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Electrical Engineering and Computer Science

Defense Notices

EECS MS and PhD Defense Notices for

All students and faculty are welcome to attend the final defense of EECS graduate students completing their M.S. or Ph.D. degrees. Defense notices for M.S./Ph.D. presentations for this year and several previous years are listed below in reverse chronological order.

Students who are nearing the completion of their M.S./Ph.D. research should schedule their final defenses through the EECS graduate office at least THREE WEEKS PRIOR to their presentation date so that there is time to complete the degree requirements check, and post the presentation announcement online.




Upcoming Defense Notices


MD AMIMUL EHSAN - Enabling Technologies for Three-dimensional (3D) Integrated Circuits (ICs): Through Silicon Via (TSV) Modeling and Analysis

PhD Comprehensive Defense (EE)

When & Where:
August 15, 2016
1:00 pm
246 Nichols Hall
Committee Members:
Yang Yi, Chair
Chris Allen
Ron Hui
Lingjia Liu
Judy Wu*

Abstract: [ Show / Hide ]
Three-dimensional (3D) integrated circuits (ICs) offer a promising near-term solution for pushing beyond Moore’s Law because of their compatibility with current technology. Through silicon vias (TSVs) provide electrical connections that pass vertically through wafers or dies to generate high-performance interconnects, which allows for higher design densities through shortened connection lengths. In recent years, we have seen tremendous technological and economic progress in adoption of 3D ICs with TSVs for mainstream commercial use.
Along with the need for low-cost and high-yield process technology, the successful application of TSV technology requires further optimization of the TSV electrical modeling and design. In the millimeter wave (mmW) frequency range, the root mean square (rms) height of the through silicon via (TSV) sidewall roughness is comparable to the skin depth and hence becomes a critical factor for TSV modeling and analysis. The impact of TSV sidewall roughness on electrical performance, such as the loss and impedance alteration in the mmW frequency range, is examined and analyzed. The second order small analytical perturbation method is applied to obtain a simple closed-form expression for the power absorption enhancement factor of the TSV. In this study, we propose an accurate and efficient electrical model for TSVs which considers the TSV sidewall roughness effect, the skin effect, and the metal oxide semiconductor (MOS) effect. The accuracy of the model is validated through a comparison of circuit model behavior for full wave electromagnetic field simulations up to 100 GHz.
Another advanced neurophysiological computing system that can incorporate 3D integration could provide massive parallelism with fast and energy efficient links. While the 3D neuro-inspired system offers a fantastic level of integration, it becomes inordinately arduous for the designer to model, merely because of the innumerable interconnected elements. When a TSV array is utilized in a 3D neuromorphic system, crosstalk has a malefic effect upon the system’s signal to noise ratio; the result is an overall deterioration of system performance. To countervail the crosstalk, we propose a novel optimized TSV array pattern by applying the force directed optimization algorithm.



RACHAD ATAT - Communicating over Internet Things: Security, Energy-Efficiency, Reliability and Low-Latency

PhD Comprehensive Defense (EE)

When & Where:
August 12, 2016
10:00 am
250 Nichols Hall
Committee Members:
Lingjia Liu, Chair
Yang Yi, Co-Chair
Shannon Blunt
Jim Rowland
James Sterbenz
David Nualart*

Abstract: [ Show / Hide ]
The Internet of Things (IoT) is expected to revolutionize the world through its myriad applications in health-care, public safety, environmental management, vehicular networks, industrial automation, etc. Some of the concepts related to IoT include Machine Type Communications (MTC), Low power Wireless Personal Area Networks (LoWPAN), wireless sensor networks (WSN) and Radio-Frequency Identification (RFID). Characterized by large amount of traffic with smart decision making with little or no human interaction, these different networks pose a set of challenges, among which security, energy, reliability and latency are the most important ones. First, the open wireless medium and the distributed nature of the system introduce eavesdropping, data fabrication and privacy violation threats. Second, the large number of IoT devices are expected to operate in a self-sustainable and self-sufficient manner without degrading system performance. That means energy efficiency is critical to prolong devices' lifetime. Third, many IoT applications require the information to be successfully transmitted in a reliable and timely manner, such as emergency response and health-care scenarios. To address these challenges, we propose low-complexity approaches by exploiting the physical layer and using stochastic geometry as a powerful tool to accurately model the spatial locations of ''things''. This helps provide a tractable analytical framework to provide solutions for the mentioned challenges of IoT.



OMAR BARI - Ensembles of Text and Time-Series Models for Automatic Generation of Financial Trading Signals

PhD Dissertation Defense (CS)

When & Where:
August 9, 2016
10:00 am
2001B Eaton Hall
Committee Members:
Arvin Agah, Chair
Joseph Evans
Andy Gill
Jerzy Grzymala-Busse
Sara Wilson*

Abstract: [ Show / Hide ]
Event Studies in finance have focused on traditional news headlines to assess the impact an event has on a traded company. The increased proliferation of news and information produced by social media content has disrupted this trend. Although researchers have begun to identify trading opportunities from social media platforms, such as Twitter, almost all techniques use a general sentiment from large collections of tweets. Though useful, general sentiment does not provide an opportunity to indicate specific events worthy of affecting stock prices.





Past Defense Notices


VAISHNAVI YADALAM - Real Time Video Streaming over a Multihop Ad Hoc Network

MS Project Defense (CoE)

When & Where:
July 25, 2016
2:00 pm
Room 1 Eaton Hall
Committee Members:
Aveek Dutta, Chair
Victor Frost
Richard Wang

Abstract: [ Show / Hide ]
High rate data transmission is very common in cellular and wireless local area networks. It is achievable because of its wired backbone where only the first or the last hop is wireless, commonly known as wireless “last-mile” link. With this type of infrastructure network, it is not surprising to achieve the desired performance of wirelessly-transmitted video. However, the current challenge is to transmit an enunciated and a high quality real time video over multiple wireless hops in an ad hoc network. The performance of multiple wireless hops to transmit a high quality video is limited by data rate, bandwidth of wireless channel and interference from adjacent channels. These factors constrain the applications for a wireless multihop network but are fundamental to military tactical network solutions. The project addresses and studies the effect of packet sensitivity, latency, bitrate and bandwidth on the quality of video for line of sight and non-line of sight test scenarios. It aims to achieve the best visual user experience at the receiver end on transmission over multiple wireless hops. Further, the project provides an algorithm for placement of drones in sub-terrain environment to stream real time videos for border surveillance to monitor and detect unauthorized activity.



YANG TIAN - Integrating Textual Ontology and Visual Features for Content Based Search in an Invertebrate Paleontology Knowledgebase

MS Thesis Defense (CS)

When & Where:
July 22, 2016
2:00 pm
246 Nichols Hall
Committee Members:
Bo Luo, Chair
Fengjun Li
Richard Wang

Abstract: [ Show / Hide ]
The Treatise on Invertebrate Paleontology (TIP) is a definitive work completed by more than 300 authors in the field of Paleontology, covering all categories of invertebrate animals. The digital version for TIP is consisted of multiple PDF files, however, these files are just a clone of paper version and are not well formatted, which makes it hard to extract structured data using only straightforward methods. In order to make fossil and extant records in TIP organized and searchable from a web interface, a digital library which is called Invertebrate Paleontology Knowledgebase (IPKB) was built for information sharing and querying in TIP. It is consisted of a database which stores records of all fossils and extant invertebrate animals, and a web interface which provides an online access.
The existing IPKB system provides a general framework for TIP information showing and searching, however, it has very limited search functions, only allowing users querying by pure text. Details of structural properties in the fossil descriptions are not carefully taken into consideration. Moreover, sometimes users cannot provide correct and rich enough query terms. Although authors of TIP are all paleontologists, the expected users of IPKB may not be that professional.
In order to overcome this limitation and bring more powerful search features into the IPKB system, in this thesis, we present a content-based search function, which allow users to search using textual ontology descriptions and images of fossils. First, this thesis describes the work done by previous research on IPKB system. Except for the original text and image processing approaches, we also present our new efforts on improving these original methods. Second, this thesis presents the algorithm and approach adopted in the construction of content-based search system for IPKB. The search functions in the old IPKB system did not consider the differences among morphological details of certain regions of fossils. Three major parts are discussed in detail: (1) Textual ontology based search. (2) Image based search. (3) Text-image based search.



