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IEEE eLearning Library Series on Biometrics

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    Author:Bergstrom, Mats
    Sponsored by:IEEE Engineering in Medicine and Biology Society
    Tutorial Level: Intermediate
    Publication Date: Sept-2013
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    Drug development is a very costly endeavor with a low probability of success for each individual drug candidate. Typically only 5% of programs will result in a registered drug. Authorities charged with overseeing drug development efforts, such as the US FDA are advocating novel approaches for drug development with earlier studies in man combined with novel trial designs, increased attention to biomarkers and a focus on mechanistic understanding. This tutorial is the first of two presentations on the use of molecular imaging in drug development in light of these novel directives.

    The purpose of this first module is to discuss the potential uses for molecular imaging in drug development in a modernized strategy. This approach to development can be characterized as "question-based drug development.” – It consists of defining and trying to answer the relevant questions, questions which are mechanistic in nature and are key in distinct phases during the development of novel drugs.

    Keywords: Drug Development, molecular imaging, imaging

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Bergstrom, Mats
    Sponsored by:IEEE Engineering in Medicine and Biology Society
    Tutorial Level: Intermediate
    Publication Date: Sept-2013
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    In the IEEE eLearning tutorial “Including Medical Imaging in Drug Development – To Answer the Relevant Question,” we discussed different options for using imaging in the drug development process, and described the Question -Based Drug Development paradigm. Imaging can play an important role because it is non-invasive, repeatable and generates quantitative data over the whole anatomical site of disease. It can also suggest the mechanism by which the drug works.

    This tutorial is the second of two presentations on the use of molecular imaging in drug development. In this second tutorial we will focus on the relevance of the data from the standpoint of quantitation accuracy and precision; that is, we’ll be considering how to certify confidence in the data generated in an imaging study used for making important decisions.

    Keywords: Drug Development, molecular imaging, imaging

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Bezdek, James C.
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Introductory
    Publication Date: Dec-2008
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This tutorial - 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.

    Keywords: Alternating optimization , Classifier Design , Cluster Analysis , Cluster Count Extraction , Cluster Validity , Compact, Separated clusters , Competitive Learning , Dendogram , Distinguished features , Equal content

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Bezdek, James C.
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Intermediate
    Publication Date: Dec-2008
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    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.

    Keywords: Alternating optimization , Classifier Design , Cluster Analysis , Cluster Count Extraction , Cluster Validity , Compact, Separated clusters , Competitive Learning , Dendogram , Distinguished features

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Bezdek, James C.
    Sponsored by:
    IEEE Computational Intelligence Society
    Tutorial Level: Advanced
    Publication Date: Jan-2009
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This last tutorial 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.

    Keywords: Alternating optimization , Classifier Design , Cluster Analysis , Cluster Count Extraction , Cluster Validity , Compact, Separated clusters , Competitive Learning , Dendogram , Distinguished features , Equal content

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Perlovsky, Leonid
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Intermediate/advanced
    Publication Date: Jul-2005
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    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 tutorial 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 tutorial 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 tutorial 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.

    Keywords: aesthetic emotion , applications , beauty , behavior , cognition , combinatorial complexity , concept-models , concepts , conscious , dynamic logic , emotions

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Authors:Piuri, Vincenzo andScotti, Fabio
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Introductory/Intermediate
    Publication Date: Feb-2007
    Run Time: 1:00:
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    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.

    Keywords: Analog architecture , Composite system , Configuration , Digital Architecture , Evolutionary computing , Fuzzy system , High-level system design , Neural networks , Operating life , Processor , ambient

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Principe, Jose
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Introductory/Intermediate
    Publication Date: Dec-2006
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time


    Abstract
    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.

    Keywords: Euclidean and Cauchy Schwartz pdf Distances , Information forces , Information potential , Information theoretic learning , Kernel Annealing , Mermaid algorithm , Nonparameteric entropy estimation , Stochastic Information Gradient

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Gori, Marco
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Intermediate
    Publication Date: Aug-2009
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This tutorial 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.

    Keywords: Artificial neuron , Backpropagation , Batch-mode learning , Cross-validation , Generalization , Local minima , Multilayer perceptron , On-line learning , Premature saturation , Supervised learning

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Mendel, Jerry
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Introductory
    Publication Date: Oct-2008
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This course will provide an introduction to and an overview of type-2 fuzzy sets (T2 FSs) and systems. It will also address the following:
    • Locate type-2 fuzzy sets and systems in an educational taxonomy, so that the student 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
    • Provide careful definitions and pictures of the new terminology of T2 FSs
    • Explain the importance of interval type-2 fuzzy sets over more general T2 FSs
    • 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)
    • Explain how T2 FSs are used in a rule-based system (a fuzzy logic system-FLS)
    • Describe the detailed computations that are used for an interval T2 FLS, relying mostly on graphical pictures, and compare those computations with their type-1 counterparts
    • Explain the major obstacle to using a T2 FLS in a real-time application and how that obstacle has been overcome
    The course will conclude with a plug for the applications course and a short reading list.

    Keyword: KM Algorithms , WM uncertainty bounds , centroid: type-reduction , firing interval , footprint of uncertainty , fuzzy logic , fuzzy set , lower membership functions , output processing , representation theorem , type-2 fuzzy logic system , type-2 fuzzy set , upper membership functions

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Lucas, Simon M.
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Introductory
    Publication Date: Jun-2010
    Run Time: 1:30:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This tutorial provides a practical introduction to game strategy learning with function approximation architectures. The tutorial will cover the two main approaches to learning game strategy: evolution (including co-evolution), and temporal difference learning, and also discuss some ways of hybridizing these.

