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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

PAST DEFENSE NOTICES


Calen Carabajal - Development of Compact UWB Transmit Receive Modules and Filters on Liquid Crystal Polymer for Radar

When & Where:

September 23, 2020 - 2:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Carl Leuschen, Chair
Fernando Rodriguez-Morales (Co-Chair)
Christopher Allen

Abstract

Microwave and mm-wave radars have been used extensively for remote sensing applications, and ultra-wideband (UWB) radars have provided particular utility in geophysical research due to their ability to resolve narrowly-spaced targets or media interface levels. With increased availability of unmanned aerial vehicles (UAS) and expanded application of microwave radars into other realms requiring portability, miniaturization of radar systems is a crucial goal. This thesis presents the design and development of various microwave components for a compact, airborne snow-probing radar with multi-gigahertz bandwidth and cm-scale vertical resolution.
 
First, a set of UWB, compact transmit and receive modules with custom power sequencing circuits is presented. These modules were rapid-prototyped as an initial step toward the miniaturization of the radar’s front-end, using a combination of custom and COTS circuits. The transmitter and receiver modules operate in the 2–18 GHz range. Laboratory and field tests are discussed, demonstrating performance that is comparable to previous, connectorized implementations of the system, while accomplishing a 5:1 size reduction.
 
Next, a set of miniaturized band-pass and low-pass filters is developed and demonstrated. This work addressed the lack of COTS circuits with adequate performance in a sufficiently small form factor that is compatible with the planar integration required in a multi-chip module.
 
The filters presented here were designed for manufacture on a multi-layer liquid crystal polymer (LCP) substrate. A detailed trade study to assess the effects of potential manufacturing tolerances is presented. A framework for the automated creation of panelized design variations was developed using CAD tools. Thirty-two design variations with two different types of launches (microstrip and grounded co-planar waveguide) were successfully simulated, fabricated and tested, showing good electrical performance both as individual filters and cascaded to offer outstanding out-of-band rejection. The size of the new filters is 1 cm x 1 cm x 150 µm, a vertical reduction of over 90% and a reduction of total cascaded length by over 80%.

Kunal Karnik - Augment drone GPS telemetry data onto its Optical Flow lines

When & Where:

September 10, 2020 - 1:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Andy Gill, Chair
Drew Davidson
Prasad Kulkarni

Abstract

Optical flow is the apparent displacement of objects, surfaces and edges in a visual scene caused because of the relative motion between an observer and the scene. This apparent displacement (parallax) is used to render optical flow lines for such objects which hold invaluable information about their motion. In this research, we apply this technique to study a video file. We will locate pixels of objects with strong optical flow displacements. Which will enable us to identify an aerial multirotor craft (drone) from possible object pixels. Further we will not only mark the drones path using optical flow lines, but we will also add value to the video file by augmenting the drone’s 3D Telemetry data onto its optical flow lines.


Guojun Xiong - Distributed Filter Design and Power Allocation for Small-Cell MIMO Network

When & Where:

August 24, 2020 - 1:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Taejoon Kim, Chair
Morteza Hashemi
Erik Perrins

Abstract

The deluge of wireless data traffic catalyzed by the growing number of data-intensive devices has motivated the deployment of small-cell in fifth-generation (5G) networks. A primary challenge for deploying dense small-cell networks comes from the lack of practical techniques that efficiently handle the increased network interference at a low cost. This has aroused considerable interest in the distributed precoder/combiner coordination techniques that leverage the channel reciprocity, while relying on the local channel state information (CSI) available at each communication end. In this project, a distributed approach is proposed to the problem of signal-to-interference-plus-noise-ratio (SINR)-guaranteed power minimization (SGPM) for multicell multiuser (MCMU) multiple-input multiple-output (MIMO) systems. Unlike prior SGPM approaches, the technique is based on solving necessary and sufficient optimality conditions, which are derived by decomposing the original problem into forward and backward (FB) subproblems, while ensuring the strong duality of each subproblems. The proposed distributed SGPM algorithm makes use of FB adaptation and Jacobi recursion for iterative filter design and power allocation, respectively, which guarantees target SINR performance as well as its convergence to a stationary point. Simulation results illustrate the enhanced power efficiency with the performance guarantees of the proposed method compared to the existing distributed techniques.


