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Surgical education for residents is provided through observership and participation with attending surgeons, cadaveric sessions and use of textbook illustrations. This paper describes the development of virtual medical instruments as a flexible, safe, and scalable learning option that can provide real-time feedback for the resident and increase entrustability by the attending surgeon when the resident is in the operating room. In orthopedic surgery, total hip arthroplasty (THA) is reproducible and hence was chosen as the simulated training surgery. The virtual medical instruments developed for this research is set in a fully immersive 3D virtual environment that can allow for innovative ways to train students and provide a new way of experiential education. With the goal of making the training anatomically accurate, users can interact with the virtual operating environment in real-time to ensure proper instrument usage. The virtual procedure also provides realistic visualization of the surrounding operating room and anatomical environment around the surgery site, marking important landmarks to the surgeon to enable effective surgical training.

Hip implants are extremely common procedures that are performed to relieve pain and restore function to the hip joint. Historically, most implants would last for the duration of the patient’s life, approximately 15 years after implantation; however, due to medical advancements patients are living longer and outliving the life of their implant. The most common cause for implant failure is the failure of the bond between the implant and the bone, occurring due to the translation or rotation of the implant. Once an implant has failed, a very invasive, costly, and painful revision surgery is required. There is significant advantage in the early detection of implant loosening. This paper presents a radial basis function based image processing technique to detect minute 3-D rotations of a hip implant from 2-D X-ray images. Comparing these rotations for a particular hip implant over time can alert orthopedic surgeons of trends that might lead to impending gross loosening of the implant and enable early correction.

Large scale distributed visualizations are routinely used by organizations in both the commercial and government sectors for training and simulation. These simulations are of particular importance in the research associated with the United States Federal Aviation Administration's current work on updating the National Airspace System to the Next Generation Air Transportation System. This new system will include the integration of unmanned aerial systems into civil airspace for routine flights. In this paper we propose a system for integrating unmanned aerial system visual observers into the distributed simulation capability of the Federal Aviation Administration using large-scale virtual reality systems. We demonstrate the replication of terrain surrounding flight tests in virtual environments and the generation of the observer's views from both ground-based and airborne scenarios.

Valves play a critical role in rocket-engine test stands because they are essential for the cryogen transport mechanisms that are vital to test operations. Sensors that are placed on valves monitor the pressure, temperature, flow rate, valve position, and any other features that are required for diagnosing their functionality. Integrated system-health management (ISHM) algorithms have been used to identify and evaluate anomalous operating conditions of systems and subsystems (e.g., valves and valve components) on complex structures, such as rocket test stands. In order for such algorithms to be useful, there is a need to develop realistic models for the most common and problem-prone elements. Furthermore, the user needs to be provided with efficient tools to explore the nature of the anomaly and its possible effects on the element, as well as its relationship to the overall system state. This paper presents the development of an intelligent-valve framework that is capable of tracking and visualizing events of the large linear actuator valve (LLAV) in order to detect anomalous conditions. The framework employs a combination of technologies, including a dynamic data exchange data-transfer protocol, autoassociative neural networks, empirical and physical models, and virtual-reality environments. The diagnostic procedure that is developed has the ability to be integrated into existing ISHM systems and can be used for assessing the integrity of rocket-engine test-stand components.

To keep up with rapidly advancing technology, numerous innovations to the electrical and computer engineering (ECE) curriculum, learning methods and pedagogy have been envisioned, tested, and implemented. It is safe to say that no single approach will work for all of the diverse ECE technologies and every type of learner. However, a few key innovations appear useful in keeping undergraduate students motivated to learn, resilient to technology evolution, and oriented amid the overload of new information and ECE applications. Engineering clinics, similar to their medical clinic counterparts, provide project-based experiences within the core of an ECE education that enable transformation of the entire curriculum toward an outcomes-oriented, student-centered, total-quality environment. Clinics and project-based learning approaches build skills that give the students confidence and motivation to continuously self-learn and adapt as the technologies around them give way to new, more effective paradigms. Perhaps more importantly, engineering clinic experiences provide numerous opportunities for students to experience the holism of true engineering problem-solving approaches and the ranges of potential technology solutions. This paper reviews the clinic innovations that will enable ECE education to become more effective in the midst of the present plethora of information and technology. Assessment results are provided and are very encouraging. This paper concludes that agile learning environments, created to graduate engineers who can be rapidly productive in the professional and research worlds, are enhanced by clinic and/or project-based learning experiences in the ECE curriculum.

It has been shown that the flow and shear characteristics of granular particles such as soils are significantly dependent on the shape of the particles. This is important from a practical viewpoint because a fundamental understanding of granular behavior will lead to an improved understanding of soil stability and influence the design of structural foundations. Furthermore, the calculation of soil stability and, consequently, structural stability is particularly useful during earthquake events. In previous work, we have demonstrated the applicability of X-ray and optical tomography measurements for characterizing 3-D shapes of natural sands and manufactured granular particles. In this paper, we extend the work to measure the arrangement and the orientation of an assemblage of such particles. A combination of X-ray computed tomography (CT) for measuring the coordinates of the individual particles and an iterative adaptive thresholding technique for computing the local variations in porosity is employed to generate porosity maps. Such maps can be used to gain a more fundamental understanding of the shear characteristics of granular particles. In this paper, we demonstrate the success of our technique by exercising the method on several sets of granular particles-glass beads (used as a control), natural Michigan Dune and Daytona Beach sand, and processed Dry #1 sand.

Integrated Systems Health Management (ISHM) consists of processes managing erroneous conditions that systems may encounter during their operational life by either designing out failures early on or defending and mitigating any possible failures. A successful implementation of ISHM consists of the following four components: data sensors, computations, data sinks, and visualization modules. In this paper, we explore the use of virtual reality (VR) platforms as a candidate for developing ISHM visualization modules. VR allows for a complete and spatially accurate 3-D model of a system to be displayed in real time. It provides a medium for improved data assimilation and analysis through its core tenants of immersion, interaction, and navigation. Furthermore, VR allows for integrating graphical, functional, and measurement data in the same platform-providing for the development of subsequent risk-analysis modules. The research objectives of this paper are focused on creating a detailed visual model of a multisensor rocket engine test facility inside a VR platform and demonstrating the capability of the VR platform in integrating graphical, measurement, and health data in an immersive, navigable, and interactive manner. A human-based performance evaluation of the VR platform is also presented. These research objectives are addressed using an example of a multisensor rocket-engine portable test stand at the National Aeronautics and Space Administration (NASA) Stennis Space Center's E-3 test facility.

Three dimensional tools for anatomical and radiological education for medical students are not novel. However, there has been limited ability to apply these tools into clinical practice. We speculate the reason for this situation is that although anatomical education software exists for a myriad of devices, and multiple applications exist for hand held devices, these platforms do not provide for intuitive correlation of clinical information from multiple diagnostic imaging procedures, nor do they provide users with any measure of prognostic capability. In this paper, we demonstrate the development of a virtual scalpel in the 3-D, immersive, navigable and interactive environment provided by a CAVE™. We propose that such an environment allows for the integrated visualization of medical images obtained from multiple radiological imaging platforms including CT, MRI, and PET. We intend to show such a system is advantageous not only for anatomical education but also for clinicians as a useful tool in surgical practice.

Magnetic flux leakage techniques are used extensively to detect and characterize defects in natural gas transmission pipelines. This paper presents a novel approach for training a multiresolution, hierarchical wavelet basis function (WBF) neural network for the three-dimensional characterization of defects from magnetic flux leakage signals. Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelets respectively. The centers of the basis functions are calculated using a dyadic expansion scheme and a k-means clustering algorithm. The results indicate that significant advantages over other neural network based defect characterization schemes could be obtained, in that the accuracy of the predicted defect profile can be controlled by the resolution of the network. The feasibility of employing a WBF neural network is demonstrated by predicting defect profiles from both simulation data and experimental magnetic flux leakage signals.

This paper presents a technique for solving inverse problems in magnetostatic nondestructive evaluation (NDE), using finite element models. In-line inspection of ferromagnetic gas pipelines containing pipe-wall defects, is chosen as the candidate NDE process. The signal inversion technique consists of iteratively solving the forward problem by updating the finite element mesh, rather than the material properties of the finite elements. Preliminary simulation results obtained using a 2D finite element model are presented.

The magnetic flux leakage method, used for nondestructive evaluation of ferromagnetic objects, generates greyscale images that are representative of the integrity of the specimen. Defective areas typically appear as bright regions in the image. Unfortunately, the task of defect characterization becomes more challenging due to the effects of variations in the test parameters associated with the experiment. One such test parameter is the permeability of the test object. Conventional invariant pattern recognition algorithms are not capable of performing invariance transformations to compensate for such variations. This paper describes a novel technique that uses wavelet basis functions to provide selective invariant features and eliminate image intensity variations from undesirable changes in operational variables. The performance of the invariance transformation is demonstrated by applying the method to magnetic flux leakage images obtained using a finite element simulation of in-line inspection of natural gas transmission pipelines.

Magnetostatic methods that are used for the nondestructive evaluation (NDE) of ferromagnetic materials can be modeled by partial differential equations. Conventional numerical methods such as finite difference, finite elements or boundary integral methods for large problems typically require an iterative matrix inversion scheme to solve the system of equations. Parallel implementation of such schemes is extremely difficult due to the data dependencies in the matrices. Monte Carlo methods offer an alternative approach by reducing the differential equation to simple, independent, random processes that are inherently amenable to parallel implementation. This paper investigates the use of Monte Carlo methods for modeling magnetostatic flux leakage NDE phenomena. The model is validated by comparing the results with those obtained with finite element models. The approach offers significant reduction in computational burden especially for 3-D problems.

Magnetic flux leakage (MFL) methods are used extensively for inspecting ferromagnetic materials. The analysis of the MFL signal is however fraught with problems associated with the sensitivity of the signal to a number of factors such as the MFL sensor velocity and variations in the permeability of the test specimen. The interpretation can be simplified if the signals can be processed to attain invariance to these conditions. This paper presents novel methods for obtaining permeability invariant and velocity invariant MFL signals.

This paper presents a finite element model for simulating electrodynamic and magnetostrictive vibration absorber systems. The model employs a decoupled formulation to compute motion induced voltages in a magnetomechanical coupled system. The model is first validated by comparison with experimental measurements. An application of the model for optimization of the system damping performance using model predictions, is presented. The model is also used to calculate the frequency response of a magnetostrictive actuator. >

In this paper, a novel method for modeling the B-H characteristics of ferromagnetic materials is discussed. The method finds application in numerical finite element models for computing the leakage fields in magnetostatic nondestructive testing. The method is computationally simple and employs a neural network approach using Radial Basis Functions for representing the hysteresis loop as a closed curve and expands a periodic contour function in a Fourier series. The second technique, though parsimonious in terms of data storage requirements, is computationally more intensive.

Anomalous indications in monitoring equipment onboard U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship's crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this paper, we present algorithms for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments. One of the principal advantages of this technique is that the method can be applied to monitor legacy shipboard systems and environments where high-quality, color video may not be available.

This paper presents our ongoing work in the area of using virtual reality (VR) environments for the Integrated Systems Health Management (ISHM) of rocket engine test stands. Specifically, this paper focuses on the development of an intelligent valve model that integrates into the control center at NASA Stennis Space Center. The intelligent valve model integrates diagnostic algorithms and 3D visualizations in order to diagnose and predict failures of a large linear actuator valve (LLAV). The diagnostic algorithm uses auto-associative neural networks to predict expected values of sensor data based on the current readings. The predicted values are compared with the actual values and drift is detected in order to predict failures before they occur. The data is then visualized in a VR environment using proven methods of graphical, measurement, and health visualization. The data is also integrated into the control software using an ActiveX plug-in.

It has been shown that the flow and shear characteristics of granular particles such as soils are significantly dependent on the shape of the particles. This is important from a practical viewpoint because a fundamental understanding of granular behavior will lead to an improved understanding of soil stability and influence the design of structural foundations. Furthermore, the calculation of soil stability and consequently structural stability is particularly useful during earthquake events. In previous work, we have demonstrated the applicability of X-ray and optical tomography measurements for characterizing 3-D shapes of natural sands and manufactured granular particles. In this paper, we extend the work to measure the arrangement and orientation of an assemblage of such particles. A combination of X-ray CT for measuring the coordinates of the individual particles, and basic image processing techniques for computing the local variations in packing density are employed to generate density maps. Such maps can be used to gain a more fundamental understanding of the shear characteristics of granular particles. In this paper, we demonstrate the success of our technique by exercising the method on two sets of granular particles - glass beads (used as a control) and Michigan Dune sand.

This paper presents our ongoing work in the area of using virtual reality (VR) environments for the integrated systems health management of complex test environments, such as those found in a rocket engine test stand. Specifically, the paper focuses on the design, development and implementation of a hardware-software framework for acquiring, interfacing, integrating and analyzing multiple sensor measurements for display inside of a VR environment. IEEE and OSA-CBM standards are used to design the acquisition and interface methods. Commercial off-the-shelf software is used for analysis and integration. Results demonstrating the application of our method to a rocket engine test stand are presented.

This paper presents our continuing work in the area of using virtual reality (VR) environments for the integrated systems health management of complex test environments, such as those found in a rocket engine test stand. Specifically, the paper focuses on the design and development of a software framework for integrating and displaying multiple sensor measurements. Various forms of data are displayed inside the VR environment in conjunction with the associated graphics which correspond to the physical structure of the test-stand (piping, valves, sensors, etc.). Results demonstrating the application of our method for visualizing scalar, vector and system-state data are presented.

Granular material packing is of considerable importance when considering the stability and shear strength of granular media. It has been shown in various studies that the shear strength of granular media is influenced by inherent particle characteristics such as shape, angularity, surface texture and mineralogy. In this study, granular material packing at the grain scale is studied to see how packing is influenced by these inherent particle characteristics and if there is any evidence of an intermediate scale larger than the grain scale. Studies will also show clear evidence of the influence of shape, angularity and surface texture on the granular packing obtained.

Complex systems can be challenging to operate, maintain, and repair. The US Navy is interested in developing data fusion techniques that include video to both assist the shippsilas crew in monitoring machinery and also to allow the crew to get ship-to-shore assistance, usually called distance support, for repairs. This paper reports on a work-in-progress integrating sensor data, streaming and stored video data and processed video data with virtual technologies and 3D modeling to create a data fusion technology. First the hardware and software requirements and development will be discussed. Then progress with the human-system integration and data fusion aspects will be discussed. Finally, the paper will conclude with remarks on future work.

Integrated systems health management (ISHM) consists of processes managing erroneous conditions that systems may encounter during their operational life by either designing out failures early on, or the ability to defend and mitigate any possible failures. A successful implementation of ISHM consists of the following four components: data sensor, computations, data sinks, and visualization modules. In this paper we explore the use of virtual reality (VR) platforms as candidate for developing ISHM visualization modules. VR allows for a complete and spatially accurate 3D model of a system to be displayed in real time. It provides a medium for improved data assimilation and analysis through its core attributes of immersion, interaction and navigation. Furthermore, VR allows for integrating graphical, functional and measurement data in the same platform - this allows for the development of subsequent risk-analysis modules. The effectiveness of deploying VR as a key component in an ISHM environment is demonstrated using an example of a multi-sensor rocket-engine portable test stand at NASA - Stennis Space Center's E-3 test facility.

Integrated systems health management (ISHM) is one of several approaches to building automated diagnostic and prognostic systems. We describe a unique architecture for ISHM along with other key technology elements such as Smart Sensors complying with the IEEE 1451.x family of standards, and user interaction methods. Technology readiness levels (TRL) and functional capability levels (FCL) are useful metrics for evaluating the readiness of each contributing technology and identifying those technologies that need further development.

This paper presents a technique that can be used to fuse data from multiple sensors that are employed in nondestructive evaluation (NDE) applications, specifically for the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage (MFL), ultrasonic (UT) and thermal imaging methods. The results presented in this paper indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.

The properties of geomaterial aggregates, mainly flow and shear characteristics, are significantly dependent on the shapes of the particles in the mixture. 3D particle models that have been previously developed to quantify this dependence have proved complex and expensive. This paper describes an optical tomography technique for the acquisition of real particle data that can be characterized using 3D shape descriptors. The algebraic reconstruction technique (ART) is used to synthesize 3D particle shapes from 2D tomography projections. Results demonstrating the efficacy of the method on a set of sand mixtures are presented.

We have previously introduced Learn ++ , an en- semble of classifiers based algorithm capable of incre- mental learning from additional data, and pointed to its fea- sibility in data fusion applications. In this contribution, we provide additional details, updated results and insight on how such a system can be used in integrating complemen- tary knowledge provided by different data sources obtained from different sensors. Essentially, the algorithm generates an ensemble of classifiers using data from each source, and combines these classifiers using a weighted voting proce- dure. The weights are determined based on the individual classifier's training performance as well as the observed or predicted reliability of each data source.

Shear behavior and liquefaction susceptibility of geomaterial aggregates have been determined to be dependent on particle shape and angularity. However, quantification of this dependence is a challenging task owing to a dearth of quantitative models for particle shape and the difficulty of modeling angular particle assemblies. The situation becomes more complex when the quantitative models are required to synthesize arbitrary 3-D particle shapes that are representative of specific sand mixtures. The authors have recently described a method for quantitative identification of 3-D particle morphology estimated from projective two-dimensional representations. This paper extends prior work and describes the design and development of an automated three-dimensional particle synthesis algorithm using tomographic reconstruction techniques. A database of 2-D and 3-D images has been generated by optical and X-ray scans of the following sands: #1 Dry Sand, Daytona Beach, Standard Melt, Rhode Island, Hawaii Kahala Beach, Michigan Dune and Hawaii Ala Wai Surfer’s Beach. The algebraic reconstruction technique (ART) has been used to characterize 3-D particle shapes from this database. This paper demonstrates the consistency, separability, and uniqueness of the 3-D shape descriptor algorithm as well as its ability to synthesize 3-D particle shapes representative of the respective aggregate mixtures.

The percentage of radiodense tissue in a mammogram has been used as a marker for determining breast cancer risk. In this paper, we present an image segmentation technique for identifying tissue and non-tissue regions of a digitized X-ray image. This procedure constitutes a vital step prior to subsequent processing for estimating the amount of radiodense tissue. The process involves the generation of a segmentation mask developed by using discrete wavelet transform techniques. Initial results have been promising, demonstrating the feasibility of the approach.

Several measurement modalities have been developed over the years for various nondestructive testing and evaluation (NDT and E) applications, such as ultrasonic, magnetic flux leakage, and eddy current testing, all of which have been used extensively in pipeline defect identification. While it is generally believed that different testing modalities provide complementary information, only a single testing modality is typically used for a given application. This is part due to lack of effective, computationally feasible data fusion algorithms that are applicable to NDT and E signals. Such an algorithm capable of data fusion can combine information from two or more different sources of data, giving more insight and confidence to the data analysis than a decision that would otherwise be based on either of the sources alone. Learn++, previously introduced as an incremental learning algorithm, was applied to a NDT and E data fusion application. Specifically, we generated two ensembles of classifiers, one trained on ultrasonic signals, and the other on corresponding magnetic flux leakage signals obtained from stainless steel samples that contained five classes of discontinuities: crack, pitting, weld, mechanical damage, and no discontinuity. We have observed that the prediction ability of the automated classification system, as measured by the accuracy and reliability of the classification performance on validation data, was significantly improved when the two data sources were combined using Learn++.