Research on digital image watermark encryption based on hyperchaos

The digital watermarking technique embeds meaningful information into one or more watermark images hidden in one image, in which it is known as a secret carrier. It is difficult for a hacker to extract or remove any hidden watermark from an image, and especially to crack so called digital watermark. The combination of digital watermarking technique and traditional image encryption technique is able to greatly improve anti-hacking capability, which suggests it is a good method for keeping the integrity of the original image. The research works contained in this thesis include: (1)A literature review the hyperchaotic watermarking technique is relatively more advantageous, and becomes the main subject in this programme. (2)The theoretical foundation of watermarking technologies, including the human visual system (HVS), the colour space transform, discrete wavelet transform (DWT), the main watermark embedding algorithms, and the mainstream methods for improving watermark robustness and for evaluating watermark embedding performance. (3) The devised hyperchaotic scrambling technique it has been applied to colour image watermark that helps to improve the image encryption and anti-cracking capabilities. The experiments in this research prove the robustness and some other advantages of the invented technique. This thesis focuses on combining the chaotic scrambling and wavelet watermark embedding to achieve a hyperchaotic digital watermark to encrypt digital products, with the human visual system (HVS) and other factors taken into account. This research is of significant importance and has industrial application value

Classification of Biomedical Signals using the Dynamics of the False Nearest Neighbours (DFNN) Algorithm

* This study was supported in part by the Natural Sciences and Engineering Research Council of Canada, and by the Gastrointestinal Motility Laboratory (University of Alberta Hospitals) in Edmonton, Alberta, Canada.Accurate and efficient analysis of biomedical signals can be facilitated by proper identification based on their dominant dynamic characteristics (deterministic, chaotic or random). Specific analysis techniques exist to study the dynamics of each of these three categories of signals. However, comprehensive and yet adequately simple screening tools to appropriately classify an unknown incoming biomedical signal are still lacking. This study is aimed at presenting an efficient and simple method to classify model signals into the three categories of deterministic, random or chaotic, using the dynamics of the False Nearest Neighbours (DFNN) algorithm, and then to utilize the developed classification method to assess how some specific biomedical signals position with respect to these categories. Model deterministic, chaotic and random signals were subjected to state space decomposition, followed by specific wavelet and statistical analysis aiming at deriving a comprehensive plot representing the three signal categories in clearly defined clusters. Previously recorded electrogastrographic (EGG) signals subjected to controlled, surgically-invoked uncoupling were submitted to the proposed algorithm, and were classified as chaotic. Although computationally intensive, the developed methodology was found to be extremely useful and convenient to use

Entropy in Dynamic Systems

In order to measure and quantify the complex behavior of real-world systems, either novel mathematical approaches or modifications of classical ones are required to precisely predict, monitor, and control complicated chaotic and stochastic processes. Though the term of entropy comes from Greek and emphasizes its analogy to energy, today, it has wandered to different branches of pure and applied sciences and is understood in a rather rough way, with emphasis placed on the transition from regular to chaotic states, stochastic and deterministic disorder, and uniform and non-uniform distribution or decay of diversity. This collection of papers addresses the notion of entropy in a very broad sense. The presented manuscripts follow from different branches of mathematical/physical sciences, natural/social sciences, and engineering-oriented sciences with emphasis placed on the complexity of dynamical systems. Topics like timing chaos and spatiotemporal chaos, bifurcation, synchronization and anti-synchronization, stability, lumped mass and continuous mechanical systems modeling, novel nonlinear phenomena, and resonances are discussed

Measurements, Models, Systems and Design

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Periodic solutions of autonomous systems under discretization

The existence of a sequence of periodic trajectories of a general one-step numerical scheme corresponding to a null sequence of constant time-steps is established under the assumption that the autonomous ordinary differential equation has an isolated periodic solution with non-zero topological index. The convergence of the linearly interpolated numerical curve to the original invariant curve with respect to the Hausdorff metric is also shown

Optical Signal Processing With Discrete-Space Metamaterials

As digital circuits are approaching the limits of Moore’s law, a great deal of efforthas been directed to alternative computing approaches. Among them, the old concept of optical signal processing (OSP) has attracted attention, revisited in the light of metamaterials and nano-photonics. This approach has been successful in realizing basic mathematical operations, such as derivatives and integrals, but it is difficult to be applied to more complex ones. Inspired by digital filters, we propose a radically new OSP approach, able to realize arbitrary mathematical operations over a nano-photonic platform. We demonstrate this concept for the case of spatial differentiation, image compression and color encoding through a heuristic design based on a waveguide with periodic arrays of input/output channels at its opposite walls

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

Signal processing approaches to diagnosis of esophageal motility disorders

Esophageal Motility Disorders (EGMDs) are a group of abnormalities characterized by the muscular dysfunction of the esophagus in the transportation of food from the oral cavity to the stomach. EGMDs typically cause chronic problems and affect a vast and ever-increasing number of the global population. The diagnosis of EGMDs mainly relies on a key test presently used to study the esophagus motility, known as esophageal manometry (EGM). EGM involves pressure measurements inside the esophagus, which provide information pertaining to its contractions. The diagnosis process is mainly based on visual inspection of the EGM test results to find certain characteristics of the manometric patterns. There are several factors that make such inspection tedious. For instance, manometry test results are often contaminated with a considerable amount of noise, (e.g. noise from external environment) and artifacts, (e.g. respiration artifacts) leading to a longer and more complex diagnosis process. As such, the diagnosis based on visual inspection is prone to human error and demands extensive amount of expert's time. This thesis introduces new signal processing approaches to provide an accurate means for the diagnosis of EGMDs as well as to reduce the amount of time spent on the diagnosis process. Specifically, a new technique known as wavelet decomposition (WD) is applied to the filtering of the EGM data. A nonlinear pulse detection technique (NPDT) is applied to the de-noised data leading to extraction of diagnostically important information i.e. esophageal pulses. Such information is used to generate a model using a statistical pulse modeling (SPM) technique, which can classify the EGM patterns. The proposed approaches are applied to the EGM data of 20 patients and compared with those from existing techniques. Such comparisons illustrate the advantages of the proposed approaches in terms of accuracy and efficiency. As part of this thesis, a new circuit-based approach is proposed for the treatment of Gastroesophageal Reflux Disease (GERD), i.e. the most prevalent disease caused by EGMDs. The objective is to provide a framework for further research towards the implementation of the proposed approach for GERD treatment