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"A Primer on Cluster Analysis: I. Models and Algorithms” by James C. Bezdek sponsored by the IEEE Computational Intelligence Society from the IEEE World Congress on Computational Intelligence (WCCI) This course- the first in a series of three - provides a foundation for understanding the field of cluster analysis in unlabeled data. The target audience for this course comprises undergraduate and graduate students majoring in engineering and science, as well as practicing engineers and scientists interested in either research about or applications of clustering to real world problems such as data mining, image analysis and bioinformatics. The subject matter is widely available in a number of standard textbooks given in the references below. The course begins with a discussion of the general nature of clustering. Three problems are identified: tendency assessment, partitioning and validation. Two types of data are discussed: object vector data, and pair wise objects relational data. Next, I develop the mathematical structure needed to carry clustering algorithms, discussing the notions of similarity, label vectors, partition matrices (U) and point prototypes (V). The second part of the course contains a description (and pseudo code) for one algorithm each from the four major categories of clustering methods. Specifically, I discuss and illustrate with a numerical example: (i) the U only model for single linkage clustering; (ii) the V only model for clustering with Kohonen's self-organizing map; (iii) the (U,V) model for clustering with the hard and fuzzy c-means models; and (iv) the (U,V,+) model for clustering using the expectation-maximization algorithm for Gaussian mixture decomposition. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Journal Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millennium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
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"A Primer on Clustering: II. Tendency Assessment and Cluster Validity" by James C. Bezdek sponsored by the IEEE Computational Intelligence Society This course - the second in a series of three - discusses several approaches to the first and third problems of clustering identified in module I - viz., pre-clustering tendency assessment and post-clustering cluster validation. The target audience comprises advanced undergraduate and graduate students majoring in engineering and science, and practicing engineers and scientists interested in either research about or applications of clustering to real world problems such as data mining, image analysis and bioinformatics. Some of subject matter in this course is available in textbooks (most notably some of the material about cluster validity functionals), and some of the subject matter is the object of (my) current research. The references contain pointers to some excellent papers on these Topics, and on a number of related or competitive methods that have been proposed and studied by others. I begin with a simple numerical example that establishes the necessity for both assessment and validity. Then, I discuss the visual assessment of tendency family of algorithms (VAT, sVAT and coVAT). These algorithms produce images that enable a user to make useful guesses about the number of clusters to seek in relational data before proceeding with a partitioning method for finding the clusters. Since object data can always be converted to relational form by computing pair wise distances, these methods are well defined for all types of unlabeled numerical data. The coVAT algorithm provides a means for estimating the number of clusters in each of the four problems associated with rectangular relational data: row clusters, column clusters, joint (pure) clusters, and mixed co-clusters. The second half of this course presents some examples of cluster validation using scalar measures or indices of cluster validity. Several examples from each of the three major categories (crisp, fuzzy and probabilistic) of indices are presented. This course concludes with a numerical example that compares 23 indices of all three types on clusters in 12 sets of data drawn from mixtures of Gaussian distributions having either 3 or 6 components. (SOME) indices of all three types do pretty well in this example, while others do very badly. I don't think this problem has a general "solution", but since we use clustering in many, many applications, we keep trying to find good indices to validate algorithmic outputs. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millenium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
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"A Primer on Clustering: III. Fuzzy Cluster Analysis in Very Large Scale Data Sets" by James C. Bezdek sponsored by the IEEE Computational Intelligence Society This last module in the series discusses just one approach to the interesting and important problem of clustering in very large (VL) data. The target audience is graduate students majoring in engineering and science, and practicing engineers and scientists interested in either research about or applications of clustering applied to very large real world problems that occur in data mining, image analysis and bioinformatics. Almost none of the subject matter in this course is available in textbooks; almost all of it is the object of (my own) current research, and as such, it reflects my own bias, prejudices, background and interests. I have supplied references that contain pointers to many nice papers on these Topics that use