Region-Based Watermarking of Biometric Images: Case Study in Fingerprint Images

In this paper, a novel scheme to watermark biometric images is proposed. It exploits the fact that biometric images, normally, have one region of interest, which represents the relevant part of information processable by most of the biometric-based identification/authentication systems. This proposed scheme consists of embedding the watermark into the region of interest only; thus, preserving the hidden data from the segmentation process that removes the useless background and keeps the region of interest unaltered; a process which can be used by an attacker as a cropping attack. Also, it provides more robustness and better imperceptibility of the embedded watermark. The proposed scheme is introduced into the optimum watermark detection in order to improve its performance. It is applied to fingerprint images, one of the most widely used and studied biometric data. The watermarking is assessed in two well-known transform domains: the discrete wavelet transform (DWT) and the discrete Fourier transform (DFT). The results obtained are very attractive and clearly show significant improvements when compared to the standard technique, which operates on the whole image. The results also reveal that the segmentation (cropping) attack does not affect the performance of the proposed technique, which also shows more robustness against other common attacks

Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging

Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is possible to obtain usable estimates for these boundaries, even in the presence of large amounts of noise. The technique is applied to parametric boundary data and has potential applications in biomedical imaging. It should be considered as one of several techniques to improve the visualization of images

Self-Learning Power Control in Wireless Sensor Networks

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X

Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent. A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150m² can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery

Adaptation of the IEEE 802.11 protocol for inter-satellite links in LEO satellite networks

Knowledge of the coefficient of thermal expansion (CTE) of a ceramic material is important in many application areas. Whilst the CTE can be measured, it would be useful to be able to predict the expansion behaviour of multiphase materials.. There are several models for the CTE, however, most require a knowledge of the elastic properties of the constituent phases and do not take account ofthe microstructural features of the material. If the CTE could be predicted on the basis of microstructural information, this would then lead to the ability to engineer the microstructure of multiphase ceramic materials to produce acceptable thermal expansion behaviour. To investigate this possibility, magnesia-magnesium aluminate sp~el (MMAS) composites, consisting of a magnesia matrix and magnesium aluminate s~ne'l (MAS) particles, were studied. Having determined a procedure to produce MAS fr alumina and magnesia, via solid state sintering, magnesia-rich compositions wit ~ various magnesia contents were prepared to make the MMAS composites. Further, the l\.1MAS composites prepared from different powders (i.e. from an alumina-magnesia mixture ahd from a magnesia-spinel powder) were compared. Com starch was added into the powder mixtures before sintering to make porous microstructures. Microstructural development and thermal expansion behaviour ofthe MMAS composites were investigated. Microstructures of the MAS and the MMAS composites as well as their porous bodies were quaritified from backscattered electron micrographs in terms of the connectivity of solids i.e. solid contiguity by means of linear intercept counting. Solid contiguity decreased with increasing pore content and varied with pore size, pore shape and pore distribution whereas the phase contiguity depended strongly on the chemical composition and was less influenced by porosity. ' The thermal expansion behaviour of the MAS and the MMAS composites between 100 and 1000 °C was determined experimentally. Variation in the CTE ofthe MAS relates to the degree of spinel formation while the thermal expansion of the MMAS composites depends strongly on phase content. However, the MMAS composites with similar phase compositions but made from different manufacturing processes showed differences in microstructural features and thermal expansion behaviour. Predictions of the CTE values for composites based on a simple rule-of-mixtures (ROM) using volume fraction were compared with the measured data. A conventional ROM accurately predicted the effective CTE of a range of dense alumina-silicon carbide particulate composites but was not very accurate for porous multiphase structures. It provided an upper bound prediction as all experimental values were lower. Hence, the conventional ROM was modified to take account of quantitative microstructural parameters obtained from solid contiguity. The modified ROM predicted lower values and gave a good agreement with the experimental data. Thus, it has been shown that quantitative microstructural information can be used to predict the CTE of multiphase ceramic materials with complex microstructures.EThOS - Electronic Theses Online ServiceGBUnited Kingdo