250 research outputs found
An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network
The low-cost, user-friendly, and convenient nature of Automatic Fingerprint
Recognition Systems (AFRS) makes them suitable for a wide range of
applications. This spreading use of AFRS also makes them vulnerable to various
security threats. Presentation Attack (PA) or spoofing is one of the threats
which is caused by presenting a spoof of a genuine fingerprint to the sensor of
AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure
intended to protect AFRS against fake or spoof fingerprints created using
various fabrication materials. In this paper, we have proposed a Convolutional
Neural Network (CNN) based technique that uses a Generative Adversarial Network
(GAN) to augment the dataset with spoof samples generated from the proposed
Open Patch Generator (OPG). This OPG is capable of generating realistic
fingerprint samples which have no resemblance to the existing spoof fingerprint
samples generated with other materials. The augmented dataset is fed to the
DenseNet classifier which helps in increasing the performance of the
Presentation Attack Detection (PAD) module for the various real-world attacks
possible with unknown spoof materials. Experimental evaluations of the proposed
approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and
2019 competition databases. An overall accuracy of 96.20\%, 94.97\%, and
92.90\% has been achieved on the LivDet 2015, 2017, and 2019 databases,
respectively under the LivDet protocol scenarios. The performance of the
proposed PAD model is also validated in the cross-material and cross-sensor
attack paradigm which further exhibits its capability to be used under
real-world attack scenarios
Optimal Surface Fitting of Point Clouds Using Local Refinement : Application to GIS Data
This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines
Automated characterisation of Deep-sea imagery using Machine Learning: implications for future conservation and mineral extraction
This thesis aimed to develop a methodology using Machine Learning (ML) techniques for the interpretation of deep-sea resources. The deep-sea hosts diverse ecosystems and valuable resources, but potential environmental implications, particularly from mining activities, necessitate effective management strategies. Detailed maps of the sea floor are therefore a necessity, yet such maps have to date only been produced based on manual interpretation which is time consuming and subjective. The study focused on assessing the potential of ML methods to map deep-sea features based on photomosaic and bathymetry data in order to take the first steps in developing an automated, objective, and time-saving technique. This thesis’s method accurately identified and classified features like chimneys at the hydrothermal vent fields, providing insights for resource interpretation and conservation. Integrating ML methods into deep-sea resource management is crucial. The methodology enhances understanding of complex techniques, such as Convolutional Neural Networks (CNN) and Object-Based Image Analysis (OBIA) to overcome a seabed characterization. Simultaneously describing the parameters utilised to achieve a meaningful classification. ML algorithms analyze large data volumes, extract patterns, and predict feature distributions, aiding targeted conservation measures and sustainable resource exploitation. The methodology successfully mapped hydrothermal chimneys in two study areas yet producer accuracies (0,7%) were higher than user accuracies (0,64%), indicating that there were other landforms that shared similar features. The methodology also helps assess potential environmental implications of future mining, supporting informed decision-making and mitigation strategies. It serves also as a foundation for future research to aim at overcoming problems related to incomplete spatial coverage, attempt to better utilize shape and spatial parameters within the OBIA refinement, try to identify more background classes for excluding them from the model, etc.Master's Thesis in Earth ScienceGEOV399MAMN-GEO
Heterotypic Interactions in the Complex Environments of Living Tissue
Collective cell behavior such as the formation of boundaries and collective cell motion is crucial for numerous biological functions including development, wound healing, and homeostasis. In this thesis, I investigate how changes to heterotypic behavior can drive collective behavior in models for confluent tissue, tissue with no gaps between cells. First, I examine how cell collectives can integrate signals from their environment to climb biochemical gradients when individual cells cannot. We identify two possible mechanisms that could drive this collective climbing behavior and develop an open-source framework that can be used to couple a biochemical gradient to any intercellular interaction. I also show that the advection of this gradient by cells has a minor impact in physically relevant regimes. Next, I construct a graph neural network to make predictions about the fluidity of cell tissue based on the tissue structure. Using this framework the neural network accurately predicts shear modulus and edge tensions in a spring vertex model. Next, we analyze the differences between the 3D vertex and Voronoi models. The systems share the same energy and many of the same geometric properties of cell tissue on heterotypic interfaces. However, we discover that there are differences in cell orientation on the interface boundary between cell types driven by a difference in discontinuous restoring force for cells to exit this boundary. Then, we examine the stratified epithelium as a model system with many layers of heterotypic cell interfaces. We identify changes to heterotypic interfacial tension as one possible mechanism for cells to migrate through tissue boundaries. We also create a toy model to accurately represent the integrin-based adhesions between cells and extracellular matrix in real tissue and use this model as a way to inform a similar addition to the 3D vertex model. Finally, we create a model for hair follicle development in the stratified epithelium. In conjunction with our experimental collaborators, we identify a dominant mechanism for the cell shape and tissue morphology changes seen during development. The model predicts a difference in tissue flow between the mechanisms investigated that is confirmed by experiments. All of the work I have done demonstrates how changes to individual cells, especially changes to heterotypic interactions, can drive large-scale changes in tissue behavior
Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab
The agriculture sector, due to its significant dependence on climate patterns and water availability, is highly vulnerable to changing climate patterns. Pakistan is an agrarian economy with 30% of its land area under cultivation and 93% of its water resources being utilized for agricultural production. Therefore, the changing climate patterns may adversely affect the agriculture and water resources of the country. This study was conducted to assess the climate variations over the major agro-climatic zones of Sindh and Punjab, which serve as an important hub for the production of major food and cash crops in Pakistan. For this purpose, the climate data of 21 stations were analyzed using the Mann–Kendall test and Sen's slope estimator method for the period 1990–2022. The results obtained from the analysis revealed that, in Sindh, the mean annual temperature rose by ~0.1 to 1.4°C, with ~0.1 to 1.2°C in cotton-wheat Sindh and 0.8 to 1.4°C in rice-other Sindh during the study period. Similarly, in Punjab, the mean annual temperature increased by ~0.1 to 1.0°C, with 0.6 to 0.9°C in cotton-wheat Punjab and 0.2 to 0.6°C in rainfed Punjab. Seasonally, warming was found to be highest during the spring season. The precipitation analysis showed a rising annual precipitation trend in Sindh (+30 to +60 mm) and Punjab (+100 to 300 mm), while the monsoon precipitation increased by ~50 to 200 mm. For winter precipitation, an upward trend was found in mixed Punjab, while the remaining stations showed a declining pattern. Conclusively, the warming temperatures as found in the analysis may result in increased irrigation requirements, soil moisture desiccation, and wilting of crops, ultimately leading to low crop yield and threatening the livelihoods of local farmers. On the other hand, the increasing precipitation may favor national agriculture in terms of less freshwater withdrawals. However, it may also result in increased rainfall-induced floods inundating the crop fields and causing water logging and soil salinization. The study outcomes comprehensively highlighted the prevailing climate trends over the important agro-climatic zones of Pakistan, which may aid in devising an effective climate change adaptation and mitigation strategy to ensure the state of water and food security in the country
Optimal Surface Fitting of Point Clouds Using Local Refinement
This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines.publishedVersio
Human History and Digital Future
Korrigierter Nachdruck. Im Kapitel "Wallace/Moullou: Viability of Production and Implementation of Retrospective Photogrammetry in Archaeology" wurden die Acknowledgemens enfternt.The Proceedings of the 46th Annual Conference on Computer Applications and Quantitative Methods in Archaeology, held between March 19th and 23th, 2018 at the University of TĂĽbingen, Germany, discuss the current questions concerning digital recording, computer analysis, graphic and 3D visualization, data management and communication in the field of archaeology. Through a selection of diverse case studies from all over the world, the proceedings give an overview on new technical approaches and best practice from various archaeological and computer-science disciplines
Discussing Changes in Historical Human–Environmental Dynamics Through Ecosystem Services Interactions and Future Scenarios in a Rural-Mining Region of Central Appalachians
The aim of this dissertation was to investigate how recent processes of land-change induced by humans contributed to the shaping and alteration of the current landscape in a headwater system of Central Appalachians in West Virginia (US), to understand the interactions and tradeoffs among ecosystems services and address potential solutions for targeting more sustainable human-environment interactions in a region that is deeply grounded on extractive economies. The multitiered objective was addressed through different research phases in order to unfold and disentangle a series of complex problems that the study area presents. Three main phases were used; they corresponded to distinct chapters within this study.
The first paper analyzed land-cover transitions, from 1976 to 2016, using Multi-Level Intensity Analysis and Difference Components methods. Two land cover classifications were derived explicitly for this study using remote sensing methods and obtained with segmentation analysis and machine learning algorithms from historical high-resolution aerial images (1-2 meters) and ancillary data. Results allowed the author to distinguish between surface mining areas produced before and after the enactment of the Surface Mining Control and Reclamation Act (SMCRA, 1977), discuss differences among distinct socio-technical phases, and differentiate the main drivers and outcomes of landscape change processes in the area.
The historical information and knowledge gained in the first step were used to inform the second chapter, whose objective was to analyze the interactions among ecosystem services and derive their bundles. Ecosystem services models were obtained using InVEST, and a custom model was explicitly defined to link water quality changes to freshwater ecosystem services. The results identified significant losses of carbon sequestration, habitat quality, and freshwater ecosystem services in areas subjected to Mountaintop Removal mining. The findings spatially located different ecosystem services bundles characterized by distinct human-environment relationships and complex anthropogenic drivers not limited to coal mining processes. The study identified the appropriate spatial scale for targeting specific management actions and implementing conservation, as well as development-restoration strategies, in areas characterized by similar social-ecological processes and deeply altered ecosystems.
In the third essay, the identification of ecosystem services bundles allowed the author to delineate two distinct social-ecological systems characterized by surface coal extraction and reclamation processes produced during different historical phases. These areas were discussed as separate case studies within a time interval of seventy years, from the recent past (1976) to future scenarios (2045). The scenarios were based on a backcasting approach integrated by ecosystem services models and the analysis of functional changes within the two social-ecological units analyzed. The results highlighted differences in the flow of ecosystem services due to the intensity of mining and the different and incremental reclamation approaches used in the scenarios. The comparison of threats and opportunities within each scenario, identified, in the discussion section, a range of plausible hypotheses and solutions the stakeholders and communities of the region should face if they want to rehabilitate the social and ecological conditions to promote a more sustainable approach for the future of these places
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo SimĂŁo Diniz Dalmolin
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