Difference between revisions of "Tutorials" - 19th International Conference on Information Fusion

Revision as of 15:07, 23 February 2016

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List of Tutorials of FUSION 2016

T1 Bayesian Multiple Target Tracking

Presenter: Lawrence D. Stone, Roy L. Streit

T2 Bayesian Networks and Trust Fusion with Subjective Logic

Presenter: Audun Jøsang

T3 Multisensor-Multitarget Tracker/Fusion Engine Development and Performance Evaluation for Realistic Scenarios

Presenter: T. Kirubarajan

T4 An Introduction to Track-to-Track Fusion and the Distributed Kalman Filter

Presenter: Felix Govaers

T5 A Finite-Set Statistics Prime

Presenter: Ronald Mahler

T6 Information Quality in Information Fusion and Decision Making

Presenter: Galina Rogova

T7 Multitarget Tracking and Multisensor Information Fusion

Presenter: Yaakov Bar-Shalom

T8 Overview of High-Level Information Fusion Theory, Models, and Representations

Presenter: Erik Blasch

T9 Quantum Physics Methods For Nonlinear Filtering

Presenter: Bhashyam Balaji and Fred Daum

T10 Basic Concepts in Multiobject Estimation

Presenter: Daniel Clark, Emmanuel D. Delande, and Jérémie Houssineau

T11 System-of-Systems Concepts, Opportunities and Issues for Information Fusion

Presenter: Alan Steinberg

T12 Implementations of Random-Finite-Set-Based Multi-Target Filters

Presenter: Ba-Ngu Vo and Ba-Tuong Vo

T13 Tracking and Sensor Data Fusion – Methodological Framework and Selected Applications

Presenter: Wolfgang Koch

T14 Multistatic Exploration – Introduction to Modern Patutoialive Radar and Multistatic Tracking & Data Fusion

Presenter: Wolfgang Koch

T15 Big Data Fusion and Analytics

Presenter: Subrata Das

T16 Object Tracking, Sensor Fusion and Situational Awareness for Assisted- and Self-Driving Vehicles: Problems, Solutions and Directions

Presenter: T. Kirubarajan

T17 Emerging Quantum Technologies for Fusion

Presenter: Bhashyam Balaji

T18 Maneuvering Target Tracking: Overview and Nonlinear Filtering Methods

Presenter: X. Rong Li and Vesselin P. Jilkov

T19 Integration of Information to Identify Objects in Big Data

Presenter: Grace S. Shieh

T20 Extended Object Tracking: Theory and Applications

Presenter: Karl Granström, Stephan Reuter, and Marcus Baum

T21 Probabilistic Situation Atutoialetutoialment for Abnormal Interaction Detection

Presenter: Carlo Regazzoni and Lucio Marcenaro

T22 Multitarget Tracking and Sensor Calibration in Centralized and Distributed Networks

Presenter: Murat Uney, Simon Julier, and Daniel Clark

T23 Information Fusion in Resource-Limited Camera Networks

Presenter: Andrea Cavallaro and Juan C. SanMiguel

T24 Introduction to Bayesian Filtering and Smoothing

Presenter: Simo Särkkä

T25 Sensor Fusion for Intelligent Vehicles

Presenter: Bharanidhar Duraisamy, Ting Yuan, Tilo Schwarz, and Martin Fritzsche

T26 Multisensor Data Fusion in Wireless Sensor and Actuator Networks

Presenter: Claudio Miceli de Farias

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-| class="MainPageBG" style="width:100%; border:1px solid #fff784; background:#fffff7; vertical-align:top; color:#000;" |
+| class="MainPageBG" style="width:100%; border:1px solid #f2ea7e; background:#ffffe8; vertical-align:top; color:#000;" |
-{| id="mp-left" style="width:100%; vertical-align:top; background:#fffff7;"
+{| id="mp-left" style="width:100%; vertical-align:top; background:#ffffe8;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #fff784; text-align:left; color:#000; padding:0.2em 0.4em;">T4 An Introduction to Track-to-Track Fusion and the Distributed Kalman Filter</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f2ea7e; text-align:left; color:#000; padding:0.2em 0.4em;">T4 An Introduction to Track-to-Track Fusion and the Distributed Kalman Filter</h2>
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 <!--        T9 Quantum Physics Methods For Nonlinear Filtering        -->
-| class="MainPageBG" style="width:100%; border:1px solid #fff784; background:#fffff7; vertical-align:top; color:#000;" |
+| class="MainPageBG" style="width:100%; border:1px solid #f2ea7e; background:#ffffe8; vertical-align:top; color:#000;" |
-{| id="mp-left" style="width:100%; vertical-align:top; background:#fffff7;"
+{| id="mp-left" style="width:100%; vertical-align:top; background:#ffffe8;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #fff784; text-align:left; color:#000; padding:0.2em 0.4em;">T9 Quantum Physics Methods For Nonlinear Filtering</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f2ea7e; text-align:left; color:#000; padding:0.2em 0.4em;">T9 Quantum Physics Methods For Nonlinear Filtering</h2>
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 <!--        T14 Multistatic Exploration – Introduction to Modern Patutoialive Radar and Multistatic Tracking & Data Fusion        -->
-| class="MainPageBG" style="width:100%; border:1px solid #fff784; background:#fffff7; vertical-align:top; color:#000;" |
+| class="MainPageBG" style="width:100%; border:1px solid #f2ea7e; background:#ffffe8; vertical-align:top; color:#000;" |
-{| id="mp-left" style="width:100%; vertical-align:top; background:#fffff7;"
+{| id="mp-left" style="width:100%; vertical-align:top; background:#ffffe8;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #fff784; text-align:left; color:#000; padding:0.2em 0.4em;">T14 Multistatic Exploration – Introduction to Modern Patutoialive Radar and Multistatic Tracking & Data Fusion</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f2ea7e; text-align:left; color:#000; padding:0.2em 0.4em;">T14 Multistatic Exploration – Introduction to Modern Patutoialive Radar and Multistatic Tracking & Data Fusion</h2>
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 <!--        T19 Integration of Information to Identify Objects in Big Data        -->
-| class="MainPageBG" style="width:100%; border:1px solid #fff784; background:#fffff7; vertical-align:top; color:#000;" |
+| class="MainPageBG" style="width:100%; border:1px solid #f2ea7e; background:#ffffe8; vertical-align:top; color:#000;" |
-{| id="mp-left" style="width:100%; vertical-align:top; background:#fffff7;"
+{| id="mp-left" style="width:100%; vertical-align:top; background:#ffffe8;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #fff784; text-align:left; color:#000; padding:0.2em 0.4em;">T19 Integration of Information to Identify Objects in Big Data</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f2ea7e; text-align:left; color:#000; padding:0.2em 0.4em;">T19 Integration of Information to Identify Objects in Big Data</h2>
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 <!--        T24 Introduction to Bayesian Filtering and Smoothing        -->
-| class="MainPageBG" style="width:100%; border:1px solid #fff784; background:#fffff7; vertical-align:top; color:#000;" |
+| class="MainPageBG" style="width:100%; border:1px solid #f2ea7e; background:#ffffe8; vertical-align:top; color:#000;" |
-{| id="mp-left" style="width:100%; vertical-align:top; background:#fffff7;"
+{| id="mp-left" style="width:100%; vertical-align:top; background:#ffffe8;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #fff784; text-align:left; color:#000; padding:0.2em 0.4em;">T24 Introduction to Bayesian Filtering and Smoothing</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#fff7bd; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f2ea7e; text-align:left; color:#000; padding:0.2em 0.4em;">T24 Introduction to Bayesian Filtering and Smoothing</h2>
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