C-DIST

We present new distance computation algorithms using hierarchies of rectangular swept spheres. Each bounding volume of the tree is described as the Minkowski sum of a rectangle and a sphere, and fits tightly to the underlying geometry. We present accurate and efficient algorithms to build the hierarchies and perform distance queries between the bounding volumes. We also present traversal techniques for accelerating distance queries using coherence and priority directed search. These algorithms have been used to perform proximity queries for applications including virtual prototyping, dynamic simulation, and motion planning on complex models. As compared to earlier algorithms based on bounding volume hierarchies for separation distance and approximate distance computation, our algorithms have achieved significant speedups on many benchmarks.

Often, designers and developers of serious games (and virtual simulations in general) strive for high fidelity (realism). However, real-time high-fidelity rendering of complex environments across sensory modalities such as vision, audition, and haptic (sense of touch) is still beyond our computational reach. Previous work has demonstrated that multimodal effects can be considerable, to the extent that a large amount of detail of one sense may be ignored or enhanced by the presence of other sensory inputs. Taking advantage of such multimodal effects, perceptual-based rendering—whereby the rendering parameters are adjusted based on the perceptual system—can be employed to limit computational processing. Motivated by the general lack of emphasis given to the understanding of audio rendering in virtual environments and games, we have started investigating multimodal (audiovisual) interactions within such virtual environments. Our work has shown that sound can directly affect visual fidelity perception and task performance within a virtual environment. These effects can be very individualized, whereby the influence of sound is dependent on various individual factors including musical listening preferences, suggesting the importance of individualizing the virtual environment to each user. This chapter begins with an overview of virtual environments and serious gaming’s open problems, with an emphasis on fidelity, and multimodal interactions, and the implications that these may have on performance and computational requirements. A detailed summary of our own prior work will be provided along with insight and suggestions that may guide designers and developers of serious games and virtual learning environments in general. Although the chapter is contextualized in the use of serious games in health professions education, the information provided is generalizable across a variety of domains.

In many interactive computer graphics applications, users are represented as virtual characters called avatars. Collision detection is a pre-requisite in interactive computer graphics application so that appropriate response and realistic behaviour can be generated. However, it will not be easy to anticipate collisions since objects' motion are dependant on user interaction and avatar manipulation. Ideally, collision detection needs to be done efficiently, with accurate result in the shortest time possible. However, due to its computationally intensive nature, collision detection could cause a bottleneck to the system. A variety of techniques, data structures and algorithms were proposed to accelerate the collision detection process. Acceleration data structures are widely used not only to promote faster collision detection, but also in the field of ray tracing and deformable objects simulation. This paper discusses the use of bounding volume hierarchy as an acceleration data structure in collision detection for avatar in virtual environment, as well as similar methods used in interactive ray tracing and deformable objects simulation. Based on these findings, method for fast bounding volume hierarchy construction is proposed as a first step towards efficient avatar collision detection.

Virtual reality applications such as computer games form a large portion of the computing industry. The increasing expectations for immersive and interactive entertainment require fast graphics rendering with a lot of details and high-fidelity physics simulation. Interference and collision detection between general polygonal models, including all those tasks in the simulated motion of solids which cannot penetrate one another, has been widely explored. The most recent works have been focused on increasing the number of collision queries per second to an interactive rate, assuming that the computing resources such as memory, CPU capacity, and network bandwidth are sufficient all the time. However, the performance of a collision detection algorithm may vary in real environments where the available computing resources are not sufficient or are dynamic at runtime. Such an unstable performance directly affects the interactive rate of many applications, which is not acceptable. This drawback has been noticed extensively by practitioners. Little research work has been introduced to systematically solve this problem. One way out of this problem is to use a technique to dynamically adjust the cost of the collision detection (CD) task according to the measurements of available resources. In this dissertation, a solution to the problem is proposed. The problem is analyzed under three different physical environments: single low end computer, clustered multiple-machine setting, and distributed environment. An extensive comparative study of several methods is done in the context of polygonal models. A new approach, "multi-resolution collision detection", is proposed. It performs time-critical and exact interference detection on continuous level of detail (LOD) representations of arbitrary triangle meshes undergoing rigid body motion. The goal of this work is to extend the bounding volume hierarchy (BVH) based collision detection approach from static to dynamic in order to adapt the cost of collision detection to the available resource in either centralized or distributed environments. Several adaptive mesh refinement criteria based on viewing distance, application context, and movement velocity are proposed. Global and local mesh refinement algorithms and a BVH CD algorithm are introduced. They are computationally simple and achieve reliable real-time performance. The complexity of the proposed algorithms is analyzed and proved. Measurements are taken to compare with the analytical results and strong agreements are achieved. The major application areas considered are distributed virtual environments (DVEs) and interactive haptic applications.

