Browsing by Author "Dr. Edward Grant, Committee Member"

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Actuation and Control Strategies for Miniature Robotic Surgical Systems
(2002-08-12) Stevens, Jason Michael; Dr. Edward Grant, Committee Member; Dr. Gregory Buckner, Committee Chair; Dr. M.K. Ramasubramanian, Committee Member
Over the past 20 years, tremendous advancements have been made in the fields of minimally invasive surgery (MIS) and minimally invasive robotic assisted (MIRA) surgery. Benefits from MIS include reduced pain and trauma, reduced risks of post-operative complications, shorter recovery times, and more aesthetically pleasing results. MIRA approaches have extended the capabilities of MIS by introducing three-dimensional vision, eliminating tremors, and enabling the precise articulation of smaller instruments. These advancements come with their own drawbacks, however. Robotic systems used in MIRA procedures are large, costly, and do not offer the miniaturized articulation necessary to facilitate tremendous advancements in MIS. This research tests the hypothesis that miniature actuation can overcome some of the limitations of current robotic systems by demonstrating accurate, repeatable control of a small end-effector. A 10X model of a two link surgical manipulator is developed, using antagonistic shape memory alloy (SMA) wires as actuators, to simulate motions of a surgical end-effector. Artificial neural networks (ANNs) are used in conjunction with real-time visual feedback to "learn" the inverse system dynamics and control the manipulator endpoint trajectory. Experimental results are presented for indirect, on-line learning and control. Manipulator tip trajectories are shown to be accurate and repeatable to within 0.5 mm. These results confirm that SMAs can be effective actuators for miniature surgical robotic systems, and that intelligent control can be used to accurately control the trajectory of these systems.
Decentralized Autonomous Control of Aerospace Vehicle Formations
(2003-06-05) Levedahl, Blaine Alexander; Dr. Larry Silverberg, Committee Chair; Dr. Ashok Gopalarathnam, Committee Member; Dr. Edward Grant, Committee Member
Two approaches for the autonomous control of aerospace vehicle formations are developed. The development of the approaches relies on fundamental work in the areas of distributed control; specifically modal, robust, optimal, and decentralized control. The algorithms are shown to satisfy five separation principles that simplify design and enable the algorithms to be implemented reliably. The autonomous controllers uniformly dampen the modes of the formation (global control) using a decentralized approach and a nearest-neighbor approach. A numerical example illustrates robust formation changes from 9-vehicle (3 x 3) grids to V-type formations.
Design and Development of a Cross Platform Interactive Image Processing System
(2005-11-28) Krish, Karthik; Dr. Wesley E. Snyder, Committee Chair; Dr. Joel Trussell, Committee Member; Dr. Edward Grant, Committee Member
The objective of this thesis is to design and develop a cross-platform software system which will enable the user to perform image processing/analysis interactively. The software system will have the ability to render images of any data-type and visualize them in many different ways. The software will also integrate many commonly used image processing and analysis algorithms, that can be run on the images. The cross-platform nature of the tool will help in making sure that a uniform interface is presented to the user irrespective of the underlying architecture. The system is designed to be modular which makes it suitable for future expansion.
Performance of Microcalcification Detection Algorithms
(2005-07-21) Srivastava, Vaibhav; Dr. Wesley Snyder, Committee Chair; Dr. Edward Grant, Committee Member; Dr. David Lalush, Committee Member; Dr. Huaiyu Dai, Committee Member
Breast cancer is the most common malignancy in women and is three times more common than all gynecologic malignancies put together. The incidence of breast cancer has been increasing steadily from an incidence of 1:20 in 1960 to 1:8 women today. Seventy percent of all breast cancers are found through breast self-exams. However not all lumps are detectable by touch and mammography is a low-dose X-ray examination that can detect breast cancer up to two years before it is large enough to be felt. Some patterns of microcalcifications (small white deposits of calcium) give an early indication of cancer. Their small size makes their detection difficult for the radiologist. This brings in the role of CAD (Computer Aided Diagnosis) which serve as an assistant to the radiologist. In this thesis we have investigated the performance of three state of the art CAD techniques for the detection of microcalcifications in mammograms. First, is a wavelet based technique which applies an adaptive wavelet filter to the input mammogram. Then it calculates HOS (Higher Order Statistics) values for maxima locations that are determined from the input image by an empirical method. This is followed by determination of a threshold using a cross entropy based thresholding algorithm. The thresholded image gives the locations of microcalcifications. Second, is a technique that pre-processes the input mammogram with a tophat morphological filter which only preserves objects that are smaller than the size of the filter used in pre-processing. This is again followed by the determination of a threshold using the same thresholding algorithmas in the first technique. The thresholded image indicates the positions of microcalcifications. We have also done an investigation of co-occurrence matrix based entropy thresholding schemes. We found that two dimensional matrix based algorithms perform better than three dimensional based algorithms. Although both fail in case of images with high dynamic range which make them unsuitable for medical imaging. However the cross entropy based method performed better than co-occurrence matrix based techniques for both low as well as high resolution images. Third, is a technique that makes use of a high pass filter for pre-processing. Classification of a location as a microcalcification is done by a SVM (Support Vector Machine) classifier using a scheme called SEL(Successive Enhancement Learning). These techniques have been compared by the use of ROC curves and we found out that the SVM based technique gives the lowest false positives for high detection rate. However cross entropy method does gives lower false positives for detection rates lower than 65%.
Signal Processing using Wavelets for Enhancing Electronic Nose Performance
(2007-07-19) Phaisangittisagul, Ekachai; Dr. Edward Grant, Committee Member; Dr. H. Troy Nagle, Committee Chair; Dr. Charles Smith, Committee Member; Dr. Mark White, Committee Member
In recent years, many new technologies of electronic devices that mimic the mammalian olfactory system, electronic noses (e-noses), have been developed in many research institutions and commercial organizations around the world. These devices have been used in a wide range of applications such as food and beverage quality, environmental monitoring, medical diagnosis. Over the past decade, many researchers have spent a great deal of effort improving e-nose performance and also extended the use of the e-nose devices, not only for discriminating or classifying different odor samples, but also for quantifying an ingredient of a given odor sample. This dissertation focuses on two technical areas. First, an implementation of an e-nose signal processing system is developed to improve classification performance for small portable devices with fast response times and reduced cost. Second, the signal processing system is extended to odor mixture analysis. The advances made this research are based on a modern signal processing technique, specifically wavelet analysis. Ultimately, the performance of e-nose devices is highly dependent on the quality of features from the sensors' response. Therefore, a new transient feature extraction method using wavelet decomposition to capture the transient sensor's response has been developed. The evaluation of these transient features shows promising results in terms of classification performance, number of sensors employed in the e-nose device, and simplification of the classifier. For handling different types of odor samples, a simplified multiple classifier system is developed based on an "odor type signature." Analyzing mixtures of odors is a challenge for e-nose systems. Herein a new method is developed for predicting a sensor's response to mixtures of odors. The combination of wavelet decomposition and reconstruction is adopted to implement the mixed odor sensor-response predictor.
Structural Health Monitoring and Detection of Progressive and Existing Damage using Artificial Neural Networks-Based System Identification
(2003-03-10) Saadat, Soheil; Dr. Mohammad N. Noori, Committee Chair; Dr. Gregory D. Buckner, Committee Co-Chair; Dr. Tadatoshi Furukawa, Committee Member; Dr. Edward Grant, Committee Member; Dr. Melur K. Ramasubramanian, Committee Member
In recent decades, the growing number of civil and aerospace structures has accelerated the development of damage detection and health monitoring approaches. Many are based upon non-destructive and non-invasive sensing and analysis of structural characteristics, and most use structural response information to identify the existence, location, and time of damage. Model based techniques such as parametric and non-parametric system identification seek to identify changes in the parameters of a dynamic structural model. Restoring forces in real structures can exhibit highly non-linear characteristics, thus accurate non-linear system identification is critical. Parametric system identification approaches are commonly used, but these require a priori assumptions about restoring force characteristics. Non-parametric approaches do not require such information, but they typically lack direct associations between the model and the structural dynamics, providing limited utility for accurate health monitoring and damage detection. This dissertation presents a novel 'Intelligent Parameter Varying' (IPV) health monitoring and damage detection technique that accurately detects the existence, location, and time of damage occurrence without any assumptions about the constitutive nature of structural non-linearities. This technique combines the advantages of parametric techniques with the non-parametric capabilities of artificial neural networks by incorporating artificial neural networks into a traditional parametric model. This hybrid approach benefits from the effectiveness of traditional modeling approaches and from the adaptation and learning capabilities of artificial neural networks. The generality of this IPV approach makes it suitable to a wide range of dynamic systems, including those with non-linear and time-varying characteristics. This IPV technique is demonstrated using a lumped-mass structural model with an embedded array of artificial neural networks. These networks identify the non-linear and time-varying storing forces that would be difficult or impossible to model using traditional modeling techniques. This approach preserves direct associations between the model and the underlying system dynamics, making it ideally suited for health monitoring. Backpropagation of error is used to identify the 'optimal' network parameters from recorded acceleration responses. Chapter 1 presents an introduction to commonly used health monitoring and damage detection strategies, discusses their advantages and shortcomings, and identifies the building blocks of an effective health monitoring and damage detection strategy. Chapter 2 presents the principles of modeling and system identification. Different modeling and optimization techniques are introduced and their relevance to health monitoring and damage detection are identified. Chapter 3 introduces artificial neural networks, in particular Radial Basis Function Networks (RBFNs), for function approximation as related to the development of the IPV technique. Chapter 4 presents the development and implementation of the IPV technique. It includes development of: (1) a computational model of a typical three-story, base-excited structure, (2) computational models for elastic, elasto-plastic, and hysteretic restoring forces, (3) structural damage mechanisms, (4) structural response simulations to synthetic and recorded ground excitations, and (5) the IPV technique implementation. Chapter 5 is devoted to studying the effects of changes in artificial neural network parameters on IPV accuracy and performance. Chapter 6 is devoted to studying the effects of measurement noise on IPV accuracy. Chapter 7 identifies the main advantages of IPV over other techniques and provides future research directions.
Three Dimensional Electrophotographic Printing Through Layered Manufacturing : An Exploration into Personal Fabrication
(2004-06-03) Mahale, Tushar Ramkrishna; Dr. Edward Grant, Committee Member; Dr. Ola L. A. Harrysson, Committee Member; Dr. Denis Cormier, Committee Chair
A machine capable of making 'anything' has always existed in the realm of science fiction. The advent of the Rapid Prototyping machines partially fulfilled the realization of a personal fabricator by breaking the boundaries on the geometric form that could be realized through a machine in a single set up. The proliferation of the rapid prototyping machines into the industry and finally for domestic use, has been hampered by their costs, size and process limitations. The current trends in the Rapid Prototyping industry has been to develop machines capable of manufacturing parts in functionally graded materials. In order to achieve this, there is a need to develop means to precisely deposit a controlled combination of materials within the volume of a part. Electrophotography has been used for decades for monochrome and multicolor dry toner printing. The application of electrophotography for the generation of 3D parts through layered manufacturing has been left mostly unexplored. This thesis suggests guidelines for the development of an electrophotography based rapid prototyping process that would be cost effective in comparison with current commercial rapid prototyping technologies, as well as have the capability of depositing multiple materials. The initial research involved attempts to adapt a commercial electrophotographic printer to print in 3D. Later, experiments were conducted to indigenously build an electrophotography based layered manufacturing system. The research involved the development of transmission systems, development of power supplies to facilitate electrostatic charging, testing of polygon mirror based laser-scanning system, development of fusing and pressing station and experiments with multiple materials. Though a electrophotography based rapid prototyping machine was not realized at the end of this research, substantial evidence was generated to validate future research towards the development of such a system. Future work would involve the development of a completely automated system. Upon the completion of this system, further research could be carried out in the fields of personal fabrication, micro Rapid Prototyping, materials with directional properties, bio and materials, direct write technologies for printing circuits and functionally graded materials.