Acceleration is a central aim of clinical and technical research in magnetic resonance imaging (MRI) today, with the potential to increase robustness, accessibility and patient comfort, reduce cost, and enable entirely new kinds of examinations. A key component in this endeavor is image reconstruction, as most modern approaches build on advanced signal and image processing. Here, deep learning (DL)-based methods have recently shown considerable potential, with numerous publications demonstrating benefits for MRI reconstruction. However, these methods often come at the cost of an increased risk for subtle yet critical errors. Therefore, the aim of this thesis is to advance DL-based MRI reconstruction, while ensuring high quality and fidelity with measured data. A network architecture specifically suited for this purpose is the variational network (VN). To investigate the benefits these can bring to non-Cartesian cardiac imaging, the first part presents an application of VNs, which were specifically adapted to the reconstruction of accelerated spiral acquisitions. The proposed method is compared to a segmented exam, a U-Net and a compressed sensing (CS) model using qualitative and quantitative measures. While the U-Net performed poorly, the VN as well as the CS reconstruction showed good output quality. In functional cardiac imaging, the proposed real-time method with VN reconstruction substantially accelerates examinations over the gold-standard, from over 10 to just 1 minute. Clinical parameters agreed on average. Generally in MRI reconstruction, the assessment of image quality is complex, in particular for modern non-linear methods. Therefore, advanced techniques for precise evaluation of quality were subsequently demonstrated. With two distinct methods, resolution and amplification or suppression of noise are quantified locally in each pixel of a reconstruction. Using these, local maps of resolution and noise in parallel imaging (GRAPPA), CS, U-Net and VN reconstructions were determined for MR images of the brain. In the tested images, GRAPPA delivers uniform and ideal resolution, but amplifies noise noticeably. The other methods adapt their behavior to image structure, where different levels of local blurring were observed at edges compared to homogeneous areas, and noise was suppressed except at edges. Overall, VNs were found to combine a number of advantageous properties, including a good trade-off between resolution and noise, fast reconstruction times, and high overall image quality and fidelity of the produced output. Therefore, this network architecture seems highly promising for MRI reconstruction. / Eine Beschleunigung des Bildgebungsprozesses ist heute ein wichtiges Ziel von klinischer und technischer Forschung in der Magnetresonanztomographie (MRT). Dadurch könnten Robustheit, Verfügbarkeit und Patientenkomfort erhöht, Kosten gesenkt und ganz neue Arten von Untersuchungen möglich gemacht werden. Da sich die meisten modernen Ansätze hierfür auf eine fortgeschrittene Signal- und Bildverarbeitung stützen, ist die Bildrekonstruktion ein zentraler Baustein. In diesem Bereich haben Deep Learning (DL)-basierte Methoden in der jüngeren Vergangenheit bemerkenswertes Potenzial gezeigt und eine Vielzahl an Publikationen konnte deren Nutzen in der MRT-Rekonstruktion feststellen. Allerdings besteht dabei das Risiko von subtilen und doch kritischen Fehlern. Daher ist das Ziel dieser Arbeit, die DL-basierte MRT-Rekonstruktion weiterzuentwickeln, während gleichzeitig hohe Bildqualität und Treue der erzeugten Bilder mit den gemessenen Daten gewährleistet wird. Eine Netzwerkarchitektur, die dafür besonders geeignet ist, ist das Variational Network (VN). Um den Nutzen dieser Netzwerke für nicht-kartesische Herzbildgebung zu untersuchen, beschreibt der erste Teil dieser Arbeit eine Anwendung von VNs, welche spezifisch für die Rekonstruktion von beschleunigten Akquisitionen mit spiralen Auslesetrajektorien angepasst wurden. Die vorgeschlagene Methode wird mit einer segmentierten Rekonstruktion, einem U-Net, und einem Compressed Sensing (CS)-Modell anhand von qualitativen und quantitativen Metriken verglichen. Während das U-Net schlecht abschneidet, zeigen die VN- und CS-Methoden eine gute Bildqualität. In der funktionalen Herzbildgebung beschleunigt die vorgeschlagene Echtzeit-Methode mit VN-Rekonstruktion die Aufnahme gegenüber dem Goldstandard wesentlich, von etwa zehn zu nur einer Minute. Klinische Parameter stimmen im Mittel überein. Die Bewertung von Bildqualität in der MRT-Rekonstruktion ist im Allgemeinen komplex, vor allem für moderne, nichtlineare Methoden. Daher wurden anschließend forgeschrittene Techniken zur präsizen Analyse von Bildqualität demonstriert. Mit zwei separaten Methoden wurde einerseits die Auflösung und andererseits die Verstärkung oder Unterdrückung von Rauschen in jedem Pixel eines untersuchten Bildes lokal quantifiziert. Damit wurden lokale Karten von Auflösung und Rauschen in Rekonstruktionen durch Parallele Bildgebung (GRAPPA), CS, U-Net und VN für MR-Aufnahmen des Gehirns berechnet. In den untersuchten Bildern zeigte GRAPPA gleichmäßig eine ideale Auflösung, aber merkliche Rauschverstärkung. Die anderen Methoden verhalten sich lokal unterschiedlich je nach Struktur des untersuchten Bildes. Die gemessene lokale Unschärfe unterschied sich an den Kanten gegenüber homogenen Bildbereichen, und Rauschen wurde überall außer an Kanten unterdrückt. Insgesamt wurde für VNs eine Kombination von verschiedenen günstigen Eigenschaften festgestellt, unter anderem ein guter Kompromiss zwischen Auflösung und Rauschen, schnelle Laufzeit, und hohe Qualität und Datentreue der erzeugten Bilder. Daher erscheint diese Netzwerkarchitektur als ein äußerst vielversprechender Ansatz für MRT-Rekonstruktion.

