Presentation Schedule

Fall 2016

Abstract

Slide #Last Name First Name Title & Abstract of Presentation Ph.D Advisor Day & Time Venue

4 Akbar Md Navid Widely Linear Estimation with Complex Data Mohammad SaquibNovember 28

11:00-12:00 amECSN

4.728

5 Arjona Angarita Ricardo Javier Performance Analysis of the IEEE 802.11 Distributed

Coordination FunctionAndrea Fumagalli

October 281:00-3:00pm

ECSN 3.524

6 Arunachalam Harish BabuA Review of Methods for Whole Slide Histopathological

Image Analysis for Tumor Detection Ovidiu Daescu

October 19 12:00-1:00 pm

ECSS 4.910

7 Cao Beiming Silent Speech Recognition Using Inversely Mapped

Articulatory DataJun Wang

October 271:00-2:00 pm

BSB 11.102E

8 Challapalli Niharika Tight Bounds for Compressed Sensing Algorithms Mathukumalli

VidyasagarOctober 12

10:00-11:00 amECSS 3.910

9 Chen Yingping Integrated Isolated Power Converter Using Active

Rectification and Closed-Loop CRM Control for Secondary Side Regulation in E-Meters

Dongsheng MaNovember 282:00-3:00 pm

ECSN 3.804

10Ershad

LangroodiMarzieh

Identification of Meaningful Skill Assessment Metrics Using the Wisdome of Crowd

Ann MajewiczOctober 13

9:00-10:00 amECSN 2.704

11 Erturk Feyzullah A Method for Online Incipient Fault Detection in SiC

MOSFETs Bilal Akin

October 129:30-10:30 am

ECSN 4.702

12 Gour Riti Robust Routing in Networks with Probabilistic Failures Jason JueNovember 16 1:00-2:00 pm

ECSS 4.910

13 Hao YiyaNondeterministic Sound Source Localization with

Smartphones in CrowdsensingIssa Panahi

October 2811:00-12:00 pm

ECSS 3.910

Table of Contents

Abstract

Slide #Last Name First Name Title & Abstract of Presentation Ph.D Advisor Day & Time Venue

14 Jha Sumit Analysis of Driver Visual Attention Carlos BussoNovember 102:00-3:00 pm

ECSN 4.702

15 Kumar SauravExtremum Seeking Control for Multi-Objective Optimization

Problems Robert D Gregg

October 272:00-3:00 pm

ECSN 4.728

16 Li SenAn Energy-Stored Quasi-Z-Source Inverter for Application to

Photovoltaic Power System Poras Balsara

November 17 10:30-11:30 am

ECSN 4.728

17 Li Xi Optical Antenna Enhanced Light Emitting Devices Qing GuNovember 183:30-5:00 pm

ECSN 4.728

18 Liu Jiawei Noise Subspace-Based Iterative Technique for Direction

Finding Mohammad Saquib

October 26 10:00-11:00 am

ECSN 4.728

19 Lotfi Mahsa Prediction of Time to Tumor Recurrence in Ovarian Cancer

using Sparse Regression Methods Mathukumalli

VidyasagarOctober 12

12:00-1:00 pmECSS 3.910

20 NimmalapudiSai Govinda

Rao Self-Correcting Op-Amp Input Offset Using Analog Floating

Gate Memory Andrew Marshall

November 810:00-11:00 am

ECSS 3.503

21 YelleswarapuVenkata Pavan

Kumar Concepts and Methods in Optimization of Phase Noise in LC

VCOs Kenneth O

November 29 10:00-11:00 am

ECSN 3.802

22 Zhao Zhongfan Literature Review of Fast Extremum Seeking Control for

Wiener-Hammerstein Plants Yaoyu Li

November 28 9:30-11:30 am

ECSN 4.702

Widely Linear Estimation with

Complex Data

Mean square estimation of complex and normal datais not linear as in the real case but widely linear. Thepurpose of this correspondence is to calculate theoptimum widely linear mean square estimate and topresent its main properties. The advantage withrespect to linear procedure is especially analyzed.

