Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 30 of 44
Full-Text Articles in Entire DC Network
Deep Generative Sculpting Models For Single Image 3d Reconstruction, Jason Jennings
Deep Generative Sculpting Models For Single Image 3d Reconstruction, Jason Jennings
Computer Science and Engineering Dissertations
In the field of computer vision, learning representations of images is an important task. This dissertation introduces deep generative sculpting models (DGSM), deep learning models that learn 3D representations of objects from 2D images. DGSMs use convolutional networks combined with a differentiable renderer to attempt to "sculpt" a base 3D mesh, such as a sphere, to faithfully represent an object in the scene, and render it to reconstruct the input image. The core methodology revolves around the encoding of the input image into latent variables. These variables are decoded into interpretable scene parameters, describing the object's translation, rotation, scale, texture, …
Constructing Large Open-Source Corpora And Leveraging Language Models For Simulink Toolchain Testing And Analysis, Sohil Lal Shrestha
Constructing Large Open-Source Corpora And Leveraging Language Models For Simulink Toolchain Testing And Analysis, Sohil Lal Shrestha
Computer Science and Engineering Dissertations
In several safety-critical industries such as automotive, aerospace, healthcare, and industrial automation, MATLAB/Simulink has emerged as the de-facto standard tool for system modeling and analysis, model compilation into executable code, and code deployment onto embedded hardware. Within the context of cyber-physical system (CPS) development, it is imperative to both rigorously test the development tools, such as MathWorks’ Simulink, and understand modeling practices and model evolution. The existing body of work faces limitations primarily stemming from two factors: (1) contemporary testing methodologies often prove inefficient in identifying critical toolchain bugs due to a paucity of explicit toolchain specifications and (2) there …
Enhancing The Classification Of Autism Spectrum Disorder From Rs-Fmri Functional Connectivity Data Using Temporal Information, Mihir Yashwant Ingole
Enhancing The Classification Of Autism Spectrum Disorder From Rs-Fmri Functional Connectivity Data Using Temporal Information, Mihir Yashwant Ingole
Computer Science and Engineering Theses
Autism Spectrum Disorder (ASD) affects the patient’s cognitive development which leads to difficulties in social functioning, daily tasks, and independent living. This necessitates intervention at an early age to take preventive measures and provide vital care. Manual diagnosis methods like Autism Diagnostic Observation Schedule (ADOS) assessment adopts symptom-based criteria which typically manifest at a later age. To automate this process, correlations computed from BOLD (Blood Oxygen-level dependent) signals obtained through resting state functional magnetic resonance imaging (rs-fMRI) data of patients across sparse brain regions has been used recently as a measure of functional connectivity. The goal of this study is …
Model Optimization And Applications In Deep Learning, Chengchen Mao
Model Optimization And Applications In Deep Learning, Chengchen Mao
Electrical Engineering Dissertations
ABSTRACT: Machine learning refers to a machine or an algorithm that draws experience from data. A certain pattern is found to build a model, which is used to solve real problems. Deep learning, an important branch and extension of machine learning, employs a neural network structure containing multiple hidden layers. It learns critical features of the data by combining lower-level features to form more abstract higher-level representations of attribute categories or features. In this dissertation, deep learning network models were applied to sense-through-foliage target detection and extended with Rake structure. The deep learning network models had a large number of …
Neural Network Architecture Optimization Using Reinforcement Learning, Raghav Vadhera
Neural Network Architecture Optimization Using Reinforcement Learning, Raghav Vadhera
Computer Science and Engineering Dissertations
Deep learning has emerged as an increasingly valuable tool, employed across a myriad of applications. However, the intricacies of deep learning systems, stemming from their sensitivity to specific network architectures, have rendered them challenging for non-experts to harness, thus highlighting the need for automatic network architecture optimization. Prior research predominantly optimizes a network for a single problem through architecture search, necessitating extensive training of various architectures during optimization.\\ To tackle this issue and unlock the potential for transferability across tasks, this dissertation presents a groundbreaking approach that employs Reinforcement Learning to develop a network optimization policy based on an abstract …
Toward A Deeper Integration Of Low-Fidelity Sketches Into Mobile Application Development, Soumik Mohian
Toward A Deeper Integration Of Low-Fidelity Sketches Into Mobile Application Development, Soumik Mohian
Computer Science and Engineering Dissertations
Mobile application development often starts with creating low-fidelity sketches of user interfaces. Integrating these sketches into the software development process can reduce repetition, narrow the gap between user perception and final implementation, and improve app resilience. In this study, we introduce the DoodleUINet dataset, which comprises over 10K sketches of UI elements. Our Doodle2App tool converts low-fidelity sketches into a single-page, compilable Android app. At the same time, our PSDoodle provides an interactive, partial sketch-based search engine with a top-10 screen retrieval accuracy comparable to the state-of-the-art SWIRE line of work but with a 50% reduction in the average required …