Jason Baxter - An FPGA Implementation of Carrier Phase and Symbol Timing Synchronization for 16-APSK

When & Where:

August 19, 2020 - 2:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Erik Perrins, Chair
Taejoon Kim
Carl Leuschen

Abstract

Proper synchronization between a transmitter and receiver, in terms of carrier phase and symbol timing, is critical for reliable communication. Carrier phase synchronization is related to the frequency translation hardware, where perfect synchronization means that the local oscillators of the transmitter’s upconverter and receiver’s downconverter are aligned in phase and frequency. Timing synchronization is related to the analog-to-digital converter in the receiver, where perfect synchronization means that samples of the received signal are taken at transmitted symbol times. Perfect synchronization is unlikely in practical systems for a number of reasons, including hardware limitations and the independence of the transmitter and receiver. This thesis explores an FPGA implementation of a PLL-based carrier phase and symbol timing synchronization subsystem as part of a 16-APSK aeronautical telemetry receiver. The theory behind this subsystem is presented, and the hardware implementation of each component is described. Results demonstrate successful demodulation of a test signal, and system performance is shown to be comparable to double-precision floating point simulations in terms of error vector magnitude, synchronization lock time, and BER.


Adam Petz - An Infrastructure for Faithful Execution of Remote Attestation Protocols

When & Where:

August 14, 2020 - 2:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Perry Alexander, Chair
Drew Davidson
Andy Gill
Prasad Kulkarni
Emily Witt

Abstract

Security decisions often rely on trust.  An emerging technology for gaining trust in a remote computing system is remote attestation.  Remote attestation is the activity of making a claim about properties of a target by supplying evidence to an appraiser over a network.  Although many existing approaches to remote attestation wisely adopt a layered architecture–where the bottom layers measure layers above–the dependencies between components remain static and measurement orderings fixed.  Further, they are often restricted to a specialized embedded platform, or only perform shallow measurements on a component of interest without considering the trustworthiness of its context or the attestation mechanism itself.  For modern computing environments with diverse topologies, we can no longer fix a target architecture any more than we can fix a protocol to measure that architecture.

Copland is a domain-specific language and formal framework that provides a vocabulary for specifying the goals of layered attestation protocols.  It remains configurable by measurement capability, participants, and platform topology, yet provides a precise reference semantics that characterizes system measurement events and evidence handling; a foundation for comparing protocol alternatives.  The aim of this work is to refine the Copland semantics to a more fine-grained notion of attestation manager execution–a high-privilege thread of control responsible for invoking attestation services and bundling evidence results.  This refinement consists of two cooperating components called the Copland Compiler and the Attestation Virtual Machine (AVM).  The Copland Compiler translates a Copland specification into a sequence of primitive attestation instructions to be executed in the AVM.  These components are implemented as functional programs in the Coq proof assistant and proved correct with respect to the Copland reference semantics.  This formal connection is critical in order to trust that protocol specifications are faithfully carried out by the attestation manger implementation.  We also explore synthesis of appraisal routines that leverage the same formally verified infrastructure to interpret evidence generated by Copland protocols and make trust decisions.


Xiaohan Zhang - Golf Ball Detection and Tracking Based on Convolutional Neural Networks

When & Where:

July 16, 2020 - 2:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Richard Wang, Chair
Bo Luo
Cuncong Zhong

Abstract

With the rapid growth in artificial intelligence (AI), AI technologies have completely changed our lives. Especially in the sports field, AI starts to play the role in auxiliary training, data management, and systems that analyze training performance for athletes. Golf is one of the most popular sports in the world, which frequently utilize video analysis during training. Video analysis falls into the computer vision category. Computer vision is the field that benefited most during the AI revolution, especially the emerging of deep learning. 


This thesis focuses on the problem of real-time detection and tracking of a golf ball from video sequences. We introduce an efficient and effective solution by integrating object detection and a discrete Kalman model. For ball detection, five classical convolutional neural network based detection models are implemented, including Faster R-CNN, SSD, RefineDet, YOLOv3, and its lite version, YOLOv3 tiny. At the tracking stage, a discrete Kalman filter is employed to predict the location of the golf ball based on its previous observations. As a trade-off between the detection accuracy and detection time, we took advantage of image patches rather than the entire images for detection. In order to train the detection models and test the tracking algorithm, we collect and annotate a collection of golf ball dataset. Extensive experimental results are performed to demonstrate the effectiveness of the proposed technique and compare the performance of different neural network models.


Ronald Moore - AIDA: An Assistant for Workers with Intellectual and Developmental Disabilities

When & Where:

June 12, 2020 - 11:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Andrew Williams, Chair
Arvin Agah
Michael Branicky
Richard Wang

