Mountain Scholar :: Browsing by Author "Jayasumana, Anura P., committee member"

Browsing by Author "Jayasumana, Anura P., committee member"

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Open Access
An integrated retrieval framework for multiple polarization, multiple frequency radar networks
(Colorado State University. Libraries, 2015) Hardin, Joseph C., author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul, committee member; Cheney, Margaret, committee member
Radar networks form the backbone of severe weather and remote sensing in throughout most of the world. These networks provide diverse measurements of weather phenomenon, but ultimately are measuring indirect parameters rather than detecting the physics of the situation. One of the long standing goals of weather remote sensing is to relate the measurements from the various instruments to the physics that give rise to the measurements. Weather radar networks give both a better spatial coverage than single radars, as well as providing multiple looks at the environment. Newly developed radar networks have started to incorporate multiple frequencies and multiple polarizations to take advantage of attributes of different radar frequencies. Raindrops occupy different scattering regimes based on the frequency of the radar being used. Based on this, multiple radars at different wavelengths provide unique information about the microphysical characteristics of the atmosphere. Nonetheless, very little work has been conducted on fusing multiple radar measurements at heterogeneous frequencies to improve microphysical retrievals. This work presents a forward variational algorithm for multiple radar fusion that retrieves microphysical parameters from the atmosphere. The single radar case and the multiple radar case will both be addressed. Ground instrumentation will be used for verification, and the spatial and temporal variability of precipitation microphysics will be discussed.
Open Access
An intelligent, mobile network aware middleware framework for energy efficient offloading in smartphones
(Colorado State University. Libraries, 2017) Khune, Aditya Dilip, author; Pasricha, Sudeep, advisor; Jayasumana, Anura P., committee member; Gesumaria, Bob, committee member
Offloading mobile computations is an innovative technique that is being explored by researchers for reducing energy consumption in mobile devices and for achieving better application response time. Offloading refers to the act of transferring computations from a mobile device to servers in the cloud. There are many challenges in this domain that are not dealt with effectively yet, and thus offloading is far from being adopted in the design of current mobile architectures. We believe that there is a need to verify the effectiveness of computation offloading in terms of both response time and energy consumption, to highlight its potential in real smartphone applications. The effect of varying network technologies such as 3G, 4G, and Wi-Fi on the performance of offloading systems is also a major concern that needs to be addressed. In this thesis, we study the behavior of a set of real smartphone applications, in both local and offload processing modes. Our experiments identify the advantages and disadvantages of offloading for various mobile networks. Further, we propose a middleware framework that uses Reinforcement Learning to make reward-based offloading decisions effectively. Our framework allows a smartphone to consider suitable contextual information to determine when it makes sense to offload, and to select between available networks (3G, 4G, or Wi-Fi) when offloading mode is active. We tested our framework in both simulated and real environments, across various applications, to demonstrate how energy consumption can be minimized in mobile systems that are capable of supporting offloading.
Open Access
Application of systems engineering to complex systems and system of systems
(Colorado State University. Libraries, 2017) Sturdivant, Rick L., author; Chong, Edwin K. P., advisor; Sega, Ronald M., committee member; Jayasumana, Anura P., committee member; Atadero, Rebecca, committee member
This dissertation is an investigation of system of systems (SoS). It begins with an analysis to define, with some rigor, the similarities and differences between complex systems and SoS. With this foundation, the baseline concept is development for several different types of systems and they are used as a practical approach to compare and contrast complex systems versus SoS. The method is to use a progression from simple to more complex systems. Specifically, a pico hydro electric power generation system, a hybrid renewable electric power generation system, a LEO satellites system, and Molniya orbit satellite system are investigated. In each of these examples, systems engineering methods are applied for the development of a baseline solution. While these examples are complex, they do not rise to the level of a SoS. In contrast, a multi-spectral drone detection system for protection of airports is investigated and a baseline concept for it is generated. The baseline is shown to meet the minimum requirements to be considered a SoS. The system combines multiple sensor types to distinguish drones as targets. The characteristics of the drone detection system which make it a SoS are discussed. Since emergence is considered by some to be a characteristic of a SoS, it is investigated. A solution to the problem of determining if system properties are emergent is presented and necessary and sufficient conditions for emergence are developed. Finally, this work concludes with a summary and suggestions for additional work.
Open Access
CASA real-time multi-Doppler retrieval system
(Colorado State University. Libraries, 2011) Zhang, Sean X., author; Chandrasekaran, V., advisor; Bringi, V. N., committee member; Jayasumana, Anura P., committee member; Mielke, Paul W., committee member
Doppler synthesis of 3D wind products is of great practical importance to observing and understanding severe weather features such as tornadoes and microbursts. It becomes very effective for severe weather events if this modeling can be performed in real-time. A real-time multi-Doppler retrieval system forms an important product of modern weather radar networks. Challenging constraints exists between computing performance, high data resolution, and other quality issues. This Thesis describes the implementation of the operational Real-time Multi-Doppler Retrieval System (R-MDRS) of the Center for Collaborative Adaptive Sensing of the Atmosphere Engineering Research Center (CASA ERC). The R-MDRS is seamlessly integrated into CASA's Distributed Collaborative Adaptive Sensing (DCAS) operational framework and exhibit robust performance that strikes balance between high resolution and real-time processing speeds. A detailed technical description of the CASA R-MDRS implementation is given, including design approach that builds around two core components of the tool: interpolation to a common grid and Doppler synthesis. The R-MDRS generates 3D Wind products in step with network scanning modes and has been effective at detecting convective cells and tornadic activities. Data from 2009 and 2010 weather events are presented and analyzed for evaluating processing time as well as factors that effect data accuracy. These factors include Dual-Doppler candidate pair selection, advection correction, and variations in wind calculation techniques.
