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Yuichi Motai, Ph.D. - VCU College of Engineering. Engineering West Hall, Room 212, Richmond, VA, US

Yuichi Motai, Ph.D. Yuichi Motai, Ph.D.

Associate Professor, Department of Electrical and Computer Engineering | VCU College of Engineering

Engineering West Hall, Room 212, Richmond, VA, UNITED STATES

Associate Professor of the Department of Electrical and Computer Engineering of Virginia Commonwealth University.



Yuichi Motai, Ph.D. Publication Yuichi Motai, Ph.D. Publication Yuichi Motai, Ph.D. Publication Yuichi Motai, Ph.D. Publication




Motai Lab




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 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 supervised over 15 graduate students including currently enrolling 6 Ph.D. students, and has published 4 research books, more than 60 journals and conferences papers. His research interests are in the broad area of sensory intelligence, especially of medical imaging, computer vision, and robotics.

Industry Expertise (8)

Education/Learning Research Electrical Engineering Industrial Automation Computer Software Health and Wellness Information Technology and Services Writing and Editing

Areas of Expertise (10)

Intelligent Systems with Adaptive Tracking Online Classification Methodologies Medical Imaging Pattern Recognition Computer Vision Robotics Biomedical Applications Sensory Intelligence Data Analysis Data Science

Education (3)

Purdue University: Ph.D., Electrical and Computer Engineering 2002

Kyoto University: M.E., Applied Systems Science 1993

Keio University: B.E., Instrumentation Engineering 1991

Affiliations (5)

  • Virginia Commonwealth University
  • Sigma Xi (Scientific Research Society)
  • IEEE (Institute of Electrical and Electronics Engineers) Senior member
  • ACM (Association for Computing Machinery) Lifetime member
  • ASEE (American Society for Engineering Education)

Media Appearances (1)

Editor's Note

IEEE Intelligent transportation systems magazine  print


Review of the Book Predicting Vehicle Trajectory [Book Review] Christos-Nikolaos E. Anagnostopoulos
IEEE Intelligent Transportation Systems Magazine
Year: 2017, Volume: 9, Issue: 3
Pages: 156 - 157, DOI: 10.1109/MITS.2017.2711255


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Research Grants (3)

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

NSF $448000


Research Objectives and Approaches

The objective of this research is to contribute to the interdisciplinary topic on STEM and basic medical science, specifically Patient-Centered Health Informatics Applications, with the newly proposed techniques that stand to benefit from the investigator?s expertise in engineering and from the collaboration with medical experts.

The approach is to study the medical data from several institutions comprehensively with the dynamics of all the datasets in their entirety such as non-linearity with kernel factors, and the network characteristics of whole multiple database distribution, rather than applying traditional techniques of prediction and classification to the very limited number of small medical testbeds.

Intellectual Merit

When the proposed adaptive tracking method is used on soft tissue tumors, radiosurgery systems maintain precise targeting of the tumor by predicting tumor motion using a motion tracking system. The successful development of the proposed dynamic classification method will substantially advance the clinical implementation of cancer screening, promote the early diagnosis of colon cancer, lead to an improved screening rate, and ultimately contribute toward reducing the mortality due to colon cancer.

Broader impacts

The proposed data-intensive solutions can save millions of cancer patients every year. The expected outcomes will be applied to medical problems and benefit society as a whole by enhancing the quality of all our lives, through unprecedented advances in the early diagnosis and treatment of cancer. The advancements in the developed framework will make use of and expand the Nation's cyber infrastructure and high performance computing capability.

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

U.S. ARMY Research Laboratory $30163


Robot Vision

NIH R01 CA159471-07 Subaward – Washington University

National Institutes of Health $25099


Medical Imaging

Courses (4)

Signals and Systems I

Presents the concept of linear continuous-time and discrete-time signals and systems, their classification, and analysis and design using mathematical models. Topics to be covered: the concepts of linear systems and classification of these systems, continuous-time linear systems and differential and difference equations, convolution, frequency domain analysis of systems, Fourier series and Fourier transforms and their application, and continuous-time to discrete-time conversion, and Laplace transformation and Transfer function representation.

Dynamic and Multivariable Systems

This course covers the use of state space methods to model analog and digital linear and nonlinear systems. Emphasis is placed on the student gaining mathematical modeling experience, performing sensitivity and stability analysis and designing compensators to meet systems specifications. Topics treated will include a review of root locus and frequency design methods, linear algebraic equations, state variable equations, state space design and digital control systems (principles and case studies). The students will use complex dynamic systems for analysis and design. The laboratory will consist of modeling and control demonstrations and experiments single-input/single-output and multivariable systems, analysis and simulation using matlab control toolbox and other control software.