Abstract

Roughly 1 in 5 people in the United States have an intellectual or developmental disability (IDD), which is a substantial amount of the population. In the realm of human-robot interaction, there have been many attempts to help these individuals lead more productive and independent lives. However, many of these solutions focus on helping individuals with IDD develop social skills. For the solutions that do focus on helping people with IDD increase their work productivity, many of these involve giving the user control over a robot that augments the worker’s capabilities. In this thesis, it is posited that an autonomous agent could effectively assist workers with IDD, thereby increasing their productivity. The artificially intelligent disability assistant (AIDA) is an autonomous agent that uses social scaffolding techniques to assist workers with IDD. Before designing the system, data was gathered by observing workers with IDD perform tasks in a light manufacturing facility.
To test the hypothesis, an initial Wizard-of-Oz (WoZ) experiment was conducted where subjects had to assemble a box using only either their dominant or non-dominant hand. During the experiment, subjects could ask the robot for assistance, but a human operator controlled whether the robot provided a response. After the experiment, subjects were required to complete a feedback survey. Additionally, this feedback was used to refine and build the autonomous system for AIDA.
The autonomous system is composed of data collection and processing modules, a scaffolding algorithm module, and robot action output modules. This system was tested in a simulated experiment using video recordings from the initial experiment. The results of the simulated experiment provide support for the hypothesis that an autonomous agent using social scaffolding techniques can increase the productivity of workers with IDD. In the future, it is desired to test the current system in a real-time experiment before using it on workers with IDD.

 


Sairath Bhattacharjya - A Novel Zero-Trust Framework to Secure IoT Communications

When & Where:

June 5, 2020 - 10:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Hossein Saiedian, Chair
Alex Bardas
Fengjun Li

Abstract

The phenomenal growth of the Internet of Things (IoT) has highlighted the security and privacy concerns associated with these devices. The research literature on the security architectures of IoT makes evident that we need to define and formalize a framework to secure the communications among these devices. To do so, it is important to focus on a zero-trust framework that will work on the principle premise of "trust no one, verify everyone" for every request and response.

In this thesis, we emphasize the immediate need for such a framework and propose a zero-trust communication model for IoT that addresses security and privacy concerns. We employ the existing cryptographic techniques to implement the framework so that it can be easily integrated into the current network infrastructures. The framework provides an end-to-end security framework for users and devices to communicate with each other privately. It is often stated that it is difficult to implement high-end encryption algorithm within the limited resource of an IoT device. For our work, we built a temperature and humidity sensor using NodeMCU V3 and were able to implement the framework and successfully evaluate and document its efficient operation. We defined four areas for evaluation and validation, namely, security of communications, memory utilization of the device, response time of operations, and cost of its implementation. For every aspect we defined a threshold to evaluate and validate our findings. The results are satisfactory and are documented. Our framework provides an easy-to-use solution where the security infrastructure acts as a backbone for every communication to and from the IoT devices.


Royce Bohnert - Experiments with mmWave Radar

When & Where:

June 2, 2020 - 11:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Christopher Allen, Chair
Erik Perrins
James Stiles

Abstract

The IWR6843 mmWave radar device from Texas Instruments (TI) is a complete FMCW radar system-on-chip operating in the 60 to 64 GHz frequency range. The IWR6843ISK is an evaluation platform which includes the IWR6843 connected to patch antennas on a PCB. In this project, the viability of using the IWR6843 sensor for short-range detection of small, high-velocity targets is investigated. Some of the limitations of the device are explored and a specific radar configuration is proposed. To confirm the applicability of the proposed configuration, a similar configuration is used with the IWR6843ISK-ODS evaluation platform to observe the launch of a foil-wrapped dart. The evaluation platform is used to collect raw data, which is then post-processed in a Python program to generate a range-doppler heatmap visualization of the data.


Matthew Taylor - Defending Against Typosquatting Attacks In Programming Language-Based Package Repositories

When & Where:

May 14, 2020 - 10:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Drew Davidson, Chair
Alex Bardas
Bo Luo

Abstract

Program size and complexity have dramatically increased over time.  To reduce their work-load, developers began to utilize package managers.  These packages managers allow third-party functionality, contained in units called packages, to be quickly imported into a project.  Due to their utility, packages have become remarkably popular. The largest package repository, npm, has more than 1.2 million publicly available packages and serves more than 80 billion package downloads per month.  In recent years,  this popularity has attracted the attention of malicious users. Attackers have the ability to upload packages which contain malware. To increase the number of victims, attackers regularly leverage a tactic called typosquatting, which involves giving the malicious package a name that is very similar to the name of a popular package.  Users who make a typo when trying to install the popular package fall victim to the attack and are instead served the malicious payload. The consequences of typosquatting attacks can be catastrophic. Historical typosquatting attacks have exported passwords, stolen cryptocurrency, and opened reverse shells.This thesis focuses on typosquatting attacks in package repositories.  It explores the extent to which typosquatting exists in npm and PyPI (the de facto standard package repositories for Node.js and Python, respectively), proposes a practical defense against typosquatting attacks, and quantifies the efficacy of the proposed defense.  The presented solution incurs an acceptable temporal overhead of 2.5% on the standard package installation process and is expected to affect approximately 0.5% of all weekly package downloads. Furthermore, it has been used to discover a particularly high-profile typosquatting perpetrator, which was then reported and has since been deprecated by npm.  Typosquatting is an important yet preventable problem.  This thesis recommends pack-ages creators to protect their own packages with a technique called defensive typosquatting and repository maintainers to protect all users through augmentations to their package managers or automated monitoring of the package namespace.


Jacob Fustos - ​​Attacks and Defenses against Speculative Execution Based Side Channels

When & Where:

May 13, 2020 - 10:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Heechul Yun, Chair
Alex Bardas
Drew Davidson

Abstract

Modern high-performance processors utilize techniques such as speculation and out-of-order execution to improve performance. Unfortunately, the recent Spectre and Meltdown exploits take advantage of these techniques to circumvent the security of the system. As speculation and out-of-order execution are complex features meant to enhance performance, full mitigation of these exploits often incurs high overhead and partial defenses need careful considerations to ensure attack surface is not left vulnerable.  In this work, we explore these attacks deeper,  both how they are executed and how to defend against them.   
 
We first propose a novel micro-architectural extension, SpectreGuard, that takes a data-centric approach to the problem. SpectreGuard attempts to reduce the performance penalty that is common with Spectre defenses by allowing software and hardware to work together. This collaborative approach allows software to tag secrets at the page granularity, then the underlying hardware can optimize secret data for security, while optimizing all other data for performance. Our research shows that such a combined approach allows for the creation of processors that can both achieve a high level of security while maintaining high performance.
 
We then propose SpectreRewind, a novel strategy for executing speculative execution attacks. SpectreRewind reverses the flow of traditional speculative execution attacks, creating new covert channels that transmit secret data to instructions that appear to execute logically before the attack even takes place. We find this attack vector can bypass some state-of-the-art proposed hardware defenses, as well as increase attack surface for certain Meltdown-type attacks on existing machines. Our research into this area helps towards completing the understanding of speculative execution attacks so that defenses can be designed with the knowledge of all attack vectors.

Venkata Siva Pavan Kumar Nelakurthi - Venkata Siva Pavan Kumar Nelakurthi

When & Where:

May 8, 2020 - 2:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Jerzy Grzymala-Busse, Chair
Prasad Kulkarni
Guanghui Wang

Abstract

In data mining, rule induction is a process of extracting formal rules from decision
tables, where the later are the tabulated observations, which typically consist of few
attributes, i.e., independent variables and a decision, i.e., a dependent variable. Each
tuple in the table is considered as a case, and there could be n number of cases for a
table specifying each observation. The efficiency of the rule induction depends on how
many cases are successfully characterized by the generated set of rules, i.e., ruleset.
There are different rule induction algorithms, such as LEM1, LEM2, MLEM2. In the real
world, datasets will be imperfect, inconsistent, and incomplete. MLEM2 is an efficient
algorithm to deal with such sorts of data, but the quality of rule induction largely
depends on the chosen classification strategy. We tried to compare the 16 classification
strategies of rule induction using MLEM2 on incomplete data. For this, we
implemented MLEM2 for inducing rulesets based on the selection of the type of
approximation, i.e., singleton, subset or concept, and the value of alpha for calculating
probabilistic approximations. A program called rule checker is used to calculate the
error rate based on the classification strategy specified. To reduce the anomalies, we
used ten-fold cross-validation to measure the error rate for each classification. Error
rates for the above strategies are being calculated for different datasets, compared, and
presented.​

Charles Mohr - Design and Evaluation of Stochastic Processes as Physical Radar Waveforms

When & Where:

May 7, 2020 - 10:00 AM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Shannon Blunt, Chair
Christopher Allen
Carl Leuschen
James Stiles
Zsolt Talata

Abstract

Recent advances in waveform generation and in computational power have enabled the design and implementation of new complex radar waveforms. Still, even with these advances in computation, in a pulse agile mode, where the radar transmits unique waveforms at every pulse, the requirement to design physically robust waveforms which achieve good autocorrelation sidelobes, are spectrally contained, and have a constant amplitude envelope for high power operation, can require expensive computation equipment and can impede real time operation. This work addresses this concern in the context of FM noise waveforms which have been demonstrated in recent years in both simulation and in experiments to achieve low autocorrelation sidelobes through the high dimensionality of coherent integration when operating in a pulse agile mode. However while they are effective, the approaches to design these waveforms requires the optimization of each individual waveform making them subject to the concern above.

