Skip redundant pieces

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


SHAHAMMADULLAH SHAIK - Semi Supervised Entity Recognition in noisy-text

MS Project Defense (CS)

When & Where:
January 29, 2018
1:00 pm
2001B Eaton Hall
Committee Members:
Bo Luo, Chair
Jerzy Grzymala-Busse
Prasad Kulkarni

Abstract: [ Show / Hide ]
Many of the existing Named Entity Recognition (NER) solutions are built based on news corpus data with proper syntax. Existing systems might not lead to highly accurate results when applied on user generated data such as tweets which can feature sloppy spelling, concept drift, and limited contextualization of terms and concepts due to length constraints. The models described in this paper are based on Linear Chain Conditional Random Fields (CRFs) and use the BIO encoding scheme. The considered features include Glove vectors (Global vectors for word representations), word clusters built using Brown clustering algorithm and Part-of-Speech (PoS) Tag induction algorithm, gazetteer features and Regular expression features. Word clusters are built using the unlabeled tweet data. I have considered 11 entity types for building the model. The data used in these models is from Proceedings of the 2nd Workshop on Noisy User-generated Text and web crawled tweets. I have compared the results obtained by using different combination of features which shows the importance of pre-trained word vector representations, pre-trained brown clusters and clusters formed using labeled and unlabeled data.



PATRICK McCORMICK - Design and Optimization of Physical Waveform-Diverse Emissions

PhD Comprehensive Defense (EE)

When & Where:
January 29, 2018
12:30 pm
246 Nichols Hall
Committee Members:
Shannon Blunt, Chair
Chris Allen
Alessandro Salandrino
Jim Stiles
Emily Arnold*

Abstract: [ Show / Hide ]
With the advancement of arbitrary waveform generation techniques, new radar transmission modes can be designed via precise control of the waveform's time-domain signal structure. The finer degree of emission control for a waveform (or multiple waveforms via a digital array) presents an opportunity to reduce ambiguities in the estimation of parameters within the radar backscatter. While this freedom opens the door to new emission capabilities, one must still consider the practical attributes for radar waveform design. Constraints such as constant amplitude (to maintain sufficient power efficiency) and continuous phase (for spectral containment) are still considered prerequisites for high-powered radar waveforms. These criteria are also applicable to the design of multiple waveforms emitted from an antenna array in a multiple-input multiple-output (MIMO) mode.

In this work, two spatially-diverse radar emission design methods are introduced that provide constant amplitude, spectrally-contained waveforms. The first design method, denoted as spatial modulation, designs the radar waveforms via a polyphase-coded frequency-modulated (PCFM) framework to steer the coherent mainbeam of the emission within a pulse. The second design method is an iterative scheme to generate waveforms that achieve a desired wideband and/or widebeam radar emission. However, a wideband and widebeam emission can place a portion of the emitted energy into what is known as the `invisible' space of the array, which is related to the storage of reactive power that can damage a radar transmitter. The proposed design method purposefully avoids this space and a quantity denoted as the Fractional Reactive Power (FRP) is defined to assess the quality of the result.

The design of FM waveforms via traditional gradient-based optimization methods is also considered. A waveform model is proposed that is a generalization of the PCFM implementation, denoted as coded-FM (CFM), which defines the phase of the waveform via a summation of weighted, predefined basis functions. Therefore, gradient-based methods can be used to minimize a given cost function with respect to a finite set of optimizable parameters. A generalized integrated sidelobe metric is used as the optimization cost function to minimize the correlation range sidelobes of the radar waveform.



RAKESH YELLA - A Comparison of Two Decision Tree Generating Algorithms CART and Modified ID3

MS Project Defense (CS)

When & Where:
January 29, 2018
10:30 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Man Kong
Prasad Kulkarni

Abstract: [ Show / Hide ]
In Data mining, Decision Tree is a type of classification model which uses a tree-like data structure to organize the data to obtain meaningful information. We may use Decision Tree for important predictive analysis in data mining.

In this project, we compare two decision tree generating algorithms CART and the modified ID3 algorithm using different datasets with discrete and continuous numerical values. A new approach to handle the continuous numerical values is implemented in this project since the basic ID3 algorithm is inefficient in handling the continuous numerical values. In the modified ID3 algorithm, we discretize the continuous numerical values by creating cut-points. The decision trees generated by the modified algorithm contain fewer nodes and branches compared to basic ID3.

The results from the experiments indicate that there is statistically insignificant difference between CART and modified ID3 in terms of accuracy on test data. On the other hand, the size of the decision tree generated by CART is smaller than the decision tree generated by modified ID3.



SRUTHI POTLURI - A Web Application for Recommending Movies to Users

MS Project Defense (CS)

When & Where:
January 26, 2018
11:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Man Kong
Bo Luo

Abstract: [ Show / Hide ]
Recommendation systems are becoming more and more important with increasing popularity of e-commerce platforms. An ideal recommendation system recommends preferred items to the user. In this project, an algorithm named item-item collaborative filtering is implemented as premise. The recommendations are smarter by going through movies similar to the movies of different ratings by the user, calculating predictions and recommending those movies which have high predictions. The primary goal of the proposed recommendation algorithm is to include user’s preference and to include lesser known items in recommendations. The proposed recommendation system was evaluated on basis of Mean Absolute Error(MAE) and Root Mean Square Error(RMSE) against 1 Million movie rating involving 6040 users and 3900 movies. The implementation is made as a web-application to simulate the real-time experience for the user.