Automatic Control

This course covers the design and analysis of linear feedback systems. Emphasis is placed upon the student gaining mathematical modeling experience and performing sensitivity and stability analysis. The use of compensators to meet systems design specifications will be treated. Topics include: an overview and brief history of feedback control, dynamic models, dynamic response, basic properties of feedback, root-locus, frequency response and state space design methods. The laboratory will consist of modeling and control demonstrations and experiments single-input/single-output and multivariable systems, analysis and simulation using matlab/simulink and other control system analysis/design/implementation software.

Pattern Recognition

This course will give an introduction to statistical pattern classification. The fundamental background for the course is probability theory, especially those fundamental topics summarized in Appendix of the text. The course is suitable for students, in engineering, mathematics, and computer science, who have a basic background in calculus, linear algebra, and probability theory, and who have some interest in exploring the field of pattern recognition. The course will closely follow the material in the text, and will be surveyed through the most chapters 1-10 (except 7). The intention is to spend about 3 class periods discussing material selectively covered in each chapter. Students are expected to read each chapter, prior to the start of the second class on that chapter.

Selected Articles (31)

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


Heterogeneous Data Analysis (HDA) is proposed to address a learning problem of medical image databases of Computed Tomographic Colonography (CTC). The databases are generated from clinical CTC images using a Computer-aided Detection (CAD) system, the goal of which is to aid radiologists' interpretation of CTC images by providing highly accurate, machine-based detection of colonic polyps. We aim to achieve a high detection accuracy in CAD in a clinically realistic context, in which additional CTC cases of new patients are added regularly to an existing database. In this context, the CAD performance can be improved by exploiting the heterogeneity information that is brought into the database through the addition of diverse and disparate patient populations. In the HDA, several quantitative criteria of data compatibility are proposed for efficient management of these online images. After an initial supervised offline learning phase, the proposed online learning method decides whether the online data are heterogeneous or homogeneous. Our previously developed Principal Composite Kernel Feature Analysis (PC-KFA) is applied to the online data, managed with HDA, for iterative construction of a linear subspace of a high-dimensional feature space by maximizing the variance of the non-linearly transformed samples. The experimental results showed that significant improvements in the data compatibility were obtained when the online PC-KFA was used, based on an accuracy measure for long-term sequential online datasets. The computational time is reduced by more than 93% in online training compared with that of offline training.

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Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning IEEE Journal of Translational Engineering in Health and Medicine


Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter-fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and inter-fraction fuzzy deep learning (IIFDL), where FDL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of IIFDL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.

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Trajectory Estimations Using Smartphones IEEE Transactions on Industrial Electronics


This paper investigates whether the smartphones' built-in sensors can accurately predict future trajectories for a possible implementation in a vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) system. If smartphones could be used, vehicles without the V2V/V2I technology could use them to tap into the V2V/V2I infrastructure and help to populate the gap of vehicles off the V2V/V2I grid. To evaluate this, we set up a dead-reckoning system that uses Kalman filters to predict the future trajectory of a vehicle, information that could be used in a V2V/V2I system to warn drivers if the trajectories of vehicles will intersect at the same time. Then, we use a vehicle with accelerometer, GPS, and speedometer sensors mounted on it and evaluate its accuracy in predicting the future trajectory. Afterward, we place a smartphone securely on the vehicle's dashboard, and we use its internal accelerometer and GPS to feed the same dead reckoning and Kalman filter setup to predict the future trajectory of the vehicle. We end by comparing both results and evaluating if a smartphone can achieve similar accuracy in predicting the future trajectory of a vehicle. Our results show that some smartphones could be used to predict a future position, but the use of their accelerometer sensors introduces some measurements that can be incorrectly interpreted as spatial changes.

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Intelligent Forecasting using Dead Reckoning with Dynamic Errors IEEE Transactions on Industrial Informatics


A method for integrating, processing, and analyzing sensing data from vehicle-mounted sensors for intelligent forecasting and decision-making is introduced. This dead reckoning with dynamic errors (DRWDEs) is for a large-scale integration of distributed resources and sensing data intervehicle collision avoidance system. This sensor fusion algorithm is introduced to predict the future trajectory of a vehicle. Current systems that predict a vehicle’s future trajectory, necessary in a network of collision avoidance systems, tend to have a lot of errors when the vehicles are moving in a nonstraight path. Our system has been designed with the objective of improving the estimations during curves. To evaluate this system, our research uses a Garmin 16HVS GPS sensor, an AutoEnginuity OBDII ScanTool, and a Crossbow three-axis accelerometer. Using Kalman filters (KFs), a dynamic noise covariance matrix merged together with an interacting multiple models (IMMs) system, our DRWDE produces the future position estimation of where the vehicle will be 3 s later in time. The ability to handle the change in noise, depending on unavailable sensor measurements, permits a flexibility to use any type of sensor and still have the system run at the fastest frequency available. Compared with a more common KF implementation that runs at the rate of its slowest sensor (1 Hz in our setup), our experimental results showed that our DRWDE (running at 10 Hz) yielded more accurate predictions (25%–50% improvement) during abrupt changes in the heading of the vehicle.