ANIL PEDIREDLA - Information Revelation and Privacy in Online Social Networks

MS Project Defense (CS)

When & Where:
July 22, 2016
12:00 pm
250 Nichols Hall
Committee Members:
Bo Luo, Chair
Fengjung Li
Richard Wang

Abstract: [ Show / Hide ]
Participation in social networking sites has dramatically increased in recent years. Services such as Linkedin, Facebook, or Twitter allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. The relation between privacy and a person’s social network is multi-faced. At certain occasions we want information about ourselves to be know only to a limited set of people, and not to strangers. Privacy implications associated with online social networking depend on the level of identifiability of the information provided, its possible recipients, and its possible uses. Even social networking websites that do not openly expose their users’ identities may provide enough information to identify profile’s owner.



SERGIO LEON CUEN - Visualization and Performance Analysis of N-Body Dynamics Comparing GPGPU Approaches

MS Project Defense (CS)

When & Where:
July 20, 2016
9:00 am
2001B Eaton Hall
Committee Members:
Jim Miller, Chair
Man Kong
Suzanne Shontz

Abstract: [ Show / Hide ]
With the advent of general-purpose programming tools and newer GPUs, programmers now have access to a more flexible general-purpose approach to using GPUs for something other than graphics. With single instruction stream, multiple data streams (SIMD), the same instruction is executed by multiple processors using different data streams. GPUs are SIMD computers that exploit data-level parallelism by applying the same operations to multiple items of data in parallel. There are many areas where GPUs can be used for general-purpose computing. We have chosen to focus on a project in the astrophysics area of scientific computing called N-body simulation which computes the evolution of a system of bodies that interact with each other. Each body represents an object such as a planet or a star, and each exerts a gravitational force on all the others. It is performed by using a numerical integration method to compute the interactions among the system of bodies, and begins with the initial conditions of the system which are the masses and starting position and velocity of every body. These data are repeatedly used to compute the gravitational force between all bodies of the system to show updates on screen. We investigate alternative implementation approaches to the problem in an attempt to determine the factors that maximize its performance, including speed and accuracy. Specifically, we compare an OpenCL approach to one based on using OpenGL Compute Shaders. We select these two for comparison to generate real-time interactive displays with OpenGL. Ultimately, we anticipate our results will be generalizable to other APIs (e.g., CUDA) as well as to applications other than the N-Body problem. A comparison of various numerical integration and memory optimization techniques is also included in our analysis in an attempt to understand how they work in the SIMD GPGPU environment and how they contribute to our performance metrics. We conclude that, for our particular implementation of the problem, taking advantage of efficiently using local memory considerably increases performance.



BHARGHAVA DESU - VIN Database Application to Assist National Highway Traffic Safety Agency

MS Project Defense (CoE)

When & Where:
June 10, 2016
1:00 pm
246 Nichols Hall
Committee Members:
Prasad Kulkarni, Chair
Andy Gill
Richard Wang

Abstract: [ Show / Hide ]
The number of vehicle manufacturers and the number of vehicles produced have been significantly increasing each year. With more vehicles on road, the number of accidents on the National Highways in the US increased notably. NHTSA (National Highway Traffic Safety Agency) is a federal agency which works towards preventing vehicle crashes and their attendant costs. They plan and execute several operations and control measures to find and solve the problems causing accidents. One such initiative is to analyze the primary causes of all the vehicle crashes and maintain a streamlined data of vehicle Identification catalog customized for DOT and NHTSA. Maintaining a data on about 250+ millions of vehicles and analyze them needs a robust database and an application for its maintenance. At StrongBridge Corporation, we developed VPICLIST, an application for NHTSA to assist their analytic projects with data entry and pattern decoding of VIN information catalog. The application employs precise pattern matching techniques to dump data into distributed databases which in turn collaborate to a central database of NHTSA. It allows decoding of VIN each at a time by the public and also decoding thousands of VINS simultaneously for internal use of NHTSA. To hold and operate upon several PBs of data, insertion and retrieval process of the application emulates a distributed architecture. The application is developed in Java and uses Oracle enterprise database for distributed small collections and NoSQL system for the central database.



VENKATA SUBRAMANYA HYMA YADAVALLI - Framework for Shear Wave Velocity 2D Profiling with Topography

MS Project Defense (CoE)

When & Where:
June 9, 2016
11:00 am
246 Nichols Hall
Committee Members:
Prasad Kulkarni, Chair
Perry Alexander
Heechul Yun

Abstract: [ Show / Hide ]
The study of shear wave velocity (Vs) of near surface materials has been one of the primary areas of interest in seismic analyses. ‘Vs’ serves as the best indicator in evaluating the stiffness of a material from its shear modulus. One of the economical methods to obtain Vs profiling information is through the analysis of dispersion property of surface waves. SurfSeis4 - Software developed by the Kansas Geological Survey (KGS) utilizes Multichannel Analysis of Surface Waves (MASW) method to obtain shear wave velocity 2D (Surface location and depth) profiling. The profiling information is obtained in the form of a grid through inversion of dispersion curves. The Vs 2D map module of SurfSeis4, integrates the functionality of interpolating this grid to approximate the variation of shear wave velocity across the surface locations. The current project is an extension of the existing SurfSeis4 Vs 2D mapping module in its latest release of SurfSeis5 that incorporates topography in shear wave velocity variation and facilitates users with advanced image interpolation options.



LIYAO WANG - High Current Switch for Switching Power Supplies

MS Project Defense (EE)

When & Where:
June 8, 2016
2:00 pm
2001B Eaton Hall
Committee Members:
Jim Stiles, Chair
Chris Allen
Glenn Prescott

Abstract: [ Show / Hide ]
One of the main components in switching power supply is switch. However, there are two main negative issues the switch will cause in a switching power supply. The first one is that the power dissipation of the switch will be unimaginable high, especially when the current go through the switch gets higher. Secondly, because there are so many parasitic inductances and capacitances in the circuit, transient will cause problems when the operating state of the switch changes. In this project, P-Spice is used to design a qualify swith and suppress the negative effect as much as possible. The purpose of this project is to design a switch for hardware design in switching power supplies. Therefore, all the components used in P-Spice simulation are the actual models which is able to get from electronic market, and all the situations which may be happen in hardware design will be consider in the simulation. Both Mosfet and bipolar transistor switch will be discussed in the project. The project will give solutions for reducing the power dissipation cause by the switch and transient problems.



MANOGNA BHEEMINENI - Implementation and Comparison of FSA Filters

MS Project Defense (EE)

When & Where:
June 7, 2016
9:00 am
246 Nichols Hall
Committee Members:
Fengjun Li, Chair
Victor Frost
Bo Luo

Abstract: [ Show / Hide ]
Packet Filtering is a process of filtering the packets based on the filters rules that are being defines by the user. The focus of this project is to implement and compare the performance of two different packet filtering techniques (SFSA and PFSA), that uses FSA(finite state automaton) for the filtering process. Stateless FSA(SFSA) is a packet filtering technique where an FSA is generated based on the input packet and the filtering criteria. Then succeed early algorithm is applied to the automaton which simplifies by the automaton by shortening long trails to the the final state which reduces the packet filtering time. It also uses transition compaction algorithm which helps in avoiding certain areas in packet inspection which are not necessary for packet filtering.
PFSA (predicates of FSA) does the filtering based on predicates generated by the predicate evaluator. In this filtering process the FSA generated as state transitions which depend on the input symbol and also the predicate value. In order to simplify the FSA algorithms like predicates Cartesian product and predicates anticipation algorithms are being used. These algorithms consider all states that are possible and merge them to make the FSA deterministic. There is also a proto FSA that is being generated for the predicates to speed up the filtering process.



