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Public Lecture on the Analysis of Internet Traffic

12 Sep 2011

Title: Building a Model of Organic Link Traffic

Speaker: Dr. Michael A. Dewar

Date: Thursday 22nd September at 1100

Venue: Engineering Building Lecture Room 1

Abstract: At Bitly we study behaviour on the internet by capturing clicks on shortened URLs. This link traffic comes in many forms yet, when studying human behaviour, we're only interested in using 'organic' traffic: the traffic patterns caused by actual humans clicking on links that have been shared on the social web. To extract these patterns, we employ Python/Numpy, streaming Hadoop and some Machine Learning to create a model of organic traffic patterns based on bitly's click logs. This model lets us extract the traffic we’re interested in from the variety of patterns generated by inorganic entities following bitly links.

Public Lectures on Control Design (5, 6 Apr)

30 Mar 2011

The Department of Systems and Control Engineering organized a series of two public lectures on Control Systems Design. The speaker was

Assoc. Prof. Ing. Marek Kubalcik

Tomas Bata University, Zlin, Czech Republic.

The two lectures were held as follows:

Library of Adaptive MIMO Controllers Based on Polynomial Methods

Date: Tuesday, 5 April 2011
Time: 1600hrs ~ 1700hrs
Venue: Room EB7, Engineering Building

Predictive Control of Multivariable Systems

Date: Wednesday, 6 April 2011
Time: 1600hrs ~ 1700hrs
Venue: Room EB7, Engineering Building

Prof. Kubalcik is an Associate Professor in the Department of Process Control at Tomas Bata University.

Abstracts

5 April: Library of Adaptive MIMO Controllers based on Polynomial Methods

The lecture will present a design of self – tuning controllers for two input – two output systems. The synthesis of the controllers is based on polynomial methods. Various configurations of the closed loop system were considered and also several decoupling compensators were applied. The controllers were incorporated into a library designed under MATLAB – Simulink environment. The library enables their design and verification. The implemented controllers can be verified both by simulation and real – time control of laboratory models. Design of particular controllers and description of recursive identification method will be presented as well as several simulations and examples.

6 April: Predictive Control of Multivariable Systems

The lecture will be focused on design and implementation of predictive control algorithms for control of multivariable systems. Linear models of multivariable systems used in model predictive control will be introduced. Various ways of derivation of multi – step – ahead predictors will be presented and compared. Computation of optimal control will be given both for unconstrained and constrained cases. Implementation of predictive controllers for processes with constraints will be described as well as realization of adaptive predictive controllers. Simulations and results of experimental examples will also be presented.

Public lectures on Unmanned Robotic Vehicles

04 Mar 2011

Public Lectures on Unmanned Robotic Vehicles

The Department of Systems and Control Engineering organized a series of two public lectures on Unmanned Robotic Vehicles. The speaker was Dr. Christopher Clark, California Polytechnic State University, USA who delivered the following two lectures:

• Underwater Robotics: Applications Driven by Science

o Date: Thursday,10th March 2011

o Time:16:00 ~ 17:00

o Venue: Room EB2, Engineering Building

• Coordinating Multiple Autonomous Vehicles

o Date: Thursday,17th March 2011

o Time:16:00 ~ 17:00

o Venue: Room EB2, Engineering Building

Dr. Clark is the Director of the Laboratory for Autonomous and Intelligent Robotics at California Polytechnic State University, USA. He has used robots to explore archeological sites in Malta.

Abstracts

10th March: Underwater Robotics: Applications Driven by Science

Over the past 40 years underwater robots have been used to explore and observe the earth's underwater environments that are either too dangerous, difficult, or expensive for humans. These robots are often classified as Remotely Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs), or gliders. ROVs are controlled by human operators by sending (joystick) control signals through a tether to robot below surface. AUVs are able to navigate autonomously through open water, tracking pre-programmed paths through open water. Gliders have similar navigation capabilities, but use buoyancy propulsion systems that limit maneuverability while extending mission lengths.

This talk will cover Cal Poly's recent research thrust in the use of such underwater robots for applications in Oceanography, Marine Biology, Arctic Science, and Archeology. In the last few years, the speaker has deployed robots in Canada, Norway, the Arctic, Malta, and the California coastal area. These expeditions, while driven by the needs of social and natural scientists, are made successful through the use of recent developments in computing technology. Examples to be presented include, AUV planning for reducing errors in a regional ocean modeling system, Bayesian filtering for ice detection in AUV under ice navigation, construction of 4 dimensional Dissolved Oxygen maps with AUV sampling, estimation of shark behavioral modes for tracking, and underwater mapping of ancient tunnels with mapping algorithms. Results from these experiences will be presented, highlighting successes, failures, and future directions.

17th March: Coordinating Multiple Autonomous Vehicles

For years researchers have been developing multi-robot systems with the goal of increasing the robustness, mission success rates, force generated, and spatio-temporal sampling as compared with single robot systems. Today, the applications of these systems are growing, and becoming a requirement in some sectors.

With the benefits of having multiple autonomous robots or vehicles accomplishing common goals, comes the necessity to coordinate their motion. This talk will present a variety of motion coordination strategies, and how they may be applied. Examples projects include multi-robot motion planning with probabilistic road-maps, distributed multi-robot boundary tracking, optimal lane assignment for highway vehicles, and finally complete and scalable multi-robot motion planning in tunnels.

Talk on Image Processing for Geological Exploration

25 Jun 2010

Date: 2nd July 2010

Time: 12:00 to 13:00

Location: Room EB1, Engineering Building

The Department of Systems and Control Engineering is hosting Dr. Patrick McGuire from The Department of Geophysical Sciences, University of Chicago to present a talk on image processing techniques for geological exploration entitled:

The Cyborg Astrobiologist: Teaching computers to find uncommon or novel areas of geological scenery in real-time.

Abstract:
In prior work, we have developed computer algorithms for real-time novelty detection and rarity mapping for astrobiological and geological exploration. These algorithms were tested at astrobiological field sites using mobile computing platforms – originally with a wearable computer connected to a digital video camera, but more recently with a phone camera connected wirelessly to a local or remote server computer. The image features used in the novelty detection and rarity mapping in prior work were based only upon RGB or HSI color. In this work, we describe an image-compression technique of Heidemann and Ritter that is capable of: (i) detecting novel textures in a series of images, as well as of: (ii) alerting the user to the similarity of a new image to a previously-observed texture. This image-compression technique has been implemented and tested using our Astrobiology phone-cam system, which employs Bluetooth communication to send images to a local netbook server in the field for the image-compression analysis. By providing more advanced capabilities for similarity detection and novelty detection, this image-compression technique could be useful in giving more scientific autonomy to robotic planetary rovers, and in assisting human astronauts in their geological exploration.
References
Bartolo, A., et al. “The Cyborg Astrobiologist: Porting from a Wearable Computer to the Astrobiology Phone-cam”, International Journal of Astrobiology, vol. 6, issue 4, pp. 255-261 (2007).
Gross, C., et al.“The Cyborg Astrobiologist: testing a novelty detection algorithm at the Mars Desert Research Station (MDRS), Utah,” LPSCXLI, Lunar and Planetary Science Conf., Houston, Texas, extended abstract #2457 (2010).
Heidemann, G. and H. Ritter, “Compression for Visual Pattern Recognition”, Proc. of the Third International Symposium on Communications, Control and Signal Processing (ISCCSP 2008), St. Julians, Malta, IEEE, pp. 1520-1523 (2008).
McGuire, P.C., et al. “The Cyborg Astrobiologist: First Field Experience”, International Journal of Astrobiology, vol. 3, issue 3, pp. 189-207 (2004).
McGuire, P.C., et al. “The Cyborg Astrobiologist: Scouting Red Beds for Uncommon Features with Geological Significance”, International Journal of Astrobiology, vol. 4, issue 2, pp. 101-113 (2005).
McGuire, P.C., et al. “The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah”, International Journal of Astrobiology, Vol. 9, pp. 11-27 (2010).
Bonnici, A., et al. “The Cyborg Astrobiologist: Compressing Images for the Matching of Prior Textures and for the Detection of Novel Textures “, European Planetary Science Congress, Rome (2010, submitted).

