Improved digital watermarking schemes using DCT and neural techniques

The present thesis investigates the copyright protection by utilizing the digital watermarking of images. The basic spatial domain technique DCT based frequency based technique were studied and simulated. Most recently used Neural Network based DCT Scheme is also studied and simulated. The earlier used Back Propagation Network (BPN) is replaced by Radial Basis Function Neural Network (RBFNN) in the proposed scheme to improve the robustness and overall computation requirements. Since RBFNN requires less number of weights during training, the memory requirement is also less as compared to BPN. Keywords : Digital Watermarking, Back Propagation Network (BPN), Hash Function, Radial Basis Function Neural Network (RBFNN), and Discrete Cosine Transform (DCT). Watermarking can be considered as a special technique of steganography where one message is embedded in another and the two messages are related to each other in some way. The most common examples of watermarking are the presence of specific patterns in currency notes, which are visible only when the note is held to light, and logos in the background of printed text documents. The watermarking techniques prevent forgery and unauthorized replication of physical objects. In digital watermarking a low-energy signal is imperceptibly embedded in another signal. The low-energy signal is called the watermark and it depicts some metadata, like security or rights information about the main signal. The main signal in which the watermark is embedded is referred to as the cover signal since it covers the watermark. In recent years the ease with which perfect copies can be made has lead large-scale unauthorized copying, which is a great concern to the music, film, book and software publishing industries. Because of this concern over copyright issues, a number of technologies are being developed to protect against illegal copying. One of these technologies is the use of digital watermarks. Watermarking embeds an ownership signal directly into the data. In this way, the signal is always present with the data. Analysis Digital watermarking techniques were implemented in the frequency domain using Discrete Cosine Transform (DCT). The DCT transforms a signal or image from the spatial domain to the frequency domain. Also digital watermarking was implemented using Neural Networks such as: 1. Back Propagation Network (BPN) 2. Radial Basis Function Neural Network (RBFNN) Digital watermarking using RBFNN was proposed which improves both security and robustness of the image. It is based on the Cover’s theorem which states that nonlinearly separable patterns can be separated linearly if the pattern is cast nonlinearly into a higher dimensional space. RBFNN contains an input layer, a hidden layer with nonlinear activation functions and an output layer with linear activation functions. Results The following results were obtained:- 1. The DCT based method is more robust than that of the LSB based method in the tested possible attacks. DCT method can achieve the following two goals: The first is that illegal users do not know the location of the embedded watermark in the image. The second is that a legal user can retrieve the embedded watermark from the altered image. 2. The RBFNN network is easier to train than the BPN network. The main advantage of the RBFNN over the BPN is the reduced computational cost in the training stage, while maintaining a good performance of approximation. Also less number of weights are required to be stored or less memory requirements for the verification and testing in a later stage

Explainable Machine Learning for Hydrogen Diffusion in Metals and Random Binary Alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared-errors between 98-119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies.By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importances, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. This framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.Comment: 36 pages, 8 figures, supplemental materia

Engineering the Electron-Hole Bilayer Tunneling Field-Effect Transistor

The electron-hole (EH) bilayer tunneling field-effect transistor promises to eliminate heavy-doping band tails enabling a smaller subthreshold swing voltage. Nevertheless, the electrostatics of a thin structure must be optimized for gate efficiency. We analyze the tradeoff between gate efficiency versus ON-state conductance to find the optimal device design. Once the EH bilayer is optimized for a given ON-state conductance, Si, Ge, and InAs all have similar gate efficiency, around 40%-50%. Unlike Si and Ge, only the InAs case allows a manageable work function difference for EH bilayer transistor operation.National Science Foundation (U.S.). Center for Energy Efficient Electronics Science (Award 0939514

Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning

An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures and chemistry they can exhibit. While black box machine learning-based algorithms can predict these properties with some success, they do not directly provide the basis on which these predictions are made, therefore complicating the a priori design of novel materials exhibiting a desired property value. In this work we demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H2 pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. Elucidation of this simple structure–property relationship is valid across a range of compositions, metal substitutions, and structural classes exhibited by intermetallic hydrides. This permits rational targeting of novel intermetallics for high-pressure hydrogen storage (low-stability hydrides) by their descriptor values, and we predict a known intermetallic to form a low-stability hydride (as confirmed by density functional theory calculations) that has not yet been experimentally investigated

Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability

Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet, the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial and error. Data-driven materials’ discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here, we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitate a data-driven approach that does not rely on exact crystal structures to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based on not only targeted thermodynamic properties but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70× increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient material selection for various applications, especially in areas such as metal hydride-based hydrogen compressors, actuators, and heat pumps