Open Access
Compiling dataflow graphs into hardware
(Colorado State University. Libraries, 2005) Rinker, Robert E., author; Najjar, Walid, advisor; Böhm, Wim, committee member; Grit, Dale H., committee member; Jayasumana, Anura P., committee member
Conventional computers are programmed by supplying a sequence of instructions that perform the desired task. A reconfigurable processor is "programmed" by specifying the interconnections between hardware components, thereby creating a "hardwired" system to do the particular task. For some applications such as image processing, reconfigurable processors can produce dramatic execution speedups. However, programming a reconfigurable processor is essentially a hardware design discipline, making programming difficult for application programmers who are only familiar with software design techniques. To bridge this gap, a programming language, called SA-C (Single Assignment C, pronounced "sassy"), has been designed for programming reconfigurable processors. The process involves two main steps - first, the SA-C compiler analyzes the input source code and produces a hardware-independent intermediate representation of the program, called a dataflow graph (DFG). Secondly, this DFG is combined with hardware-specific information to create the final configuration. This dissertation describes the design and implementation of a system that performs the DFG to hardware translation. The DFG is broken up into three sections: the data generators, the inner loop body, and the data collectors. The second of these, the inner loop body, is used to create a computational structure that is unique for each program. The other two sections are implemented by using prebuilt modules, parameterized for the particular problem. Finally, a "glue module" is created to connect the various pieces into a complete interconnection specification. The dissertation also explores optimizations that can be applied while processing the DFG, to improve performance. A technique for pipelining the inner loop body is described that uses an estimation tool for the propagation delay of the nodes within the dataflow graph. A scheme is also described that identifies subgraphs with the dataflow graph that can be replaced with lookup tables. The lookup tables provide a faster implementation than random logic in some instances.
Open Access
Data mining and spatiotemporal analysis of modern mobile data
(Colorado State University. Libraries, 2019) Fang, Luoyang, author; Yang, Liuqing, advisor; Jayasumana, Anura P., committee member; Luo, Jie, committee member; Wang, Haonan, committee member
Modern mobile network technologies and smartphones have successfully penetrated nearly every aspect of human life due to the increasing number of mobile applications and services. Massive mobile data generated by mobile networks with timestamp and location information have been frequently collected. Mobile data analytics has gained remarkable attention from various research communities and industries, since it can broadly reveal the human spatiotemporal mobility patterns from the individual level to an aggregated one. In this dissertation, two types of spatiotemporal modeling with respect to human mobility behaviors are considered, namely the individual modeling and aggregated modeling. As for individual spatiotemporal modeling, location privacy is studied in terms of user identifiability between two mobile datasets, merely based on their spatiotemporal traces from the perspective of a privacy adversary. The success of user identification then hinges upon the effective distance measures via user spatiotemporal behavior profiling. However, user identification methods depending on a single semantic distance measure almost always lead to a large portion of false matches. To improve user identification performance, we propose a scalable multi-feature ensemble matching framework that integrates multiple explored spatiotemporal models. On the other hand, the aggregated spatiotemporal modeling is investigated for network and traffic management in cellular networks. Traffic demand forecasting problem across the entire mobile network is first studied, which is considered as the aggregated behavior of network users. The success of demand forecasting relies on effective modeling of both the spatial and temporal dependencies of the per-cell demand time series. However, the main challenge of the spatial relevancy modeling in the per-cell demand forecasting is the uneven spatial distribution of cells in a network. In this work, a dependency graph is proposed to model the spatial relevancy without compromising the spatial granularity. Accordingly, the spatial and temporal models, graph convolutional and recurrent neural networks, are adopted to forecast the per-cell traffic demands. In addition to demand forecasting, a per-cell idle time window (ITW) prediction application is further studied for predictive network management based on subscribers' aggregated spatiotemporal behaviors. First, the ITW prediction is formulated into a regression problem with an ITW presence confidence index that facilitates direct ITW detection and estimation. To predict the ITW, a deep-learning-based ITW prediction model is proposed, consisting of a representation learning network and an output network. The representation learning network is aimed to learn patterns from the recent history of demand and mobility, while the output network is designed to generate the ITW predicts with the learned representation and exogenous periodic as inputs. Upon this paradigm, a temporal graph convolutional network (TGCN) implementing the representation learning network is also proposed to capture the graph-based spatiotemporal input features effectively.
