Master’s Thesis Defense: Qi Xia
“Feasibility and Security Analysis of Wideband Ultrasonic Radio for Smart Home Applications”
Wednesday, April 10, 2019
11 a.m.
211 Schorr Center
Committee Members: Dr. Qiben Yan (Advisor), Dr. Hongfeng Yu and Dr. Lisong Xu
Abstract: Smart home Internet-of-Things (IoT) accompanied with smart home apps has witnessed a tremendous growth in the past few years. Yet, smart home IoT devices as well as their apps’ security and privacy have raised serious concerns, as they are getting increasingly complicated each day, expected to store and exchange extremely sensitive personal data, always on and connected, and commonly exposed to any users in a sensitive environment. Nowadays wireless smart home IoT devices rely on electromagnetic wave-based radio-frequency (RF) technology to establish fast and good quality network connections. However, RF has its limitations that can negatively affect the smart home user experience and even cause serious security issue, such as crowded spectrum resources and wide-spreading RF waves. To overcome those limitations, people have to use technology for sophisticated time and frequency division management and rely on the assumptions that the attackers have limited computational power, which is resource consuming and insecure to powerful attackers. In this paper we proposed URadio, a wideband ultrasonic communication system, using electrostatic ultrasonic transducers. We design and develop two different types of transducer membranes using two types of extremely thin materials, Aluminized Mylar Film (AMF) and reduced Graphene Oxide (rGO), for assembling transducers, which achieve at least 45 times more bandwidth than commercial transducers. Equipped with the new wideband transducers, an OFDM communication system is deigned to better utilize the available 600 kHz wide bandwidth. Our experiments show that URadio can achieve unprecedentedly 4.8 Mbps data rate with a communication range of 17 cm. The attainable communication range is increased to 31 cm and 35 cm with data rates of 1.2 Mbps and 0.6 Mbps using QPSK and BPSK, respectively. Although the current wideband system only supports short range communication, it is expected to extend the trasmission range with better acoustic engineering. Also, by doing experiments of measuring the ultrasonic adversaries’ eavesdropping and jamming performance, we proved that our system is physically secure even when exchanging plaintext data.
Master’s Thesis Defense: Bruno Vieira Resende Silva
“GAINDroid: General Automated Incompatibility Notifier for Android Applications”
Wednesday, April 10, 2019
4:15 p.m.
103C Avery Hall
Committee Members: Dr. Hamid Bagheri (Advisor) Dr. ThanhVu Nguyen and Dr. Witawas Srisa-an
Abstract: With the ever-increasing popularity of mobile devices over the last decade, mobile apps and the frameworks upon which they are built frequently change. This rapid evolution leads to a confusing jumble of devices and applications utilizing differing features even within the same framework. For Android apps and devices, representing over 80% of the market share, mismatches between the version of the Android operating system installed on a device and the version of the app installed, can lead to several run-time crashes, providing a poor user experience.
This thesis presents GAINDroid, an analysis approach, backed with a class-loader based program analyzer, that automatically detects three types of mismatches to which an app may be vulnerable across versions of the Android API it supports.
Unlike all prior techniques that focus on identifying a particular problem, such as callback APIs issues, GAINDroid has the potential to greatly increase the scope of the analysis by automatically and effectively analyzing various sources of incompatibilities that may lead an app to crash at run-time.
We applied GAINDroid to 3,590 real-world apps and compared the results of our analysis against state-of-the-art tools. The experimental results demonstrate its ability to outperform the existing analysis techniques in terms of both the number and type of mismatches correctly identified as well as run-time performance of the analysis.
Master’s Thesis Defense: Suraj Gampa
“A Data-Driven Approach For Detecting Stress In Plants Using Hyperspectral Imagery”
Thursday, April 11, 2019
8:30 a.m.
211 Schorr Center
Committee Members: Dr. Ashok Samal and Dr. Sruti Das Choudhury (Co-Advisors), Dr. Vinod Variyam
Abstract: A phenotype is an observable characteristic of an individual and is a function of its genotype and its growth environment. Individuals with different genotypes are impacted differently by exposure to the same environment. Therefore, phenotypes are often used to understand morphological and physiological changes in plants as a function of genotype and biotic and abiotic stress conditions. Phenotypes that measure the level of stress can help mitigate the adverse impacts on the growth cycle of the plant. Image-based plant phenotyping has the potential for early stress detection by means of computing responsive phenotypes in a non-intrusive manner. A large number of plants grown and imaged under a controlled environment in a high-throughput plant phenotyping (HTPP) system, are increasingly becoming accessible to research communities. They can be useful to compute novel phenotypes for early stress detection.
In early stages of stress induction, plants manifest responses in terms of physiological changes rather than morphological, making it difficult to detect using visible spectrum cameras which use only three wide spectral bands in the 380nm - 740 nm wavelength range. In contrast, hyperspectral imaging can capture spectral information for narrow spectral bands (5nm) across a broad range of wavelengths (350nm - 2500nm). Hyperspectral imagery (HSI), therefore, provides rich spectral information that can help identify and track even small changes in plant physiology in response to stress.
In this research, a data-driven approach has been developed to identify regions in plants that manifest abnormal reflectance patterns after stress induction. Reflectance patterns of age-matched unstressed plants are first characterized. The normal and stressed reflectance patterns are used to train a classifier that can predict if a point in the plant is stressed or not. Stress maps of a plant can be generated from its HSI and can be used to track the temporal propagation of stress. These stress maps are used to compute novel phenotypes that represent the level of stress in a plant and the stress trajectory over time. The data-driven approach is validated using a dataset of sorghum plants exposed to drought stress in a LemnaTec Scanalyzer 3D HTPP system.
Master’s Thesis Defense: Mahmoud Habibnezhad
Thursday, April 11, 2019
9:30 a.m.
211 Schorr Center
Committee Members: Dr. Peter Revesz (Advisor), Dr. Chris Bourke and Dr. Ashok Samal
“Image Processing Algorithms for Elastin Lamellae Inside Cardiovascular Arteries”
Abstract: Automated image processing methods are greatly needed to replace the tedious, manual histology analysis still performed by many physicians. This thesis focuses on pathological studies that express the essential role of elastin lamella in the resilience and elastic properties of the arterial blood vessels. Due to the stochastic nature of the shape and distribution of the elastin layers, their morphological features appear as the best candidates to develop a mathematical formulation for the resistance behavior of elastic tissues. However, even for trained physicians and their assistants, the pertaining measurement procedures are highly error-prone and prolonged. This thesis successfully integrates such techniques in an image processing application that results in an increased speed and accuracy. In particular, the image processing algorithms can identify and count the elastin lamella within a 10% error on average. Modifications of the elastin-lamella counting algorithm can also recognize artery boundaries and calculate the continuity index and the distribution of elastin layers.