A PODS-based Extended Kalman Filter: Quantifying Sensing Uncertainties in Automatic Bird Species Detection
|
|
- Benjamin Russell
- 5 years ago
- Views:
Transcription
1 IEEE ICRA Workshop on Uncertainty in Automation, May 9, 2011 A PODS-based Extended Kalman Filter: Quantifying Sensing Uncertainties in Automatic Bird Species Detection Dezhen Song Associate Professor Dept. of Computer Science and Engineering, Texas A&M University
2 Thanks to: Ni Qin, Yiliang Xu, Chang Young Kim, Wen Li, TAMU Ken Goldberg, UC Berkeley Ron Rohrbach, Cornell Lab of Ornithology John Fitzpatrick, Cornell Lab of Ornithology David Luneau, U Arkansas Hopeng Wang and Jingtain Liu, Nankai University John Rappole, Smithsonian Selma Glasscock, Welder Wildlife Foundation National Science Foundation The Nature Conservancy Arkansas Game and Fish Commission U.S. Fish and Wildlife Service Arkansas Electric Cooperative Cache River National Wildlife Refuge
3 Biological observation is arduous, expensive, dangerous, lonely
4 Assisting the search for IBWO
5 Detecting Rare Birds Low occurrence (e.g., <10 times per year) Short duration (e.g., < 1 sec. in FOV) Huge video data for human identification. Setup and maintenance in remote environments.
6 Design Goals Accuracy low false negative Data reduction filtering the targeted bird Easy to setup and maintain monocular vision system
7 Related Work Natural cameras DeerCam Africa web cams at the Tembe Elephant part Tiger web cams James Reserve Wildlife Observatory Crane Cam Swan Cam
8 Related Work Motion detection and tracking Elgammal, Grimson, Isard Periodic motion detection Culter, Ran, Briassouli 3D inference using monocular vision Ribnick, Hoiem, Saxena
9 Related Work Kalman Filter SLAM, tracking, recognition Convergence ample observation data manageable noise less than 11 data points significant image noise
10 Bird detection problem Input targeted bird body length l b and speed range V=[v min,v max ]. a sequence of n images containing a moving object Output to determine if the object is a bird of targeted species
11 Assumptions Static monocular camera High resolution Narrow FOV Single bird in FOV Motion segmentation Constant bird velocity High flying speed Narrow camera FOV
12 Observation 1: Invariant body length in Steady flight
13 Invariant body length in steady flight [u t,v t ] T [u h,v h ] T z=[u h,v h,u t,v t ] T (observation)
14 Bird Body Axis Filtering Observation 2: Body axis orientation close to tangent line of trajectory during steady flight Flying trajectory Bird body axis B θ θ Difference between θ and θ on 61bird sequences: o µ = 0.8 ; σ = 8.3 b ( u, v ) B t t ( u, v ) B b z= argmax l, s.t. θ [ θ 2 σ, θ + 2 σ ] h h o b b
15 Modeling A Flying Bird [x,y,z] T P tail Kinematics: Tail: x xl & b / v t t t T P tail = [ x, y, z ] = y yl & b / v z zl & b / v [u t,v t ] T [u h,v h ] T camera center z Image plane y x Pin-hole model:
16 Extended Kalman Filter x(k) x(k+1) z(k) z(k+1) Image plane z y camera center x
17 Determine Species for Noise-free Cases False Image plane camera center Targeted range True
18 Estimation with Observation Noises Image plane camera center
19 Probable Observation Data Set (PODS) ( ) [ h S ( ) ] 1 k = u k ± τ S ( ) [ h ( ) ] 2 k = v k ± τ t S3( k) = [ u ( k) ± τ ] S ( ) [ t ( ) ] 4 k = v k ± τ S( k) = S ( k) S ( k) S ( k) S ( k) Image plane camera center Targeted range 1 n 1 n 1 n PODS: Z : = {Z : z( ) S( ) and (X : ) < } k k ε δ
20 EKF Convergence Metrics
21 PODS-EKF Decision-making: I 1: n (Z ) = 1: n 1 (accept) if V V Φ and Z Φ 0 (reject) otherwise PODS: Z = {Z z( k) S( k) and ε(x ) < δ } 1 : n 1 : n 1 : n Targeted range Dezhen Song and Yiliang Yu, Low False Negative Filter for Detecting Rare Bird Species from Short Video Segments using a Probable Observation Data Set-based EKF Method, IEEE Transactions on Image Processing, vol. 19, no. 9, Sept. 2010, pp
22 PODS-EKF Approximate Computation % = arg min ε(x 1: n 1: n Z ) z( k ) S( k ) Subject to: Z = {Z z( k) S( k) and ε (X ) < δ } 1 : n 1 : n 1 : n Targeted range Dezhen Song and Yiliang Yu, Low False Negative Filter for Detecting Rare Bird Species from Short Video Segments using a Probable Observation Data Set-based EKF Method, IEEE Transactions on Image Processing, vol. 19, no. 9, Sept. 2010, pp
23 Dezhen Song and Yiliang Yu, Low False Negative Filter for Detecting Rare Bird Species from Short Video Segments using a Probable Observation Data Set-based EKF Method, IEEE Transactions on Image Processing, vol. 19, no. 9, Sept. 2010, pp
24 Algorithm
25 Experiments Both simulated and real data A desktop PC with an Intel Core 2 Duo 2.13GHz CPU and 2GB RAM Matlab 7.0 (motion detection) and Visual C (PODS-EKF) Arecont AV3100 camera Bird species tested:
