Defense Notices

The non-ending growth of data traffic resulting from the continuing emergence of high-data-rate-demanding applications sets huge capacity requirements on optical interconnects and transport networks. This requires optical communication schemes in these networks to make the best possible use of the available optical spectrum per a single optical channel to enable transmission of multiple tens of tera-bits per second per a single fiber core in high capacity transport networks. Therefore, advanced modulation formats are required to be used in conjunction with energy-efficient and robust transceiver schemes. Important challenges facing these goals are the stringent requirements on the characteristics of optical components comprising these systems. Especially the laser sources. Laser phase noise is one of the most important performance-limiting factors in systems with high spectral efficiency. In this research work, we study the effects of different laser phase noise characteristics on the performance of different optical communication schemes. A novel, simple and accurate phase noise characterization technique is proposed. Experimental results show that the proposed technique is very accurate in estimating the performance of lasers in coherent systems employing digital phase recovery techniques. A novel multi-heterodyne scheme for characterizing the phase noise of laser frequency comb sources is also proposed and validated by experimental results. This proposed scheme is the first one of its type capable of measuring the differential phase noise between multiple spectral lines instantaneously by a single measurement. Moreover, extended relations between system performance and detailed characteristics of laser phase noise are also analyzed and modeled. The results of this study show that the commonly-used metric to estimate the performance of lasers with a specific phase recovery scheme, linewidth-symbol-period product, is not necessarily accurate for all types of lasers, and description of FM-noise power spectral profile is required for accurate performance estimation. We also propose an energy- and cost-efficient transmission scheme suitable for metro and long-reach data-center-interconnect links based on direct detection of field-modulated optical signals with advanced modulation formats, allowing for higher spectral efficiency. The proposed system combines the Kramers-Kronig coherent receiver technique, with the use of quantum-dot multi-mode laser sources, to generate and transmit multi-channel optical signals using a single diode laser source. Experimental results of the proposed system show that high modulation formats can be employed, with high robustness against laser phase noise and frequency drifting.

Resource limited embedded systems provide a great challenge to programming using functional languages.  Although these embedded systems cannot be programmed directly with Haskell, I show that an embedded domain specific language is able to be used to program them, and provides a user friendly environment for both prototyping and full development.  The Arduino line of microcontroller boards provide a versatile, low cost and popular platform for development of these resource limited systems, and I use these boards as the platform for my DSL research.

First, I provide a shallowly embedded domain specific language, and a firmware interpreter, allowing the user to program the Arduino while tethered to a host computer.  Shallow EDSLs allow a programmer to program using many of the features of a host language and its syntax, but sacrifice performance.  Next, I add a deeply embedded version, allowing the interpreter to run standalone from the host computer, as well as allowing the code to be compiled to C and then machine code for efficient operation.   Deep EDSLs provide better performance and flexibility, through the ability to manipulate the abstract syntax tree of the DSL program, but sacrifice syntactical similarity to the host language.   Using Haskino, my EDSL designed for Arduino microcontrollers, and a compiler plugin for the Haskell GHC compiler, I show a method for combining the best aspects of shallow and deep EDSLs. The programmer is able to write in the shallow EDSL, and have it automatically transformed into the deep EDSL.  This allows the EDSL user to benefit from powerful aspects of the host language, Haskell, while meeting the demanding resource constraints of the small embedded processing environment.

Energy harvesting from solar sources in an attempt to increase efficiency has sparked interest in many communities to develop more energy harvesting applications for renewable energy topics. Advanced technical methods are required to ensure the maximum available power is harnessed from the photovoltaic (PV) system. This dissertation proposed a new discrete-in-time extremum-seeking (ES) based technique for tracking the maximum power point of a photovoltaic array. The proposed method is a true maximum power point tracker that can be implemented with reasonable processing effort on an expensive digital controller. The dissertation presents a stability analysis of the proposed method to guarantee the convergence of the algorithm.

Two types of PV systems were designed and comprehensive frame work of control design was considered for a stand-alone and a three-phase grid connected system.

Grid-tied systems commonly have a two-stage power electronics interface which is necessitated due to the inherent limitation of the DC-AC (Inverter) power converging stage. However, a one stage converter topology, denoted as Quasi-Z-source inverter (q-ZSI) was selected that interface the PV panel which overcomes the inverter limitations to harvest the maximum available power.

A powerful control scheme called Model Predictive Control with Finite Set (MPC-FS) was designed to control the grid connected system. The predictive control was selected to achieve a robust controller with superior dynamic response in conjunction with the extremum-seeking algorithm to enhance the system behavior.

