Defense Notices

With the advent of general-purpose programming tools and newer GPUs, programmers now have access to a more flexible general-purpose approach to using GPUs for something other than graphics. With single instruction stream, multiple data streams (SIMD), the same instruction is executed by multiple processors using different data streams. GPUs are SIMD computers that exploit data-level parallelism by applying the same operations to multiple items of data in parallel. There are many areas where GPUs can be used for general-purpose computing. We have chosen to focus on a project in the astrophysics area of scientific computing called N-body simulation which computes the evolution of a system of bodies that interact with each other. Each body represents an object such as a planet or a star, and each exerts a gravitational force on all the others. It is performed by using a numerical integration method to compute the interactions among the system of bodies, and begins with the initial conditions of the system which are the masses and starting position and velocity of every body. These data are repeatedly used to compute the gravitational force between all bodies of the system to show updates on screen. We investigate alternative implementation approaches to the problem in an attempt to determine the factors that maximize its performance, including speed and accuracy. Specifically, we compare an OpenCL approach to one based on using OpenGL Compute Shaders. We select these two for comparison to generate real-time interactive displays with OpenGL. Ultimately, we anticipate our results will be generalizable to other APIs (e.g., CUDA) as well as to applications other than the N-Body problem. A comparison of various numerical integration and memory optimization techniques is also included in our analysis in an attempt to understand how they work in the SIMD GPGPU environment and how they contribute to our performance metrics. We conclude that, for our particular implementation of the problem, taking advantage of efficiently using local memory considerably increases performance.

The number of vehicle manufacturers and the number of vehicles produced have been significantly increasing each year. With more vehicles on road, the number of accidents on the National Highways in the US increased notably. NHTSA (National Highway Traffic Safety Agency) is a federal agency which works towards preventing vehicle crashes and their attendant costs. They plan and execute several operations and control measures to find and solve the problems causing accidents. One such initiative is to analyze the primary causes of all the vehicle crashes and maintain a streamlined data of vehicle Identification catalog customized for DOT and NHTSA. Maintaining a data on about 250+ millions of vehicles and analyze them needs a robust database and an application for its maintenance. At StrongBridge Corporation, we developed VPICLIST, an application for NHTSA to assist their analytic projects with data entry and pattern decoding of VIN information catalog. The application employs precise pattern matching techniques to dump data into distributed databases which in turn collaborate to a central database of NHTSA. It allows decoding of VIN each at a time by the public and also decoding thousands of VINS simultaneously for internal use of NHTSA. To hold and operate upon several PBs of data, insertion and retrieval process of the application emulates a distributed architecture. The application is developed in Java and uses Oracle enterprise database for distributed small collections and NoSQL system for the central database.

The study of shear wave velocity (Vs) of near surface materials has been one of the primary areas of interest in seismic analyses. ‘Vs’ serves as the best indicator in evaluating the stiffness of a material from its shear modulus. One of the economical methods to obtain Vs profiling information is through the analysis of dispersion property of surface waves. SurfSeis4 - Software developed by the Kansas Geological Survey (KGS) utilizes Multichannel Analysis of Surface Waves (MASW) method to obtain shear wave velocity 2D (Surface location and depth) profiling. The profiling information is obtained in the form of a grid through inversion of dispersion curves. The Vs 2D map module of SurfSeis4, integrates the functionality of interpolating this grid to approximate the variation of shear wave velocity across the surface locations. The current project is an extension of the existing SurfSeis4 Vs 2D mapping module in its latest release of SurfSeis5 that incorporates topography in shear wave velocity variation and facilitates users with advanced image interpolation options.

One of the main components in switching power supply is switch. However, there are two main negative issues the switch will cause in a switching power supply. The first one is that the power dissipation of the switch will be unimaginable high, especially when the current go through the switch gets higher. Secondly, because there are so many parasitic inductances and capacitances in the circuit, transient will cause problems when the operating state of the switch changes. In this project, P-Spice is used to design a qualify swith and suppress the negative effect as much as possible. The purpose of this project is to design a switch for hardware design in switching power supplies. Therefore, all the components used in P-Spice simulation are the actual models which is able to get from electronic market, and all the situations which may be happen in hardware design will be consider in the simulation. Both Mosfet and bipolar transistor switch will be discussed in the project. The project will give solutions for reducing the power dissipation cause by the switch and transient problems.

Packet Filtering is a process of filtering the packets based on the filters rules that are being defines by the user. The focus of this project is to implement and compare the performance of two different packet filtering techniques (SFSA and PFSA), that uses FSA(finite state automaton) for the filtering process. Stateless FSA(SFSA) is a packet filtering technique where an FSA is generated based on the input packet and the filtering criteria. Then succeed early algorithm is applied to the automaton which simplifies by the automaton by shortening long trails to the the final state which reduces the packet filtering time. It also uses transition compaction algorithm which helps in avoiding certain areas in packet inspection which are not necessary for packet filtering. 
PFSA (predicates of FSA) does the filtering based on predicates generated by the predicate evaluator. In this filtering process the FSA generated as state transitions which depend on the input symbol and also the predicate value. In order to simplify the FSA algorithms like predicates Cartesian product and predicates anticipation algorithms are being used. These algorithms consider all states that are possible and merge them to make the FSA deterministic. There is also a proto FSA that is being generated for the predicates to speed up the filtering process. 