related or competitive methods that have been proposed and studied by others. I begin with a characterization of VL data. For me, this means any data set that you cannot load into your computer. Not an objective definition, but a definition that is easy to understand and practical, because there is a data set too big for any computer you use, and hence, VL for you. There are two main approaches to clustering in VL data; distributed clustering, and progressing sampling followed by extension. I discuss the first approach briefly, but it seems much more difficult to me than the second approach. Next, I define progressive sampling followed by (non-iterative) extension. This idea is pretty general: it can accelerate most (but not all) iterative algorithms that estimate parameters with loadable data (this is true for both clustering and classifier design!), and, it provides a means for approximating the outputs of many algorithms for unloadable data. So, one of the main points of this third course is to establish the basic ideas of progressive sampling and extension. The method of clustering in VL data by (sampling + extension) is developed and illustrated with four clustering algorithms: (i) extended fast fuzzy c-means (eFFCM) for segmentation of VL images; generalized fast fuzzy c-means (geFFCM) for clustering in VL object data (VL sets of feature vectors in p dimensions); (iii) generalized fast expectation maximization (geFEM) for clustering by Gaussian mixture decomposition in VL object data; and (iv), extended non-Euclidean relational fuzzy c-means (eNERF) for clustering in VL (square) relational data. These four methods are presented in the spirit of active research - i.e., parts of them clearly need improvement and more testing, and I expect much of this material to be replaced by better approaches as our understanding of clustering using this approach matures. After completing this course you should be able to develop an understanding of:
Jim received the BS in Civil Engineering from U. of Nevada (Reno, 1969); and the PhD in Applied Mathematics Cornell University, 1973. Jim's interests include woodworking, optimization, motorcycles, pattern recognition, fine cigars, fishing, image processing, computational neural networks, blues music, and clustering in very large data sets. Jim is past president of NAFIPS, IFSA and the IEEE NNC (aka CIS), is the founding editor of the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems, is a fellow of the IEEE and IFSA, and is a recipient of the IEEE 3rd Millenium , IEEE Fuzzy Systems Pioneer and IEEE Frank Rosenblatt medals.
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"A Software Design Method for Embedded Systems" by Don Berndt, sponsored by the IEEE Computer Society This course introduces an innovative architecture and software design method specifically for embedded devices. The proposed architecture consists of a system of Finite State Machine-encoded application tasks, an executive to provide for the concurrent execution of task FSMs, and useful system services such as a system timer, inter-task communication and system calls. The course presents an overview of the extensive use of embedded systems throughout various industries. A discussion of features and processes common to many embedded systems is then presented to emphasize their distinctive role in both critical and non-critical real-time applications. A thorough treatment of Finite State Machines is presented, demonstrating their usefulness in the analysis and design of both abstract and physical systems. Many C-language examples are presented, showing how a task FSM can be directly coded from its state diagram, and how internal state logic can be conveyed using two basic flowcharting symbols. From a systems perspective, the course presents those services integral to most embedded systems: a runtime executive, a system timer, inter-task communication and valuable systems calls. The course demonstrates how these services are highly integrated into this architecture of FSM-based tasks. Finally, the course offers a discussion and examples of how to document the design of embedded system software using this method. To ensure a successful project, the quality of communication among team members is enhanced by utilizing the document chain recommended within this course. The value of a properly documented design will pay dividends during the software life-cycle, and especially, during validation and verification activities. The Mealy/Moore paradigm of Finite State Machines has been harnessed by the community of digital hardware designers for decades. As described within the content of this course, these basic concepts have been abstracted into the software realm, resulting in excellent application performance through synergy with the underlying hardware in which it runs. This introductory course will benefit both hardware and software engineers, as well as those managing embedded systems projects. A working knowledge of the C-language is required to understand the concepts presented in this undergraduate/masters level course. After completing this course you should be able to develop an understanding of:
Don Berndt has been an IEEE member and independent consultant since 1991. He has provided numerous FDA-compliant embedded software systems for medical devices since that time. He is a former MTS at Bell Labs in Holmdel, NJ where he designed firmware for office-based products. As a senior engineer at GE Space Systems Flight Processor group, Mr. Berndt was involved in the design of DoD satellite communication systems.