The proposed method exhibited better performance in comparison to conventional Maximum Power Point Tracking (MPPT) methods and require less computational effort than the complex mathematical methods.​

Remote Procedure Calls are an integral part of the internet of things and cloud computing. However, remote procedures, by their very nature, have an expensive overhead cost of a network round trip. There have been many optimizations to amortize the network overhead cost, including asynchronous remote calls and batching requests together.

In this dissertation, we present a principled way to batch procedure calls together, called the Remote Monad. The support for monadic structures in languages such as Haskell can be utilized to build a staging mechanism for chains of remote procedures. Our specific formulation of remote monads uses natural transformations to make modular and composable network stacks which can automatically bundle requests into packets by breaking up monadic actions into ideal packets. By observing the properties of these primitive operations, we can leverage a number of tactics to maximize the size of the packets.

We have created a framework which has been successfully used to implement the industry standard JSON-RPC protocol, a graphical browser-based library, an efficient byte string implementation, a library to communicate with an Arduino board and database queries all of which have automatic bundling enabled. We demonstrate that the result of this investigation is that the cost of implementing bundling for remote monads can be amortized almost for free, when given a user-supplied packet transportation mechanism.

Many important machine learning and data mining algorithms rely on a measure to provide a notion of distance or dissimilarity. Naive metrics such as the Euclidean distance are incapable of leveraging task-specific information, and consider all features as equal. A learned distance metric can become much more effective by honing in on structure specific to a task. Additionally, it is often extremely desirable for a metric to be sparse, as this vastly increases the ability to interpret the distance metric. In this dissertation, we explore several current problems in distance metric learning and put forth solutions which make use of structured sparsity.

The first contribution of this dissertation begins with a classic approach in distance metric learning and address a scenario where distance metric learning is typically inapplicable, i.e., the case of learning on heterogeneous data in a high-dimensional input space. We construct a projection-free distance metric learning algorithm which utilizes structured sparse updates and successfully demonstrate its application to learn a metric with over a billion parameters.

The second contribution of this dissertation focuses on an intriguing regression-based approach to distance metric learning. Under this regression approach there are two sets of parameters to learn; those which parameterize the metric, and those defining the so-called ``virtual points''. We begin with an exploration of the metric parameterization and develop a structured sparse approach to robustify the metric to noisy, corrupted, or irrelevant data. We then focus on the virtual points and develop a new method for learning the metric and constraints together in a simultaneous manner. It is demonstrate through empirical means that our approach results in a distance metric which is more effective than the current state of-the-art.

Machine learning algorithms have recently become ingrained in an incredibly diverse amount of technology. The focus of this dissertation is to develop more effective techniques to learn a distance metric. We believe that this work has the potential for broad-reaching impacts, as learning a more effective metric typically results in more accurate metric-based machine learning algorithms.

Data mining is a computing process of finding meaningful patterns in large sets of data. These patterns are then analyzed and used to make predictions for the future. One form of data mining is to extract rules from data sets. There are various rule induction algorithms, such as LEM1 (Learning from Examples Module Version 1), LEM2 (Learning from Examples Module Version 2) and MLEM2(Modified Learning from Examples Module Version 2). Most of the rule induction algorithms require the input data with only discretized attributes. If the input data contains numerical attributes, we need to convert them into discrete values (intervals) before performing rule induction, this process is called discretization. In this project, we discuss an implementation of LEM2 which generates the rules from data with numerical and symbolic attributes. The accuracy of the rules generated by LEM2 is measured by computing the error rate by a program called rule checker using ten-fold cross-validation and holdout methods. ​

Dynamic Binary Translators(DBTs) have a variety of uses, like instrumentation, profiling, security, portability, etc. In order for the desired application to run with these enhanced additional features(not originally part of its design), it is to be run under the control of Dynamic Binary Translator. The application can be thought of as the guest application, to be run with in a controlled environment of the translator, which would be the host application. That way, the intended application execution flow can be enforced by the translator, thereby inducing the desired behavior in the application on the host platform(combination of Operating System and Hardware). Depending on the implementation of the translator(host application), the guest application can either have code compiled for the host platform, or a different platform. It would be the responsibility of the translator to make appropriate code/binary translation of the guest application code, to be run on the host platform.

