Digital Commons Network^™

Integrating Fuzzy Decisioning Models With Relational Database Constructs, Erin-Elizabeth A. Durham

Computer Science Dissertations

Human learning and classification is a nebulous area in computer science. Classic decisioning problems can be solved given enough time and computational power, but discrete algorithms cannot easily solve fuzzy problems. Fuzzy decisioning can resolve more real-world fuzzy problems, but existing algorithms are often slow, cumbersome and unable to give responses within a reasonable timeframe to anything other than predetermined, small dataset problems. We have developed a database-integrated highly scalable solution to training and using fuzzy decision models on large datasets. The Fuzzy Decision Tree algorithm is the integration of the Quinlan ID3 decision-tree algorithm together with fuzzy set theory …

Data Assimilation For Agent-Based Simulation Of Smart Environment, Minghao Wang

Computer Science Dissertations

Agent-based simulation of smart environment finds its application in studying people’s movement to help the design of a variety of applications such as energy utilization, HAVC control and egress strategy in emergency situation. Traditionally, agent-based simulation is not dynamic data driven, they run offline and do not assimilate real sensor data about the environment. As more and more buildings are equipped with various sensors, it is possible to utilize real time sensor data to inform the simulation. To incorporate the real sensor data into the simulation, we introduce the method of data assimilation. The goal of data assimilation is to …

Distributed Particle Filters For Data Assimilation In Simulation Of Large Scale Spatial Temporal Systems, Fan Bai

Computer Science Dissertations

Assimilating real time sensor into a running simulation model can improve simulation results for simulating large-scale spatial temporal systems such as wildfire, road traffic and flood. Particle filters are important methods to support data assimilation. While particle filters can work effectively with sophisticated simulation models, they have high computation cost due to the large number of particles needed in order to converge to the true system state. This is especially true for large-scale spatial temporal simulation systems that have high dimensional state space and high computation cost by themselves. To address the performance issue of particle filter-based data assimilation, this …

Visualizing Spatio-Temporal Data, Ayush Shrestha

Computer Science Dissertations

The amount of spatio-temporal data produced everyday has sky rocketed in the recent years due to the commercial GPS systems and smart devices. Together with this, the need for tools and techniques to analyze this kind of data have also increased. A major task of spatio-temporal data analysis is to discover relationships and patterns among spatially and temporally scattered events. However, most of the existing visualization techniques implement a top-down approach i.e, they require prior knowledge of existing patterns. In this dissertation, I present my novel visualization technique called Storygraph which supports bottom-up discovery of patterns. Since Storygraph presents and …

Maximum Energy Subsampling: A General Scheme For Multi-Resolution Image Representation And Analysis, Yanjun Zhao

Computer Science Dissertations

Image descriptors play an important role in image representation and analysis. Multi-resolution image descriptors can effectively characterize complex images and extract their hidden information.

Wavelets descriptors have been widely used in multi-resolution image analysis. However, making the wavelets transform shift and rotation invariant produces redundancy and requires complex matching processes. As to other multi-resolution descriptors, they usually depend on other theories or information, such as filtering function, prior-domain knowledge, etc.; that not only increases the computation complexity, but also generates errors.

We propose a novel multi-resolution scheme that is capable of transforming any kind of image descriptor into its multi-resolution …

Hiv Drug Resistant Prediction And Featured Mutants Selection Using Machine Learning Approaches, Xiaxia Yu

Computer Science Dissertations

HIV/AIDS is widely spread and ranks as the sixth biggest killer all over the world. Moreover, due to the rapid replication rate and the lack of proofreading mechanism of HIV virus, drug resistance is commonly found and is one of the reasons causing the failure of the treatment. Even though the drug resistance tests are provided to the patients and help choose more efficient drugs, such experiments may take up to two weeks to finish and are expensive. Because of the fast development of the computer, drug resistance prediction using machine learning is feasible.

In order to accurately predict the …

Algorithms For Viral Population Analysis, Nicholas Mancuso

Computer Science Dissertations

The genetic structure of an intra-host viral population has an effect on many clinically important phenotypic traits such as escape from vaccine induced immunity, virulence, and response to antiviral therapies. Next-generation sequencing provides read-coverage sufficient for genomic reconstruction of a heterogeneous, yet highly similar, viral population; and more specifically, for the detection of rare variants. Admittedly, while depth is less of an issue for modern sequencers, the short length of generated reads complicates viral population assembly. This task is worsened by the presence of both random and systematic sequencing errors in huge amounts of data. In this dissertation I present …

Optimization Techniques For Next-Generation Sequencing Data Analysis, Adrian Caciula

Computer Science Dissertations

High-throughput RNA sequencing (RNA-Seq) is a popular cost-efficient technology with many medical and biological applications. This technology, however, presents a number of computational challenges in reconstructing full-length transcripts and accurately estimate their abundances across all cell types.

Our contributions include (1) transcript and gene expression level estimation methods, (2) methods for genome-guided and annotation-guided transcriptome reconstruction, and (3) de novo assembly and annotation of real data sets. Transcript expression level estimation, also referred to as transcriptome quantification, tackle the problem of estimating the expression level of each transcript. Transcriptome quantification analysis is crucial to determine similar transcripts or unraveling gene …

Row Compression And Nested Product Decomposition Of A Hierarchical Representation Of A Quasiseparable Matrix, Mary Hudachek-Buswell

Computer Science Dissertations

This research introduces a row compression and nested product decomposition of an nxn hierarchical representation of a rank structured matrix A, which extends the compression and nested product decomposition of a quasiseparable matrix. The hierarchical parameter extraction algorithm of a quasiseparable matrix is efficient, requiring only O(nlog(n))operations, and is proven backward stable. The row compression is comprised of a sequence of small Householder transformations that are formed from the low-rank, lower triangular, off-diagonal blocks of the hierarchical representation. The row compression forms a factorization of matrix A, where A = QC, Q is the product of the Householder transformations, and …

Data Assimilation Based On Sequential Monte Carlo Methods For Dynamic Data Driven Simulation, Haidong Xue

Computer Science Dissertations

Simulation models are widely used for studying and predicting dynamic behaviors of complex systems. Inaccurate simulation results are often inevitable due to imperfect model and inaccurate inputs. With the advances of sensor technology, it is possible to collect large amount of real time observation data from real systems during simulations. This gives rise to a new paradigm of Dynamic Data Driven Simulation (DDDS) where a simulation system dynamically assimilates real time observation data into a running model to improve simulation results. Data assimilation for DDDS is a challenging task because sophisticated simulation models often have: 1) nonlinear non-Gaussian behavior 2) …

Ontology-Based Search Algorithms Over Large-Scale Unstructured Peer-To-Peer Networks, Rasanjalee Dissanayaka Mudiyanselage

Computer Science Dissertations

Peer-to-Peer(P2P) systems have emerged as a promising paradigm to structure large scale distributed systems. They provide a robust, scalable and decentralized way to share and publish data.The unstructured P2P systems have gained much popularity in recent years for their wide applicability and simplicity. However efficient resource discovery remains a fundamental challenge for unstructured P2P networks due to the lack of a network structure. To effectively harness the power of unstructured P2P systems, the challenges in distributed knowledge management and information search need to be overcome. Current attempts to solve the problems pertaining to knowledge management and search have focused on …

Activity-Aware Sensor Networks For Smart Environments, Debraj De

Computer Science Dissertations

The efficient designs of Wireless Sensor Network protocols and intelligent Machine Learning algorithms, together have led to the advancements of various systems and applications for Smart Environments. By definition, Smart Environments are the typical physical worlds used in human daily life, those are seamlessly embedded with smart tiny devices equipped with sensors, actuators and computational elements. Since human user is a key component in Smart Environments, human motion activity patterns have key importance in building sensor network systems and applications for Smart Environments. Motivated by this, in this thesis my work is focused on human motion activity-aware sensor networks for …