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"Advanced Protocols for Wireless Ad-hoc Networks" by Katayoun (Kathy) Sohrabi sponsored by the IEEE Vehicular Technology Society The Advanced Protocols on Wireless Ad-hoc Networks will illustrate that in Ad-Hoc networks where there is no underlying fixed infrastructure, tasks such as network self-organization, mobility management, adaptive route detection for unicast and multicast applications, and provisioning of Gateway functionality to interconnect the ad-hoc network to the rest of the Internet space must be handled according to rules that are unique to the ad-hoc nature of the system. Topics related to support of QoS at various network layers will also be discussed, with emphasis on layers 2, 3, and 4 of the network. We will investigate the performance of these protocols in terms of their level of scalability to different sizes, and traffic loads. Topics of security will also be discussed. After completing this course you should be able to develop an understanding of:
Kathy Sohrabi has been studying, working with, and designing wireless ad-hoc networks and systems. She is currently the Lead Network Architect for Sensoria Corporation, a maker of wireless and sensor networking products.
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"Advanced Universal Plug and Play Technology Topics” by Jack Weast, sponsored by IEEE Educational Activities This advanced course will be a valuable resource for software developers who are implementing UPnP technology in their products. It provides detailed information about building UPnP Audio/Video products (such as those compatible with media players like Sony’s Playstation 3), and hands-on demonstrations of development tools helpful for anyone building UPnP-based products. The Advanced course also provides an introduction to UPnP Security. After completing you should be able to develop an understanding of:
Jack Weast is a Staff Engineer at Intel Corporation who has been an active participant in UPnP technologies for over 7 years.
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“Biometrics: Solutions for Security and Authentication” by Kostas Plataniotis, sponsored by the IEEE Educational Activities Board This course will provide an overview of the study of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. I will present the fundamentals of biometrics and biometric systems. The course will delve into why biometrics is a solution for security and authentication. Face, gait and ECG based biometrics will be covered. Biometrics and encryption will also be discussed, and the course will conclude with a discussion of future steps. After completing you should be able to develop an understanding of:
Konstantinos N. (Kostas) Plataniotis received his B. Eng. degree in Computer Engineering & Informatics from University of Patras, Greece in 1988 and his M.S. and Ph.D. degrees in Electrical Engineering from Florida Institute of Technology (Florida Tech), Melbourne, Florida, in 1992 and 1994 respectively. He was an Assistant Professor with the Computer Science Department at Ryerson University, Ontario, Canada from July 1997 to June 1999. Dr. Plataniotis is currently an Associate Professor with The Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto in Toronto, Ontario, Canada. He is also an Adjunct Professor with the School of Computer Science at Ryerson University, Toronto, Ontario, Canada.
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"Calibration and Error Correction Techniques for Network Calibration" by Doug Rytting, sponsored by the IEEE Microwave Theory and Techniques Society The accuracy of Vector-Network-Analyzer (VNA) measurements depends critically on calibration and error correction techniques. This course will cover the evolution of conventional VNA calibration methods from the start of network analysis through the development of new calibration methods. After completing this course you should be able to develop an understanding of:
Doug Rytting Graduated with a BSEE from Utah State University and MSEE from Stanford University. Joined HP in June 1966 and worked on virtually all microwave network analyzers introduced since 1966. Hardware designer on the 8405 vector voltmeter and 8410 network analyzers. Hardware project manager of the 8540, 8541, and 8542 automatic network analyzers, Section manager of the 8505, 8754 RF network analyzers, and the 8510C microwave network analyzer family. Managed the development of the 8340 microwave synthesized sources and the startup of the microwave CAE design software. Provided technology support for the RFMT (RF Manufacturing Test) system products. Doug Rytting is now retired and consulting on microwave measurements and calibration techniques.
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“Cellular Wave Computers--Via Million Processing” by Tamas Roska, sponsored by the IEEE Circuits and Systems Society The cellular wave computer architecture, based on the CNN universal machine principle, has been implemented recently in many different physical forms. The mixed mode CMOS, the emulated digital (cell wise or as aggregated arrays), FPGA, DSP, as well as optical implementations are the main examples. In many cases, the sensory array is integrated as well. This course will begin with an introduction which will provide a historical overview, mind inspired and brain inspired computing models, the role of spatial address of a processor, new directions and products in computing The technology scenario. After completing you should be able to develop an understanding of:
Dr. Roska is a co-inventor of the CNN Universal Machine (with Leon O. Chua) and the analogic CNN Bionic Eye (with Frank S. Werblin and Leon O.Chua), US patents of UC Berkeley. During the last 15 years he has received two NSF grants, four ONR grants, two EU Grants and several Hungarian Grants. He has been a founding member of two spin/off companies, one in Berkeley and one in Budapest.