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Smart Anomaly Prediction in Non-stationary CT Colonography Screening IEEE Transactions on Industrial Informatics


To enhance the quality of economically efficient healthcare, we propose a preventive planning service for next-generation screening based on a longitudinal prediction. This newly proposed framework may bring important advancements in prevention by identifying the early stages of cancer, which will help in further diagnoses and initial treatment planning. The preventive service may also solve the obstacles of cost and availability of scanners in screening. For nonstationary medical data, anomaly detection is the key problem in the prediction of cancer staging. To address anomaly detection in a huge stream of databases, we applied a composite kernel to the prediction of cancer staging for the first time. The proposed longitudinal analysis of composite kernels (LACK) is designed for the prediction of anomaly status and cancer stage for further diagnosis and the future likelihood of cancer stage progression. The prediction error of LACK is relatively small even if the prediction is made far ahead of time. The computation time for nonstationary learning is reduced by 33% compared with stationary learning.

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Cloud Colonography: Distributed Medical Testbed over Cloud IEEE Transactions on Cloud Computing


Cloud Colonography is proposed in this paper, using different types of cloud computing environments. The sizes of the databases from the Computed Tomographic Colonography (CTC) screening tests among several hospitals are explored. These networked databases are going to be available in the near future via cloud computing technologies. Associated Multiple Databases (AMD) was developed in this study to handle multiple CTC databases. When AMD is used for assembling databases, it can achieve very high classification accuracy. The proposed AMD has the potential to play as a core classifier tool in the cloud computing framework. AMD for multiple institutions databases yields high detection performance of polyps using Kernel principal component analysis (KPCA). Two cases in the proposed cloud platform are private and public. We adapted a University cluster as a private platform, and Amazon Elastic Compute Cloud (EC2) as a public. The computation time, memory usage, and running costs were compared using three respresentative databases between private and public cloud environments. The proposed parallel processing modules improved the computation time, especially for the public cloud enviroments. The successful development of a cloud computing environment that handles large amounts of data will make Cloud Colonography feasible for a new health care service.

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Dynamic 3D Surface Reconstruction and Motion Modeling from a Pan-Tilt-Zoom Camera International Journal of Computers in Industries


3D surface reconstruction and motion modeling has been integrated in several industrial applications. Using a pan–tilt–zoom (PTZ) camera, we present an efficient method called dynamic 3D reconstruction (D3DR) for recovering the 3D motion and structure of a freely moving target. The proposed method estimates the PTZ measurements to keep the target in the center of the field of view (FoV) of the camera with the same size. Feature extraction and tracking approach are used in the imaging framework to estimate the target's translation, position, and distance. A selection strategy is used to select keyframes that show significant changes in target movement and directly update the recovered 3D information. The proposed D3DR method is designed to work in a real-time environment, not requiring all frames captured to be used to update the recovered 3D motion and structure of the target. Using fewer frames minimizes the time and space complexity required. Experimental results conducted on real-time video streams using different targets to prove the efficiency of the proposed method. The proposed D3DR has been compared to existing offline and online 3D reconstruction methods, showing that it uses less execution time than the offline method and uses an average of 49.6% of the total number of frames captured.

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Smart Colonography Computing for Distributed Medical Databases with Group Kernel Feature Analysis ACM Transactions on Intelligent Systems and Technologies


Computer-Aided Detection (CAD) of polyps in Computed Tomographic (CT) colonography is currently very limited since a single database at each hospital/institution doesn't provide sufficient data for training the CAD system's classification algorithm. To address this limitation, we propose to use multiple databases, (e.g., big data studies) to create multiple institution-wide databases using distributed computing technologies, which we call smart colonography. Smart colonography may be built by a larger colonography database networked through the participation of multiple institutions via distributed computing. The motivation herein is to create a distributed database that increases the detection accuracy of CAD diagnosis by covering many true-positive cases. Colonography data analysis is mutually accessible to increase the availability of resources so that the knowledge of radiologists is enhanced. In this article, we propose a scalable and efficient algorithm called Group Kernel Feature Analysis (GKFA), which can be applied to multiple cancer databases so that the overall performance of CAD is improved. The key idea behind the proposed GKFA method is to allow the feature space to be updated as the training proceeds with more data being fed from other institutions into the algorithm. Experimental results show that GKFA achieves very good classification accuracy.