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Indiana University-Purdue University Indianapolis (IUPUI) / During recent years, the field of computer vision has evolved rapidly. Convolutional Neural Networks (CNNs) have become the chosen default for implementing computer vision tasks. The popularity is based on how the CNNs have successfully performed the well-known computer vision tasks such as image annotation, instance segmentation, and others with promising outcomes. However, CNNs have their caveats and need further research to turn them into reliable machine learning algorithms. The disadvantages of CNNs become more evident as the approach to breaking down an input image becomes apparent. Convolutional neural networks group blobs of pixels to identify objects in a given image. Such a technique makes CNNs incapable of breaking down the input images into sub-parts, which could distinguish the orientation and transformation of objects and their parts. The functions in a CNN are competent at learning only the shift-invariant features of the object in an image. The discussed limitations provides researchers and developers a purpose for further enhancing an effective algorithm for computer vision. The opportunity to improve is explored by several distinct approaches, each tackling a unique set of issues in the convolutional neural network’s architecture. The Capsule Network (CapsNet) which brings an innovative approach to resolve issues pertaining to affine transformations by sharing transformation matrices between the different levels of capsules. While, the Residual Network (ResNet) introduced skip connections which allows deeper networks to be more powerful and solves vanishing gradient problem. The motivation of these fusion of these advantageous ideas of CapsNet and ResNet with Squeeze and Excite (SE) Block from Squeeze and Excite Network, this research work presents SE-Residual Capsule Network (SE-RCN), an efficient neural network model. The proposed model, replaces the traditional convolutional layer of CapsNet with skip connections and SE Block to lower the complexity of the CapsNet. The performance of the model is demonstrated on the well known datasets like MNIST and CIFAR-10 and a substantial reduction in the number of training parameters is observed in comparison to similar neural networks. The proposed SE-RCN produces 6.37 Million parameters with an accuracy of 99.71% on the MNIST dataset and on CIFAR-10 dataset it produces 10.55 Million parameters with 83.86% accuracy.

Yes / Today, computer based face recognition is a mature and reliable mechanism which is being practically utilised for many access control scenarios. As such, face recognition or authentication is predominantly performed using ‘perfect’ data of full frontal facial images. Though that may be the case, in reality, there are numerous situations where full frontal faces may not be available — the imperfect face images that often come from CCTV cameras do demonstrate the case in point. Hence, the problem of computer based face recognition using partial facial data as probes is still largely an unexplored area of research. Given that humans and computers perform face recognition and authentication inherently differently, it must be interesting as well as intriguing to understand how a computer favours various parts of the face when presented to the challenges of face recognition. In this work, we explore the question that surrounds the idea of face recognition using partial facial data. We explore it by applying novel experiments to test the performance of machine learning using partial faces and other manipulations on face images such as rotation and zooming, which we use as training and recognition cues. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the cheek. We also study the effect of face recognition subject to facial rotation as well as the effect of recognition subject to zooming out of the facial images. Our experiments are based on using the state of the art convolutional neural network based architecture along with the pre-trained VGG-Face model through which we extract features for machine learning. We then use two classifiers namely the cosine similarity and the linear support vector machines to test the recognition rates. We ran our experiments on two publicly available datasets namely, the controlled Brazilian FEI and the uncontrolled LFW dataset. Our results show that individual parts of the face such as the eyes, nose and the cheeks have low recognition rates though the rate of recognition quickly goes up when individual parts of the face in combined form are presented as probes.

