Defense Notices

Science advances not only because of new discoveries, but also due to revolutionary ideas drawn from accumulated data. The quality of studies in paleontology, in particular, depends on accessibility of fossil data. This research builds an intelligent system based on brachiopod fossil images and their descriptions published in Treatise on Invertebrate Paleontology. The project is still developing and some significant achievements will be discussed here. 
This thesis has two major parts. The first part describes the digitization, organization and integration of information extracted from the Treatise. The Treatise is in PDF format and it is non-trivial to convert large volumes into a structured, easily accessible digital library. Three important topics will be discussed: (1) how to extract data entries from the text, and save them in a structured manner; (2) how to crop individual specimen images from figures automatically, and associate each image with text entries; (3) how to build a search engine to perform both keyword search and natural language search. The search engine already has a web interface and many useful tasks can be done with ease. 
Verbal descriptions are second-hand information of fossil images and thus have limitations. The second part of the thesis develops an algorithm to compare fossil images directly, without referring to textual information. After similarities between fossil images are calculated, we can use the results in image search, fossil classification, and so on. The algorithm is based on deformable templates, and utilizes expectation propagation to find the optimal deformation. Specifically, I superimpose a ``warp'' on each image. Each node of the warp encapsulates a vector of local texture features, and comparing two images involves two steps: (1) deform the warp to the optimal configuration, so the energy function is minimized; and (2) based on the optimal configuration, compute the distance of two images. Experiment results confirmed that the method is reasonable and robust.

Distributed computations are a very important aspect of modern computing, especially given the rise of distributed systems used for applications such as web search, massively multiplayer online games, financial trading, and cloud computing. When running these computations across several physical machines it becomes much more difficult to determine exactly what is occurring on each system at a specific point in time. This is due to each server having an independent clock, thus making event timestamps inherently inaccurate across machine boundaries. Another difficulty with evaluating distributed experiments is the coordination required to launch daemons, executables, and logging across all machines, followed by the necessary gathering of all related output data. The goal of this research is to overcome these obstacles and construct a single, global timeline of events from all servers. 
We employ high-resolution clock synchronization to bring all servers within microseconds as measured by a modified version of the Network Time Protocol implementation. Kernel and user-level events with wall-clock timestamps are then logged during basic network socket experiments. These data are then collected from each server and merged into a single dataset, sorted by timestamp, and plotted on a timeline. The entire experiment, from setup to teardown to data collection, is coordinated from a single server. The timeline visualizations provide a narrative of not only how packets flow between servers, but also how kernel interrupt handlers and other events shape an experiment's execution.

Billions of dollars are lost every year to successful cyber attacks that are fundamentally enabled by software vulnerabilities. Modern cyber attacks increasingly threaten individuals, organizations, and governments, causing service disruption, inconvenience,and costly incident response. Given that such attacks are primarily enabled by software vulnerabilities, this work examines whether or not existing change metrics, along with architectural modularity and maintainability metrics can be used to predict modules and files that might be analyzed or tested further to excise vulnerabilities prior to release. 
The problem addressed by this research is the residual vulnerability problem, or vulnerabilities that evade detection and persist in released software. Many modern software projects are over a million lines of code, composed of reused components of varying maturity. The sheer size of modern software, along with the reuse of existing open source modules, complicates the questions of where to look, and in what order to look, for residual vulnerabilities. Prediction models based on various code and change metrics (e.g.,churn) have shown promise as indicators of vulnerabilities at the file level. 
This work investigates whether change metrics, along with architectural metrics quantifying modularity and maintainability can be used to identify attack-prone modules. In addition to identifying or predicting attack prone files, this work also examines prioritization and ranking said predictions.

