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.

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Heat and Mass Transfer

This article describes a new electrical configuration for driving piezoelectric benders. The ‘Biased Bipolar’ configuration is compatible with parallel-polled, bimorph and multimorph benders. The new configuration is similar to the standard three-wire drive method where the top electrode is biased with a DC voltage and the bottom electrode is grounded. However, the new configuration uses an alternate DC bias voltage and adjusted range for the central electrode which allows the full range of positive and negative electric fields to be utilized. Using this technique, the predicted deflection and force can be increased by a factor of 2.2 compared to the standard two wire configuration and 1.3 times for the standard three wire configuration. These predictions were verified experimentally where the measured factor of improvement in displacement and force was of 2.4 and 1.3 compared to the standard two-wire and three-wire configurations.

/pdf/a-new-electrical-configuration-for-improving-the-range-of-29kwsskz7u.pdf

A new electrical configuration for improving the range of piezoelectric bimorph benders

An experiment has been developed to examine the behavior of a titanium-water loop heat pipe under standard and elevated acceleration fields. The loop heat pipe was mounted on a 2.44 meter-diameter centrifuge table on edge with heat applied to the evaporator via a mica heater and heat rejected using a high-temperature polyalphaolefin oil coolant loop. The loop heat pipe was tested under the following parametric ranges: heat load at the evaporator 100 ≤ Q in ≤ 600 W, heat load at the compensation chamber 0 ≤ Q cc ≤ 50 W, radial acceleration 0 ≤ a r ≤ 10 g. For stationary operation, the evaporative heat transfer coefficient decreased monotonically with heat load whereas the thermal resistance decreased to a minimum then increased. Heat input to the compensation chamber was found to increase the evaporative heat transfer coefficient and decrease the thermal resistance for Q in = 500 W. Transient periodic flow reversal in the loop heat pipe was found for some cases, which was likely due to vapor bubble formation in the primary wick. Operation in an elevated acceleration environment revealed that dry out was dependent on both Q in and a r , and the ability for the loop heat pipe to reprime after an acceleration event that induced dry out was influenced by the evaporator temperature. The evaporative heat transfer coefficient and thermal resistance were found not to be significantly dependent on radial acceleration. However, the evaporator wall superheat was found to increase slightly with radial acceleration at high heat loads.

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Titanium-Water Loop Heat Pipe Operating Characteristics Under Standard and Elevated Acceleration Fields

Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. The aim of this paper is to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage.

/pdf/finite-time-learning-control-using-frequency-response-data-36l10s28lc.pdf

Finite-Time Learning Control Using Frequency Response Data With Application to a Nanopositioning Stage

Piezoelectric transducers are known to exhibit less hysterisis when driven with current or charge rather than voltage. Despite this advantage, such methods have found little practical application due to the poor low frequency response of present current and charge driver designs. This paper introduces the compliance feedback current driver containing a secondary voltage feedback loop to prevent DC charging of capacitive loads and to compensate for any voltage or current offsets in the driver circuit. Low frequency bandwidths in the milli-Hertz range can be achieved.

/pdf/improved-current-and-charge-amplifiers-for-driving-1pydvzatz2.pdf

Improved current and charge amplifiers for driving piezoelectric loads

Many forms of scanning probe microscopy require a piezoelectric actuator to vary the probe-sample distance. Examples include constant-force atomic force microscopy and constant-current scanning tunneling microscopy. In such modes, the topography of the sample is reconstructed from the voltage applied to the vertical piezoelectric actuator. However, piezoelectric actuators exhibit significant hysteresis which can produce up to 14% uncertainty in the reproduced topography. In this work, a charge drive is used to linearize the vertical piezoelectric actuator which reduces the error from 14% to 0.65%.

/pdf/quantitative-scanning-probe-microscope-topographies-by-144ptik0xk.pdf

Andrew J. Fleming

Papers

A new electrical configuration for improving the range of piezoelectric bimorph benders

Titanium-Water Loop Heat Pipe Operating Characteristics Under Standard and Elevated Acceleration Fields

Finite-Time Learning Control Using Frequency Response Data With Application to a Nanopositioning Stage

Improved current and charge amplifiers for driving piezoelectric loads

Quantitative scanning probe microscope topographies by charge linearization of the vertical actuator.