DEBABRATA MAJHI - IRIM: Interesting Rule Induction Module with Handling Missing Attribute Values

MS Project Defense (CS)

When & Where:
January 24, 2018
11:00 am
2001B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Bo Luo

Abstract: [ Show / Hide ]
In the current era of big data, huge amount of data can be easily collected, but the unprocessed data is not useful on its own. It can be useful only when we are able to find interesting patterns or hidden knowledge. The algorithm to find interesting patterns is known as Rule Induction Algorithm. Rule induction is a special area of data mining and machine learning in which formal rules are extracted from a dataset. The extracted rules may represent some general or local (isolated) patterns related to the data.
In this report, we will focus on the IRIM (Interesting Rule Inducing Module) which induces strong interesting rules that covers most of the concept. Usually, the rules induced by IRIM provides interesting and surprising insight to the expert in the domain area.
The IRIM algorithm was implemented using Python and pySpark library, which is specially customize for data mining. Further, the IRIM algorithm was extended to handle the different types of missing data. Then at the end the performance of the IRIM algorithm with and without missing data feature was analyzed. As an example, interesting rules induced from IRIS dataset are shown.





Past Defense Notices


SUSHIL BHARATI - Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles

MS Thesis Defense (EE)

When & Where:
January 22, 2018
11:00 am
246 Nichols Hall
Committee Members:
Richard Wang, Chair
Bo Luo
Suzanne Shontz

Abstract: [ Show / Hide ]
Vision-based autonomous navigation of UAVs in real-time is a very challenging problem, which requires obstacle detection, tracking, and depth estimation. Although the problems of obstacle detection and tracking along with 3D reconstruction have been extensively studied in computer vision field, it is still a big challenge for real applications like UAV navigation. The thesis intends to address these issues in terms of robustness and efficiency. First, a vision-based fast and robust obstacle detection and tracking approach is proposed by integrating a salient object detection strategy within a kernelized correlation filter (KCF) framework. To increase its performance, an adaptive obstacle detection technique is proposed to refine the location and boundary of the object when the confidence value of the tracker drops below a predefined threshold. In addition, a reliable post-processing technique is implemented for an accurate obstacle localization. Second, we propose an efficient approach to detect the outliers present in noisy image pairs for the robust fundamental matrix estimation, which is a fundamental step for depth estimation in obstacle avoidance. Given a noisy stereo image pair obtained from the mounted stereo cameras and initial point correspondences between them, we propose to utilize reprojection residual error and 3-sigma principle together with robust statistic based Qn estimator (RES-Q) to efficiently detect the outliers and accurately estimate the fundamental matrix. The proposed approaches have been extensively evaluated through quantitative and qualitative evaluations on a number of challenging datasets. The experiments demonstrate that the proposed detection and tracking technique significantly outperforms the state-of-the-art methods in terms of tracking speed and accuracy, and the proposed RES-Q algorithm is found to be more robust than other classical outlier detection algorithms under both symmetric and asymmetric random noise assumptions.



MOHSEN ALEENEJAD - New Modulation Methods and Control Strategies for Power Electronics Inverters

PhD Dissertation Defense (EE)

When & Where:
January 19, 2018
3:00 pm
Room 1 Eaton Hall
Committee Members:
Reza Ahmadi, Chair
Glenn Prescott
Alessandro Salandrino
Jim Stiles
Huazhen Fang*

Abstract: [ Show / Hide ]
The DC to AC power Converters (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. Nevertheless, the superior characteristics come at the price of a more complex topology with an increased number of power electronic switches. The increased number of power electronics switches results in more complicated control strategies for the inverter. Moreover, as the number of power electronic switches increases, the chances of fault occurrence 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 simple control strategies for normal and fault-tolerant operation of multilevel inverters has always been an interesting topic for researchers in related areas. The purpose of this dissertation is to develop new control and fault-tolerant strategies for the multilevel power inverter. For the normal operation of the inverter, a new high switching frequency technique is developed. The proposed method extends the utilization of the dc link voltage while minimizing the dv/dt of the switches. In the event of a fault, the line voltages of the faulty inverters are unbalanced and cannot be applied to the three phase loads. For the faulty condition of the inverter, three novel fault-tolerant techniques are developed. The proposed fault-tolerant strategies generate balanced line voltages without bypassing any healthy and operative inverter element, makes better use of the inverter capacity and generates higher output voltage. These strategies exploit the advantages of the Selective Harmonic Elimination (SHE) and Space Vector Modulation (SVM) methods in conjunction with a slightly modified Fundamental Phase Shift Compensation (FPSC) technique to generate balanced voltages and manipulate voltage harmonics at the same time. The proposed strategies are applicable to several classes of multilevel inverters with three or more voltage levels.



XIAOLI LI - Constructivism Learning

PhD Dissertation Defense (CS)

When & Where:
January 19, 2018
1:00 pm
246 Nichols Hall
Committee Members:
Luke Huan, Chair
Victor Frost
Bo Luo
Richard Wang
Alfred Ho*

Abstract: [ Show / Hide ]
Aiming to achieve the learning capabilities possessed by intelligent beings, especially human, researchers in machine learning field have the long-standing tradition of borrowing ideas from human learning, such as reinforcement learning, active learning, and curriculum learning. Motivated by a philosophical theory called "constructivism", in this work, we propose a new machine learning paradigm, constructivism learning. The constructivism theory has had wide-ranging impact on various human learning theories about how human acquire knowledge. To adapt this human learning theory to the context of machine learning, we first studied how to improve leaning performance by exploring inductive bias or prior knowledge from multiple learning tasks with multiple data sources, that is multi-task multi-view learning, both in offline and lifelong setting. Then we formalized a Bayesian nonparametric approach using sequential Dirichlet Process Mixture Models to support constructivism learning. To further exploit constructivism learning, we also developed a constructivism deep learning method utilizing Uniform Process Mixture Models.