Open Access
Electronic scan weather radar: scan strategy and signal processing for volume targets
(Colorado State University. Libraries, 2013) Nguyen, Cuong Manh, author; Chandra, Chandrasekar V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul W., committee member; Notaros, Branislav, committee member
Following the success of the WSR-88D network, considerable effort has been directed toward searching for options for the next generation of weather radar technology. With its superior capability for rapidly scanning the atmosphere, electronically scanned phased array radar (PAR) is a potential candidate. A network of such radars has been recommended for consideration by the National Academies Committee on Weather Radar Technology beyond NEXRAD. While conventional weather radar uses a rotating parabolic antenna to form and direct the beam, a phased array radar superimposes outputs from an array of many similar radiating elements to yield a beam that is scanned electronically. An adaptive scan strategy and advanced signal designs and processing concepts are developed in this work to use PAR effectively for weather observation. An adaptive scan strategy for weather targets is developed based on the space-time variability of the storm under observation. Quickly evolving regions are scanned more often and spatial sampling resolution is matched to spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time while the measurement accuracy controls the dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. In this research, a signal model for polarimetric phased array weather radar (PAWR) is presented and analyzed. The electronic scan mechanism creates a complex coupling of horizontal and vertical polarizations that produce the bias in the polarimetric variables retrieval. Methods for bias correction for simultaneous and alternating transmission modes are proposed. It is shown that the bias can be effectively removed; however, data quality degradation occurs at far off boresight directions. The effective range for the bias correction methods is suggested by using radar simulation. The pulsing scheme used in PAWR requires a new ground clutter filtering method. The filter is designed to work with a signal covariance matrix in the time domain. The matrix size is set to match the data block size. The filter's design helps overcome limitations of spectral filtering methods and make efficient use of reducing ground clutter width in PAWR. Therefore, it works on modes with few samples. Additionally, the filter can be directly extended for staggered PRT waveforms. Filter implementation for polarimetric retrieval is also successfully developed and tested for simultaneous and alternating staggered PRT. The performance of these methods is discussed in detail. It is important to achieve high sensitivity for PAWR. The use of low-power solid state transmitters to keep costs down requires pulse compression technique. Wide-band pulse compression filters will partly reduce the system sensitivity performance. A system for sensitivity enhancement (SES) for pulse compression weather radar is developed to mitigate this issue. SES uses a dual-waveform transmission scheme and an adaptive pulse compression filter that is based on the self-consistency between signals of the two waveforms. Using SES, the system sensitivity can be improved by 8 to 10 dB.
Open Access
Frequency diversity wideband digital receiver and signal processor for solid-state dual-polarimetric weather radars
(Colorado State University. Libraries, 2012) Mishra, Kumar Vijay, author; Chandra, Chandrasekar V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul W., committee member
The recent spate in the use of solid-state transmitters for weather radar systems has unexceptionably revolutionized the research in meteorology. The solid-state transmitters allow transmission of low peak powers without losing the radar range resolution by allowing the use of pulse compression waveforms. In this research, a novel frequency-diversity wideband waveform is proposed and realized to extenuate the low sensitivity of solid-state radars and mitigate the blind range problem tied with the longer pulse compression waveforms. The latest developments in the computing landscape have permitted the design of wideband digital receivers which can process this novel waveform on Field Programmable Gate Array (FPGA) chips. In terms of signal processing, wideband systems are generally characterized by the fact that the bandwidth of the signal of interest is comparable to the sampled bandwidth; that is, a band of frequencies must be selected and filtered out from a comparable spectral window in which the signal might occur. The development of such a wideband digital receiver opens a window for exciting research opportunities for improved estimation of precipitation measurements for higher frequency systems such as X, Ku and Ka bands, satellite-borne radars and other solid-state ground-based radars. This research describes various unique challenges associated with the design of a multi-channel wideband receiver. The receiver consists of twelve channels which simultaneously downconvert and filter the digitized intermediate-frequency (IF) signal for radar data processing. The product processing for the multi-channel digital receiver mandates a software and network architecture which provides for generating and archiving a single meteorological product profile culled from multi-pulse profiles at an increased data date. The multi-channel digital receiver also continuously samples the transmit pulse for calibration of radar receiver gain and transmit power. The multi-channel digital receiver has been successfully deployed as a key component in the recently developed National Aeronautical and Space Administration (NASA) Global Precipitation Measurement (GPM) Dual-Frequency Dual-Polarization Doppler Radar (D3R). The D3R is the principal ground validation instrument for the precipitation measurements of the Dual Precipitation Radar (DPR) onboard the GPM Core Observatory satellite scheduled for launch in 2014. The D3R system employs two broadly separated frequencies at Ku- and Ka-bands that together make measurements for precipitation types which need higher sensitivity such as light rain, drizzle and snow. This research describes unique design space to configure the digital receiver for D3R at several processing levels. At length, this research presents analysis and results obtained by employing the multi-carrier waveforms for D3R during the 2012 GPM Cold-Season Precipitation Experiment (GCPEx) campaign in Canada.