Text classification is a fundamental natural language processing task. However, in real-world applications, class distributions are usually skewed, e.g., due to inherent class imbalance. In addition, the task difficulty changes based on the underlying language. When rich morphological structure and high ambiguity are exhibited, natural language understanding can become challenging. For example, Arabic, ranked the fifth most widely used language, has a rich morphological structure and high ambiguity that result from Arabic orthography. Thus, Arabic natural language processing is challenging. Several studies employ Long Short- Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), but Graph Convolutional Networks (GCNs) have not yet been investigated for the task. Sequence- based models can successfully capture semantics in local consecutive text sequences. On the other hand, graph-based models can preserve global co-occurrences that capture non- consecutive and long-distance semantics. A text representation approach that combines local and global information can enhance performance in practical class imbalance text classification scenarios. Yet, multi-view graph-based text representations have received limited attention. In this research, first we introduce Multi-view Minority Class Text Graph Convolutional Network (MMCT-GCN), a transductive multi-view text classification model that captures textual graph representations for the minority class alongside sequence-based text representations. Experimental results show that MMCT-GCN obtains consistent improvements over baselines. Second, we develop an Arabic Bidirectional Encoder Representations from Transformers (BERT) Graph Convolutional Network (AraBERT-GCN), a hybrid model that combines the large-scale pre-trained models that encode the local context and semantics alongside graph-based features that are capable of extracting the global word co-occurrences in non-consecutive extended semantics by only one or two hops. Experimental results show that AraBERT-GCN outperforms the state-of-the-art (SOTA) on our Arabic text datasets. Finally, we propose an Arabic Multidimensional Edge Graph Convolutional Network (AraMEGraph) designed for text classification that encapsulates richer and context-aware representations of word and phrase relationships, thus mitigating the impact of the complexity and ambiguity of the Arabic language. / Doctor of Philosophy / The text classification task is an important step in understanding natural language. However, this task has many challenges, such as uneven data distributions and language difficulty. For example, Arabic is the fifth most spoken language. It has many different word forms and meanings, which can make things harder to understand. Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are widely utilized for text classification. However, another kind of network called graph convolutional network (GCN) has yet to be explored for this task. Graph-based models keep track of how words are connected, even if they are not right next to each other in a sentence. This helps with better understanding the meaning of words. On the other hand, sequence-based models do well in understanding the meaning of words that are right next to each other. Mixing both types of information in text understanding can work better, especially when dealing with unevenly distributed data. In this research, we introduce a new text classification method called Multi-view Minority Class Text Graph Convolutional Network (MMCT-GCN). This model looks at text from different angles and combines information from graphs and sequence-based models. Our experiments show that this model performs better than other ones proposed in the literature. Additionally, we propose an Arabic BERT Graph Convolutional Network (AraBERT-GCN). It combines pre-trained models that understand words in context and graph features that look at how words relate to each other globally. This helps AraBERT- GCN do better than other models when working with Arabic text. Finally, we develop a special network called Arabic Multidimensional Edge Graph Convolutional Network (AraMEGraph) for Arabic text. It is designed to better understand Arabic and classify text more accurately. We do this by adding special edge features with multiple dimensions to help the network learn the relationships between words and phrases.

The last decade has seen great advances in Next-Generation Sequencing technologies, and, as a result, there has been a rise in the number of genomes sequenced each year. In 2017, there were as many as 10,000 new organisms sequenced and added into the RefSeq Database. Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state-of-the-art algorithms falter. Plinko leverages multi-view convolutional neural networks and the pre-defined taxonomy tree structure to improve multi-level taxonomy prediction. In the Plinko strategy, each network takes advantage of different word usage patterns corresponding to different levels of evolutionary divergence. Plinko has the advantages of relatively low storage, GPGPU parallel training and inference, making the solution portable, and scalable with anticipated genome database growth. To the best of our knowledge, Plinko is the first to use multi-view convolutional neural networks as the core algorithm in a compositional,alignment-free approach to taxonomy prediction. / Master of Science / Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. Given species diversity on Earth, taxonomy prediction gets challenging with (i) increasing number of species (labels) to classify and (ii) decreasing input (DNA) size. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state-of-the-art algorithms falter. Three major challenges in taxonomy prediction are (i) large dataset sizes (order of 109 sequences) (ii) large label spaces (order of 103 labels) and (iii) low resolution inputs (100 base pairs or less). Plinko leverages multi-view convolutional neural networks and the pre-defined taxonomy tree structure to improve multi-level taxonomy prediction for hard to classify sequences under the three conditions stated above. Plinko has the advantage of relatively low storage footprint, making the solution portable, and scalable with anticipated genome database growth. To the best of our knowledge, Plinko is the first to use multi-view convolutional neural networks as the core algorithm in a compositional, alignment-free approach to taxonomy prediction.

