Defense Notices

The objective of this project is to leverage the open source Angular JS, Node JS, and Ionic Framework along with Cordova to develop “A Hybrid Mobile Application” for students and “A Responsive Web Application” for professor to conduct classroom centered “Dynamic Tests”. Dynamic Tests are the test taking environments where questions can be posted to students in the form of quizzes during a classroom setup. Guided by the specifications set by the professor, students answer and submit the quiz from their mobile devices. The results are generated instantaneously after the completion of the test session and can be viewed by the professor. The web application performs statistical analysis of the responses by considering the factors that the professor had set to measure the students’ performance. This advanced methodology of test taking is highly beneficial as it gives a clear picture to the professor the level of understanding of all the students in any chosen topic immediately after the test. It helps to improvise the teaching methods. This is also very advantageous to students since it helps them to come out of their hesitation to clarify their doubts as their marks become the measure of their understanding which is directly uncovered before the professor. This application overall improves the classroom experience to help students gain higher standards.

Most of the present classification algorithms require the input data with discretized attributes. If the input data contains numerical attributes, we need to convert such attributes into discrete values (intervals) before performing classification. Discretization algorithms for real value attributes are very important for applications such as artificial intelligence and machine learning. In this project we discuss an implementation of the ChiMerge algorithm for discretization of numerical attributes, a robust algorithm, which uses X2 statistic to determine interval similarity as it constructs intervals in a bottom-up merging process. ChiMerge provides a reliable summarization of numerical attributes and determines the number of intervals. 

The primary goal of data visualization is to communicate information clearly and efficiently via statistical graphs, plots and information graphics. It makes complex data more accessible, understandable and usable. The goal of this project is to build a graphical user interface called RULEVIZ to visualize the rules, induced by LERS (Learning from Examples using Rough Set Theory) data mining system in the form of directed graphs. LERS is a technique used to induce a set of rules from examples given in the form of a decision table. Such rules are used to classify unseen data. The RULEVIZ is developed as a web application where the user uploads the rule set and the data set from which the rule set is visualized in the graphical format and is rendered on the web browser. Every rule is taken sequentially, and all the conditions of that rule are visualized as nodes connected by undirected edges. The last condition is connected to the concept by a directed edge. The RULEVIZ offers custom filtering options for the user to filter the rules based on factors like the number of conditions and conditional probability or strength. The RULEVIZ also has interactive capabilities to filter out rule sets and manipulate the generated graph for a better look and feel.

Remote sensing with radar systems on airborne platforms is key for wide-area data collection to estimate the impact of ice and snow masses on rising sea levels. NASA P-3B and DC-8, as well as other platforms, successfully flew with multiple versions of the Snow Radar developed at CReSIS. Compared to these manned missions, the Global Hawk UAV can support flights with long endurance, complex flight paths and flexible altitude operation up to 70,000 ft. This thesis documents the process of adapting the 2-18 GHz Snow radar to meet the requirements for operation on manned and unmanned platforms from 700 ft to 70,000 ft. The primary focus of this work is the development of an improved microwave chirp generator implemented with frequency multipliers. The x16 frequency multiplier is composed of a series of x2 frequency multiplication stages, overcoming some of the limitations encountered in previous designs. At each stage, undesired harmonics are kept out of the band and filtered. The miniaturized design presented here reduces reflections in the chain, overall size, and weight as compared to the earlier large and heavy connectorized chain. Each stage is implemented by a drop-in type modular design operating at microwaves and millimeter waves; and realized with commercial surface-mount ICs, wire-bondable chips, and custom filters. DC circuits for power regulation and sequencing are developed as well. Another focus of this thesis is the development of band-pass filters using different distributed element filter technologies. Multiple edge-coupled band pass filters are fabricated on alumina substrate based on the design and optimization in computer-aided design (CAD) tools. Interdigital cavity filter models developed in-house are validated by full-wave EM simulation and measurements. Overall, the measured results of the modular frequency multiplier and filters match with the expected responses from original design and co-simulation outputs. The design files, test setups, and simulation models are generalized to use with any similar or new designs in the future. 

