Computational Studies of Liquid Droplet on Liquid Infused Surfaces

Surfaces that can repel any liquid are highly desired in various aspect of life for their wide range of beneficial applications. They can be utilised to enhance production processes, to simplify maintenance processes, to preserve surfaces from fouling, and many more. Ironically, one effective way to repel a liquid from a surface is by infusing the surface with another liquid which acts as a lubricant. Such surfaces are called liquid infused surfaces (LIS). The presence of the lubricant introduces rich interplay between the interfacial properties of the solid surface and the other fluid phases, which leads to many new interfacial phenomena. We employ two numerical methods for studying the behaviour of a liquid droplet on a LIS. First, we use the lattice Boltzmann method, which is powerful for studying the dynamic evolution of the system. Second, we use the phase field energy minimisation method, which is efficient for finding the equilibrium states of the system. In this thesis, we show how these numerical methods can be exploited to explore a wide range of LIS parameters and to confirm our theoretical predictions. We start by examining the equilibrium properties of the LIS system and demonstrate that the droplet morphology strongly depends on the choice of liquids used for the droplet-lubricant combination. The droplet morphology, in turn, affects the translational and rotational dynamics of the droplet under the influence of an external body force. Interestingly, we found a complex interplay between contact line pinning and viscous dissipation at the lubricant ridge, which become dominant at large and small apparent angles, respectively. Our investigations further demonstrate that the relative importance of viscous dissipation at the lubricant ridge depends on the drop to lubricant viscosity ratio, as well as on the shape of the wetting ridge. Next, we demonstrate spontaneous bidirectional motion of droplets on liquid infused surfaces in the presence of a topographical gradient, in which the droplets can move either toward the denser or the sparser solid fraction area. We show that the key factor determining the direction of motion is the preferential wetting of the droplet on the solid surface and on the lubricant film, which depends on the choice of the droplet-lubricant combination. Finally, we study how the pinning force of droplet on LIS is controlled by the solid surface fraction, the lubricant wetting angles, and the various fluid surface tensions. We derive an analytical prediction for contact angle hysteresis and numerically test the theory. We also discuss why a droplet on a liquid infused surface with partially wetting lubricants typically experiences stronger pinning compared to a droplet on a classical superhydrophobic surface

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Acquisition, Modeling, and Augmentation of Reflectance for Synthetic Optical Flow Reference Data

This thesis is concerned with the acquisition, modeling, and augmentation of material reflectance to simulate high-fidelity synthetic data for computer vision tasks. The topic is covered in three chapters: I commence with exploring the upper limits of reflectance acquisition. I analyze state-of-the-art BTF reflectance field renderings and show that they can be applied to optical flow performance analysis with closely matching performance to real-world images. Next, I present two methods for fitting efficient BRDF reflectance models to measured BTF data. Both methods combined retain all relevant reflectance information as well as the surface normal details on a pixel level. I further show that the resulting synthesized images are suited for optical flow performance analysis, with a virtually identical performance for all material types. Finally, I present a novel method for augmenting real-world datasets with physically plausible precipitation effects, including ground surface wetting, water droplets on the windshield, and water spray and mists. This is achieved by projecting the realworld image data onto a reconstructed virtual scene, manipulating the scene and the surface reflectance, and performing unbiased light transport simulation of the precipitation effects

Automatic Reconstruction of Textured 3D Models

Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models

An Overview of BRDF Models

This paper is focused on the Bidirectional Reflectance Distribution Function (BRDF) in the context of algorithms for computational production of realistic synthetic images. We provide a review of most relevant analytical BRDF models proposed in the literature which have been used for realistic rendering. We also show different approaches used for obtaining efficient models from acquired reflectance data, and the related function fitting techniques, suitable for using that data in efficient rendering algorithms. We consider algorithms for computation of BRDF integrals, by using Monte-Carlo based numerical integration. In this context, we review known techniques to design efficient BRDF sampling schemes for both analytical and measured BRDF models.The authors have been partially supported by the Spanish Research Program under project TIN2004-07672-C03-02 and the Andalusian Research Program under project P08-TIC-03717

Action recognition from RGB-D data

In recent years, action recognition based on RGB-D data has attracted increasing attention. Different from traditional 2D action recognition, RGB-D data contains extra depth and skeleton modalities. Different modalities have their own characteristics. This thesis presents seven novel methods to take advantages of the three modalities for action recognition. First, effective handcrafted features are designed and frequent pattern mining method is employed to mine the most discriminative, representative and nonredundant features for skeleton-based action recognition. Second, to take advantages of powerful Convolutional Neural Networks (ConvNets), it is proposed to represent spatio-temporal information carried in 3D skeleton sequences in three 2D images by encoding the joint trajectories and their dynamics into color distribution in the images, and ConvNets are adopted to learn the discriminative features for human action recognition. Third, for depth-based action recognition, three strategies of data augmentation are proposed to apply ConvNets to small training datasets. Forth, to take full advantage of the 3D structural information offered in the depth modality and its being insensitive to illumination variations, three simple, compact yet effective images-based representations are proposed and ConvNets are adopted for feature extraction and classification. However, both of previous two methods are sensitive to noise and could not differentiate well fine-grained actions. Fifth, it is proposed to represent a depth map sequence into three pairs of structured dynamic images at body, part and joint levels respectively through bidirectional rank pooling to deal with the issue. The structured dynamic image preserves the spatial-temporal information, enhances the structure information across both body parts/joints and different temporal scales, and takes advantages of ConvNets for action recognition. Sixth, it is proposed to extract and use scene flow for action recognition from RGB and depth data. Last, to exploit the joint information in multi-modal features arising from heterogeneous sources (RGB, depth), it is proposed to cooperatively train a single ConvNet (referred to as c-ConvNet) on both RGB features and depth features, and deeply aggregate the two modalities to achieve robust action recognition