26 Convergence of different EFKs on Rock Pigeon
27 Simulation on three birds
28 Physical Experiment on Rock Pigeon Insects, falling leaves, other birds, etc.
29 ROC Curves for Rock Pigeon Area under ROC curve: 91.5% in Simulation; 95.0% in Experiment.
30
31
32
33
34
35
36
37 Data reduction Oct Oct Motion detection: TB to GB PODS-EKF: GB to MB (~960 images) Overall reduction rate: %
38 What we found Pileated woodpecker (cousin of IBWO)
39 Northern flicker (smaller than IBWO)
40 Red-tailed Hawk (larger than IBWO)
41 Conclusion Low false negative bird filter: PODS-EKF Cope with insufficient noisy observation data 95% area under ROC curve % data reduction
42 Current and Future Work Examine wing-flapping motion Wingbeat frequency is unique for each species
43 Wing Kinematic Model
44 Current & Future Work: AnyFish Collaborators: Mr. Ji Zhang, Dr. Gil Rosenthal, and Dr. Wei Yan
45 Thanks! Websites:
46
47 Seagull: Mean 2.74 Hz S.D Hz Gliding component Wingbeat frequency component Harmonic component
Two-stage Pedestrian Detection Based on Multiple Features and Machine Learning
38 3 Vol. 38, No. 3 2012 3 ACTA AUTOMATICA SINICA March, 2012 1 1 1, (Adaboost) (Support vector machine, SVM). (Four direction features, FDF) GAB (Gentle Adaboost) (Entropy-histograms of oriented gradients,
More informationAn improved active contour model based on level set method
1 015 1 ( ) Journal of East China Normal University (Natural Science) No. 1 Jan. 015 : 1000-5641(015)01-0161-11, (, 0006) :,., (SPF),.,,.,,,,. : ; ; ; : O948 : A DOI: 10.3969/j.issn.1000-5641.015.01.00
More informationEVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE FILTER
EVALUATING SYMMETRIC INFORMATION GAP BETWEEN DYNAMICAL SYSTEMS USING PARTICLE FILTER Zhen Zhen 1, Jun Young Lee 2, and Abdus Saboor 3 1 Mingde College, Guizhou University, China zhenz2000@21cn.com 2 Department
More informationThe Detection Techniques for Several Different Types of Fiducial Markers
Vol. 1, No. 2, pp. 86-93(2013) The Detection Techniques for Several Different Types of Fiducial Markers Chuen-Horng Lin 1,*,Yu-Ching Lin 1,and Hau-Wei Lee 2 1 Department of Computer Science and Information
More informationHandling parametric and non-parametric additive faults in LTV Systems
1 / 16 Handling parametric and non-parametric additive faults in LTV Systems Qinghua Zhang & Michèle Basseville INRIA & CNRS-IRISA, Rennes, France 9th IFAC SAFEPROCESS, Paris, France, Sept. 2-4, 2015 2
More informationStatistical Filters for Crowd Image Analysis
Statistical Filters for Crowd Image Analysis Ákos Utasi, Ákos Kiss and Tamás Szirányi Distributed Events Analysis Research Group, Computer and Automation Research Institute H-1111 Budapest, Kende utca
More informationTuning of Extended Kalman Filter for nonlinear State Estimation
OSR Journal of Computer Engineering (OSR-JCE) e-ssn: 78-0661,p-SSN: 78-877, Volume 18, ssue 5, Ver. V (Sep. - Oct. 016), PP 14-19 www.iosrjournals.org Tuning of Extended Kalman Filter for nonlinear State
More informationUAV Navigation: Airborne Inertial SLAM
Introduction UAV Navigation: Airborne Inertial SLAM Jonghyuk Kim Faculty of Engineering and Information Technology Australian National University, Australia Salah Sukkarieh ARC Centre of Excellence in
More informationERROR COVARIANCE ESTIMATION IN OBJECT TRACKING SCENARIOS USING KALMAN FILTER
ERROR COVARIANCE ESTIMATION IN OBJECT TRACKING SCENARIOS USING KALMAN FILTER Mr K V Sriharsha K.V 1, Dr N V Rao 2 1 Assistant Professor, Department of Information Technology, CVR College of Engg (India)
More informationUsing the Kalman Filter to Estimate the State of a Maneuvering Aircraft
1 Using the Kalman Filter to Estimate the State of a Maneuvering Aircraft K. Meier and A. Desai Abstract Using sensors that only measure the bearing angle and range of an aircraft, a Kalman filter is implemented
More informationIn 1-D, all we needed was x. For 2-D motion, we'll need a displacement vector made up of two components: r = r x + r y + r z
D Kinematics 1. Introduction 1. Vectors. Independence of Motion 3. Independence of Motion 4. x-y motions. Projectile Motion 3. Relative motion Introduction Using + or signs was ok in 1 dimension but is
More informationEmpirical Analysis of Invariance of Transform Coefficients under Rotation
