Defense Notices

In nano-photonics, the control of optical signals is based on tuning of the material optical properties in which the electromagnetic field propagates, and thus the choice of materials and of the physical modulation mechanism plays a crucial role. Several materials such as graphene, Indium Tin Oxide (ITO), and vanadium di-oxide (VO2) investigated here have attracted a great deal of attention in the nanophotonic community because of their remarkable tunability. This dissertation will include both theoretical modeling and experimental characterization of functional electro-optic materials and their applications in guided-wave photonic structures. 
We have characterized the complex index of graphene in near infrared (NIR) wavelength through the reflectivity measurement on a SiO2/Si substrate. The measured complex indices as the function of the applied gate electric voltage agreed with the prediction of the Kubo formula. 
We have performed the mathematical modeling of permittivity of ITO based on the Drude Model. Results show that ITO can be used as a plasmonic material and performs better than noble metals for applications in NIR wavelength region. Additionally, the permittivity of ITO can be tuned by carrier density change through applied voltage. An electro-optic modulator (EOM) based on plasmonically enhanced graphene has been proposed and modeled. We show that the tuning of graphene chemical potential through electrical gating is able to switch on and off the ITO plasmonic resonance. This mechanism enables dramatically increased electro-absorption efficiency. 
Another novel photonic structure we are investigating is a multimode EOM based on the electrically tuned optical absorption of ITO in NIR wavelengths. The capability of mode-multiplexing increases the functionality per area in a nanophotonic chip. Proper design of ITO structure based on the profiles of y-polarized TE11 and TE21 modes allows the modulation of both modes simultaneously and differentially. 
We have experimentally demonstrated the ultrafast changes of optical properties associated with dielectric-to-metal phase transition of VO2. This measurement is based on a fiber-optic pump-probe setup in NIR wavelength. Instantaneous optical phase modulation of the probe was demonstrated during pump pulse leading edge, which could be converted into an intensity modulation of the probe through an optical frequency discriminator 

Alias resolution or disambiguation is the process of determining which IP addresses belong to the same router. The focus of this project is the feature extraction aspect of the AliasCluster alias resolution technique. This technique uses five features extracted from traceroutes and uses a Naive Bayesian approach to resolve router aliases. The features extracted are the common subnet, percentage out-degree match for hop count ≤ 3, percentage out-degree match for hop count ≤ 4, percentage hop-count match for hop count ≤ 3, and percentage hop-count match for hop count ≤ 4. Using traceroutes from publicly available databases, the common subnet feature is determined by finding the number of bits common to two addresses, and the out-degree match is found by checking the number of interfaces in the downpath that appear in common to two addresses. The hop-count match is determined in a approach similar to the out-degree match, with an additional condition that the common interfaces must appear at the same hop count. In this project, algorithms to extract these features are implemented in Python and the feature distributions are compared to those described in the original AliasCluster work.

Future wireless networks will face a compound challenge of supporting large traffic volumes, providing ultra-reliable and low latency connections to ultra-dense mobile devices. To meet this challenge, various new technologies have been introduced among which mutual-information accumulation (MIA), an advanced physical (PHY) layer coding technique, has been shown to significantly improve the network performance. Since the PHY layer is the fundamental layer, MIA could potentially impact various network layers of a wireless network. Accordingly, the understanding of improving network design based on MIA is far from being fully developed. In the proposed research, we target to 1) apply MIA techniques to various wireless networks such as cognitive radio networks, device-to-device networks, etc; 2) mathematically characterize the performance of such networks employing MIA; 3) use hardware to demonstrate the performance of MIA for a simple wireless network using the Universal Software Radio Peripherals (USRPs).

System Security has been one of the primary focus areas for embedded devices in recent times. The pervasion of embedded devices over a wide range of applications ranging from routers to RFID badge controls emphasizes the need for System Security. Any security compromise may result in manipulation, damage or loss of crucial data leading to unwarranted results. A conventional approach towards system security is the use of static analysis tools on source code. However, very few of these tools operate at the system level. This project envisions measuring (Looking at a given device and analyzing what is present)firmware of Gumstix, an embedded device running poky version of Linux and build a model that serves as an input to Action Notation Modelling Language (ANML) planner. An ANML planner can be later on used to generate a check list of vulnerabilities, which is out of scope for this project. 

