Defense Notices

The advancement in the field of embedded technology has resulted in its extensive use in almost all the modern electronic devices. Hence, unlike in the past, there is a very crucial need to develop system security tools for these devices. So far most of the research has been concentrated either on security for general computer systems or on static analysis of embedded systems. In this project, we develop tools that explore and monitor the run-time properties of programs/applications as well as the inter-process communication. We also present a case studies in which these tools are be used on a Gumstix (an embedded system) running Poky Linux system to monitor a particular program as well as print out a graph of all inter-process communication on the system.

Having stable dielectric properties extending to frequencies over 110 GHz, Liquid Crystal Polymer (LCP) materials are a new and promising substrate alternative for low-cost production of planar microwave circuits. This project focused on the design of several microwave filter structures using multiple layers for operation in the 2-18 GHz and 10-14 GHz bands. Circuits were simulated and optimized using EDA tools, obtaining good results over the bands of interest. The results show that it is feasible to fabricate these structures on dielectric substrates compatible with off-site manufacturing facilities. It is likewise shown that LCP technology can yield a 3-5x area reduction as compared to cavity-type filters, making them much easier to integrate in a planar circuit.

A cross-layer framework in the Software Defined Networking domain is pro- posed to study the resilience in OpenFlow-based multipath communication. A testbed has been built, using Brocade OpenFlow switches and Dell Poweredge servers. The framework is evaluated against regional challenges. By using differ- ent adjacency matrices, various topologies are built. The behavior of OpenFlow multipath-based communication is studied in case of a single path failure, splitting of traffic and also with multipath TCP enabled traffic. The behavior of different coupled congestion algorithms for MPTCP is also studied. A Web framework is presented to demonstrate the OpenFlow experiment by importing the network topologies and then executing and analyzing user defined regional attacks.

One of the main applications of rough set theory is rule induction. If the input data set contains inconsistencies, using rough set theory leads to inducing certain and possible rule sets. 
In this project, the concept of a maximal consistent block is applied to formulate a new approximation to a concept in the incomplete data set with a higher level of accuracy. This method does not require change in the size of the original incomplete data set. Two interpretations of missing attribute values are discussed: lost values and “do not care” conditions. The main objective is to compare maximal consistent blocks and characteristics sets in terms of cardinality of lower and upper approximations. Four incomplete data sets are used for experiments with varying levels of missing information. The next objective is to compare the decision rules induced and cases covered by both techniques. The experiments show that the both techniques provide the same lower approximations for all the datasets with “do not care” conditions. The best results are achieved by maximal consistent blocks for upper approximations for three datasets and there is a tie for the other data set. 

In data mining, rules are the most popular symbolic representation of knowledge. Classification of data and extracting of classification rules from the data is a difficult process, and there are different approaches to this process. One such approach is inductive learning. Inductive learning involves the process of learning from examples - where a system tries to induce a set of rules from a set of observed examples. Inductive learning methods produce distinct concept descriptions when given identical training data and there are questions about the quality of the different rule sets produced. This project work is aimed at comparing and analyzing the rule sets induced by different inductive learning systems. In this project, three different algorithms AQ, CART and C4.5 are used to induce rule sets from different data sets. An analysis is carried out in terms of the total number of rules and the total number of conditions present in the rules. These space complexity measures such as rule count and condition count show that AQ tends to produce more complex rule sets than C4.5 and CART. AQ algorithm has been implemented as a part of project and is used to induce the rule sets.

License plate Recognition method is a technique to detect license plate numbers from the vehicle images. This method has become an important part of our life with an increase in traffic and crime every now and then. It uses computer vision and pattern recognition technologies. Various techniques have been proposed so far and they work best within boundaries.This detection technique helps in finding the accurate location of license plates and extracting characters of the plates. The license plate detection is a three-stage process that includes license plate detection, character segmentation and character recognition. The first stage is the extraction of the number plate as it occupies a small portion of the whole image. After tracking down the license plate, localizing of the characters is done. The character recognition is the last stage of the detection and template matching is the most common method used for it. The results achieved by the above experiment were quite accurate which showed the robustness of the investigated algorithm.

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.