Large-scale spatial observations of the global sea ice thickness and distribution rely on multiple satellite-based altimeters. Laser altimeters, such as the GLAS instrument aboard ICESat-1 and ATLAS instrument aboard ICESat-2, measure freeboard which is the snow and ice thickness above mean sea level. Deriving sea-ice thickness from these data requires estimating the snow depth on the sea ice. Current means of estimating the snow depth are climatological history, daily precipitation products, and/or data from passive microwave sensors, such as AMSR-E. Radar altimeters, such as SIRAL aboard CryoSat-2, do not have sufficient vertical range resolution to resolve both the air-snow and snow-ice interfaces over sea-ice. Additionally, there is significant ambiguity on the location of the peak return due to penetration into the snow cover. Regardless of the sensor, any error in snow-depth estimation amplifies sea-ice thickness errors due to the assumption of hydrostatic equilibrium used in deriving sea-ice thickness. There clearly is a need for an airborne sensor to provide spatially large-scale measurements of the snow cover in both polar regions to improve the accuracy of sea-ice thickness estimates and provide validation for the satellite-based sensors. 
The Snow Radar was developed at the Center for Remote Sensing of Ice Sheets and deployed as part of NASA Operation IceBridge since 2009 to directly measure snow thickness over sea ice. The radar is an ultra-wideband, frequency-modulated, continuous-wave radar now working over the frequency range of 2 GHz to 8 GHz, resulting in a vertical range resolution of approximately 4 cm after post-processing. The radar has been shown to be capable of detecting snow depth over sea ice from 10 cm to more than 2 meters and results from the radar compare well to multiple in-situ measurements and passive-microwave measurements. 
The focus of the proposed research is estimation of useful geophysical properties of snow-covered sea ice beyond snow depth and subsequent refinement and validation of the snow depth extraction. Geophysical properties of interest are: snow density and wetness, air-snow and snow-ice surface roughness, and sea ice temperature and salinity. Through forward modeling of the radar backscatter response and the corresponding inversion, large-scale estimation of these properties may be possible.

Android is one of the most popular operating systems (OS) for mobile touchscreen devices, including smart-phones and tablet computers. Dalvik is a process virtual machine (VM) that provides an abstraction layer over the Android OS, and runs the Java-based Android applications. The first goal of this project is to construct a development environment for conveniently investigating the properties of Android's Dalvik VM on contemporary x86 and ARM architectures. The normal development environment restricts the Dalvik VM to run on top of Android, and requires an updated Android image to be built and installed on the target device after any change to the Dalvik code. This update-build-install process unnecessarily slows down any Dalvik VM exploration. We have now discovered a (undisclosed) configuration that enables us to study the Dalvik VM as a stand-alone application on top of the Linux OS. 
The second goal of this project is to understand the translation/compilation sub-system in the Dalvik VM, experiment with various modifications to determine the best translation parameters, and compare the Dalvik VM's just-in-time (JIT) compilation characteristics (such as quality of code generated and compilation time) on the x86 and ARM systems with a state-of-the-art Java VM. As expected, we find that JIT compilation is able to significantly improve application performance over basic interpretation. Comparing Dalvik's generated code quality with the Java HotSpot VM, we observe that Dalvik's ARM target is a much more mature compared to Dalvik-x86. However, Dalvik's simple trace-based compilation generates code quality that is much worse as compared to HotSpot. Finally, our experiments also reveal the most effective JIT compilation parameters for the Dalvik VM, and its effect of benchmark performance and memory usage.

I investigate an optical Nyquist-WDM Bit Error Rate (BER) detection system. A transmitter and receiver system is simulated, using Matlab and Simulink, to form a working algorithm and to study the effects of the different processes of the data chain. The inherent lack of phase information in the N-WDM scheme presents unique challenges and requires a precise phase recovery system to accurately decode a message. Furthermore, resource constraints are applied by a cost-effective Field Programmable Gate Array (FPGA). To compensate for the speed, gate, and memory constraints of a budget FPGA, several techniques are employed to design the best possible receiver. I study the resource intensive operations and vary their resource utilization to discover the effect on the BER. To conclude, a full VHDL design is delineated, including peripheral initialization, input data sorting and storage, timing synchronization, state machine and control signal implementation, N-WDM demodulation, phase recovery, QAM decoding, and BER calculation.

