Defense Notices

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. 

The project presents the development of modified differential evolution algorithm based on harmony search algorithm for linear aperiodic array synthesis. The modified algorithm has the combine capability from the classical DE as well as harmony search algorithm. This differential evolution algorithm method optimizes a problem by iteratively trying to improve a solution with regards to given measure of quality. The objective is to optimize the linear aperiodic arrays with a minimum peak side lobe level (PSSL). The algorithm follows the steps of initializing the model parameters and generate corresponding base vectors followed by selection of two spacing vectors from the base vectors. Perform mutation and crossover in order to generate a new spacing vector. By calculation of PSSL along with execution of selection operation in DE, we update the vector base. Finally we adjust the parameters to meet the criteria, otherwise the iteration starts all over from the selection of two spacing vectors randomly. Numerical results shows that the HSDEA gives us a better PSSL performance. Comparison of PSSL using HSDEA and other differential evolution algorithm are performed which proves that the algorithm in study produces better PSSL performance with less number of evaluations.

Event Studies research focuses on the statistical impact that an event has on a traded company. In Finance, a financial press-release announcing company earnings is an example of an event. Unlike earnings announcements, media events may arise unexpectedly. By using the framework of an Event Study, this proposal will explore unexpected events in modern media -- particularly Twitter. Measuring statistical impact is not the central goal. Instead, listed here are the selected implementation objectives. Utilizing natural language processing, identify events on Twitter that influence stock prices of firms. Create text and time-series models, by applying machine learning techniques, to classify events. Develop quantitative trading strategies by associating prediction outputs as trading signals. The implementation objectives combine Event Studies and Machine Learning to produce an actionable system that guides trading decisions.

Nowadays, companies, governments, and civilians are moving towards using remote sensing drones for tasks that are too expensive, too risky, or too mundane for humans to do in order to retrieve visual intelligence. With this new age of drones being used for work, it is crucial to understand what goes into designing and constructing sensor systems, and how to build a vision system that preserves image integrity so that it can be successful in supplying data from aerial reconnaissance missions. This work focuses on the development of two such sensor systems, one containing a single camera and the other containing a rigid pair of cameras for implementation in unmanned aerial vehicles (UAVs) for the purpose of geographic information system (GIS) and surveillance applications. Calibration results for the cameras used in each system are given, and 
an analysis of camera capture frequency and synchronization is presented to 
understand how various automated camera trigger methods affect the integrity of image data during UAV flights. 

This thesis presents the design and implementation of high power, wide bandwidth transmit/receive (T/R) switches and modules for use in multi-channel ice-penetrating imaging radars. The switches were designed to address the lack of standard off-the shelf (COTS) devices that meet our technical requirements. 
The design of these switches was accomplished using electronic design automation (EDA) tools and implemented with quadrature hybrids and actively biased PIN diodes. Three different circuits were developed for three different frequency bands: 160-230 MHz (VHF band), 150-600 MHz (VHF/UHF), and 10-45 MHz (HF band). The circuits are capable of transmitting at least 1000 W of peak power and exhibit an insertion loss lower than 1.3 dB for 160-230 MHz, 1.6 dB for 150-600 MHz, and 1.95 dB for 10-45 MHz ranges. A fourth, miniaturized prototype for the 150-600 MHz range was implemented for use in future multi-channel systems. The circuits developed exhibit turn-on times better than 1.3 µs for the VHF/UHF circuits; and 2.1 µs for the HF circuits. The turn-off times were better than 200 ns for the first two bands and 1.36 µs for the HF band. Both the VHF and VHF/UHF have been demonstrated in field operations with two different radar systems.