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Heterogeneous data analysis: Online learning for medical-image-based diagnosis

Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning

Trajectory Estimations Using Smartphones

Intelligent Forecasting using Dead Reckoning with Dynamic Errors

Smart Anomaly Prediction in Non-stationary CT Colonography Screening

Cloud Colonography: Distributed Medical Testbed over Cloud

Dynamic 3D Surface Reconstruction and Motion Modeling from a Pan-Tilt-Zoom Camera

Smart Colonography Computing for Distributed Medical Databases with Group Kernel Feature Analysis

Kernel Association for Classification and Prediction: a Survey

Scale-Invariant Tracking Optical Flow in Tracking using a Pan-Tilt-Zoom Camera

Design of a Fiber-optic Sensing Mosquito Trap

Tracking Human Motion With Multichannel Interacting Multiple Model

Local Intensity Feature Tracking and Motion Modeling for Respiratory Signal Extraction in Cone Beam CT Projections

Customized Prediction of Respiratory Motion with Clustering from Multiple Patient Interaction

Irregular Breathing Classification from Multiple Patient Datasets using Neural Networks

Principal Composite Kernel Feature Analysis: Data-Dependent Kernel Approach

A Multiple Model Approach to Tracking Head Orientation with Delta Quaternions

Interpolation Volume Calibration: A Multisensor Calibration Technique for Electromagnetic Trackers

Self-Reproduction for Articulated Behaviors with Dual Humanoid Robots using On-line Decision Tree Classification

Respiratory Motion Estimation With Hybrid Implementation of Extended Kalman Filter

Head Orientation Prediction: Delta Quaternion versus Quaternion

Very Low Frequency Electromagnetic Detector With Data Acquisition

Hand-Eye Calibration Applied to Viewpoint Selection for Robotic Vision

Video-Based Tracking and Incremental Learning Applied to Rodent Behavioral Activity under Near-Infrared Illumination

An Interactive Framework for Acquiring Vision Models of 3D Objects from 2D images

Respiratory motion prediction and perspective correction for free-breathing arterial spin labeled perfusion MRI of the kidney

Multicolumn RBF Network

A Novel Method of Cone Beam CT Projection Binning base on Image Registration

Acoustic Fault Detection Technique for High Power Insulators

Human Behavior-based Target Tracking with an Omni-directional Thermal Camera

Dynamic 3D Reconstruction of Human Targets via an Omni-directional Infrared Sensor

Vegetation Segmentation for Sensor Fusion of Omnidirectional Far-infrared and Visual Stream

Detecting Infrared Maritime Targets Overwhelmed in Sun Glitters by Anti-Jitter Spatiotemporal Saliency

Direct Spherical Calibration of Omnidirectional Far Infrared Camera System

Thermal Multisensor Fusion for Collaborative Robotics

Elastographic Tomosynthesis from X-ray Strain Imaging of Breast Cancer

Electric Load Forecasting Model Using a Multicolumn Deep Neural Networks

Visual Perception for Multiple Human-Robot Interaction from Motion Behavior

Robust Detection of Infrared Maritime Targets for Autonomous Navigation

DeepFuseNet of Omnidirectional Far-infrared and Visual Stream for Vegetation Detection

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI

Prediction and Classification of Respiratory Motion

Data-Variant Kernel Analysis

Augmented reality (AR) systems are often used to superimpose virtual objects or information on a scene to improve situational awareness. Delays in the display system or inaccurate registration of objects destroy the sense of immersion a user experiences when using AR systems. AC electromagnetic trackers are ideal for these applications when combined with head orientation prediction to compensate for display system delays. Unfortunately, these trackers do not perform well in environments that contain conductive or ferrous materials due to magnetic field distortion without expensive calibration techniques. In our work we focus on both the prediction and distortion compensation aspects of this application, developing a "small footprint" predictive filter for display lag compensation and a simplified calibration system for AC magnetic trackers.  In the first phase of our study we presented a novel method of tracking angular head velocity from quaternion orientation using an Extended Kalman Filter in both single model (DQEKF) and multiple model (MMDQ) implementations. In the second phase of our work we have developed a new method of mapping the magnetic field generated by the tracker without high precision measurement equipment. This method uses simple fixtures with multiple sensors in a rigid geometry to collect magnetic field data in the tracking volume. We have developed a new algorithm to process the collected data and generate a map of the magnetic field distortion that can be used to compensate distorted measurement data.  Table of Contents: List of Tables / Preface / Acknowledgments / Delta Quaternion Extended Kalman Filter / Multiple Model Delta Quaternion Filter / Interpolation Volume Calibration / Conclusion / References / Authors' Biographies 

Latency and Distortion of Electromagnetic Trackers for Augmented Reality Systems (Synthesis Lectures on Algorithms and Software in Engineering)

This book concentrates on improving the prediction of a vehicle’s future trajectory, particularly on non-straight paths. Having an accurate prediction of where a vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. The US DOT will be mandating that all vehicle manufacturers begin implementing V2V and V2I systems, so very soon collision avoidance systems will no longer rely on line of sight sensors, but instead will be able to take into account another vehicle’s spatial movements to determine if the future trajectories of the vehicles will intersect at the same time. Furthermore, the book introduces the reader to some improvements when predicting the future trajectory of a vehicle and presents a novel temporary solution on how to speed up the implementation of such V2V collision avoidance systems. Additionally, it evaluates whether smartphones can be used for trajectory predictions, in an attempt to populate a V2V collision avoidance system faster than a vehicle manufacturer can.

Predicting Vehicle Trajectory

Signals and Systems I

Dynamic and Multivariable Systems

Automatic Control

Pattern Recognition

Digital Signal Processing

Yuichi Motai, Ph.D. is currently an associate professor of Electrical and Computer Engineering at the Virginia Commonwealth University, Richmond, VA, USA after having moved from the University of Vermont. He had a visiting appointment in Radiology Department at Harvard Medical School, MA, USA. He was born in Japan, studied in both Japan and the USA, and completed his Ph.D. with the Robot Vision Laboratory in the School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana in 2002. His first laboratory work was in Biomedical Instrumentation Laboratory at Keio University in 1990-1991, where he wrote his bachelor thesis on a psycho-physiological experiment, color matching between computer display and actual object. He wrote his master thesis on 3D object reconstruction by depth from focus using a single camera at Image Informatics laboratory at Kyoto University in 1991-1993. He then spent 4 years as a tenured research scientist in an industrial laboratory called Intelligent Systems Lab in the image processing division where he built an infrared image sensing systems for detecting human behavior. In 1997, he started working for his Ph.D. in the School of Electrical and Computer Engineering at Purdue University. His thesis was on a 3D robot vision system for acquiring an object model with human-computer interaction framework. He spent more than 3 years on this project funded by Ford Motor Company. He has been selected for more than 10 prestigious fellowships and awards, and has been invited to more than 50 research seminars from the top universities and research companies. He has granted over 10 Ph.D as an adviser, and has published 4 research books, more than 100 journals and conferences papers as a corresponding author. He was ONR SFRP Visiting Summer Senior Faculty, Naval Research Lab, Naval Surface Warfare Center, Dahlgren, VA, AFOSR-AFRL/RI Summer Faculty Fellowship Program (SFFP) at Air Force Research Lab, Rome AFB, NY, and AFRL-ASEE SFFP at the Air Force Research Lab in Hanscom AFB, MA. He was a winner of NSF CAREER Award in the Division of Electrical, Communications, and Cyber System at the Directorate for Engineering, and awarded 10+ peer-reviewed grants from both internal and external sources as the PI. His research interests are in the broad area of sensory intelligence, especially of medical imaging, computer vision, and robotics.

System Safety

Intelligent Systems with Adaptive Tracking

Online Classification Methodologies

Medical Imaging

Computer Vision

Robotics

Biomedical Applications

Sensory Intelligence

Data Analysis

Data Science

Software and Development

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Fall2017

Editor's Note

CAREER: Engineering Data-intensive Prediction and Classification for Medical Testbeds with Nonlinear, Distributed, and Interdisciplinary Approaches

CRADA ARL JWS 15-18-01 Omni-Directional Infrared Imagery to Enhance Localization and Tracking Algorithms

NIH R01 CA159471-07 Subaward – Washington University

Associate Professor, Department of Electrical and Computer Engineering

Do you have a knack for math and physics? Maybe a deep love for problem solving? If so, you’ve got a lot in common with Seonyeong Park, an Electrical & Computer Engineering Ph.D. student. Listen to how she’s applying all her passions in a rapidly growing field to enhance medical imaging for radiation therapy.

Motai Lab

Laurence Demo

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