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"Computational Intelligence: Natural Information Processing" by Leonid Perlovsky, sponsored by the IEEE Computational Intelligence Society This course covers the rapidly evolving field of Computational Intelligence and focuses on the current understanding of the fundamental principles of working the mind, their computational implementations, and practical applications. This course covers mind mechanisms, including concepts, emotions, instincts, behavior, language, cognition, understanding, thinking, intuitions, conscious and unconscious, abilities for formation of symbols and aesthetic feelings. Computational techniques are given for these mechanisms and abilities. The goal of this course is to provide a basic mathematical understanding of the working of the mind. Its secondary goal is to demonstrate practical applications of these mechanisms for pattern recognition, tracking, fusion, search engines, and for integrated systems combining sensor signals and communication data. Lastly, this course will outline future research directions. Historical and current difficulties in developing intelligent systems (IS) and applications will be discussed along with how the mind and new computational techniques overcome these difficulties. By the end of this course, learners will be familiar with several general applications addressed by IS, computational difficulties encountered over fifty years, and basic novel approaches to overcoming these difficulties. After completing this course you should be able to develop an understanding of:
Dr. Leonid Perlovsky is Principal Research Physicist and Technical Advisor at the Air Force Research Laboratory/SNHE.
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“Cooperative Control of Multiagent Systems: Synthesis and Experimentation” by Camille-Alain Rabbath, sponsored by the IEEE Systems, Man & Cybernetics Society This course illustrates the various attributes needed in such systems and the complexity inherent to the design. Cooperative systems are currently limited in capacity and in availability, partly due to this so-called complexity and to the multifaceted nature of design and analysis. This course will focus on the well-known problem of multiagent path planning, with brief discussions of advanced techniques for formation flight health management. The optimization problem and its solution will be cast in the framework of dynamic programming and Markov decision processes, typical of problems of optimization under uncertainty. A discussion of the results of numerical simulations, integrating decision-making with closed-loop dynamics of the air vehicles, for both formation flight and path planning, will conclude the course. After completing you should be able to develop an understanding of:
C.A. Rabbath is currently Defence Scientist at Defence Research and Development Canada - Valcartier. He also holds adjunct professorship positions at Concordia University and McGill University, Montreal, Canada. Dr. Rabbath received the PhD degree in 1999 from McGill University. He then worked in industry from 1999 to 2002 in control systems design, and in modeling and simulation of aerospace and robotic systems.
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“Cyber Security of Industrial Control Systems (ICS)” by Joseph Weiss, sponsored by IEEE Educational Activities This course will begin with an introduction to industrial control systems (SCADA, DCS, PLC, RTU, IED, field devices, meters, etc) and will explain what makes control systems different than business IT. Potential mitigation approaches including policies and technologies will be discussed. Example control system cyber events and their ramifications will be presented. Finally, current industry and government activities to secure ICS will be discussed. After completing you should be able to develop an understanding of:
Joseph Weiss is an industry expert on control systems and electronic security of control systems, with more than 30 years of experience in the energy industry. He serves as KEMA's leading expert on control system cyber security.
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"Cyber Security of Substation Control and Diagnostic Systems" by Joseph Weiss, sponsored by the IEEE Power and Energy Society This course will familiarize learners with the need to strengthen the protection of the control systems used in the industry against cyber (electronic) threats. The control systems addressed include SCADA systems, IEDs, substation automation systems, and distribution control systems. The course will identify the major threats, and outline practical suggestions about how the security of these systems may be enhanced. After completing this course you should be able to develop an understanding of:
Joseph Weiss is an industry expert on control systems and electronic security of control systems, with more than 30 years of experience in the energy industry. He serves as KEMA's leading expert on control system cyber security.