Open Access
Green communication and security in wireless networks based on Markov decision process and semivariance optimization
(Colorado State University. Libraries, 2020) Elsherif, Fateh, author; Chong, Edwin K. P., advisor; Jayasumana, Anura P., committee member; Luo, J. Rockey, committee member; Atadero, Rebecca, committee member
Wireless networking has become an integral part of our everyday life. Certainly, wireless technologies have improved many aspects of the way people communicate, interact, and perform tasks, in addition to enabling new use cases, such as massive machine-type communications and industry verticals, among others. While convenient, these technologies impose new challenges and introduce new design problems. In this dissertation, we consider three problems in wireless networking. Specifically, we formulate optimization problems in green communication and security, and develop computationally efficient solutions to these optimization problems. First, we study the problem of base station (BS) dynamic switching for energy efficient design of fifth generation (5G) cellular networks and beyond. We formulate this problem as a Markov decision process (MDP) and use an approximation method known as policy rollout to solve it. This method employs Monte Carlo sampling to approximate the Q-value. In this work, we introduce a novel approach to design an energy-efficient algorithm based on MDP to control the ON/OFF switching of BSs; we exploit user mobility and location information in the selection of the optimal control actions. We start our formulation with the simple case of one-user one-ON. We then gradually and systematically extend this formulation to the multi-user multi-ON scenario. Simulation results show the potential of our novel approach of exploiting user mobility information within the MDP framework to achieve significant energy savings while providing quality-of-service guarantees. Second, we study the problem of jamming-aware-multi-path routing in wireless networks. Multipath routing is a technique for transmitting data from one or more source node(s) to one or more destination node(s) over multiple routing paths. We study the problem of wireless jamming-mitigation multipath routing. To address this problem, we propose a new framework for mitigating jamming risk based on semivariance optimization. Semivariance is a mathematical quantity used originally in finance and economics to measure the dispersion of a portfolio return below a risk-aversion benchmark. We map the problem of jamming-mitigation multipath routing to that of portfolio selection within the semivariance risk framework. Then we use this framework to design a new, and computationally feasible, RF-jamming mitigation algorithm. We use simulation to study the properties of our method and demonstrate its efficacy relative to a competing scheme that optimizes the jamming risk in terms of variance instead of semivariance. To the best of our knowledge, our work is the first to use semivariance as a measure of jamming risk. Directly optimizing objective functions that involve exact semivariance introduces certain computational issues. However, there are approximations to the semivariance that overcome these issues. We study semivariance problems—from the literature of finance and economics—and survey their solutions. Based on one of these solutions, we develop an efficient algorithm for solving semivariance optimization problems. Efficiency is imperative for many telecommunication applications such as tactile Internet and Internet of Things (recall that these types of applications have stringent constraints on latency and computing power). Our algorithm provides a general approach to solving semivariance optimization problems, and can be used in other applications. Last, we consider the problem of multiple--radio-access technology (multi-RAT) connectivity in heterogeneous networks (HetNets). Recently, multi-RAT connectivity has received significant attention—both from industry and academia—because of its potential as a method to increase throughput, to enhance communication reliability, and to minimize communication latency. We introduce a new approach to the problem of multi-RAT traffic allocation in HetNets. We propose a new risk-averse multi-RAT connectivity (RAM) algorithm. Our RAM algorithm allows trading off expected throughput for risk measured in throughput semivariance. Here we also adopt semivariance as a measure of throughput dispersion below a risk-aversion--throughput benchmark. We then formulate the multi-RAT connectivity problem as a semivariance-optimization problem. However, we tackle a different optimization problem in this part of the research. The objective function of the optimization problem considered here is different from the objective function of the optimization problem above that also uses semivariance to quantify risk (because the underlying standard form of portfolio selection is different). In addition, the set of constraints is different in this optimization problem: We introduce new capacity constraints to account for the stochastic capacity of the involved wireless links. We also introduce a new performance metric, the risk-adjusted throughput; risk-adjusted throughput is the ratio between the expected throughput and the throughput semideviation, where semideviation is the square root of semivariance. We evaluate the performance of our algorithm through simulation of a system with three radio-access technologies: 4G LTE, 5G NR, and WiFi. Simulation results show the potential gains of using our algorithm.
Open Access
Integration of an unmanned aircraft system and ground-based remote sensing to estimate spatially distributed crop evapotranspiration and soil water deficit throughout the vegetation soil root zone
(Colorado State University. Libraries, 2016) Hathaway, Jeffrey Calvin, author; Chávez, José L., advisor; Niemann, Jeffrey D., committee member; Jayasumana, Anura P., committee member; Zhang, Huihui, committee member
