Computed Tomography (CT) finds applications across domains like medical diagnosis, security screening, and scientific research. In medical imaging, CT allows physicians to diagnose injuries and disease more quickly and accurately than other imaging techniques. However, CT is one of the most significant contributors of radiation dose to the general population and the required radiation dose for scanning could lead to cancer. On the other hand, a shallow radiation dose could sacrifice image quality causing misdiagnosis. To reduce the radiation dose, sparse-view CT, which includes capturing a smaller number of projections, becomes a promising alternative. However, the image reconstructed from linearly interpolated views possesses severe artifacts. Recently, Deep Learning-based methods are increasingly being used to interpret the missing data by learning the nature of the image formation process. The current methods are promising but operate mostly in the image domain presumably due to lack of projection data. Another limitation is the use of simulated data with less sparsity (up to 75%). This research aims to interpolate the missing sparse-view CT in the sinogram domain using deep learning. To this end, a residual U-Net architecture has been trained with patch-wise projection data to minimize Euclidean distance between the ground truth and the interpolated sinogram. The model can generate highly sparse missing projection data. The results show improvement in SSIM and RMSE by 14% and 52% respectively with respect to the linear interpolation-based methods. Thus, experimental sparse-view CT data with 90% sparsity has been successfully interpolated while improving CT image quality. / Master of Science / Computed Tomography is a commonly used imaging technique due to the remarkable ability to visualize internal organs, bones, soft tissues, and blood vessels. It involves exposing the subject to X-ray radiation, which could lead to cancer. On the other hand, the radiation dose is critical for the image quality and subsequent diagnosis. Thus, image reconstruction using only a small number of projection data is an open research problem. Deep learning techniques have already revolutionized various Computer Vision applications. Here, we have used a method which fills missing highly sparse CT data. The results show that the deep learning-based method outperforms standard linear interpolation-based methods while improving the image quality.

This project represents an important first step in determining the fundamental aesthetically relevant features of sound. Though there has been much effort in revealing the features learned by a deep neural network (DNN) trained on visual data, little effort in applying these techniques to a network trained on audio data has been performed. Importantly, these efforts in the audio domain often impose strong biases about relevant features (e.g., musical structure). In this project, a DNN is trained to mimic the acoustic aesthetic judgment of a professional composer. A unique corpus of sounds and corresponding professional aesthetic judgments is leveraged for this purpose. By applying a variation of Google's "DeepDream" algorithm to this trained DNN, and limiting the assumptions introduced, we can begin to listen to and examine the features of sound fundamental for aesthetic judgment. / Master of Science / The question of what makes a sound aesthetically “interesting” is of great importance to many, including biologists, philosophers of aesthetics, and musicians. This project serves as an important first step in determining the fundamental aesthetically relevant features of sound. First, a computer is trained to mimic the aesthetic judgments of a professional composer; if the composer would deem a sound “interesting,” then so would the computer. During this training, the computer learns for itself what features of sound are important for this classification. Then, a variation of Google’s “DeepDream” algorithm is applied to allow these learned features to be heard. By carefully considering the manner in which the computer is trained, this algorithmic “dreaming” allows us to begin to hear aesthetically salient features of sound.

Society's newfound dependence on wireless transmission systems has driven demand for access to the electromagnetic (EM) spectrum to an all-time high. In particular, wireless applications related to the fifth generation (5G) of cellular technology along with statically allocated radar systems have contributed to the increasing scarcity of the sub 6 GHz frequency bands. As a result, development of Dynamic Spectrum Access (DSA) techniques for sharing these frequencies has become a critical research area for the greater wireless community. Since among incumbent systems, radars are the largest consumers of spectrum in the sub 6 GHz regime, and are being used increasingly for civilian applications such as traffic control, adaptive cruise control, and collision avoidance, the need for radars which can adaptively tune specific transmission parameters in an intelligent manner to promote coexistence with other systems has arisen. Thus, fully-aware, dynamic, cognitive radar has been proposed as target for radars to evolve towards. In this thesis, we extend current research thrusts towards cognitive radar to utilize Reinforcement Learning (RL) techniques which allow a radar system to learn desired behavior using information obtained from past transmissions. Since radar systems inherently interact with their electromagnetic environment, it is natural to view the use of reinforcement learning techniques as a straightforward extension to previous adaptive techniques. However, in designing learning algorithms for radar systems, we must carefully define goal-driven rewards, formalize the learning process, and consider an appropriate amount of environmental information. In this thesis, we apply well-established and emerging reinforcement learning approaches to meet the demands of modern radar coexistence problems. In particular, function estimation using deep neural networks is examined, as Deep RL presents a scalable learning framework which allows many environmental states to be considered in the decision-making process. We then show how these techniques can be used to improve traditional radar performance metrics, such as interference avoidance, spectral efficiency, and target detectibility with simulated and experimental results. We also compare the learning techniques to each other and naive approaches, such as fixed bandwidth radar and avoiding interference reactively. Finally, online learning strategies are considered which aim to balance the fundamental learning trade-off between exploration and exploitation. We show that online learning techniques can be used to select individual waveforms or applied as a high-level controller in a hierarchical learning scheme based on the biologically inspired concept of metacognition. The general use of RL techniques provides a robust framework for decision making under uncertainty that is more flexible than previously proposed cognitive radar strategies. Further, the wide array of RL models and algorithms allow the underlying structure to be applied to both small and large-scale radar scenarios. / Master of Science / Society's newfound dependence on wireless transmission systems has driven demand for control of the electromagnetic (EM) spectrum to an all-time high. In particular, federal spectrum auctions and the fifth generation of wireless technologies have contributed to the scarcity of frequency bands below 6GHz. These frequencies are widely used by both radar and communications systems due to favorable propagation characteristics. However, current radar systems typically occupy a fixed bandwidth and are tend to be poorly equipped to share their allocated spectrum with other users, which has become a necessity given the growth of wireless traffic. In this thesis, we study learning algorithms which enable a radar to optimize its electromagnetic pulses based on feedback from received signals. In particular, we are interested in reinforcement learning algorithms which allow a radar to learn optimal behavior based on rewards defined by a human. Using these algorithms, radar system designers can choose which metrics may be most important for a given radar application which can then be optimized for the given setting. However, scaling reinforcement learning to real-world problems such as radar optimization is often difficult due to the massive scope of the problem. Here we attempt to identify potential issues with implementation of each algorithm and narrow in on algorithms that are well-suited for real-time radar operation.

Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. National Cancer Institute estimated that 91,270 new case and 9,320 deaths are expected in 2018 caused by melanoma. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is dermoscopy, which leads to improved diagnose accuracy compared to traditional ABCD criteria. But reading and examining dermoscopic images is a time-consuming and complex process. Therefore, computerized analysis methods of dermoscopic images have been developed to assist the visual interpretation of dermoscopic images. The automatic segmentation of skin lesion attributes is a key step in computerized analysis of dermoscopic images. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to help the diagnosis of melanoma based on dermoscopic images. In this thesis, I develop a deep learning based approach to automatically segment the attributes from dermoscopic skin lesion images. The approach described in the thesis achieved the Jaccard index of 0.477 on the official test dataset, which ranked 5th place in the challenge. / Master of Science / Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is called dermoscopy. It has become increasingly conveniently to use dermoscopic device to image the skin in recent years. Dermoscopic lens are available in the market for individual customer. When coupling the dermoscopic lens with smartphones, people are be able to take dermoscopic images of their skin even at home. However, reading and examining dermoscopic images is a time-consuming and complex process. It requires specialists to examine the image, extract the features, and compare with criteria to make clinical diagnosis. The time-consuming image examination process becomes the bottleneck of fast diagnosis of melanoma. Therefore, computerized analysis methods of dermoscopic images have been developed to promote the melanoma diagnosis and to increase the survival rate and save lives eventually. The automatic segmentation of skin lesion attributes is a key step in computerized analysis of dermoscopic images. In this thesis, I developed a deep learning based approach to automatically segment the attributes from dermoscopic skin lesion images. The segmentation result from this approach won 5th place in a public competition. It has the potential to be utilized in clinic application in the future.