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"Home Networking Standards" by Marie-José Montpetit sponsored by the IEEE Communications Society In recent years the converging digital technologies of television, publishing, telephony and computers, the so-called multimedia revolution, have prompted the deployment of a multitude of high-speed applications from streaming video to the World Wide Web to deskTop video editing. An immediate impact of this evolution is the ever-increasing demand for Internet bandwidth, the more intelligent use of already available resources and the use of overlay networks in the home. Because of the emergence of the home networks, the nature of networking itself is changing dramatically. It is moving away from supporting mainly home business and education applications and into the infotainment world where video and audio are now predominant and IPTV is emerging as the “killer app.” In this environment the all-IP network is emerging as the network layer of choice. Hence in Internet terms the home becomes another subnet. But IP does not describe the whole Home Networking solutions. What are also needed are technologies to transport the information and software and middleware to enable the development of those solutions that will allow the Home Network to be “user friendly.” After completing this course you should be able to develop an understanding of:
Marie-José Montpetit received her PhD in Computer and Electrical Engineering from the Ecole Polytechnique in Montreal, Canada. She is currently at Motorola in the Connected Homes Solutions Division.
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"Implementations of Computational Intelligence Techniques" by Vincenzo Piuri and Fabio Scotti sponsored by the IEEE Computational Intelligence Society Computational Intelligence techniques are a powerful and adaptable approach to tackle problems and cases for which the conventional technologies have not been proved sufficiently effective. These results are achieved by mimicking some aspects of the knowledge representation and processing performed by the brain. The computational efforts implied by these approaches are usually quite relevant. Practical use of computational Intelligence technologies is constrained by limits (e.g., on performance, cost, accuracy) imposed by the envisioned application. The choice of the most appropriate implementation approach becomes therefore fundamental in order to find best balance among the computational intelligence characteristics and the application constraints. A methodological perspective becomes also useful in order to tackle the design of a dedicated system encompassing computational Intelligence components as most efficiently as possible. After completing this course you should be able to develop an understanding of:
Vincenzo PIURI obtained the Ph.D. in Computer Engineering in 1989, at Politecnico di Milano, Italy. From 1992 to September 2000, he was Associate Professor in Operating Systems at Politecnico di Milano. Since October 2000 he is Full Professor in Computer Engineering at the University of Milano, Italy. He was Visiting Professor at the University of Texas at Austin during the summers from 1993 to 1999. Fabio Scotti received the Dr. Ing. degree in electronic engineering in 1998 from Politecnico di Milano, Milano, Italy, where he is currently pursuing the Ph.D. degree. His research interests include signal and image processing, neural technologies for image processing, and optical technologies.
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"Information Theoretic Learning" by Jose C. Principe, sponsored by the IEEE Computational Intelligence Society This course examines Information Theory and our efforts to develop an information-theoretic criterion which can be utilized in adaptive filtering and neurocomputing. The main aim of our research is to develop new signal processing techniques by going beyond the basic assumptions of Linearity, Gaussianity and Stationarity. By capturing higher order statistics of data using Information Theory, we solve a variety of problems in Biomedical Signal Processing, Communications and Machine Learning. In the context of Information Theory, “information” is a precise, fully characterized mathematical quantity. The core process toward learning from examples in both biological and artificial systems is extracting information directly from data. The concept of learning from examples begins with a data set which globally conveys information about a real-world event, with the goal of capturing the information in the parameters of a learning machine. The information exists in a “distributed” mode in the data set, and, after successful training, is “condensed” in the parameters of the learning machine. Here, we develop information-theoretic criteria which can train directly from samples of linear or nonlinear mappers, either for entropy or mutual information maximization or minimization. After completing this course you should be able to develop an understanding of:
Jose C. Principe is Distinguished Professor of Electrical and Biomedical Engineering at the University of Florida, Gainesville, where he teaches advanced signal processing and artificial neural networks (ANNs) modeling. He is BellSouth Professor and Founder and Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL).
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"Introduction to Developing Embedded Systems" by Kim Fowler, sponsored by the IEEE Instrumentation & Measurement Society This course introduces important issues in preparing, designing, and developing a product covering areas such as:
After completing this course you should be able to develop an understanding of:
Kim Fowler has spent over 22 years in the design, development, and project management of medical, military, and satellite equipment. He developed many different kinds of embedded systems at The Johns Hopkins University Applied Physics Laboratory; he currently manages technical programs there.
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“Introduction to Multilayer Perceptrons” by Marco Gori, sponsored by the IEEE Computational Intelligence Society The course introduces multilayer perceptrons in a self-contained way by providing motivations, architectural issues, and the main ideas behind the Backpropagation learning algorithm. In addition, the course shows how multilayer perceptrons can be successfully used in real-world applications After completing you should be able to develop an understanding of:
Marco Gori received the Laurea in electronic engineering from University of Florence, Italy, in 1984, and the Ph.D. degree in 1990 from University of Bologna, Italy, working partially as a visiting student at the School of Computer Science (McGill University, Montreal). In 1992, he became an associate professor at University of Florence and, in November 1995, he joined the University di Siena, where he is currently full professor of computer science. Dr. Gori is a fellow of the IEEE and of the ECCAI.