Speaker Biography:

Dr. Patrick Mc. Guire is a Senior Research Associate of the Department of the Geophysical Sciences at the University of Chicago, USA. He recieved the B.A. degree in Physics and Mathematics in 1989 from the University of Chicago and the Ph.D degree in Physics in 1994 from the University of Arizona.
Dr. Patrick C. McGuire has interests in computer vision, Mars exploration, climate science, and is working on modelling the formation and stability of methane hydrates underneath the seafloor. His broad background in astrobiology, astronomy/astrophysics, complex systems, neural networks, computer vision, and robotics is the springboard from which his current interests in both climate science and Mars exploration have proceeded. After his Ph.D. in astrophysics and neural networks, he worked in industry for two years prior to working on neural networks, telescope instrumentation and astronomy on the MMT Telescope in Arizona. He then worked in Germany and Spain for almost six years on neural networks, robotics, computer vision, and astrobiology. He has led the development of the Cyborg Astrobiologist system, including field tests at several Mars analog sites in Spain, Malta and Utah. Since 2005, he has led the development of the atmospheric and thermal correction system for CRISM multispectral mapping by the Mars Reconnaissance Orbiter. Together with Navid Serrano and others, he won a NASA/JPL New Technology Report Software Award in 2009 for “Using CTX image features to predict HiRISE-equivalent rock density”. His teaching experience includes introductory courses in astronomy, planetary science, and climate science. Since 2008, he has spent part of each year as a research fellow in Berlin. In 2009, he began a research position at the University of Chicago.

Talk on Modelling and Signal Processing

25 May 2010

Date: 25th May 2010

Time: 12:00 to 13:00

Location: Gateway Building, room GW205

The Department of Systems and Control Engineering hosted Professor Yuriy Shmaliy from the University of Guanajuato, Mexico who presented a seminar on modeling and signal processing entitled:

Discrete-time optimal and unbiased FIR estimation of state space models

Abstract:

Optimal estimation of signal parameters and system models is often required to formalize a posteriori knowledge about undergoing processes in the presence of noise. Therefore, filtering, smoothing, and prediction have become key tools of statistical signal, image, and speech processing and found applications in algorithms of various electronic systems. Very often, estimation is provided using methods of linear optimal filtering employing either finite impulse response (FIR) or infinite impulse response (IIR) structures. First fundamental works on discrete-time optimal linear filtering of stationary random processes were published in 1939-1941 by Kolmogorov as mathematically-oriented. Soon after, Wiener solved the problem for engineering applications in continuous-time and Levinson used the Wiener error criterion in filter design and prediction. The solutions by Wiener were all in the frequency domain, presuming IIR solutions. A FIR modification to the Wiener filter was made by Zadeh and Ragazzini. Thereafter, Johnson extended Zadeh-Ragazzini's results to discrete time. The roots of optimal FIR filtering can be found namely in these basic works. Despite the inherent bounded input/bounded output stability and robustness against temporary model uncertainties and round-off errors, practical interest to FIR filtering weakened after Kalman and Bucy presented in 1960-1961 complete results on the theory of linear filtering of nonstationary Gaussian processes. In contrast to the FIR solutions implying large computational burden and memory, the recursive IIR Kalman-Bucy algorithm has appeared to be simple, accurate, and fast. That has generated an enormous number of papers devoted to the investigation and application of this filter. It then has been shown that the Kalman filter is a nice solution if the model is distinct, there are no uncertainties, and noise sources are all white sequences. Otherwise, the algorithm may become unstable and its estimate may diverge. An interest to FIR structures has grown in recent decades owing to a dramatic development in computational resources. In receding horizon predictive control, significant results on optimal linear FIR filtering of Gaussian processes have been achieved by Jazwinski, Liu and Liu, Ling and Lim, and Kwon et al. For image processing, predictive FIR filtering has been proposed by Heinonen and Neuvo and thereafter developed by many authors. For polynomial models, FIR structures were used by Wang to design a nonlinear filter, by Zhou and Wang in the FIR-median hybrid filters, and a number of publications keep growing.