Irrigation is the largest consumer of fresh water and produces over 40% of the world’s food and fiber supply. As the world’s population continues to grow rapidly, the increased demands on fresh water will force the agricultural community to improve the efficiency and productivity of irrigation systems, while reducing overall water usage. In order to address the requirements of increased efficiency and productivity in agricultural water use, the agricultural community has begun to focus on the development of precision agriculture (PA) irrigation management systems for use with irrigated agriculture. Remote sensing (RS) is at the forefront of the PA movement, allowing the estimation of spatially distributed crop water requirements on a large-scale basis. Techniques using ground, aerial and space-borne RS platforms, have been developed to estimate actual crop evapotranspiration (ETa) and soil water deficit (SWD) for use in PA irrigation management systems. The ability to monitor the ETa and SWD allows irrigators to manage their irrigation to increase efficiency and decrease overall water use while maintaining crop yields goals. Historically, remote sensing data, such as spectral reflectance and thermal infrared (TIR) imagery, were provided by ground or space-borne RS platforms, like NASA’s Landsat 8 satellites. Though these methods are effective at estimating ETa over large areas, their lack of spatial and temporal resolution limit their effectiveness for application in PA irrigation management systems. In order to address the required spatial and temporal resolutions required for PA systems, Colorado State University (CSU) developed an unmanned aircraft system (UAS) RS platform capable of collecting high spatial and temporal resolution data in the TIR, near-infrared (NIR), red and green bands of the electromagnetic spectrum. During the summer of 2015, CSU conducted four flights over corn at the Agriculture Research Development and Education Center (ARDEC), near Fort Collins, CO, with the Tempest UAS RS platform in order to collect thermal and multispectral imagery. The RS data collected over the ARDEC test location were used in three studies. The first was the comparison of the raw RS data to the ground-based RS data collected during the RS overpasses. The second study used the Tempest RS data to estimate the ETa using four methods: two methods based on the surface energy balance (Two-Source Energy Balance (TSEB) and the Surface Aerodynamic Temperature (SAT)), one method based on the TIR imagery (Crop Water Stress Index (CWSI)), and one method based on the spectral reflectance imagery (reflectance-based crop coefficients (kcbrf)) and reference ET. Remote sensing derived ETa estimates were compared to ETa derived using neutron probe soil moisture sensors. The third study utilized the RS derived ETa and the Hybrid Soil Water Balance method to estimate the SWD for comparison with the neutron probe derived SWD. Results showed that the Tempest RS data was in good agreement with the ground-based data as demonstrate by the low RMSE of the raw data, ETa and SWD calculations (TIR = 5.68 oC, NIR = 5.26 % reflectance, red = 3.51 % reflectance, green = 7.31 % reflectance, TSEB ETa = 0.89 mm/d, Hybrid SWD = 16.19 mm/m). The accuracy of the results of the Tempest UAS RS platform suggests that UAS RS platforms have the potential to increase the accuracy of ETa and SWD estimation for use in the application of a PA irrigation management system.
Open Access
Machine learning techniques for energy optimization in mobile embedded systems
(Colorado State University. Libraries, 2012) Donohoo, Brad Kyoshi, author; Pasricha, Sudeep, advisor; Anderson, Charles, committee member; Jayasumana, Anura P., committee member
Mobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for communication, business, and social interactions. While performance, capabilities, and design are all important considerations when purchasing a mobile device, a long battery lifetime is one of the most desirable attributes. Battery technology and capacity has improved over the years, but it still cannot keep pace with the power consumption demands of today's mobile devices. This key limiter has led to a strong research emphasis on extending battery lifetime by minimizing energy consumption, primarily using software optimizations. This thesis presents two strategies that attempt to optimize mobile device energy consumption with negligible impact on user perception and quality of service (QoS). The first strategy proposes an application and user interaction aware middleware framework that takes advantage of user idle time between interaction events of the foreground application to optimize CPU and screen backlight energy consumption. The framework dynamically classifies mobile device applications based on their received interaction patterns, then invokes a number of different power management algorithms to adjust processor frequency and screen backlight levels accordingly. The second strategy proposes the usage of machine learning techniques to learn a user's mobile device usage pattern pertaining to spatiotemporal and device contexts, and then predict energy-optimal data and location interface configurations. By learning where and when a mobile device user uses certain power-hungry interfaces (3G, WiFi, and GPS), the techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression, and k-nearest neighbor, are able to dynamically turn off unnecessary interfaces at runtime in order to save energy.
Open Access
Microphysical retrieval and profile classification for GPM dual-frequency precipitation radar and ground validation
(Colorado State University. Libraries, 2013) Le, Minda, author; Chandrasekar, V. Chandra, advisor; Jayasumana, Anura P., committee member; Mielke, Paul W., committee member; Notaros, Branislav, committee member
The Global Precipitation Measurement (GPM) mission, planned as the next satellite mission following the Tropical Rainfall Measurement Mission (TRMM), is jointly sponsored by the National Aeronautic and Space Administration (NASA) of USA and the Japanese Aerospace Exploration Agency (JAXA) with additional partners, the Centre National d'Études Spatiales (CNES), the Indian Space Research Organization (ISRO), the National Oceanic and Atmospheric Administration (NOAA), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and others. The core satellite of GPM mission will be equipped with a dual-frequency precipitation radar (DPR) operating at Ku- (13.6 GHz) and Ka- (35.5 GHz) band with the capability to cover ±65° latitude of the earth. One primary goal of the DPR is to improve accuracy in estimation of drop size distribution (DSD) parameters of precipitation particles. The estimation of the DSD parameters helps achieve more accurate estimation of precipitation rates. The DSD is also centrally important in the determination of the electromagnetic scattering properties of precipitation media. The combination of data from the two frequency channels, in principle, can provide more accurate estimates of DSD parameters than the TRMM Precipitation radar (TRMM PR) with Ku- band channel only. In this research, a methodology is developed to retrieve DSD parameters for GPM-DPR. Profile classification is a critical module in the microphysical retrieval system for GPM-DPR. The nature of microphysical models and equations for use in the DSD retrieval algorithm are determined by the precipitation type of each profile