SREENIVAS VEKAPU - Chemocaffe: A Platform providing Deep Learning as a Service to Cheminformatics Researchers

MS Project Defense (CS)

When & Where:
June 6, 2016
11:00 am
2001B Eaton Hall
Committee Members:
Luke Huan, Chair
Man Kong
Prasad Kulkarni

Abstract: [ Show / Hide ]
Neural Networks were studied and applied to many research problems from a long time. With gaining popularity of deep neural networks in the area of machine learning, many researchers in various domains want to try deep learning framework. Deep learning requires lot of memory and high processing power. One way of doing it faster is to make use of GPUs which use distributed and parallel processing, thereby increasing speed. But because of the computation (lot of vector and matrix operations) deep learning requires, expensive infrastructure required (GPUs and clusters), hardware and software installation overhead, not many researchers prefer deep learning. The current application is a solution to cheminformatics problems using Convolutional Architecture for Fast Feature Embedding (Caffe) deep learning framework. The application provides a framework/service to researchers who want to try deep learning on their datasets. The application accepts datasets from users along with options for hyper parameter configuration, runs cross fold validation on the training dataset, and makes predictions on the test dataset. The (tuning) results of running caffe on the training dataset and predictions made on test dataset are sent to user via an email. The current version supports binary classification that predicts activity/inactivity of a chemical compound based on molecular fingerprints which are binary features.



YUFEI CHENG - Future Internet Routing Design for Massive Failures and Attacks

PhD Dissertation Defense (EE)

When & Where:
June 6, 2016
9:00 am
246 Nichols Hall
Committee Members:
James Sterbenz, Chair
Jiannong Cao
Victor Frost
Fengjun Li
Deep Medhi
Gary Minden
Michael Vitevitch*

Abstract: [ Show / Hide ]
Given the high complexity and increasing traffic load of the current Internet, the geographically-correlated challenge caused by large-scale disasters or malicious attacks pose a significant threat to dependable network communications. To understand its characteristics, we start our research by first proposing a critical-region identification mechanism. Furthermore, the identified regions are incorporated into a new graph resilience metric, compensated Total Geographical Graph Diversity (cTGGD), which is capable of characterizing and differentiating resiliency levels for different topologies. We further propose the path geodiverse problem (PGD) that requires the calculation of a number of geographically disjoint paths, and two heuristics with less complexity compared to the optimal algorithm. We present two flow-diverse multi-commodity flow problems, a linear minimum-cost and a nonlinear delay-skew optimization problem to study the tradeoff among cost, end-to-end delay, and traffic skew on different geodiverse paths. We further prototype and integrate the solution from above models into our cross-layer resilient protocol stack, ResTP--GeoDivRP. Our protocol stack is implemented in the network simulator ns-3 and emulated in the KanREN testbed. By providing multiple geodiverse paths, our protocol stack provides better path protection than Multipath TCP (MPTCP) against geographically-correlated challenges. Finally, we analyze the mechanism attackers could utilize to maximize the attack impact and demonstrate the effectiveness of a network restoration plan.



HARSHITH POTU - Android Application for Interactive Teaching

MS Project Defense (CoE)

When & Where:
June 3, 2016
10:00 am
250 Nichols Hall
Committee Members:
Prasad Kulkarni, Chair
Esam El-Araby
Andy Gill

Abstract: [ Show / Hide ]
In a world with enormously growing technologies and applications, most people use smart
devices. This provides a means to develop smart applications that will be help students learn effectively.
In this project, we develop a smart android application which will provide digital means of
interaction between the professors and students. Instead of using traditional emails for every
discussion, this application helps to broadcast multiple messages to the class through a single
click. The students will also be able to follow multiple professors and participate in the active
discussions. And also this application allows the users to send personal messages to the other
users in order to participate in an active discussion. It provides unique logins to every student
and professor. It uses mongoDB as the database and "parse" backend as a service.The main
inspiration for this project was an application called Tophat.



ABDULMALIK HUMAYED - Security Protection for Smart Cars — A CPS Perspective

PhD Comprehensive Defense (CS)

When & Where:
May 31, 2016
2:00 pm
246 Nichols Hall
Committee Members:
Bo Luo, Chair
Arvin Agah
Prasad Kulkarni
Heechul Yun
Prajna Dhar*

Abstract: [ Show / Hide ]
As the passenger vehicles evolve to be “smart”, electronic components, including communication, intelligent control and entertainment, are continuously introduced to new models and concept vehicles. The new paradigm introduces new features and benefits, but also brings new security issues, which is often overlooked in the industry as well as in the research community.

Smart cars are considered cyber-physical systems (CPS) because of their integration of cyber- and physical- components. In recent years, various threats, vulnerabilities, and attacks have been discovered from different models of smart cars. In the worst- case scenario, external attackers may remotely obtain full control of the vehicle by exploiting an existing vulnerability.

In this research, we investigate smart cars’ security from a CPS’ perspective and derive a taxonomy of threats, vulnerabilities, attacks, and controls. In addition, we investigate three security solutions that would improve the security posture of automotive networks. First, as automotive networks are highly vulnerable to Denial of Service (DoS) attacks, we investigate a solution that effectively mitigates such attacks, namely ID-Hopping. In addition, because several attacks have successfully exploited the poor separation between critical and non-critical components in the automotive networks, we propose to investigate the effectiveness of firewalls and Intrusion Detection Systems (IDS) to prevent and detect such exploitations. To evaluate our proposals, we built a test bench that is composed of five microcontrollers and a communication bus to simulate an automotive network. Simulations and experiments performed with the testbed demonstrates the effectiveness of ID-hopping against DoS attacks.




CAITLIN McCOLLISTER - Predicting Author Traits Through Topic Modeling of Multilingual Social Media Text

MS Thesis Defense (CS)

When & Where:
May 31, 2016
12:00 pm
246 Nichols Hall
Committee Members:
Bo Luo, Chair
Arvin Agah
Luke Huan

Abstract: [ Show / Hide ]
One source of insight into the motivations of a modern human being is the text they write and post for public consumption online, in forms such as personal status updates, product reviews, or forum discussions. The task of inferring traits about an author based on their writing is often called "author profiling." One challenging aspect of author profiling in today’s world is the increasing diversity of natural languages represented on social media websites. Furthermore, the informal nature of such writing often inspires modifications to standard spelling and grammatical structure which are highly language-specific.
These are some of the dilemmas that inspired a series of so-called "shared task" competitions, in which many participants work to solve a single problem in different ways, in order to compare their methods and results. This thesis describes our submission to one author profiling shared task in which 22 teams implemented software to predict the age, gender, and certain personality traits of Twitter users based on the content of their posts to the website. We will also analyze the performance and implementation of our system compared to those of other teams, all of which were described in open-access reports.
The competition organizers provided a labeled training dataset of tweets in English, Spanish, Dutch, and Italian, and evaluated the submitted software on a similar but hidden dataset. Our approach is based on applying a topic modeling algorithm to an auxiliary, unlabeled but larger collection of tweets we collected in each language, and representing tweets from the competition dataset in terms of a vector of 100 topics. We then trained a random forest classifier based on the labeled training dataset to predict the age, gender and personality traits for authors of tweets in the test set. Our software ranked in the top half of participants in English and Italian, and the top third in Dutch.



ANIRUDH NARASIMMAN - Arcana: Private Tweets on a Public Microblog Platform

MS Thesis Defense (CS)

When & Where:
May 31, 2016
10:00 am
250 Nichols Hall
Committee Members:
Bo Luo, Chair
Luke Huan
Prasad Kulkarni

Abstract: [ Show / Hide ]
As one of the world’s most famous online social networks (OSN), Twitter now has 320 million monthly active users. Accompanying the large user group and abundant personal information, users increasingly realize the vulnerability of tweets and have reservations of showing certain tweets to different follower groups, such as colleagues, friends and other followers. However, Twitter does not offer enough privacy protection or access control functions. Users can just set an account as protected, which results in only the user’s followers seeing the tweet. The protected tweet does not appear in the public domain, third party sites and search engines cannot access the tweet. However, a protected account cannot distinguish between different follower groups or users who use multiple accounts. To serve the demand of the user so that they can restrict the access of each tweet to certain follower groups, we propose a browser plug-in system, which utilizes CP-ABE (Ciphertext Policy Attribute based encryption), allowing the user to select followers based on predefined attributes. Through simple installation and pre-setting, the user can encrypt and decrypt tweets conveniently and can avoid the fear of information leakage.