Object detection and tracking is an important research topic in the computer vision field with numerous practical applications. Although great progress has been made, both in object detection and tracking over the last decade, it is still a big challenge in real-time applications like automated navigation of an unmanned aerial vehicle and collision avoidance with a forward looking camera. An automated and robust object tracking approach is proposed by integrating a kernelized correlation filter framework with an adaptive object detection technique based on minimum barrier distance transform. The proposed tracker is automatically initialized with salient object detection and the detected object is localized in the image frame with a rectangular bounding box. An adaptive object redetection strategy is proposed to refine the location and boundary of the object, when the tracking correlation response drops below a certain threshold. In addition, reliable pre-processing and post-processing methods are applied on the image frames to accurately localize the object. Extensive quantitative and qualitative experimentation on challenging datasets have been performed to verify the proposed approach. Furthermore, the proposed approach is comprehensively examined with six other recent state-of-the-art¬ trackers, demonstrating that the proposed approach greatly outperforms these trackers, both in terms of tracking speed and accuracy. 

With the motivation to develop a resilient and survivable networking system that can cope with challenges posed by the rapid growth in networking technologies and use paradigms and the impairments of TCP and UDP, we propose a general-purpose, configurable and adaptable multipath-capable transport-layer protocol called ResTP. By supporting cross- layering, ResTP allows service tuning by the upper application layer while promptly reacting to the underlying network dynamics by using the feedback from the lower layer. Our composable ResTP not only has the flexibility to provide services to different application classes operating across various network environments, its selection of mechanisms also increases the resilience level of the system in which it is deployed since the design of ResTP is guided by a set of principles derived from the ResiliNets framework. Moreover, the implementation of ResTP employs modular programming to minimize the complexity while increasing its extensibility. Hence, the addition of any new algorithms to ResTP would require only some small changes to the existing code. Last but not least, many ResTP components, including its header, are optimized to reduce unnecessary overhead. In this proposal, we introduce ResTP’s key functionalities, present some preliminary simulation results of ResTP in comparison with TCP and UDP in ns-3, and discuss our plan towards the completion and analysis of the protocol. The results show that ResTP is a promising transport-layer protocol for Future Internet (FI) resilience. 

Remote Procedure Calls (RPCs) are an integral part of the internet of things. After the introduction of RPCs, there have been a number of optimizations to amortize the network overhead, including the addition of asynchronous calls and batching requests together. In Haskell, we have discovered a principled way to compose procedure calls together using the Remote Monad mechanism. A remote monad has primitive operations that evaluate outside the local runtime system and is a generalization of RPCs. Remote Monads use natural transformations to make modular and composable network stacks which can automatically bundle requests into packets in a principled way, making them easy to adapt for a number of applications. We have created a framework which has been successfully used to implement JSON-RPC, a graphical browser-based library, an efficient bytestring implementation, and database queries. The result of this investigation is that the cost of implementing bundling for remote monads can be amortized almost for free, if given a user-supplied packet transportation mechanism.

Covert systems are designed to operate at a low probability of detection in order to provide system protection at the physical layer level. The classical approach to covert communications aims at hiding the covert signal in noise by lowering the power spectral density of the signal to a level that makes it indistinguishable from that of the noise. However, the increasing demand for modern covert systems that can provide better protection against intercept receivers (IRs) and provides higher data rates has shifted the focus to the design of Ad-Hoc covert networks (ACNs) that can hide their transmission in the RF spectrum of primary networks (PNs). The early work on exploiting the RF band of other wireless systems has been promising; however, the difficulties in modeling such environments, and analyzing the impact on/from the primary network have limited the work on this crucial subject. In this work, we provide the first comprehensive analyses of a covert network that exploits the RF band of an OFDM-based primary network to achieve covertness. A spectrum access algorithm is presented which would allow the ACN to transmit in the RF spectrum of the PN with minimum interference. Next, we use stochastic geometry to model both the OFDM-based PN as well as the ACN. Using stochastic geometry would also allow us to provide a comprehensive analysis for two metrics, namely an aggregate metric and a ratio metric. These two metrics quantify the covertness and performance of the covert network from the perspective of the IR and the ACN, respectively. The two metrics are used to determine the detectability limits of an ACN by an IR. The two metrics along with the proposed spectrum access algorithm will be used to provide a comprehensive discussion the design the ACN for a target covertness level, and analyze the effect of the PN parameters on the ACN expected performance. This work also addresses the question of trade-off between the ACN covertness and its achievable throughput. The overall research work illustrates the strong potential for using man-made transmissions as a mask for covert communications. 