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“Introduction to Type-2 Fuzzy Sets and Systems” by Jerry Mendel, sponsored by the IEEE Computational Intelligence Society This course will provide an introduction to, and an overview of, type-2 fuzzy sets (T2 FSs) and systems. It will locate type-2 fuzzy sets and systems in an educational taxonomy, so that the learner will appreciate from the onset the importance of studying such fuzzy sets; explain what a T2 FS is, how it is different from a type-1 FS, and why it is needed. The course will also provide careful definitions and pictures of the new terminology of T2 FSs and explain the importance of interval type-2 fuzzy sets over more general T2 FSs. The course will also explain important representations for a T2 FS (one is very good for computing, and another is very good for quickly developing the structure of the solution to a new theoretical problem) After completing you should be able to develop an understanding of:
Jerry M. Mendel received the Ph.D. degree in electrical engineering from the Polytechnic Institute of Brooklyn, Brooklyn, NY. Currently he is Professor of Electrical Engineering and Systems Architecting Engineering at the University of Southern California in Los Angeles, where he has been since 1974. He has published over 470 technical papers and is author and/or editor of eight books.
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“Introduction to Universal Plug and Play Technology” by Jack Weast, sponsored by IEEE Educational Activities Networked devices should be as easy for consumers to set up as stereo equipment—when you plug it in and turn it on, it just works. Universal Plug and Play technology can make this happen. Traditionally, network peripherals have not been easy to install. Recent standards such as Universal Serial Bus (USB) and Plug-and-Play have improved the situation so that devices are automatically detected and device drivers automatically installed. Yet, networked devices, like Internet gateways and networked printers, still require manual setup and configuration. After completing you should be able to develop an understanding of:
Jack Weast is a Staff Engineer at Intel Corporation who has been an active participant in UPnP technologies for over 7 years.
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"Introduction to Wireless Ad-hoc Networks" by Katayoun (Kathy) Sohrabi sponsored by the IEEE Vehicular Technology Society Introduction to Wireless Ad-hoc Networks will provide a technical overview and introduction to the Topic of wireless ad-hoc networks. Wireless Ad-hoc networks will be defined. Major requirements and challenges of wireless ad-hoc networks will be covered. The solution space, and related technologies at different layers will be discussed. After completing this course you should be able to develop an understanding of:
Kathy Sohrabi has been studying, working with, and designing wireless ad-hoc networks and systems. She is currently the Lead Network Architect for Sensoria Corporation, a maker of wireless and sensor networking products.
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“Methods & Models of Collaborative Computational Intelligence” by Witold Pedrycz sponsored by the IEEE Computational Intelligence Society and the IEEE Systems, Man and Cybernetics Society There are rapidly emerging needs to deal with distributed sources of data (sensors and sensor networks, web sites, databases). While recognizing their limited accessibility at a global level (associated with technical constraints and/or privacy issues) and fully acknowledging benefits of collaborative processing, we propose a concept of Collaborative Computational Intelligence (CI), and collaborative fuzzy models, in particular. The variety of possible mechanisms of interaction is organized into a setting of the C3 interaction paradigm (communication – collaboration – consensus). This helps us offer a coherent taxonomy of various schemes of interaction which in the sequel implies a certain development of a suite of algorithms. In this setting, the role granular information in the establishing of the mechanisms of interaction plays a pivotal role. We consider distributed fuzzy models and fuzzy modeling. In particular, we elaborate on the key design issues concerning fuzzy rule-based systems with local functional models occurring at their conclusion parts and show how the fundamental modes of interaction are exploited here. It will be demonstrated that more advanced constructs such as type-2 fuzzy sets emerge naturally in distributed fuzzy modeling and come with a well-defined semantics of their membership functions by being fully reflective of the character of the underlying distributed data. In the context of collaborative fuzzy modeling, we bring forward a concept experience–consistent fuzzy system identification showing how fuzzy models built on a basis of limited data can benefit from taking advantage of the past experience conveyed in the form of previously constructed fuzzy models. Detailed algorithmic considerations embrace several design scenarios in which we apply the mechanism of experience consistency at the level of conditions and conclusions of the rules. We also show that a level of achieved experience-driven consistency can be quantified through fuzzy sets (fuzzy numbers) of the parameters of the local models standing in the conclusion parts of the rules this leading to the emergence of granular constructs of fuzzy modeling. After completing you should be able to develop an understanding of:
Witold Pedrycz received the M.Sc., and Ph.D., D.Sci. all from the Silesian University of Technology, Gliwice, Poland. He is a Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. Dr. Pedrycz is an IEEE Fellow and IFSA Fellow. His main research interests encompass fundamentals of Computational Intelligence, Granular Computing, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published vigorously in these areas. He is an author of 11 research monographs and over 250 journal papers published in highly reputable journals. His research is highly cited and he is also on the list Highly cited researcher on ISI HighlyCited.com. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of Computational Intelligence, Granular Computing, fuzzy sets and neurocomputing. He was a Program Chair of the 2007 Int. Conf on Machine Learning and Cybernetics, August 19-22, 2007, Hong Kong. He was also a General Chair of NAFIPS 2004, June 24-26, 2004, Banff, Alberta- a flagship conference of the NAFIPS Society. He currently serves as Editor-in-Chief of IEEE Transactions on Systems Man and Cybernetics-part A. He is an Associate Editor of Transactions on Fuzzy Systems. He is also on editorial boards of over 10 international journals. Dr Pedrycz is also an Editor-in-Chief of Information Sciences. NEXT Dr. Pedrycz is the past president of IFSA and the past president of NAFIPS. Dr. Pedrycz is a recipient of the prestigious Norbert Wiener Award which is one of the two highest awards of the IEEE Systems, Man, and Cybernetics Society. He is also a recipient of the K.S. Fu from NAFIPS.
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"Nanotechnology 101 Part 1" by H.-S. Philip Wong, sponsored by the IEEE Solid-State Circuits Society This course provides an introduction to the emerging opportunities in novel nanoscale devices and fabrication techniques, with particular emphasis on the implications for circuit and system designers. Topics covered include: fundamentals of device physics and materials science at the nanoscale, and the ITRS Emerging Research Devices (memory & logic). After completing this course you should be able to develop an understanding of:
H.-S. Philip Wong joined the IBM T. J. Watson Research Center, Yorktown Heights, New York, in 1988. In September, 2004, he joined Stanford University as Professor of Electrical Engineering.
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"Nanotechnology 101 Part 2" by H.-S. Philip Wong, sponsored by the IEEE Solid-State Circuits Society This course expands on the materials presented in "Nanotechnology 101 Part 1," and delves further to cover Topics such as: nanotubes, nanowires, and nanoparticles, molecular devices, nanofabrication techniques and their impact on device layout. An assessment of the level of maturity for the proposed devices will be given. After completing this course you should be able to develop an understanding of:
H.-S. Philip Wong joined the IBM T. J. Watson Research Center, Yorktown Heights, New York, in 1988. In September, 2004, he joined Stanford University as Professor of Electrical Engineering.
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"Planning and Performing Failure Mode and Effects Analysis on Software" by Nathaniel Ozarin, sponsored by the IEEE Reliability Society Failure Mode Effects Analyses (FMEA) have proven to be an effective method for improving the reliability of hardware systems but many still consider software FMEAs to be problematic. This course provides a proven methodology and a detailed example for planning and performing FMEAs on software. An introduction to Software FMEA and relation to Hardware FMEA will be provided along with a step-by-step approach to performing software FMEA--using excerpts from a real example. After completing this course you should be able to develop an understanding of:
Nathaniel Ozarin is a senior engineering consultant at The Omnicon Group Inc., a company specializing in reliability and safety analysis for the military, medical, industrial, and transportation industries.
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"Real-Time Computer Systems with Applications" by Phil Laplante, sponsored by the IEEE Computer Society This broad overview of techniques in real-time systems design and analysis provides a practical and quick introduction to the subject. The treatment is pragmatic and example-oriented, drawing on extensive experience rather than abstract and theoretically rigorous derivations; but it covers a great deal of territory, including real-time operating systems, software system design, and performance analysis and optimization, among others. After completing this course you should be able to develop an understanding of:
Dr. Phillip Laplante is Associate Professor of Software Engineering at the Penn State Great Valley School of Graduate Studies.