International Journal of Engineering Research and Development e-issn: 2278-67X, p-issn: 2278-8X, www.ijerd.com Volume, Issue 5 (May 25), PP.43-5 Empirical Analysis of Invariance of Transform Coefficients
More informationDevelopment of a Deep Recurrent Neural Network Controller for Flight Applications
Development of a Deep Recurrent Neural Network Controller for Flight Applications American Control Conference (ACC) May 26, 2017 Scott A. Nivison Pramod P. Khargonekar Department of Electrical and Computer
More informationHuman Pose Tracking I: Basics. David Fleet University of Toronto
Human Pose Tracking I: Basics David Fleet University of Toronto CIFAR Summer School, 2009 Looking at People Challenges: Complex pose / motion People have many degrees of freedom, comprising an articulated
More informationThe Use of Short-Arc Angle and Angle Rate Data for Deep-Space Initial Orbit Determination and Track Association
The Use of Short-Arc Angle and Angle Rate Data for Deep-Space Initial Orbit Determination and Track Association Dr. Moriba Jah (AFRL) Mr. Kyle DeMars (UT-Austin) Dr. Paul Schumacher Jr. (AFRL) Background/Motivation
More informationAttitude Determination System of Small Satellite
Attitude Determination System of Small Satellite Satellite Research Centre Jiun Wei Chia, M. Sheral Crescent Tissera and Kay-Soon Low School of EEE, Nanyang Technological University, Singapore 24 th October
More informationEKF and SLAM. McGill COMP 765 Sept 18 th, 2017
EKF and SLAM McGill COMP 765 Sept 18 th, 2017 Outline News and information Instructions for paper presentations Continue on Kalman filter: EKF and extension to mapping Example of a real mapping system:
More informationRisk Management of Portfolios by CVaR Optimization
Risk Management of Portfolios by CVaR Optimization Thomas F. Coleman a Dept of Combinatorics & Optimization University of Waterloo a Ophelia Lazaridis University Research Chair 1 Joint work with Yuying
More informationMonitoring and data filtering II. Dynamic Linear Models
Monitoring and data filtering II. Dynamic Linear Models Advanced Herd Management Cécile Cornou, IPH Dias 1 Program for monitoring and data filtering Friday 26 (morning) - Lecture for part I : use of control
More informationA Bregman alternating direction method of multipliers for sparse probabilistic Boolean network problem
A Bregman alternating direction method of multipliers for sparse probabilistic Boolean network problem Kangkang Deng, Zheng Peng Abstract: The main task of genetic regulatory networks is to construct a
More informationProbabilistic Graphical Models
Probabilistic Graphical Models Lecture 5 Bayesian Learning of Bayesian Networks CS/CNS/EE 155 Andreas Krause Announcements Recitations: Every Tuesday 4-5:30 in 243 Annenberg Homework 1 out. Due in class
More informationResearch Article Convex Polyhedron Method to Stability of Continuous Systems with Two Additive Time-Varying Delay Components
Applied Mathematics Volume 202, Article ID 689820, 3 pages doi:0.55/202/689820 Research Article Convex Polyhedron Method to Stability of Continuous Systems with Two Additive Time-Varying Delay Components
More informationTarget tracking and classification for missile using interacting multiple model (IMM)
Target tracking and classification for missile using interacting multiple model (IMM Kyungwoo Yoo and Joohwan Chun KAIST School of Electrical Engineering Yuseong-gu, Daejeon, Republic of Korea Email: babooovv@kaist.ac.kr
More informationTennis player segmentation for semantic behavior analysis
Proposta di Tennis player segmentation for semantic behavior analysis Architettura Software per Robot Mobili Vito Renò, Nicola Mosca, Massimiliano Nitti, Tiziana D Orazio, Donato Campagnoli, Andrea Prati,
More informationOpen Access Target Tracking Algorithm Based on Improved Unscented Kalman Filter
Send Orders for Reprints to reprints@benthamscience.ae he Open Automation and Control Systems Journal, 2015, 7, 991-995 991 Open Access arget racking Algorithm Based on Improved Unscented Kalman Filter
More informationLecture 13 Visual Inertial Fusion
Lecture 13 Visual Inertial Fusion Davide Scaramuzza Outline Introduction IMU model and Camera-IMU system Different paradigms Filtering Maximum a posteriori estimation Fix-lag smoothing 2 What is an IMU?