This dissertation takes a different approach. Since these FM noise waveforms are meant to be noise like in the first place, the waveforms here are instantiated as the sample functions of a stochastic process which has been specially designed to produce spectrally contained, constant amplitude waveforms with noise like cancellation of sidelobes. This makes the waveform creation process little more computationally expensive than pulling numbers from a random number generator (RNG) since the optimization designs a waveform generating function (WGF) itself rather than each waveform themselves. This goal is achieved by leveraging gradient descent optimization methods to reduce the expected frequency template error (EFTE) cost function for both the pulsed stochastic waveform generation (StoWGe) waveform model and a new CW version of StoWGe denoted CW-StoWGe. The effectiveness of these approaches and their ability to generate useful radar waveforms is analyzed using several stochastic waveform generation metrics developed here. The EFTE optimization is shown through simulation to produce WGFs which generate FM noise waveforms in both pulsed and CW modes which achieve good spectral containment and autocorrelation sidelobes. The resulting waveforms will be demonstrated in both loopback and in open-air experiments to be robust to physical implementation.


Michael Stees - Optimization-based Methods in High-Order Mesh Generation and Untangling

When & Where:

May 1, 2020 - 2:30 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Suzanne Shontz, Chair
Perry Alexander
Prasad Kulkarni
Jim Miller
Weizhang Huang

Abstract

High-order numerical methods for solving PDEs have the potential to 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 a discretization of the domain that accurately captures key geometric features.  In the presence of curved boundaries, this requires a high-order curvilinear mesh.  Consequently, there is a lot of interest in high-order mesh generation methods.  The majority of such methods warp a high-order straight-sided mesh through the following three step process.  First, they add additional nodes to a low-order mesh to create a high-order straight-sided mesh.  Second, they move the newly added boundary nodes onto the curved domain (i.e., apply a boundary deformation).  Finally, they compute the new locations of the interior nodes based on the boundary deformation.  We have developed a mesh warping framework based on optimal weighted combinations of nodal positions.  Within our framework, we develop methods for optimal affine and convex combinations of nodal positions, respectively.  We demonstrate the effectiveness of the methods within our framework on a variety of high-order mesh generation examples in two and three dimensions.  As with many other methods in this area, the methods within our framework do not guarantee the generation of a valid mesh.  To address this issue, we have also developed two high-order mesh untangling methods.  These optimization-based untangling methods formulate unconstrained optimization problems for which the objective functions are based on the unsigned and signed angles of the curvilinear elements.  We demonstrate the results of our untangling methods on a variety of two-dimensional triangular meshes.


Farzad Farshchi - Deterministic Memory Systems for Real-time Multicore Processors

When & Where:

April 27, 2020 - 3:00 PM
Zoom Meeting, please contact jgrisafe@ku.edu for link

Committee Members:

Heechul Yun, Chair
Esam Eldin Mohamed Aly
Prasad Kulkarni
Rodolfo Pellizzoni
Shawn Keshmiri

Abstract

With the emergence of autonomous systems such as self-driving cars and drones, the need for high-performance real-time embedded systems is increasing. On the other hand, the physics of the autonomous systems constraints size, weight, and power consumption (known as SWaP constraints) of the embedded systems. A solution to satisfy the need for high performance while meeting the SWaP constraints is to incorporate multicore processors in real-time embedded systems. However, unlike unicore processors, in multicore processors, the memory system is shared between the cores. As a result, the memory system performance varies widely due to inter-core memory interference. This can lead to over-estimating the worst-case execution time (WCET) of the real-time tasks running on these processors, and therefore, under-utilizing the computation resources. In fact, recent studies have shown that real-time tasks can be slowed down more than 300 times due to inter-core memory interference.

In this work, we propose novel software and hardware extensions to multicore processors to bound the inter-core memory interference in order to reduce the pessimism of WCET and to improve time predictability. We introduce a novel memory abstraction, which we call Deterministic Memory, that cuts across various layers of the system: the application, OS, and hardware. The key characteristic of Deterministic Memory is that the platform—the OS and hardware—guarantees small and tightly bounded worst-case memory access timing.  Additionally, we propose a drop-in hardware IP that enables bounding the memory interference by per-core regulation of the memory access bandwidth at fine-grained time intervals. This new IP, which we call the Bandwidth Regulation Unit (BRU), does not require significant changes to the processor microarchitecture and can be seamlessly integrated with the existing microprocessors. Moreover, BRU has the ability to regulate the memory access bandwidth of multiple cores collectively to improve bandwidth utilization. As for future work, we plan to further improve bandwidth utilization by extending BRU to recognize memory requests accessing different levels of the memory hierarchy (e.g. LLC and DRAM). We propose to fully evaluate these extensions on open-source software and hardware and measure their effectiveness with realistic case studies.

Waqar Ali - Deterministic Scheduling of Real-Time Tasks on Heterogeneous Multicore Platforms

When & Where:

April 9, 2020 - 2:00 PM
https://zoom.us/j/484640842?pwd=TDAyekxtRDVaTHF0K1NlbU5wNFVtUT09 - The password for the meeting is 005158.