Abstract:

Md Navid AkbarNovember 28, 2016 │ 11:00 am-12:00 pm │ ECSN 4.728

PhD Advisor: Dr. Mohammad Saquib

Performance Analysis of the

IEEE 802.11 Distributed

Coordination Function

To present an analytical model to compute the802.11DCF throughput, in the assumption of finitenumber of terminals and ideal channel conditions. Themodel is applied to the basic access and RTS/CTSaccess mechanisms with an extensive throughputperformance evaluation.

Abstract:

Ricardo Javier Arjona AngaritaOctober 28, 2016 │ 1:00 pm-3:00 pm │ ECSN 3.524

PhD Advisor: Dr. Andrea Fumagalli

A Review of Methods for Whole

Slide Histopathological Image

Analysis for Tumor Detection

Histopathological image analysis, an important sub-field in Pathology

informatics, deals with identifying patterns and extracting features

from whole slide images that help in aiding tumor prognosis or

diagnosis. In most of the research studies, histopathological images

used for tumor analysis, contain data that is rich in information about

the cellular level details. This presentation covers various image

processing algorithms that are used in tumor studies, their limitations

and explains why it is difficult to develop a standard pipeline of

algorithms that are applicable for all types of tumor.

Abstract:

Harish Babu ArunachalamOctober 19, 2016 │ 12:00–1:00 pm │ ECSS 4.910

PhD Advisor: Dr. Ovidiu Daescu

Silent Speech Recognition

Using Inversely Mapped Articulatory Data

Silent speech recognition (SSR) is the process of converting non-audio (sensors) signals to

speech in the form of text. The primary application of SSR is assisting people with difficulties

in phonation such as laryngectomees, who have undergone a surgical removal of larynx for

treating laryngeal cancer. Articulatory movements (e.g., tongue, lips) are less affected by the

impairment of larynx compared to normal acoustic signals. In this study, I use the movement

signal of four sensors (upper lip, lower lip, tongue tip, tongue back) attached to speakers'

articulators in my experiments. In this talk, I will present the experiments I have done on silent

speech recognition including using articulatory movement data from healthy speakers,

laryngectomees, and whispered speech that were recently collected in our lab. Two speaker

normalization approaches, Procrustes matching, a physiological approach, and fMLLR, a

data-driven approach have been used to reduce the speaker articulation variation. The

experimental results indicated the effectiveness of these approaches. I am currently exploring

inverse mapping approaches (acoustic-to-articulatory mapping), which will be used for

generating articulatory data from a large acoustic dataset. Then we will have the possibility to

avoid collecting a large articulatory movement dataset, which is logistically difficult.

Abstract:

Beiming CaoOctober 27, 2016 │ 1:00–2:00 pm │ BSB 11.102E

PhD Advisor: Dr. Jun Wang

Tight Bounds for Compressed

Sensing Algorithms

The LASSO and the Elastic Net (EN) formulations are among the most popular approaches

for sparse regression and compressed sensing. We introduce a new optimization formulation

for sparse regression and compressed sensing, called CLOT (Combined L-One and Two),

wherein the regularizer is a convex combination of the L1 - and L2 -norms. This formulation

differs from the Elastic Net (EN) formulation, in which the regularizer is a convex combination

of the L1 – and L2 -norm squared. This seemingly simple modification has fairly significant

consequences. In particular, it is shown that the EN formulation does not achieve robust

recovery of sparse vectors in the context of compressed sensing, whereas the new CLOT

formulation does so. Also, like EN but unlike LASSO, the CLOT formulation achieves the

grouping effect, wherein coefficients of highly correlated columns of the measurement (or

design) matrix are assigned roughly comparable values. It is noteworthy that LASSO does not

have the grouping effect and EN does not achieve robust sparse recovery. Therefore the

CLOT formulation combines the best features of both LASSO (robust sparse recovery) and

EN (grouping effect).

Abstract:

Niharika ChallapalliOctober 12, 2016 │ 10:00–11:00 am │ ECSS 3.910

PhD Advisor: Dr. Mathukumalli Vidyasagar

Integrated Isolated Power Converter Using Active

Rectification and Closed-Loop CRM Control for

Secondary Side Regulation in E-Meters

An integrated isolated power converter is presented to mitigate efficiency, accuracy and

cost challenges on secondary side regulation. It employs time-multiplexing scheme to

seamlessly power the non-isolated primary and the isolated secondary output. With the

coordination of proposed primary state detection (PSD) circuit, the secondary side output

is accurately regulated and automatically synchronized to the primary side without the use

of bulky transformers or optocouplers. In collaboration with the PSD, an active rectifier is

adopted, which replaces conventional power diode and LDO for efficiency enhancement.

Designed in a 0.35µm BCD process, the converter achieves closed-loop regulations on

both the non-isolated and the isolated output at 5V, with an input source of 24V. Thanks to

the proposed circuits and control scheme, the peak efficiency at secondary side is

improved by 12.4%, with the output variation reduced to 0.6% in a load range of 500mA.

Abstract:

Yingping ChenNovember 28, 2016 │ 2:00-3:00pm │ECSN 3.804

PhD Advisor: Dr. Dongsheng Ma

Identification of Meaningful Skill

Assessment Metrics Using the

Wisdome of Crowd

Robotic surgery requires skills which differ from that of open and laparoscopic surgery.

Training surgical residents to master these techniques is an ongoing field of research. In this

study we aim to find the data metrics (motion and physiological data) which best correlates

to the crowd’s choices of word from the semantic labeling lexicon of surgical expertise. Three

experts, three intermediates and three novices participated in our study. They completed two

tasks on the da Vinci surgical simulator: a ring and rail task and a a suture sponge task.

Different data measurement were acquired using sensors which recorded limb (hand and

arm) acceleration and angular velocity and joint (shoulder, elbow, wrist) positions. Posture

videos of the subject performing the task as well as videos from the task being performed

were rated by crowd workers and the best correlations between the data metrics and crowd

assignments were found. Through this we were able to find the best measurement data

metric for the word choices in our lexicon.

Abstract:

Marzieh Ershad LangroodiOctober 13, 2016 │ 9:00–10:00 am │ ECSN 2.704

PhD Advisor: Dr. Ann Majewicz

A Method for Online Incipient

Fault Detection in SiC MOSFETs

This presentation introduces a comprehensive study on degradation monitoring of

SiC MOSFETs and propose a method to detect incipient faults for early warning in

power converters and smart gate drivers. During the accelerated ageing tests (power

cycling) several electrical parameters are recorded to analyze critical signatures and

precursors for early fault detection. Among those, gate leakage current is identified as

one of the most practical precursor which exhibit consistent changes throughout the

aging and relatively easy to monitor. The proposed method is experimentally justified

which can be integrated to a gate driver to monitor the FETs condition. This method

naturally fits to the applications which cannot tolerate interrupts caused by

unpredicted failures. Due to its simple scheme and low cost, it can potentially be

embedded into commercial gate drivers featuring improved reliability options.

Abstract:

Feyzullah ErturkOctober 12, 2016│ 9:30–10:30 am │ECSN 4.702

PhD Advisor: Dr. Bilal Akin

Robust Routing in Networks with

Probabilistic Failures

I will be presenting a journal paper from IEEE/ACM Transactions on Networking titled Robust

Routing in Networks with Probabilistic Failures, by Hyang-Won Lee, Eytan Modiano, Kayi Lee.

The abstract of the paper is mentioned below. I will also be including some part of my research

work towards the end of the presentation which is related to ensuring survivability for multi-

domain optical networks. Abstract of the journal paper—We develop diverse routing schemes

for dealing with multiple, possibly correlated, failures. While disjoint path protection can

effectively deal with isolated single link failures, recovering from multiple failures is not

guaranteed. In particular, events such as natural disasters or intentional attacks can lead to

multiple correlated failures, for which recovery mechanisms are not well understood. We take a

probabilistic view of network failures where multiple failure events can occur simultaneously,

and develop algorithms for finding diverse routes with minimum joint failure probability.

Moreover, we develop a novel Probabilistic Shared Risk Link Group (PSRLG) framework for

modeling correlated failures. In this context, we formulate the problem of finding two paths with

minimum joint failure probability as an integer nonlinear program (INLP) and develop

approximations and linear relaxations that can find nearly optimal solutions in most cases.

Abstract:

Riti GourNovember 16, 2016│ 1:00-2:00 pm │ECSS 4.910

PhD Advisor: Dr. Jason Jue

Nondeterministic Sound

Source Localization with

Smartphones in Crowdsensing

The proliferation of smartphones nowadays has enabled many crowd assisted applications including audio-

based sensing. In such applications, detected sound sources are meaningless without location information.

However, it is challenging to localize sound sources accurately in a crowd using only microphones

integrated in smartphones without existing infrastructures, such as dedicated microphone sensor systems.

The main reason is that a smartphone is a nondeterministic platform that produces large and unpredictable

variance in data measurements. Most existing localization methods are deterministic algorithms that are ill

suited or cannot be applied to sound source localization using only smartphones. In this paper, we propose

a distributed localization scheme using nondeterministic algorithms. We use the multiple possible

outcomes of nondeterministic algorithms to weed out the effect of outliers in data measurements and

improve the accuracy of sound localization. We then proposed to optimize the cost function using least

absolute deviations rather than ordinary least squares to lessen the influence of the outliers. To evaluate

our proposal, we conduct a testbed experiment with a set of 16 Android devices and 9 sound sources. The

experiment results show that our nondeterministic localization algorithm achieves a root mean square error

(RMSE) of 1.19 m, which is close to the Cramer-Rao bound (0.8 m). Meanwhile, the best RMSE of

compared deterministic algorithms is 2.64 m.

Abstract:

Yiya HaoOctober 28, 2016 │ 11:00 am-12:00pm │ ECSS 3.910

PhD Advisor: Dr. Issa Panahi

Analysis of Driver Visual Attention

Monitoring driver behavior is crucial in the design of advanced driver assistance systems (ADAS)

that can detect driver actions, providing necessary warnings when not attentive to driving tasks.

The visual attention of a driver is an important aspect to consider, as most driving tasks require

visual resources. Previous work has investigated algorithms to detect driver visual attention by

tracking the head or eye movement. While tracking pupil can give an accurate direction of visual

attention, estimating gaze on vehicle environment is a challenging problem due to changes in

illumination, head rotations, and occlusions (e.g. hand, glasses). Instead, this study investigates

the use of the head pose as a coarse estimate of the driver visual attention. The key challenge is

the non-trivial relation between head and eye movements while glancing to a target object, which

depends on the driver, the underlying cognitive and visual demand, and the environment. First, we

evaluate the performance of a state-of-the-art head pose detection algorithm over natural driving

recordings, which are compared with ground truth estimations derived from AprilTags attached to a

headband. Then, the study proposes regression models to estimate the drivers’ gaze based on the

head position and orientation, which are built with data from natural driving recordings. The

proposed system achieves high accuracy over the horizontal direction, but moderate/low

performance over the vertical direction. We compare results while our participants were driving,

and when the vehicle was parked.

Abstract:

Sumit JhaNovember 10, 2016 │ 2:00-3:00pm │ ECSN 4.702

PhD Advisor: Dr. Carlos Busso

Extremum Seeking Control for Multi-

Objective Optimization Problems

Many real world applications require multiple conflicting objectives to be optimized. However,

explicit functions mapping the system variables to outputs are often unknown, making traditional

optimization approaches impossible. Extremum Seeking Control (ESC) is one method to tackle

this problem by estimating the local gradient of the objective functions. Extensive literature on

ESC focuses on single objective optimization, which use sinusoids as the dither signal. An

inherent disadvantage with the sinusoidal nature of the dither is that after convergence the output

continues to move around in a neighborhood of the optimal set-point, with the size of the

neighborhood proportional to the amplitude of the dither signal. This presentation focuses on using

square waves as dithers and proves that the square wave can produce a constant output, in

contrast to the typical sinusoid where the undesirable dithering effect still exists at the output. The

basic ESC scheme is extended to execute Multiple Gradient Descent Algorithm (MGDA) to solve a

Multi Objective Optimization Problem (MOOP). A two-stage ESC estimates the local gradient of

each objective function, then estimates the optimal weighting of gradients to move to the Pareto

front. This eliminates the need for a decision maker as required in a priori scalarization solutions a

MOOP. Simulation results show that ESC using MGDA is able to find the Pareto optimal solutions,

starting from different initial conditions. An application of a simple ESC is illustrated on auto- tuning

the PD gains of the knee and the ankle joints for a prosthetic controller.

Abstract:

Saurav KumarOctober 27, 2016 │ 2:00-3:00 pm │ ECSN 4.728

PhD Advisor: Dr. Robert D Gregg

An Energy-Stored Quasi-Z-Source

Inverter for Application to

Photovoltaic Power System

This presentation starts with the introduction to the concept and control method of Z-

source converter. Then it comes to present a new topology of the energy-stored quasi-Z-

source inverter (qZSI) to overcome the shortcoming of the existing solutions in

photovoltaic (PV) power system. Two strategies are discussed with the related design

principles to control the new energy-stored qZSI. They can control the inverter output

power, track the PV panel’s maximum power point, and manage the battery power,

simultaneously. The voltage boost and inversion, and energy storage are integrated in a

single-stage inverter. An experimental prototype is built to test the proposed circuit and

the two discussed control methods. The obtained results verify the theoretical analysis

and prove the effectiveness of the proposed control of the inverter’s input and output

powers and battery power regardless of the charging or discharging situation. A real PV

panel is used in the grid-tie test of the proposed energy-stored qZSI, which demonstrates

three operational modes suitable for application in the PV power system.

Abstract:

Sen LiNovember 17, 2016 │ 10:30-11:30 am │ ECSN 4.728

PhD Advisor: Dr. Poras Balsara

Optical Antenna Enhanced Light

Emitting Devices

Small on-chip light emitting devices are desirable for potential applications, including optical

communication, biomedical sensors, high resolution imaging and spectroscopy. However,

diffraction limit and high threshold have hold back the miniaturization of such devices. To overcome

restrictions imposed by the diffraction limit in all-dielectric devices, plasmonics, which uses metals

for extreme light concentration and manipulation in nanostructures, is introduced. Recent studies

on plasmonic lasers have shown the possibility to squeeze the effective mode volume into

subwavelength range. As nano-emitters of small aperture typically suffer from impedance

mismatch, resulting in the detectable output power to be much less than the already minuscule

emitted power, the idea of applying the optical antenna concept to the nano-emitter design

becomes very attractive. Similar to its radio frequency and microwave counterparts, optical antenna

serves as a transducer to convert localized energy to the energy of free propagating radiation, and

vice versa. Thus, nano-antenna can bridge the impedance mismatch between nano-emitters and

free space radiation, and concurrently enhance electric field confinement to increase radiation

efficiency of the device. Furthermore, it is feasible for designers to make trade-offs between high

electric field localization, broad bandwidth, high directivity by selecting different antenna types. The

small size of optical antennas also enables on-chip phased array configuration for even more

complex functions such as beam steering.

Abstract:

Xi LiNovember 18, 2016 │ 3:30-5:00pm │ ECSN 4.728

PhD Advisor: Dr. Qing Gu

Noise Subspace-Based Iterative

Technique for Direction Finding

In the area of array signal processing, direction of arrival (DoA) estimation is a widely

studied topic. In this estimation process, the noise subspace of the received signal

covariance matrix is often utilized and obtained through numerical methods. We explicitly

derive an algebraic expression of the noise subspace when the number of signal sources

present is less than the number of elements of a uniform linear array (ULA). This expression

of the noise subspace is then used to formulate a constrained minimization problem to

obtain the DoAs of all the sources in a scene in the presence of spatially white noise of

identical power. This noise subspace-based estimation (NISE) algorithm iteratively solves

for each source’s DoA, potentially yielding (depending on the number of iterations) lower

complexity than existing DoA estimation algorithms, such as fast root-MUSIC (FRM), while

exhibiting performance advantages for a low number of time samples and low signal-to-

noise ratio (SNR). The convergence of NISE is then proven mathematically. In addition, it is

shown how NISE can readily incorporate prior knowledge into the DoA estimation process.

Abstract:

Jiawei LiuOctober 26, 2016 │ 10:00-11:00am │ ECSN 4.728

PhD Advisor: Dr. Mohammad Saquib

Prediction of Time to Tumor

Recurrence in Ovarian Cancer using

Sparse Regression Methods

Ovarian cancer is the most fatal and the most aggressive gynecological malignancy. It is also

considered to be the fourth commonest cause of cancer deaths worldwide and also the cause of half

of the deaths related to gynecological cancers. Therefore, cancer prognosis plays an important role in

the evaluation and the treatment of a cancer patient. Sparse regression seems to be a useful tool in

predicting the time to tumor recurrence in Ovarian cancer. By applying sparse regression methods

such as CLOT and LASSO to the gene expressions of Ovarian cancer samples, one would be able to

prognosticate the time of tumor recurrence and also to compute the concordance index which is a

prognostic factor in Ovarian cancer. Due to the high dimensionality of gene expression data,

recursive feature elimination method is also recommended for the regression methods in order to

decrease the number of features used. In this study, we have applied two sparse regression

methods, CLOT and LASSO to four different Ovarian cancer datasets and computed the

concordance index for these datasets. Results show that sparse regression methods are more

successful in the computation of the concordance index for Ovarian cancer than previous methods in

overall.

