Defense Notices

Ice shelves are sensitive indicators of climate change and play a critical role in the stability of ice sheets and oceanic currents. Basal melting of ice shelves affect both the mass balance of the ice sheet and the global climate system. This melting and refreezing influences the development of Antarctic Bottom Water, which help drive the oceanic thermohaline circulation, a critical component of the global climate system. Basal melt rates can be estimated through traditional glaciological techniques, relying on conversation of mass. However, this requires accurate knowledge of the ice movement, surface accumulation and ablation, and firn compression. Boreholes can provide direct measurement of melt rates, but only provide point estimates and are difficult and expensive to perform. Satellite altimetry measurements have been heavily relied upon for the past few decades. Thickness and melt rate estimates require the same conservation of mass a priori knowledge, with the additional assumption that the ice shelf is in hydrostatic equilibrium. Even with newly available, ground truthed density and geoid estimates, satellite data derived ice shelf thickness and melt rate estimates suffers from relatively course spatial resolution and interpolation induced error. Non destructive radio echo sounding (RES) measurements from long range airborne platforms provide best solution for fine spatial and temporal resolution over long survey traverses and only require a priori knowledge of firn density and surface accumulation. Previously, RES data derived basal melt rate experiments have been limited to ground based experiments with poor coverage and spatial resolution. To improve upon this, an airborne multi channel wideband radar has been developed for the purpose of imaging shallow ice and ice shelves. A moving platform and cross track antenna array will allow for fine resolution 3 D imaging of basal topography. An initial experiment will use a ground based system to image shallow ice and generate 3 D imagery as a proof of concept. This will then be applied to ice shelf data collected by an airborne system.

Residing between the network layer and the application layer, the transport 
layer exchanges application data using the services provided by the network. Given the unreliable nature of the underlying network, reliable data transfer has become one of the key requirements for those transport-layer protocols such as TCP. Studying the various mechanisms developed for TCP to increase the correctness of data transmission while fully utilizing the network's bandwidth provides us a strong background for our study and development of our own resilient end-to-end transport protocol. Given this motivation, in this thesis, we study the dierent 
TCP's error control and congestion control techniques by simulating them under dierent network scenarios using ns-3. For error control, we narrow our research to acknowledgement methods such as cumulative ACK - the traditional TCP's way of ACKing, SACK, NAK, and SNACK. The congestion control analysis covers some TCP variants including Tahoe, Reno, NewReno, Vegas, Westwood, Westwood+, and TCP SACK.

Mobile traffic is expected to grow at an annual compound rate of 66% in the next 3 years, while among the data types that account for this growth mobile video has the highest growth rate. Since most video applications are delay-sensitive, the delay-sensitive traffic will be the dominant traffic over future wireless communications. Consequently, 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 video, online gaming, and voice-over-IP (VoIP)). Past work on delay-sensitive communications has generally overlooked the 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 the delay-violation probability and the probability of decoding error to identify the fundamental trade-offs among wireless system parameters such as channel fading speed, average received signal-to-noise ratio (SNR), MCS, and user perceived quality of service. We will model the underlying wireless channel by a finite-state Markov chain, use channnel dispersion to track the probability of decoding error and the coding delay for a given MCS, and focus on the asymptotic decay rate of buffer occupancy for queueing delay analysis. The proposed work will be used to obtain fundamental bounds on the performance of queued systems over wireless communication channels.

The high level of functionality and flexibility of modern wideband wireless networks, LTE and WiMAX, have made them the preferred technology for providing mobile internet connectivity. The high performance of these systems comes from adopting several innovative techniques such as Orthogonal Frequency Division Multiplexing (OFDM), Automatic Modulation and Coding (AMC), and Hybrid Automatic Repeat Request (HARQ). However, this flexibility also opens the door for network exploitation by other ad-hoc networks, like Device-to-Device technology, or covert systems. In this work effort, we provide the theoretical foundation for a new ad-hoc wireless covert system that hides its transmission in the RF spectrum of an OFDM-based wideband network (Target Network), like LTE. The first part of this effort will focus on designing the covert waveform to achieve a low probability of detection (LPD). Next, we compare the performance of several available detection methods in detecting the covert transmission, and propose a detection algorithm that would represent a worst case scenario for the covert system. Finally, we optimize the performance of the covert system in terms of its throughput, transmission power, and interference on/from the target network.

Computer networks are prone to targeted attacks and natural disasters that could disrupt its normal operation and services. Adding links to form a full mesh yields the most resilient network but it incurs unfeasible high cost. In this research, we investigate the resilience improvement of real-world network via adding a cost-efficient set of links. Adding a set of link to get optimal solution using exhaustive search is impracticable given the size of communication network graphs. Using a greedy algorithm, a feasible solution is obtained by adding a set of links to improve network connectivity by increasing a graph robustness metric such as algebraic connectivity or total path diversity. We use a graph metric called flow robustness as a measure for network resilience. To evaluate the improved networks, we apply three centrality-based attacks and study their resilience. The flow robustness results of the attacks show that the improved networks are more resilient than the non-improved networks.