Committee Members:

Heechul Yun, Chair
Esam Eldin Mohamed Aly
Drew Davidson
Prasad Kulkarni
Shawn Keshmiri

Abstract

Scheduling of real-time tasks involves analytically determining whether each task in a group of periodic tasks can finish before its deadline. This problem is well understood for unicore platforms and there are exact schedulability tests which can be used for this purpose. However, in multicore platforms, sharing of hardware resources between simultaneously executing real-time tasks creates non-deterministic coupling between them based on their requirement of the shared hardware resource(s) which significantly complicates the schedulability analysis. The standard practice is to over-estimate the worst-case execution time (WCET) of the real-time tasks, by a constant factor (e.g, 2x), when determining schedulability on these platforms. Although widely used, this practice has two serious flaws. Firstly, it can make the schedulability analysis overly pessimistic because all tasks do not interfere with each other equally. Secondly, recent findings have shown that for tasks that do get affected by shared resource interference, they can experience extreme (e.g., >300X) WCET increases on commercial-of-the-shelf (COTS) multicore platforms, in which case, a schedulability analysis incorporating a blanket interference factor of 2x for every task cannot give accurate results. Apart from the problem of WCET estimation, the established schedulability analyses for multicore platforms are inherently pessimistic due to the effect of carry-in jobs from high priority tasks. Finally, the increasing integration of hardware accelerators (e.g., GPU) on SoCs complicates the problem further because of the nuances of scheduling on these devices which is different from traditional CPU scheduling.

 

We propose a novel approach towards scheduling of real-time tasks on heterogeneous multicore platforms with the aim of increased determinism and utilization in the online execution of real-time tasks and decreased pessimism in the offline schedulability analysis. Under this framework, we propose to statically group different real-time tasks into a single scheduling entity called a virtual-gang. Once formed, these virtual-gangs are to be executed one-at-a-time with strict regulation on interference from other sources with the help of state-of-the-art techniques for performance isolation in multicore platforms. Using this idea, we can achieve three goals. Firstly, we can limit the effect of shared resource interference which can exist only between tasks that are part of the same virtual-gang. Secondly, due to one-gang-at-a-time policy, we can transform the complex problem of scheduling real-time tasks on multicore platforms into simple and well-understood problem of scheduling these tasks on unicore platforms. Thirdly, we can demonstrate that it is easy to incorporate scheduling on integrated GPUs into our framework while preserving the determinism of the overall system. We show that the virtual-gang formation problem can be modeled as an optimization problem and present algorithms for solving it with different trade-offs. We propose to fully implement this framework in the open-source Linux kernel and evaluate it both analytically using generated tasksets and empirically with realistic case-studies.


Amir Modarresi - Network Resilience Architecture and Analysis for Smart Homes

When & Where:

March 23, 2020 - 1:00 PM
https://kansas.zoom.us/j/228154773

Committee Members:

Victor Frost, Chair
Morteza Hashemi
Fengjun Li
Bo Luo
John Symons

Abstract

The Internet of Things (IoT) is evolving rapidly to every aspect of human life including, healthcare, homes, cities, and driverless vehicles that makes humans more dependent on the Internet and related infrastructure. While many researchers have studied the structure of the Internet that is resilient as a whole, new studies are required to investigate the resilience of the edge networks in which people and \things" connect to the Internet. Since the range of service requirements varies at the edge of the network, a wide variety of technologies with different topologies are involved. Though the heterogeneity of the technologies at the edge networks can improve the robustness through the diversity of mechanisms, other issues such as connectivity among the utilized technologies and cascade of failures would not have the same effect as a simple  network. Therefore, regardless of the size of networks at the edge, the structure of these networks is complicated and requires appropriate study.

In this dissertation, we propose an abstract model for smart homes, as part of one of the fast-growing networks at the edge, to illustrate the heterogeneity and complexity of the network structure. As the next step, we make two instances of the abstract smart home model and perform a graph-theoretic analysis to recognize the fundamental behavior of the network to improve its robustness. During the process, we introduce a formal multilayer graph model to highlight the structures, topologies, and connectivity of various technologies at the edge networks and their connections to the Internet core. Furthermore,  we propose another graph model, technology interdependence graph, to represent the connectivity of technologies. This representation shows the degree of connectivity among technologies and illustrates which technologies are more vulnerable to link and node failures.

Moreover, the dominant topologies at the edge change the node and link vulnerability, which can be used to apply worst-case scenario attacks. Restructuring of the network by adding new links associated with various protocols to maximize the robustness of a given network can have distinctive outcomes for different robustness metrics. However, typical centrality metrics usually fail to identify important nodes in multi-technology networks such as smart homes. We propose four new centrality metrics to improve the process of identifying important nodes in multi-technology networks and recognize vulnerable nodes. Finally, we study over 1000 different smart home  topologies to examine the resilience of the networks with typical and the proposed centrality metrics.