PRATHAP KUMAR VALSAN - Towards Achieving Predictable Memory Performance on Multi-core Based Mixed Criticality Embedded Systems

MS Thesis Defense (CoE)

When & Where:
May 27, 2016
11:00 am
250 Nichols Hall
Committee Members:
Heechul Yun, Chair
Esam El-Araby
Prasad Kulkarni

Abstract: [ Show / Hide ]
The shared resources in multi-core systems, mainly the memory subsystem(caches and DRAM), if not managed properly would affect the predictability of real-time tasks in the presence of co-runners. In this work, we first studied the design of COTS DRAM controllers and its impact on predictability and, proposed a DRAM controller design, called MEDUSA, to provide predictable memory performance in multi-core based real-time systems. In our approach, the OS partially partitions DRAM banks into reserved banks and shared banks. The reserved banks are exclusive to each core to provide predictable timing while the shared banks are shared by all cores to efficiently utilize the resources. MEDUSA has two separate queues for read and write requests, and it prioritizes reads over writes. In processing read requests, MEDUSA employs a two-level scheduling algorithm that prioritizes the memory requests to the reserved banks in a Round Robin fashion to provide strong timing predictability. In processing write requests, MEDUSA largely relies on the FR-FCFS for high throughput. We implemented MEDUSA in a cycle-accurate full-system simulator. The results show that MEDUSA achieves up to 91% better worst-case performance for real-time tasks while achieving up to 29% throughput improvement for non-real-time tasks

Second, we studied the contention at shared caches and its impact on predictability. We demonstrate that the prevailing cache partition techniques does not necessarily ensure predictable cache performance in modern COTS multi-core platforms that use non-blocking caches to exploit memory-level-parallelism (MLP). Through carefully designed experiments using three real COTS multi-core platforms (four distinct CPU architectures) and a cycle-accurate full system simulator, we show that special hardware registers in non-blocking caches, known as Miss Status Holding Registers (MSHRs), which track the status of outstanding cache-misses, can be a significant source of contention. We propose a hardware and system software (OS) collaborative approach to efficiently eliminate MSHR contention for multi-core real-time systems.We implement the hardware extension in a cycle-accurate full-system simulator and the scheduler modification in Linux 3.14 kernel. In a case study, we achieve up to 19% WCET reduction (average: 13%) for a set of EEMBC benchmarks compared to a baseline cache partitioning setup.



LEI SHI - Multichannel Sense-and-Avoid Radar for Small UAVs

PhD Dissertation Defense (EE)

When & Where:
May 24, 2016
10:00 am
2001B Eaton Hall
Committee Members:
Chris Allen, Chair
Glenn Prescott
Jim Stiles
Heechul Yun
Lisa Friis*

Abstract: [ Show / Hide ]
This dissertation investigates the feasibility of creating a multichannel sense-and-avoid radar system for small fixed-wing unmanned aerial vehicles (UAVs, also known as sUAS or drones). These aircraft are projected to have a significant impact on the U.S. economy in both the commercial and government sectors, however, their lack of situation awareness has caused the FAA to strictly limit their use. Through this dissertation, a miniature, multichannel, FMCW radar system was created with a small enough size, weight, and power (SWaP) that would allow it to be mounted onboard a sUAS providing inflight target detection. The primary hazard to avoid are general aviation (GA) aircraft such as a Cessna 172 which was estimated to have a radar cross section (RCS) of approximately 1 sqr meter. The radar system is capable of locating potential hazards in range, Doppler, and 3-dimensional space using a patent pending 2-D FFT process and interferometry. The initial prototype system has a detection range of approximately 800 m, with 360-degree azimuth coverage, and +/- 15-degree elevation coverage and draws less than 20 W. From the radar data, target detection, tracking, and the extrapolation of the target behavior in 6-degree of freedom was demonstrated.



RANJITH SOMPALLI - Implementation of Invertebrate Paleontology Knowledge base using Integration of Textual Ontology & Visual Features

MS Project Defense (CS)

When & Where:
May 20, 2016
2:00 pm
2001B Eaton Hall
Committee Members:
Bo Luo, Chair
Jerzy Grzymala-Busse
Richard Wang

Abstract: [ Show / Hide ]
The Treatise on Invertebrate Paleontology is the most authoritative compilation of the invertebrate fossil records. The quality of studies in paleontology, in particular depends on the accessibility of fossil data. Unfortunately, the PDF version of Treatise currently available is just a scanned copy of the paper publications and the content is in no way organized to facilitate search and knowledge discovery. This project builds an Information Retrieval based system, to extract the fossil descriptions, images and other available information from Treatise. This project is divided into two parts. The first part deals with the extraction of the text and images from the Treatise, organize the information in a structured format and store in a relational database, build a search engine to browse fossil data. Extracting text requires identifying common textual patterns and a text parsing algorithm is developed to identify the patterns and organize the information in a structural format. Images are extracted using the image processing techniques like image segmentation, morphological operations etc., and then associated with the corresponding textual descriptions. A Search engine is built to efficiently browse the extracted information and also the web interface provides options to perform many useful tasks with ease. The second part of this research focuses on the implementation of Content Based Information Retrieval System. All images from treatise are grayscale fossil images and identifying the matching images based on the visual image features is a very difficult task. Hence, we employed an approach that integrates textual and visual features to identify matching images. Textual features are extracted from the description of the fossils and using statistical approaches and Parts of Speech tagging approaches, an ontology is generated, that forms attribute – property pairs explaining how a region looks like in each shell. Popular image features like SIFT, GIST, and HOG features are extracted from fossil images. Both the textual and image features are then integrated to extract the information related to the fossil image matching the query image.



NAGABHUSHANA GARGESHWARI MAHADEVASWAMY - How Duplicates Affect the Error Rate of Data Sets During Validation

MS Project Defense (CS)

When & Where:
May 20, 2016
12:00 pm
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
In data mining, duplicate data plays a huge role in deciding the set of rules. In this project, an analysis has been made on finding the impact of duplicates in the input data set on the rule set. The effect of duplicates is being analyzed using the error rate factor. Error rate is calculated by comparing the obtained rule set against the testing part of input data. The results of experiments have shown decrement of error rate with the increase of percentage of duplicates in the input data set, which demonstrates that the duplicate data plays a crucial role in validation process of machine learning. LEM2 algorithm and rule checker application have been implemented as a part of project. LEM2 algorithm is used to induce the rule set for the given input data set and rule checker application is used to calculate the error rate.



GOWTHAM GOLLA - Developing Novel Machine Learning Algorithms to Improve Sedentary Assessment for Youth Health Enhancement

MS Project Defense (CS)

When & Where:
May 18, 2016
11:00 am
2001B Eaton Hall
Committee Members:
Luke Huan, Chair
Jerzy Grzymala-Busse
Jordan Carlson

Abstract: [ Show / Hide ]
Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Even though wearable devices like accelerometers (e.g. activPAL) are considered as the standard for assessing physical activity in research, the machine learning algorithms that we propose will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. In order to achieve this, we collected two datasets, one is laboratory-controlled dataset and second is free-living dataset. We trained machine learning classifiers on both datasets and compared their behaviors on these datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\walk. We have also compared manually constructed Hidden Markov model(HMM) with automated HMM from existing software on both datasets to better understand the algorithm and existing software. When we tested on the laboratory-controlled dataset and the free-living dataset, the manually constructed HMM gave more F1-Macro score.



RITANKAR GANGULY - Graph Search Algorithms and Their Applications

MS Project Defense (CS)

When & Where:
May 12, 2016
2:00 pm
2001B Eaton Hall
Committee Members:
Man Kong, Chair
Nancy Kinnersley
Jim Miller

Abstract: [ Show / Hide ]
Depth- First Search (DFS) and Breadth- First Search are two of the most extensively used graph traversal algorithms to compile information about the graph in linear time. These two graph traversal mechanisms overlay a path to explore further the applications based on them that are widely used in Network Engineering, Web Analytics, Social Networking, Postal Services and Hardware Implementations. The difference between DFS and BFS results in the order in which they explore vertices and the implementation techniques for storing the discovered but un-processed vertices in the graph. BFS algorithm usually needs less time but consumes more computer memory than a DFS implementation. DFS algorithm is based on LIFO mechanism and is implemented using stack. BFS algorithm is based on FIFO technique and is realized using a queue. The order in which the vertices are visited using DFS or BFS can be realized with the help of a tree. The type of graph (directed or undirected) along with the edges of these trees form the basis of all the applications on BFS or DFS. Determining the shortest path between vertices of an un-weighted graph can be used in network engineering to transfer data packets. Checking for the presence of cycle can be critical in minimizing redundancy in telecommunications and is extensively used by social networking websites these days to analyse information as how people are connected. Finding bridges in a graph or determining the set of articulation vertices help minimize vulnerability in network design. Finding the strongly connected components in a graph can be used by model checkers in computer science. Determining an Euler circuit in a graph can be used by the postal service industries and the algorithm can be successfully implemented with linear running time using enhanced data structures. This survey project briefly defines and explains the basics of DFS and BFS traversal and explores some of the applications that are based on these algorithms.