With increasing civilian use of unmanned aerial vehicles (UAVs), flight safety of these unmanned devices in populated area has become one of the most concerned issues among the operators and users. To reduce the rate of colliding, anti-collision systems based on airborne radar system and enhanced autopilot programs are developed. However, for most civilian UAVs being made of non-metal materials which has considerably low radar cross-section (RCS), those UAVs are really hard or even impossible to be detected by radars. This project aims to design a light-weight UAV mounted RCS enhancement device that can increase the visibility of the UAV for airborne radars which work in the frequency band near 
1.445 GHz. In this project, a 40% YAK54 radio controlled UAV is used as the subject UAV. The report also concentrates on the design of passive Van Atta Array reflector approach.

This thesis presents the implementation and characterization of a VHF high power amplifier developed for the Multi-channel Coherent Radar Depth Sounder/Imager (MCoRDS/I) system. MCoRDS/I is used to collect data on the thickness and basal topography of polar ice sheets, ice sheet margins, and fast-flowing glaciers from airborne platforms. Previous surveys have indicated that higher transmit power is needed to improve the performance of the radar, particularly when flying over challenging areas. 
The VHF high power amplifier system presented here consists of a 50-W driver amplifier and a 1-kW output stage operating in Class C. Its performance was characterized and optimized to obtain the best tradeoff between linearity, output power, efficiency, and conducted and radiated noise. A waveform pre-distortion technique to correct for gain variations (dependent on input power and operating frequency) was demonstrated using digital techniques. 
The amplifier system is a modular unit that can be expanded to handle a larger number of transmit channels as needed for future applications. The system can support sequential transmit/receive operations on a single antenna by using a high-power circulator and a duplexer circuit composed of two 90° hybrid couplers and anti-parallel diodes. The duplexer is advantageous over switches based on PIN-diodes due to the moderately high power handling capability and fast switching time. The system presented here is also smaller and lighter than previous implementations with comparable output power levels.

The scope of this research is a blind mobile wireless channel state recognition (CSR) algorithm that detects channel time and frequency dispersion. Hidden Markov Models (HMM) are utilized to represent the statistical relationship between these hidden channel dispersive state process and an observed received waveform process. The HMMs provide sufficient sensitivity to detect the hidden channel dispersive state process. First-order and second-order statistical features are assumed to be sufficient to discriminate channel state from the receive waveform observations. State hard decisions provide sufficient information, and can be combined, to increase the reliability of a time block channel state estimate. To investigate the feasibility of the proposed CSR algorithm, this research effort has architected, designed, and verified a blind statistical feature recognition process capable of detecting whether a mobile wireless channel is coherent, single time, single frequency, or dual dispersive. Channel state waveforms are utilized to compute the transition and output probability parameters for a set of feature recognition HMMs. Time and frequency statistical features are computed from consecutive sample blocks and input into the set of trained HMMs which compute a state sequence conditional probability for each feature. The conditional probabilities identify how well the input waveform statistically agrees with the previous training waveforms. Hard decisions were produced from each feature state probability estimate and combined to produce a single output channel dispersive state estimate for each input time block. To verify the CSR algorithm performance, combinations of state sequence blocks were input to the process and state recognition accuracy was characterized. Initial results suggest that CSR based on blind waveform statistical feature recognition is feasible.

In conventional database access control models, access control policies are explicitly specified for each role against each data object manually. Nowadays, in large-scale content-centric data sharing, 
conventional approaches could be impractical due to exponential explosion and the sensitivity of data objects. In this proposal, we first introduce Content-Based Access Control (CBAC), an innovative access control model for content-centric information sharing. As a complement to conventional access control models, the CBAC model makes access control decisions based on the content similarity between user credentials and data content automatically. In CBAC, each user is allowed by a meta-rule to access “a subset” of the designated data objects of the whole database, while the boundary of the subset is dynamically determined by the textual content of data objects. We then present an enforcement mechanism for CBAC that exploits Oracle’s Virtual Private Database (VPD). To further improve the performance of the proposed approach, we introduce a content-based blocking mechanism to improve the efficiency of CBAC enforcement to further 
reveal a more relavant part of the data objects comparing with the user credentials and data content. We also utilized a tagging mechanism for more accurate textual content matching for short text snippets (e.g. short VarChar attributes) to extract topics other than pure word occurences to 
represent the content of data. Experimental results show that CBAC makes reasonable access control decisions with a small overhead.