Qiaozhi Wang - Towards the Understanding of Private Content -- Content-based Privacy Assessment and Protection in Social Networks

When & Where:

March 2, 2020 - 1:30 PM
246 Nichols Hall

Committee Members:

Bo Luo, Chair
Fengjun Li
Guanghui Wang
Heechul Yun
Prajna Dhar

Abstract

In the wake of the Facebook data breach scandal, users begin to realize how vulnerable their per-sonal data is and how blindly they trust the online social networks (OSNs) by giving them an inordinate amount of private data that touch on unlimited areas of their lives. In particular, stud-ies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. Additionally, friends on social media platforms are also found to be adversarial and may leak one’s private in-formation. Threats from within users’ friend networks – insider threats by human or bots – may be more concerning because they are much less likely to be mitigated through existing solutions, e.g., the use of privacy settings. Therefore, we argue that the key component of privacy protection in social networks is protecting sensitive/private content, i.e. privacy as having the ability to control dissemination of information. A mechanism to automatically identify potentially sensitive/private posts and alert users before they are posted is urgently needed.

In this dissertation, we propose a context-aware, text-based quantitative model for private information assessment, namely PrivScore, which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first solicit diverse opinions on the sensitiveness of private information from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to generate a per-sonalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs. Such a mechanism could also benefit non-human users such as social media chatbots.​


Mohammad Saad Adnan - Corvus: Integrating Blockchain with Internet of Things Towards a Privacy Preserving, Collaborative and Accountable, Surveillance System in a Smart Community

When & Where:

March 2, 2020 - 9:00 AM
246 Nichols Hall

Committee Members:

Bo Luo, Chair
Alex Bardas
Fengjun Li

Abstract

The Internet of Things is a rapidly growing field that offers improved data collection, analysis and automation as solutions for everyday problems. A smart-city is one major example where these solutions can be applied to issues with urbanization. And while these solutions can help improve the quality of life of the citizens, there are always security & privacy risks. Data collected in a smart-city can infringe upon the privacy of users and reveal potentially harmful information. One example is a surveillance system in a smart city. Research shows that people are less likely to commit crimes if they are being watched. Video footage can also be used by law enforcement to track and stop criminals. But it can also be harmful if accessible to untrusted users. A malicious user who can gain access to a surveillance system can potentially use that information to harm others. There are researched methods that can be used to encrypt the video feed, but then it is only accessible to the system owner. Polls show that public opinion of surveillance systems is declining even if they provide increased security because of the lack of transparency in the system. Therefore, it is vital for the system to be able to do its intended purpose while also preserving privacy and holding malicious users accountable.  


To help resolve these issues with privacy & accountability and to allow for collaboration, we propose Corvus, an IoT surveillance system that targets smart communities. Corvus is a collaborative blockchain based surveillance system that uses context-based image captioning to anonymously describe events & people detected. These anonymous captions are stored on the immutable blockchain and are accessible by other users. If they find the description from another camera relevant to their own, they can request the raw video footage if necessary. This system supports collaboration between cameras from different networks, such as between two neighbors with their own private camera networks.  This paper will explore the design of this system and how it can be used as a privacy-preserving, but translucent & accountable approach to smart-city surveillance. Our contributions include exploring a novel approach to anonymizing detected events and designing the surveillance system to be privacy-preserving and collaborative.


Sandip Dey - Analysis of Performance Overheads in DynamoRIO Binary Translator

When & Where:

February 3, 2020 - 3:00 PM
2001 B Eaton Hall

Committee Members:

Prasad Kulkarni, Chair
Jerzy Grzymala-Busse
Esam Eldin Mohamed Aly

Abstract

Dynamic binary translation is the process of translating instruction code from one architecture to another while it executes, i.e., dynamically. As modern applications are becoming larger, more complex and more dynamic, the tools to manipulate these programs are also becoming increasingly complex. DynamoRIO is one such dynamic binary translation tool that targets the most common IA-32 (a.k.a. x86) architecture on the most popular operating systems - Windows and Linux. DynamoRIO includes applications ranging from program analysis and understanding to profiling, instrumentation, optimization, improving software security, and more. However, even considering all of these optimization techniques, DynamoRIO still has the limitations of performance and memory usage, which restrict deployment scalability. The goal of my thesis is to break down the various aspects which contribute to the overhead burden and evaluate which factors directly contribute to this overhead. This thesis will discuss all of these factors in further detail. If the process can be streamlined, this application will become more viable for widespread adoption in a variety of areas. We have used industry standard Mi benchmarks in order to evaluate in detail the amount and distribution of the overhead in DynamoRIO. Our statistics from the experiments show that DynamoRIO executes a large number of additional instructions when compared to the native execution of the application. Furthermore, these additional instructions are involved in building the basic blocks, linking, trace creation, and resolution of indirect branches, all of which in return contribute to the frequent exiting of the code cache. We will discuss in detail all of these overheads, show statistics of instructions for each overhead, and finally show the observations and analysis in this defense.


