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Articles 1 - 9 of 9
Full-Text Articles in Physical Sciences and Mathematics
Learning Discrete Hidden Markov Models From State Distribution Vectors, Luis G. Moscovich
Learning Discrete Hidden Markov Models From State Distribution Vectors, Luis G. Moscovich
LSU Doctoral Dissertations
Hidden Markov Models (HMMs) are probabilistic models that have been widely applied to a number of fields since their inception in the late 1960’s. Computational Biology, Image Processing, and Signal Processing, are but a few of the application areas of HMMs. In this dissertation, we develop several new efficient learning algorithms for learning HMM parameters. First, we propose a new polynomial-time algorithm for supervised learning of the parameters of a first order HMM from a state probability distribution (SD) oracle. The SD oracle provides the learner with the state distribution vector corresponding to a query string. We prove the correctness …
Broadcast In Sparse Conversion Optical Networks Using Fewest Converters, Tong Yi
Broadcast In Sparse Conversion Optical Networks Using Fewest Converters, Tong Yi
LSU Doctoral Dissertations
Wavelengths and converters are shared by communication requests in optical networks. When a message goes through a node without a converter, the outgoing wavelength must be the same as the incoming one. This constraint can be removed if the node uses a converter. Hence, the usage of converters increases the utilization of wavelengths and allows more communication requests to succeed. Since converters are expensive, we consider sparse conversion networks, where only some specified nodes have converters. Moreover, since the usage of converters induces delays, we should minimize the use of available converters. The Converters Usage Problem (CUP) is to use …
Active Security Mechanisms For Wireless Sensor Networks And Energy Optimization For Passive Security Routing, Lydia Ray
LSU Doctoral Dissertations
Wireless sensor networks consisting of numerous tiny low power autonomous sensor nodes provide us with the remarkable ability to remotely view and interact with the previously unobservable physical world. However, incorporating computation intensive security measures in sensor networks with limited resources is a challenging research issue. The objective of our thesis is to explore different security aspects of sensor networks and provide novel solutions for significant problems. We classify security mechanisms into two categories - active category and passive category. The problem of providing a secure communication infrastructure among randomly deployed sensor nodes requires active security measurements. Key pre-distribution is …
An Empirical Study Of Imputation Techniques For Software Data Sets, Sumanth Yenduri
An Empirical Study Of Imputation Techniques For Software Data Sets, Sumanth Yenduri
LSU Doctoral Dissertations
Software Project Effort/Cost/Time Estimation has been one of the hot topics of research in the current software engineering industry. Solutions for effort/cost/time estimation are in great demand. Knowledge of accurate effort/cost/time estimates early in the software project life cycle enables project managers manage and exploit resources efficiently. The constraints of cost and time can also be met. To this day, most companies rely on their historical database of past project data sets to predict estimates for future projects. Like other data sets, software project data sets also suffer from numerous problems. The most important problem is they contain missing/incomplete data. …
Adaptive Remote Visualization System With Optimized Network Performance For Large Scale Scientific Data, Mengxia Zhu
Adaptive Remote Visualization System With Optimized Network Performance For Large Scale Scientific Data, Mengxia Zhu
LSU Doctoral Dissertations
This dissertation discusses algorithmic and implementation aspects of an automatically configurable remote visualization system, which optimally decomposes and adaptively maps the visualization pipeline to a wide-area network. The first node typically serves as a data server that generates or stores raw data sets and a remote client resides on the last node equipped with a display device ranging from a personal desktop to a powerwall. Intermediate nodes can be located anywhere on the network and often include workstations, clusters, or custom rendering engines. We employ a regression model-based network daemon to estimate the effective bandwidth and minimal delay of a …
Automatic Segmentation Of Magnetic Resonance Images Of The Brain, Kirk V. N. Spence
Automatic Segmentation Of Magnetic Resonance Images Of The Brain, Kirk V. N. Spence
LSU Doctoral Dissertations
Magnetic resonance imaging (MRI) is a technique used primarily in medical settings to produce high quality images of the human body’s internal anatomy. Each image is of a thin slice through the body, with the typical distance between slices being a few millimeters. Brain segmentation is the delineation of one or more anatomical structures within images of the brain. It promotes greater understanding of spatial relationships to aid in such tasks as surgical planning and clinical diagnoses, particularly when the segmented outlines from each image slice are displayed together as a surface in three-dimensions. A review of the literature indicates …
Efficient Automatic Correction And Segmentation Based 3d Visualization Of Magnetic Resonance Images, Mikhail V. Milchenko
Efficient Automatic Correction And Segmentation Based 3d Visualization Of Magnetic Resonance Images, Mikhail V. Milchenko
LSU Doctoral Dissertations
In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs. The goal of this research was to develop algorithms suitable for fast and effective correction and advanced visualization of digital MR image volumes with minimal human operator interaction. This research has resulted in a number of techniques for automated processing of MR image volumes, including a novel MR inhomogeneity correction algorithm derivative surface fitting (dsf), automatic tissue detection algorithm …
Biologically Inspired Learning System, Patrick Mcdowell
Biologically Inspired Learning System, Patrick Mcdowell
LSU Doctoral Dissertations
Learning Systems used on robots require either a-priori knowledge in the form of models, rules of thumb or databases or require that robot to physically execute multitudes of trial solutions. The first requirement limits the robot’s ability to operate in unstructured changing environments, and the second limits the robot’s service life and resources. In this research a generalized approach to learning was developed through a series of algorithms that can be used for construction of behaviors that are able to cope with unstructured environments through adaptation of both internal parameters and system structure as a result of a goal based …
Medical Image Enhancement, Alina Monica Trifas
Medical Image Enhancement, Alina Monica Trifas
LSU Doctoral Dissertations
Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified …