Defense Notices

Simulating the mechanics of a beating heart requires the numerical solution of partial differential equations. An application like this is a good candidate for high order computational methods that deliver higher solution accuracy at a lower cost than their low order counterparts. 
To fully leverage these high order computational methods, they must be paired with an accurate discretization of the domain. For a geometry like the heart, this requires a high order mesh. Thus robust high order mesh generation is a critical component to the widespread adoption of high order computational methods for numerically solving partial differential equations. Toward this end, we are developing high order mesh generation and untangling methods. As our first step, we have developed an optimization-based second order mesh generation method that employs triangles and tetrahedra. We will also develop generation methods for quadrilateral and hexahedral elements. Finally, we will develop untangling methods that can be used to untangle our generated meshes, as well as untangle any tangled elements that occur during motion (e.g. the beating of the heart). 

Unmanned Aerial Vehicles (UAVs) are increasingly demanded in civil, military and research purposes. However, they also possess serious threats to the society because faults in UAVs can lead to physical damage or even loss of life. While increasing their intelligence, for example, adding vision-based sense-and-avoid capability, has a potential to reduce the safety threats, increased software complexity and the need for higher 
computing performance create additional challenges—software bugs and transient hardware faults—that must be addressed to realize intelligent and safe UAV systems. 
This work present a fault tolerant system design for UAVs. Our proposal is to use two heterogeneous hardware and software platforms with distinct reliability and performance characteristics: High-Assurance (HA) and High-Performance (HP) platforms. The HA platform focuses on simplicity and 
verfiability in software and uses a simple and transient fault tolerant processor, while the HP platform focuses on intelligence and functionality in software and uses a complex and high performance processor. During the normal operation, the HP platform is responsible for controlling the UAV. However, if it fails due to transient hardware faults or software bugs, the HA platform will take over until the HP platform recovers. 
We have implemented the proposed design on an actual UAV using a low-cost Arduino and a high-performance Tegra TK1 multicore platform. Our case-studies show that our design can improve safety without compromising performance and intelligence of the UAV. 

Scene understanding in both static images and dynamic videos is the ultimate goal in computer vision. As two important sub-tasks of this endeavor, object detection and tracking have been extensively studied in the past decades, however, the problem is still not well addressed. The main challenge is that the appearance of objects is affected by a number of factors, such as scale, occlusion, illumination, and so on. Recently, deep learning has attracted lots of interests in the computer vision community. However, how to tackle these challenges in object detection and tracking is still an open problem. In this work, we propose a method for detecting objects in images using a single deep neural network, which can be optimized end-to-end and predict the object bounding boxes and class probabilities in one evaluation. To handle the challenges in object tracking, we propose a framework, which consists of a novel deep Convolutional Neural Networks (CNNs) to effectively generate robust spatial appearance, and a Long Short-term Memory (LSTM) network that incorporates temporal information to achieve long-term object tracking accuracy in real-time.

According to Moore’s law the number of transistors per square inch double every two years. Scaling down technology reduces size and cost however, also increases the number of problems. Our current computers using Von-Neumann architectures are seeing progressive difficulties not only due to scaling down the technology but also due to grid-lock situation in its architecture. As a solution to this, scientists came up architectures whose function resembles that of the brain. They called these brains inspired architectures, neuromorphic computers. The building block of the brain is the neuron which encodes, decodes and processes the data. The neuron is known to accept sensory information and converts this information into a spike train. This spike train is encoded by the neuron using different ways depending on the situation. Rate encoding, temporal encoding, population encoding, sparse encoding and rate-order encoding are a few encoding schemes said to be used by the neuron. These different neural encoding schemes are discussed as the primary focus of the thesis. A comparison between these different schemes is also provided for better understanding, thus helping in the design of an efficient neuromorphic computer. This thesis also focusses on hardware implementation of a neuron. Leaky Fire and Integrate neuron model has been used in this work which uses spike-time dependent encoding. Different neuron models are discussed with a comparison as to which model is effective under which circumstances. The electronic neuron model was implemented using 180nm CMOS Technology using Global Foundries PDK libraries. Simulation results for the neuron are presented for different inputs and different excitation currents. These results show the successful encoding of sensory information into a spike train.

