Computing the output distribution and selection probabilities of a stack filter from the DNF of its positive Boolean function

Many nonlinear filters used in practise are stack filters. An algorithm is presented which calculates the output distribution of an arbitrary stack filter S from the disjunctive normal form (DNF) of its underlying positive Boolean function. The so called selection probabilities can be computed along the way.Comment: This is the version published in Journal of Mathematical Imaging and Vision, online first, 1 august 201

Consensus Based Networking of Distributed Virtual Environments

Distributed Virtual Environments (DVEs) are challenging to create as the goals of consistency and responsiveness become contradictory under increasing latency. DVEs have been considered as both distributed transactional databases and force-reflection systems. Both are good approaches, but they do have drawbacks. Transactional systems do not support Level 3 (L3) collaboration: manipulating the same degree-of-freedom at the same time. Force-reflection requires a client-server architecture and stabilisation techniques. With Consensus Based Networking (CBN), we suggest DVEs be considered as a distributed data-fusion problem. Many simulations run in parallel and exchange their states, with remote states integrated with continous authority. Over time the exchanges average out local differences, performing a distribued-average of a consistent, shared state. CBN aims to build simulations that are highly responsive, but consistent enough for use cases such as the piano-movers problem. CBN's support for heterogeneous nodes can transparently couple different input methods, avoid the requirement of determinism, and provide more options for personal control over the shared experience. Our work is early, however we demonstrate many successes, including L3 collaboration in room-scale VR, 1000's of interacting objects, complex configurations such as stacking, and transparent coupling of haptic devices. These have been shown before, but each with a different technique; CBN supports them all within a single, unified system

Pupil wavefront manipulation for the compensation of mask topography effects in optical nanolithography

As semiconductor optical lithography is pushed to smaller dimensions, resolution enhancement techniques have been required to maintain process yields. For some time, the customization of illumination coherence at the source plane has allowed for the control of diffraction order distribution across the projection lens pupil. Phase shifting at the mask plane has allowed for some phase control as well. However, geometries smaller than the imaging wavelength introduce complex wavefront effects that cannot be corrected at source or mask planes. Three dimensional mask topography effects can cause a pitch dependent defocus (δBF), which can decrease the useable depth of focus (UDOF) across geometry of varying density. Wavefront manipulation at the lens pupil plane becomes necessary to provide the degrees of freedom needed to correct for such effects. The focus of this research is the compensation of such wavefront phase error realized through manipulation of the lens pupil plane, specifically in the form of spherical aberration. The research does not attempt to improve the process window for one particular feature, but rather improve the UDOF in order to make layouts with multiple pitches possible for advanced technology nodes. The research approach adopted in this dissertation includes rigorous simulation, analytical modeling, and experimental measurements. Due to the computational expense of rigorous calculations, a smart genetic algorithm is employed to optimize multiple spherical aberration coefficients. An analytical expression is formulated to predict the best focus shifts due to spherical aberration applied in the lens pupil domain. Rigorously simulated trends of best focus (BF) through pitch and orientation have been replicated by the analytical expression. Experimental validation of compensation using primary and secondary spherical aberration is performed using a high resolution wavefront manipulator. Subwavelength image exposures are performed on four different mask types and three different mask geometries. UDOF limiting δBF is observed on the thin masks for contact holes, and on thick masks for both one directional (1D) and two directional (2D) geometries. For the contact holes, the applied wavefront correction decreases the δBF from 44 nm to 7 nm and increases the UDOF to 109 nm, an 18% improvement. For the 1D geometries on a thick mask, the through pitch UDOF is increased from 59 nm to 108 nm, an 83% improvement. Experimental data also shows that an asymmetric wavefront can be tuned to particular geometries, providing a UDOF improvement for line ends under restricted processing conditions. The experimental data demonstrates that pupil wavefront manipulation has the capability to compensate for mask topography induced δBF. This dissertation recommends that corrective spherical aberration coefficients be used to decrease pitch dependent best focus, increase process yield, and ultimately expand the design domain over parameters such as mask materials and mask feature densities. The effect of spherical aberration applied in the pupil plane is to provide a wavefront solution that is equivalent to complex multiple-level mask compensation methods. This will allow the advantages of thicker masks to be explored for further applications in semiconductor optical lithography

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

Light sheet adaptive optics microscope for 3D live imaging

Optical microscopy is still the main research tool for many biological studies. Indeed with the advent of genetic manipulation and specifically, the use of fluorescent protein expressing in animals and plants it has actually seen a renaissance in the past ten years, in particular with the development of novel techniques such as CARS, PALM, STORM, STED and SPIM. In all of microscopy methods one has to look through the sample at some point. The sample thus adds an additional and uncontrolled optical path, which leads to aberrations in the final image. Adaptive optics (AO) is a way of removing these unwanted aberrations which can cause image degradation and even potentially artifacts within the image. This thesis is concerned with the implementation of AO in non scanning microscopes and presents some novel methods both in wavefront sensored and sensorless configurations. A first implementation of AO on the emission path of a light sheet microscope is also presented