322 research outputs found

    Randomness Tests for Binary Sequences

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    Cryptography is vital in securing sensitive information and maintaining privacy in the today’s digital world. Though sometimes underestimated, randomness plays a key role in cryptography, generating unpredictable keys and other related material. Hence, high-quality random number generators are a crucial element in building a secure cryptographic system. In dealing with randomness, two key capabilities are essential. First, creating strong random generators, that is, systems able to produce unpredictable and statistically independent numbers. Second, constructing validation systems to verify the quality of the generators. In this dissertation, we focus on the second capability, specifically analyzing the concept of hypothesis test, a statistical inference model representing a basic tool for the statistical characterization of random processes. In the hypothesis testing framework, a central idea is the p-value, a numerical measure assigned to each sample generated from the random process under analysis, allowing to assess the plausibility of a hypothesis, usually referred to as the null hypothesis, about the random process on the basis of the observed data. P-values are determined by the probability distribution associated with the null hypothesis. In the context of random number generators, this distribution is inherently discrete but in the literature it is commonly approximated by continuous distributions for ease of handling. However, analyzing in detail the discrete setting, we show that the mentioned approximation can lead to errors. As an example, we thoroughly examine the testing strategy for random number generators proposed by the National Institute of Standards and Technology (NIST) and demonstrate some inaccuracies in the suggested approach. Motivated by this finding, we define a new simple hypothesis test as a use case to propose and validate a methodology for assessing the definition and implementation correctness of hypothesis tests. Additionally, we present an abstract analysis of the hypothesis test model, which proves valuable in providing a more accurate conceptual framework within the discrete setting. We believe that the results presented in this dissertation can contribute to a better understanding of how hypothesis tests operate in discrete cases, such as analyzing random number generators. In the demanding field of cryptography, even slight discrepancies between the expected and actual behavior of random generators can, in fact, have significant implications for data security

    FPGA-Based Implicit-Explicit Real-time Simulation Solver for Railway Wireless Power Transfer with Nonlinear Magnetic Coupling Components

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    Railway Wireless Power Transfer (WPT) is a promising non-contact power supply solution, but constructing prototypes for controller testing can be both costly and unsafe. Real-time hardware-in-the-loop simulation is an effective and secure testing tool, but simulating the dynamic charging process of railway WPT systems is challenging due to the continuous changes in the nonlinear magnetic coupling components. To address this challenge, we propose an FPGA-based half-step implicit-explicit (IMEX) simulation solver. The proposed solver adopts an IMEX algorithm to solve the piecewise linear and nonlinear parts of the system separately, which enables FPGAs to solve nonlinear components while achieving high numerical stability. Additionally, we divide a complete integration step into two half-steps to reduce computational time delays. Our proposed method offers a promising solution for the real-time simulation of railway WPT systems. The novelty of our approach lies in the use of the IMEX algorithm and the half-step integration method, which significantly improves the accuracy and efficiency of the simulation. Our simulations and experiments demonstrate the effectiveness and accuracy of the proposed solver, which provides a new approach for simulating and optimizing railway WPT systems with nonlinear magnetic coupling components

    3D Representation Learning for Shape Reconstruction and Understanding

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    The real world we are living in is inherently composed of multiple 3D objects. However, most of the existing works in computer vision traditionally either focus on images or videos where the 3D information inevitably gets lost due to the camera projection. Traditional methods typically rely on hand-crafted algorithms and features with many constraints and geometric priors to understand the real world. However, following the trend of deep learning, there has been an exponential growth in the number of research works based on deep neural networks to learn 3D representations for complex shapes and scenes, which lead to many cutting-edged applications in augmented reality (AR), virtual reality (VR) and robotics as one of the most important directions for computer vision and computer graphics. This thesis aims to build an intelligent system with dynamic 3D representations that can change over time to understand and recover the real world with semantic, instance and geometric information and eventually bridge the gap between the real world and the digital world. As the first step towards the challenges, this thesis explores both explicit representations and implicit representations by explicitly addressing the existing open problems in these areas. This thesis starts from neural implicit representation learning on 3D scene representation learning and understanding and moves to a parametric model based explicit 3D reconstruction method. Extensive experimentation over various benchmarks on various domains demonstrates the superiority of our method against previous state-of-the-art approaches, enabling many applications in the real world. Based on the proposed methods and current observations of open problems, this thesis finally presents a comprehensive conclusion with potential future research directions

    CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting

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    Contrastive Language-Image Pre-training (CLIP) starts to emerge in many computer vision tasks and has achieved promising performance. However, it remains underexplored whether CLIP can be generalized to 3D hand pose estimation, as bridging text prompts with pose-aware features presents significant challenges due to the discrete nature of joint positions in 3D space. In this paper, we make one of the first attempts to propose a novel 3D hand pose estimator from monocular images, dubbed as CLIP-Hand3D, which successfully bridges the gap between text prompts and irregular detailed pose distribution. In particular, the distribution order of hand joints in various 3D space directions is derived from pose labels, forming corresponding text prompts that are subsequently encoded into text representations. Simultaneously, 21 hand joints in the 3D space are retrieved, and their spatial distribution (in x, y, and z axes) is encoded to form pose-aware features. Subsequently, we maximize semantic consistency for a pair of pose-text features following a CLIP-based contrastive learning paradigm. Furthermore, a coarse-to-fine mesh regressor is designed, which is capable of effectively querying joint-aware cues from the feature pyramid. Extensive experiments on several public hand benchmarks show that the proposed model attains a significantly faster inference speed while achieving state-of-the-art performance compared to methods utilizing the similar scale backbone.Comment: Accepted In Proceedings of the 31st ACM International Conference on Multimedia (MM' 23