    We also look at how the choice of input features and function approximation architecture has a critical impact on what is learned, as well as how it is interfaced to the game (e.g. as a value estimator or as an action selector). Incremental and co-evolutionary methods of learning complex skills are described. In addition to standard MLPs, attention is also given to N-Tuple systems, as these have recently shown great potential to learn quickly and effectively, and to evolutionary methods for selecting subsets of the input vector to use and neural network topologies to process it with.

    Each method will be demonstrated with reference to some simple fragments of software, illustrating how the learning algorithm is connected with the game and with the function approximation architecture. Example games will include Othello, Simulated Car Racing, and Ms. Pac-Man.

    Keywords: AI , Artificial Intelligence , Gaming , temporal learning

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Pedrycz, Witold
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Intermediate
    Publication Date: May-2009
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    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.

    Keywords: Collaborative clustering , Collaborative systems , Granular computing , Information granules , Learning-interpretability tradeoff , Logic neurons , Objective function (performance index)

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author: Gu, Claire
    Sponsored by:IEEE Engineering in Medicine and Biology Society
    Tutorial Level: Introductory
    Publication Date: Nov-2008
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time


    Abstract

    Early diagnostics of diseases is the key to treatment, cure, and fatality prevention. Various biomedical sensors are available or being developed to achieve early disease diagnostics with non-invasive or minimally invasive techniques, such as magnetic resonance imaging (MRI), ultrasonic imaging, X-ray imaging, CT scan, optical coherent tomography (OCT), endoscopy, microscopy, spectroscopy, etc. Among these techniques, optical technologies, including various microscopy and spectroscopy approaches, provide the possibility to observe a large range of objects, from organs, cells, to molecules, with fast (ideally real-time) response and high spatial and spectral resolutions. In addition, to make the diagnostic tests of diseases, such as cancers, more accessible to the general public it is important to provide easy early diagnostic tools packaged as portable information devices. Such early diagnostics portable information devices must be highly sensitive, disease specific, reliable, inexpensive, easy to fabricate, fast, and compact.

    This course will provide an overview of various optical biomedical sensors, including both imaging and spectroscopic techniques, and introduce some recent developments in biomedical sensors, such as nanoparticle surface enhanced Raman scattering (SERS) and its application in compact molecular sensors. Specifically, the following topics will be discussed: interaction of light with tissues, cells, and molecules; bioimaging including optical microscopy, endoscopic imaging, fluorescence imaging, and optical tomography; spectroscopy including absorption spectroscopy, fluorescence spectroscopy, and Raman spectroscopy; optical fiber surface enhanced Raman probes for biomedical applications.

    Keywords: D-Shaped Fiber , Green Fluorescent Protein , Liquid Core Photonic Crystal Fiber , Mitosis , Optical Coherence Tomography , Photonic Crystal Fiber , Quantum Dots , Raman Scattering , Surface Enhanced Raman Scattering , absorption spectroscopy , cell , chromosomes

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Paradiso, Rita
    Sponsored by:IEEE Engineering in Medicine and Biology Society
    Tutorial Level: Introductory
    Publication Date: Mar-2009
    Run Time: 2:25:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    Smart Fabrics and Interactive Textile (SFIT) based systems are conceived as the integration into textile of sensors, actuators, computing, and a power source, with the whole being part of an interactive communication network. Such systems could only be envisaged through a combination of advances in fields as fiber and polymer research, advanced material processing, microelectronics, signals processing, nanotechnologies, and telecommunication.Textile is the common platform where smart materials in the form of fibers are integrated, where the properties of the material are augmented through combination of chemical surfaces processes, and where the structure of the fabric allows the use of redundant sensor configurations.

    Promising recent developments in material processing, device design and system configuration enable the scientific and industrial community to concentrate efforts on the realization of smart textiles.

    This course will discuss the use of textile materials for sensing functions. Textile technology for sensors fabrication will be presented. Methods for characterizations will also be discussed and examples of specific applications will be presented. The course will also provide an overview of future developments.

    Keywords: Autonomic Nervous System , Bipolar Einthoven Leads , Breathing Rate , Central Nervous System , Electroactive Polymers , Knitted Piezoresistive Fabric , Power Spectral Density , Pregelled Disposable Electrodes , Smart Fabrics and Interactive Textile

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.

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    Author:Hagras, Hani
    Sponsored by:IEEE Computational Intelligence Society
    Tutorial Level: Introductory/intermediate
    Publication Date: Jun-2008
    Run Time: 1:00:00
    CEUs: .3
    PDHs: 3
    ECSA CPD (Category 1 - Development Activities): 1 - Includes study time

    Abstract
    This course deals with handling uncertainties which pose challenges to real world applications operating in 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 such as control of marine diesel engines, autonomous outdoor mobile robots, embedded agents and ambient intelligent environments; these deal with how we can embed very efficient computational intelligence and type-2 techniques in small computing and memory platforms. The course will present a very clear description of type-2 Fuzzy Logic Controllers (FLCs), their design and their various applications in handling the uncertainties in various real world applications. Different examples will be provided.

    Keywords: AIE , Ambient Intelligent Environments , Co- Processor , DC , Direct Current , FLCs , FOU, Footprint Of Uncertainty , Fuzzy Logic Controllers , Gas , Genetic Algorithms , Intelligent Dormitory , Karnik-Mendel , Neuro, Neural Networks based , iDorm

    For individuals not subscribed to the IEEE eLearning Library, this course is available for individual purchase.