Defense Notices

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.  

In the current era of big data, huge amount of data can be easily collected, but the unprocessed data is not useful on its own. It can be useful only when we are able to find interesting patterns or hidden knowledge. The algorithm to find interesting patterns is known as Rule Induction Algorithm. Rule induction is a special area of data mining and machine learning in which formal rules are extracted from a dataset. The extracted rules may represent some general or local (isolated) patterns related to the data.
In this report, we will focus on the IRIM (Interesting Rule Inducing Module) which induces strong interesting rules that covers most of the concept. Usually, the rules induced by IRIM provides interesting and surprising insight to the expert in the domain area.
The IRIM algorithm was implemented using Python and pySpark library, which is specially customize for data mining. Further, the IRIM algorithm was extended to handle the different types of missing data. Then at the end the performance of the IRIM algorithm with and without missing data feature was analyzed. As an example, interesting rules induced from IRIS dataset are shown.

Vision-based autonomous navigation of UAVs in real-time is a very challenging problem, which requires obstacle detection, tracking, and depth estimation. Although the problems of obstacle detection and tracking along with 3D reconstruction have been extensively studied in computer vision field, it is still a big challenge for real applications like UAV navigation. The thesis intends to address these issues in terms of robustness and efficiency. First, a vision-based fast and robust obstacle detection and tracking approach is proposed by integrating a salient object detection strategy within a kernelized correlation filter (KCF) framework. To increase its performance, an adaptive obstacle detection technique is proposed to refine the location and boundary of the object when the confidence value of the tracker drops below a predefined threshold. In addition, a reliable post-processing technique is implemented for an accurate obstacle localization. Second, we propose an efficient approach to detect the outliers present in noisy image pairs for the robust fundamental matrix estimation, which is a fundamental step for depth estimation in obstacle avoidance. Given a noisy stereo image pair obtained from the mounted stereo cameras and initial point correspondences between them, we propose to utilize reprojection residual error and 3-sigma principle together with robust statistic based Qn estimator (RES-Q) to efficiently detect the outliers and accurately estimate the fundamental matrix. The proposed approaches have been extensively evaluated through quantitative and qualitative evaluations on a number of challenging datasets. The experiments demonstrate that the proposed detection and tracking technique significantly outperforms the state-of-the-art methods in terms of tracking speed and accuracy, and the proposed RES-Q algorithm is found to be more robust than other classical outlier detection algorithms under both symmetric and asymmetric random noise assumptions.

The DC to AC power Converters (so-called Inverters) are widely used in industrial applications. The multilevel inverters are becoming increasingly popular in industrial apparatus aimed at medium to high power conversion applications.  In comparison to the conventional inverters, they feature superior characteristics such as lower total harmonic distortion (THD), higher efficiency, and lower switching voltage stress.  Nevertheless, the superior characteristics come at the price of a more complex topology with an increased number of power electronic switches. The increased number of power electronics switches results in more complicated control strategies for the inverter. Moreover, as the number of power electronic switches increases, the chances of fault occurrence of the switches increases, and thus the inverter’s reliability decreases. Due to the extreme monetary ramifications of the interruption of operation in commercial and industrial applications, high reliability for power inverters utilized in these sectors is critical.  As a result, developing simple control strategies for normal and fault-tolerant operation of multilevel inverters has always been an interesting topic for researchers in related areas.  The purpose of this dissertation is to develop new control and fault-tolerant strategies for the multilevel power inverter.  For the normal operation of the inverter, a new high switching frequency technique is developed.  The proposed method extends the utilization of the dc link voltage while minimizing the dv/dt of the switches. In the event of a fault, the line voltages of the faulty inverters are unbalanced and cannot be applied to the three phase loads. For the faulty condition of the inverter, three novel fault-tolerant techniques are developed. The proposed fault-tolerant strategies generate balanced line voltages without bypassing any healthy and operative inverter element, makes better use of the inverter capacity and generates higher output voltage. These strategies exploit the advantages of the Selective Harmonic Elimination (SHE) and Space Vector Modulation (SVM) methods in conjunction with a slightly modified Fundamental Phase Shift Compensation (FPSC) technique to generate balanced voltages and manipulate voltage harmonics at the same time.  The proposed strategies are applicable to several classes of multilevel inverters with three or more voltage levels.

Aiming to achieve the learning capabilities possessed by intelligent beings, especially human, researchers in machine learning field have the long-standing tradition of borrowing ideas from human learning, such as reinforcement learning, active learning, and curriculum learning.  Motivated by a philosophical theory called  "constructivism", in this work, we propose a new machine learning paradigm, constructivism learning.   The constructivism theory has had wide-ranging impact on various human learning theories about how human acquire knowledge.  To adapt this human learning theory to the context of machine learning, we first studied how to improve leaning performance by exploring inductive bias or prior knowledge from multiple learning tasks with multiple data sources, that is multi-task multi-view learning, both in offline and lifelong setting.  Then we formalized a Bayesian nonparametric approach using sequential Dirichlet Process Mixture Models to support constructivism learning.  To further exploit constructivism learning, we also developed a constructivism deep learning method utilizing Uniform Process Mixture Models.