Depth inference is a fundamental problem of computer vision with a broad range of potential applications. Monocular depth inference techniques, particularly shape from shading dates back to as early as the 40's when it was first used to study the shape of the lunar surface. Since then there has been ample research to develop depth inference algorithms using monocular cues. Most of these are based on physical models of image formation and rely on a number of simplifying assumptions that do not hold for real world and natural imagery. Very few make use of the rich statistical information contained in real world images and their 3D information. There have been a few notable exceptions though. The study of statistics of natural scenes has been concentrated on outdoor natural scenes which are cluttered. Statistics of scenes of single objects has been less studied, but is an essential part of daily human interaction with the environment. This thesis focuses on studying the statistical properties of single objects and their 3D imagery, uncovering some interesting trends, which can benefit shape inference techniques. I acquired two databases: Single Object Range and HDR (SORH) and the Eton Myers Database of single objects, including laser-acquired depth, binocular stereo, photometric stereo and High Dynamic Range (HDR) photography. The fractal structure of natural images was previously well known, and thought to be a universal property. However, my research showed that the fractal structure of single objects and surfaces is governed by a wholly different set of rules. Classical computer vision problems of binocular and multi-view stereo, photometric stereo, shape from shading, structure from motion, and others, all rely on accurate and complete models of which 3D shapes and textures are plausible in nature, to avoid producing unlikely outputs. Bayesian approaches are common for these problems, and hopefully the findings on the statistics of the shape of single objects from this work and others will both inform new and more accurate Bayesian priors on shape, and also enable more efficient probabilistic inference procedures.

The work presented in this thesis explores the use of geographic data and geostatistical methods to estimate path loss for cognitive radio networks. Path loss models typically employed in this scenario use a general terrain type (i.e., urban, suburban, or rural) and possibly a digital elevation model to predict excess path loss over the free space model. Additional descriptive knowledge of the local environment can be used to make more accurate path loss predictions. This research focuses on the use of visible imagery, digital elevation models, and terrain classification systems for predicting localized propagation characteristics. A low-cost data collection platform was created and used to generate a sufficiently large spectrum measurement set for machine learning. A series of path loss models were fitted to the data using linear and nonlinear methods. These models were then used to create a radio environment map depicting estimated signal strength. All of the models created have good cross-validated prediction results when compared to existing path loss models, although some of the more flexible models had a tendency to overfit the data. A number of geostatistical models were fitted on the data as well. 
These models have the advantage of not requiring the transmitter location in order to create a model. The geostatistical models performed very well when given a sufficient density of observations but were not able to generalize as well as some of the regression models. An analysis of the geographical data sets indicated that each had a significant measurable effect on path loss estimation, with the medium resolution imagery and elevation data providing the greatest increase in accuracy. Finally, these models were compared to number of existing path loss models, demonstrating a gain in usable spectrum for cognitive radio network use.

Tactical networks, networks designed to facilitate command and control capabilities for militaries, have key attributes that differ from the commercial Internet. Characterizing, modeling, and ex- ploiting our understanding of these differences is the focus of this research. 
The differences between tactical and commercial networks can be found primarily in the areas of access bandwidth, access diversity, access latency, core latency, subnet distribution, and network infrastructure. In this work we characterize and model these differences. These key attributes affect research into issues such as peer-to-peer protocols, service discovery, and server selection among others, as well as the deployment of services and systems in tactical networks. Researchers traditionally struggle with measuring, analyzing, or testing new ideas on tactical networks due to a lack of direct access, and thus this characterization is crucial to evolving this research field. 
In this work we develop a topology generator that creates realistic tactical networks that can be visualized, analyzed, and emulated. 
Topological features including geographically constrained line of sight networks, high density low bandwidth satellite networks, and the latest high bandwidth on- the-move networks are captured. All of these topological features can be mixed to create realistic networks for many different tactical scenarios. A web based visualization tool is developed, as well as the ability to export topologies to the Mininet network virtualization environment. 
Finally, state-of-the-art server selection algorithms are reviewed and found to perform poorly for tactical networks. We develop a collaborative algorithm tailored to the attributes of tactical networks, and utilize our generated networks to assess the algorithm, finding a reduction in utilized bandwidth and a significant reduction in client to server latency as key improvements.

Multitask Learning is a learning framework which explores the concept of sharing training information among multiple related tasks to improve the generalization error of each task. The benefits of multitask learning have been shown both empirically and theoretically. There are a number of fields that benefit from multitask learning, including toxicology. However, the current multitask learning algorithms make a very important key assumption that all the tasks are related to each other in a similar fashion in multitask learning. The users often do not have the knowledge of which tasks are related and train all tasks together. This results in sharing of training information even among the unrelated tasks. Training unrelated tasks together can cause a negative transfer and deteriorate the performance of multitask learning. For example, consider the case of predicting in vivo toxicity of chemicals at various endpoints from the chemical structure. Toxicity at all the endpoints are not related. Since, biological networks are highly complex, it is also not possible to predetermine which endpoints are related. Thus, training all the endpoints together may cause a negative effect on the overall performance. This proposal aims at developing algorithms which make use of task relationship models to further improve the generalization error and prevent transfer of information among the unrelated tasks. The algorithms proposed here either learn the task relationships or utilize the known task relationships in the learning framework. Further, these algorithms will be utilized to predict toxicity of chemicals at various endpoints using the chemical structures and the results of multiple in vitro assays performed on these chemicals.

A rule set is a popular symbolic representation of knowledge derived from 
data. A rule induction is an important technique of data mining or machine 
learning. Many rule induction algorithms are widely used, such as LEM1, LEM2 and MLEM2. Some of these algorithms perform better on special data, e. g., on inconsistent data set or data sets with missing attribute values. This work discusses basic ideas of the MLEM2 algorithm, especially, how it handles data sets with numeric attribute values. Additionally, a comparison of the performance of different discretization options of the MLEM2 algorithm is also included.