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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


SALLY SAJADIAN - Model Predictive Control of Impedance Source Inverter for Photovoltaic Applications

PhD Comprehensive Defense (EE)

When & Where:
October 3, 2017
11:00 am
2001B Eaton Hall
Committee Members:
Reza Ahmadi, Chair
Glenn Prescott
Alessandro Salandrino
Jim Stiles
Huazhen Fang*

Abstract: [ Show / Hide ]
A model predictive controlled power electronics interface (PEI) based on impedance source inverter for photovoltaic (PV) applications is proposed in this work. The proposed system has the capability of operation in both grid-connected and islanded mode. Firstly, a model predictive based maximum power point tracking (MPPT) method is proposed for PV applications based on single stage grid-connected Z-source inverter (ZSI). This technique predicts the future behavior of the PV side voltage and current using a digital observer that estimates the parameters of the PV module. Therefore, by predicting a priori the behavior of the PV module and its corresponding effects on the system, it improves the control efficacy. The proposed method adaptively updates the perturbation size in the PV voltage using the predicted model of the system to reduce oscillations and increase convergence speed. The operation of the proposed method is verified experimentally. The experimental results demonstrate fast dynamic response to changes in solar irradiance level, small oscillations around maximum power point at steady-state, and high MPPT effectiveness from low to high solar irradiance level.
The second part of this work focuses on the dual-mode operation of the proposed PEI based on ZSI with capability to operate in islanded and grid-connected mode. The transition from islanded to grid-connected mode and vice versa can cause significant deviation in voltage and current due to mismatch in phase, frequency, and amplitude of voltages. The proposed controller using MPC offers seamless transition between the two modes of operations. The main predictive controller objectives are direct decoupled power control in grid-connected mode and load voltage regulation in islanded mode. The proposed direct decoupled active and reactive power control in grid connected mode enables the dual-mode ZSI to behave as a power conditioning unit for ancillary services such as reactive power compensation. The proposed controller features simplicity, seamless transition between modes of operations, fast dynamic response, and small tracking error in steady state condition of controller objectives. The operation of the proposed system is verified experimentally.





Past Defense Notices


NAVAJIT BARUAH - Scheduler Based Solution for Maintaing Performance of Real-Time Processes in Presence of Memory Contention in Multi-Core Systems

MS Thesis Defense (CoE)

When & Where:
July 28, 2017
10:30 am
250 Nichols Hall
Committee Members:
Heechul Yun, Chair
Prasad Kulkarni
Gary Minden

Abstract: [ Show / Hide ]
Multi-core processors are being increasingly used in resource constraint real-time embedded platforms. They provide improved performance with lesser power consumption and true parallelism by running multiple processes on multiple processing cores concurrently. As many processes of different priority levels are running simultaneously in a real-time multi-core system there is interference between processes as they content for the shared resources like cache, bus and memory. This leads to difficulties in maintaining deterministic performance of real-time processes particularly for those ones which need more memory (cache, dram) in situations where they co-run with similar non-real-time processes. This project tries to address this problem by making intelligent scheduling decisions in the existing Linux scheduler subsystem.

The memory contention problem is demonstrated using standard SPEC SPEC2006 benchmarks. Real-time processes which have higher cache misses suffer most amount (upto 4 times) of slowdown in their instruction per cycle (IPC). Using last level cache misses as an indicator of process’s memory needs the project provides a priority based solution to dynamically lower the co-running normal priority processes which have high memory demands if they interfere with co-running real-time processes. The evaluation of the approach performed using two representative benchmarks in SPEC CPU2006 suite showed that the IPC slowdown was reduced by around 50% in two core system and 25% in case of four core system in the worst case scenario. Modifications were made to the Linux’s Completely Fair Scheduler while maintaining the core scheduler hierarchy. The memory intensity of a process is determined by reading the last level cache misses from the CPU Performance Measurement registers.



CHAO LAN - Learning with Multiple Views of Data: Some Theoretical and Practical Contributions

PhD Dissertation Defense (CS)

When & Where:
July 25, 2017
1:00 pm
246 Nichols Hall
Committee Members:
Luke Huan, Chair
Lingjia Liu
Bo Luo
Xintao Wu
Hongguo Xu*

Abstract: [ Show / Hide ]
In machine learning applications, instances are usually describable by multiple views (i.e. feature sets), each somewhat sufficient for the learning task. In this dissertation, we investigated several issues of learning with multi-view instances, in both statistical setting and matrix setting.

In statistical setting, we first theoretically investigate the possibility of training an accurate prediction model using as few unlabeled multi-view data as possible, and concluded such possibility by improving the state-of-art unlabeled sample complexity of semi-supervised multi-view learning by a logarithm factor. We then benchmarked the performance of a new and simple multi-view multi-class learning method, and showed it consistently outperforms the state-of-art. We finally investigated the application of multi-view clustering methods in social circle detection on ego-networks.

In matrix setting, we investigated the negative transfer problem in the popular collective matrix factorization (CMF) method for multi-view feature matrix recovery. We first developed a theoretical characterization of negative transfer in the CMF estimator, as the decrease of its ideal mini-max learning rate by a root function. We then showed the ideal rate is tight (up to a constant factor), by deriving a matching PAC upper bound for it. Finally, we proposed an algorithmic variant of CMF to mitigate its negative transfer effect.

At the end, we briefly discussed an application of multi-view learning in fairness-aware machine learning.



LEI YANG - Security and Privacy in the Internet of Things

PhD Dissertation Defense (CS)

When & Where:
July 25, 2017
10:30 am
246 Nichols Hall
Committee Members:
Fengjun Li, Chair
Luke Huan
Prasad Kulkarni
James Sterbenz
Yong Zeng*

Abstract: [ Show / Hide ]
The Internet of Things (IoT) is an emerging paradigm that seamlessly integrates electronic devices with sensing and computing capability into the Internet to achieve intelligent processing and optimized controlling, which enables us to do things in a way that we never before imagined. However, as IoT are creating tremendous benefits, significant security and privacy concerns arise such as cyberattacks and personal information leakage. Theoretically, when everything is connected, everything is at risk. The ubiquity of connected things gives adversaries more attack vectors, and thus more catastrophic consequences by cybercrimes. Therefore, it is very critical to move fast to address these rising security and privacy concerns before severe disasters happen. In this dissertation, we mainly address the challenges for IoT applications in two domains: (1) how to protect sensitive data during storage, dissemination and utilization; (2) how to protect identity and defend against cyberattacks by leveraging anonymous communication techniques such as Tor.

First, we present a reliable, searchable and privacy-preserving e-healthcare system, which takes advantage of emerging cloud storage and IoT infrastructure and enables healthcare service providers to realize remote patient monitoring in a secure and regulatory compliant manner. Then, we turn our attention to the data analysis in IoT applications. We propose a cloud-assisted, privacy-preserving machine learning classification scheme over encrypted data for IoT devices. Finally, we explore the problem of privacy-preserving targeted broadcast in IoT, and propose two multi-cloud-based outsourced-ABE. They enable the receivers to partially outsource the computationally expensive decryption operations to the clouds, while preventing attributes from being disclosed.

In the second part of this dissertation, we present how to use Tor to protect IoT devices from cyberattacks. The performance problem of Tor makes it less appealing for bandwidth-intensive IoT applications. Therefore, we first explore the utilization of resources in Tor and propose a multipath routing scheme to use idle resources to support those applications. Then, we propose a scheme to enhance the security of smart home by integrating Tor hidden services into IoT gateway. Finally, we propose a multipath-routing based architecture for Tor hidden services to enhance its resistance against traffic analysis attacks.



XI CHEN - A Two-layer Text Classification for Private Tweets

MS Project Defense (CS)

When & Where:
July 24, 2017
2:00 pm
2001B Eaton Hall
Committee Members:
Bo Luo, Chair
Fengjun Li
Jerzy Grzymala-Busse

Abstract: [ Show / Hide ]
In the last 15 years, online social networks (OSNs) have become one of the most popular social tools by providing online communication platforms for users. With the increase in popularity of Twitter and Facebook, people are willing to post everything in their life on these online social networks. Tweet as one of the biggest platforms of OSNs provided us a large amount of such public data worldwide. Meanwhile, with the fast progress in data science, classification of tweets to different categories has become to an interesting and important research area for data scientists. We consider that identifying sensitive tweets is very important for government and OSNs providers. For example, Tweet data already used for improving crime prediction nowadays.

In my project, I applied different text classification approaches to classify tweets from 13 different categories. We first applied inverse document frequency (IDF) as term weight for directly classification. Afterward, we implemented a two-layer classification that including two steps: identification and classification. In this two-layer classification, we first identify tweets to sensitive tweets and non-sensitive tweets. Then we classify sensitive tweets into 12 pre-defined categories in the second layer of our classifier. We applied IDF, TF-IDF and doc2vec as features for the classification. By comparing the classification results, we found that two-layer classification gives better result than one-layer classification. Also, the deep learning method of training doc2vec to find the similarity between sentences/paragraphs gives the best result in my project. In my project, I applied different feature extraction methods for testing my two-layer classification and analyze the performance for each approach. Finally, our results show that doc2vec has advantage in tweet classification than other traditional feature extraction models.




HAYDER ALMOSA - Downlink Achievable Rate Analysis for FDD Massive MIMO Systems

PhD Comprehensive Defense (EE)

When & Where:
June 26, 2017
1:30 pm
250 Nichols Hall
Committee Members:
Lingjia Liu, Chair
Shannon Blunt
Ron Hui
Erik Perrins
Hongyi Cai*

Abstract: [ Show / Hide ]
Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, is a very promising direction for 5G due to its ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO system, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this research proposal, we propose an efficient downlink beamforming method to address the challenging of downlink training overhead. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink (UL) DoAs and downlink (DL) DoDs, we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, we derive an efficient downlink beamforming method based on downlink CSIT estimated at the BS. By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that select the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with the conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional method in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming method.



SUSANNA MOSLEH - Intelligent Interference Mitigation for Multi-cell Multi-user MIMO Networks with Limited Feedback

PhD Comprehensive Defense (EE)

When & Where:
June 20, 2017
9:30 am
250 Nichols Hall
Committee Members:
Lingjia Liu, Chair
Victor Frost
Ron Hui
Erik Perrins
Jian Li*

Abstract: [ Show / Hide ]
Nowadays, wireless communication are becoming so tightly integrated in our daily lives, especially with the global spread of laptops, tablets and smartphones. This has paved the way to dramatically increasing wireless network dimensions in terms of subscribers and amount of flowing data. With the rapidly growing data traffic, interference has become a major limitation in wireless networks. To deal with this issue and in order to increase the spectral efficiency of wireless networks, various interference mitigation techniques have been suggested among which interference alignment (IA) has been shown to significantly improve network performance. However, how to practically use IA to mitigate inter-cell interference in a downlink multi-cell multi-user MIMO networks still remains an open problem. Besides, more recently, the attention of researchers has been drawn to a new technique for improving the spectral efficiency, namely, massive/full dimension multiple-input multiple-output. Although massive MIMO/FD-MIMO brings a large diversity gain to the network, its practical implementation poses a research challenge. Moreover, new techniques that can mitigate interference impact in such systems remain unexplored. To address these challenges, this proposed research targets to 1) develop an IA technique for downlink multi-cell multi-user MIMO networks; 2) mathematically characterize the performance of IA with limited feedback; and 3) evaluate the performance analysis of IA technique (with/without limited feedback) in massive MIMO/FD-MIMO networks. Preliminary results show that IA with limited feedback significantly increase the spectral efficiency of downlink multi-cell multi-user MIMO networks.



RACHAD ATAT - Enabling Cyber-Physical Communication in 5G Cellular Networks: Challenges, Solutions and Applications

PhD Dissertation Defense (EE)

When & Where:
June 14, 2017
1:30 pm
246 Nichols Hall
Committee Members:
Lingjia Liu, Chair
Yang Yi, Co-Chair
Shannon Blunt
Jim Rowland
James Sterbenz
Jin Feng*

Abstract: [ Show / Hide ]
Cyber-physical systems (CPS) are expected to revolutionize the world through a myriad of applications in health-care, disaster event applications, environmental management, vehicular networks, industrial automation, and so on. The continuous explosive increase in wireless data traffic, driven by the global rise of smartphones, tablets, video streaming, and online social networking applications along with the anticipated wide massive sensors deployments, will create a set of challenges to network providers, especially that future fifth generation (5G) cellular networks will help facilitate the enabling of CPS communications over current network infrastructure.
In this dissertation, we first provide an overview of CPS taxonomy along with its challenges from energy efficiency, security, and reliability. Then we present different tractable analytical solutions through different 5G technologies, such as device-to-device (D2D) communications, cell shrinking and offloading, in order to enable CPS traffic over cellular networks. These technologies also provide CPS with several benefits such as ubiquitous coverage, global connectivity, reliability and security. By tuning specific network parameters, the proposed solutions allow the achievement of balance and fairness in spectral efficiency and minimum achievable throughout among cellular users and CPS devices. To conclude, we present a CPS mobile-health application as a case study where security of the medical health cyber-physical space is discussed in details.



HAO CHEN - Mutual Information Accumulation over Wireless Networks:Fundamentals and Applications

PhD Dissertation Defense (EE)

When & Where:
June 14, 2017
10:00 am
250 Nichols Hall
Committee Members:
Lingjia Liu, Chair
Shannon Blunt
Victor Frost
Yang Yi
Zsolt Talata*

Abstract: [ Show / Hide ]
Future wireless networks will face a compound challenge of supporting large traffic volumes, providing ultra-reliable and low latency connections to ultra-dense mobile devices. To meet this challenge, various new technologies have been introduced among which mutual-information accumulation (MIA), an advanced physical (PHY) layer coding technique, has been shown to significantly improve the network performance. Since the PHY layer is the fundamental layer, MIA could potentially impact various network layers of a wireless network. Accordingly, the understanding of improving network design based on MIA is far from being fully developed. The purpose of this dissertation is to study the fundamental performance improvement of MIA over wireless networks and to apply these fundamental results to guide the design of practical systems, such as cognitive radio networks and massive machine type communication networks.
This dissertation includes three parts. The first part of this dissertation presents the fundamental analysis of the performance of MIA over wireless networks. To begin with, we first analyze the asymptotic performance of MIA in an infinite 2-dimensional(2-D) grid network. Then, we investigate the optimal routing in cognitive radio networks with MIA and derive the closed-form cooperative gain obtained by applying MIA in cognitive radio networks. Finally, we characterize the performance of MIA in random networks using tools from stochastic geometry.
The second and third part of this dissertation focuses on the application of MIA in cognitive radio networks and massive machine type communication networks. An optimization problem is formulated to identify the cooperative routing and optimal resources allocation to minimize the transmission delay in underlay cognitive radio networks with MIA. Efficient centralized as well as distributed algorithms are developed to solve this cross-layer optimization problem using the fundamental properties obtained in the first part of this dissertation. A new cooperative retransmission strategy is developed for massive MTC networks with MIA. Theoretical analysis of the new developed retransmission strategy is conducted using the same methodology developed in the fundamental part of this dissertation. Monte Carlo simulation results and numerical results are presented to verify our analysis as well as to show the performance improvement of our developed strategy.



HAMID MAHMOUDI - Modulated Model Predictive Control for Power Electronic Converters

PhD Comprehensive Defense (EE)

When & Where:
June 13, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Reza Ahmadi, Chair
Chris Allen
Glenn Prescott
Alessandro Salandrino
Jim Stiles
Huazhen Fang*

Abstract: [ Show / Hide ]
Advanced switching algorithms and modulation methods for power electronics converters controlled with model predictive control (MPC) strategies have been proposed in this work. The methods under study retain the advantage of conventional MPC methods in programing the nonlinear effects of the converter into the design calculations to improve the overall dynamic and steady state performance of the system and builds upon that by offering new modulation technique for MPC to minimize the voltage and current ripples through using a fixed switching frequency. The proposed method is easy to implement and provides flexibility to prioritize different objectives of the system against each other using the objective weighting factor. To demonstrate the effectiveness of the proposed method, it has been used to overcome the stability problems caused by a constant power load (CPL) in a multi converter system as a case study.
In addition, to further evaluate the merits of the proposed method, it has been used to control modular multilevel converters (MMCs) in voltage source converter-high voltage DC (VSC-HVDC) systems. The proposed method considers the nonlinear properties of the MMC into the design calculations while minimizing the line total harmonic distortion (THD), circulating current ripple and steady-state error by generating modulated switching signals with a fixed switching frequency. In this work, the predictive modeling of the MMC is provided. Next, the proposed control method is described. Then, the application of the proposed method to a MMC system is detailed. Experimental results from the systems under study illustrate the effectiveness of proposed strategies.




MD AMIMUL EHSAN - Enabling Technologies for 3D ICs: TSV Modeling and Analysis

PhD Dissertation Defense (EE)

When & Where:
June 9, 2017
10:30 am
246 Nichols Hall
Committee Members:
Yang Yi, Chair
Ron Hui
Lingjia Liu
Alessandro Salandrino
Judy Wu*

Abstract: [ Show / Hide ]
Through silicon via (TSV) based three-dimensional (3D) integrated circuit (IC) aims to stack and interconnect dies or wafers vertically. This forefront technology offers a promising near-term solution for further miniaturization and the performance improvement of electronic systems and follows a more than Moore strategy.
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 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 following the second order small perturbation method. Then, an accurate and efficient electrical model for TSVs has been proposed considering the TSV sidewall roughness effect, the skin effect, and the metal oxide semiconductor (MOS) effect.
However, the emerging application of 3D integration involves an advanced bio-inspired computing system which is currently experiencing an explosion of interest. In neuromorphic computing, the high density membrane capacitor plays a key role in the synaptic signaling process, especially in the spike firing analog implementation of neurons. We proposed a novel 3D neuromorphic design architecture in which the redundant and dummy TSVs are reconfigured as membrane capacitors. This modification has been achieved by taking advantage of the metal insulator semiconductor (MIS) structure along the sidewall, strategically engineering the fixed oxide charges in depletion region surrounding the TSVs, and the addition of oxide layer around the bump without changing any process technology. Without increasing the circuit area, this reconfiguration of TSVs can result in substantial power consumption reduction and a significant boost to chip performance and efficiency. Also, depending on the availability of the TSVs, we proposed a novel CAD framework for TSV assignments based on the force-directed optimization and linear perturbation.




SANTOSH MALYALA - Estimation of Ice Basal Reflectivity of Byrd Glacier using RES Data

MS Thesis Defense (EE)

When & Where:
June 9, 2017
9:00 am
317 Nichols Hall
Committee Members:
Carl Leuschen, Chair
Jilu Li, Co-Chair
Chris Allen
John Paden

Abstract: [ Show / Hide ]
Ice basal reflectivity is much needed for the determination of ice basal conditions and for the accurate modeling of ice sheet to estimate the future global mean sea level rise. Reflectivity values can be determined from the received radio echo sounding data if the power loss caused by different components along the two-way transmission of EM wave are accurately compensated.

For the large volume of received radio echo sounding data collected over Byrd glacier in 2011-2012 with multichannel radar, the spherical spreading loss caused due to two-way propagation, power reduction due to roughness and relative englacial attenuation are compensated to estimate the relative reflectivity values of the Byrd glacier.
In order to estimate the scattered incoherent power component due to roughness, the distributions of echo amplitudes returned from air-firn interface and from ice – bed interface are modeled to estimate RMS height variations. The englacial attenuation rate of wave for two-way propagation along the ice depth is modeled using the observed data. The estimated air-firn interface roughness parameters are relatively cross verified using the Neal’s method and with the correlations from the Landsat image mosaic of Antarctica. Estimated relative basal reflectivity values are validated using the cross-over analysis and abruptness index measurements. From the Byrd relative reflectivity map, the corresponding echograms at the locations of potential subglacial water systems are checked for the observable lake features.
The obtained results are checked for correlations with previously predicted lake locations and subglacial flow paths. While the results doesn’t exactly match with the previously identified locations with elevation changes, high relative reflectivity values are observed close to those locations, aligning exactly or close to previously predicted flow paths providing a new window into the hydrological network of the glacial. Relative reflectivity values are clustered to indicate the different potential basal conditions beneath the Byrd glacier



RAVALI GINJUPALLI - A Rule Checker and K-Fold Cross Validation for Incomplete Data Sets

MS Project Defense (CS)

When & Where:
June 1, 2017
3:00 pm
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Gary Minden
Suzanne Shontz






Abstract: [ Show / Hide ]
Rule induction is an important technique of data mining or machine learning. Knowledge is frequently expressed by rules in many areas of AI, including rule based expert systems. The machine learning/ data mining system LERS (Learning from Examples based on Rough Sets) induces a set of rules from examples and classifies new examples using the set of rules induced previously by LERS. LERS induces rules based on supervised learning. The MLEM2 algorithm is a rule induction algorithm in which rule induction, discretization, and handling missing attribute values are all conducted simultaneously. A rule checker is implemented to classify new cases using the rules induced by MLEM2 algorithm. MLEM2 algorithm induces certain and possible rule sets. Bucket Brigade algorithm is implemented to
classify new examples. K-fold cross-validation technique is implemented to measure the performance of MLEM2 algorithm. The objective of this project is to find out the efficiency of the MLEM2 rule induction method for incomplete data set.



DHWANI SAXENA - A Modification of the Characteristic Relation for Incomplete Data Sets

MS Project Defense (CS)

When & Where:
May 30, 2017
1:30 pm
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Perry Alexander
Prasad Kulkarni

Abstract: [ Show / Hide ]
Rough set theory is a popular approach for decision rule induction. However, it requires the objects in the information system to be completely described. Many real life data sets are incomplete, so we cannot directly apply rough set theory for rule induction. A characteristic relation is used to deal with incomplete information systems in which ‘do not care’ data coexist with lost data. There are scenarios in which two objects that do not have the same known value are indiscernible and on the other hand the two objects which have a lot of equivalent known values are very likely to be in different classes. To rectify such situations, a modification of the characteristic relation was introduced. This project implements rule induction from the modification of the characteristic relation for incomplete data sets.



AHMED SYED - Maximal Consistent Block Technique for Rule Acquisition in Incomplete Information Systems

MS Project Defense (CS)

When & Where:
May 30, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Perry Alexander
Prasad Kulkarni

Abstract: [ Show / Hide ]
In this project, an idea of the maximal consistent block is applied to formulate a new approximation to a concept in incomplete data sets. The maximal consistent blocks have smaller cardinality compared to characteristic sets. Because of this, the generated upper approximations will be smaller in size. Two interpretations of missing attribute values are discussed: lost values and “do not care” conditions. Four incomplete data sets are used for experiments with varying levels of missing information. Maximal Consistent Blocks and Characteristics Sets are compared in terms of cardinality of lower and upper approximations. The next objective is to compare the decision rules induced and cases covered by both techniques. The experiments show that 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.



AMUKTHA CHAKILAM - A Modified ID3 Algorithm for Continuous Numerical Attributes Using Cut Point Approach

MS Project Defense (CS)

When & Where:
May 26, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Perry Alexander
Prasad Kulkarni

Abstract: [ Show / Hide ]
Data classification is a methodology of data mining used to organize data by relevant categories to obtain meaningful information. A model is generated from the input training set which is used to classify the test data into predetermined groups or classes. One of the most widely used models is a decision tree which uses a tree like structure to list all possible outcomes. Decision tree is an important predictive analysis method in Data Mining as it requires minimum effort from the users for data interpretation.

This project implements ID3, an algorithm for building decision tree using information gain metric. Furthermore, through illustrating the basic ideas of ID3, this project also addresses the inefficiency of ID3 in handling continuous numerical attributes. A cut point approach is presented to discretize the numeric attributes into discrete intervals and enable ID3 functionality for them. Experiments show that such decision trees contain fewer number of nodes and branches in contrast to a tree obtained by basic ID3 algorithm. This modified algorithm can be used to classify real valued domains containing symbolic and numeric attributes with multiple discrete outcomes.



LUKE DODGE - Rule Induction on Data Sets with Set-Value Attributes

MS Thesis Defense (CS)

When & Where:
May 25, 2017
3:00 pm
1 Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Arvin Agah
Bo Luo

Abstract: [ Show / Hide ]
Data sets may have instances where multiple values are possible which are described as set-value attributes. The established LEM2 algorithm does not handle data sets with set-value attributes. To solve this problem, a parallel approach was used during LEM2's execution to avoid preprocessing data. Changing the creation of characteristic sets and attribute-value blocks to include all values for each case allows LEM2 to induce rules on data sets with set-value attributes. The ability to create a single local covering for set-value data sets increases the variety of data LEM2 can process.



SIRISHA THIPPABHOTLA - Applying Machine Learning Algorithms for Predicting Gender based on Voice

MS Project Defense (CS)

When & Where:
May 25, 2017
1:00 pm
1415A LEEP2
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
Machine learning is being applied in many domains of research. One such research area is the automation of gender prediction. The goal of this project is to determine a person’s gender based on his/her voice. Although it may seem like a simple task for any human to recognize this, the difficulty lies in the process of training a computer to do this job for us. This project is implemented by training models based on input data of voice samples from both male and female voices. The voice samples considered were from different datasets, with varying frequencies, noise ratios etc. This input data is passed through various machine learning models, with/without parameter tuning, to compare results. A comparative analysis of multiple machine learning algorithms was conducted, and the prediction with the highest accuracy is displayed as output for the given input voice sample.



SUNDEEP GANJI - A Hybrid Web Application For Conducting In Class Quizzes

MS Project Defense (CoE)

When & Where:
May 25, 2017
9:00 am
1415A LEEP2
Committee Members:
Prasad Kulkarni, Chair
Jerzy Grzymala-Busse
Gary Minden

Abstract: [ Show / Hide ]
Every student comes to the class with a smart phone, and they are constantly distracted. It has become a tough challenge for the instructors to keep the students focused on the lectures. The idea of this project is to build a hybrid responsive web application which helps the instructors to post questions between their discussions. The students can give their responses through their smart phones instantly. This enables the instructor to analyze the understanding of the students on the current topic through various statistics which are generated instantly. The instructors can improve their teaching methods while the students who are less interactive can give their voice along with others in the class and check their understanding.

This application allows the instructor to add or edit courses in their account, add students to their courses, create or edit quizzes beforehand, post questions in different formats to the students, and analyze results through various kinds of plots. On the otherhand, a Student can view the courses he is added in to by his/her instructor, submit his/her responses for the quizzes posted. This application simplifies the process of conducting in-class quizzes and offers the students and the instructors an enhanced classroom experience.



ALI MAHMOOD - Design, Integration, and Deployment of UAS-borne HF/VHF Ice Depth Sounding Radar and Antenna System

MS Thesis Defense (EE)

When & Where:
May 23, 2017
10:30 am
317 Nichols Hall
Committee Members:
Carl Leuschen, Chair
Fernando Rodriguez-Morales, Co-Chair
Chris Allen

Abstract: [ Show / Hide ]
The dynamic thinning of fast-flowing glaciers is so poorly understood that its potential impact on sea level rise remains unpredictable. Therefore, there is a dire need to predict the behavior of these ice bodies by understanding their bed topography and basal conditions, particularly near their grounding lines (the limit between grounded ice and floating ice). The ability to detect previous VHF radar returns in some key glacier regions is limited by strong clutter caused by severe ice surface roughness, volume scatter, and increased attenuation induced by water inclusions and debris.
The work completed in the context of this thesis encompasses the design, integration, and field testing of a new compact light-weight radar and antenna system suitable for low-frequency operation onboard Uninhabited Aerial Systems (UASs). Specifically, this thesis presents the development of two tapered dipole antennas compatible with a 4-meter wingspan UAS. The bow-tie shaped antenna resonates at 35 MHz, and the meandering and resistively loaded element radiates at 14 MHz. Also discussed are the methods and tools used to achieve the necessary bandwidth while mitigating the electromagnetic coupling between the antennas and on-board avionics in a fully populated UAS. The influence of EM coupling on the 14 MHz antenna was nominal due to relatively longer wavelength. However, its input impedance had to be modified by resistive loading in order to avoid high power reflections back to the transmitter. The antenna bandwidths were further enhanced by employing impedance matching networks that resulted in 17.3% and 7.1% bandwidths at 35 MHz and 14 MHz, respectively.
Finally, a compact 4 lbs. system was validated during the 2013-2014 Antarctic deployment, which led to echo sounding of more challenging temperate ice in the Arctic Circle. The thesis provides results obtained from data collected during a field test campaign over the Russell glacier in Greenland compared with previous data obtained with a VHF depth sounder system operated onboard a manned aircraft.



KELLY RODRIGUEZ - Analysis of Extracellular Recordings and Temporal Encoding in Delayed-Feedback Reservoir

MS Thesis Defense (EE)

When & Where:
May 22, 2017
3:30 pm
1 Eaton Hall
Committee Members:
Yang Yi, Chair
Randolph Nudo, Co-Chair
Shannon Blunt

Abstract: [ Show / Hide ]
Technological advancements in analog and digital systems have enabled new approaches to study networks of physical and artificial neurons. In biological systems, a standard method to record neuronal activity is through cortically implanted micro electrode arrays (MEAs). As advances in hardware continue to push channel counts of commercial MEAs upwards, it becomes imperative to develop automatic methods for data acquisition and analysis with high accuracy and throughput. Reliable, low latency methods are critical in closed-loop neuroprosthetic paradigms such as spike-timing dependent applications where the activity of a single neuron triggers specific stimuli with millisecond precision. This work presents an adapted version of an online spike detection algorithm, previously employed successfully on in vitro recordings, that has been improved to work under more stringent in vivo environments subject to additional sources of variability and noise. The algorithm’s performance was compared with other commonly employed detection techniques for neural data on a newly developed and highly tunable extracellular recording model that features variable firing rates, adjustable SNRs, and multiple waveform characteristics. The testing framework was created from in vivo recordings collected during quiescence and electrical stimulation periods. The algorithm presents superior performance and efficiency in all evaluated conditions. Furthermore, we propose a methodology for online signal integrity analysis from MEA recordings and quantification of neuronal variability across different experimental settings. This work constitutes a stepping stone toward the creation of large scale neural data processing pipelines and aims to facilitate reproducibility in activity dependent experiments by offering a method for unifying various metrics calculated from single unit activity. Precise spike detection becomes crucial for experiments studying temporal in addition to rate coding mechanisms. To further study and exploit the potential of temporal coding, a delay-feedback-based reservoir (DFB) has been implemented in software. This artificial network is found to be capable of processing spikes encoded from a benchmark task with performance comparable to that of more complex networks. This work allows us to corroborate the capabilities of temporal coding in a minimally-complex system suitable for implementation in physical hardware and inclusion in low-power circuit applications where computational power is also necessary.



SALEH ESHTAIWI - A New Model Predictive Control Technique Based Maximum Power Point Tracking For Photovoltaic Systems

PhD Comprehensive Defense (EE)

When & Where:
May 22, 2017
1:00 pm
2001B Eaton Hall
Committee Members:
Reza Ahmadi, Chair
Chris Allen
Jerzy Grzymala-Busse
Ron Hui
Elaina Sutley*

Abstract: [ Show / Hide ]
The worldwide energy demand is being increased day by day, anticipated to increase for 48% from 2012 to 2040. The distributed generation (DG) including renewable energy resources such as wind and solar are part of the solution in terms of lowering electricity cost, power reliability, and environmental concerns and therefore must function efficiently. Designing a robust maximum power point tracking (MPPT) technique can ensure maximized energy harvesting from PV solar systems and increases conversion efficiency which is the significant hindrance for their growth. The maximum power point (MPP) varies with intrinsic and climate changes nonlinearly. Thus, MPPT methods are expected to seek the MPP regardless of the solar module and ambient changes. The proposed method is based on the concept of Model Predictive Control (MPC) with unique properties. MPC is a powerful class of controllers that uses a system modeling to predict future behavior and optimize performance objectives. Unlike the traditional techniques that are prone to lose a tracking direction and their consequences on the stability, the proposed technique treats the photovoltaic (PV) module as a plant and uses a digital observer for predicting the behavior of the PV module and tracking the MPP. Further, it unifies the simplicity of implementation, enhances the overall dynamics performance and is robust against atmosphere changes.



ELI SYMM - Wavelets in Electromagnetic Profile Inversion

MS Project Defense (EE)

When & Where:
May 22, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Jim Stiles, Chair
Chris Allen
Ron Hui

Abstract: [ Show / Hide ]
Historical subsurface sensing methods applied to planar ice and snow sheets rely on underlying assumptions about the physical situation governing volumetric backscatter. Namely, the stratification of the natural medium under investigation consists of layered material with distinctly different dielectric properties. While appropriate for recovering sharp spatial discontinuities in the relative permittivity, the layer stripping approach [1] is not applicable to smooth permittivity variations about a common mean. In this project we developed techniques to model both the forward scattering from one-dimensional permittivity variation and the inverse problem - estimating the permittivity profile from the reflected energy. The underlying assumption is that smoothly varying inhomogeneities may be decomposed into wavelet basis functions which efficiently represent natural perturbations about an effective mean. Potential applications for this method are in ground penetrating radar, ionospheric sounding, nondestructive evaluation, and medical imaging.



MICHAEL STEES - Robust High Order Mesh Generation and Untangling

PhD Comprehensive Defense (CS)

When & Where:
May 19, 2017
3:00 pm
317 Nichols Hall
Committee Members:
Suzanne Shontz, Chair
Perry Alexander
Prasad Kulkarni
Jim Miller
Weizhang Huang*

Abstract: [ Show / Hide ]
Simulating the mechanics of a beating heart requires the numerical solution of partial differential equations. An application like this is a good candidate for high order computational methods that deliver higher solution accuracy at a lower cost than their low order counterparts.
To fully leverage these high order computational methods, they must be paired with an accurate discretization of the domain. For a geometry like the heart, this requires a high order mesh. Thus robust high order mesh generation is a critical component to the widespread adoption of high order computational methods for numerically solving partial differential equations. Toward this end, we are developing high order mesh generation and untangling methods. As our first step, we have developed an optimization-based second order mesh generation method that employs triangles and tetrahedra. We will also develop generation methods for quadrilateral and hexahedral elements. Finally, we will develop untangling methods that can be used to untangle our generated meshes, as well as untangle any tangled elements that occur during motion (e.g. the beating of the heart).



PRASANTH VIVEKANANDAN - A Simplex Architecture for Intelligent and Safe Unmanned Aerial Vehicles

MS Thesis Defense (CoE)

When & Where:
May 9, 2017
2:00 pm
250 Nichols Hall
Committee Members:
Heechul Yun, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
Unmanned Aerial Vehicles (UAVs) are increasingly demanded in civil, military and research purposes. However, they also possess serious threats to the society because faults in UAVs can lead to physical damage or even loss of life. While increasing their intelligence, for example, adding vision-based sense-and-avoid capability, has a potential to reduce the safety threats, increased software complexity and the need for higher
computing performance create additional challenges—software bugs and transient hardware faults—that must be addressed to realize intelligent and safe UAV systems.
This work present a fault tolerant system design for UAVs. Our proposal is to use two heterogeneous hardware and software platforms with distinct reliability and performance characteristics: High-Assurance (HA) and High-Performance (HP) platforms. The HA platform focuses on simplicity and
verfiability in software and uses a simple and transient fault tolerant processor, while the HP platform focuses on intelligence and functionality in software and uses a complex and high performance processor. During the normal operation, the HP platform is responsible for controlling the UAV. However, if it fails due to transient hardware faults or software bugs, the HA platform will take over until the HP platform recovers.
We have implemented the proposed design on an actual UAV using a low-cost Arduino and a high-performance Tegra TK1 multicore platform. Our case-studies show that our design can improve safety without compromising performance and intelligence of the UAV.




YUANWEI WU - Learning Deep Neural Networks for Object Detection and Tracking

PhD Comprehensive Defense (EE)

When & Where:
May 8, 2017
3:00 pm
317 Nichols Hall
Committee Members:
Richard Wang,Chair
Arvin Agah
Lingjia Liu
Bo Luo
Haiyang Chao*

Abstract: [ Show / Hide ]
Scene understanding in both static images and dynamic videos is the ultimate goal in computer vision. As two important sub-tasks of this endeavor, object detection and tracking have been extensively studied in the past decades, however, the problem is still not well addressed. The main challenge is that the appearance of objects is affected by a number of factors, such as scale, occlusion, illumination, and so on. Recently, deep learning has attracted lots of interests in the computer vision community. However, how to tackle these challenges in object detection and tracking is still an open problem. In this work, we propose a method for detecting objects in images using a single deep neural network, which can be optimized end-to-end and predict the object bounding boxes and class probabilities in one evaluation. To handle the challenges in object tracking, we propose a framework, which consists of a novel deep Convolutional Neural Networks (CNNs) to effectively generate robust spatial appearance, and a Long Short-term Memory (LSTM) network that incorporates temporal information to achieve long-term object tracking accuracy in real-time.



LAKSHMI KOUTHA - Advanced Encoding Schemes and their Hardware Implementations for Brain Inspired Computing

MS Thesis Defense (EE)

When & Where:
May 5, 2017
1:00 pm
2001B Eaton Hall
Committee Members:
Yang Yi, Chair
Chris Allen
Glenn Prescott

Abstract: [ Show / Hide ]
According to Moore’s law the number of transistors per square inch double every two years. Scaling down technology reduces size and cost however, also increases the number of problems. Our current computers using Von-Neumann architectures are seeing progressive difficulties not only due to scaling down the technology but also due to grid-lock situation in its architecture. As a solution to this, scientists came up architectures whose function resembles that of the brain. They called these brains inspired architectures, neuromorphic computers. The building block of the brain is the neuron which encodes, decodes and processes the data. The neuron is known to accept sensory information and converts this information into a spike train. This spike train is encoded by the neuron using different ways depending on the situation. Rate encoding, temporal encoding, population encoding, sparse encoding and rate-order encoding are a few encoding schemes said to be used by the neuron. These different neural encoding schemes are discussed as the primary focus of the thesis. A comparison between these different schemes is also provided for better understanding, thus helping in the design of an efficient neuromorphic computer. This thesis also focusses on hardware implementation of a neuron. Leaky Fire and Integrate neuron model has been used in this work which uses spike-time dependent encoding. Different neuron models are discussed with a comparison as to which model is effective under which circumstances. The electronic neuron model was implemented using 180nm CMOS Technology using Global Foundries PDK libraries. Simulation results for the neuron are presented for different inputs and different excitation currents. These results show the successful encoding of sensory information into a spike train.



PENG SENG TAN - Addressing Spectrum Congestion by Spectrally-Cooperative Radar Design

PhD Dissertation Defense (EE)

When & Where:
May 5, 2017
10:30 am
250 Nichols Hall
Committee Members:
Jim Stiles, Chair
Shannon Blunt, Co-Chair
Chris Allen
Lingjia Liu
Tyrone Duncan*

Abstract: [ Show / Hide ]
Due to the increasing need for greater Radio Frequency (RF) spectrum by mobile apps like Facebook and Instagram, high data-rate communication protocols like 5G and the Internet of Things, it has led to the issue of spectrum congestion as radar systems have traditionally maintain the largest share of the RF spectrum. To resolve the spectrum congestion problem, it has become even necessary for users from both types of systems to coexist within a finite spectrum allocation. However, this then leads to other problems such as the increased likelihood of mutual interference experienced by all users that are coexisting within the finite spectrum.

In this dissertation, we propose to address the problem of spectrum congestion via a two-step approach. The first step of this approach involves designing an optimal sparse spectrum allocation scheme to radar systems such that the radar range resolution performance can be maintained with a smaller resulting bandwidth at a cost of degraded sidelobe performance. The second step of this approach involves designing radar waveforms that possesses good spectral containment property by expanding the framework of Polyphase-coded Frequency Modulated (PCFM) waveforms to higher-order representations such that these waveforms will mitigate issues of interference experienced by other systems when both systems are coexisting within the same band.



CHENYUAN ZHAO - Energy Efficient Spike-Time-Dependent Encoder Design for Neuromorphic Computing System

PhD Comprehensive Defense (EE)

When & Where:
May 3, 2017
3:30 pm
250 Nichols Hall
Committee Members:
Yang Yi, Chair
Lingjia Liu, Co-Chair
Luke Huan
Suzanne Shontz
Yong Zeng*

Abstract: [ Show / Hide ]
Von Neumann Bottleneck, which refers to the limited throughput between the CPU and memory, has already become the major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain the increasing attentions as compact and energy-efficient computing platforms. As one of the most crucial components in the neuromorphic computing systems, neural encoder transforms the stimulus (input signals) into spike trains. In this report, I will present my research work on spike-time-dependent encoding schemes and its relevant energy efficient encoders’ design. The performance comparison among rate encoding, latency encoding, and temporal encoding would be discussed in this report. The proposed neural temporal encoder allows efficient mapping of signal amplitude information into a spike time sequence that represents the input data and offers perfect recovery for band-limited stimuli. The simulation and measurement results show that the proposed temporal encoder is proven to be robust and error-tolerant.




XIAOLI LI - Constructivism Learning: A Learning Paradigm for Transparent and Reliable Predictive Analytics

PhD Comprehensive Defense (CS)

When & Where:
May 2, 2017
3:30 pm
246 Nichols Hall
Committee Members:
Luke Huan, Chair
Victor Frost
Jerzy Grzymala-Busse
Bo Luo
Alfred Tat-Kei Ho*

Abstract: [ Show / Hide ]
With an increasing trend of adoption of machine learning in various real-world problems, the need for transparent and reliable models has become apparent. Especially in some socially consequential applications, such as medical diagnosis, credit scoring, and decision making in educational systems, it may be problematic if humans cannot understand and trust those models. To this end, in this work, we propose a novel machine learning algorithm, constructivism learning. To achieve transparency, we formalized a Bayesian nonparametric approach using sequential Dirichlet Process Mixture of prediction models to support constructivism learning. To achieve reliability, we exploit two strategies, reducing model uncertainty and increasing task construction stability by leveraging techniques in active learning and self-paced learning.





JOSEPH ST. AMAND - Local Metric Learning

PhD Comprehensive Defense (EE)

When & Where:
May 2, 2017
9:00 am
250 Nichols Hall
Committee Members:
Luke Huan, Chair
Prasad Kulkarni
Jim Miller
Richard Wang
Bozenna Pasik-Duncan*

Abstract: [ Show / Hide ]
Distance metrics are concerned with learning how objects are similar, and are a critical component of many machine learning algorithms such as k-nearest neighbors and kernel machines. Traditional metrics are unable to adapt to data with heterogenous interactions in the feature space. State of the art methods consider learning multiple metrics, each in some way local to a portion of the data. Selecting how the distance metrics are local to the data is done apriori, with no known best approach.
In this proposal, we address the local metric learning scenario from three complementary perspectives. In the first direction, we consider a spatial approach, and develop an efficient Frank-Wolfe based technique to learn local distance metrics directly in a high-dimensional input space. We then consider a view-local perspective, where we associate each metric with a separate view of the data, and show how the approach naturally evolves into a multiple kernel learning problem. Finally, we propose a new function for learning a metric which is based on a newly discovered operator called the t-product, here we show that our metric is composed of multiple parts, with each portion local to different interactions in the input space.



MARK GREBE - Domain Specific Languages for Small Embedded Systems

PhD Comprehensive Defense (CS)

When & Where:
May 1, 2017
12:00 pm
246 Nichols Hall
Committee Members:
Andy Gill, Chair
Perry Alexander
Prasad Kulkarni
Suzanne Shontz
Kyle Camarda*

Abstract: [ Show / Hide ]
Resource limited embedded systems provide a great challenge to programming using functional languages. Although we cannot program these embedded systems directly with Haskell, we show than an embedded domain specific language is able to be used to program them, providing a user friendly environment for both prototyping and full development. The Arduino line of microcontroller boards provide a versatile, low cost and popular platform for development of these resource limited systems, and we use this as the platform for our DSL research.

First we provide a shallowly embedded domain specific language and a firmware interpreter, allowing the user to program the Arduino while tethered to a host computer. Second, we add a deeply embedded version, allowing the interpreter to run standalone from the host computer, as well as allowing us to compile the code to C and then machine code for efficient operation. Finally, we develop a method of transforming the shallowly embedded DSL syntax into the deeply embedded DSL syntax automatically,



RUBAYET SHAFIN - Performance Analysis of Parametric Channel Estimation for 3D Massive MIMO/FD-MIMO OFDM Systems

MS Thesis Defense (EE)

When & Where:
April 25, 2017
2:00 pm
250 Nichols Hall
Committee Members:
Lingjia Liu, Chair
Erik Perrins
Yang Yi

Abstract: [ Show / Hide ]
With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is critical. In this thesis, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive- MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the root mean square error (RMSE) of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system.



DANIEL HEIN - A New Approach for Predicting Security Vulnerability Severity in Attack Prone Software Using Architecture and Repository Mined Change Metrics

PhD Dissertation Defense (CS)

When & Where:
April 21, 2017
2:00 pm
1 Eaton Hall
Committee Members:
Hossein Saiedian, Chair
Arvin Agah
Perry Alexander
Prasad Kulkarni
Nancy Mead
Reza Barati*

Abstract: [ Show / Hide ]
Billions of dollars are lost every year to successful cyber attacks that are fundamentally enabled by software vulnerabilities. Modern cyber attacks increasingly threaten individuals, organizations, and governments, causing service disruption, inconvenience, and costly incident response. Given that such attacks are primarily enabled by software vulnerabilities, this work examines the efficacy of using change metrics, along with architectural burst and maintainability metrics, to predict modules and files that should be analyzed or tested further to excise vulnerabilities prior to release.

The problem addressed by this research is the residual vulnerability problem, or vulnerabilities that evade detection and persist in released software. Many modern software projects are over a million lines of code, and composed of reused components of varying maturity. The sheer size of modern software, along with the reuse of existing open source modules, complicates the questions of where to look, and in what order to look, for residual vulnerabilities.

Traditional code complexity metrics, along with newer frequency based churn metrics (mined from software repository change history), are selected specifically for their relevance to the residual vulnerability problem. We compare the performance of these complexity and churn metrics to architectural level change burst metrics, automatically mined from the git repositories of the Mozilla Firefox Web Browser, Apache HTTP Web Server, and the MySQL Database Server, for the purpose of predicting attack prone files and modules.

We offer new empirical data quantifying the relationship between our selected metrics and the severity of vulnerable files and modules, assessed using severity data compiled from the NIST National Vulnerability Database, and cross-referenced to our study subjects using unique identifers defined by the Common Vulnerabilities and Exposures (CVE) vulnerability catalog. Our results show that architectural level change burst metrics can perform well in situations where more traditional complexity metrics fail as reliable estimators of vulnerability severity. In particular, results from our experiments on Apache HTTP Web Server indicate that architectural level change burst metrics show high correlation with the severity of known vulnerable modules, and do so with information directly available from the version control repository change-set (i.e., commit) history.




CHENG GAO - Mining Incomplete Numerical Data Sets

PhD Comprehensive Defense (CS)

When & Where:
April 19, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Arvin Agah
Bo Luo
Tyrone Duncan
Xuemin Tu*

Abstract: [ Show / Hide ]
Incomplete and numerical data are common for many application domains. There have been many approaches to handle missing data in statistical analysis and data mining. To deal with numerical data, discretization is crucial for many machine learning algorithms. However, most of the discretization algorithms cannot be applied to incomplete data sets.

Multiple Scanning is an entropy based discretization method. Previous research shown it outperforms commonly used discretization methods: Equal Width or Equal Frequency discretization. In this work, Multiple Scanning is tested on C4.5 and MLEM2 on incomplete datasets. Results show for some data sets, the setup utilizing Multiple Scanning as preprocessing performs better, for the other data sets, C4.5 or MLEM2 should be used by themselves. Our conclusion is that there are no universal optimal solutions for all data sets. Setup should be custom-made.




SUMIAH ALALWANI - Experiments on Incomplete Data Sets Using Modifications to Characteristic Relation

MS Thesis Defense (CS)

When & Where:
April 10, 2017
11:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
Rough set theory is a useful approach for decision rule induction, which is applied, to large life data sets. Lower and upper approximations of concepts values are used to induce rules for incomplete data sets. In our research we will study validity of modifications suggested to characteristic relation. We discuss the implementation of modifications to characteristic relation, and the local definability of each modified set. We show that all suggested modifications sets are not locally definable except for maximal consistent blocks that are restricted to data set with “do not care” conditions. A comparative analysis was conducted for characteristic sets and modifications in terms of cardinality of lower and upper approximations of each concept and decision rules induced by each modification. In this thesis, experiments were conducted on four incomplete data sets with lost and “do not care “ conditions. LEM2 algorithm was implemented to induce certain and possible rules form the incomplete data set. To measure the classification average error rate for induced rules, ten-fold cross validation was implemented. Our results show that there is no significant difference between the qualities of rule induced from each modification.



DANIEL GOMEZ GARCIA ALVESTEGUI - Ultra-Wideband Radar for High-Throughput-Phenotyping of Wheat Canopies

PhD Comprehensive Defense (EE)

When & Where:
March 30, 2017
10:00 am
250 Nichols Hall
Committee Members:
Carl Leuschen, Chair
Chris Allen
Ron Hui
Fernando Rodriguez-Morales
David Braaten*

Abstract: [ Show / Hide ]
Increasing the rate of crop yield is an important issue to meet projected future crop production demands. Breeding efforts are being made to rapidly improve crop yields and make them more stress-resistance. Accelerated molecular breeding techniques, in which desirable plant physical traits are selected based on genetic markers, rely on accurate and rapid methods to link plant genotypes and phenotypes. Advances in next-generation-DNA sequencing have made genotyping a fast and efficient process. In contrast, methods for characterizing physical traits remain inefficient.
The height of wheat crop is an important trait as it may be related to yield and biomass. It is also an indicator of plant growth-stage. Recent high-throughput-phenotyping experiments have used sensing techniques to measure canopy height based on ultrasound sonar and cameras. The main drawback of these methods is that the ground topography is not directly measured.
In contrast to current sensors, ultra-wideband radars have the potential to take distance measurements to the top of the canopy and the ground simultaneously. We propose the study of ultra-wideband radar for measuring wheat crop heights. Specifically, we propose to study the effects of canopy constituents on the ranging radar-return or impulse-response, as well as on the frequency-response. First, a numerical simulator will be developed to accurately calculate the radar response at different canopy conditions. Second, a parametric study will be performed with aforementioned simulator. Lastly, an estimation algorithm for crop canopy heights will be developed, based on the parametric study.



ALI ABUSHAIBA - Maximum Power Point Tracking for Photvoltaic Systems Using a Discreet in Time Extremum Seeking Algorithm

PhD Comprehensive Defense (EE)

When & Where:
March 28, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Reza Ahmadi, Chair
Ken Demarest
Glenn Prescott
Alessandro Salandrino
Huazhen Fang*

Abstract: [ Show / Hide ]
Energy harvesting from solar sources in an attempt to increase efficiency has sparked interest in many communities to develop more energy harvesting applications for renewable energy topics. Advanced technical methods are required to ensure the maximum available power is harnessed from the photovoltaic (PV) system. This work proposes a new discrete-in-time extremum-seeking based technique for tracking the maximum power point of a photovoltaic array. The proposed method is a true maximum power point tracker that can be implemented with reasonable processing effort on an expensive digital controller. The approach is to study the stability analysis of the proposed method to guarantee the convergence of the algorithm. The proposed method should exhibit better performance in comparison to conventional Maximum Power Point Tracking (MPPT) methods and require less computational effort than the complex mathematical methods.




JAISNEET BHANDAL - Classification of Private Tweets using Tweets Content

MS Project Defense (CS)

When & Where:
March 27, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Bo Luo, Chair
Jerzy Grzymala-Busse
Prasad Kulkarni

Abstract: [ Show / Hide ]
Online social networks (OSNs) like Twitter provide an open platform for users to easily convey their thoughts and ideas from personal experiences to breaking news. With the increasing popularity of Twitter and the explosion of tweets, we have observed large amounts of potentially sensitive/private messages being published to OSNs inadvertently or voluntarily. The owners of these messages may become vulnerable to online stalkers or adversaries, and they often regret posting such messages. Therefore, identifying tweets that reveal private/sensitive information is critical for both the users and the service providers. However, the definition of sensitive information is subjective and different from person to person. To develop a privacy protection mechanism that is customizable to fit the needs of diverse audiences, it is essential to accurately and automatically identify and classify potentially sensitive tweets.
In this project, we adopted a two-step approach - private tweet identification, and private tweet classification. We make the first attempt to classify private tweets into two main categories, sensitive and nonsensitive - private tweet identification, followed by private tweet classification where we categorize the sensitive tweets into 13 pre-defined topics. We consider private tweet identification and private tweet classification as dual-problems. Progress towards one of them will eventually benefit the other. We used a 2-layer classification approach, where we explore different combinations of classifiers, and analyze the performance of each combination.



JONATHAN LYLE - A Digital Approach to Bistatic Radar Synchronization via GPS PPS

MS Project Defense (CoE)

When & Where:
March 16, 2017
10:30 am
246 Nichols Hall
Committee Members:
Carl Leuschen, Chair
Chris Allen
Jilu Li

Abstract: [ Show / Hide ]
Bistatic Radar systems utilize physically separate transmit and receive systems to collect information that monostatic systems cannot. One issue with developing bisatic systems is guaranteeing synchronization between the transmitters and receivers. This project presents a purely digital method for improving synchronization of a bistatic system based on the GPS PPS signal, and using step-time for both transmitter and receiver timing. The issue of bistatic synchronization is simulated in Matlab and then modified to utilize the proposed step-time adjustment to show that the method works in theory. This method is then implemented in hardware on the digital system of CReSIS’s ‘HF Sounder’ radar system, and then tested to verify that the proposed method can be implemented in hardware and that it improves performance.



TYLER WADE - AOT Vs. JIT: Impact of Profile Data on Code Quality

MS Thesis Defense (CS)

When & Where:
March 8, 2017
3:00 pm
246 Nichols Hall
Committee Members:
Prasad Kulkarni, Chair
Perry Alexander
Heechul Yun

Abstract: [ Show / Hide ]
Just-in-time (JIT) compilation during program execution and
ahead-of-time (AOT) compilation during software installation are
alternate techniques used by managed language virtual machines
(VM) to generate optimized native code while simultaneously
achieving binary code portability and high execution performance.
JIT compilers typically collect profile information at run-time to
enable profile-guided optimizations (PGO) to customize the gener-
ated native code to different program inputs/behaviors. AOT com-
pilation removes the speed and energy overhead of online profile
collection and dynamic compilation, but may not be able to achieve
the quality and performance of customized native code. The goal
of this work is to investigate and quantify the implications of the
AOT compilation model on the quality of the generated native code
for current VMs.
First, we quantify the quality of native code generated by the
two compilation models for a state-of-the-art (HotSpot) Java VM.
Second, we determine how the amount of profile data collected af-
fects the quality of generated code. Third, we develop a mechanism
to determine the accuracy or similarity of different profile data for a
given program run, and investigate how the accuracy of profile data
affects its ability to effectively guide PGOs. Finally, we categorize
the profile data types in our VM and explore the contribution of
each such category to performance.



LOHITH NANUVALA - An Implementation of the MLEM2 Algorithm

MS Project Defense (CS)

When & Where:
February 24, 2017
1:00 pm
1 Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Richard Wang

Abstract: [ Show / Hide ]
Data mining is the process of finding meaningful information from data. Data mining can be used in several areas like business, medicine, education etc. It allows us to find patterns in the data and make predictions for the future. One form of data mining is to extract rules from data sets. In this project we discuss an implementation of one of the data mining algorithms called MLEM2 (Modified Learning from Examples Module, version 2). This algorithm uses the concept of blocks of attribute-value pairs. It is also robust and generates rules for both complete and incomplete data sets with numeric and symbolic attributes. A rule checker has been developed which is used to evaluate the rule sets produced by MLEM2. The accuracy of the rules is measured by computing the error rate which is the ratio of the number of incorrectly classified cases to the total number of all cases. Experiments are conducted on different kinds of data sets (complete, incomplete, numeric and symbolic) using 10-fold cross validation method.



ASHWINI BALACHANDRA - Implementation of Truncated Lévy Walk Mobility Model in ns-3

MS Project Defense (EE)

When & Where:
January 31, 2017
1:30 pm
246 Nichols Hall
Committee Members:
James Sterbenz, Chair
Victor Frost
Fengjun Li

Abstract: [ Show / Hide ]
Mobility models generate the mobility patterns of the nodes in a given system. Mobility models help us to analyze and study the characteristic of new and existing systems. Various mobility models implemented in network simulation tools like ns-3 does not model the patterns of human mobility. The main idea of this project is to implement the truncated Lévy walk mobility model in ns-3. The model has two variations, in the first variation, the flight length and pause time of the nodes are determined from the truncated Pareto distribution and in the second variation, Lévy distribution models the flight length and pause time distributions and the values are obtained by Lévy α-stable random number generator. The mobility patterns of the nodes are generated and analyzed for the model by changing various model attributes. Further studies can be done to understand the behavior of these models for different ad hoc networking protocols.