However, there will be a run-time/execution-time overhead in the translator, when performing the additional tasks to run the guest application in a controlled fashion. This run-time overhead has been limiting the usage of DBT's on a large scale, where response times can be critical. There is often a trade-off between the benefits of using a DBT against the overall application response time. So, there is a need to research/explore ways of faster application execution through DBT's(given their large code-base).

With the evolution of the multi-core and GPU hardware architectures, paralleization of software can be employed through multiple threads, which can concurrently run parts of code and potentially doing more work at the same time. The proper design of parallel applications or parallelizing parts of existing code, can lead to faster application run-time's, by taking advantage of the hardware architecture support to parallel programs.

We explore the possibility of improving the performance of a DBT named DynamoRIO. The basic idea is to improve its performance by speeding-up the process of guest code translation, through multiple threads translating multiple pieces of code concurrently. In an ideal case, all the required code blocks for application execution would be available ahead of time(eager translation), without any wait/overhead at run-time, and also giving it the enhanced features through the DBT. For efficient run-time eager translation there is also a need for heuristics, to better predict the next likely code block to be executed. That could potentially bring down the less productive code translations at run-time. The goal is to get application speed-up through eager translation, coupled with block prediction heuristics, leading to an execution time close to that of native run.

With the advancement of arbitrary waveform generation techniques, new radar transmission modes can be designed via precise control of the waveform's time-domain signal structure. The finer degree of emission control for a waveform (or multiple waveforms via a digital array) presents an opportunity to reduce ambiguities in the estimation of parameters within the radar backscatter. While this freedom opens the door to new emission capabilities, one must still consider the practical attributes for radar waveform design. Constraints such as constant amplitude (to maintain sufficient power efficiency) and continuous phase (for spectral containment) are still considered prerequisites for high-powered radar waveforms. These criteria are also applicable to the design of multiple waveforms emitted from an antenna array in a multiple-input multiple-output (MIMO) mode.

In this work, two spatially-diverse radar emission design methods are introduced that provide constant amplitude, spectrally-contained waveforms. The first design method, denoted as spatial modulation, designs the radar waveforms via a polyphase-coded frequency-modulated (PCFM) framework to steer the coherent mainbeam of the emission within a pulse. The second design method is an iterative scheme to generate waveforms that achieve a desired wideband and/or widebeam radar emission. However, a wideband and widebeam emission can place a portion of the emitted energy into what is known as the `invisible' space of the array, which is related to the storage of reactive power that can damage a radar transmitter. The proposed design method purposefully avoids this space and a quantity denoted as the Fractional Reactive Power (FRP) is defined to assess the quality of the result.

The design of FM waveforms via traditional gradient-based optimization methods is also considered. A waveform model is proposed that is a generalization of the PCFM implementation, denoted as coded-FM (CFM), which defines the phase of the waveform via a summation of weighted, predefined basis functions. Therefore, gradient-based methods can be used to minimize a given cost function with respect to a finite set of optimizable parameters. A generalized integrated sidelobe metric is used as the optimization cost function to minimize the correlation range sidelobes of the radar waveform.

In Data mining, Decision Tree is a type of classification model which uses a tree-like data structure to organize the data to obtain meaningful information. We may use Decision Tree for important predictive analysis in data mining. 

In this project, we compare two decision tree generating algorithms CART and the modified ID3 algorithm using different datasets with discrete and continuous numerical values. A new approach to handle the continuous numerical values is implemented in this project since the basic ID3 algorithm is inefficient in handling the continuous numerical values. In the modified ID3 algorithm, we discretize the continuous numerical values by creating cut-points. The decision trees generated by the modified algorithm contain fewer nodes and branches compared to basic ID3. 

The results from the experiments indicate that there is statistically insignificant difference between CART and modified ID3 in terms of accuracy on test data. On the other hand, the size of the decision tree generated by CART is smaller than the decision tree generated by modified ID3. 

Recommendation systems are becoming more and more important with increasing popularity of e-commerce platforms. An ideal recommendation system recommends preferred items to the user. In this project, an algorithm named item-item collaborative filtering is implemented as premise. The recommendations are smarter by going through movies similar to the movies of different ratings by the user, calculating predictions and recommending those movies which have high predictions. The primary goal of the proposed recommendation algorithm is to include user’s preference and to include lesser known items in recommendations. The proposed recommendation system was evaluated on basis of Mean Absolute Error(MAE) and Root Mean Square Error(RMSE) against 1 Million movie rating involving 6040 users and 3900 movies. The implementation is made as a web-application to simulate the real-time experience for the user.