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Kernel Association for Classification and Prediction: a Survey IEEE Transactions on Neural Networks and Learning Systems


Kernel association (KA) in statistical pattern recognition used for classification and prediction have recently emerged in a machine learning and signal processing context. This survey outlines the latest trends and innovations of a kernel framework for big data analysis. KA topics include offline learning, distributed database, online learning, and its prediction. The structural presentation and the comprehensive list of references are geared to provide a useful overview of this evolving field for both specialists and relevant scholars.

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


An efficient method for tracking a target using a single Pan-Tilt-Zoom (PTZ) camera is proposed. The proposed Scale-Invariant Optical Flow (SIOF) method estimates the motion of the target and rotates the camera accordingly to keep the target at the center of the image. Also, SIOF estimates the scale of the target and changes the focal length relatively to adjust the Field of View (FoV) and keep the target appear in the same size in all captured frames. SIOF is a feature-based tracking method. Feature points used are extracted and tracked using Optical Flow (OF) and Scale-Invariant Feature Transform (SIFT). They are combined in groups and used to achieve robust tracking. The feature points in these groups are used within a twist model to recover the 3D free motion of the target. The merits of this proposed method are (i) building an efficient scale-invariant tracking method that tracks the target and keep it in the FoV of the camera with the same size, and (ii) using tracking with prediction and correction to speed up the PTZ control and achieve smooth camera control. Experimental results were performed on online video streams and validated the efficiency of the proposed method SIOF, comparing with OF, SIFT, and other tracking methods. The proposed SIOF has around 36% less average tracking error and around 70% less tracking overshoot than OF.

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Design of a Fiber-optic Sensing Mosquito Trap IEEE Sensors Journal


Mosquito traps offer researchers and health officials a reasonable estimate of mosquito abundances to assess the spatial and temporal occurrences of mosquito-transmitted pathogens. Existing traps, however, have issued efficient design to detect mosquito and energy consumption of the device. We designed a novel mosquito collection device that sensitively detects the presence of a mosquito via a fiber-optic sensor. In this prototype, a pushing capture mechanism selectively powers and efficiently captures live mosquitoes without destroying identifying morphological features of the specimens. Because the trap sensor selectively powers the capture mechanism, it allows for greatly reduced power consumption when compared with existing continuously operated devices. With appropriate programming, the fans ON and OFF based on the triggering of a fiber-optic sensor detected and counted each mosquito that entered the trap. This trapping platform can be used with a variety of power sources including renewable sources (e.g., solar, wind, or hydroelectric power) in remote settings. The experimental results show a high success ratio 93%-100% for detection of live mosquitoes.

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Tracking Human Motion With Multichannel Interacting Multiple Model IEEE Transactions on Industrial Informatics


Tracking human motion with multiple body sensors has the potential to promote a large number of applications such as detecting patient motion, and monitoring for home-based applications. With multiple sensors, the tracking system architecture and data processing cannot perform the expected outcomes because of the limitations of data association. For the collaborative and intelligent applications of motion tracking (Polhemus Liberty AC magnetic tracker), we propose a human motion tracking system with multichannel interacting multiple model estimator (MC-IMME). To figure out interactive relationships among distributed sensors, we used a Gaussian mixture model (GMM) for clustering. With a collaborative grouping method based on GMM and expectation-maximization algorithm for distributed sensors, we can estimate the interactive relationship with multiple body sensors and achieve the efficient target estimation to employ a tracking relationship within a cluster. Using multiple models with filter divergence, the proposed MC-IMME can achieve the efficient estimation of the measurement and the velocity from measured datasets of human sensory data. We have newly developed MC-IMME to improve overall performance with a Markov switch probability and a proper grouping method. The experiment results shows that the prediction overshoot error can be improved on average by 19.31% by employing a tracking relationship.