Eric Schweisberger - Optical Limiting via Plasmonic Parametric Absorbers

When & Where:

January 30, 2020 - 10:00 AM
2001 B Eaton Hall

Committee Members:

Alessandro Salandrino , Chair
Kenneth Demarest
Rongqing Hui

Abstract

Optical sensors are increasingly prevalent devices whose costs tend to increase with their sensitivity. A hike in sensitivity is typically associated with fragility, rendering expensive devices vulnerable to threats of high intensity illumination. These potential costs and even security risks have generated interest in devices that maintain linear transparency under tolerable levels of illumination, but can quickly convert to opaque when a threshold is exceeded. Such a device is deemed an optical limiter. Copious amounts of research have been performed over the last few decades on optical nonlinearities and their efficacy in limiting. This work provides an overview of the existing literature and evaluates the applicability of known limiting materials to threats that vary in both temporal and spectral width. Additionally, we introduce the concept of plasmonic parametric resonance (PPR) and its potential for devising a new limiting material, the plasmonic parametric absorber (PPA). We show that this novel material exhibits a reverse saturable absorption behavior and promises to be an effective tool in the kit of optical limiter design.


Muhammad Saad Adnan - Corvus: Integrating Blockchain with Internet of Things Towards a Privacy Preserving, Collaborative and Accountable, Surveillance System in a Smart Community

When & Where:

January 22, 2020 - 9:00 AM
246 Nichols Hall

Committee Members:

Bo Luo, Chair
Alex Bardas
Fengjun Li

Abstract

The Internet of Things is been a rapidly growing field that offers improved data collection, analysis and automation as solutions for everyday problems. A smart-city is one major example where these solutions can be applied to issues with urbanization. And while these solutions can help improve the quality of live of the citizens, there are always security & privacy risks. Data collected in a smart-city can infringe upon the privacy of users and reveal potentially harmful information. One example is a surveillance system in a smart city. Research shows that people are less likely to commit crimes if they are being watched. Video footage can also be used by law enforcement to track and stop criminals. But it can also be harmful if accessible to untrusted users. A malicious user who can gain access to a surveillance system can potentially use that information to harm others. There are researched methods that can be used to encrypt the video feed, but then it is only accessible to the system owner. Polls show that public opinion of surveillance systems is declining even if they provide increased security because of the lack of transparency in the system. Therefore, it is vital for the system to be able to do its intended purpose while also preserving privacy and holding malicious users accountable. 

To help resolve these issues with privacy & accountability and to allow for collaboration, we propose Corvus, an IoT surveillance system that targets smart communities. Corvus is a collaborative blockchain based surveillance system that uses context-based image captioning to anonymously describe events & people detected. These anonymous captions are stored on the immutable blockchain and are accessible by other users. If they find the description from another camera relevant to their own, they can request the raw video footage if necessary. This system supports collaboration between cameras from different networks, such as between two neighbors with their own private camera networks. This paper will explore the design of this system and how it can be used as a privacy-preserving, but translucent & accountable approach to smart-city surveillance. Our contributions include exploring a novel approach to anonymizing detected events and designing the surveillance system to be privacy-preserving and collaborative.


Lumumba Harnett - Reduced Dimension Optimal and Adaptive Mismatch Processing for Interference Cancellation

When & Where:

January 13, 2020 - 10:00 AM
246 Nichols Hall

Committee Members:

Shannon Blunt, Chair
Christopher Allen
Erik Perrins
James Stiles
Richard Hale

Abstract

Interference has been a subject of interest to radars for generations due to its ability to degrade performance. Commercial radars can experience radio frequency (RF) interference from a different RF service (such as radio broadcasting, television broadcasting, communications, satellites, etc.) if it operates simultaneously in the same spectrum. The RF spectrum is a finite asset that is regulated to mitigate interference and maximum resources. Recently, shared spectrum have been proposed to accommodate the growing commercial demand of communication systems.  Airborne radars, performing ground moving target indication (GMTI), encounter interference from clutter scattering that may mask slow-moving, low-power targets. Least-squares (LS) optimal and re-iterative minimum-mean square error (RMMSE) adaptive mismatch processing recent advancements are proposed for GMTI and shared spectrum. Each estimation technique reduces sidelobes, provides less signal-to-noise loss, and less resolution degradation than windowing. For GMTI, LS and RMMSE filters are considered with angle-Doppler filters and pre-existing interference cancellation techniques for better detection performance. Application specific reduce rank versions of the algorithms are also introduced for real-time operation. RMMSE is further considered to separate radar and mobile communication systems operating in the same RF band to mitigate interference and information loss.


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