The ability to understand occlusion and disocclusion is critical in analyzing motion and forecasting changes. For example, when we see a car gradually blocks our view of a human figure, we know that either the car or the human is moving. We also know that the human behind the car will be visible again if we move to other positions. As many vision-based intelligent systems need to handle and react to visual data with potentially intensive motions, it is therefore beneficial to incorporate the occlusion reasoning into such systems. In this thesis, we study how we can improve the performance of vision-based deep learning models by harnessing the power of occlusion handling. We first visit the problem of optical flow estimation for motion analysis. We present a deep learning module that builds upon occlusion handling methods in classic Computer Vision literature. Our results show performance improvement in occluded regions on standard benchmarks, as well as real-world applications. We then examine the problem of view synthesis for 3D photography. We propose an inpainting method that leverages local color and depth context for novel view synthesis. We validate the proposed inpainting approach with a series of quantitative and qualitative experiments, and demonstrate promising results in predicting plausible content in occluded regions. / Master of Science / Human has the ability to understand occlusion, and make use of such knowledge to make predictions about motions and occluded contents. For example, when we see a car gradually blocks our view of a human figure, we know that either the car or the human is moving. We also know that the human behind the car will be visible again if we move to other positions. In this thesis, we study how we can replicate such an ability to artificial intelligence systems. We first investigate the effect of occlusion reasoning in the task of predicting motion. Our experimental results show that a system equipped with our occlusion reasoning module can better capture the motions happening in image sequences. Next, we examine the problem of hallucinating visual contents that are blocked in an image. We develop a model that can produce plausible content in occluded regions. In our experiments, we show that given one single RGB image with an estimated depth map, our model can produce a corresponding 3D photo by hallucinating the structures that are not visible in the image.

In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. It encompasses the characteristics of a scale, a mode, and a tune, with none of them fully describing it, rendering the Raga a unique concept in ICM. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Identifying and categorizing the Raga is challenging due to its dynamism and complex structure as well as the polyphonic nature of ICM. Hence, Raga recognition—identify the constituent Raga in an audio file—has become an important problem in music informatics with several known prior approaches. Advancing the state of the art in Raga recognition paves the way to improving other Music Information Retrieval tasks in ICM, including transcribing notes automatically, recommending music, and organizing large databases. This thesis presents a novel melodic pattern-based approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is hierarchically processed and split into subsequences and gamaka sequences to mimic a textual document structure, so our model can learn the resulting tonal and temporal sequence patterns using a Recurrent Neural Network. Although training and testing on these smaller sequences, we predict the Raga for the entire audio excerpt, with the accuracy of 90.3% for the Carnatic Music Dataset and 95.6% for the Hindustani Music Dataset, thus outperforming prior approaches in Raga recognition. / Master of Science / In Indian Classical Music (ICM), the Raga is a musical piece's melodic framework. The Raga is a unique concept in ICM, not fully described by any of the fundamental concepts of Western classical music. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Raga recognition refers to identifying the constituent Raga in an audio file, a challenging and important problem with several known prior approaches and applications in Music Information Retrieval. This thesis presents a novel approach to recognizing Ragas by representing this task as a document classification problem, solved by applying a deep learning technique. A digital audio excerpt is processed into a textual document structure, from which the constituent Raga is learned. Based on the evaluation with third-party datasets, our recognition approach achieves high accuracy, thus outperforming prior approaches.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

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Thesis (Ph.D.) OGI School of Science & Engineering at OHSU, June 2008. / Includes bibliographical references (leaves 138-145).

Thesis (M.S.)--Oregon State University, 2004. / Printout. Includes bibliographical references (leaves 45-46). Also available on the World Wide Web.