Defense Notices

In real world environments, the speech signals received by our ears are usually a combination of different sounds that include not only the target speech, but also acoustic interference like music, background noise, and competing speakers. This interference has negative effect on speech perception and degrades the performance of speech processing applications such as automatic speech recognition (ASR), speaker identification, and hearing aid devices. One way to solve this problem is using source separation algorithms to separate the desired speech from the interfering sounds. Many source separation algorithms have been proposed to improve the performance of ASR systems and hearing aid devices, but it is still challenging for these systems to work efficiently in noisy and reverberant environments. On the other hand, humans have a remarkable ability to separate desired sounds and listen to a specific talker among noise and other talkers. Inspired by the capabilities of human auditory system, a popular method known as auditory scene analysis (ASA) was proposed to separate different sources in a two stage process of segmentation and grouping. The main goal of source separation in ASA is to estimate time frequency masks that optimally match and separate noise signals from a mixture of speech and noise. In this work, multiple algorithms are proposed to improve upon source separation in noisy and reverberant acoustic environment. First, a simple and novel algorithm is proposed to increase the discriminability between two sound sources by scaling (magnifying) the head-related transfer function of the interfering source. Experimental results from applications of this algorithm show a significant increase in the quality of the recovered target speech. Second, a time frequency masking-based source separation algorithm is proposed that can separate a male speaker from a female speaker in reverberant conditions by using the spatial cues of the source signals. Furthermore, the proposed algorithm has the ability to preserve the location of the sources after separation. Three major aims are proposed for supervised speech separation based on deep neural networks to estimate either the time frequency masks or the clean speech spectrum. Firstly, a novel monaural acoustic feature set based on a gammatone filterbank is presented to be used as  the input of the deep neural network (DNN) based speech separation model, which shows significant improvement in objective speech intelligibility and speech quality in different testing conditions. Secondly, a complementary binaural feature set is proposed to increase the ability of source separation in adverse environment with non-stationary background noise and high reverberation using 2-channel recordings. Experimental results show that the combination of spatial features with this complementary feature set improves significantly the speech intelligibility and speech quality in noisy and reverberant conditions. Thirdly, a novel dilated convolution neural network is proposed to improve the generalization of the monaural supervised speech enhancement model to different untrained speakers, unseen noises and simulated rooms. This model increases the speech intelligibility and speech quality of the recovered speech significantly, while being computationally more efficient and requiring less memory in comparison to other models. In addition, the proposed model is modified with recurrent layers and dilated causal convolution layers for real-time processing. This model is causal which makes it suitable for implementation in hearing aid devices and ASR system, while having fewer trainable parameters and using only information about previous time frames in output prediction. The main goal of the proposed algorithms are to increase the intelligibility and the quality of the recovered speech from noisy and reverberant environments, which has the potential to improve both speech processing applications and signal processing strategies for hearing aid and cochlear implant technology.

The non-ending growth of data traffic resulting from the continuing emergence of Internet applications with high data-rate demands sets huge capacity requirements on optical interconnects and transport networks. This requires the adoption of optical communication technologies that can make the best possible use of the available bandwidths of electronic and electro-optic components to enable data transmission with high spectral efficiency (SE). Therefore, advanced modulation formats are required to be used in conjunction with energy-efficient and cost-effective transceiver schemes, especially for medium- and short-reach applications. Important challenges facing these goals are the stringent requirements on the characteristics of optical components comprising these systems, especially laser sources. Laser phase noise is one of the most important performance-limiting factors in systems with high spectral efficiency. In this research work, we study the effects of the spectral characteristics of laser phase noise on the characterization of lasers and their impact on the performance of digital coherent and self-coherent optical communication schemes. The results of this study show that the commonly-used metric to estimate the impact of laser phase noise on the performance, laser linewidth, is not reliable for all types of lasers. Instead, we propose a Lorentzian-equivalent linewidth as a general characterization parameter for laser phase noise to assess phase noise-related system performance. Practical aspects of determining the proposed parameter are also studied and its accuracy is validated by both numerical and experimental demonstrations. Furthermore, we study the phase noises in quantum-dot mode-locked lasers (QD-MLLs) and assess the feasibility of employing these devices in coherent applications at relatively low symbol rates with high SE. A novel multi-heterodyne scheme for characterizing the phase noise of laser frequency comb sources is also proposed and validated by experimental results with the QD-MLL. This proposed scheme is capable of measuring the differential phase noise between multiple spectral lines instantaneously by a single measurement. Moreover, we also propose an energy-efficient and cost-effective transmission scheme based on direct detection of field-modulated optical signals with advanced modulation formats, allowing for higher SE compared to the current pulse-amplitude modulation schemes. The proposed system combines the Kramers-Kronig self-coherent receiver technique, with the use of QD-MLLs, to transmit multi-channel optical signals using a single diode laser source without the use of the additional RF or optical components required by traditional techniques. Semi-numerical simulations based on experimentally captured waveforms from practical lasers show that the proposed system can be used even for metro scale applications. Finally, we study the properties of phase and intensity noise changes in unmodulated optical signals passing through saturated semiconductor optical amplifiers for intensity noise reduction. We report, for the first time, on the effect of phase noise enhancement that cannot be assessed or observed by traditional linewidth measurements. We demonstrate the impact of this phase noise enhancement on coherent transmission performance by both semi-numerical simulations and experimental validation.

The memory for events is a central component in human cognition, but we have yet to see artificial agents that can demonstrate the same range of event memory capabilities as humans. Some machine learning systems are capable of behaving as if they remember and reason about events, but often times, their behavior is produced by an ad hoc assemblage of opaque statistical algorithms which yield little new insights on the nature of event memory. We propose a novel, psychologically plausible theory of event memory with an accompanying implementation which affords integrated agents the ability to remember events, present details about their past experiences, and reason about future events. We propose to demonstrate such event memory reasoning capabilities in three different experiments. First, we evaluate the fundamental capabilities of our theory to explain different event memory phenomena, such as remembering. Second, we aim to show that our event memory theory provides a unified framework for building intelligent agents that generate explanations of their own behavior and  make inferences about the goals and intentions of other actors. Third, we evaluate whether our event memory theory facilitates cooperative behavior of computational agents in human-robot teams. The proposed work will be completed in December 2020. If our efforts are successful, we believe it will change the way humans interact with autonomous agents. People will better understand why robots, self-driving vehicles, and other agents behave the way they do, and as a result, will know when to trust them. This in turn will speed adoption of autonomous systems not only in military settings but, in everyday life.

Deep learning (DL) has dramatically improved the state-of-the-art performances in broad applications of computer vision, such as image recognition, object detection, semantic/instance segmentation, and point cloud analysis. However, the reasons for such huge empirical success of DL still keep elusive theoretically. In this dissertation, to understand DL and improve its efficiency, robustness, and interpretability, we theoretically investigate optimization algorithms for training deep models and empirically explore deep learning for unsupervised learning tasks in point-cloud analysis and image classification. 

1). Optimization for Training Deep Models: Neural network training is one of the most difficult optimization problems involved in DL. Recently, to understand the global optimality in DL has attracted a lot of attention. However, we observe that conventional DL solvers have not been developed intentionally to seek for such global optimality. In this dissertation, we propose a novel approximation algorithm, BPGrad, towards optimizing deep models globally via branch and pruning. Empirically an efficient solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation. 

2). Deep Learning for Unsupervised Learning Tasks: The architecture of neural networks is of central importance for many visual recognition tasks. In this dissertation, we focus on the emerging field of unsupervised learning for point clouds analysis and image classification. 

2.1) For point cloud analysis, we propose a novel unsupervised approach to jointly learn the 3D object model and estimate the 6D poses of multiple instances of the same object in a single end-to-end deep neural network framework, with applications to depth-based instance segmentation. Extensive experiments evaluate our technique on several object models and a varying number of instances in 3D point clouds. Compared with popular baselines for instance segmentation, our model not only demonstrates competitive performance, but also learns a 3D object model that is represented as a 3D point cloud. 

2.2) For low-quality image classification, we propose a simple while effective unsupervised deep feature transfer network to address the degrading problem of the state-of-the-art recognition algorithms on low-quality images. No fine-tuning is required in our method. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high-resolution images, and transfers the distinguishing features to low-resolution features space. Extensive experiments show that the proposed transfer network achieves significant improvements over the baseline method. 

In today's world of digital data, the field of data mining has come into the limelight. In data mining, patterns in data are found and accordingly can be analyzed further. Processing data as deep as possible is relevant in case of pattern recognition in huge data sets. In this whole process, we try to understand the data well in order to gain some useful results out of it. For the data to be analyzed correctly, it is better if it is complete and consistent.

We compare the effect of incomplete and inconsistent data in this project. The algorithm used for generating rules is the Modified Learning from Example Module version 2 (MLEM2). We used a single local probabilistic approach for all the datasets. We took 141 datasets into consideration for the error rate comparison of incomplete and inconsistent data. We used ten-fold cross validation and computed average error rate for each of the datasets. From our experiments, we observed that the error rate for incomplete data is greater than the error rate for inconsistent data.

