Dissecting global air traffic data to discern different types and trends of transnational human mobility

Human mobility across national borders is a key phenomenon of our time. At the global scale, however, we still know relatively little about the structure and nature of such transnational movements. This study uses a large dataset on monthly air passenger traffic between 239 countries worldwide from 2010 to 2018 to gain new insights into (a) mobility trends over time and (b) types of mobility. A time series decomposition is used to extract a trend and a seasonal component. The trend component permits—at a higher level of granularity than previous sources—to examine the development of mobility between countries and to test how it is affected by policy and infrastructural changes, economic developments, and violent conflict. The seasonal component allows, by measuring the lag between initial and return motion, to discern different types of mobility, from tourism to seasonal work migration. Moreover, the exact shape of seasonal mobility patterns is extracted, allowing to identify regular mobility peaks and nadirs throughout the year. The result is a unique classification of trends and types of mobility for a global set of country pairs. A range of implications and possible applications are discussed. Open-Access-Publikationsfonds 2019 Document type: Articl

Biometric identification with 3D fingerprints acquired through optical coherence tomography

Orientador : Prof. Dr. Luciano SilvaCoorientador : Profª. Olga Regina Pereira BellonTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 28/06/2016Inclui referências : f. 75-82Área de concentraçãoResumo: Um método para se obter impressões digitais 3D da derme e da epiderme a partir de imagens em alta resolução adquiridas utilizando Tomografia de Coerência Ótica (OCT) é proposto neste trabalho. Este método, resolve limitações das técnicas de reconstrução 3D de impressões digitais que empregam múltiplas câmeras/triangulação ou iluminação estruturada, tais como variações de resolução do centro para as bordas das impressões digitais 3D causadas por erros de reconstrução, sensibilidade a baixa iluminação e contraste insuficiente. Uma técnica de busca e identificação baseados em padrões inovativos, os "mapas KH " (usados para a segmentação de regiões de superfície em imagens de intensidade e de profundidade), extraídos computando as curvaturas Gaussiana (K) e média (H) de uma região de interesse na vizinhança das minúcias (denominada nuvem de minúcia), é apresentada. Grandes bases de mapas KH, uma para cada nuvem de minúcia identificada, podem ser construídos com essa técnica. A estratégia de busca e identificação, em duas etapas, baseia-se primeiro em padrões locais de gradientes (LGP) dos mapas KH, para reduzir o espaço de busca dentro da base, seguidos de uma comparação que utiliza uma medida de similaridade, a correlação cruzada normalizada dos padrões pré-selecionados com o LGP com os que se quer identificar. A acuracidade do método e sua compatibilidade com os métodos correntes, comparável ou superior à dos métodos 2D, é verificada através da identificação biométrica de impressões digitais 3D utilizando duas bases de imagens, uma adquirida através da tecnologia OCT e a outra gentilmente cedida pela Universidade Politécnica de Hong Kong. A base de imagens OCT, a primeira adquirida com essa tecnologia, é composta de imagens coletadas de onze voluntários em duas sessões de escaneamento e contém imagens de dedos de pessoas com diferentes idades, gênero e etnias e contém casos de cicatrizes, calos e alterações, tais como abrasão e arranhões. Uma base de impressões digitais 2D, obtida dos mesmos voluntários através de um leitor regular de impressões digitais, foi adquirida para permitir uma comparação da técnica proposta com os métodos de identificação tradicionais. A aplicabilidade do método proposto à identificação de impressões digitais alteradas, deterioradas acidentalmente ou intencionalmente, é investigada. Nesses casos, a impressão digital 3D extraída da derme e compatível com a da epiderme é empregada. A identificação destas impressões 3D alteradas é testada utilizando a base de imagens adquiridas com OCT. A acuracidade da técnica é comparada com a obtida utilizando os métodos tradicionais 2D usando os gráficos de taxas de Falsa Aceitação e Falsa Rejeição (FAXxFRR) e de Características Cumulativas de Identificação (CMC). Impressões digitais 2D, extraídas a partir das impressões digitais 3D simulando o rolamento do dedo durante a aquisição (rolamento virtual), foram geradas e sua compatibilidade com as bases de imagens 2D foi testada. Um conjunto de medidas de