More informationDynamic Data Modeling, Recognition, and Synthesis. Rui Zhao Thesis Defense Advisor: Professor Qiang Ji
Dynamic Data Modeling, Recognition, and Synthesis Rui Zhao Thesis Defense Advisor: Professor Qiang Ji Contents Introduction Related Work Dynamic Data Modeling & Analysis Temporal localization Insufficient
More informationLarge-Scale Behavioral Targeting
Large-Scale Behavioral Targeting Ye Chen, Dmitry Pavlov, John Canny ebay, Yandex, UC Berkeley (This work was conducted at Yahoo! Labs.) June 30, 2009 Chen et al. (KDD 09) Large-Scale Behavioral Targeting
More informationGeneralized Zero-Shot Learning with Deep Calibration Network
Generalized Zero-Shot Learning with Deep Calibration Network Shichen Liu, Mingsheng Long, Jianmin Wang, and Michael I.Jordan School of Software, Tsinghua University, China KLiss, MOE; BNRist; Research
More informationA Probabilistic Representation for Dynamic Movement Primitives
A Probabilistic Representation for Dynamic Movement Primitives Franziska Meier,2 and Stefan Schaal,2 CLMC Lab, University of Southern California, Los Angeles, USA 2 Autonomous Motion Department, MPI for
More informationModel Identification and Attitude Control Scheme for a Micromechanical Flying Insect
Model Identification and Attitude Control Scheme for a Micromechanical Flying Insect Xinyan Deng, Luca Schenato and Shankar Sastry Department of Electrical Engineering and Computer Sciences University
More informationConstrained State Estimation Using the Unscented Kalman Filter
16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France June 25-27, 28 Constrained State Estimation Using the Unscented Kalman Filter Rambabu Kandepu, Lars Imsland and
More informationVision-based Control Laws for Distributed Flocking of Nonholonomic Agents
Vision-based Control Laws for Distributed Flocking of Nonholonomic Agents Nima Moshtagh, Ali Jadbabaie, Kostas Daniilidis GRASP Laboratory, University of Pennsylvania, Philadelphia, PA 94 Email: {nima,
More informationNew Developments in Tail-Equivalent Linearization method for Nonlinear Stochastic Dynamics
New Developments in Tail-Equivalent Linearization method for Nonlinear Stochastic Dynamics Armen Der Kiureghian President, American University of Armenia Taisei Professor of Civil Engineering Emeritus
More informationESTIMATOR STABILITY ANALYSIS IN SLAM. Teresa Vidal-Calleja, Juan Andrade-Cetto, Alberto Sanfeliu
ESTIMATOR STABILITY ANALYSIS IN SLAM Teresa Vidal-Calleja, Juan Andrade-Cetto, Alberto Sanfeliu Institut de Robtica i Informtica Industrial, UPC-CSIC Llorens Artigas 4-6, Barcelona, 88 Spain {tvidal, cetto,
More informationMarkov chain Monte Carlo methods for visual tracking
Markov chain Monte Carlo methods for visual tracking Ray Luo rluo@cory.eecs.berkeley.edu Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720
More informationValidating Expensive Simulations with Expensive Experiments: A Bayesian Approach
Validating Expensive Simulations with Expensive Experiments: A Bayesian Approach Dr. Arun Subramaniyan GE Global Research Center Niskayuna, NY 2012 ASME V& V Symposium Team: GE GRC: Liping Wang, Natarajan
More informationImproved Kalman Filter Initialisation using Neurofuzzy Estimation
Improved Kalman Filter Initialisation using Neurofuzzy Estimation J. M. Roberts, D. J. Mills, D. Charnley and C. J. Harris Introduction It is traditional to initialise Kalman filters and extended Kalman
More informationMcGill University Department of Electrical and Computer Engineering
McGill University Department of Electrical and Computer Engineering ECSE 56 - Stochastic Control Project Report Professor Aditya Mahajan Team Decision Theory and Information Structures in Optimal Control
More informationA Simple Model for Sequences of Relational State Descriptions
A Simple Model for Sequences of Relational State Descriptions Ingo Thon, Niels Landwehr, and Luc De Raedt Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Heverlee,
More informationEnvironmental Response Management Application
Environmental Response Management Application Coastal Response Research Center Nancy Kinner, Michele Jacobi, Rob Braswell, Kurt Schwehr & Amy Merten RRT III May 14, 2008 1 Talk Outline Overview of Center
