There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Field-assisted sintering technology/Spark plasma sintering is a low voltage, direct current (DC) pulsed current activated, pressure-assisted sintering, and synthesis technique, which has been widely applied for materials processing in the recent years. After a description of its working principles and historical background, mechanical, thermal, electrical effects in FAST/SPS are presented along with the role of atmosphere. A selection of successful materials development including refractory materials, nanocrystalline functional ceramics, graded, and non-equilibrium materials is then discussed. Finally, technological aspects (advanced tool concepts, temperature measurement, finite element simulations) are covered.

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Field-Assisted Sintering Technology/Spark Plasma Sintering: Mechanisms, Materials, and Technology Developments

Review on titanium and titanium based alloys as biomaterials for orthopaedic applications.

Shape-Memory Materials

Electron backscattering diffraction analysis of secondary cleavage cracks in a reactor pressure vessel steel

Microstructural evolution of helium-irradiated 6H–SiC subjected to different irradiation conditions and annealing temperatures: A multiple characterization study

Miroslav Karlík

Papers

Electron backscattering diffraction analysis of secondary cleavage cracks in a reactor pressure vessel steel

Microstructural evolution of helium-irradiated 6H–SiC subjected to different irradiation conditions and annealing temperatures: A multiple characterization study

Mechanical properties of spark plasma sintered FeAl intermetallics

The interaction of natural ageing with straining in a twin-roll cast AlMgSi automotive sheet

Characterization of strain-induced martensitic transformation in A301 stainless steel by Barkhausen noise measurement