Trends toward large-scale integration and the high-power application of green energy resources necessitate the advent of efficient power converter topologies, multilevel converters. Multilevel inverters are effective solutions for high power and medium voltage DC-to-AC conversion due to their higher efficiency, provision of system redundancy, and generation of near-sinusoidal output voltage waveform. Recently, modular multilevel converter (MMC) has become increasingly attractive. To improve the harmonic profile of the output voltage, there is the need to increase the number of output voltage levels. However, this would require increasing the number of submodules (SMs) and power semi-conductor devices and their associated gate driver and protection circuitry, resulting in the overall multilevel converter to be complex and expensive. Specifically, the need for large number of bulky capacitors in SMs of conventional MMC is seen as a major obstacle. This work proposes an MMC-based multilevel converter that provides the same output voltage as conventional MMC but has reduced number of bulky capacitors. This is achieved by introduction of an extra middle arm to the conventional MMC. Due to similar dynamic equations of the proposed converter with conventional MMC, several previously developed control methods for voltage balancing in the literature for conventional MMCs are applicable to the proposed MMC with minimal effort. Comparative loss analysis of the conventional MMC and the proposed multilevel converter under different power factors and modulation indexes illustrates the lower switching loss of proposed MMC. In addition, a new voltage balancing technique based on carrier-disposition pulse width modulation for modular multilevel converter is proposed.
The second part of this work focuses on an improved control of MMC-based high-power DC/DC converters. Medium-voltage DC (MVDC) and high-voltage DC (HVDC) grids have been the focus of numerous research studies in recent years due to their increasing applications in rapidly growing grid-connected renewable energy systems, such as wind and solar farms. MMC-based DC/DC converters are employed for collecting power from renewable energy sources. Among various developed DC/DC converter topologies, MMC-based DC/DC converter with medium-frequency (MF) transformer is a valuable topology due to its advantages. Specifically, they offer a significant reduction in the size of the MMC arm capacitors along with the ac-link transformer and arm inductors due to the ac-link transformer operating at medium frequencies. As such, this work focuses on improving the control of isolated MMC-based DC/DC (IMMDC) converters. The single phase shift (SPS) control is a popular method in IMMDC converter to control the power transfer. This work proposes conjoined phase shift-amplitude ratio index (PSAR) control that considers amplitude ratio indexes of MMC legs of MF transformer’s secondary side as additional control variables. Compared with SPS control, PSAR control not only provides wider transmission power range and enhances operation flexibility of converter, but also reduces current stress of medium-frequency transformer and power switches of MMCs. An algorithm is developed for simple implementation of the PSAR control to work at the least current stress operating point. Hardware-in-the-loop results confirm the theoretical outcomes of the proposed control method.

To design highly precise 3D object detection approaches for autonomous vehicle has been a crucial topic recently. Shallow machine learning methods such as clustering, support vector machines fail to accomplish multi-modal tasks for self-driving vehicle, while deep-learning based methods gain great success in regressing accurate 3D bound boxes and pose estimation of objects in complicated road scene. Though deep neural networks designed for LiDAR points and monocular-view inputs achieve highest performance in 3D object detection, binocular-views based networks suffer from intrinsic ambiguities therefore yielding less precise regressions. To remedy the ambiguities, we propose an efficient module to bridge the gap between 2D objection detection on stereopsis and real LiDAR points. Experiments on challenging KITTI dataset show that our method outperforms state-of-the-arts binocular-views based methods.

For the past several years, with almost every system being upgraded and digitized, data are getting generated and collected in huge amounts. But there is no use of collecting huge amounts of data unless we can make sense out of it. Generating rules from datasets helps to predict the possible outcomes from given datasets. The predictions are never error free and so all we can do is create rules from datasets that are as accurate as possible. Rule induction from large datasets can be based on different principles and rules induced from applying different methods lead to rulesets with different levels of accuracy. Some rules are more accurate than others. In this project, the goal is to compare two approaches of rule induction, from characteristic sets and from maximal consistent blocks. The aim is to study the error rates of rules generated by taking either of the two approaches. To validate the error rates of the rulesets, 10-fold cross validation method is applied. 

In George Orwell's 1984, there is fear regarding what “Big Brother”, knows due to the fact that even thoughts could be “heard”. Though we are not quite to this point, it should concern us all in what data we are transferring, both intentionally and unintentionally, and whether or not that data is being “leaked”. In this work, we consider the evolving landscape of IoT devices and the threat posed by the pervasive botnets that have been forming over the last several years. We look at two specific cases in this work. One being the practical application of a botnet system actively executing a Man in the Middle Attack against SSH, and the other leveraging the same paradigm as a case of eavesdropping on Internet Protocol (IP) cameras. For the latter case, we construct a web portal for interrogating IP cameras directly for information that they may be exposing. ​

With the rise of software defined radios (SDR) and the trend towards integrating more RF components into MMICs the cost and complexity of multichannel radar development has gone down. High-speed RF data converters have seen continuous increases in both sampling rate and resolution, further rendering a growing subset of components in an RF chain unnecessary. A recent development in this trend is the Xilinx RFSoC, which integrates multiple high speed data converters into the same package as an FPGA. The Center for Remote Sensing of Ice Sheets (CReSIS) is regularly upgrading its suite of sensor platforms spanning from HF depth sounders to Ka band altimeters. A radar platform was developed around the RFSoC to demonstrate the capabilities of the chip when acting as a digital backend and evaluate its role in future radar designs at CReSIS. A new ultra-wideband (UWB) FMCW RF frontend was designed that consists of multiple transmit and receive modules operating at microwave frequencies with multi-GHz bandwidth. An antenna array was constructed out of printed-circuit elements to validate radar system performance. Firmware developed for the RFSoC enables radar features that will prove useful in future sensor platforms used for the remote sensing of snow, soil moisture, or crop canopies.