PF samples (with plantar fasciitis disorder) using only asymptomatic samples is proposed. This model implies the following: feature analysis, building a normality model by training the one-class SVDD classifier using only asymptomatic PF training datasets, and computing novelty scores using the trained SVDD classifier, training and testing asymptomatic datasets, and testing symptomatic datasets of the PF dataset. The performance evaluation results showed that the proposed approaches used in this study obtained favourable results compared to other methods reported in the literature

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

Aeronautical engineering: A cumulative index to a continuing bibliography

This bibliography is a cumulative index to the abstracts contained in NASA SP-7037(210) through NASA SP-7037(221) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract number, report number, and accession number indexes

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor