Simulations of Error Propagation for Prioritizing Data Accuracy Improvements in Multi-Criteria Satisficing Decision Making Scenarios

This research addresses the need for models that guide data quality design and resource allocation decisions. Broadly, our research problem is: Given an information system that utilizes a set of data sources for producing required information, how can we determine the gain in information accuracy and, subsequently, the economic return if the accuracy of a chosen data source is improved? An earlier paper by the author approaches this problem through a construct and a model. The construct, named damage, is defined as the change in information accuracy that results from improving the accuracy of a chosen data source. The model that is provided together with this construct enables its quantification as well as a simple ranking of inputs according to the damage that errors in each inflict. The model suits environments in which the data are applied mostly by satisficing, multi-criteria decisions, such as databases. This paper reports on a series of Monte Carlo Simulations that validate the ranking component of the model under conjunctive decisions, and, in addition, explore and characterize special conditions in which a predicted ranking is not assured to be correct

A Framework For Assessing Water Quality, Prioritizing Recovery Potential, And Analyzing Placement Of Best Management Practices

Motivated by the U.S. EPA goals, this research developed a framework to support identification and restoration of nutrient-impaired water bodies. The study objectives were developing total nitrogen (TN) and total phosphorus (TP) prediction models, evaluating the impact of social indicators on assessing recovery potential, and developing a spatial decision support system for choice and placement of best management practices (BMPS). An artificial neural network was used to develop TN and TP predictive regional models for U.S. lakes using easily measurable and cost-effective variables. The performance of models was superior for regions trained with larger datasets and/or regions with lower temperature and precipitation variability. The use of datasets larger than existing records and obtained from homogeneous climatic region was suggested to achieve the desired performance. The impact of social indicators on assessing a recovery potential was studied by comparing four watersheds using ecological, stressor, and social indicators. Social indicators were grouped into socio-economic, organizational, and information and planning subcategories. The existing U.S. EPA recovery potential screening tool prioritizes restoration for a water body with the most favorable ecological and social condition as well as the least stressing factors. In the present study, water bodies ranked lowest were observed with lower social scores associated with lower socio-economic conditions. This could mean a manager would take a water body with lower socio-economic condition as the lowest priority for restoration. It is suggested that such prioritization plan should carefully incorporate community goals in a prioritization effort because restoration supports an improvement of quality of life. A spatial decision support system was developed with the necessary information to assess nitrogen (n) pollution and methods to estimate an annual exported n load into Beasley Lake, Mississippi. A decision analysis of choice and placement of BMPS was performed based on performance, site suitability, and establishment cost criteria. From this analysis, a BMP scenario that reduces 25% of the exported load at an establishment and an annual opportunity cost-to-performance ratios of 148 $/kg and 29$/kg, respectively, was developed. The presented approach supports similar efforts when the use of existing watershed models is limited by data availability

A Model of Error Propagation in Satisficing Decisions and its Application to Database Quality Management

This study centers on the accuracy dimension of information quality and models the relationship between input accuracy and output accuracy in a popular class of applications. Such applications consist of dichotomous decisions or judgments that are implemented through conjunction of selected criteria. Initially, this paper introduces a model that designates a single decision rule which employs a single binary conjunction operation. This model is extended to handle multiple, related decision rules that consist of any number of binary conjunction operations. Finally, application of the extended model is illustrated through the example of an online hotel reservation database. This example demonstrates how the new model can be utilized for ranking and quantifying the damage that errors in different database attributes inflict. Numerical estimates of the model can be integrated into cost-benefit analyses that assess alternative data accuracy enhancements or process or system designs

Radio Resource Management Optimization For Next Generation Wireless Networks

The prominent versatility of today’s mobile broadband services and the rapid advancements in the cellular phones industry have led to a tremendous expansion in the wireless market volume. Despite the continuous progress in the radio-access technologies to cope with that expansion, many challenges still remain that need to be addressed by both the research and industrial sectors. One of the many remaining challenges is the efficient allocation and management of wireless network resources when using the latest cellular radio technologies (e.g., 4G). The importance of the problem stems from the scarcity of the wireless spectral resources, the large number of users sharing these resources, the dynamic behavior of generated traffic, and the stochastic nature of wireless channels. These limitations are further tightened as the provider’s commitment to high quality-of-service (QoS) levels especially data rate, delay and delay jitter besides the system’s spectral and energy efficiencies. In this dissertation, we strive to solve this problem by presenting novel cross-layer resource allocation schemes to address the efficient utilization of available resources versus QoS challenges using various optimization techniques. The main objective of this dissertation is to propose a new predictive resource allocation methodology using an agile ray tracing (RT) channel prediction approach. It is divided into two parts. The first part deals with the theoretical and implementational aspects of the ray tracing prediction model, and its validation. In the second part, a novel RT-based scheduling system within the evolving cloud radio access network (C-RAN) architecture is proposed. The impact of the proposed model on addressing the long term evolution (LTE) network limitations is then rigorously investigated in the form of optimization problems. The main contributions of this dissertation encompass the design of several heuristic solutions based on our novel RT-based scheduling model, developed to meet the aforementioned objectives while considering the co-existing limitations in the context of LTE networks. Both analytical and numerical methods are used within this thesis framework. Theoretical results are validated with numerical simulations. The obtained results demonstrate the effectiveness of our proposed solutions to meet the objectives subject to limitations and constraints compared to other published works

Uncovering Hidden Diversity in Plants

One of the greatest challenges to human civilization in the 21st century will be to provide global food security to a growing population while reducing the environmental footprint of agriculture. Despite increasing demand, the fundamental issue of limited genetic diversity in domesticated crops provides windows of opportunity for emerging pandemics and the insufficient ability of modern crops to respond to a changing global environment. The wild relatives of crop plants, with large reservoirs of untapped genetic diversity, offer great potential to improve the resilience of elite cultivars. Utilizing this diversity requires advanced technologies to comprehensively identify genetic diversity and understand the genetic architecture of beneficial traits. The primary focus of the dissertation is developing computational tools to facilitate variant discovery and trait mapping for plant genomics. In Chapter 1, I benchmarked the performance of variant discovery algorithms based on simulated and diverse plant datasets. The comparison of sequence aligners found that BWA-MEM consistently aligned the most plant reads with high accuracy, whereas Bowtie2 had a slightly higher overall accuracy. Variant callers, such as GATK HaplotypCaller and SAMtools mpileup, were shown to significantly differ in their ability to minimize the frequency of false negatives and maximize the discovery of true positives. A cross-reference experiment of Solanum lycopersicum and Solanum pennellii reference genomes revealed significant limitations of using a single reference genome for variant discovery. Next, I demonstrated that a machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff filtering strategy, resulting in a significantly higher number of true positive and fewer false-positive variants. Finally, I developed a 2-step imputation method resulted in up to 60% higher accuracy than direct LD-based imputation methods. In Chapter 2, I focused on developing a trait mapping algorithm tailored for plants considering the high levels of diversity found in plant datasets. This novel trait mapping framework, HapFM, had the ability to incorporate biological priors into the mapping model to identify casual haplotypes for traits of interest. Compared to conventional GWAS analyses, the haplotype-based approach significantly reduced the number of variables while aggregating small effect SNPs to increase mapping power. HapFM could account for LD between haplotype segments to infer the causal haplotypes directly. Furthermore, HapFM could systemically incorporate biological priors into the probability function during the mapping process resulting in greater mapping resolution. Overall, HapFM achieves a balance between powerfulness, interpretability, and verifiability. In Chapter 3, I developed a computational algorithm to select a pan-genome cohort to maximize the haplotype representativeness of the cohort. Increasing evidence suggest that a single reference genome is often inadequate for plant diversity studies due to extensive sequence and structural rearrangements found in many plant genomes. HapPS was developed to utilize local haplotype information to select the reference cohort. There are three steps in HapPS, including genome-wide block partition, representative haplotype identification, and genetic algorithm for reference cohort selection. The comparison of HapPS with global-distance-based selection showed that HapPS resulted in significantly higher block coverage in the highly diverse genic regions. The GO-term enrichment analysis of the highly diverse genic region identified by HapPS showed enrichment for genes involved in defense pathways and abiotic stress, which might identify genomic regions involved in local adaptation. In summary, HapPS provides a systemic and objective solution to pan-genome cohort selection

Learning-based Nonlinear MPC for Quadrotor Control

openThis work aims at investigate the application of different learning based techniques for the enhancement of the Nonlinear Model Predictive Control (NMPC) framework, in the context of trajectory control for a quadrotor unmanned aerial vehicle (UAV). In particular, a gaussian process regression technique and a neural network approach are both taken into account in order to improve the knowledge of the model that constitutes the basis of the effectiveness of the NMPC.This work aims at investigate the application of different learning based techniques for the enhancement of the Nonlinear Model Predictive Control (NMPC) framework, in the context of trajectory control for a quadrotor unmanned aerial vehicle (UAV). In particular, a gaussian process regression technique and a neural network approach are both taken into account in order to improve the knowledge of the model that constitutes the basis of the effectiveness of the NMPC

Advanced Access Schemes for Future Broadband Wireless Networks

International audienc

An Approach for Guiding Developers to Performance and Scalability Solutions

This thesis proposes an approach that enables developers who are novices in software performance engineering to solve software performance and scalability problems without the assistance of a software performance expert. The contribution of this thesis is the explicit consideration of the implementation level to recommend solutions for software performance and scalability problems. This includes a set of description languages for data representation and human computer interaction and a workflow