More informationCovariance Tracking Algorithm on Bilateral Filtering under Lie Group Structure Yinghong Xie 1,2,a Chengdong Wu 1,b
Applied Mechanics and Materials Online: 014-0-06 ISSN: 166-748, Vols. 519-50, pp 684-688 doi:10.408/www.scientific.net/amm.519-50.684 014 Trans Tech Publications, Switzerland Covariance Tracking Algorithm
More informationBAYESIAN ESTIMATION OF UNKNOWN PARAMETERS OVER NETWORKS
BAYESIAN ESTIMATION OF UNKNOWN PARAMETERS OVER NETWORKS Petar M. Djurić Dept. of Electrical & Computer Engineering Stony Brook University Stony Brook, NY 11794, USA e-mail: petar.djuric@stonybrook.edu
More informationHIGHER ORDER SLIDING MODES AND ARBITRARY-ORDER EXACT ROBUST DIFFERENTIATION
HIGHER ORDER SLIDING MODES AND ARBITRARY-ORDER EXACT ROBUST DIFFERENTIATION A. Levant Institute for Industrial Mathematics, 4/24 Yehuda Ha-Nachtom St., Beer-Sheva 843, Israel Fax: +972-7-232 and E-mail:
More informationCONTROL OF ROBOT CAMERA SYSTEM WITH ACTUATOR S DYNAMICS TO TRACK MOVING OBJECT
Journal of Computer Science and Cybernetics, V.31, N.3 (2015), 255 265 DOI: 10.15625/1813-9663/31/3/6127 CONTROL OF ROBOT CAMERA SYSTEM WITH ACTUATOR S DYNAMICS TO TRACK MOVING OBJECT NGUYEN TIEN KIEM
More informationTarget Localization and Circumnavigation Using Bearing Measurements in 2D
Target Localization and Circumnavigation Using Bearing Measurements in D Mohammad Deghat, Iman Shames, Brian D. O. Anderson and Changbin Yu Abstract This paper considers the problem of localization and
More informationBAYESIAN MACHINE LEARNING.
BAYESIAN MACHINE LEARNING frederic.pennerath@centralesupelec.fr What is this Bayesian Machine Learning course about? A course emphasizing the few essential theoretical ingredients Probabilistic generative
More informationTowards Reduced-Order Models for Online Motion Planning and Control of UAVs in the Presence of Wind
Towards Reduced-Order Models for Online Motion Planning and Control of UAVs in the Presence of Wind Ashray A. Doshi, Surya P. Singh and Adam J. Postula The University of Queensland, Australia {a.doshi,
More informationCompressive Sensing of Sparse Tensor
Shmuel Friedland Univ. Illinois at Chicago Matheon Workshop CSA2013 December 12, 2013 joint work with Q. Li and D. Schonfeld, UIC Abstract Conventional Compressive sensing (CS) theory relies on data representation
More informationYARN TENSION PATTERN RETRIEVAL SYSTEM BASED ON GAUSSIAN MAXIMUM LIKELIHOOD. Received July 2010; revised December 2010
International Journal of Innovative Computing, Information and Control ICIC International c 2011 ISSN 1349-4198 Volume 7, Number 11, November 2011 pp. 6261 6272 YARN TENSION PATTERN RETRIEVAL SYSTEM BASED
More informationTARGET DETECTION WITH FUNCTION OF COVARIANCE MATRICES UNDER CLUTTER ENVIRONMENT
TARGET DETECTION WITH FUNCTION OF COVARIANCE MATRICES UNDER CLUTTER ENVIRONMENT Feng Lin, Robert C. Qiu, James P. Browning, Michael C. Wicks Cognitive Radio Institute, Department of Electrical and Computer
More informationRevenue Maximization in a Cloud Federation
Revenue Maximization in a Cloud Federation Makhlouf Hadji and Djamal Zeghlache September 14th, 2015 IRT SystemX/ Telecom SudParis Makhlouf Hadji Outline of the presentation 01 Introduction 02 03 04 05
More informationAutonomous Agent Behaviour Modelled in PRISM A Case Study
Autonomous Agent Behaviour Modelled in PRISM A Case Study Ruth Hoffmann 1, Murray Ireland 1, Alice Miller 1, Gethin Norman 1, and Sandor Veres 2 1 University of Glasgow, Glasgow, G12 8QQ, Scotland 2 University
More informationNonlinear Optimization for Optimal Control Part 2. Pieter Abbeel UC Berkeley EECS. From linear to nonlinear Model-predictive control (MPC) POMDPs
Nonlinear Optimization for Optimal Control Part 2 Pieter Abbeel UC Berkeley EECS Outline From linear to nonlinear Model-predictive control (MPC) POMDPs Page 1! From Linear to Nonlinear We know how to solve
More informationSemidefinite Facial Reduction for Euclidean Distance Matrix Completion
Semidefinite Facial Reduction for Euclidean Distance Matrix Completion Nathan Krislock, Henry Wolkowicz Department of Combinatorics & Optimization University of Waterloo First Alpen-Adria Workshop on Optimization
More informationUNCOOPERATIVE OBJECTS POSE, MOTION AND INERTIA TENSOR ESTIMATION VIA STEREOVISION