Neural Networks were studied and applied to many research problems from a long time. With gaining popularity of deep neural networks in the area of machine learning, many researchers in various domains want to try deep learning framework. Deep learning requires lot of memory and high processing power. One way of doing it faster is to make use of GPUs which use distributed and parallel processing, thereby increasing speed. But because of the computation (lot of vector and matrix operations) deep learning requires, expensive infrastructure required (GPUs and clusters), hardware and software installation overhead, not many researchers prefer deep learning. The current application is a solution to cheminformatics problems using Convolutional Architecture for Fast Feature Embedding (Caffe) deep learning framework. The application provides a framework/service to researchers who want to try deep learning on their datasets. The application accepts datasets from users along with options for hyper parameter configuration, runs cross fold validation on the training dataset, and makes predictions on the test dataset. The (tuning) results of running caffe on the training dataset and predictions made on test dataset are sent to user via an email. The current version supports binary classification that predicts activity/inactivity of a chemical compound based on molecular fingerprints which are binary features.

Given the high complexity and increasing traffic load of the current Internet, the geographically-correlated challenge caused by large-scale disasters or malicious attacks pose a significant threat to dependable network communications. To understand its characteristics, we start our research by first proposing a critical-region identification mechanism. Furthermore, the identified regions are incorporated into a new graph resilience metric, compensated Total Geographical Graph Diversity (cTGGD), which is capable of characterizing and differentiating resiliency levels for different topologies. We further propose the path geodiverse problem (PGD) that requires the calculation of a number of geographically disjoint paths, and two heuristics with less complexity compared to the optimal algorithm. We present two flow-diverse multi-commodity flow problems, a linear minimum-cost and a nonlinear delay-skew optimization problem to study the tradeoff among cost, end-to-end delay, and traffic skew on different geodiverse paths. We further prototype and integrate the solution from above models into our cross-layer resilient protocol stack, ResTP--GeoDivRP. Our protocol stack is implemented in the network simulator ns-3 and emulated in the KanREN testbed. By providing multiple geodiverse paths, our protocol stack provides better path protection than Multipath TCP (MPTCP) against geographically-correlated challenges. Finally, we analyze the mechanism attackers could utilize to maximize the attack impact and demonstrate the effectiveness of a network restoration plan. 

In a world with enormously growing technologies and applications, most people use smart 
devices. This provides a means to develop smart applications that will be help students learn effectively. 
In this project, we develop a smart android application which will provide digital means of 
interaction between the professors and students. Instead of using traditional emails for every 
discussion, this application helps to broadcast multiple messages to the class through a single 
click. The students will also be able to follow multiple professors and participate in the active 
discussions. And also this application allows the users to send personal messages to the other 
users in order to participate in an active discussion. It provides unique logins to every student 
and professor. It uses mongoDB as the database and "parse" backend as a service.The main 
inspiration for this project was an application called Tophat. 

As the passenger vehicles evolve to be “smart”, electronic components, including communication, intelligent control and entertainment, are continuously introduced to new models and concept vehicles. The new paradigm introduces new features and benefits, but also brings new security issues, which is often overlooked in the industry as well as in the research community. 

Smart cars are considered cyber-physical systems (CPS) because of their integration of cyber- and physical- components. In recent years, various threats, vulnerabilities, and attacks have been discovered from different models of smart cars. In the worst- case scenario, external attackers may remotely obtain full control of the vehicle by exploiting an existing vulnerability. 

In this research, we investigate smart cars’ security from a CPS’ perspective and derive a taxonomy of threats, vulnerabilities, attacks, and controls. In addition, we investigate three security solutions that would improve the security posture of automotive networks. First, as automotive networks are highly vulnerable to Denial of Service (DoS) attacks, we investigate a solution that effectively mitigates such attacks, namely ID-Hopping. In addition, because several attacks have successfully exploited the poor separation between critical and non-critical components in the automotive networks, we propose to investigate the effectiveness of firewalls and Intrusion Detection Systems (IDS) to prevent and detect such exploitations. To evaluate our proposals, we built a test bench that is composed of five microcontrollers and a communication bus to simulate an automotive network. Simulations and experiments performed with the testbed demonstrates the effectiveness of ID-hopping against DoS attacks. 

One source of insight into the motivations of a modern human being is the text they write and post for public consumption online, in forms such as personal status updates, product reviews, or forum discussions. The task of inferring traits about an author based on their writing is often called "author profiling." One challenging aspect of author profiling in today’s world is the increasing diversity of natural languages represented on social media websites. Furthermore, the informal nature of such writing often inspires modifications to standard spelling and grammatical structure which are highly language-specific. 
These are some of the dilemmas that inspired a series of so-called "shared task" competitions, in which many participants work to solve a single problem in different ways, in order to compare their methods and results. This thesis describes our submission to one author profiling shared task in which 22 teams implemented software to predict the age, gender, and certain personality traits of Twitter users based on the content of their posts to the website. We will also analyze the performance and implementation of our system compared to those of other teams, all of which were described in open-access reports. 
The competition organizers provided a labeled training dataset of tweets in English, Spanish, Dutch, and Italian, and evaluated the submitted software on a similar but hidden dataset. Our approach is based on applying a topic modeling algorithm to an auxiliary, unlabeled but larger collection of tweets we collected in each language, and representing tweets from the competition dataset in terms of a vector of 100 topics. We then trained a random forest classifier based on the labeled training dataset to predict the age, gender and personality traits for authors of tweets in the test set. Our software ranked in the top half of participants in English and Italian, and the top third in Dutch.