Multi-dimensional digital signals have become an intertwined part of day to day life, from digital images and videos used to capture and share life experiences, to more powerful scene representations such as light field images, which open the gate to previously challenging tasks, such as post capture refocusing or eliminating visible occlusions from a scene. This dissertation delves into the world of multi-dimensional signal processing and introduces a tool of particular use for gradient based solutions of well-known signal processing problems. Specifically, a technique to reconstruct a signal from a given gradient data set is developed in the case of two dimensional (2-D), three dimensional (3-D) and four dimensional (4-D) digital signals. The reconstruction technique is multiresolution in nature, and begins by using the given gradient to generate a multi-dimensional Haar wavelet decomposition of the signals of interest, and then reconstructs the signal by Haar wavelet synthesis, performed on successive resolution levels. The challenges in developing this technique are non-trivial and are brought about by the applications at hand. For example, in video content replacement, the gradient data from which a video sequence needs to be reconstructed is a combination of gradient values that belong to different video sequences. In most cases, such operations disrupt the conservative nature of the gradient data set. The effects of the non-conservative nature of the newly generated gradient data set are attenuated by using an iterative Poisson solver at each resolution level during the reconstruction. A second and more important challenge is brought about by the increase in signal dimensionality. In a previous approach, an intermediate extended signal with symmetric region of support is obtained, and the signal of interest is extracted from it. This approach is reasonable in 2-D, but becomes less appealing as the signal dimensionality increases. To avoid generating data that is then discarded, a new approach is proposed, in which signal extension is no longer performed. Instead, different procedures are suggested to generate a non-symmetric Haar wavelet decomposition of the signals of interest. In the case of 2-D and 3-D signals, ways to obtain this decomposition exactly from the given gradient data and the average value of the signal are proposed. In addition, ways to approximate a subset of decomposition coefficients are introduced and the visual consequences of such approximations are studied in the special case of 2-D digital images. Several ways to approximate the same subset of decomposition coefficients are developed in the special case of 4-D light field images. Experiments run on various 2-D, 3-D and 4-D test signals are included to provide an insight on the performance of the reconstruction technique. The value of the multi-dimensional reconstruction technique is then demonstrated by including it in a number of signal processing applications. First, an efficient algorithm is developed with the purpose of combining information from the gradient of a set of 2-D images with different regions in focus or different exposure times, with the purpose of generating an all-in-focus image or revealing details that were lost due to improper exposure setting. Moving on to 3-D signal processing applications, two video editing problems are studied and gradient based solutions are presented. In the first one, the objective is to seamlessly place content from one video sequence in another, while in the second one, to combine elements from two video sequences and generate a transparency effect. Lastly, a gradient based technique for editing 4-D scene representations (light fields) is presented, as well as a technique to combine information from two light fields with the purpose of generating a light field with more details of the imaged scene. All these applications show that the developed technique is a reliable tool for gradient domain based solutions of signal processing problems. / Graduate

abstract: Moore's law has been the most important driving force for the tremendous progress of semiconductor industry. With time the transistors which form the fundamental building block of any integrated circuit have been shrinking in size leading to smaller and faster electronic devices.As the devices scale down thermal effects and the short channel effects become the important deciding factors in determining transistor architecture.SOI (Silicon on Insulator) devices have been excellent alternative to planar MOSFET for ultimate CMOS scaling since they mitigate short channel effects. Hence as a part of thesis we tried to study the benefits of the SOI technology especially for lower technology nodes when the channel thickness reduces down to sub 10nm regime. This work tries to explore the effects of structural confinement due to reduced channel thickness on the electrostatic behavior of DG SOI MOSFET. DG SOI MOSFET form the Qfinfet which is an alternative to existing Finfet structure. Qfinfet was proposed and patented by the Finscale Inc for sub 10nm technology nodes. As part of MS Thesis we developed electrostatic simulator for DG SOI devices by implementing the self consistent full band Schrodinger Poisson solver. We used the Empirical Pseudopotential method in conjunction with supercell approach to solve the Schrodinger Equation. EPM was chosen because it has few empirical parameters which give us good accuracy for experimental results. Also EPM is computationally less expensive as compared to the atomistic methods like DFT(Density functional theory) and NEGF (Non-equilibrium Green's function). In our workwe considered two crystallographic orientations of Si,namely [100] and [110]. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

abstract: The quest to find efficient algorithms to numerically solve differential equations isubiquitous in all branches of computational science. A natural approach to address this problem is to try all possible algorithms to solve the differential equation and choose the one that is satisfactory to one's needs. However, the vast variety of algorithms in place makes this an extremely time consuming task. Additionally, even after choosing the algorithm to be used, the style of programming is not guaranteed to result in the most efficient algorithm. This thesis attempts to address the same problem but pertinent to the field of computational nanoelectronics, by using PETSc linear solver and SLEPc eigenvalue solver packages to efficiently solve Schrödinger and Poisson equations self-consistently. In this work, quasi 1D nanowire fabricated in the GaN material system is considered as a prototypical example. Special attention is placed on the proper description of the heterostructure device, the polarization charges and accurate treatment of the free surfaces. Simulation results are presented for the conduction band profiles, the electron density and the energy eigenvalues/eigenvectors of the occupied sub-bands for this quasi 1D nanowire. The simulation results suggest that the solver is very efficient and can be successfully used for the analysis of any device with two dimensional confinement. The tool is ported on www.nanoHUB.org and as such is freely available. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