Due to the increasing need for greater Radio Frequency (RF) spectrum by mobile apps like Facebook and Instagram, high data-rate communication protocols like 5G and the Internet of Things, it has led to the issue of spectrum congestion as radar systems have traditionally maintain the largest share of the RF spectrum. To resolve the spectrum congestion problem, it has become even necessary for users from both types of systems to coexist within a finite spectrum allocation. However, this then leads to other problems such as the increased likelihood of mutual interference experienced by all users that are coexisting within the finite spectrum. 

In this dissertation, we propose to address the problem of spectrum congestion via a two-step approach. The first step of this approach involves designing an optimal sparse spectrum allocation scheme to radar systems such that the radar range resolution performance can be maintained with a smaller resulting bandwidth at a cost of degraded sidelobe performance. The second step of this approach involves designing radar waveforms that possesses good spectral containment property by expanding the framework of Polyphase-coded Frequency Modulated (PCFM) waveforms to higher-order representations such that these waveforms will mitigate issues of interference experienced by other systems when both systems are coexisting within the same band. 

Von Neumann Bottleneck, which refers to the limited throughput between the CPU and memory, has already become the major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain the increasing attentions as compact and energy-efficient computing platforms. As one of the most crucial components in the neuromorphic computing systems, neural encoder transforms the stimulus (input signals) into spike trains. In this report, I will present my research work on spike-time-dependent encoding schemes and its relevant energy efficient encoders’ design. The performance comparison among rate encoding, latency encoding, and temporal encoding would be discussed in this report. The proposed neural temporal encoder allows efficient mapping of signal amplitude information into a spike time sequence that represents the input data and offers perfect recovery for band-limited stimuli. The simulation and measurement results show that the proposed temporal encoder is proven to be robust and error-tolerant. 

With an increasing trend of adoption of machine learning in various real-world problems, the need for transparent and reliable models has become apparent. Especially in some socially consequential applications, such as medical diagnosis, credit scoring, and decision making in educational systems, it may be problematic if humans cannot understand and trust those models. To this end, in this work, we propose a novel machine learning algorithm, constructivism learning. To achieve transparency, we formalized a Bayesian nonparametric approach using sequential Dirichlet Process Mixture of prediction models to support constructivism learning. To achieve reliability, we exploit two strategies, reducing model uncertainty and increasing task construction stability by leveraging techniques in active learning and self-paced learning. 

Distance metrics are concerned with learning how objects are similar, and are a critical component of many machine learning algorithms such as k-nearest neighbors and kernel machines. Traditional metrics are unable to adapt to data with heterogenous interactions in the feature space. State of the art methods consider learning multiple metrics, each in some way local to a portion of the data. Selecting how the distance metrics are local to the data is done apriori, with no known best approach. 
In this proposal, we address the local metric learning scenario from three complementary perspectives. In the first direction, we consider a spatial approach, and develop an efficient Frank-Wolfe based technique to learn local distance metrics directly in a high-dimensional input space. We then consider a view-local perspective, where we associate each metric with a separate view of the data, and show how the approach naturally evolves into a multiple kernel learning problem. Finally, we propose a new function for learning a metric which is based on a newly discovered operator called the t-product, here we show that our metric is composed of multiple parts, with each portion local to different interactions in the input space. 

Resource limited embedded systems provide a great challenge to programming using functional languages. Although we cannot program these embedded systems directly with Haskell, we show than an embedded domain specific language is able to be used to program them, providing 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 we use this as the platform for our DSL research. 

First we provide a shallowly embedded domain specific language and a firmware interpreter, allowing the user to program the Arduino while tethered to a host computer. Second, we add a deeply embedded version, allowing the interpreter to run standalone from the host computer, as well as allowing us to compile the code to C and then machine code for efficient operation. Finally, we develop a method of transforming the shallowly embedded DSL syntax into the deeply embedded DSL syntax automatically.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is critical. In this thesis, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive- MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the root mean square error (RMSE) of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system.