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Local Intensity Feature Tracking and Motion Modeling for Respiratory Signal Extraction in Cone Beam CT Projections IEEE Transactions on Biomedical Engineering


Accounting for respiration motion during imaging can help improve targeting precision in radiation therapy. We propose local intensity feature tracking (LIFT), a novel markerless breath phase sorting method in cone beam computed tomography (CBCT) scan images. The contributions of this study are twofold. First, LIFT extracts the respiratory signal from the CBCT projections of the thorax depending only on tissue feature points that exhibit respiration. Second, the extracted respiratory signal is shown to correlate with standard respiration signals. LIFT extracts feature points in the first CBCT projection of a sequence and tracks those points in consecutive projections forming trajectories. Clustering is applied to select trajectories showing an oscillating behavior similar to the breath motion. Those “breathing” trajectories are used in a 3-D reconstruction approach to recover the 3-D motion of the lung which represents the respiratory signal. Experiments were conducted on datasets exhibiting regular and irregular breathing patterns. Results showed that LIFT-based respiratory signal correlates with the diaphragm position-based signal with an average phase shift of 1.68 projections as well as with the internal marker-based signal with an average phase shift of 1.78 projections. LIFT was able to detect the respiratory signal in all projections of all datasets.

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Customized Prediction of Respiratory Motion with Clustering from Multiple Patient Interaction ACM Transactions on Intelligent Systems and Technology


Information processing of radiotherapy systems has become an important research area for sophisticated radiation treatment methodology. Geometrically precise delivery of radiotherapy in the thorax and upper abdomen is compromised by respiratory motion during treatment. Accurate prediction of the respiratory motion would be beneficial for improving tumor targeting. However, a wide variety of breathing patterns can make it difficult to predict the breathing motion with explicit models. We proposed a respiratory motion predictor, that is, customized prediction with multiple patient interactions using neural network (CNN). For the preprocedure of prediction for individual patient, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the intraprocedure, the proposed CNN used neural networks (NN) for a part of the prediction and the extended Kalman filter (EKF) for a part of the correction. The prediction accuracy of the proposed method was investigated with a variety of prediction time horizons using normalized root mean squared error (NRMSE) values in comparison with the alternate recurrent neural network (RNN). We have also evaluated the prediction accuracy using the marginal value that can be used as the reference value to judge how many signals lie outside the confidence level. The experimental results showed that the proposed CNN can outperform RNN with respect to the prediction accuracy with an improvement of 50%.

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Irregular Breathing Classification from Multiple Patient Datasets using Neural Networks IEEE Transactions on Information Technology in Biomedicine


Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this paper is that the classifier using neural networks can provide clinical merit for the statistical quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The proposed irregular breathing classification used a regular ratio to decide whether or not the current breathing patterns were regular. The sensitivity, specificity, and receiver operating characteristiccurve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients' breathing patterns validated the proposed irregular breathing classifier.

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Principal Composite Kernel Feature Analysis: Data-Dependent Kernel Approach IEEE Transactions on Knowledge Data Engineering


Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace using a simple pattern classifier.

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A Multiple Model Approach to Tracking Head Orientation with Delta Quaternions IEEE Transactions on Cybernetics


Virtual reality and augmented reality environments using helmet-mounted displays create a sense of immersion by closely coupling user head motion to display content. Delays in the presentation of visual information can destroy the sense of presence in the simulation environment when it causes a lag in the display response to user head motion. The effect of display lag can be minimized by predicting head orientation, allowing the system to have sufficient time to counteract the delay. In this paper, anew head orientation prediction technique is proposed that uses a multiple delta quaternion (DQ) extended Kalman filter to track angular head velocity and angular head acceleration. This method is independent of the device used for orientation measurement, relying on quaternion orientation as the only measurement data. A new orientation prediction algorithm is proposed that estimates future head orientation as a function of the current orientation measurement and a predicted change in orientation, using the velocity and acceleration estimates. Extensive experimentation shows that the new method improves head orientation prediction when compared to single filter DQ prediction.

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Interpolation Volume Calibration: A Multisensor Calibration Technique for Electromagnetic Trackers IEEE Transactions on Robotics


AC electromagnetic trackers are well suited for head tracking but are adversely affected by conductive and ferromagnetic materials. Tracking performance can be improved by mapping the tracking volume to produce coefficients that correct position and orientation (PnO) measurements caused by stationary distorting materials. The mapping process is expensive and time consuming, requiring complicated high-precision equipment to provide registration of the measurements to the source reference frame. In this study, we develop a new approach to mapping that provides registration of mapping measurements without precision equipment. Our method, i.e., the interpolation volume calibration system, uses two simple fixtures, each with multiple sensors in a rigid geometry, to determine sensor PnO in a distorted environment without mechanical measurements or other tracking technologies. We test our method in a distorted tracking environment, constructing a lookup table of the magnetic field that is used as the basis for distortion compensation. The new method compares favorably with the traditional approach providing a significant reduction in cost and effort.

