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

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Thank you for reading principles of optics electromagnetic theory of propagation interference and diffraction of light. As you may know, people have search hundreds times for their favorite novels like this principles of optics electromagnetic theory of propagation interference and diffraction of light, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their computer.

Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

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

Thermophysical Properties Research Literature Retrieval Guide Edited by Y. S. Touloukian, J. K. Gerritsen and N. Y. Moore Second edition, revised and expanded. Book 1: Pp. xxi + 819. Book 2: Pp.621. Book 3: Pp. ix + 1315. (New York: Plenum Press, 1967.) n.p.

/pdf/heat-and-mass-transfer-1yaijfx8vp.pdf

Heat and Mass Transfer

This article compares the performance of three feedback control methodologies for high performance nanopositioning applications. Integral resonance damping control is a new approach for controlling mechanical systems. In this approach, the system resonances are actively damped rather than inverted which maximizes the closed-loop bandwidth and provides robustness to changes in the resonance frequencies. This technique is comprehensively compared to the standard methods of PI and inversion control in a practical environment. A five times improvement in the settling-time and bandwidth is demonstrated.

/pdf/an-experimental-comparison-of-pi-inversion-and-damping-ykiwik768k.pdf

An experimental comparison of PI, inversion, and damping control for high performance nanopositioning

This article derives design guidelines for integrating strain gauges into the electrodes of piezoelectric bending actuators. The proposed sensor can estimate the actuator tip displacement in response to an applied voltage and an external applied tip force. The actuator load force is also estimated with an accuracy of 8% full scale by approximating the actuator response with a linear model. The applications of this work include micro-grippers and pneumatic valves, which both require the measurement of deflection and load force. At present, this is achieved by external sensors. However, this work shows that these functions can be integrated into the actuator electrodes.

Position and force sensing using strain gauges integrated into piezoelectric bender electrodes

A fundamental component of the z-axis feedback loop in amplitude modulation atomic force microscopy is the demodulator. It dictates both bandwidth and noise in the amplitude and phase estimate of the cantilever deflection signal. In this paper, we derive a linear time-invariant model of a closed-loop demodulator with user definable tracking bandwidth and sensitivity to other frequency components. A direct demodulator design method is proposed based on the reformulation of the Lyapunov filter as a modulated-demodulated controller in closed loop with a unity plant. Simulation and experimental results for a higher order Lyapunov filter as well as Butterworth and Chebyshev type demodulators are presented.

Direct Design of Closed-loop Demodulators for Amplitude Modulation Atomic Force Microscopy

Optimal Placement of Actuators and Sensors

http://www.precisionmechatronicslab.com/wp-content/publications/C06c.pdf

Andrew J. Fleming

Papers

An experimental comparison of PI, inversion, and damping control for high performance nanopositioning

Position and force sensing using strain gauges integrated into piezoelectric bender electrodes

Direct Design of Closed-loop Demodulators for Amplitude Modulation Atomic Force Microscopy

Optimal Placement of Actuators and Sensors

High-performance control of a pzt scanner