avaliação de qualidade foram aplicados às bases de imagens de impressões digitais 3D e sua correspondência aos escores de identificação foi analisada para determinar aqueles que podem contribuir para melhorar a acuracidade da identificação. Palavras-chave: Impressões digitais 3D. Identificação Biométrica. Tomografia de Coerência Ótica.Abstract: A method to obtain epidermal and dermal 3D fingerprints from high-resolution images acquired using Optical Coherence Tomography (OCT) is proposed. This method addresses limitations of current 3D reconstruction techniques that employ multiple cameras/triangulation or structured illumination such as depth and resolution variations from the center to the borders of the fingerprint caused by reconstruction errors, sensitivity to low illumination and poor contrast. The availability of these 3D fingerprints allowed the creation of new matching methods that benefit from the rich information available in 3D. A 3D fingerprint matching technique based on novel patterns, the KH maps (used to surface region segmentation in range and intensity images), extracted by computing the Gaussian and mean curvatures (SILVA; BELLON; GOTARDO, 2001) from a region of interest around the minutiae, named minutiae clouds is presented. Large databases of KH maps, one for each identified minutiae cloud can be built. The matching strategy, a two-step approach, relies on local gradient patterns (LGP) of the KH maps to narrow the search space, followed by a similarity matching, the normalized cross correlation of patterns being matched. The accuracy and matching compatibility, comparable or improved in relation to the 2D matching methods, is verified through matching 3D fingerprints from two databases one acquired using OCT and a public database gently made available by the Hong Kong Polytechnic University. The OCT database, the first 3D database acquired using Optical Coherence Tomography, to our knowledge, is made of images collected from eleven volunteers in two scanning sessions and contains images of people of different ages, genders and ethnicities and also cases of scars, calluses and alterations as abrasion and scratches. A 2D fingerprint database, scanned from the same volunteers using a regular fingerprint reader was also obtained for comparison with traditional matching methods. We investigate the applicability of our method to the identification of altered fingerprints, damaged unintentionally or accidentally. In these cases, the 3D dermal fingerprint, compatible with the epidermis fingerprint, is employed. Matching with 3D dermal and epidermal fingerprints is tested in the OCT database. Matching accuracy is compared with the obtained using traditional matching 2D methods by using False Acceptance and False rejection rate (FARxFRR) and Cumulative Matching Characteristics (CMC) graphs. Unwrapped fingerprints, 2D fingerprints extracted from 3D fingerprints by virtual unrolling were generated and tested for compatibility with 2D databases. A set of quality evaluation measures were employed to the 3D fingerprint databases and their correspondence to the matching scores was analyzed to identify those that can contribute to improve the matching accuracy. Key-words: 3D Fingerprints. Biometric identification. Optical Coherence Tomography

Information Extraction and Data Fusion for Nondestructive Evaluation of Concrete Bridge Decks

The objective of the dissertation is to improve and extend the application of impact echo (IE) and ground penetrating radar (GRP) methods in the field of concrete condition evaluation. At the beginning, an ensemble empirical mode decomposition (EEMD) approach is proposed to decompose the IE testing data into different spectral composition for defect signal extraction. The EEMD approach overcomes the challenge of extracting reflected P-wave from the IE signal that may contain strong surface wave. Then, to realize direct visualization of internal defects of concrete structures, an automated data fusion and visualization process is developed based on IE testing with source-receiver arrays. Both the simulation and experimental results demonstrate that the proposed method can effectively extract delamination regions from the IE data. In the end, the f-x variational mode decomposition (f-x VMD) method is adopted to remove the direct wave clutter of GPR Data from RC bridge decks, which are the main problem hindering the discrimination of the target of interest. The superiority and effectiveness of proposed methods are demonstrated in simulation, experiment, and field test environments over the average background subtraction method and F–K filter with dip relaxation method.Ph.D