Fire dynamics is a complex process involving multi-mode heat transfer, reacting fluid flow, and the reaction of combustible materials. High-fidelity predictions of fire behavior using computational fluid dynamics (CFD) models come at a significant computational cost where simulation times are often measured in hours, days, or even weeks. A new simulation method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make predictions of new inputs. The field of image analysis has seen significant advancements in ANN performance by using feature based layers in the network architecture. Inspired by these advancements, a generalized procedure to design ANNs to make spatially resolved predictions in multi-physics applications is presented and applied to different fire applications. A deep convolutional inverse graphics network (DCIGN) was developed to predict the two-dimensional spatially resolved spread of a wildland fire. The network uses an image stack corresponding to the spatially resolved landscape, weather, and current fire perimeter (which can be obtained from measurements) to predict the fire perimeter six hours in the future. A transpose convolutional neural network (TCNN) was developed to predict the spatially resolved thermal flow field in a compartment fire from coarse zone fire model predictions. The network uses thirty-five parameters describing the geometry of the room and the ventilation conditions to predict the full-field temperature and velocity throughout the room. The data for use in training and testing both networks was generated using high-fidelity CFD fire simulations. Overall, the ANN predictions in each network agree with simulation predictions for validation scenarios. The computational time to evaluate the ANNs is 10,000x faster than the high-fidelity fire simulations. This work represents a first step in developing super real-time full-field fire predictions for different applications. / Ph. D. / The National Fire Protection Agency estimates the total cost of fire in the United States at $300 billion annually. In 2017 alone, there were 3,400 civilian fire fatalities, 14,670 civilian fire injuries, and an estimated $23 billion direct property loss in the United States. Large scale fires in the wildland urban interface (WUI) and in large buildings still represent a significant hazard to life, property, and the environment. Researchers and fire safety engineers often use computer simulations to predict the behavior of a fire to assist in reducing the hazard of fire. Unfortunately, typical simulations of fire scenarios may take hours, days, or even weeks to run which limits their use to small areas or sections of buildings. A new method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make new predictions of fire behavior. Inspired by advancements in the field of image processing, this research developed a procedure to use machine learning to make rapid high resolution predictions of fire behavior. An ANN was developed to predict the perimeter of a wildland fire six hours in the future based on a set of images corresponding to the landscape, weather, and current fire perimeter, all of which can be obtained directly from measurements (US Geological Survey, Automated Surface Observation System, and satellites). In addition, an ANN was developed to predict high-resolution temperature and velocity fields within a floor of a building based on predictions from a coarse model. The data for use in training and testing these networks was generated using high-resolution fire simulations. Overall, the network predictions agree well with simulation predictions for new scenarios. In addition, the time to run the model is 10,000x faster than the typical simulations. The work presented herein represents a first step in developing high resolution computer simulations for different fire scenarios that run very quickly.

Multiple antennas help in combating the destructive effects of fading as well as improve the spectral efficiency of a communication system. Receive diversity techniques like maximal ratio receive combining have been popular means of introducing multiple antennas into communication systems. Space-time block codes present a way of introducing transmit diversity into the communication system with similar complexity and performance as maximal ratio receive combining. In this thesis we study the performance of space-time block codes in Rayleigh fading channel. In particular, the quasi-static assumption on the fading channel is removed to study how the space-time block coded system behaves in fast fading. In this context, the complexity versus performance trade-off for a space-time block coded receiver is studied. As a means to improve the performance of space-time block coded systems concatenation by convolutional codes is introduced. The improvement in the diversity order by the introduction of convolutional codes into the space-time block coded system is discussed. A general analytic expression for the error performance of a space-time block coded system is derived. This expression is utilized to obtain general expressions for the error performance of convolutionally concatenated space-time block coded systems utilizing both hard and soft decision decoding. Simulation results are presented and are compared with the analytical results. / Master of Science