    Design and Control of Power Converters 2019

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    In this book, 20 papers focused on different fields of power electronics are gathered. Approximately half of the papers are focused on different control issues and techniques, ranging from the computer-aided design of digital compensators to more specific approaches such as fuzzy or sliding control techniques. The rest of the papers are focused on the design of novel topologies. The fields in which these controls and topologies are applied are varied: MMCs, photovoltaic systems, supercapacitors and traction systems, LEDs, wireless power transfer, etc

    Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

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    Power converters and electric machines represent essential components in all fields of electrical engineering. In fact, we are heading towards a future where energy will be more and more electrical: electrical vehicles, electrical motors, renewables, storage systems are now widespread. The ongoing energy transition poses new challenges for interfacing and integrating different power systems. The constraints of space, weight, reliability, performance, and autonomy for the electric system have increased the attention of scientific research in order to find more and more appropriate technological solutions. In this context, power converters and electric machines assume a key role in enabling higher performance of electrical power conversion. Consequently, the design and control of power converters and electric machines shall be developed accordingly to the requirements of the specific application, thus leading to more specialized solutions, with the aim of enhancing the reliability, fault tolerance, and flexibility of the next generation power systems

    Image and Video Forensics

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    Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

    Multimedia Forensics

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    This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

    Model-based Reinforcement Learning of Nonlinear Dynamical Systems

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    Model-based Reinforcement Learning (MBRL) techniques accelerate the learning task by employing a transition model to make predictions. In this dissertation, we present novel techniques for online learning of unknown dynamics by iteratively computing a feedback controller based on the most recent update of the model. Assuming a structured continuous-time model of the system in terms of a set of bases, we formulate an infinite horizon optimal control problem addressing a given control objective. The structure of the system along with a value function parameterized in the quadratic form provides flexibility in analytically calculating an update rule for the parameters. Hence, a matrix differential equation of the parameters is obtained, where the solution is used to characterize the optimal feedback control in terms of the bases, at any time step. Moreover, the quadratic form of the value function suggests a compact way of updating the parameters that considerably decreases the computational complexity. In the convergence analysis, we demonstrate asymptotic stability and optimality of the obtained learning algorithm around the equilibrium by revealing its connections with the analogous Linear Quadratic Regulator (LQR). Moreover, the results are extended to the trajectory tracking problem. Assuming a structured unknown nonlinear system augmented with the dynamics of a commander system, we obtain a control rule minimizing a given quadratic tracking objective function. Furthermore, in an alternative technique for learning, a piecewise nonlinear affine framework is developed for controlling nonlinear systems with unknown dynamics. Therefore, we extend the results to obtain a general piecewise nonlinear framework where each piece is responsible for locally learning and controlling over some partition of the domain. Then, we consider the Piecewise Affine (PWA) system with a bounded uncertainty as a special case, for which we suggest an optimization-based verification technique. Accordingly, given a discretization of the learned PWA system, we iteratively search for a common piecewise Lyapunov function in a set of positive definite functions, where a non-monotonic convergence is allowed. Then, this Lyapunov candidate is verified for the uncertain system. To demonstrate the applicability of the approaches presented in this dissertation, simulation results on benchmark nonlinear systems are included, such as quadrotor, vehicle, etc. Moreover, as another detailed application, we investigate the Maximum Power Point Tracking (MPPT) problem of solar Photovoltaic (PV) systems. Therefore, we develop an analytical nonlinear optimal control approach that assumes a known model. Then, we apply the obtained nonlinear optimal controller together with the piecewise MBRL technique presented previously

    Techniques améliorées pour la cryptanalyse des primitives symétriques

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    This thesis proposes improvements which can be applied to several techniques for the cryptanalysis of symmetric primitives. Special attention is given to linear cryptanalysis, for which a technique based on the fast Walsh transform was already known (Collard et al., ICISIC 2007). We introduce a generalised version of this attack, which allows us to apply it on key recovery attacks over multiple rounds, as well as to reduce the complexity of the problem using information extracted, for example, from the key schedule. We also propose a general technique for speeding key recovery attacks up which is based on the representation of Sboxes as binary decision trees. Finally, we showcase the construction of a linear approximation of the full version of the Gimli permutation using mixed-integer linear programming (MILP) optimisation.Dans cette thĂšse, on propose des amĂ©liorations qui peuvent ĂȘtre appliquĂ©es Ă  plusieurs techniques de cryptanalyse de primitives symĂ©triques. On dĂ©die une attention spĂ©ciale Ă  la cryptanalyse linĂ©aire, pour laquelle une technique basĂ©e sur la transformĂ©e de Walsh rapide Ă©tait dĂ©jĂ  connue (Collard et al., ICISC 2007). On introduit une version gĂ©nĂ©ralisĂ©e de cette attaque, qui permet de l'appliquer pour la rĂ©cupĂ©ration de clĂ© considerant plusieurs tours, ainsi que le rĂ©duction de la complexitĂ© du problĂšme en utilisant par example des informations provĂ©nantes du key-schedule. On propose aussi une technique gĂ©nĂ©rale pour accĂ©lĂ©rer les attaques par rĂ©cupĂ©ration de clĂ© qui est basĂ©e sur la reprĂ©sentation des boĂźtes S en tant que arbres binaires. Finalement, on montre comment on a obtenu une approximation linĂ©aire sur la version complĂšte de la permutation Gimli en utilisant l'optimisation par mixed-integer linear programming (MILP)
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