MICHAEL BLECHA - Implementation of a 2.45GHz Power Amplifier for use in Collision Avoidance Radar

MS Project Defense (EE)

When & Where:
May 10, 2016
2:30 pm
2001B Eaton Hall
Committee Members:
Chris Allen, Chair
Glenn Prescott
Jim Stiles

Abstract: [ Show / Hide ]
The integration of a RF power amplifier into a Collision Avoidance Radar will increase the maximum detection distance of the radar. Increasing the maximum detection distance will allow a radar system mounted on an Unmanned Aircraft Vehicle to observe obstacles earlier and give the UAV more time to react. The UAVradars project has been miniaturized to support operation on an unmanned aircraft and could benefit from an increase in maximum detection distance.
The goal of this project is to create a one watt power amplifier for the 2.4GHz-2.5GHz band that can be integrated into the UAVradars project. The amplifier will be powered from existing power supplies in the radar system and must be small and lightweight to support operation on board the UAV in flight. This project will consist of the schematic and layout design, simulations, fabrication, and characterization of the power amplifier. The power amplifier will be designed to fit into the current system with minimal system modifications required.



HARSHUL ROUTHU - A Comparison of Two Decision Tree Generating Algorithms C4.5 and CART Based on Testing Datasets with Missing Attribute Values

MS Project Defense (CS)

When & Where:
May 10, 2016
10:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
In data mining, missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. Classification of missing data is a challenging task. One of the most popular techniques for classifying missing data is decision tree induction.
In this project, we compare two decision tree generating algorithms CART and C4.5 with their original implementations on different datasets with missing attribute values, taken from University of California Irvine (UCI). The comparative analysis of these two implementations is carried out in terms of accuracy on training and testing data, and decision tree complexity based on its depth and size. Results from experiments show that there is statistically insignificant difference between C4.5 and CART in terms of accuracy on testing data and complexity of the decision tree. On the other hand, accuracy on training data is significantly better for CART compared to C4.5.



HADEEL ALABANDI - A Survey of Metrics Employed to Assess Software Security

MS Thesis Defense (CS)

When & Where:
May 9, 2016
3:00 pm
246 Nichols Hall
Committee Members:
Prasad Kulkarni, Chair
Andy Gill
Heechul Yun

Abstract: [ Show / Hide ]
Measuring and assessing software security is a critical concern as it is undesirable to develop risky and insecure software. Various measurement approaches and metrics have been defined to assess software security. For researchers and software developers, it is significant to have different metrics and measurement models at one place either to evaluate the existing measurement approaches, to compare between two or more metrics or to be able to find the proper metric to measure the software security at a specific software development phase. There is no existing survey of software security metrics that covers metrics available at all the software development phases. In this paper, we present a survey of metrics used to assess and measure software security, and we categorized them based on software development phases. Our findings reveal a critical lack of automated tools, and the necessity to possess detailed knowledge or experience of the measured software as the major hindrances in the use of existing software security metrics.




HARISH SAMPANGI - Delay Feedback Reservoir (DFR) Design in Neuromorphic Computing Systems and its Application in Wireless Communications

MS Project Defense (EE)

When & Where:
May 9, 2016
2:00 pm
2001B Eaton Hall
Committee Members:
Yang Yi, Chair
Glenn Prescott
Jim Rowland

Abstract: [ Show / Hide ]
As semiconductor technologies continue to scale further into the nanometer regime, it is important to study how non-traditional computer architectures may be uniquely suited to take advantage of the novel behavior observed for many emerging technologies. Neuromorphic computing system represents a type of non-traditional architecture encompassing evolutionary. Reservoir computing, a computational paradigm inspired on neural systems, has become increasingly popular for solving a variety of complex recognition and classification problems. The traditional reservoir computing methods employs three different layers – the input layer, the reservoir and the output layer. The input layer feeds the input signals to the reservoir via fixed random weighted connections. These weights will scale the input that is given to the nodes, creating different input scaling for the input nodes. The second layer, which is called the reservoir, usually consists of a large number of randomly connected nonlinear nodes, constituting a recurrent network. Finally, the output weights are extracted from the output layer. Contrary to this traditional approach, the delayed feedback reservoir replaces the entire network of connected non-liner nodes just with a single nonlinear node subjected to delayed feedback. This approach does not only provide a drastic simplification of the experimental implementation of artificial neural networks for computing purposes, it also demonstrates the huge computational processing power hidden in even the simplest delay-dynamical system. Previous implementation of reservoir computing using the echo state network has been proven efficient for channel estimation in wireless Orthogonal Frequency-Division Multiplexing (OFDM) systems. This project aims at verifying the performance of DFR in channel estimation, by calculating its bit error rate (BER) and comparing it with other standard techniques like the LS and MMSE.



AUDREY SEYBERT - Analysis of Artifacts Inherent to Real-Time Radar Target Emulation

MS Thesis Defense (EE)

When & Where:
May 9, 2016
10:00 am
246 Nichols Hall
Committee Members:
Chris Allen, Chair
Shannon Blunt
Jim Stiles

Abstract: [ Show / Hide ]
Executing high-fidelity tests of radar hardware requires real-time fixed-latency target emulation. Because fundamental radar measurements occur in the time domain, real-time fixed latency target emulation is essential to producing an accurate representation of a radar environment. Radar test equipment is further constrained by the application-specific minimum delay to a target of interest, a parameter that limits the maximum latency through the target emulator algorithm. These time constraints on radar target emulation result in imperfect DSP algorithms that generate spectral artifacts. Knowledge of the behavior and predictability of these spectral artifacts is the key to identifying whether a particular suite of hardware is sufficient to execute tests for a particular radar design. This work presents an analysis of the design considerations required for development of a digital radar target emulator. Further considerations include how the spectral artifacts inherent to the algorithms change with respect to the radar environment and an analysis of how effectively various DSP algorithms can be used to produce an accurate representation of simple target scenarios. This work presents a model representative of natural target motion, a model that is representative of the side effects of digital target emulation, and finally a true HDL simulation of a target.



CHRISTOPHER SEASHOLTZ - Security and Privacy Vulnerabilities in Unmanned Aerial Vehicles

MS Project Defense (CoE)

When & Where:
May 6, 2016
3:30 pm
246 Nichols Hall
Committee Members:
Bo Luo, Chair
Joe Evans
Fengjun Li

Abstract: [ Show / Hide ]
In the past few years, UAVs have become very popular amongst the average citizen. Much like their military counterpart, these UAVs provide the ability to be controlled by computers, instead of a remote controller. While this may not appear to be a major security issue, the information gained from compromising a UAV can be used for other malicious activities. To understand potential attack surfaces of various UAVs, this paper presents the theory behind multiple possible attacks, as well as implementations of a select number of attacks mentioned. The main objective of this project was to obtain complete control of a UAV while in flight. Only a few of the attacks demonstrated, or mentioned, provide this ability. The remaining attacks mentioned provide information that can be used in conjunction with others in order to provide full control, or complete knowledge, of a system. Once the attacks have been proven possible, measures for proper defense must be taken. For each attack described in this paper, possible countermeasures will be given and explained.



