Philipp Lamby

Compressed Sensing and Electron Microscopy

Compressed sensing (CS) is a relatively new approach to signal acquisition which has as its goal to minimize the number of measurements needed of the signal in order to guarantee that it is captured to a prescribed accuracy. It is natural to inquire whether this new subject has a role to play in electron microscopy (EM). In this chapter, we shall describe the foundations of CS and then examine which parts of this new theory may be useful in EM.

High-Quality Image Formation by Nonlocal Means Applied to High-Angle Annular Dark-Field Scanning Transmission Electron Microscopy (HAADF–STEM)

Abstract : We outline a new systematic approach to extracting high resolution information from HAADF-STEM images which will be beneficial to the characterization of beam sensitive materials. The idea is to treat several, possibly many low electron dose images with specially adapted digital image processing concepts at a minimum allowable spatial resolution. Our goal is to keep the overall cumulative electron dose as low as possible while still staying close to an acceptable level of physical resolution. We shall present the main conceptual imaging concepts and restoration methods that we believe are suitable for carrying out such a program and, in particular, allow one to correct special acquisition artifacts which result in blurring, aliasing, rastering distortions and noise.

Application of B-spline techniques to the modeling of airplane wings and numerical grid generation

In the present paper we give account of an effort that aimed at the unification of the whole geometric preprocessing that preceded the wind tunnel readings with a realistic airplane wing model in a recent research project. This preprocessing includes the automated generation of the CAD models which were used for the manufacturing of the multi-parted wing-fuselage configuration and the generation of the numerical grids for the corresponding numerical simulations. Due to the constraints of the project it was decided to employ only exact, watertight, untrimmed B-Spline representations.

A Unified Approach to the Modeling of Airplane Wings and Numerical Grid Generation Using B-Spline Representations

In this article we summarize the development of a unified platform for treating the entire range of geometric preprocessing tasks that preceded the wind tunnel readings and the numerical simulations performed in the collaborative research center SFB 401. In particular, this includes the automated generation of the CAD models which were used for manufacturing multi-parted wing-fuselage configurations as well as the generation of the numerical grids for the corresponding, adaptive numerical simulations.

Tree approximation of the long wave radiation parameterization in the NCAR CAM global climate model

The computation of Global Climate Models (GCMs) presents significant numerical challenges. This paper presents new algorithms based on sparse occupancy trees for learning and emulating the long wave radiation parameterization in the NCAR CAM climate model. This emulation occupies by far the most significant portion of the computational time in the implementation of the model. From the mathematical point of view this parameterization can be considered as a mapping R^2^2^0->R^3^3 which is to be learned from scattered data samples (x^i,y^i), i=1,...,N. Hence, the problem represents a typical application of high-dimensional statistical learning. The goal is to develop learning schemes that are not only accurate and reliable but also computationally efficient and capable of adapting to time-varying environmental states. The algorithms developed in this paper are compared with other approaches such as neural networks, nearest neighbor methods, and regression trees as to how these various goals are met.