UNCOOPERATIVE OBJECTS POSE, MOTION AND INERTIA TENSOR ESTIMATION VIA STEREOVISION M. Lavagna, V. Pesce, and R. Bevilacqua 2 Politecnico di Milano, Aerospace Science and Technology Dept, Via La Masa 34,
More informationPose tracking of magnetic objects
Pose tracking of magnetic objects Niklas Wahlström Department of Information Technology, Uppsala University, Sweden Novmber 13, 2017 niklas.wahlstrom@it.uu.se Seminar Vi2 Short about me 2005-2010: Applied
More informationCSC487/2503: Foundations of Computer Vision. Visual Tracking. David Fleet
CSC487/2503: Foundations of Computer Vision Visual Tracking David Fleet Introduction What is tracking? Major players: Dynamics (model of temporal variation of target parameters) Measurements (relation
More informationBetter Simulation Metamodeling: The Why, What and How of Stochastic Kriging
Better Simulation Metamodeling: The Why, What and How of Stochastic Kriging Jeremy Staum Collaborators: Bruce Ankenman, Barry Nelson Evren Baysal, Ming Liu, Wei Xie supported by the NSF under Grant No.
More informationIMPROVEMENTS IN ACTIVE NOISE CONTROL OF HELICOPTER NOISE IN A MOCK CABIN ABSTRACT
IMPROVEMENTS IN ACTIVE NOISE CONTROL OF HELICOPTER NOISE IN A MOCK CABIN Jared K. Thomas Brigham Young University Department of Mechanical Engineering ABSTRACT The application of active noise control (ANC)
More informationDISTURBANCE OBSERVER BASED CONTROL: CONCEPTS, METHODS AND CHALLENGES
DISTURBANCE OBSERVER BASED CONTROL: CONCEPTS, METHODS AND CHALLENGES Wen-Hua Chen Professor in Autonomous Vehicles Department of Aeronautical and Automotive Engineering Loughborough University 1 Outline
More informationPROBABILISTIC REASONING OVER TIME
PROBABILISTIC REASONING OVER TIME In which we try to interpret the present, understand the past, and perhaps predict the future, even when very little is crystal clear. Outline Time and uncertainty Inference:
More informationShankar Shivappa University of California, San Diego April 26, CSE 254 Seminar in learning algorithms
Recognition of Visual Speech Elements Using Adaptively Boosted Hidden Markov Models. Say Wei Foo, Yong Lian, Liang Dong. IEEE Transactions on Circuits and Systems for Video Technology, May 2004. Shankar
More informationLinear Discrete-time State Space Realization of a Modified Quadruple Tank System with State Estimation using Kalman Filter
Journal of Physics: Conference Series PAPER OPEN ACCESS Linear Discrete-time State Space Realization of a Modified Quadruple Tank System with State Estimation using Kalman Filter To cite this article:
More informationAutonomous Vision Based Detection of Non-stellar Objects Flying in Formation with Camera Point of View
Autonomous Vision Based Detection of Non-stellar Objects Flying in Formation with Camera Point of View As.Prof. M. Benn (1), Prof. J. L. Jørgensen () (1) () DTU Space, Elektrovej 37, 4553438, mb@space.dtu.dk,
More informationLecture 4: Perceptrons and Multilayer Perceptrons
Lecture 4: Perceptrons and Multilayer Perceptrons Cognitive Systems II - Machine Learning SS 2005 Part I: Basic Approaches of Concept Learning Perceptrons, Artificial Neuronal Networks Lecture 4: Perceptrons
More informationCS 5522: Artificial Intelligence II
CS 5522: Artificial Intelligence II Particle Filters and Applications of HMMs Instructor: Wei Xu Ohio State University [These slides were adapted from CS188 Intro to AI at UC Berkeley.] Recap: Reasoning
More informationGlobal Behaviour Inference using Probabilistic Latent Semantic Analysis
Global Behaviour Inference using Probabilistic Latent Semantic Analysis Jian Li, Shaogang Gong, Tao Xiang Department of Computer Science Queen Mary College, University of London, London, E1 4NS, UK {jianli,
More informationOVERLAPPING ANIMAL SOUND CLASSIFICATION USING SPARSE REPRESENTATION
OVERLAPPING ANIMAL SOUND CLASSIFICATION USING SPARSE REPRESENTATION Na Lin, Haixin Sun Xiamen University Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry
More informationA Background Layer Model for Object Tracking through Occlusion
A Background Layer Model for Object Tracking through Occlusion Yue Zhou and Hai Tao UC-Santa Cruz Presented by: Mei-Fang Huang CSE252C class, November 6, 2003 Overview Object tracking problems Dynamic