: This report describes all SBIR Phase 2 effort to validate a mathematical model mid conduct a design study of the Air Force Dynamic Environment The Dynamic Environment Simulator, or DES, is a three degree-of-freedom centrifuge operated by Armstrong Laboratories at Wright-Patterson AFB. The effort was conducted under Contract F41624-96-C-6027 through the Armstrong Laboratory and was entitled Model Validation and Design Study of the Dynamic Environment Simulator. This Phase 2 project had four primary objectives, which were to: (1) Validate the Phase 1 software model of the DES; (2) Conduct a design study of DES improvements; (3) Perform a cost/benefit analysis of the possible DES improvements; and (4) Demonstrate the commercial potential of the model and the DES.

Introduction: Identification of noninvasive biomarkers to predict tumor biology and provide prognostic indicators in ER positive breast cancer are of essence. [18F]fluoroestradiol (FES) is an FDA-approved tracer that measures functional ER expression and predicts response to ER-targeted therapy. The goal of this study was to test novel 4D analysis of dynamic volumetric FES PET patient imaging data to predict outcomes in patients with metastatic ER positive breast cancer undergoing endocrine therapy. Methods: We utilized the Rad-Fit method, previously tested in 4D analysis of dynamic [18F]fluorodexosyglucose (FDG) breast cancer patient data sets, to identify and characterize intratumor subregions through unsupervised clustering of tumor voxels according to each voxel’s time-activity information, summarized by functional principal components (Chitalia, PMID: 3367764). The analysis was performed on a set of dynamic FES PET data taken over a single body section with one or more tumor sites from a previously studied cohort of 38 patients with locally advanced or metastatic breast cancer taken prior to endocrine therapy (Linden, PMID: 1668272). The FES imaging data were analyzed using the Rad-fit method and radiomics measures were compared to patient outcome data (PFS and OS) collected in the original study. Tumors were clustered from two up to twelve clusters and the optimal clustering of each tumor was chosen by utilizing a scaled silhouette score. The mean, standard deviation, minimum, and maximum values of the 4D features of subregion sum of squared error (SSE), distance between sub regions, and the total number of intratumor subregions were used to build prognostic models of overall survival (OS) and progression free survival (PSF). We employed Kaplan-Meyer plots to determine if our prognostic models could significantly separate our population into high and low risk groups. Results: 4D features significantly separated this cohort into a high or low risk group for OS in a single tumor per subject (C=0.73, p=0.0018) or averaging all features per subject (C=0.65, p<0.00025) model. Mean and maximum distance are the most significant radiomic features in the Cox-PH model with all features. Combining the significant radiomic predictors of maximum and mean distance with tumor site SUV Max resulted in improved separation of high and low risk groups for the single tumor (C=0.71, p=0.0014) and average tumor (C=0.61, p=0.00018) models. Conclusions: In this exploratory study, we show that 4D radiomic features extracted from dynamic FES PET images using the established Rad-Fit method can add significantly to prediction of OS in advanced ER positive breast cancer. Metrics of tumor subregion distance, reflective of the overall tumor heterogeneity in FES PET images, appear to perform as the best radiomic predictors for risk stratification of OS. Supported in part by Komen Grant SAC232145 and NIH/NCI Funding R50 CA-211270 Citation Format: Carla Zeballos Torrez, Andrew Chen, Lanell M. Peterson, Mark Muzi, Jennifer M. Specht, Eric A. Cohen, Hannah M. Linden, Despina Kontos, David A. Mankoff. Novel 4D radiomics applied to dynamic FES PET images to improve prediction of ER-positive breast cancer outcomes for ER-targeted therapy [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS11-05.

The present study contributes the detailed design, Implementation and comparative studies of supervised classification algorithms such as Nearest Neighbor (NN),k-Nearest Neighbor (k-NN) and Minimum Mean Distance (MMD) along with the distance metrics Maximum, Sum (Manhattan) and Euclidean distances. Classification involves training and testing phases. Training phase teaches the classifier about the types of sample images to classify during the testing phase and saves the class names in a classifier file. Testing phase classifies an unknown sample image into one of the class from classifier file. Total numbers of sample images were divided into the ratio of 30:70 for training and testing respectively. Then classification procedure was performed on sample images for each algorithm with each distance metric. The training and testing VIs for particle and color classification were designed and implemented by using LabVIEW. All the algorithms and distance metrics were analyzed, compared for best results and the maximum accuracy algorithm along with its distance metric is going to implement in real time object sorting application.

This paper examines the problem of simulating angular velocity fields that may for example, be experienced by a pilot during a supermaneuverable flight trajectory. The ability to simulate angular velocity fields has important applications in the study of certain physiological effects encountered by pilots during these unusual flight scenarios. The simulations are to be performed using a centrifuge simulator called the Dynamic Environment Simulator (DES) located in the Armstrong Laboratory of Wright-Patterson Air Force Base. Some of the kinematic issues relating to the motion control of the DES are examined in the paper. >

Kernel functions may be used in robotics for comparing different poses of a robot, such as in collision checking, inverse kinematics, and motion planning. These comparisons provide distance metrics often based on joint space measurements and are performed hundreds or thousands of times a second, continuously for changing environments. We introduce a new kernel function based on forward kinematics (FK) to compare robot manipulator configurations. We integrate our new FK kernel into our proxy collision checker, Fastron, that previously showed significant speed improvements to collision checking and motion planning. With the new FK kernel, we realize a two-fold speedup in proxy collision check speed, 8 times less memory, and a boost in classification accuracy from 74% to over 95% for a 7 degrees-of-freedom robot arm compared to the previously-used radial basis function kernel. Compared to state-of-the-art geometric collision checkers, with the FK kernel, collision checks are now 9 times faster. To show the broadness of the approach, we apply Fastron FK in OMPL across a wide variety of motion planners, showing unanimously faster robot planning.

