An automatic focusing and stigmating system for the SEM

A digital minicomputer has been interfaced with a scanning electron microscope, and programmed to control the excitations of the objective lens and the stigmator of the microscope. The electron beam is scanned by a digital scan generator and the digitised video signal is used for computations. To focus the microscope, a parameter related to the 'sharpness' of the image is maximised, and to set the stigmator, the directional information in the above- and below-focus images is used. | A digital minicomputer has been interfaced with a scanning electron microscope, and programmed to control the excitations of the objective lens and the stigmator of the microscope. The electron beam is scanned by a digital scan generator and the digitized video signal is used for computations. To focus the microscope, a parameter related to the 'sharpness' of the image is maximized, and to set the stigmator, the directional information in the above and below-focus images is used.

Topographical analysis in the SEM using an automatic focusing technique

A novel technique for automated topographical analysis in the SEM has been investigated. It utilizes a 16-bit minicomputer arranged to act as an automatic focusing unit. The computer is coupled to the objective lens of the microscope, by means of a digital to analogue converter, and may regulate the excitation of the lens under program control. Further digital-to-analogue converters allow the computer to act as a programmable scan generator by applying ramp waveforms to the scan amplifiers, permitting the beam to be swept over a small sub-region of the field of interest. The video signal is sampled and applied to an analogue-to-digital converter; the resultant binary numbers are stored in computer memory as an array of values representing relative image intensities within a subregion. A differencing algorithm applied to the collected data allows the level of objective lens excitation to be found at which the sharpness of the image is optimized, and the excitation may be related to the working distance for that subregion through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm.

MPC: A paradigm shift for automatic control

Model Predictive Control (MPC) represents a major paradigm shift in the field of automatic control. This radically affects synthesis techniques (illustrated by control of an unstable system) and underlying concepts (illustrated by control of a multivariable system), as well as lifting the control engineer's focus from prescriptions to specifications ('what' not 'how', illustrated by emulation of a conventional autopilot). Part of the objective of this paper is to emphasize the significance of this paradigm shift. Another part is to consider the fact that this shift was missed for many years by the academic community, and what this tells us about teaching and research in the field.

The modified Kanerva model for automatic speech recognition

A parallel processing network derived from Kanerva's associative memory theory Kanerva 1984 is shown to be able to train rapidly on connected speech data and recognize further speech data with a label error rate of 0·68%. This modified Kanerva model can be trained substantially faster than other networks with comparable pattern discrimination properties. Kanerva presented his theory of a self-propagating search in 1984, and showed theoretically that large-scale versions of his model would have powerful pattern matching properties. This paper describes how the design for the modified Kanerva model is derived from Kanerva's original theory. Several designs are tested to discover which form may be implemented fastest while still maintaining versatile recognition performance. A method is developed to deal with the time varying nature of the speech signal by recognizing static patterns together with a fixed quantity of contextual information. In order to recognize speech features in different contexts it is necessary for a network to be able to model disjoint pattern classes. This type of modelling cannot be performed by a single layer of links. Network research was once held back by the inability of single-layer networks to solve this sort of problem, and the lack of a training algorithm for multi-layer networks. Rumelhart, Hinton & Williams 1985 provided one solution by demonstrating the "back propagation" training algorithm for multi-layer networks. A second alternative is used in the modified Kanerva model. A non-linear fixed transformation maps the pattern space into a space of higher dimensionality in which the speech features are linearly separable. A single-layer network may then be used to perform the recognition. The advantage of this solution over the other using multi-layer networks lies in the greater power and speed of the single-layer network training algorithm. © 1989.

Acceleration of an unstructured hybrid mesh RANS solver by porting to GPU architectures

The modern CFD process consists of mesh generation, flow solving and post-processing integrated into an automated workflow. During the last several years we have developed and published research aimed at producing a meshing and geometry editing system, implemented in an end-to-end parallel, scalable manner and capable of automatic handling of large scale, real world applications. The particular focus of this paper is the associated unstructured mesh RANS flow solver and the porting of it to GPU architectures. After briefly describing the solver itself, the special issues associated with porting codes using unstructured data structures are discussed - followed by some application examples. Copyright © 2011 by W.N. Dawes.

Enticing general engineering undergrads into signal processing with Photo-Editing and image search

The University of Cambridge is unusual in that its Department of Engineering is a single department which covers virtually all branches of engineering under one roof. In their first two years of study, our undergrads study the full breadth of engineering topics and then have to choose a specialization area for the final two years of study. Here we describe part of a course, given towards the end of their second year, which is designed to entice these students to specialize in signal processing and information engineering topics for years 3 and 4. The course is based around a photo editor and an image search application, and it requires no prior knowledge of the z-transform or of 2-dimensional signal processing. It does assume some knowledge of 1-D convolution and basic Fourier methods and some prior exposure to Matlab. The subject of this paper, the photo editor, is written in standard Matlab m-files which are fully visible to the students and help them to see how specific algorithms are implemented in detail. © 2011 IEEE.