ARIJIT BASU - Analyzing Bag of Visual Words for Efficient Content Based Image Retrieval and Classification

MS Project Defense (CS)

When & Where:
May 6, 2016
11:00 am
250 Nichols Hall
Committee Members:
Richard Wang, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
Content Based Image Retrieval also known as QBIC (Query by Image Content) is a retrieval technique where detailed analysis of the features of an image is done for retrieving similar images from the image base. Content refers to any kind of information that can derived from the image itself like textures, color, shape which are primarily global features and local features like Sift, Surf, Hog etc. Content Based image retrieval as opposed to traditional text based image retrieval has been in the limelight for quite a while owing to its contribution in putting away too much responsibility from the end user and trying to bridge the semantic gap between low level features and high level human perception.
Image Categorization is the process of classifying distinct image categories based on image features extracted from a subset of images or the entire database from each category followed by feeding it to a machine learning classifier which predicts the category labels eventually. Bag of Words Model is a very well known flexible model that represents an image as a histogram of visual patches. The idea originally comes from application of Bag of Words model in document retrieval and texture classification. Clustering is a very important aspect of the BOW model. It helps in grouping identical features from the entire dataset and hence feeding it to the Support Vector Machine Classifier. The SVM classifier takes into account every image that has been represented as a bag of visual features after clustering and then performs quality predictions. In this work we first apply the Bag of Words on well known datasets and then obtain accuracy parameters like Confusion Matrix, MCC, (Matthews Correlation Coefficient) and other statistical measures. For Feature selection we considered SURF Features owing to their rotation and scale invariant characteristics. The model has been trained and applied on two well known datasets Caltech 101 and Flickr- 25K followed by detailed performance analysis in different scenarios.




SOUMYAJIT SARKAR - Biometric Analysis of Human Ear Recognition Using Traditional Approach

MS Project Defense (CS)

When & Where:
May 4, 2016
11:00 am
246 Nichols Hall
Committee Members:
Richard Wang, Chair
Jerzy Grzymala-Busse
Bo Luo

Abstract: [ Show / Hide ]
Biometric ear authentication has received enormous popularity in recent years due to its uniqueness for each and every individual, even for identical twins. In this paper, two scale and rotation invariant feature detectors, SIFT and SURF, are adopted for recognition and authentication of ear images. An extensive analysis has been made on how these two descriptors work under certain real-life conditions; and a performance measure has been given. The proposed technique is evaluated and compared with other approaches on two data sets. Extensive experimental study demonstrates the effectiveness of the proposed strategy. Robust Estimation algorithm has been implemented to remove several false matches and improved results have been provided. Deep Learning has become a new way to detect features in objects and is also used extensively for recognition purposes. Sophisticated deep learning techniques like Convolutional Neural Networks(CNNs) have also been implemented and analysis has been done.Deep Learning Models need a lot of data to give a good result, unfortunately ear datasets available publicly are not very large and thus CNN simulations are being carried out on other state of the art datasets related to this research for evaluation of the model.



RUXIN XIE - Single-fiber-laser-based-multimodal coherent Raman System

PhD Dissertation Defense (EE)

When & Where:
April 21, 2016
2:30 pm
250 Nichols Hall
Committee Members:
Ron Hui, Chair
Chris Allen
Shannon Blunt
Victor Frost
Carey Johnson*

Abstract: [ Show / Hide ]
Coherent Raman scattering (CRS) is an appealing technique for spectroscopy and microscopy, due to its selectivity and sensitivity. We designed and built single-fiber-laser-based coherent Raman scattering spectroscopy and microscopy system which can automatically maintain frequency synchronization between pump and Stokes beam. The Stokes frequency shift is generated by soliton self-frequency shift (SSFS) through a photonic crystal fiber. The impact of pulse chirping on the signal power reduction of coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) have been investigate through theoretical analysis and experiment.

Our multimodal system provides measurement diversity among CARS, SRS and photothermal, which can be used for comparison and offering complementary information. Distribution of hemoglobin in human red blood cells and lipids in sliced mouse brain sample have been imaged. Frequency and power dependency of photothermal signal is characterized.
Based on the polarization dependency of the third-order susceptibility of the material, the polarization switched SRS method is able to eliminate the nonresonant photothermal signal from the resonant SRS signal. Red blood cells and sliced mouse brain samples were imaged to demonstrate the capability of the proposed technique. The result shows that polarization switched SRS removes most of the photothermal signal.



MAHITHA DODDALA - Properties of Probabilistic Approximations Applied to Incomplete Data

MS Project Defense (CS)

When & Where:
March 25, 2016
11:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Man Kong
Bo Luo

Abstract: [ Show / Hide ]
The main focus of the project is to discuss mining of incomplete data which we find frequently in real-life records. For this, I considered the probabilistic approximations as they have a direct application to mining incomplete data. I have examined the results obtained from the experiments conducted on eight real-life data sets taken from University of California at Irvine Machine Learning Repository. I also investigated the properties of singleton, subset, and concept approximations and corresponding consistencies. The main objective was to compare the global and local approximations and generalize the consistency definition for incomplete data with two interpretations of missing attribute values: lost values and "do not care" conditions. In addition to this comparison, the most useful approach among singleton, subset and concept approximations is also tested for which the conclusion is the best approach would be selected with the help of tenfold cross validation after applying all three approaches. Also it’s shown that even if there exist six types of consistencies, there are only four distinct consistencies of incomplete data as two pairs of such consistencies are equivalent.



ROHIT YADAV - Automatic Text Summarization of Email Corpus Using Importance of Sentences

MS Project Defense (CS)

When & Where:
March 15, 2016
11:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
With the advent of Internet, the data being added online have been increasing at an enormous rate. Though search engines use information retrieval (IR) techniques to facilitate the search requests from users, the results may not always be effective or the efficiency of results according to a search query may not be high. The user has to go through certain web pages before getting at the web page he/she needs. This problem of information overload can be solved using automatic text summarization. Summarization is a process of obtaining an abridged version of documents so that user can have a quick understanding of the document. A new technique to produce a summary of an original text is investigated in this project.
Email threads from the World Wide Web consortium’s sites (W3C) corpus are used in this system.Our system is based on identification and extraction of important sentences from the input document. Apart from common IR features like term frequency and inverse document frequency, novel features such as Term Frequency-Inverse Document Frequency,subject words, sentence position and thematic words have also been implemented. The model consists of four stages. The pre-processing stage converts the unstructured (all those things that can't be so readily classified) text into structured (any data that resides in a fixed field within a record or file). In the first stage each sentence is partitioned into the list of tokens and stop words are removed. The second stage is to extract the important key phrases in the text by implementing a new algorithm through ranking the candidate words. The system uses the extracted keywords/key phrases to select the important sentence. Each sentence is ranked depending on many features such as the existence of the keywords/key phrase in it, the relation between the sentence and the title by using a similarity measurement and other many features. The third stage of the proposed system is to extract the sentences with the highest rank. The fourth stage is the filtering stage where sentences from email threads are ranked as per features and summaries are generated. This system can be considered as a framework for unsupervised learning in the field of text summarization.



ARJUN MUTHALAGU - Flight Search Application

MS Project Defense (CS)

When & Where:
March 15, 2016
9:00 am
250 Nichols Hall
Committee Members:
Prasad Kulkarni, Chair
Andy Gill
Jerzy Grzymala-Busse

Abstract: [ Show / Hide ]
“Flight-search” application is an Angular JS application implemented in a client side architecture. The application displays the flight results from different airline companies based on the input parameters. The application also has custom filtering conditions and custom pagination, which a user can interact with to filter the result and also limit the results displayed in the browser. The application uses QPX Express API to pull data for the flight searches.



SATYA KUNDETI - A comparison of Two Decision Tree Generating Algorithms: C4.5 and CART Based on Numerical Data

MS Project Defense (CS)

When & Where:
February 29, 2016
11:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Luke Huan
Bo Luo

Abstract: [ Show / Hide ]
In Data Mining, classification of data is a challenging task. One of the most popular techniques for classifying data is decision tree induction. In this project, two decision tree generating algorithms CART and C4.5, using their original implementations, are compared on different numerical data sets, taken from University of California Irvine (UCI). The comparative analysis of these two implementations is carried out in terms of accuracy and decision tree complexity. Results from experiments show that there is statistically insignificant difference(5% level of significance, two-tailed test)between C4.5 and CART in terms of accuracy. On the other hand, decision trees generated by C4.5 and CART have significant statistical difference in terms of their complexity.



