An FPGA-Based System for Tracking Digital Information Transmitted via Peer-to-Peer Protocols

This thesis addresses the problem of identifying and tracking digital information that is shared using peer-to-peer file transfer and Voice over IP (VoIP) protocols. The goal of the research is to develop a system for detecting and tracking the illicit dissemination of sensitive government information using file sharing applications within a target network, and tracking terrorist cells or criminal organizations that are covertly communicating using VoIP applications. A digital forensic tool is developed using an FPGA-based embedded software application. The tool is designed to process file transfers using the BitTorrent peer-to-peer protocol and VoIP phone calls made using the Session Initiation Protocol (SIP). The tool searches a network for selected peer-to-peer control messages using payload analysis and compares the unique identifier of the file being shared or phone number being used against a list of known contraband files or phone numbers. If the identifier is found on the list, the control packet is added to a log file for later forensic analysis. Results show that the FPGA tool processes peer-to-peer packets of interest 92% faster than a software-only configuration and is 99.0% accurate at capturing and processing BitTorrent Handshake messages under a network traffic load of at least 89.6 Mbps. When SIP is added to the system, the probability of intercept for BitTorrent Handshake messages remains at 99.0% and the probability of intercept for SIP control packets is 97.6% under a network traffic load of at least 89.6 Mbps, demonstrating that the tool can be expanded to process additional peer-to-peer protocols with minimal impact on overall performance

Drive-Through Covered Charging Station for Battery-Powered Vehicles

The batteries of battery-powered vehicles can be charged with chargers that use mechanical contact plates which are prone to contamination and corrosion from the elements. Current hands-free charging mechanisms, while preferable, are not well suited for uncontrolled and unsupervised use in certain operating environments. This disclosure describes an outdoor contactless charging mechanism for vehicles powered by rechargeable batteries that addresses these issues. The mechanism employs a covered space with inductive charging primary antennas that are aligned with the vehicle secondary antennas with precision via a protected track with a center curb

Anisotropic Diffusion Stencils: From Simple Derivations over Stability Estimates to ResNet Implementations

Anisotropic diffusion processes with a diffusion tensor are important in image analysis, physics, and engineering. However, their numerical approximation has a strong impact on dissipative artefacts and deviations from rotation invariance. In this work, we study a large family of finite difference discretisations on a 3 x 3 stencil. We derive it by splitting 2-D anisotropic diffusion into four 1-D diffusions. The resulting stencil class involves one free parameter and covers a wide range of existing discretisations. It comprises the full stencil family of Weickert et al. (2013) and shows that their two parameters contain redundancy. Furthermore, we establish a bound on the spectral norm of the matrix corresponding to the stencil. This gives time step size limits that guarantee stability of an explicit scheme in the Euclidean norm. Our directional splitting also allows a very natural translation of the explicit scheme into ResNet blocks. Employing neural network libraries enables simple and highly efficient parallel implementations on GPUs

Deep spatial and tonal data optimisation for homogeneous diffusion inpainting

Difusion-based inpainting can reconstruct missing image areas with high quality from sparse data, provided that their location and their values are well optimised. This is particularly useful for applications such as image compression, where the original image is known. Selecting the known data constitutes a challenging optimisation problem, that has so far been only investigated with model-based approaches. So far, these methods require a choice between either high quality or high speed since qualitatively convincing algorithms rely on many time-consuming inpaintings. We propose the frst neural network architecture that allows fast optimisation of pixel positions and pixel values for homogeneous difusion inpainting. During training, we combine two optimisation networks with a neural network-based surrogate solver for difusion inpainting. This novel concept allows us to perform backpropagation based on inpainting results that approximate the solution of the inpainting equation. Without the need for a single inpainting during test time, our deep optimisation accelerates data selection by more than four orders of magnitude compared to common model-based approaches. This provides real-time performance with high quality results

CNN-based Euler's Elastica Inpainting with Deep Energy and Deep Image Prior

Euler's elastica constitute an appealing variational image inpainting model. It minimises an energy that involves the total variation as well as the level line curvature. These components are transparent and make it attractive for shape completion tasks. However, its gradient flow is a singular, anisotropic, and nonlinear PDE of fourth order, which is numerically challenging: It is difficult to find efficient algorithms that offer sharp edges and good rotation invariance. As a remedy, we design the first neural algorithm that simulates inpainting with Euler's Elastica. We use the deep energy concept which employs the variational energy as neural network loss. Furthermore, we pair it with a deep image prior where the network architecture itself acts as a prior. This yields better inpaintings by steering the optimisation trajectory closer to the desired solution. Our results are qualitatively on par with state-of-the-art algorithms on elastica-based shape completion. They combine good rotation invariance with sharp edges. Moreover, we benefit from the high efficiency and effortless parallelisation within a neural framework. Our neural elastica approach only requires 3x3 central difference stencils. It is thus much simpler than other well-performing algorithms for elastica inpainting. Last but not least, it is unsupervised as it requires no ground truth training data.Comment: In Proceedings of the 10th European Workshop on Visual Information Processing, Lisbon, 202