IEEE
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"Reliability Analysis of Computer Based Systems Using Dynamic Fault Trees" by Joanne Bechta Dugan, sponsored by the IEEE Reliability Society Redundant or fault tolerant computer-based systems provide several challenges to reliability analysis and probabilistic risk assessment. Computer systems which are designed to achieve high reliability frequently employ high levels of redundancy, dynamic redundancy management and complex fault and error recovery techniques. It is precisely this flexibility and adaptability inherent in fault tolerant computer systems that makes analysis problematic. In this course, Dynamic Fault Tree (DFT) modeling techniques for handling these difficulties are described. The dynamic fault tree methodology extends the traditional (also called static) fault tree methodology by providing special gates to capture specific types of sequence dependencies that arise in modeling computer-based systems. The DFT is automatically converted to the equivalent Markov model for solution; the solution results are translated back to the FT framework. The DFT model seeks to address these difficulties by providing a methodology for including Markov modeling within the fault tree framework, thus extending the applicability of the fault tree model, and by extension, the Probabilistic Risk Assessment methodology. In addition to sequence-dependent failure scenarios, reliability analysis of fault tolerant computer systems must include the notion of fault coverage. A covered fault is one that can be tolerated by the system, that is, the system is able to utilize redundant components to continue correct operation. However, an uncovered fault may occur that could defeat the fault tolerance mechanisms and result in immediate system failure. The DFT approach allows the analyst to include consideration of fault coverage. In this course we introduce the DFT approach and apply the special gates to the analysis of several example systems. Subsequent sections discuss fault coverage and its impact on reliability analysis. After completing this course you should be able to develop an understanding of:
Joanne Bechta Dugan is currently Professor of Electrical and Computer Engineering at the University of Virginia.
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"Software Safety for Aerospace Applications" by Alan Tribble, sponsored by the IEEE Aerospace & Electronic Systems Society This course provides an overview of software safety as it relates to the safety of the overall computing system. In particular, learners will gain an understanding of the various software safety standards used in the aircraft industry, traditional safety analysis techniques, and current research and development efforts in the field. After completing this course you should be able to develop an understanding of:
Alan Tribble has over sixteen years of industrial experience and is currently with Rockwell Collins, Advanced Technology Center.
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“Type-2 Fuzzy Logic Controllers: Towards a New Approach for Handling Uncertainties in Real World Environments” by Hani Hagras, sponsored by the IEEE Computational Intelligence Society This course will have a large impact on a large audience as handling uncertainties will be a very important challenge to any real world application that operate in real world changing and dynamic environments. The course will present the theoretical aspects of type-2 FLCs and how to build a type-2 FLC. The course will also present many applications in different areas ranging from Control of Marine Diesel Engines, Autonomous Outdoor mobile Robots as well as Embedded Agents and Ambient Intelligent Environments which deals with how we can embed very efficient computational intelligence and type-2 techniques in small computing and memory platforms. After completing you should be able to develop an understanding of:
Prof. Hani Hagras is a Professor of Computer Science, Director of the Centre for Computational Intelligence and leader of the Fuzzy Systems Research Group in the University of Essex, UK.
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"Wireless Sensor Networks and Applications" by Dwight Borses, sponsored by the IEEE Computer Society A Wireless Sensor Network (WSN) is made up of a large number of sensors which are extremely small, low-cost, and low-power devices that collect environmental data (acoustics, light, temperature, humidity, imaging, etc.) that is then communicated through radio or optical means to infrastructure processing nodes. WSNs may consist of up to thousands of nodes, which can be deployed in very high density, in homes, highways, buildings, cities, and infrastructures for monitoring and/or controlling purposes. Applications may range from detecting and monitoring occurrences of natural disasters and homeland security, to military surveillance. This introduction to emerging WSN applications reveals how developments in micro- and nanotechnology have aided advancements in WSNs and highlights implementation of several real systems that hint of tomorrow's potential. After completing this course you should be able to develop an understanding of:
Dwight Borses has worked at startups and mature corporations and is currently a field applications engineer with National Semiconductor, which he joined in 1987.
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