In this presentation, we show that the general theory of the p-shift optimal linear FIR estimator follows straightforwardly from the real-time state space model (from n and n-1 to n) used in signal processing, rather than from the prediction model (from n to n+1) used in control. This model allows for a universal estimator intended for solving the problems of filtering (p = 0), prediction (p > 0), and smoothing (p < 0) in discrete-time and state space on a horizon of N points. In such an estimator, the initial state is self-determined by solving the discrete algebraic Riccati equation (DARE). The noise components are allowed to have arbitrary distribution and covariance functions with a particular case of white Gaussian approximation. Depending on p, the estimator is readily modified to solve several specific problems, such as the receding horizon control one (p = 1), smoothing the initial state (p = -N+1), holdover in digital communication networks (p > 0), etc. We show that the optimal FIR estimator gain is a product of the unbiased gain and the noise-dependent function composed with the covariance functions and the initial state function. An important point is that the optimal and unbiased estimates converge either when the convolution length is large, N >> 1, or if the initial state error dominates the noise components. The unbiased (near optimal) FIR estimate associated with the best linear unbiased estimator (BLUE) is considered in detail as having strong engineering features. Along with the noise power gain (NPG), this estimate can be represented in batch and recursive Kalman-like forms. A special attention is paid to the polynomial state space models as being basic for many applications. For this model, the unique low-degree polynomial gains are derived and investigated in detail. Applications are given for polynomial state space modeling, clock state estimation and synchronization, and image processing. The trade-off with the Kalman algorithm is also discussed and supported with experimental results.

Brief Biography of the Speaker:

Professor Yuriy S. Shmaliy is a Full Professor of Electronics of the School of Mechanical, Electrical, and Electronic Engineering (FIMEE) of the University of Guanajuato, Mexico. He received the B.S., M.S., and Ph.D. degrees in 1974, 1976 and 1982, respectively, from the Kharkiv Aviation Institute, Ukraine, all in Electrical Engineering. In 1992 he received the Doctor of Technical Sc. degree from the Kharkiv Railroad Institute. In March 1985, he joined the Kharkiv Military University. He serves as Full Professor beginning in 1986. Since 1999 to 2009, he has been with the Kharkiv National University of Radio Electronics.

Prof. Shmaliy has 250 Journal and Conference papers and 80 patents. His books Continuous-Time Signals (2006) and Continuous-Time Systems (2007) were published by Springer. His book GPS-Based Optimal FIR Filtering of Clock Models (2009) was published by Nova Science Publ., New York. He also contributed with several invited Chapters to books. He was rewarded a title, Honorary Radio Engineer of the USSR, in 1991. He was listed in Marquis Who's Who in the World in 1998; Outstanding People of the 20th Century, Cambridge, England in 1999; and Contemporary Who's Who, American Bibliographical Institute, in 2002. He is a Senior Member of IEEE. He has Certificates of Recognition and Appreciation from the IEEE, WSEAS, and IASTED. He serves as an Associate Editor in Recent Patents on Space Technology. He is a member of several Organizing and Program Committees of Int. Symposia. He organized and chaired several International Conferences on Precision Oscillations in Electronics and Optics. He was multiply invited to give tutorial, seminar, and plenary lectures. His current interests include optimal estimation, statistical signal processing, and stochastic system theory.

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24 May 2013
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