Defense Notices

The least-squares mismatched filter (LS MMF) is a pulse compression method used to suppress range sidelobes. Though initially derived for codes, this work provides a description of the adjustments needed such that the LS MMF can be applied to FM waveforms, a topic that had not previously been published (to the best of our knowledge). Additionally, the effects of range straddling and Doppler on the LS MMF are examined. The effects of straddling on mismatch loss is well known, what is less appreciated is the effect straddling has on the range sidelobes. This work outlines methods that alleviate some of the degradation in sidelobe levels due to straddling. Making the LS MMF more robust to Doppler is also investigated. Adaptive Pulse Compression (APC) is another pulse compression algorithm that has been adjusted to be applicable to FM waveforms. Although the derivation of these adjustments is not part of this work, the analysis via simulation and measured data are. The effects of straddling and Doppler on APC are also investigated, and improvements to APC are analyzed. Lastly, these pulse compression methods are applied to measured data, showing their viability for application in real FM-based systems. 

Optical orthogonal frequency division multiplexing (OFDM) offers high spectral efficiency, resilience to fiber distortion, and simple equalization that make it a suitable technology for next generation optical communication systems. The suitability of optical OFDM to convey data and services in the next generation of optical networks has been extensively investigated for both direct and coherent detection. The key point of OFDM is that all sub-carriers in frequency domain are orthogonal to each other in order to completely eliminate the inter-channel interface (ICI). Nyquist pulse modulation is relatively new technique in optical communication, but the format is very similar to OFDM. It can be derived by simply interchanging time and frequency domain for orthogonal sub-carriers. Therefore, Nuquist pulse modulation could be referred an orthogonal time division multiplexing (OTDM) technique. 
In this project, we investigate the design of a field programmable gate array (FPGA) based optical OFDM modulation and Nyquist pulse modulation transmitters implementing digital signal processing. The transmitters were utilized to generate QAM-OFDM signals and QAM-Nyquist signals. We study the impact of different IFFT algorithms for OFDM and different FIR filter orders for Nyquist on the system performance. In addition to that, we make some comparisons between these two modulation techniques in terms of resource requirements on FPGA, spectral efficiency and peak-to-power ratio. 

Super-Heterodyne receiver is still a predominant receiver architecture used today. In this receiver design one of the most important design trade-offs is the selection of IF frequency. The IF frequency should be low because at the higher (GHz) frequencies the signal processing circuits performs poorly and the cost goes higher. Selectivity of the receiver also affects the IF frequency as the bandwidth of the filter increases with the IF frequency so that the adjacent signals may not get enough attenuation. Another reason which makes selection of IF frequency more complicate is, it should be free from interference and we could achieve this by getting enough attenuation for Image and Murphy signals which creates mixer product terms exactly at the IF filter center frequency. 
In this project an application has been developed in the Mathematica environment which reduces the complexity in rejecting all the Murphy signals and in selecting the IF frequency. And selection of IF frequency using the application is discussed. An interface has been developed with the filter response with Image and all the Murphy signal bands positioned on it. Filter responses are shown for various filter types and orders, as well as Image and Murphy signal bands are shown for Low side, High side and Up conversion tuning solutions with the values of most problematic frequency signals in each band. 

Universal Serial Bus (USB) is a popular choice of interfacing computer systems with peripherals. With the increasing support of modern operating systems, it is now truly plug-and-play for most USB devices. However, this great convenience comes with a risk which can allow a device to perform arbitrary actions at any time while it is connected. Researchers have confirmed that a simple USB device such as a mass storage device can be disguised to have an additional function such as a keyboard. An unauthorized keyboard attachment can compromise the security of the host by allowing arbitrary keystrokes to enter the host. This undetectable threat differs from traditional virus that spreads via USB devices due to the location it is stored and the way it behaves. Therefore, it is impossible for current file-level antivirus to be aware of such risk. Currently, there is no commercially available protection for USB devices other than mass storage devices. We propose a novel way to protect the host via a software/hardware solution we named a USBWall. USBWall uses BeagleBoard Black (BBB), a low-cost open-source computer, to act as a middleware to enumerate the devices on behalf of the host. We developed a program to assist the user to identify the risk of a device. We present a simulated USB device with malicious firmware to the USBWall. Based on the results, we confirm that using the USBWall to enumerate USB devices on behalf of the host eliminates risks to the hosts.

Computer systems have moved from unicore platforms to multicore platforms in modern days as they offer higher performance and efficiency. However, when multiple programs are executed in parallel on different cores on a multicore platform, performance isolation among the programs is difficult to achieve because of contention in shared hardware resources. This is problematic for real-time applications where a certain performance guarantee must be provided. 

In this work, we first present a case study that depicts the difficulties faced by a memory intensive real-time application, WebRTC---an open source, plugin free communication framework that provides the capability of Real-Time Communications(RTC) to browsers and mobile applications---when running in a multi-core plat-form along with other memory intensive co-running applications. We then present a tool, MBProtector that dynamically protects the performance of memory intensive code sectors in real-time applications. MBProtector uses BWLOCK, a mechanism for memory bandwidth control, and Pin, a binary instrumentation framework, to automatically insert BWLOCKs in memory intensive code sections in program binary. Our evaluation shows that the tool achieves up to 60% performance improvement in WebRTC. 