More informationAbnormal Activity Detection and Tracking Namrata Vaswani Dept. of Electrical and Computer Engineering Iowa State University
Abnormal Activity Detection and Tracking Namrata Vaswani Dept. of Electrical and Computer Engineering Iowa State University Abnormal Activity Detection and Tracking 1 The Problem Goal: To track activities
More informationBiometrics: Introduction and Examples. Raymond Veldhuis
Biometrics: Introduction and Examples Raymond Veldhuis 1 Overview Biometric recognition Face recognition Challenges Transparent face recognition Large-scale identification Watch list Anonymous biometrics
More informationAvailable online at ScienceDirect. Procedia Engineering 119 (2015 ) 13 18
Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 119 (2015 ) 13 18 13th Computer Control for Water Industry Conference, CCWI 2015 Real-time burst detection in water distribution
More informationMultiscale Adaptive Sensor Systems
Multiscale Adaptive Sensor Systems Silvia Ferrari Sibley School of Mechanical and Aerospace Engineering Cornell University ONR Maritime Sensing D&I Review Naval Surface Warfare Center, Carderock 9-11 August
More informationSimultaneous Localization and Mapping (SLAM) Corso di Robotica Prof. Davide Brugali Università degli Studi di Bergamo
Simultaneous Localization and Mapping (SLAM) Corso di Robotica Prof. Davide Brugali Università degli Studi di Bergamo Introduction SLAM asks the following question: Is it possible for an autonomous vehicle
More informationPartially Observable Markov Decision Processes (POMDPs) Pieter Abbeel UC Berkeley EECS
Partially Observable Markov Decision Processes (POMDPs) Pieter Abbeel UC Berkeley EECS Many slides adapted from Jur van den Berg Outline POMDPs Separation Principle / Certainty Equivalence Locally Optimal
More informationSampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking
Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking Zhang Zu-Tao( 张祖涛 ) a)b) and Zhang Jia-Shu( 张家树 ) b) a) School of Mechanical Engineering, Southwest Jiaotong
More informationSTAR-CCM+: NACA0012 Flow and Aero-Acoustics Analysis James Ruiz Application Engineer January 26, 2011
www.cd-adapco.com STAR-CCM+: NACA0012 Flow and Aero-Acoustics Analysis James Ruiz Application Engineer January 26, 2011 Introduction The objective of this work is to prove the capability of STAR-CCM+ as
More informationArtificial Intelligence
Artificial Intelligence Roman Barták Department of Theoretical Computer Science and Mathematical Logic Summary of last lecture We know how to do probabilistic reasoning over time transition model P(X t
More informationAn Investigation of the Generalised Range-Based Detector in Pareto Distributed Clutter
Progress In Electromagnetics Research C, Vol. 85, 1 8, 2018 An Investigation of the Generalised Range-Based Detector in Pareto Distributed Clutter Graham V. Weinberg * and Charlie Tran Abstract The purpose
More informationX. F. Wang, J. F. Chen, Z. G. Shi *, and K. S. Chen Department of Information and Electronic Engineering, Zhejiang University, Hangzhou , China
Progress In Electromagnetics Research, Vol. 118, 1 15, 211 FUZZY-CONTROL-BASED PARTICLE FILTER FOR MANEUVERING TARGET TRACKING X. F. Wang, J. F. Chen, Z. G. Shi *, and K. S. Chen Department of Information
More informationA Hybrid Time-delay Prediction Method for Networked Control System
International Journal of Automation and Computing 11(1), February 2014, 19-24 DOI: 10.1007/s11633-014-0761-1 A Hybrid Time-delay Prediction Method for Networked Control System Zhong-Da Tian Xian-Wen Gao
More informationRESTORATION OF VIDEO BY REMOVING RAIN
RESTORATION OF VIDEO BY REMOVING RAIN Sajitha Krishnan 1 and D.Venkataraman 1 1 Computer Vision and Image Processing, Department of Computer Science, Amrita Vishwa Vidyapeetham University, Coimbatore,
More informationRobotics 2 AdaBoost for People and Place Detection
Robotics 2 AdaBoost for People and Place Detection Giorgio Grisetti, Cyrill Stachniss, Kai Arras, Wolfram Burgard v.1.0, Kai Arras, Oct 09, including material by Luciano Spinello and Oscar Martinez Mozos
More informationMaximum)individuals:!2! Shape)requirements:!rodent0like 4! Identity)method:!crosses!solved 5!
