Defense Notices

The shared resources in multi-core systems, mainly the memory subsystem(caches and DRAM), if not managed properly would affect the predictability of real-time tasks in the presence of co-runners. In this work, we first studied the design of COTS DRAM controllers and its impact on predictability and, proposed a DRAM controller design, called MEDUSA, to provide predictable memory performance in multi-core based real-time systems. In our approach, the OS partially partitions DRAM banks into reserved banks and shared banks. The reserved banks are exclusive to each core to provide predictable timing while the shared banks are shared by all cores to efficiently utilize the resources. MEDUSA has two separate queues for read and write requests, and it prioritizes reads over writes. In processing read requests, MEDUSA employs a two-level scheduling algorithm that prioritizes the memory requests to the reserved banks in a Round Robin fashion to provide strong timing predictability. In processing write requests, MEDUSA largely relies on the FR-FCFS for high throughput. We implemented MEDUSA in a cycle-accurate full-system simulator. The results show that MEDUSA achieves up to 91% better worst-case performance for real-time tasks while achieving up to 29% throughput improvement for non-real-time tasks 

Second, we studied the contention at shared caches and its impact on predictability. We demonstrate that the prevailing cache partition techniques does not necessarily ensure predictable cache performance in modern COTS multi-core platforms that use non-blocking caches to exploit memory-level-parallelism (MLP). Through carefully designed experiments using three real COTS multi-core platforms (four distinct CPU architectures) and a cycle-accurate full system simulator, we show that special hardware registers in non-blocking caches, known as Miss Status Holding Registers (MSHRs), which track the status of outstanding cache-misses, can be a significant source of contention. We propose a hardware and system software (OS) collaborative approach to efficiently eliminate MSHR contention for multi-core real-time systems.We implement the hardware extension in a cycle-accurate full-system simulator and the scheduler modification in Linux 3.14 kernel. In a case study, we achieve up to 19% WCET reduction (average: 13%) for a set of EEMBC benchmarks compared to a baseline cache partitioning setup. 

This dissertation investigates the feasibility of creating a multichannel sense-and-avoid radar system for small fixed-wing unmanned aerial vehicles (UAVs, also known as sUAS or drones). These aircraft are projected to have a significant impact on the U.S. economy in both the commercial and government sectors, however, their lack of situation awareness has caused the FAA to strictly limit their use. Through this dissertation, a miniature, multichannel, FMCW radar system was created with a small enough size, weight, and power (SWaP) that would allow it to be mounted onboard a sUAS providing inflight target detection. The primary hazard to avoid are general aviation (GA) aircraft such as a Cessna 172 which was estimated to have a radar cross section (RCS) of approximately 1 sqr meter. The radar system is capable of locating potential hazards in range, Doppler, and 3-dimensional space using a patent pending 2-D FFT process and interferometry. The initial prototype system has a detection range of approximately 800 m, with 360-degree azimuth coverage, and +/- 15-degree elevation coverage and draws less than 20 W. From the radar data, target detection, tracking, and the extrapolation of the target behavior in 6-degree of freedom was demonstrated.

The Treatise on Invertebrate Paleontology is the most authoritative compilation of the invertebrate fossil records. The quality of studies in paleontology, in particular depends on the accessibility of fossil data. Unfortunately, the PDF version of Treatise currently available is just a scanned copy of the paper publications and the content is in no way organized to facilitate search and knowledge discovery. This project builds an Information Retrieval based system, to extract the fossil descriptions, images and other available information from Treatise. This project is divided into two parts. The first part deals with the extraction of the text and images from the Treatise, organize the information in a structured format and store in a relational database, build a search engine to browse fossil data. Extracting text requires identifying common textual patterns and a text parsing algorithm is developed to identify the patterns and organize the information in a structural format. Images are extracted using the image processing techniques like image segmentation, morphological operations etc., and then associated with the corresponding textual descriptions. A Search engine is built to efficiently browse the extracted information and also the web interface provides options to perform many useful tasks with ease. The second part of this research focuses on the implementation of Content Based Information Retrieval System. All images from treatise are grayscale fossil images and identifying the matching images based on the visual image features is a very difficult task. Hence, we employed an approach that integrates textual and visual features to identify matching images. Textual features are extracted from the description of the fossils and using statistical approaches and Parts of Speech tagging approaches, an ontology is generated, that forms attribute – property pairs explaining how a region looks like in each shell. Popular image features like SIFT, GIST, and HOG features are extracted from fossil images. Both the textual and image features are then integrated to extract the information related to the fossil image matching the query image.

In data mining, duplicate data plays a huge role in deciding the set of rules. In this project, an analysis has been made on finding the impact of duplicates in the input data set on the rule set. The effect of duplicates is being analyzed using the error rate factor. Error rate is calculated by comparing the obtained rule set against the testing part of input data. The results of experiments have shown decrement of error rate with the increase of percentage of duplicates in the input data set, which demonstrates that the duplicate data plays a crucial role in validation process of machine learning. LEM2 algorithm and rule checker application have been implemented as a part of project. LEM2 algorithm is used to induce the rule set for the given input data set and rule checker application is used to calculate the error rate.

Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Even though wearable devices like accelerometers (e.g. activPAL) are considered as the standard for assessing physical activity in research, the machine learning algorithms that we propose will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. In order to achieve this, we collected two datasets, one is laboratory-controlled dataset and second is free-living dataset. We trained machine learning classifiers on both datasets and compared their behaviors on these datasets. The classifiers predict five postures: sit, stand, sit-stand, stand-sit, and stand\walk. We have also compared manually constructed Hidden Markov model(HMM) with automated HMM from existing software on both datasets to better understand the algorithm and existing software. When we tested on the laboratory-controlled dataset and the free-living dataset, the manually constructed HMM gave more F1-Macro score.

Depth- First Search (DFS) and Breadth- First Search are two of the most extensively used graph traversal algorithms to compile information about the graph in linear time. These two graph traversal mechanisms overlay a path to explore further the applications based on them that are widely used in Network Engineering, Web Analytics, Social Networking, Postal Services and Hardware Implementations. The difference between DFS and BFS results in the order in which they explore vertices and the implementation techniques for storing the discovered but un-processed vertices in the graph. BFS algorithm usually needs less time but consumes more computer memory than a DFS implementation. DFS algorithm is based on LIFO mechanism and is implemented using stack. BFS algorithm is based on FIFO technique and is realized using a queue. The order in which the vertices are visited using DFS or BFS can be realized with the help of a tree. The type of graph (directed or undirected) along with the edges of these trees form the basis of all the applications on BFS or DFS. Determining the shortest path between vertices of an un-weighted graph can be used in network engineering to transfer data packets. Checking for the presence of cycle can be critical in minimizing redundancy in telecommunications and is extensively used by social networking websites these days to analyse information as how people are connected. Finding bridges in a graph or determining the set of articulation vertices help minimize vulnerability in network design. Finding the strongly connected components in a graph can be used by model checkers in computer science. Determining an Euler circuit in a graph can be used by the postal service industries and the algorithm can be successfully implemented with linear running time using enhanced data structures. This survey project briefly defines and explains the basics of DFS and BFS traversal and explores some of the applications that are based on these algorithms. 

The integration of a RF power amplifier into a Collision Avoidance Radar will increase the maximum detection distance of the radar. Increasing the maximum detection distance will allow a radar system mounted on an Unmanned Aircraft Vehicle to observe obstacles earlier and give the UAV more time to react. The UAVradars project has been miniaturized to support operation on an unmanned aircraft and could benefit from an increase in maximum detection distance. 
The goal of this project is to create a one watt power amplifier for the 2.4GHz-2.5GHz band that can be integrated into the UAVradars project. The amplifier will be powered from existing power supplies in the radar system and must be small and lightweight to support operation on board the UAV in flight. This project will consist of the schematic and layout design, simulations, fabrication, and characterization of the power amplifier. The power amplifier will be designed to fit into the current system with minimal system modifications required. 

In data mining, missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. Classification of missing data is a challenging task. One of the most popular techniques for classifying missing data is decision tree induction. 
In this project, we compare two decision tree generating algorithms CART and C4.5 with their original implementations on different datasets with missing attribute values, taken from University of California Irvine (UCI). The comparative analysis of these two implementations is carried out in terms of accuracy on training and testing data, and decision tree complexity based on its depth and size. Results from experiments show that there is statistically insignificant difference between C4.5 and CART in terms of accuracy on testing data and complexity of the decision tree. On the other hand, accuracy on training data is significantly better for CART compared to C4.5. 

Measuring and assessing software security is a critical concern as it is undesirable to develop risky and insecure software. Various measurement approaches and metrics have been defined to assess software security. For researchers and software developers, it is significant to have different metrics and measurement models at one place either to evaluate the existing measurement approaches, to compare between two or more metrics or to be able to find the proper metric to measure the software security at a specific software development phase. There is no existing survey of software security metrics that covers metrics available at all the software development phases. In this paper, we present a survey of metrics used to assess and measure software security, and we categorized them based on software development phases. Our findings reveal a critical lack of automated tools, and the necessity to possess detailed knowledge or experience of the measured software as the major hindrances in the use of existing software security metrics. 

As semiconductor technologies continue to scale further into the nanometer regime, it is important to study how non-traditional computer architectures may be uniquely suited to take advantage of the novel behavior observed for many emerging technologies. Neuromorphic computing system represents a type of non-traditional architecture encompassing evolutionary. Reservoir computing, a computational paradigm inspired on neural systems, has become increasingly popular for solving a variety of complex recognition and classification problems. The traditional reservoir computing methods employs three different layers – the input layer, the reservoir and the output layer. The input layer feeds the input signals to the reservoir via fixed random weighted connections. These weights will scale the input that is given to the nodes, creating different input scaling for the input nodes. The second layer, which is called the reservoir, usually consists of a large number of randomly connected nonlinear nodes, constituting a recurrent network. Finally, the output weights are extracted from the output layer. Contrary to this traditional approach, the delayed feedback reservoir replaces the entire network of connected non-liner nodes just with a single nonlinear node subjected to delayed feedback. This approach does not only provide a drastic simplification of the experimental implementation of artificial neural networks for computing purposes, it also demonstrates the huge computational processing power hidden in even the simplest delay-dynamical system. Previous implementation of reservoir computing using the echo state network has been proven efficient for channel estimation in wireless Orthogonal Frequency-Division Multiplexing (OFDM) systems. This project aims at verifying the performance of DFR in channel estimation, by calculating its bit error rate (BER) and comparing it with other standard techniques like the LS and MMSE.