https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

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

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Vehicular ad hoc network (VANET) is an emerging new technology integrating ad hoc network, wireless LAN (WLAN) and cellular technology to achieve intelligent inter-vehicle communications and improve road traffic safety and efficiency. VANETs are distinguished from other kinds of ad hoc networks by their hybrid network architectures, node movement characteristics, and new application scenarios. Therefore, VANETs pose many unique networking research challenges, and the design of an efficient routing protocol for VANETs is very crucial. In this article, we discuss the research challenge of routing in VANETs and survey recent routing protocols and related mobility models for VANETs.

/pdf/routing-in-vehicular-ad-hoc-networks-a-survey-2fwcriwix4.pdf

Routing in vehicular ad hoc networks: A survey

Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.

Introduction to Fuzzy Logic using MATLAB

In this paper a novel encapsulated dual band planar inverted F antenna is presented designed for the specific application of gathering data on seals at sea. A mobile phone module with the above antenna is encased in epoxy resin and affixed to a seal for transmission of sensor data via the short message service over GSM in both European frequency bands. Results are presented for a prototype both in anechoic facilities and affixed to a seal at sea. Finite element software is used for optimisation.

http://ieeexplore.ieee.org/iel5/9352/29706/01353751.pdf

Design, fabrication and measurement of an encapsulated inverted F dual band antenna for the gathering of data on seals at sea using SMS over a GSM system

Nuclear Reactor Control using Diagonal Recurrent Neural Networks

The finite-difference time-domain method builds a problem space out of finite-sized Yee cells. The interfaces between heterogeneous materials can be treated in different ways. The ways of calculating the effective permittivity and conductivity at dissimilar tissue interfaces are investigated. Both homogeneous and anatomically realistic heads are considered. Importantly, it is found that although the effects of the different averaging techniques are small for homogeneous heads, they are significant with anatomically realistic heads. The effects are particularly apparent if internal structures in the head are near resonance. It is shown that the choice of averaging technique has an impact on results and therefore should be explained as a parameter of the simulation technique. Data for 0.9, 1.5 and 1.8 GHz are included here.

/pdf/effects-of-averaging-procedures-for-electrical-properties-at-4u8wfe4q1v.pdf

Effects of averaging procedures for electrical properties at the interface of dissimilar tissues in the human head with finite-difference time-domain modelling

In this paper, experiment results that verify the efficacy of the feedforward-feedback control (FF/FBC), synthesized by nonlinear programming and robust control techniques, are presented.

Feedforward-feedback control by nonlinear programming and structured singular value approach

Shannon Sweeney, Pennsylvania State University-Erie Shannon Sweeney received AS and BS degrees from West Virginia Institute of Technology and MSME degree from Case Western Reserve University. His primary teaching responsibilities are in mechanics of materials and vibrations, and his research concentrates on vibration measurement and analysis and on quality assurance. Prior to coming to Penn State he was a design engineer in industry.

Robert M. Edwards

Papers

Design, fabrication and measurement of an encapsulated inverted F dual band antenna for the gathering of data on seals at sea using SMS over a GSM system

Nuclear Reactor Control using Diagonal Recurrent Neural Networks

Effects of averaging procedures for electrical properties at the interface of dissimilar tissues in the human head with finite-difference time-domain modelling

Feedforward-feedback control by nonlinear programming and structured singular value approach

Direct Assessment Of Mechanics Of Materials Learning With Concept Inventory