Multitask Learning with task relationship modeling is a learning framework which identifies and shares training information among multiple related tasks to improve the generalization error of each task. The utilization of task relationships in static multitask learning framework, where all the tasks are known beforehand and all the data is present before the training, has been studied in considerable detail for past several years. However, in the case of lifelong multitask learning, where the tasks arrive in an online fashion and information about all the tasks is not known beforehand, modeling the task relationship is very challenging. The main contribution of this thesis is to propose a framework for modeling task relationships in lifelong multitask learning. The task relationship models in lifelong multitask learning needs to be flexible and dynamic such that it can be easily updated with each new task coming in. Also, a new task needs to readily learn its position in the existing task network using the task relationship model. Traditionally, task relationships are represented using fixed sized matrices, which describe the task network. These matrices are not capable of dynamically changing with each incoming task, and can be rather expensive to update. Here, we propose learning functions to represent the relationships between tasks. Learning functions is faster and computationally less expensive for depicting the task relationship models. The functions partition the task space such that the similar tasks remain in the same region and enforce similar tasks to depend on similar features. Learning both the task parameters and relationships is done in a supervised manner. In this thesis, we show that the algorithm we developed provides significantly better accuracy and is much faster than the state of the art lifelong learning algorithm. For some dataset, our algorithm provides a better accuracy than even the static multitask learning method.

The ReIterative Super-Resolution (RISR) was developed based on an iterative implementation of the Minimum Mean Squared Error (MMSE) estimator. A novel approach to direction of arrival estimation, coined partially constrained beamforming is introduced by building from existing work on the RISR algorithm. First, RISR is rederived with the addition of a unit gain constraint, with the result dubbed Gain Constrained RISR (GC-RISR), but the outcome exhibits some loss in resolution, so middle ground is sought between GC-RISR and RISR. By taking advantage of the similarstructure of RISR and GC-RISR, they can be combined using a geometric mean, and a weighting term is added to form a partially constrained version of RISR, which wedenote as PC-RISR. Simulations are used to characterize PC-RISR’s performance, where it is shown that the geometric weighting term can be used to control convergence. It is also demonstrated that this weighting term enables increased super-resolution capability compared to RISR, improves robustness to low sample support for super-resolving signals with low SNR, and the ability to detect and super-resolve signals with an SNR as low as -10dB given higher sample support.

Multichannel, ice sounder data can be processed to three-dimensionally map ice sheet bed topography and basal reflectivity using tomographic imaging techniques. When ultra-wideband (UWB) signals are used to interrogate a glaciological target, fine resolution maps can be obtained. These data sets facilitate both process studies of ice sheet dynamics and also continental-scale ice sheet modeling needed to predict future sea level. The socioeconomic importance of these data as well as the cost and logistical challenge of procuring them justifies the need to image ice sheet basal morphology over a wider swath. Imaging wide swaths with UWB signals poses challenges for the array processing methods that have been used to localize scattering in the cross-track dimension. Both MUltiple SIgnal Classification (MUSIC) and the Maximum Likelihood Estimator (MLE) have been applied to the ice sheet tomography problem. These techniques are formulated assuming a narrowband model of the array that breaks down in wideband signal environments when the direction of arrival (DOA) increases further off nadir. 
The Center for Remote Sensing of Ice Sheets (CReSIS) developed a UWB multichannel SAR with a large cross-track array for sounding and imaging polar ice from a Basler BT-67 aircraft. In 2013, this sensor collected data in a multibeam mode over the West Antarctic Ice Sheet to demonstrate wide swath imaging. To reliably estimate the arrival angles of echoes from the edges of the swath, a parametric space-time direction of arrival estimator was developed that obtains an estimate of the DOA by fitting the observed space-time covariance structure to a model. This thesis focuses on the development and optimization of the algorithm and describes its predicted performance based on simulation. Its measured performance is analyzed with 3D tomographic basal maps of an ice stream in West Antarctica that were generated using the technique. 

Fountain codes are forward-error correcting codes suitable for erasure channels. A binary erasure channel is a memoryless channel, in which the symbols are either transmitted correctly or they are erased. The advantage of fountain codes is that it requires few encoded symbols for decoding. The source symbols can be decoded using any set of encoded symbols. Since fountain codes are rateless, they can adapt to changing channel conditions. They are beneficial for broadcasting and multicasting applications where channels have different erasure probability. 
The project involves the implementation of two different fountain codes: LT code and Raptor code. 
The goal of the project is to measure the performance of the code based on how many encoded symbols are required for successful decoding. The encoders and decoders for the two codes are designed in Matlab. The number of encoded symbols required for decoding of the source symbols for different degree distributions are plotted. 

Wavelength division multiplexing (WDM) is a technology of combining a number of independent information-carrying signals with different wavelengths into the same fiber. This enables us to transmit several channels of high quality, large capacity optical signals in only one fiber simultaneously. WDM is the most popular long distance transmission solution nowadays, which is widely utilized in terrestrial backbone and intercontinental undersea fiber optics transmission system. Extremely effective and efficient analysis method of WDM system is always indispensable due to two reasons. In the first place, the deployment of WDM system is usually a time and money consuming project so that an accurate design is always required before construction. Secondly, optical network routing protocol is based on expeditious and veracious real-time evaluation and prediction of network performance. Two main distinct phenomena affecting the overall WDM system performance are amplified spontaneous emission (ASE) noise accumulation and nonlinear interference (NLI) due to the Kerr effect. The ASE noise has already been well understood but the calculation of NLI is complicated. A popular way called Split-Step Fourier (SSF) method, which directly solves the nonlinear propagation equation numerically is widely used to understand the pulse propagation in nonlinear dispersive media. Though the SSF method can provide an accurate result of NLI, its high computation expense prohibits satisfying the efficiency requirement mentioned above. Fortunately, Gaussian Noise (GN) model, which to a large extent resolves this issue has been proposed and developed in recent years.