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A high resolution spectrally accurate three-dimensional flow solver is developed in order to simulate convection dominated fluid flows. The governing incompressible Navier Stokes equations along with the energy equation for temperature are discretized using a second-order accurate projection method which utilizes Adams Bashforth and Backward Differentiation formula for temporal discretization of the non-linear convective and linear viscous terms, respectively. Spatial discretization is performed using a Fourier/Chebyshev spectral method. Extensive tests on three-dimensional Taylor Couette flow are performed and it is shown that the method successfully captures the different states ranging from formation of Taylor vortices to wavy vortex regime. Next, the code is validated for convection dominated flows through a comprehensive comparison of the results for two dimensional Rayleigh Benard convection with the theoretical and experimental results from the literature. Finally, fully parallel simulations, with efficient utilization of computational resources and memory, are performed on a model three-dimensional axially homogeneous Rayleigh Benard convection problem in order to explore the high Rayleigh number flows and to test the scaling of global properties.

Dans cette thèse, nous présentons notre travail de recherche dans le domaine du calcul haute performance en mécanique des fluides. Avec la demande croissante de simulations à haute résolution, il est devenu important de développer des solveurs numériques pouvant tirer parti des architectures récentes comprenant des processeurs multi-cœurs et des accélérateurs. Nous nous proposons dans cette thèse de développer un solveur efficace pour la résolution sur architectures hétérogènes CPU/GPU des équations de Navier-Stokes (NS) relatives aux écoulements 3D de fluides incompressibles.Tout d'abord nous présentons un aperçu de la mécanique des fluides avec les équations de NS pour fluides incompressibles et nous présentons les méthodes numériques existantes. Nous décrivons ensuite le modèle mathématique, et la méthode numérique choisie qui repose sur une technique de prédiction-projection incrémentale.Nous obtenons une distribution équilibrée de la charge de calcul en utilisant une méthode de décomposition de domaines. Une parallélisation à deux niveaux combinée avec de la vectorisation SIMD est utilisée dans notre implémentation pour exploiter au mieux les capacités des machines multi-cœurs. Des expérimentations numériques sur différentes architectures parallèles montrent que notre solveur NS obtient des performances satisfaisantes et un bon passage à l'échelle.Pour améliorer encore la performance de notre solveur NS, nous intégrons le calcul sur GPU pour accélérer les tâches les plus coûteuses en temps de calcul. Le solveur qui en résulte peut être configuré et exécuté sur diverses architectures hétérogènes en spécifiant le nombre de processus MPI, de threads, et de GPUs.Nous incluons également dans ce manuscrit des résultats de simulations numériques pour des benchmarks conçus à partir de cas tests physiques réels. Les résultats obtenus par notre solveur sont comparés avec des résultats de référence. Notre solveur a vocation à être intégré dans une future bibliothèque de mécanique des fluides pour le calcul sur architectures parallèles CPU/GPU. / In this PhD thesis, we present our research in the domain of high performance software for computational fluid dynamics (CFD). With the increasing demand of high-resolution simulations, there is a need of numerical solvers that can fully take advantage of current manycore accelerated parallel architectures. In this thesis we focus more specifically on developing an efficient parallel solver for 3D incompressible Navier-Stokes (NS) equations on heterogeneous CPU/GPU architectures. We first present an overview of the CFD domain along with the NS equations for incompressible fluid flows and existing numerical methods. We describe the mathematical model and the numerical method that we chose, based on an incremental prediction-projection method.A balanced distribution of the computational workload is obtained by using a domain decomposition method. A two-level parallelization combined with SIMD vectorization is used in our implementation to take advantage of the current distributed multicore machines. Numerical experiments on various parallel architectures show that this solver provides satisfying performance and good scalability.In order to further improve the performance of the NS solver, we integrate GPU computing to accelerate the most time-consuming tasks. The resulting solver can be configured for running on various heterogeneous architectures by specifying explicitly the numbers of MPI processes, threads and GPUs. This thesis manuscript also includes simulation results for two benchmarks designed from real physical cases. The computed solutions are compared with existing reference results. The code developed in this work will be the base for a future CFD library for parallel CPU/GPU computations.

With the rapidly growing computational power of modern computers, physically based rendering has found its way into real world applications. Real-time simulations and renderings of fire and smoke had become one major research interest in modern video game industry, and will continue being one important research direction in computer graphics. To visually recreate realistic dynamic fire and smoke is a complicated problem. Furthermore, to solve the problem requires knowledge from various areas, ranged from computer graphics and image processing to computational physics and chemistry. Even though most of the areas are well-studied separately, when combined, new challenges will emerge. This thesis focuses on three aspects of the problem, dynamic, real-time and realism, to propose a solution in form of a GPGPU pipeline, along with its implementation. Three main areas with application in the problem are discussed in detail: fluid simulation, volumetric radiance estimation and volumetric rendering. The weights are laid upon the first two areas. The results are evaluated around the three aspects, with graphical demonstrations and performance measurements. Uniform grids are used with Finite Difference (FD) discretization scheme to simplify the computation. FD schemes are easy to implement in parallel, especially with ComputeShader, which is well supported in Unity engine. The whole implementation can easily be integrated into any real-world applications in Unity or other game engines that support DirectX 11 or higher.