Abstract:

Mahsa LotfiOctober 12, 2016 │ 12:00-1:00 pm │ ECSS 3.910

PhD Advisor: Dr. Mathukumalli Vidyasagar

Self-Correcting Op-Amp Input Offset

Using Analog Floating Gate Memory

Keeping input offset voltage low is important in high precision Op-Amps. However, input offset

errors caused by mismatch in differential signal paths as a result of random variations are

unavoidable even with optimum layout techniques. Mismatch increases as transistor

geometries reduce, so various techniques have been developed to minimize this, including:

increasing the size of the input pair, auto-zeroing, chopper stabilization and digital trim

techniques using such as flash, fuse or EEPROM. A relatively new method, the use of Analog

Floating Gate (AFG) devices, to enable correction is being studied. AFG devices act as

analog storage and allow precise trimming of input offset. AFG devices have been included in

an operational transconductance amplifier, where they can be trimmed to correct for input

offset. The proposed methodology results in offset correction for continuous time operation,

provides low power operation, does not limit bandwidth and avoids discrete errors seen with

some correction methods. However, unlike some other methods AFG devices have a

tendency to lose charge over time, typically a few mV per year. As a result, we have

developed circuitry that automatically recalibrates the AFG charge and therefore retains Op-

amp offset targets.

Abstract:

Sai Govinda Rao NimmalapudiNovember 8, 2016 │ 10:00-11:00 am │ ECSS 3.503

PhD Advisor: Dr. Andrew Marshall

Concepts and Methods in Optimization

of Phase Noise in LC VCOs

Integrated LC voltage-controlled oscillators (VCOs) are common functional blocks in modern

radio frequency communication systems and are used as local oscillators to up-convert and

down-convert the signals. Circuit noise and device noise can disturb both the amplitude and

phase of an oscillator’s output. Because amplitude fluctuations are usually greatly attenuated,

as a result, phase noise generally dominates. Phase Noise in oscillators can be described as

random frequency fluctuations of a signal, and negatively impacts both the transmitter and

receiver chains of the system. So, it is highly important to design a low phase-noise oscillator. A

design strategy centered around an inductance selection scheme is executed to optimize the

phase noise subject to design constraints such as power dissipation, tank amplitude, tuning

range, startup condition, etc. Two modes of operation, named current-limited and voltage-limited

regions, are studied for a typical LC oscillator. Important concepts such as waste of inductance

and waste of power have been observed in these modes of oscillator operation. As a result, the

design strategy can be summarized as follows: for a given bias current, phase noise increases

with an increasing inductance in the voltage limited region, which corresponds to waste of

inductance. The phase noise also increases with the bias current in the voltage limited region,

which corresponds to waste of power.

Abstract:

Venkata Pavan Kumar YelleswarapuNovember 29, 2016 │ 10:00-11:00am │ ECSN 3.802

PhD Advisor: Dr. Kenneth O

Literature Review of Fast

Extremum Seeking Control for

Wiener-Hammerstein Plants

Extremum Seeking Control (ESC) has emerged as a class of model-free real

time optimization algorithm, which is of particular interest for control

applications where acquisition of accurate plant models for model based

approaches is cost or even prohibitive. One major drawback for the

conventional ESC scheme is the relatively slow convergence due to the

nature of time-scale separation for such framework. Remarkable research has

recently been conducted on fast dither based ESC algorithm for Wiener-

Hammerstein plants, with the primary results obtained based on semi-global

analysis. Substantial improvements have been observed. This review will

describe the relevant analysis in both continuous-time and discrete-time

domain, including a linear parameter-varying approach.

Abstract:

Zhongfan ZhaoNovember 28, 2016 │ 9:30-11:30am │ ECSN 4.702

PhD Advisor: Dr. Yaoyu Li