and the phase state of the hydrometeors. In the GPM era, the Ka- band channel enables the detection of light rain or snowfall in the mid- and high- latitudes compared to the TRMM PR (Ku- band only). GPM-DPR offers dual-frequency observations (measured reflectivity at Ku- band:Ζm (Ku) and measured reflectivity at Ka- band:Ζm (Ku)) along each vertical profile, which provide additional information for investigating the microphysical properties using the difference in measured radar reflectivities at the two frequencies, a quantity often called the measured dual-frequency ratio (DFRm) can be defined (DFRm=Ζm (Ku) — Ζm (Ka)). Both non-Rayleigh scattering effects and attenuation difference control the shape of the DFRm profile. Its pattern is determined by the forward and backscattering properties of the mixed phase and rain media and the backscattering properties of ice. Therefore, DFRm could provide better performance in precipitation type classification and hydrometeor profile characterization than TRMM PR. In this research, two methods, precipitation type classification (PCM) and hydrometeor profile characterization (HPC), are developed to perform profile classification for GPM-DPR using the DFRm profile and its range variability. The methods have been implemented into the GPM-DPR day one algorithm. Ground validation is an integral part of all satellite precipitation missions. Similar to TRMM, the GPM validation falls into the general class of validation and integration of information from space-borne observing platforms with a variety of ground-based measurements. Dual polarization ground radar is a powerful tool that can be used to address a number of important questions that arise in the validation process, especially those associated with precipitation microphysics and algorithm development. Extensive research has also been done regarding accurate retrievals of rain DSDs as well as attenuation correction for dual-polarization ground radar operating at S-, C- and X- band by using polarimetric measurements. However, polarimetric ground radar operating at a single frequency channel has limitation on DSD retrieval beyond rain region. A dual-frequency and dual-polarization Doppler radar (D3R) operating at the same frequency channels as GPM-DPR has been built. In this research, an algorithm is developed to retrieve DSD parameter for this D3R radar, which will serve as the GPM-DPR ground validation instrument.
Open Access
Modeling fuzzy criteria preference to evaluate tradespace of system alternatives
(Colorado State University. Libraries, 2018) White, Wesley Gunnar, author; Chandrasekar, V., advisor; Bradley, Thomas, committee member; Chavez, Jose, committee member; Jayasumana, Anura P., committee member
This dissertation explores techniques for evaluating system concepts using the point of diminishing marginal utility to determine a best value alternative with an optimal combination of risk, performance, reliability, and life cycle cost. The purpose of this research is to address the uncertainty of customer requirements and assess crisp and fuzzy design parameters to determine a best value system. At the time of this research, most commonly used decision analysis (DA) techniques use minimum and maximum values under a specific criterion to evaluate each alternative. These DA methods do not restrict scoring beyond the point of diminished marginal utility resulting in superfluous capabilities and overvalued system alternatives. Using these models, an alternative being evaluated could receive significantly higher scores when reported capabilities are greater than ideal customer requirements. This problem is pronounced whenever weights are applied to criteria where excessive capabilities are recorded. The techniques explored in this dissertation utilize fuzzy membership functions to restrict scoring for alternatives that provide excess capabilities beyond ideal customer requirements. This research investigates and presents DA techniques for evaluating system alternatives that determine an ideal compromise between risk, performance criteria, reliability and life cycle costs.
Open Access
Nowcasting for a high-resolution weather radar network
(Colorado State University. Libraries, 2010) Ruzanski, Evan, author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Mielke, Paul W., committee member; Notaros, Branislav M., committee member
Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories and is of great practical importance. Nowcasting using weather radar reflectivity data has been shown to be particularly useful. The Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network provides high-resolution reflectivity data amenable to producing valuable nowcasts. The high-resolution nature of CASA data requires the use of an efficient nowcasting approach, which necessitated the development of the Dynamic Adaptive Radar Tracking of Storms (DARTS) and sinc kernel-based advection nowcasting methodology. This methodology was implemented operationally in the CASA Distributed Collaborative Adaptive Sensing (DCAS) system in a robust and efficient manner necessitated by the high-resolution nature of CASA data and distributed nature of the environment in which the nowcasting system operates. Nowcasts up to 10 min to support emergency manager decision-making and 1-5 min to steer the CASA radar nodes to better observe the advecting storm patterns for forecasters and researchers are currently provided by this system. Results of nowcasting performance during the 2009 CASA IP experiment are presented. Additionally, currently state-of-the-art scale-based filtering methods were adapted and evaluated for use in the CASA DCAS to provide a scale-based analysis of nowcasting. DARTS was also incorporated in the Weather Support to Deicing Decision Making system to provide more accurate and efficient snow water equivalent nowcasts for aircraft deicing decision support relative to the radar-based nowcasting method currently used in the operational system. Results of an evaluation using data collected from 2007-2008 by the Weather Service Radar-1988 Doppler (WSR-88D) located near Denver, Colorado, and the National Center for Atmospheric Research Marshall Test Site near Boulder, Colorado, are presented. DARTS was also used to study the short-term predictability of precipitation patterns depicted by high-resolution reflectivity data observed at microalpha (0.2-2 km) to mesobeta (20-200 km) scales by the CASA radar network. Additionally, DARTS was used to investigate the performance of nowcasting rainfall fields derived from specific differential phase estimates, which have been shown to provide more accurate and robust rainfall estimates compared to those made from radar reflectivity data.
