Fractional Differential Equations

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

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

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Art of electronics

A new collection of real world applications of fractional calculus in science and engineering

The robust control of the crude oil outlet temperature uniformity in a heating furnace of a petroleum refinery is addressed. A reliable dynamic model of the nominal process has been attained using a system identification procedure based on real-time data. This procedure yields a second order model with a dominant time-delay. A PI controller embedded in a modified Smith predictor structure is therefore proposed. Sensitivity and robustness properties of this control system are analytically obtained. Based on that, a tuning procedure is developed for this control system which has lower sensitivity to disturbances than the standard Smith predictor while guaranteeing the system stability when plant parameters change. Simulations are carried out of the proposed control system and other well-known linear advanced process control systems. The comparison of the obtained results shows the superior performance of our control system in most cases, both in rejecting a variety of disturbances and in maintaining the closed-loop stability when the process parameters change.

Control of the temperature in a petroleum refinery heating furnace based on a robust modified Smith predictor.

Control-oriented model of a complex irrigation main canal pool

Flexible links combined with force and torque sensors can be used to detect obstacles in mobile robotics, as well as for surface and object recognition. These devices, called sensing antennae, perform an active sensing strategy in which a servomotor system moves the link back and forth until it hits an object. At this instant, information of the motor angles combined with force and torque measurements allow calculating the positions of the hitting points, which are valuable information about the object surface. In order to move the antenna fast and accurately, this article proposes a new closed-loop control for driving this flexible link-based sensor. The control strategy is based on combining a feedforward term and a feedback phase-lag compensator of fractional order. We demonstrate that some drawbacks of the control of these sensing devices like the apparition of spillover effects when a very fast positioning of the antenna tip is desired, and actuator saturation caused by high-frequency sensor ...

Improving the position control of a two degrees of freedom robotic sensing antenna using fractional-order controllers

/pdf/robust-fractional-order-controller-implemented-in-the-first-3ky6z9pkxb.pdf

Robust fractional order controller implemented in the first pool of the Imperial de Aragón main canal

Time Domain Tuning of a Fractional Order PIα Controller Combined with a Smith Predictor for Automation of Water Distribution in Irrigation Main Channel Pools

Vicente Feliu-Batlle

Papers

Control of the temperature in a petroleum refinery heating furnace based on a robust modified Smith predictor.

Control-oriented model of a complex irrigation main canal pool

Improving the position control of a two degrees of freedom robotic sensing antenna using fractional-order controllers

Robust fractional order controller implemented in the first pool of the Imperial de Aragón main canal

Time Domain Tuning of a Fractional Order PIα Controller Combined with a Smith Predictor for Automation of Water Distribution in Irrigation Main Channel Pools