Due to the need for greater Radio Frequency (RF) spectrum by wireless communication industries such as mobile telephony, cable/satellite and wireless internet as a result of growing consumer base and demands, it has led to the issue of spectrum congestion as radar systems have traditionally maintain the largest share of the RF spectrum. To resolve the spectrum congestion problem, it has become even necessary for users from both types of systems to coexist within a finite spectrum allocation. However, this then leads to other problems such as the increased likelihood of mutual interference experienced by all users that are coexisting within the finite spectrum. 
In this dissertation, we propose to address the problem of spectrum congestion via two independent approaches. The first approach involves designing an intelligent scheme to perform spectrum reallocation to radar systems such that the range resolution performance can be maintained with a smaller resulting bandwidth but at a cost of degraded sidelobe performance. The second approach involves designing a radar waveform that possesses good spectral containment property by utilizing the framework of Poly-phased coded Frequency Modulated (PCFM) waveforms such that the waveform will mitigate the issue of interference experienced by other users coexisting within the same band. 

Although the Internet tremendously facilitates online interaction and information exchange beyond geographic boundaries, it also enlarges attack surface for adversaries to sniff users’ privacy such as who you are, who you are talking to, and what you are saying from their communication activities over the open networks. The goal of anonymous communication networks is to protect the identity and location of a communication participant from being learned by the other participant or any third party. Tor is a most popular low-latency anonymity network. While Tor provides good privacy protection to millions of users on a daily basis, its performance and security issues are widely recognized. We anticipate that big data applications, such as anonymous video conferencing, will pose a large amount of extra traffic to Tor. The performance problem becomes a biggest obstacle impeding Tor’s further expansion, which will be aggravated in the big data era. On the other hand, it is well known that Tor is vulnerable to traffic analysis attacks, especially the end-to-end traffic confirmation attack. 
In this proposal, we target the problems discussed above and propose a solution suite to address them correspondingly. We first explore the utilization of resources and find that a large portion of low-bandwidth relays are under-utilized. Therefore, we propose a multipath routing scheme to use idle resources to support bandwidth-intensive applications, which are the efforts that we make to solve the performance problems in general Tor services. To further improve the performance, we propose to enable differentiated services in Tor. The current Tor system treats clients’ requests equally and provides the same level of protection, neglecting the heterogeneity in individuals’ anonymity needs. To address this problem, we propose a learning-based solution that can automatically recognize users’ different anonymity needs for different applications and integrates it into the currently multipath Tor design to support dynamic, self-configurable anonymous communication. Then, we propose a multipath-routing based architecture for Tor hidden services to enhance the resistance of Tor hidden services against traffic analysis attacks. 

The reliance of Unmanned Aerial Vehicles (UAVs) on Global Navigation Satellite System (GNSS) for autonomous operation represents a significant vulnerability to their reliable and secure operation due to signal interference, both incidental (e.g. terrain shadowing, ionospheric scintillation) and malicious (e.g. jamming, spoofing). An accurate and reliable alternative UAV navigation system is proposed that exploits Signals of Opportunity (SOP) thus offering superior signal strength and spatial diversity compared to satellite signals. Given prior knowledge of the transmitter's position and signal characteristics, the proposed technique utilizes triangulation to estimate the receiver's position. Dual antenna interferometry provides the received signals' Angle of Arrival (AoA) required for triangulation. Reliance on precise knowledge of the antenna system's orientation is removed by combining AoAs from different transmitters to obtain a differential Angles of Arrival (dAoAs). Analysis, simulation, and ground-based experimental techniques are used to characterize system performance; a path to miniaturized system integration is also presented. Results from these ground-based experiments show that when the received signal-to-noise ratio (SNR) is above about 45 dB (typically in within 30 km of the transmitters), the proposed method estimates the receiver's position uncertainty range from less than 20 m to about 60 m with an update rate of 10 Hz.