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


We have proposed a new repetition framework for vision-based behavior imitation by a sequence of multiple humanoid robots, introducing an on-line method for delimiting a time-varying context. This novel approach investigates the ability of a robot "student" to observe and imitate a behavior from a "teacher" robot; the student later changes roles to become the "teacher" for a naïve robot. For the many robots that already use video acquisition systems for their real-world tasks, this method eliminates the need for additional communication capabilities and complicated interfaces. This can reduce human intervention requirements and thus enhance the robots' practical usefulness outside the laboratory. Articulated motions are modeled in a three-layer method and registered as learned behaviors using color-based landmarks. Behaviors were identified on-line after each iteration by inducing a decision tree from the visually acquired data. Error accumulated over time, creating a context drift for behavior identification. In addition, identification and transmission of behaviors can occur between robots with differing, dynamically changing configurations. ITI, an on-line decision tree inducer in the C4.5 family, performed well for data that were similar in time and configuration to the training data but the greedily chosen attributes were not optimized for resistance to accumulating error or configuration changes. Our novel algorithm, OLDEX identified context changes on-line, as well as the amount of drift that could be tolerated before compensation was required. OLDEX can thus identify time and configuration contexts for the behavior data. This improved on previous methods, which either separated contexts off-line, or could not separate the slowly time-varying context into distinct regions at all. The results demonstrated the feasibility, usefulness, and potential of our unique idea for behavioral repetition and a propagating learning scheme.

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Respiratory Motion Estimation With Hybrid Implementation of Extended Kalman Filter IEEE Transactions on Industrial Electronics


The extended Kalman filter (EKF) can be used for the purpose of training nonlinear neural networks to perform desired input-output mappings. To improve the computational requirements of the EKF, Puskorius proposed the decoupled EKF (DEKF) as a practical remedy for the proper management of computational resources. This approach, however, sacrifices computational accuracy of estimates because it ignores the interactions between the estimates of mutually exclusive weights. To overcome such a limitation, therefore, we proposed hybrid implementation based on EKF (HEKF) for respiratory motion estimation, which uses the channel number for the mutually exclusive groups and the coupling technique to compensate the computational accuracy. Moreover, the authors restricted to a DEKF algorithm in which the weights connecting the inputs to a node are grouped together. If there are multiple input training sequences with respect to the time stamp, the complexity can increase by the power of the input channel number. To improve the computational complexity, we split the complicated neural network into a couple of simple neural networks to adjust separate input channels. The experimental results validated that the prediction overshoot of the proposed HEKF was improved by 62.95% in the average prediction overshoot values. The proposed HEKF showed a better performance of 52.40% improvement in the average of the prediction time horizon. We have evaluated that the proposed HEKF can outperform DEKF by comparing the prediction overshoot values, the performance of the tracking estimation value, and the normalized root-mean-squared error.

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Head Orientation Prediction: Delta Quaternion versus Quaternion IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)


Display lag in simulation environments with helmet-mounted displays causes a loss of immersion that degrades the value of virtual/augmented reality training simulators. Simulators use predictive tracking to compensate for display lag, preparing display updates based on the anticipated head motion. This paper proposes a new method for predicting head orientation using a delta quaternion (DQ)-based extended Kalman filter (EKF) and compares the performance to a quaternion EKF. The proposed framework operates on the change in quaternion between consecutive data frames (the DQ), which avoids the heavy computational burden of the quaternion motion equation. Head velocity is estimated from the DQ by an EKF and then used to predict future head orientation. We have tested the new framework with captured head motion data and compared it with the computationally expensive quaternion filter. Experimental results indicate that the proposed DQ method provides the accuracy of the quaternion method without the heavy computational burden.

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Very Low Frequency Electromagnetic Detector With Data Acquisition IEEE Transactions on Instrumentation and Measurement


Naturally occurring electromagnetic oscillating fields in the very-low-frequency (VLF) range of the spectrum, i.e., from 1 to 200 kHz, are weak and difficult to detect under normal conditions. These naturally occurring VLF electromagnetic events are observed during thunderstorms, in certain mountain winds, and during earthquakes. On the other hand, man-made VLF electromagnetic fields are stronger and have been suspected of causing negative health effects. Typical sources of these VLF emissions include television sets, video display terminals (VDTs), certain medical devices, some radio stations, and the ground-wave emergency network (GWEN) used for military communications. This paper describes the development of a triaxial ldquoVLF gaussmeter,rdquo which can be made portable. This electronic system can be used to monitor VLF electromagnetic radiation in residential and occupational environments. The ldquoVLF gaussmeterrdquo is based on a microcontroller with a built-in 10-bit A/D converter and has been designed to measure the magnetic flux density and frequency across the wide VLF bandwidth (BW). A digitized resolution of 0.2 mG is used for the 0-200-mG range, and 2-mG resolution is used for the range of 2-2000 mG. The meter has been designed to include the following features: 1) automatic or manual range selection; 2) data logging; 3) single-axis mode; 4) peak hold; 5) RS-232 communication port; and 6) analog recorder output.

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Hand-Eye Calibration Applied to Viewpoint Selection for Robotic Vision IEEE Transactions on Industrial Electronics


Viewpoint calibration is a method to manipulate hand-eye for generating calibration parameters for active viewpoint control and object grasping. In robot vision applications, accurate vision sensor calibration and robust vision-based robot control are essential for developing an intelligent and autonomous robotic system. This paper presents a new approach to hand-eye robotic calibration for vision-based object modeling and grasping. Our method provides a 1.0-pixel level of image registration accuracy when a standard Puma/Kawasaki robot generates an arbitrary viewpoint. To attain this accuracy, our new formalism of hand-eye calibration deals with a lens distortion model of a vision sensor. Our most distinguished approach of optimizing intrinsic parameters is to utilize a new parameter estimation algorithm using an extended Kalman filter. Most previous approaches did not even consider the optimal estimates of the intrinsic and extrinsic camera parameters, or chose one of the estimates obtained from multiple solutions, which caused a large amount of estimation error in hand-eye calibration. We demonstrate the power of this new method for: (1) generating 3-D object models using an interactive 3-D modeling editor; (2) recognizing 3-D objects using stereovision systems; and (3) grasping 3-D objects using a manipulator. Experimental results using Puma and Kawasaki robots are shown.

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Video-Based Tracking and Incremental Learning Applied to Rodent Behavioral Activity under Near-Infrared Illumination IEEE Transactions on Instrumentation and Measurement


This paper describes a noninvasive video tracking system for measurement of rodent behavioral activity under near-infrared (NIR) illumination, where the rodent is of a similar color to the background. This novel method allows position tracking in the dark, when rodents are generally most active, or under visible light. It also improves current video tracking methods under low-contrast conditions. We also manually extracted rodent features and classified three common behaviors (sitting, walking, and rearing) using an inductive algorithm-a decision tree (ID3). In addition, we proposed the use of a time-spatial incremental decision tree (ID5R), with which new behavior instances can be used to update the existing decision tree in an online manner. These were implemented using incremental tree induction. Open-field locomotor activity was investigated under ldquovisiblerdquo ( ), 880- and 940-nm wavelengths of NIR, as well as a ldquodarkrdquo condition consisting of a very small level of NIR illumination. A widely used NIR crossbeam-based tracking system (Activity Monitor, MED Associates, Inc., Georgia, VT) was used to record simultaneous position data for validation of the video tracking system. The classification accuracy for the set of new test data was 81.3%.

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An Interactive Framework for Acquiring Vision Models of 3D Objects from 2D images IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)


This paper presents a human-computer interaction (HCI) framework for building vision models of three-dimensional (3-D) objects from their two-dimensional (2-D) images. Our framework is based on two guiding principles of HCI: 1) provide the human with as much visual assistance as possible to help the human make a correct input; and 2) verify each input provided by the human for its consistency with the inputs previously provided. For example, when stereo correspondence information is elicited from a human, his/her job is facilitated by superimposing epipolar lines on the images. Although that reduces the possibility of error in the human marked correspondences, such errors are not entirely eliminated because there can be multiple candidate points close together for complex objects. For another example, when pose-to-pose correspondence is sought from a human, his/her job is made easier by allowing the human to rotate the partial model constructed in the previous pose in relation to the partial model for the current pose. While this facility reduces the incidence of human-supplied pose-to-pose correspondence errors, such errors cannot be eliminated entirely because of confusion created when multiple candidate features exist close together. Each input provided by the human is therefore checked against the previous inputs by invoking situation-specific constraints. Different types of constraints (and different human-computer interaction protocols) are needed for the extraction of polygonal features and for the extraction of curved features. We will show results on both polygonal objects and object containing curved features.

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


Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition.
A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7 s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in 8 healthy volunteers.
The root mean square error (RMSE) and mean absolute error (MAE) for the 8 volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm.
Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort on the part of the patient.