Open Access
Precipitation observations from high frequency spaceborne polarimetric synthetic aperture radar and ground-based radar: theory and model validation
(Colorado State University. Libraries, 2010) Fritz, Jason P., author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Notaros, Branislav M., committee member; Mielke, Paul W., committee member
Global weather monitoring is a very useful tool to better understand the Earth's hydrological cycle and provide critical information for emergency and warning systems in severe cases. Developed countries have installed numerous ground-based radars for this purpose, but they obviously are not global in extent. To address this issue, the Tropical Rainfall Measurement Mission (TRMM) was launched in 1997 and has been quite successful. The follow-on Global Precipitation Measurement (GPM) mission will replace TRMM once it is launched. However, a single precipitation radar satellite is still limited, so it would be beneficial if additional existing satellite platforms can be used for meteorological purposes. Within the past few years, several X-band Synthetic Aperture Radar (SAR) satellites have been launched and more are planned. While the primary SAR application is surface monitoring, and they are heralded as "all weather'' systems, strong precipitation induces propagation and backscatter effects in the data. Thus, there exists a potential for weather monitoring using this technology. The process of extracting meteorological parameters from radar measurements is essentially an inversion problem that has been extensively studied for radars designed to estimate these parameters. Before attempting to solve the inverse problem for SAR data, however, the forward problem must be addressed to gain knowledge on exactly how precipitation impacts SAR imagery. This is accomplished by simulating storms in SAR data starting from real measurements of a storm by ground-based polarimetric radar. In addition, real storm observations by current SAR platforms are also quantitatively analyzed by comparison to theoretical results using simultaneous acquisitions by ground radars even in single polarization. For storm simulation, a novel approach is presented here using neural networks to accommodate the oscillations present when the particle scattering requires the Mie solution, i.e., particle diameter is close to the radar wavelength. The process of transforming the real ground measurements to spaceborne SAR is also described, and results are presented in detail. These results are then compared to real observations of storms acquired by the German TerraSAR-X satellite and by one of the Italian COSMO-SkyMed satellites both operating in co-polar mode (i.e., HH and VV). In the TerraSAR-X case, two horizontal polarization ground radars provided simultaneous observations, from which theoretical attenuation is derived assuming all rain hydrometeors. A C-band fully polarimetric ground radar simultaneously observed the storm captured by the COSMO-SkyMed SAR, providing a case to begin validating the simulation model. While previous research has identified the backscatter and attenuation effects of precipitation on X-band SAR imagery, and some have noted an impact on polarimetric observations, the research presented here is the first to quantify it in a holistic sense and demonstrate it using a detailed model of actual storms observed by ground radars. In addition to volumetric effects from precipitation, the land backscatter is altered when water is on or near the surface. This is explored using TRMM, Canada's RADARSAT-1 C-band SAR and Level 3 NEXRAD ground radar data. A weak correlation is determined, and further investigation is warranted. Options for future research are then proposed.
Open Access
Quantitative analyses of software vulnerabilities
(Colorado State University. Libraries, 2011) Joh, HyunChul, author; Malaiya, Yashwant K., advisor; Ray, Indrajit, committee member; Ray, Indrakshi, committee member; Jayasumana, Anura P., committee member
There have been numerous studies addressing computer security and software vulnerability management. Most of the time, they have taken a qualitative perspective. In many other disciplines, quantitative analyses have been indispensable for performance assessment, metric measurement, functional evaluation, or statistical modeling. Quantitative approaches can also help to improve software risk management by providing guidelines obtained by using actual data-driven analyses for optimal allocations of resources for security testing, scheduling, and development of security patches. Quantitative methods allow objective and more accurate estimates of future trends than qualitative manners only because a quantitative approach uses real datasets with statistical methods which have proven to be a very powerful prediction approach in several research fields. A quantitative methodology makes it possible for end-users to assess the risks posed by vulnerabilities in software systems, and potential breaches without getting burdened by details of every individual vulnerability. At the moment, quantitative risk analysis in information security systems is still in its infancy stage. However, recently, researchers have started to explore various software vulnerability related attributes quantitatively as the vulnerability datasets have now become large enough for statistical analyses. In this dissertation, quantitative analysis is presented dealing with i) modeling vulnerability discovery processes in major Web servers and browsers, ii) relationship between the performance of S-shaped vulnerability discovery models and the skew in vulnerability datasets examined, iii) linear vulnerability discovery trends in multi-version software systems, iv) periodic behavior in weekly exploitation and patching of vulnerabilities as well as long term vulnerability discovery process, and v) software security risk evaluation with respect to the vulnerability lifecycle and CVSS. Results show good superior vulnerability discovery model fittings and reasonable prediction capabilities for both time-based and effort-based models for datasets from Web servers and browsers. Results also show that AML and Gamma distribution based models perform better than other S-shaped models with skewed left and right datasets respectively. We find that code sharing among the successive versions cause a linear discovery pattern. We establish that there are indeed long and short term periodic patterns in software vulnerability related activities which have been only vaguely recognized by the security researchers. Lastly, a framework for software security risk assessment is proposed which can allow a comparison of software systems in terms of the risk and potential approaches for optimization of remediation.