Mobile data traffic is predicted to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation on the base station, 3D “massive MIMO” has been proposed as one of the enabling technologies to significantly increase the spectral efficiency of a wireless system. In “massive MIMO ” systems, a base station will rely on the uplink sounding signals from mobile stations to figure out the spatial information to perform MIMO beam-forming. Accordingly, multi-dimensional parameter estimation of a MIMO wireless channel becomes crucial for such systems to realize the predicted capacity gains. 
In this thesis, we study separated and joint angle and delay estimation for 3D “massive MIMO” systems in mobile wireless communications. To be specific, we first introduce a separated low complexity time delay and angle estimation algorithm based on unitary transformation and derive the mean square error (MSE) for delay and angle estimation in the millimeter wave massive MIMO system. Furthermore, a matrix-based ESPRIT-type algorithm is applied to jointly estimate delay and angle, the mean square error (MSE) of which is also analyzed. Finally, we found that azimuth estimation is more vulnerable compared to elevation estimation. Simulation results suggest that the dimension of the underlying antenna array at the base station plays a critical role in determining the estimation performance. These insights will be useful for designing practical “massive MIMO” systems in future mobile wireless communications. 

Mobile traffic is expected to grow at an annual compound rate of 57% until 2019, while among the data types that account for this growth mobile video has the highest growth rate. Since a significant portion of mobile video traffic are delay-sensitive, delay-sensitive traffic will play a critical role in future wireless communications. Future mobile wireless systems will face the dual challenge of supporting large traffic volume while providing reliable service for various kinds of delay-sensitive applications (e.g., real-time conversational video, voice-over-IP, and online gaming). Past work on delay-sensitive communications has overlooked physical-layer considerations such as modulation and coding scheme (MCS), probability of decoding error, and coding delay by employing oversimplified models for the physical-layer. With the proposed research we aim to bridge information theory, communication theory and queueing theory by jointly considering queueing delay violation probability and probability of decoding error to identify fundamental trade-offs among wireless system parameters such as MCS, code blocklength, user perceived quality of service, channel fading speed, and average signal-to-noise ratio. 

We focus on the case where the channel state information is available only at the receiver, and model the underlying wireless channel by a finite-state Markov chain (FSMC). First, we derive the dispersion of the FSMC model of the Rayleigh fading channel, and the dispersion of parallel additive white Gaussian noise (AWGN) channels with discrete input alphabets (e.g., pulse amplitude modulation). The FSMC dispersion is used to track the probability of decoding error and the coding delay for a given MCS. The dispersion of parallel AWGN channels is used to track the operation of incremental redundancy type hybrid automatic request (IR-HARQ) over the Rayleigh fading channel, and hence to characterize the probability of decoding error and the coding delay of IR-HARQ for a given MCS. Second, we focus on a queueing system where data packets arrive at the transmitter, wait in the queue, and are transmitted over the Rayleigh fading channel with IR-HARQ. We invoke a two-dimensional discrete-time Markov process and develop a recursive algorithm to characterize the system throughput for a given MCS under queueing delay violation probability, and probability of decoding error constraints. 

Information used to assist biomedical and clinical research has largely comprised of data available in published sources like scientific papers and journals, or in clinical sources like patient health records, lab reports and discharge summaries. Information from such sources, though extensive and organized, is often not readily available due to its proprietary and/or privacy-sensitive nature. Collecting such information through clinical studies is expensive and the information is often limited to the diversity of the people who are involved in the study. With the growth of online social networks, more and more people openly share their health experiences with other similar patients through online healthcare forums. The data from these forum messages can act as an alternate source that provides for unrestricted, high volume, highly diverse and up-to-date information needed for assisting and guiding biomedical and pharmaceutical research. However, this data is often unstructured, noisy and scattered, making it unsuitable for use in its current form. This dissertation presents an Information Retrieval and Knowledge Discovery Framework that is capable of collecting data from online healthcare forums, extracting useful information and storing it in a structured form that facilitates knowledge discovery. A Healthcare Forum Mining Ontology developed as a part of this work is used to organize and capture the semantic relationships between patient related data like age, gender, ethnicity and habits, along with health related data like drugs, side-effects, diseases and symptoms which are extracted from the forum messages. The utility of this framework is demonstrated with the help of two applications: an Adverse Drug Reaction discovery tool that is able to assist pharmacovigilance by extracting adverse effects of drugs from forum messages and an ontology-based visualization tool that can be used for exploring and analyzing associations between patient and health related data extracted from forum messages.