   In airborne multichannel radar sounder signal processing, the collected data are most naturally represented in a cylindrical coordinate system: along-track, range, and elevation angle. The data are generally processed in each of these dimensions sequentially to focus or resolve the data in the corresponding dimension such that a 3D image of the scene can be formulated. Pulse-compression is used to process the data along the range dimension, synthetic aperture radar (SAR) processing is used to process the data in the along-track dimension, and array-processing techniques are used for the elevation angle dimension. After the first two steps, the 3D scene is resolved into toroids with constant along-track and constant range that are centered on the flight path. The targets lying in a particular toroid need to be resolved by estimating their respective elevation angles.
   In the proposed work, we focus on the array processing step, where several direction of arrival (DoA) estimation methods will be used to resolve the targets in the elevation-angle dimension, such as MUltiple Signal Classification (MUSIC) and maximum-likelihood estimation (MLE). A tracker is then used on the output of the DoA estimation to track the ice-bottom interface. We propose to use the tree re-weighted message passing algorithm or Kalman filtering, based on the array-processing technique, to track the ice-bottom. The outcome of this is a digital elevation model (DEM) of the ice-bottom. While most published work assumes a narrowband model for the array, we will use a wideband model and focus on issues related to wideband arrays. Along these lines, we propose a theoretical study to evaluate the performance of the radar products based on the array characteristics using different array-processing techniques, such as wideband MLE and focusing-matrices methods. In addition, we will investigate tracking targets using a sparse array composed of three sub-arrays, each separated by a large multiwavelength baseline. Specifically, we propose to develop and investigate the performance of a Kalman tracking solution to this wideband sparse array problem when applied to data collected by the CReSIS radar sounder.

In the 2016 presidential election, social networks showed their great power as a “modern form of communication”. With the increasing popularity of social networks, privacy concerns arise. For example, it has been shown that microblogs are revealed to audiences that are significantly larger than users' perceptions. Moreover, when users are emotional, they may post messages with sensitive content and later regret doing so.  As a result, users become very vulnerable – private or sensitive information may be accidentally disclosed, even in tweets about trivial daily activities.
Unfortunately, existing research projects on data privacy, such as the k-anonymity and differential privacy mechanisms, mostly focus on protecting individual’s identity from being discovered in large data sets. We argue that the key component of privacy protection in social networks is protecting sensitive content, i.e. privacy as having the ability to control dissemination of information. The overall objectives of the proposed research are: to understand the sensitive content of social network posts, to facilitate content-based protection of private information, and to identify different types of sensitive information.  In particular, we propose a user-centered, quantitative measure of privacy based on textual content, and a customized privacy protection mechanism for social networks. 
We consider private tweet identification and classification as dual-problems. We propose to develop an algorithm to identify all types of private messages, and, more importantly, automatically score the sensitiveness of private message.  We first collect the opinions from a diverse group of users w.r.t. sensitiveness of private information through Amazon Mechanical Turk, and analyze the discrepancies between users' privacy expectations and actual information disclosure. We then develop a computational method to generate the context-free privacy score, which is the “consensus” privacy score for average users. Meanwhile, classification of private tweets is necessary for customized privacy protection. We have made the first attempt to understand different types of private information, and to automatically classify sensitive tweets into 13 pre-defined topic categories. In proposed research, we will further include personal attitudes, topic preferences, and social context into the scoring mechanism, to generate a personalized, context-aware privacy score, which will be utilized in a comprehensive privacy protection mechanism.  

Fraud from insiders costs an estimated $3.7 trillion annually. Current fraud prevention and detection methods that include analyzing network logs, computer events, emails, and behavioral characteristics have not been successful in reducing the losses. The proposed Occupational Fraud Prevention and Detection Model uses existing data from the field of digital forensics along with text clustering algorithms, machine learning, and a growing hierarchical self-organizing map model to predict insider threats based on computer usage behavioral characteristics.

The proposed research leverages research results from information security, software engineering, data science and information retrieval, context searching, search patterns, and machine learning to build and employ a database server and workstations to support 50+ terabytes of data representing entire hard drives from work computers. Forensic software FTK and EnCase are used to generate disk images and test extraction results. Primary research tools are built using modern programming languages. The research data is derived from disk images obtained from actual investigations when fraud was asserted and other disk images when fraud was not asserted.

The research methodology includes building a data extraction tool that is a disk level reader to store the disk, partition, and operating system data in a relational database. An analysis tool is also created to convert the data into information representing usage patterns including summarization, normalization, and redundancy removal. We build a normalizing tool that uses machine learning to adjust the baselines for company, department, and job deviations.  A prediction component is developed to derive insider threat scores reflecting the anomalies from the adjusted baseline. The resulting product will allow identification of the computer users most likely to commit fraud so investigators can focus their limited resources on the suspects.

Our primarily plan to evaluate and validate our research results is via empirical study, statistical evaluation and benchmarking with tests of precision and recall from a second set of disk images.