PhD Dissertation Defense Announcement
Electrical Engineering

Polarization-insensitive all-optical dual pump-phase trans-multiplexing from 2 × 10-GBd OOKs to 10-GBd RZ-QPSK using cross-phase modulation in a passive nonlinear birefringent photonic crystal fiber

Tanvir Mahmood

2:00pm Thursday, 24 September 2015, ITE325b

Considering the network size, bit rate, spectral and channel capacity limitations, different modulation formats may be selectively used in future optical networks. Although the traditional metropolitan area networks (MANs) still use the non-return-to-zero on-off keying (NRZ-OOK) modulation format due to its technical simplicity and therefore low cost, QPSK format is more advantageous in spectrally efficient long-haul fiber optic transmission systems because of its constant power envelope, and robustness to various transmission impairments. Consequently, an important problem may arise, in particular how to route the OOK-data streams from MANs to long-haul backbone networks when the state of polarization (SOP) of the remotely generated OOK is unpredictable. Hence, the focus of this dissertation was to investigate a polarization-insensitive (PI) all-optical nonlinear optical signal processing (NOSP) method that can be implemented at the network cross-connect (X-connect) to transfer data from a remotely and a locally generated OOK data simultaneously to more effectual QPSK format for long-haul transmission. By utilizing cross-phase modulation (XPM) and inherent birefringence of the device, the work demonstrated, for the first time, PI all-optical data transfer utilizing dual pump-phase transmultiplexing (DPTM) from 2 × 10-GBd OOKs to 10-GBd RZ-QPSK in a passive nonlinear birefringent photonic crystal fiber (PCF). Polarization insensitivity was achieved by scrambling the SOP of the remotely generated OOK pump and launching the locally generated OOK pump and the probe off-axis. To mitigate polarization induced power fluctuations and detrimental effects due to nearby partially degenerate and non-degenerate four wave mixings, an optimum pump-probe detuning was also utilized. The PI DPTM RZ-QPSK demonstrated a pre-amplified receiver sensitivity penalty < 5.5 dB at 10−9 bit-error-rate (BER), relative to the FPGA-precoded RZ-DQPSK baseline in ASE-limited transmission system. The effect of the remotely generated OOK pump OSNR degradation on the PI DPTM RZ-QPSK was also investigated and it was established that 10−9 BER metric was attainable till the remotely generated OOK pump reached the threshold OSNR limit of 34 dB/0.1nm. Finally, DWDM transmission performance of the PI DPTM RZ-QPSK signal was evaluated using a 138-km long recirculating loop and it was demonstrated that the PI DPTM RZ-QPSK can be transmitted over 1,500 km before it reached ITU-T G.709 7% HD-FEC overhead limit. This propagation distance was well beyond the transmission requisites of any typical metro network (≈ 600 km). Furthermore, it was demonstrated that, within the threshold limit, OSNR degradation of the remotely generated OOK pump had minimal impact on the transmission distance of the PI DPTM RZ-QPSK before it reached 7% HD-FEC overhead limit.

Committee: Drs. Gary M. Carter (Chair), Anthony M. Johnson, Fow-Sen Choa, Tinoosh Mohsenin, Thomas E. Murphy (ECE,UMCP), William Astar

Ph.D. Dissertation Proposal

Verifiable Randomness and its Applications

Christopher Vatcher

10:30am Thursday, 24 September 2015, ITE 325b

We propose to create a public verifiable randomness beacon, to integrate with the Random-Sample Voting system, constructed to be secure against adversaries who have even almost complete control over the system’s source of public randomness including the entropy source.

By verifiable randomness, we do not mean we can prove a sequence of bits to be random. Instead, verifiability means it is possible to prove: (a) a consumer used uniform bits originating from a specific entropy source and therefore cannot lie about the bits used; and (b) the bits used were unpredictable prior to their generation and, with overwhelming probability, were free of adversarial influence. This is in contrast to ordinary public randomness where parties must agree to trust some randomness provider, who becomes a target of corruption. Verifiable randomness is an enhancement of public randomness used to perform random selection in voting, conduct random audits, preserve privacy, generate random challenges for secure multi-party computation, and public lottery draws. Random-Sample Voting specifically requires verifiable randomness for random voter selection and random audits.

Our work extends the work of Eastlake and Clark and Hengartner by considering (a) adversaries who have fine control over the entropy source and (b) physical entropy sources, which we can make verifiable.

Our specific aims include (a) creating adversary models for three entropy source abstractions based on trusted providers, sensor networks, and distributed proof-of-work systems; (b) create a verifiable random beacon that integrates each model; (c) integrate our work with the Random-Sample Voting system; and (d) integrate with NIST’s beacon and propose a verifiable randomness standard based on our work.

Our method is to weaken the trust assumption on the entropy source by introducing verifiable entropy sources, which have mechanisms for limiting adversarial influence and accumulating evidence that their outputs obey a known distribution. Combined with an appropriate randomness extractor, we can generate verifiable random bits. Using sources like weather, we will construct a verifiable randomness beacon: a public randomness provider unencumbered by generous and often unfounded trust assumptions. Such a beacon can serve as a singular gateway for accessing and aggregating multiple entropy sources without compromising the randomness provided to consumers.

Committee: Drs. Alan T. Sherman (Chair), Konstantinos Kalpakis, Weining Kang (Math/Stat), David Chaum (Random-Sample Voting), Aggelos Kiayias (University of Athens)

PhD Dissertation Proposal

Secured Embedded Many-Core Accelerator for Big Data Processing

Amey Kulkarni

2:00-4:00pm Friday, 18 September 2015, ITE 325b

I/O bandwidth and stringent delay constraints on processing time, limits the use of streaming Big Data for a large variety of real world problems. On the other hand, examining Big Data in applications such as intelligence, surveillance and reconnaissance unveils sensitive information in terms of hidden patterns or unknown correlations, thus demanding secured processing environment. In this PhD research, we propose a scalable and secured framework for a many-core accelerator architecture for efficient big data parallel processing. We propose to merge a compressive sensing-based framework to reduce IO Bandwidth and a machine learning-based framework to secure many-core communications. Four different reduced complexity architectures and two different modifications to Orthogonal Matching Pursuit (OMP) compressive sensing reconstruction algorithm are proposed. We implement the proposed OMP architectures on FPGA, ASIC, CPU/GPU and Many-Core to investigate hardware overhead cost. To secure communications within many-core, we propose two different machine learning-based Trojan detection framework which have minimal hardware overhead. To conclude this work, we aim to implement and evaluate the proposed scalable and secured many-core accelerator hardware for image and multi-channel biomedical signal processing on quad-core and sixteen-core architectures.

Committee: Drs. Tinoosh Mohsenin, (Chair), Mohamed Younis, Seung-Jun Kim, Farinaz Koushanfar (Rice University) and Houman Homayoun (George Mason University)

Ph.D. Proposal

Detecting Objects with High Accuracy and in Real-time: A Vision-based
Scene-specific Object Detector in Mobile Systems with Human-in-the-loop Training

Zheng Li

10:00am 15 September 2015, ITE 325b

In computer vision, researchers pursue to train machines to detect objects as well as humans — with high accuracy and in real-time. Though the goal of highly intelligent machine vision has been the target of research for years, machines still perform inferior to humans. Present research continues to specifically investigate new robust features types that lead to improvement of effective detection accuracy. While use of carefully hand-engineered features usually helps, it requires decades of expertise effort to design a good feature representation. Moreover, the machine-end real-time performance often suffers due to the complicated feature extraction and matching. In application where low latency is as critical as high accuracy, such as with unmanned aerial vehicles (UAVs), or assistive guidance and navigation systems for people with visual impairments, approaches to achieve lower execution times are required.

In this proposal, a vision-based Scene-Specific object Detector (SSD) is proposed which transforms the general vision problem into scene-specific sub-problems in order to incorporate scene-specific a priori knowledge to achieve higher detection accuracy and real-time performance. This SSD deeply involves human-in-the-loop training to acquire possible a priori knowledge. With the combination of human-acquired a priori information and sensed real-time information from multi-sensors, a hierarchical coarse-grain to fine-grain search scheme can be used to detect objects efficiently and robustly in a real-time hardware platform. Such a solution can achieve performance exceeding traditional state-of-the-art approaches.

Committee: Drs. Ryan Robucci (chair), Nilanjan Banerjee, Chein-I Chang, Ting Zhu

Computer Science and Electrical Engineering
University of Maryland, Baltimore County

Ph.D. Dissertation Defense

The Modeling Equation: Solving the Physically-Based
Modeling and Animation Problem with a Unified Solution

Yu Wang

12:00pm Friday, 28 August 2015, ITE 352

Physically-based modeling research in computer graphics is based largely on derivation or close approximation from physics laws defining the material behaviors. From rigid object dynamics, to various kinds of deformable objects, such as elastic, plastic, and viscous fluid flow, to their interaction, almost every natural phenomena can find a rich history in computer graphics research. Due to the nonlinear nature of almost all real world dynamics, the mathematical definition of their behavior is rarely linear. As a result, solving for the dynamics of these phenomena involves non-linear numerical solvers, which sometimes introduces numerical instability, such as volume gain or loss, slow convergence.

The contribution of this project is a unified particle-based model that implements an extended SPH solver for modeling fluid motion, integrated with rigid body deformation using shape matching. The model handles phase changes between solid and liquid, including melting and solidification, where material rigidity is treated as a function of time and particle distance to the object surface, and solid fluid coupling, where rigid body motion causes secondary fluid flow motion. Due to the stability of the fluid-rigid interplay solver, we can introduce artistic control to the framework, such as rigging, where object motion is predefined by either artistic control, or procedurally generated dynamics path. Interaction with the fluid can be indirectly achieved by rigging the rigid particles which implicitly handles rigid-fluid coupling. We used marching cubes to extract the surfaces of the objects, and applied the PN-triangles to replace the planar silhouettes with cubic approximations. We provide discussion on evaluation metrics for physically-based modeling algorithms. In addition, GPU solutions are designed for physics solvers, isosurface extraction and smoothing.

Committee: Drs. Marc Olano (CSEE; Advisor, Chair), Penny Rheingans (CSEE), Jian Chen (CSEE), Matthias Gobbert (Math), Lynn Sparling (Physics)

Ph.D. Proposal

ACCESS: An Assistive Contactless Capacitive
Electrostatic Sensing System

Alexander Nelson

12:00pm Friday, 21 August 2015, ITE 325b

The objective of ACCESS is to develop fabric capacitor sensor arrays as a holistic, wearable, touchless sensing solution. The fabric sensors are lightweight, flexible, and can therefore be integrated into items of everyday use. Further, the capacitive sensing hardware is low-power, unobtrusive, and easily maintainable. The research includes: the construction of fabric sensor prototypes and custom sensing hardware; the development of adaptive signal processing and gesture recognition; and the creation of an assistive cyber-physical interface for mobility impairment. The research is conducted with advisement from medical professionals and private consultants, and evaluated in clinical trials by individuals with upper-extremity mobility impairment. Proposed future work includes evaluation of the assistive device for computational overhead, the inclusion of personal contextual information in gesture recognition and device actuation, and investigation of a dense spatial-resolution capacitor sensor array as a low-resolution greyscale imaging system.

Committee: Drs. Nilanjan Banerjee and Ryan Robucci (Chairs), Chintan Patel, Sandy McCombe-Waller (UMB Medical School)

An interview with UMBC Computer Science Ph.D. student Kavita Krishnaswamy appeared in a recent post on the UMBC Alumni Blog.

Every so often, we’ll chat with an alum about what they do and how they got there. Today we’re talking with Kavita Krishnaswamy ’07, mathematics and computer science. Krishnaswamy has spinal muscular atrophy and has not been able to leave her house in six years. Thanks to Beam Telepresence Technology, a robotic program that allows her to remotely view and navigate spaces through her computer screen, she’s presented her doctoral thesis and attended conferences across the country. The current Ph.D. student talks about her experience with the Beam and her research on robotics and accessibility.

Read the full interview on the UMBC alumni blog.

Ph.D. Proposal Defense

Data, Energy, and Privacy Management
Techniques for Sustainable Microgrids

Zhichuan Huang

11:00am Tuesday, 11 August 2015, ITE 325b

Sustainable microgrids have gained increasing attention recently, because they can provide the power supply to places i) where the traditional power grid does not exist due to the poor economy or limited number of residences (e.g., islands); and ii) when the traditional power grid is temporally not functioning due to severe weather conditions (e.g., storms). However, in order to achieve sustainability, there are a lot of challenges to be addressed. In this thesis, we propose to investigate three key techniques in sustainable microgrids. First, we investigate the big energy data management problem and present E-Sketch, a middleware for utility companies to gather data from smart meters with much less storage and communication overhead. E-Sketch utilizes adaptive sampling to compress power consumption changes in time domain. Then frequency compression is applied to further compress the sampled data.

The second key technique is the energy management in microgrids. Because energy generation and demand in each individual home and microgrid is not matching, the key challenge of the energy management is to model the existing energy demand and propose novel energy management to reduce the overall energy usage and cost in microgrids. In this technique, we study the theoretical, technical, and economic feasibility of sustainable microgrids. To enable distributed energy management, energy consumption data of different homes needs to be shared in the microgrid. Thus an important problem is how we guarantee that the shared data can only be used for energy management but not revealing the privacy of individual homes in the microgrid. To address this problem, we leverage the unique feature of hybrid AC-DC microgrids and propose the third technique — Shepherd, a privacy protection framework to effectively protect occupants’ privacy. In Shepherd, we provide a generic model for energy consumption hiding from different types of detection techniques.

Committee: Drs. Ting Zhu (chair), Nilanjan Banerjee, Chintan Patel, and David Irwin (UMass Amherst)

Ph.D. Proposal Defense

Holistic Home Energy Management:
From Sensing to Data Analytics

David Lachut

2:00pm Tuesday, 11 August 2015, ITE 325b

As home automation tools become more prevalent, they provide great potential to assist energy conservation and promote sustainable energy use in a way that increases users’ quality of life. This paper proposes the Greenhome System: a software system for using off-the-shelf home automation components and back-end data analytics to provide intelligent home energy management capabilities primarily targeted to renewable powered homes. The system will take input from various sensors and user input to detect user activities, predict home energy consumption, and make energy consumption recommendations to users. To accomplish the project goals, the Greenhome system requires in-home hardware and software components, a mobile component for user interaction, and a server component to tie them together. These components will accomplish tasks of data collection and analysis, activity and anomaly detection, prediction, planning, and recommendation.

This project builds on prior research in several areas, combining such diverse fields as predictive analytics, data visualization and annotation, planning, and recommender systems into a holistic approach. Combining these fields will result in new adaptations and make the overall Greenhome System a novel contribution. Work has begun on the Greenhome System preliminary to this proposal, with published work on residential sensor system design and implementation, data annotation collection, and energy demand prediction. It remains to incorporate automated self-maintenance, user activity detection, and personalized recommendations into a holistic system for home energy management.

Committee: Drs. Nilanjan Banerjee (chair), Ting Zhu, Charles Nicholas, Nirmalya Roy

Computer Science and Electrical Engineering
University of Maryland, Baltimore County

MS Thesis Defense in Computer Science

Identifying Significant, Difficult and Timeless
Concepts for Cybersecurity Assessment Tools:
Results and Analysis of Two Delphi Processes

Geet Parekh

11:00am Friday, 7 August 2015, ITE 228

As part of our ongoing project to create Cybersecurity Assessment Tools (CATs), we carried out two Delphi processes to help cybersecurity experts and educators build a consensus about the core concepts and skills in the field. We present and analyze the results of these processes.

The first process identified fundamental concepts for our Cybersecurity Concept Inventory to be given to students completing any first course in cybersecurity. The second process identified skills for our Cybersecurity Curriculum Assessment, which will be given to students graduating from college headed for their first job in cybersecurity. These tests will provide infrastructure for evidence-based improvement of cybersecurity education to help universities better prepare the substantial number of cybersecurity professionals needed in America.

Thirty-six experts participated in four to five rounds of data collection. By the end of the processes, experts reached a consensus, as indicated by decreasing variations in their scoring of the importance, difficulty, and timelessness of concepts and topics that they identified. Participation by these diverse cybersecurity experts should help increase adoption of the tests.

Keywords: Cybersecurity Education, Cybersecurity Assessment Tools (CATs), Delphi Method, Concept Inventory

Committee: Drs. Alan T. Sherman (chair), Linda Oliva, Dhananjay Phatak, Chintan Patel

Remote Participation: If you wish to attend the defense remotely via Skype, please email Geet ().

Note: The Department of Defense has funded continuation of this research under BAA-003-15 via a collaborative award to UMBC and The University of Illinois at Champaign-Urbana (PIs Alan Sherman and Geoffrey Herman; CoPIs Dhananjay Phatak and Linda Oliva).