Supplementary Material Automated image-based tracking and its application in ecology Anthony I. Dell*, John A. Bender, Kristin Branson, Iain D. Couzin, Gonzalo G. de Polavieja, Lucas P.J.J. Noldus, Alfonso
More informationOptimized PSD Envelope for Nonstationary Vibration Revision A
ACCEL (G) Optimized PSD Envelope for Nonstationary Vibration Revision A By Tom Irvine Email: tom@vibrationdata.com July, 014 10 FLIGHT ACCELEROMETER DATA - SUBORBITAL LAUNCH VEHICLE 5 0-5 -10-5 0 5 10
More informationLearning Dexterity Matthias Plappert SEPTEMBER 6, 2018
Learning Dexterity Matthias Plappert SEPTEMBER 6, 2018 OpenAI OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence. OpenAI OpenAI is a non-profit
More informationA NONLINEARITY MEASURE FOR ESTIMATION SYSTEMS
AAS 6-135 A NONLINEARITY MEASURE FOR ESTIMATION SYSTEMS Andrew J. Sinclair,JohnE.Hurtado, and John L. Junkins The concept of nonlinearity measures for dynamical systems is extended to estimation systems,
More informationOn Design of Reduced-Order H Filters for Discrete-Time Systems from Incomplete Measurements
Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, 2008 On Design of Reduced-Order H Filters for Discrete-Time Systems from Incomplete Measurements Shaosheng Zhou
More informationComputational Analysis of Hovering Hummingbird Flight
Computational Analysis of Hovering Hummingbird Flight Zongxian Liang 1 and Haibo Dong 2 Department of Mechanical & Materials Engineering, Wright State University, Dayton, OH 45435 Mingjun Wei 3 Department
More informationCourse 495: Advanced Statistical Machine Learning/Pattern Recognition
Course 495: Advanced Statistical Machine Learning/Pattern Recognition Lecturer: Stefanos Zafeiriou Goal (Lectures): To present discrete and continuous valued probabilistic linear dynamical systems (HMMs
More informationExact State and Covariance Sub-matrix Recovery for Submap Based Sparse EIF SLAM Algorithm
8 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-3, 8 Exact State and Covariance Sub-matrix Recovery for Submap Based Sparse EIF SLAM Algorithm Shoudong Huang, Zhan
More informationNonlinear Filtering. With Polynomial Chaos. Raktim Bhattacharya. Aerospace Engineering, Texas A&M University uq.tamu.edu
Nonlinear Filtering With Polynomial Chaos Raktim Bhattacharya Aerospace Engineering, Texas A&M University uq.tamu.edu Nonlinear Filtering with PC Problem Setup. Dynamics: ẋ = f(x, ) Sensor Model: ỹ = h(x)
More informationUniversity of Genova - DITEN. Smart Patrolling. video and SIgnal Processing for Telecommunications ISIP40
University of Genova - DITEN Smart Patrolling 1 Smart Patrolling Detection of the intruder Tracking of the intruder A cognitive node will active an operator, describing on his mobile terminal the characteristic
More informationSIMULATION AND ASSESSMENT OF AIR IMPINGEMENT COOLING ON SQUARED PIN-FIN HEAT SINKS APPLIED IN PERSONAL COMPUTERS
20 Journal of Marine Science and Technology, Vol. 13, No. 1, pp. 20-27 (2005) SIMULATION AND ASSESSMENT OF AIR IMPINGEMENT COOLING ON SQUARED PIN-FIN HEAT SINKS APPLIED IN PERSONAL COMPUTERS Hwa-Chong
More informationRemote Sensing Techniques for Renewable Energy Projects. Dr Stuart Clough APEM Ltd
Remote Sensing Techniques for Renewable Energy Projects Dr Stuart Clough APEM Ltd What is Remote Sensing? The use of aerial sensors to detect and classify objects on Earth Remote sensing for ecological
More informationFINGERPRINT INFORMATION MAXIMIZATION FOR CONTENT IDENTIFICATION 1. Rohit Naini, Pierre Moulin
014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) FINGERPRINT INFORMATION MAXIMIZATION FOR CONTENT IDENTIFICATION 1 Rohit Naini, Pierre Moulin University of Illinois
More informationTrajectory tracking & Path-following control
Cooperative Control of Multiple Robotic Vehicles: Theory and Practice Trajectory tracking & Path-following control EECI Graduate School on Control Supélec, Feb. 21-25, 2011 A word about T Tracking and
More informationRelevant Applications of Differential Algebra in Astrodynamics
AstroNet-II International Final Conference Tossa de Mar 15-19 June 2015 Relevant Applications of Differential Algebra in Astrodynamics Pierluigi Di Lizia in collaboration with: Roberto Armellin, Alessandro
More informationA Simple Approach to the Multi-Predator Multi-Prey Pursuit Domain
A Simple Approach to the Multi-Predator Multi-Prey Pursuit Domain Javier A. Alcazar Sibley School of Mechanical and Aerospace Engineering Cornell University jaa48@cornell.edu 1. Abstract We present a different
More information