Open Access
Radar multi-sensor (RAMS) quantitative precipitation estimation (QPE)
(Colorado State University. Libraries, 2015) Willie, Delbert Darrell, author; Chandrasekar, V., advisor; Mielke, Paul, committee member; Jayasumana, Anura P., committee member; Notaros, Branislav, committee member
Quantitative precipitation estimation (QPE) continues to be one of the principal objectives for weather researchers and forecasters. The ability of radar to measure over broad spatial areas in short temporal successions encourages its application in the pursuit of accurate rainfall estimation, where radar reflectivity-rainfall (Z-R) relations have been traditionally used to derive quantitative precipitation estimation. The purpose of this research is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies used to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that occurs over a complex terrain environment, such that local radar coverage is compromised by blockage. This area of interest is concentrated around the Pigeon River Basin near Asheville, NC. In this mountainous locale, beam blockage, beam overshooting, orographic enhancement, and the unique climactic conditions complicate the development of reliable QPE's from radar. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP radars as well as the NASA NPOL research radar.
Open Access
Resource allocation for heterogeneous computing systems: performance criteria, robustness measures, optimization heuristics, and properties
(Colorado State University. Libraries, 2010) Briceno Guerrero, Luis Diego, author; Siegel, Howard Jay, advisor; Maciejewski, Anthony A., advisor; Böhm, Anton Pedro Willem, 1948-, committee member; Jayasumana, Anura P., committee member; Smith, James T., committee member
Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to maximize the combined performance and/or cost effectiveness of the system. The application environments studied in this research are: a weather data processing system, a massive multi-player on-line gaming system, and a distributed satellite image processing system. Each one of these application environments was simulated on different computation platforms. Contributions for each environment: (1) mathematical model of environment, (2) defined a performance criterion, (3) defined robustness metric, (4) designed resource allocation heuristics based on performance and robustness measures, and (5) conducted simulation studies for evaluating and comparing heuristic techniques. We consider an iterative approach that decreases the finishing time of machines by repeatedly executing a resource allocation heuristic to minimize the make span of the considered machines and tasks. For each successive iteration, the make span machine of the previous iteration and the tasks assigned to it are removed from the set of considered machines and tasks. The contribution include identifying which characteristics heuristics need to generate improvement with the iterative approach, showing that the effectiveness of the iterative approach is heuristic dependent, and deriving a theorem to identify which heuristics cannot attain improvements.
Open Access
Salient features of the D3R radar enhancements
(Colorado State University. Libraries, 2018) Joshil, Shashank S., author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; James, Susan P., committee member
D3R radar was developed to serve as a ground validation tool for the dual precipitation radar in the core satellite for the Global Precipitation Measurement mission. In order to have more flexibility in operations and improve the features of D3R, the radar was upgraded. Simulations were carried out so that the best design could be determined and implemented on the upgraded D3R (D3R 2.0). The IF subsystems and the digital receiver module which consist of arbitrary waveform generators and the digital receivers of the Ku and Ka bands were changed to support the new features. To enhance signal processing features and make the system compatible with the new design, the D3R software was also upgraded. In this thesis, the design, implementation and tests carried out during the upgrade work for the D3R are presented. The range-velocity ambiguity techniques which work well with low frequency radars pose a challenge in the case of higher frequency radars such as in D3R due to limited Doppler spectrum available. The existing method in D3R to mitigate the range ambiguity problem using random phase codes and staggered PRT is analyzed and the performance of the method is demonstrated for D3R data. The performance of random phase codes and systematic phase codes for range ambiguity mitigation and future changes in D3R 2.0 range ambiguity mitigation technique are discussed. A velocity ambiguity mitigation technique using the dual-frequency information is developed for D3R 2.0; the implementation is explained along with its performance on radar observations. The D3R 2.0 went through initial calibration and testing before being deployed to the ICE-POP field campaign in South Korea. The first results after the upgrade are presented.
Open Access
System engineering for radio frequency communication consolidation with parabolic antenna stacking
(Colorado State University. Libraries, 2020) Sugama, Clive, author; Chandrasekar, V., advisor; Jayasumana, Anura P., committee member; Bradley, Thomas H., committee member; Chavez, Jose L., committee member
This dissertation implements System Engineering (SE) practices while utilizing Model Based System Engineering (MBSE) methods through software applications for the design and development of a parabolic stacked antenna. Parabolic antenna stacking provides communication system consolidation by having multiple antennas on a single pedestal which reduces the number of U.S. Navy shipboard topside antennas. The dissertation begins with defining early phase system lifecycle processes and the correlation of these early processes to activities performed when the system is being developed. Performing SE practices with the assistance of MBSE, Agile, Lean methodologies and SE / engineering software applications reduces the likelihood of system failure, rework, schedule delays, and cost overruns. Using this approach, antenna system consolidation via parabolic antenna stacking is investigated while applying SE principles and utilizing SE software applications. SE / engineering software such as IBM Rational Software, Innoslate, Antenna Magus, ExtendSim, and CST Microwave Studio were used to perform SE activities denoted in ISO, IEC, and IEEE standards. A method to achieve multi-band capabilities on a single antenna pedestal in order to reduce the amount of U.S. Navy topside antennas is researched. An innovative approach of parabolic antenna stacking is presented to reduce the amount of antennas that take up physical space on shipboard platforms. Process simulation is presented to provide an approach to improve predicting delay times for operational availability measures and to identify process improvements through lean methodologies. Finally, this work concludes with a summary and suggestions for future work.