NAGA ANUSHA BOMMIDI - The Comparison of Performance and Complexity of Rule Sets induced from Incomplete Data

MS Project Defense (CS)

When & Where:
February 12, 2016
3:00 pm
317 Nichols Hall
Committee Members:
Jerzy Grzymala-Busse,Chair
Andy Gill
Prasad Kulkarni

Abstract: [ Show / Hide ]
The main focus of this project is to identify the best interpretation of missing attribute values in terms of performance and complexity of rule sets. This report summarizes the experimental comparison of the performance and the complexity of rule sets induced from incomplete data sets with three interpretations of missing attribute values: lost values, attribute-concept values, and “do not care” conditions. Furthermore, it details the experiments conducted using MLEM2 rule induction system on 176 data sets, using three kinds of probabilistic approximations: lower, middle and upper. The performance was evaluated using the error rate computed by ten-fold cross validation, and the complexity of rule sets was evaluated based the size of the rule sets and the number of conditions in the rule sets. The results showed that lost values were better in terms of the performance in 10 out of 24 combinations. In addition, attribute-concept values were better in 5 out of 24 combinations, and “do not care” conditions were better in 1 combination in terms of the complexity of rule sets. Furthermore, there was not even one combination of dataset and type of approximation for which both performance and complexity of rule sets were better for one interpretation of missing attributes compared to the other two.



BLAKE BRYANT - Hacking SIEMS to Catch Hackers: Decreasing the Mean Time to Respond to Security Incidents with a Novel Threat Ontology in SIEM Software

MS Thesis Defense (IT)

When & Where:
February 12, 2016
2:00 pm
2012 BEST
Committee Members:
Hossein Saiedian, Chair
Bo Luo
Gary Minden

Abstract: [ Show / Hide ]
Information security is plagued with increasingly sophisticated and persistent threats to communication networks. The development of new threat tools or vulnerability exploits often outpaces advancements in network security detection systems. As a result, detection systems often compensate by over reporting partial detections of routine network activity to security analysts for further review. Such alarms seldom contain adequate forensic data for analysts to accurately validate alerts to other stakeholders without lengthy investigations. As a result, security analysts often ignore the vast majority of network security alarms provided by sensors, resulting in security breaches that may have otherwise been prevented.

Security Information and Event Management (SIEM) software has been introduced recently in an effort to enable data correlation across multiple sensors, with the intent of producing a lower number of security alerts with little forensic value and a higher number of security alerts that accurately reflect malicious actions. However, the normalization frameworks found in current SIEM systems do not accurately depict modern threat activities. As a result, recent network security research has introduced the concept of a "kill chain" model designed to represent threat activities based upon patterns of action, known indicators, and methodical intrusion phases. Such a model was hypothesized by many researchers to result in the realization of the desired goals of SIEM software.

The focus of this thesis is the implementation of a "kill chain" framework within SIEM software. A novel "Kill chain" model was developed and implemented within a commercial SIEM system through modifications to the existing SIEM database. These modifications resulted in a new log ontology capable of normalizing security sensor data in accordance with modern threat research. New SIEM correlation rules were developed using the novel log ontology compared to existing vendor recommended correlation rules using the default model. The novel log ontology produced promising results indicating improved detection rates, more descriptive security alarms, and a lower number of false positive alarms. These improvements were assessed to provide improved visibility and more efficient investigation processes to security analysts ultimately reducing the mean time required to detect and escalate security incidents.





SHAUN CHUA - Implementation of a Multichannel Radar Waveform Generator System Controller

MS Project Defense (EE)

When & Where:
February 9, 2016
10:00 am
317 Nichols Hall
Committee Members:
Carl Leuschen, Chair
Chris Allen
Fernando Rodriguez-Morales

Abstract: [ Show / Hide ]
Waveform generation is crucial in a radar system operation. There is a recent need for an 8 channel transmitter with high bandwidth chirp signals (100 MHz – 600 MHz). As such, a waveform generator (WFG) hardware module is required for this purpose. The WFG houses 4 Direct Digital Synthesizers (DDS), and an ALTERA Cyclone V FPGA that acts as its controller. The DDS of choice is the AD9915, because its Digital to Analog Converter can be clocked at a maximum rate of 2.5 GHz, allowing plenty of room to produce the high bandwidth and high frequency chirp signals desired, and also because it supports synchronization between multiple AD9915s.

The brains behind the DDS operations are the FPGA and the radar software developed in NI LabVIEW. These two aspects of the digital systems grants the WFG highly configurable waveform capabilities. The configurable inputs that can be controlled by the user include: number of waveforms in a playlist, start and stop frequency (bandwidth of chirp signal), zero-pi mode, and waveform amplitude and phase control.

The FPGA acts as a DDS controller that directly configures and control the DDS operations, while also managing and synchronizing the operations of all DDS channels. This project details largely the development of such a controller, named Multichannel Waveform Generator (MWFG) Controller, and the necessary modifications and development in the NI LabVIEW software, so that they complement each other.




DEEPIKA KOTA - Automatic Color Detection of Colored Wires In Electric Cables

MS Project Defense (EE)

When & Where:
February 1, 2016
10:30 am
2001B Eaton Hall
Committee Members:
Jim Stiles, Chair
Ron Hui
James Rowland

Abstract: [ Show / Hide ]
An automatic Color detection system checks for the sequence of colored wires in electric cables which are ready to get crimped together. The system inspects for flat connectors with differs in type and number of wires.This is managed in an automatic way with a self learning system without any requirement of manual input from the user to load new data to the machine. The system is coupled to a connector crimping machine and once the system learns the actual sample of cable order , it automatically inspects each cable assembled by the machine. There are three methodologies based on which this automatic detection takes place 1) A self learning system 2) An algorithm for wire segmentation to extract colors from the captured images 3) An algorithm for color recognition to cope up with wires with different illuminations and insulation .The main advantage of this system is when the cables are produced in large batches ,it provides high level of accuracy and prevents false negatives in order to guarantee defect free production.



MOHAMMED ZIAUDDIN - Open Source Python Widget Application to Synchronize Bibliographical References Between Two BibTeX Repositories

MS Project Defense (CS)

When & Where:
February 1, 2016
10:00 am
246 Nichols Hall
Committee Members:
Andy Gill, Chair
Perry Alexander
Prasad Kulkarni

Abstract: [ Show / Hide ]
Bibtex is a tool to edit and manage bibliographical references in a document.Researchers face a common problem that they have one copy of their bibliographical reference databases for a specific project and a master bibliographical database file that holds all their bibliographical references. Syncing these two files is an arduous task as one has to search and modify each reference record individually. Most of the bibtex tools available either provide help in maintaining bibtex bibliographies in different file formats or searching for references in web databases but none of them provide a way to synchronize the fields of the same reference record in the two different bibtex database files.
The intention of this project is to create an application that helps academicians to keep their bibliographical references in two different databases in sync. We have created a python widget application that employs the Tkinter library for GUI and unQLite database for data storage. This application is integrated with Github allowing users to modify bibtex files present on Github.



HARISH ROHINI - Using Intel Pintools to Analyze Memory Access Patterns

MS Project Defense (CS)

When & Where:
January 29, 2016
2:00 pm
246 Nichols Hall
Committee Members:
Prasad Kulkarni, Chair
Andy Gill
Heechul Yun

Abstract: [ Show / Hide ]
Analysis of large benchmark programs can be very difficult because of their changes in memory state for every run and with billions of instructions the simulation of a whole program in general can be extremely slow. The solution for this is to simulate only some selected regions which are the most representative parts of a program, So that we can focus our analysis and optimizations on those particular regions which represent more part of the execution of a program. In order to accomplish that, we use intel’s pintool, a binary instrumentation framework which performs program analysis at run time, simpoint to get the most representative regions of a program and pinplay for the reproducible analysis of the program. This project uses these frameworks to simulate and analyze programs to provide various statistics about the memory allocations, memory reference traces, allocated memory usage across the most representative regions of the program and also the cache simulations of the representative regions.