PAVAN KUMAR MOTURU - Image Processing Techniques in Matlab GUI

MS Project Defense (CS)

When & Where:
January 31, 2017
9:00 am
246 Nichols Hall
Committee Members:
Carl Leuschen, Chair
Chris Allen
Fernando Rodriguez-Morales

Abstract: [ Show / Hide ]
Identifying missing bed in radar data is very important in sea level changes. Increase in sea level is a problem of global importance because of its impact on infrastructure. Ice sheets in the Greenland and Antarctic are melting and increasing their contribution to sea level change over the last decade. Measuring ice sheets thickness is required to estimate sea level rise. We need to use several algorithms, pre-defined functions to extract the weak bed echoes, but we don’t have a tool in Matlab which contains some important algorithms like ImageJ. We can’t process all the data in ImageJ as Matlab produces better results compared to ImageJ as some of the functions like window and symmetric selection around center in FFT domain are not implemented in ImageJ.
In this project, we will investigate the application of some image processing techniques using a GUI developed for analyzing ice sounding radargrams. One key advantage of the tool is that the image processing techniques are applied in a single GUI instead of doing separately. We apply these techniques on the data which came after applying extensive signal processing techniques. After performing these techniques, we compare the processed data with the original data.




MOHSEN ALEENEJAD - New Modulation Methods and Control Strategies for Power Converters

PhD Comprehensive Defense (EE)

When & Where:
January 30, 2017
3:00 pm
1 Eaton Hall
Committee Members:
Reza Ahmadi, Chair
Glenn Prescott
Alessandro Salandrino
Jim Stiles
Huazhen Fang*

Abstract: [ Show / Hide ]
The DC to AC power Inverters (so-called Inverters) are widely used in industrial applications. The multilevel Inverters are becoming increasingly popular in industrial apparatus aimed at medium to high power conversion applications. In comparison to the conventional inverters, they feature superior characteristics such as lower total harmonic distortion (THD), higher efficiency, and lower switching voltage stress{Malinowski, 2010 #9}{Malinowski, 2010 #9}. Nevertheless, the superior characteristics come at the price of a more complex topology with an increased number of power electronic switches. As a general rule in a Inverter topology, as the number of power electronic switches increases, the chances of fault occurrence on of the switches increases, and thus the Inverter’s reliability decreases. Due to the extreme monetary ramifications of the interruption of operation in commercial and industrial applications, high reliability for power Inverters utilized in these sectors is critical. As a result, developing fault-tolerant operation schemes for multilevel Inverters has always been an interesting topic for researchers in related areas. The purpose of this proposal is to develop new control and fault-tolerant strategies for the multilevel power Inverter. In the event of a fault, the line voltages of the faulty Inverters are unbalanced and cannot be applied to the three phase loads. This fault-tolerant strategy generates balanced line voltages without bypassing any healthy and operative Inverter element, makes better use of the Inverter capacity and generates higher output voltage. This strategy exploits the advantages of the Selective Harmonic Elimination (SHE) method in conjunction with a slightly modified Fundamental Phase Shift Compensation technique to generate balanced voltages and manipulate voltage harmonics at the same time. However, due to the distinctive requirement of the strategy to manipulate both amplitude and angle of the harmonics, the conventional SHE technique is not the suitable basis for the proposed strategy. Therefore, in this project a modified Unbalanced SHE technique which can be used as the basis for the fault-tolerant strategy is developed. The proposed strategy is applicable to several classes of multilevel Inverters with three or more voltage levels.




SIVA RAM DATTA BOBBA - Rule Induction For Numerical Data using PRISM

MS Project Defense (CS)

When & Where:
January 30, 2017
1:00 pm
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Bo Luo
James Miller

Abstract: [ Show / Hide ]
Rule induction is one of the basic and important techniques of data mining. Inducing a rule set for symbolic data is simple and straightforward, but it becomes complex when the attributes are numerical. There are several algorithms available that do the task of rule induction for symbolic data. One such algorithm is PRISM which uses conditional probability for attribute-value selection to induce a rule.
In the real world scenario, data may comprise of either symbolic or numerical attributes. It becomes difficult to induce a discriminant ruleset on the data with numerical attributes. This project provides an implementation of PRISM to handle numerical data. First, it takes as input, a dataset with numerical attributes and converts them into discrete values using the multiple scanning approach which identifies the cut-points for intervals using minimum conditional entropy. Once discretization completes, PRISM uses these discrete values to induce ruleset for each decision. Thus, this project helps to induce modular rulesets over a numerical dataset.



NILISHA MANE - Tools to Explore Run-time Program Properties

MS Project Defense (CS)

When & Where:
January 30, 2017
11:30 am
246 Nichols Hall
Committee Members:
Prasad Kulkarni,Chair
Perry Alexander
Gary Minden

Abstract: [ Show / Hide ]
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.



BRIAN MACHARIA - UWB Microwave Filters on Multilayer LCP Substrates: A Feasibility Study

MS Project Defense (EE)

When & Where:
January 30, 2017
11:00 am
317 Nichols Hall
Committee Members:
Carl Leuschen, Chair
Fernando Rodriguez-Morales-Co-Chair
Chris Allen

Abstract: [ Show / Hide ]
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.



Md. MOSHFEQUR RAHMAN - OpenFlow based Multipath Communication for Resilience

MS Thesis Defense (EE)

When & Where:
January 30, 2017
9:00 am
246 Nichols Hall
Committee Members:
James Sterbenz, Chair
Victor Frost
Fengjun Li

Abstract: [ Show / Hide ]
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.



RAGAPRABHA CHINNASWAMY - A Comparison of Maximal Consistent Blocks and Characteristics Sets for Incomplete Data Sets

MS Project Defense (CS)

When & Where:
January 25, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
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.



PRAVEEN YARLAGADDA - A Comparison of Rule Sets Generated by Algorithms: AQ, C4.5, and CART

MS Project Defense (CS)

When & Where:
January 24, 2017
2:00 pm
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Bo Luo
Jim Miller

Abstract: [ Show / Hide ]
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.



DIVYA GUPTA - Investigation of a License Plate Recognition Algorithm

MS Project Defense (EE)

When & Where:
January 24, 2017
8:00 am
250 Nichols Hall
Committee Members:
Glenn Prescott, Chair
Erik Perrins
Jim Stiles

Abstract: [ Show / Hide ]
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.



NAZMA KOTCHERLA - Hybrid Mobile and Responsive Web Application - KU Quick Quiz

MS Project Defense (CoE)

When & Where:
January 23, 2017
2:00 pm
2001B Eaton Hall
Committee Members:
Prasad Kulkarni, Chair
Perry Alexander
Jerzy Grzymala-Busse

Abstract: [ Show / Hide ]
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.



JYOTHI PRASAD PANGLURI SREEHARINAIDU - Implementation of ChiMerge Algorithm for Discretization of Numerical Attributes

MS Project Defense (CS)

When & Where:
January 23, 2017
10:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Perry Alexander
Prasad Kulkarni

Abstract: [ Show / Hide ]
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.




MOHAN KRISHNA VEERAMACHINENI - A Graphical User Interface System for Rule Visualization

MS Project Defense (CS)

When & Where:
January 20, 2017
1:00 pm
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Bo Luo
Prasad Kulkarni

Abstract: [ Show / Hide ]
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.



HARA MADHAV TALASILA - Modular Frequency Multiplier and Filters for the Global Hawk Snow Radar

MS Thesis Defense (EE)

When & Where:
January 20, 2017
12:00 pm
317 Nichols Hall
Committee Members:
John Paden, Chair
Chris Allen
Carl Leuschen
Fernando Rodriguez-Morales

Abstract: [ Show / Hide ]
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.




SOUMYAROOP NANDI - Robust Object Tracking and Adaptive Detection for Autonavigation of Unmanned Aerial Vehicle

MS Thesis Defense (EE)

When & Where:
January 9, 2017
9:00 am
246 Nichols Hall
Committee Members:
Richard Wang, Chair
Jim Rowland
Jim Stiles

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




TRUC ANH NGUYEN - ResTP: A Configurable and Adaptable Multipath Transport Protocol for Future Internet Resilience

PhD Comprehensive Defense (CS)

When & Where:
January 6, 2017
8:00 am
246 Nichols Hall
Committee Members:
James Sterbenz, Chair
Victor Frost
Bo Luo
Gary Minden
Justin Rohrer
Michael Welzl
Hyunjin Seo*

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