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Multicolumn RBF Network IEEE Transactions on Neural Networks and Learning Systems


This study proposes the multi-column RBF network (MCRN) as a method to improve the accuracy and speed of a traditional radial basis function network (RBFN). The RBFN, as a fully connected artificial neural network (ANN), suffers from costly kernel inner-product calculations due to the use of many instances as the centers of hidden units. This issue is not critical for small datasets, as adding more hidden units will not burden the computation time. However, for larger datasets, the RBFN requires many hidden units with several kernel computations to generalize the problem. The MCRN mechanism is constructed based on dividing a dataset into smaller subsets using the k-d tree algorithm. N resultant subsets are considered as separate training datasets to train N individual RBFNs. Those small RBFNs are stacked in parallel and bulged into the MCRN structure during testing. The MCRN is considered as a well-developed and easy-to-use parallel structure because each individual ANN has been trained on its own subsets and is completely separate from the other ANNs. This parallelized structure reduces the testing time compared to that of a single but larger RBFN, which cannot easily be parallelized due to its fully connected structure. Small informative subsets provide the MCRN with a regional experience to specify the problem instead of generalizing it. The MCRN has been tested on many benchmark datasets and has shown better accuracy and great improvements in training and testing times compared to a single RBFN. The MCRN also shows good results compared to those of some machine learning techniques, such as the support vector machine (SVM) and k-nearest neighbors (KNN).

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Respiratory motion prediction and prospective correction for free-breathing arterial spin labeled perfusion MRI of the kidneys Medical Physics

39. H. Song, D. Ruan, W. Liu, V. A. Stenger, R. Pohmann, M. A. F. Seara, T. Nair, S. Jung, J. Luo, Y. Motai, J. Ma, J. D. Hazle, H. M. Gach


Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition.
A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers.
The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm.
Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient.

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A Novel Method of Cone Beam CT Projection Binning base on Image Registration IEEE Transactions on Medical Imaging

S. Park, S. Kim, B. Yi, G. Hugo, H. Gach, and Y. Motai


Accurate sorting of beam projections is important in four-dimensional Cone Beam Computed Tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose Image Registration-based Projection Binning (IRPB), a novel marker-less binning method for 4D CBCT projections, which combines Intensity-based Feature Point Detection (IFPD) and Trajectory Tracking using Random sample consensus (TTR). IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets including both regular and irregular respiration. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier Transform-based method (FT), and Local Intensity Feature Tracking (LIFT) method. The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08 dB, 1.05 dB, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods.

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Smart Sensor for High Power Insulators: Acoustic Fault Detection Technique IEEE Transactions on Industrial Electronics

K. Park, Y. Motai*, and J. Yoon


Insulators are important equipment used to electrically isolate and mechanically hold wires in high-voltage power transmission systems. Faults caused by the deterioration of the insulators induce very serious problems to the power transmission line. Techniques were introduced for the acoustic detection of insulator faults by acoustic radiation noises. Radiation noises were measured from normal state insulators and fault state insulators in an anechoic chamber. The insulators used were two porcelain insulators, a cut out switch, two line posters, and a lightning arrester. A new acoustic technique determines the direction of the insulator faults using source localization with 3D microphone arrays. The advantage is to classify the fault state insulators without human inspection by considering the amount of total noises and 120Hz harmonic components. The fault detection was determined by neural network to diagnose the state automatically. The proposed technique was evaluated by distinct, real datasets and the efficacy was validated. The noise source was detected with 100.0% accuracy and the classification ratio achieved 96.7% for three typical conditions.

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Human Behavior-based Target Tracking with an Omni-directional Thermal Camera IEEE Transactions on Cognitive and Developmental Systems

E. Benrli, Y. Motai* and J. Rogers


We investigate Human Behavior-based Target Tracking from Omni-directional (O-D) thermal images for intelligent perception in unmanned systems. Current target tracking approaches are primarily focused on perspective visual and infrared band, as well as O-D visual band tracking. The target tracking from O-D images and the use of O-D thermal vision have not been adequately addressed. Thermal O-D images provide a number of advantages over other passive sensor modalities such as illumination invariance, wide field-of-view, ease of identifying heat-emitting objects, and long term tracking without interruption. Unfortunately, thermal O-D sensors have not yet been widely used due to the following disadvantages: low resolution, low frame rates, high cost, sensor noise, and an increase in tracking time. This paper outlines a spectrum of approaches which mitigate these disadvantages to enable an O-D thermal IR camera equipped with a mobile robot to track a human in a variety of environments and conditions. The CMKF (Curve Matched Kalman Filter) is used for tracking a human target based on the behavioral movement of the human and MAP (Maximum A Posteriori) based estimation is extended for the human tracking as long term which provides a faster prediction. The benefits to using our MAP based method are decreasing the prediction time of a target's position and increasing the accuracy of prediction of the next target position based on the target’s previous behavior while increasing the tracking view and lighting conditions via the view from O-D IR camera.

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