GOVIND VEDALA - Iterative SSBI Compensation in Optical OFDM Systems and the Impact of SOA Nonlinearities

MS Project Defense (EE)

When & Where:
January 28, 2016
2:00 pm
246 Nichols Hall
Committee Members:
Ron Hui, Chair
Chris Allen
Erik Perrins

Abstract: [ Show / Hide ]
Multicarrier modulation using Orthogonal Frequency Division Multiplexing (OFDM) is a best fit candidate for the next generation long-haul optical transmission systems, offering high degree of spectral efficiency and easing the compensation of linear impairments such as chromatic dispersion and polarization mode dispersion, at the receiver. Optical OFDM comes in two flavors – coherent optical OFDM (CO-OFDM) and direct detection optical OFDM (DD-OFDM), each having its own share of pros and cons. CO-OFDM is highly robust to fiber impairments and imposes a relaxation on the electronic component bandwidth requirements, but requires narrow linewidth lasers, optical hybrids and local oscillators. On the other hand DD-OFDM has relaxed laser linewidth requirement and low complexity receiver making it an attractive multicarrier system. However, DD-OFDM system suffers from signal-signal beat interference (SSBI), caused by mixing among the sub-carriers in the photo detector, which deteriorates the system performance. Previously, to mitigate the effect of SSBI, a guard band was used between optical carrier and data sideband. In this project, we experimentally demonstrate a linearly field modulated virtual single sideband OFDM (VSSB-OFDM) transmission with direct detection and digitally compensate for the SSBI using an iterative SSBI compensation algorithm.
Semiconductor optical amplifiers (SOA), with their small footprint, ultra-high gain bandwidth, and ease of integration, are attracting the attention of optical telecommunication engineers for their use in high speed transmission systems as inline amplifiers. However, the SOA gain saturation induced nonlinearities cause pulse distortion and induce nonlinear cross talk effects such as cross gain modulation especially in Wavelength Division Multiplexed systems. In this project, we also evaluate the performance of iterative SSBI compensation in an optical OFDM system, in the presence of these SOA induced nonlinearities.



KEERTHI GANTA - TCP Illinois Protocol Implementation in ns-3

MS Project Defense (EE)

When & Where:
January 27, 2016
1:00 pm
250 Nichols Hall
Committee Members:
James Sterbenz, Chair
Victor Frost
Bo Luo

Abstract: [ Show / Hide ]
The choice of congestion control algorithm has an impact on the performance of a network. The congestion control algorithm should be selected and implemented based on the network scenario in order to achieve better results. Congestion control in high speed networks and networks with large BDP is proved to be more critical due to the high amount of data at risk. There are problems in achieving better throughput with conventional TCP in the above mentioned scenario. Over the years conventional TCP is modified to pave way for TCP variants that could address the issues in high speed networks. TCP Illinois is one such protocol for high speed networks. It is a hybrid version of a congestion control algorithm as it uses both packet loss and delay information to decide on the window size. The packet loss information is used to decide on whether to increase or decrease the congestion window and delay information is used to assess the amount of increase or decrease that has to be made.



ADITYA RAVIKANTI - sheets-db: Database powered by Google Spreadsheets

MS Project Defense (CS)

When & Where:
January 27, 2016
10:00 am
2001B Eaton Hall
Committee Members:
Andy Gill, Chair
Perry Alexander
Prasad Kulkarni

Abstract: [ Show / Hide ]
The sheets-db library is a Haskell binding to Google Sheets API. sheets-db allows Haskell users to utilize google spread sheets as a light weight database. It provides various functions to create, read, update and delete rows in spreadsheets along with a way to construct simple structured queries.




NIRANJAN PURA VEDAMURTHY - Testing the Accuracy of Erlang Delay Formula for Smaller Number of TCP Flows

MS Project Defense (CoE)

When & Where:
January 27, 2016
8:00 am
246 Nichols Hall
Committee Members:
Victor Frost, Chair
Gary Minden
Glenn Prescott

Abstract: [ Show / Hide ]
The Erlang delay formula for dimensioning different networks is used to calculate the probability of congestion. Testing the accuracy of a probability of congestion found using the Erlang formula against the simulation for probability of packet loss is demonstrated in this project. The simulations are done when TCP traffic is applied through one bottleneck node. Three different source traffic models having small number of flows is considered. Simulations results for three different source traffic models is shown in terms of probability of packet loss and load supplied to the topology. Various traffic parameters are varied in order to show the impact on the probability of packet loss and to compare with the Erlang prediction for probability of congestion.



MAHMOOD HAMEED - Nonlinear Mixing in Optical Multicarrier Systems

PhD Dissertation Defense (EE)

When & Where:
January 14, 2016
2:00 pm
246 Nichols Hall
Committee Members:
Ron Hui, Chair
Shannon Blunt
Erik Perrins
Alessandro Salandrino
Carey Johnson*

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Efficient use of the vast spectrum offered by fiber-optic links by an end user with relatively small bandwidth requirement is possible by partitioning a high speed signal in a wavelength channel into multiple low-rate subcarriers. Multicarrier systems not only ensure efficient use of optical and electrical components, but also tolerate transmission impairments. The purpose of this research is to experimentally understand and minimize the impact of mixing among subcarriers in Radio-Over-Fiber (RoF) and direct detection systems, involving a nonlinear component such as a semiconductor optical amplifier. We also analyze impact of clipping and quantization on multicarrier signals and compare electrical bandwidth utilization of two popular multiplexing techniques in orthogonal frequency division multiplexing (OFDM) and Nyquist modulation.
For an OFDM-RoF system, we present a novel technique that minimizes the RF domain signal-signal beat interference (SSBI), relaxes the phase noise requirement on the RF carrier, realizes the full potential of the optical heterodyne technique, and increases the performance-to-cost ratio of RoF systems. We demonstrate a RoF network that shares the same RF carrier for both downlink and uplink, avoiding the need of an additional RF oscillator in the customer unit.
For direct detection systems, we first experimentally compare performance degradations of coherent optical OFDM and single carrier Nyquist pulse modulated systems in a nonlinear environment. We then experimentally evaluate the performance of signal-signal beat interference (SSBI) compensation technique in the presence of semiconductor optical amplifier (SOA) induced nonlinearities for a multicarrier optical system with direct detection. We show that SSBI contamination can be removed from the data signal to a large extent when the optical system operates in the linear region, especially when the carrier-to-signal power ratio is low.



SUSOBHAN DAS - Tunable Nano-photonic Devices

PhD Comprehensive Defense (EE)

When & Where:
January 12, 2016
10:00 am
246 Nichols Hall
Committee Members:
Ron Hui, Chair
Alessandro Salandrino, Co-Chair
Chris Allen
Jim Stiles
Judy Wu*

Abstract: [ Show / Hide ]
In nano-photonics, the control of optical signals is based on tuning of the material optical properties in which the electromagnetic field propagates, and thus the choice of materials and of the physical modulation mechanism plays a crucial role. Several materials such as graphene, Indium Tin Oxide (ITO), and vanadium di-oxide (VO2) investigated here have attracted a great deal of attention in the nanophotonic community because of their remarkable tunability. This dissertation will include both theoretical modeling and experimental characterization of functional electro-optic materials and their applications in guided-wave photonic structures.
We have characterized the complex index of graphene in near infrared (NIR) wavelength through the reflectivity measurement on a SiO2/Si substrate. The measured complex indices as the function of the applied gate electric voltage agreed with the prediction of the Kubo formula.
We have performed the mathematical modeling of permittivity of ITO based on the Drude Model. Results show that ITO can be used as a plasmonic material and performs better than noble metals for applications in NIR wavelength region. Additionally, the permittivity of ITO can be tuned by carrier density change through applied voltage. An electro-optic modulator (EOM) based on plasmonically enhanced graphene has been proposed and modeled. We show that the tuning of graphene chemical potential through electrical gating is able to switch on and off the ITO plasmonic resonance. This mechanism enables dramatically increased electro-absorption efficiency.
Another novel photonic structure we are investigating is a multimode EOM based on the electrically tuned optical absorption of ITO in NIR wavelengths. The capability of mode-multiplexing increases the functionality per area in a nanophotonic chip. Proper design of ITO structure based on the profiles of y-polarized TE11 and TE21 modes allows the modulation of both modes simultaneously and differentially.
We have experimentally demonstrated the ultrafast changes of optical properties associated with dielectric-to-metal phase transition of VO2. This measurement is based on a fiber-optic pump-probe setup in NIR wavelength. Instantaneous optical phase modulation of the probe was demonstrated during pump pulse leading edge, which could be converted into an intensity modulation of the probe through an optical frequency discriminator