The hardware compatibility of legged locomotion is often illustrated by Zero Moment Point (ZMP) that has been extensively studied for decades. One of the most popular models for computing the ZMP is the linear inverted pendulum (LIP) model that expresses ZMP as a linear function of the center of mass(COM) and its acceleration. In the real world, however, it may not accurately predict the true ZMP of hardware due to various reasons such as unmodeled dynamics and differences between simulation model and hardware. In this paper, we aim to improve the theoretical ZMP model by learning the real hardware dynamics from experimental data. We first optimize the motion plan using the theoretical ZMP model and collect COP data by executing the motion on a force plate. We then train a new ZMP model that maps the motion plan variable to the actual ZMP and use the learned model for finding a new hardware-compatible motion plan. Through various locomotion tasks of a quadruped, we demonstrate that motions planned for the learned ZMP model are compatible on hardware when those for the theoretical ZMP model are not. Furthermore, experiments using ZMP models with different complexities reveal that overly complex models may suffer from over-fitting even though they can potentially represent more complex, unmodeled dynamics.

This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.

The ability to localize a virtual auditory source was evaluated under varying levels of sustained (+G2) acceleration. Participants were required to judge the locations of virtual auditory cues located along the horizontal plane (elevation 0°) during exposure to 1.0, 1.6, 2.5, 4.0, 5.6, and 7.0 +Gz. The experiment was conducted at the Air Force Research Laboratory's Dynamic Environment Simulator - a man-rated, three-axis centrifuge. No significant increases in localization error were found between 1.0 and 5.6 +Gz; however, a significant increase did occur at the 7.0 +Gz level. In addition, the percentage of reversals did not vary as a function of +Gz level. Collectively, these results indicate that one's ability to localize virtual auditory cues is well maintained at various levels of sustained acceleration.

In this paper, kinematic analysis and motion planning of a quadruped robot are presented by regarding the robot as an equivalent parallel platform-type mechanism with RRRS limb structure. Based on screw theory, the mobility of the quadruped robot with different touchdown legs is analyzed, and proves that the design for degree of freedom (DOF) is available. Base on the established kinematic model of a single leg in terms of POE formula of screw theory, several typical patterns of walking motion planning are implemented and verified by the ADAMS-based simulation. In order to show a good maneuverability and potential manipulation capability of the quadruped robot as a parallel manipulator, the gait planning reflecting two rotating motion patterns (including the rotating motion and walking motion) is modeled and simulated by kinematics of the equivalent parallel manipulators. Finally, a prototype of the quadruped robot has been built. Experimental test shows the practical walking and rotating ability as desired.

The controllably dexterous workspace of a tool-point is defined. A type-synthesis process, based on screw theory and geometry, is extended to identify two 7-R robots, each of which can provide the controllably dexterous workspace. The identification process is confined to those robots that control the motion of the end-effector with seven series-connected joints, the axes for the outermost three of which are concurrent. The result for each is a suitable set of angle dimensions for the links of the arm, where the angle choices are limited to the values 0, +or- pi /2, and pi . A geometric description of the kinematics for the dominant control function is included insofar as it is required to effect the identification. Also included are comments about motion planning in a controllably dexterous workspace. >

Imbalance class problem in data mining occurs where one class known as the minority class has the significantly lower number of samples than the other classes known as the majority class(es). It affects the performance of machine learning algorithms by allowing them to show bias towards the majority class. This occurs because of the sub-concepts from the minority class. Recent studies has further divided the minority class into four sub-concepts: Safe, Borderline, Rare and Outlier using the majority-minority proportion at the neighborhood of every minority sample. Among the sub-concepts safe are easy to identify and classifiers are increasing inaccurate while classifying other subsequent sub-categories (Boarderline, Rare, Outlier). In some recent studies, heterogeneous value difference metric is used as the distance calculation mechanism for categorizing data. However, there are numerous other distance metrics whose effects on determining the sub-concepts are not explored yet. This research aimed at evaluating the effects of different distance metrics in the calculation of different sub-concepts within the minority class data. We have considered ten datasets and five distance metrics for the calculation. The datasets are divided into three categories: all categorical, mixed and fully numeric data. For the datasets with more categorical data outputs hugely differs between the distance functions. In those cases, relatively safer examples are calculated by the Euclidean and the Manhattan distance function. Our study shows that for categorizing minority data distance metrics should be chosen dataset wise.