The goal of this project is to bring together the complexity of core mathematics with programming abilities to code machine learning algorithms that can be incorporated into programming labs and exercises for graduate and undergraduate machine learning students. 
The aim of building the labs is to provide students with a learning tool to gain a better understanding of the inner workings of machine learning algorithms. Additionally, the labs aim to expose what challenges each algorithm can bring on its own. SAS Analytics brings into perspective machine learning methods by explaining that machine learning enables “high-value predictions that can guide better decisions and smart actions in real time without human intervention.”[2] Machine learning methods can be applied to a wide spectrum of domains and therefore, rather than attempting to cover all the algorithms, I have incorporated the algorithms that are widely applicable and explore key mathematical concepts. These algorithms for machine learning labs will give the students a learning approach to solving the intricacies of the underlying mathematical principles and will also help students to make better decisions about algorithm design and develop more accurate model predictions. 
Since each machine learning lab focuses on a particular algorithm, each program comes with a different challenge. To write these labs, I first had to master the material, which entailed finding the purpose of the algorithm and the statistical knowledge involved. Through these findings, I developed labs with specific designs, datasets, and evaluation metrics. A key difference between this approach and many other machine learning textbook approaches is that the students are building up these individual labs from scratch. They are asked to write, for a variety of different algorithms, the cost/loss function, the optimization procedure and even basic evaluation metrics. While it may be easier to call a function within a programming language, it is also easy to violate assumptions or requirements of these algorithms. By programming algorithms from scratch, as students must do in this lab, they are better able to draw parallels between the applied and theoretical underpinnings of these algorithms. 

Increase in sea level is a problem of global importance because of its impact on infrastructure and residents in coastal regions. Airborne and satellite observations have shown that the margins of Greenland and Antarctic ice sheets are melting and retreating, steadily increasing their contribution to sea level rise over the last decade. To understand the ice dynamics and develop models to generate accurate estimates of ice sheets’ future contribution to sea level rise, more information on ice thickness and basal conditions are required. Airborne ice penetrating radars are routinely deployed on long-range aircraft to perform ice thickness measurements, which are needed to derive information on bed topography and basal conditions. Acquiring useful radar reflections from the ice-bed interface is very challenging in regions where ice sheets are exhibiting the most rapid changes because returns from the ice-bed are very weak and often masked by the off nadir surface clutter. Advanced signal processing techniques, such as Synthetic Aperture Radar (SAR) and array processing, are required to filter the clutter and extract weak bed echoes buried in the noise. However, past attempts to detect these signals have not been completely successful because system and target-induced errors on SAR and array processing are not fully compensated. SAR processing in areas with significant surface slope degrades signal-to-noise ratio. Also, systematic and random errors in amplitude and phase between receive channels degrade the performance of array processors used to synthesize cross-track beam pattern. 
A novel Multi-look Time Domain Back Projection (MLTDBP) parallel processor has been developed to accurately model the electromagnetic wave propagation through the ice and generate echograms with better SNCR (Signal to Noise-Clutter Ratio) in the along-track dimension. A novel dynamic channel equalization method (based on null optimization) has been developed to adaptively calibrate the receive channels, giving an improved SNCR for the cross-track processing algorithms. Results from two-dimensional processing algorithms have been shown to be effective in extracting weak bed echoes, sloped internal ice layers, deep internal ice layers; and these results are also used to generate 3D ice-bed map of fast flowing Kangiata Nunaata Sermia (KNS) glacier in southwest Greenland.