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

This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

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

In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.

Iterative Learning Control: Brief Survey and Categorization

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Contents Introduction Origins of High Power Microwaves High Power Microwave Operating Regimes Future Directions in HPM Further Reading References Designing High Power Microwave Systems The Systems Approach to HPM Looking at Systems Linking Components into a System Systems Issues Scoping an Advanced System Conclusion Problems References High Power Microwave Applications Introduction High Power Microwave Weapons High Power Radar Power Beaming Space Propulsion Plasma Heating Particle Accelerators Problems References Microwave Fundamentals Introduction Basic Concepts in Electromagnetics Waveguides Periodic Slow-Wave Structures Cavities Intense Relativistic Electron Beams Magnetically Insulated Electron Layers Microwave-Generating Interactions Amplifiers and Oscillators, High- and Low-Current Operating Regimes Phase and Frequency Control Summary Problems References Enabling Technologies Introduction Pulsed Power Electron Beams and Layers Microwave Pulse Compression Antennas and Propagation Diagnostics HPM Facilities Problems Further Reading References Ultrawideband Systems UWB Defined UWB Switching Technologies UWB Antenna Technologies UWB Systems Conclusion Problems References Relativistic Magnetrons and MILOS Introduction History Design Principles Operational Features Research and Development Issues Fundamental Limitations MILOs Crossed-Field Amplifiers Summary Problems References BWOs, MWCGs, and O-Type Cerenkov Devices Introduction History Design Principles Operational Features Research and Development Issues Fundamental Limitations Summary Problems References Klystrons and Reltrons Introduction History Design Principles Operational Features Research and Development Issues Fundamental Limitations Summary Problems References Vircators, Gyrotrons and Electron Cyclotron Masers, and Free-Electron Lasers Introduction Vircators Gyrotrons and Electron Cyclotron Masers Free-Electron Lasers Summary Problems References Appendix: High Power Microwave Formulary A.1 Electromagnetism A.2 Waveguides and Cavities A.3 Pulsed Power and Beams Diodes and Beams A.4 Microwave Sources A.5 Propagation and Antennas A.6 Applications Power Beaming Plasma Heating Index

High power microwaves

Foreword by Dr. Delores Etter. Preface. Acknowledgments. List of Contributors. List of Acronyms and Abbreviations. Introduction. HPM Sources: The DOD Perspective. Gigawatt--Class Sources. Pulse Shortening. Relativistic Cerenkov Devices. Gyrotron Oscillators and Amplifiers. Active Plasma Loading of HPM Devices. Beam Transport and RF Control. Cathodes and Electron Guns. Windows and RF Breakdown. Computational Techniques. Alternative Approaches and Future Challenges. Index. About the Editors.

High-power microwave sources and technologies

Polymer materials, such as polymethylmethacrylate (PMMA), are widely used as insulators in vacuum. The insulating performance of a high-voltage vacuum system is mainly limited by surface flashover of the insulators rather than bulk breakdown. Non-thermal plasmas are an efficient method to modify the chemical and physical properties of polymer material surfaces, and enhance the surface insulating performance. In this letter, an atmospheric-pressure dielectric barrier discharge is used to treat the PMMA surface to improve the surface flashover strength in vacuum. Experimental results indicate that the plasma treatment method using Ar and CF4 (10:1) as the working gas can etch the PMMA surface, introduce fluoride groups to the surface, and then alter the surface characteristics of the PMMA. The increase in the surface roughness can introduce physical traps that can capture free electrons, and the fluorination can enhance the charge capturing ability. The increase in the surface roughness and the introduction of the fluoride groups can enhance the PMMA hydrophobic ability, improve the charge capturing ability, decrease the secondary electron emission yield, increase the surface resistance, and improve the surface flashover voltage in vacuum.

Enhanced surface flashover strength in vacuum of polymethylmethacrylate by surface modification using atmospheric-pressure dielectric barrier discharge

We report on the improvement of conditions for the rapid start of oscillations in magnetrons by increasing the amplitude of the operating wave that is responsible for the capture of electrons into spokes. This amplitude increase is achieved by using a hollow cathode with longitudinal strips removed, thereby making the cathode transparent to the wave electric field with azimuthal polarization. In addition, an optimal choice of the number and position of cathode strips provide favorable prebunching of the electron flow over the cathode for fast excitation of the operating mode. Particle-in-cell simulations of the A6 magnetron demonstrate these advantages of this novel cathode.

Rapid Start of Oscillations in a Magnetron with a "Transparent" Cathode

At the Nagaoka University of Technology (Japan), Daimon and Jiang used particle-in-cell (PIC) code simulations to demonstrate that the electronic efficiency of the A6 magnetron with axial extraction can be increased from 3% up to 37% applying different diffraction outputs, from tapered cavities in a conical horn antenna to modified expanded ones that improve magnetron matching with the antenna. This paper presents PIC code simulation results for the modified magnetron design using a transparent cathode, in contrast with Daimon and Jiang's simulations that used a solid explosive emission cathode. Furthermore, by further optimizing the magnetron parameters, we demonstrate an efficiency approaching 70% with gigawatt radiation power for an applied voltage of 400 kV. By maintaining a synchronous interaction of electrons with the operating wave, we found that the radiation power increases as the square of the diode voltage up to a diode voltage of 800 kV with short rise time that does not exceed 20 ns. In addition, we show that using a transparent cathode promotes avoiding the regime of hard excitation of magnetrons.

Edl Schamiloglu

Papers

High power microwaves

High-power microwave sources and technologies

Enhanced surface flashover strength in vacuum of polymethylmethacrylate by surface modification using atmospheric-pressure dielectric barrier discharge

Rapid Start of Oscillations in a Magnetron with a "Transparent" Cathode

70% Efficient Relativistic Magnetron With Axial Extraction of Radiation Through a Horn Antenna