Connections Between Numerical Algorithms for PDEs and Neural Networks

We investigate numerous structural connections between numerical algorithms for partial differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set of mathematical foundations from the world of PDEs to neural networks. Besides structural insights, we provide concrete examples and experimental evaluations of the resulting architectures. Using the example of generalised nonlinear diffusion in 1D, we consider explicit schemes, acceleration strategies thereof, implicit schemes, and multigrid approaches. We connect these concepts to residual networks, recurrent neural networks, and U-net architectures. Our findings inspire a symmetric residual network design with provable stability guarantees and justify the effectiveness of skip connections in neural networks from a numerical perspective. Moreover, we present U-net architectures that implement multigrid techniques for learning efficient solutions of partial differential equation models, and motivate uncommon design choices such as trainable nonmonotone activation functions. Experimental evaluations show that the proposed architectures save half of the trainable parameters and can thus outperform standard ones with the same model complexity. Our considerations serve as a basis for explaining the success of popular neural architectures and provide a blueprint for developing new mathematically well-founded neural building blocks

An FPGA-Based System for Tracking Digital Information Transmitted Via Peer-to-Peer Protocols

This paper presents a Field Programmable Gate Array (FPGA)-based tool designed to process file transfers using the BitTorrent Peer-to-Peer (P2P) protocol and VoIP phone calls made using the Session Initiation Protocol (SIP). The tool searches selected control messages in real time and compares the unique identifier of the shared file or phone number against a list of known contraband files or phone numbers. Results show the FPGA tool processes P2P packets of interest 92% faster than a software-only configuration and is 97.6% accurate at capturing and processing messages at a traffic load of 89.6 Mbps

Tactical Themes for Rangeland Research

The problems threatening the conservation and management of rangeland, over one-half of the world’s terrestrial surfaces, are significant and growing. Current assessments of drivers and externalities shaping these problems have resulted in strategies intended to result in sustainable development of these lands and their resources. However, how can individual scientists and individual research programs support the needed strategies and goals? What can we realistically contribute and accomplish? We believe that technology can connect individual scientists and their science to the problems manifest in rangelands over the world, in a more rapid exchange than has occurred in the past. Recognition of local challenges, innovations, and scientific tests of the effectiveness of our technological solutions to these problems can keep pace with rapid change and help us adapt to that change. However, to do this, we have to invest in a process of connecting science to landscapes. Our tactics are to link, openly and collaboratively, the scientific method to discrete, specific, managed landscapes. We term these collective tactics, our fundamental research theme, “Landscape Portals”. All of the elements of this theme exist currently, to various degrees, but they lack cohesion and interactive, real-time connections. Future investment requires two basic, tactical scientific behaviors: a post-normal application of science in support of land management by hypothesis and a scientific method modified to accommodate a data intensive scientific inquiry directed towards adaptive management. These behaviors support our “Landscape Portals” theme: science conducted in a highly interactive, transparent, data enriched, locally relevant, globally connected, popularly translated, and ecologically robust manner

Liver resection or combined chemoembolization and radiofrequency ablation improve survival in patients with hepatocellular carcinoma

Background/ Aims: To evaluate the long-term outcome of surgical and non-surgical local treatments of patients with hepatocellular carcinoma (HCC). Methods: We stratified a cohort of 278 HCC patients using six independent predictors of survival according to the Vienna survival model for HCC (VISUM- HCC). Results: Prior to therapy, 224 HCC patients presented with VISUM stage 1 (median survival 18 months) while 29 patients were classified as VISUM stage 2 (median survival 4 months) and 25 patients as VISUM stage 3 (median survival 3 months). A highly significant (p < 0.001) improved survival time was observed in VISUM stage 1 patients treated with liver resection ( n = 52; median survival 37 months) or chemoembolization (TACE) and subsequent radiofrequency ablation ( RFA) ( n = 44; median survival 45 months) as compared to patients receiving chemoembolization alone (n = 107; median survival 13 months) or patients treated by tamoxifen only (n = 21; median survival 6 months). Chemoembolization alone significantly (p <= 0.004) improved survival time in VISUM stage 1 - 2 patients but not (p = 0.341) in VISUM stage 3 patients in comparison to those treated by tamoxifen. Conclusion: Both liver resection or combined chemoembolization and RFA improve markedly the survival of patients with HCC