Hand Analysis From Depth Images, Mohammad Rezaei
Hand Analysis From Depth Images, Mohammad Rezaei
Computer Science and Engineering Dissertations
Hand analysis using vision systems is necessary for interaction between people and digital devices and thus is crucial in many applications relating to computer vision and human computer interaction (HCI). The proposed dissertation will explore hand analysis from depth images along two lines: hand part segmentation and 3D hand pose estimation. First, we investigate hand part segmentation from depth images, which is formulated as a semantic segmentation task. We explore a method aimed at determining for every pixel what hand part it belongs to. This method attempts to perform this task without requiring the ground-truth segmentation labels for training. It …
Advancing The Radiation Oncology Clinic With Motion Management And Automatic Treatment Planning, Damon Anton Sprouts
Advancing The Radiation Oncology Clinic With Motion Management And Automatic Treatment Planning, Damon Anton Sprouts
Bioengineering Dissertations
The leading cause of premature death (death under the age of 70) is cancer. The top five cancers for both male and female are: lung, colorectum, pancreas, breast cancer, and prostate. In 2020 there was an estimated 19.3 million new cases with an estimated 9.9 million deaths. The cancer burden is expected to grow to 28.4 million by the year 2040. Surgery, chemotherapy, and radiotherapy are the three pillars in the modern clinic for cancer treatment. In radiotherapy, ionizing radiation particles can travel through the patient body, deposit energy along the way and damage the DNA Structure. There needs to …
Deep Learning For Protein Property And Structure Prediction, Yuzhi Guo
Deep Learning For Protein Property And Structure Prediction, Yuzhi Guo
Computer Science and Engineering Dissertations
I present my work towards solving the fundamental, challenging, and valuable problem for protein property and structure prediction. Specifically, I focus on solving the problem from three critical aspects: (1) designing powerful deep learning networks for specific protein structure property prediction tasks; (2) proposing general methods that enhancing the protein sequence homologous feature, which is an important input feature of relevant tasks; (3) developing a self-supervised pre-training model for learning structure embeddings from protein tertiary structures. To evaluate the effectiveness of the developed methods, I apply several protein downstream tasks including protein secondary structure, solvent accessibility, backbone dihedral angles, protein …
Robust Noise-Based Attacks Against Audio Event Detection Systems, Rodrigo Augusto Silva Dos Santos
Robust Noise-Based Attacks Against Audio Event Detection Systems, Rodrigo Augusto Silva Dos Santos
Computer Science and Engineering Dissertations
The massive advances on the field of deep neural networks in the 2000 and 2010 decades led to an overwhelming adoption of these algorithms on all sorts of domains and applications. Under this widespread adoption scenario, it is natural that these neural networks have also been employed on safety-related use cases, bringing substantial improvements to the performance of existing as well as novel systems. Examples of these safety-inclined applications include scene recognition, object detection and tracking, speech recognition, audio event detection and classification, just to cite a few ones. Unfortunately, these neural network algorithms have been shown to be vulnerable …
Exploring Deep Learning In Finance, Abhijit Anand Anand Deshpande
Exploring Deep Learning In Finance, Abhijit Anand Anand Deshpande
Industrial, Manufacturing, and Systems Theses
Financial market analysis is process of analyzing market closely and predict the next move of market whether it will go up or down using historical data. Financial market is stochastic and has rapid changes over time, therefore it is very difficult to predict. The main goal of this work is to understand novel approaches of machine learning in finance, data parsing techniques, labelling the financial data. Furthermore, understand state of art Transformer model and implement and compare results with other traditional machine learning algorithms. Experiment carried out in python along with pytorch.
Machine Learning Methods To Improve Fairness And Prediction Accuracy On Largesocially Relevant Datasets, Bhanu Chaturvedi Jain
Machine Learning Methods To Improve Fairness And Prediction Accuracy On Largesocially Relevant Datasets, Bhanu Chaturvedi Jain
Computer Science and Engineering Dissertations
Machine learning-based decision support systems bring relief to the decision-makers in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems facilitate processing tremendous amounts of data to decipher the embedded patterns. However,these decisions can also absorb and amplify bias embedded in the data. An increasing number of applications of machine learning-based decision sup-port systems in a growing number of domains has directed the attention of stake-holders to the accuracy, transparency, interpretability, cost effectiveness, and fairness encompassed in the ensuing decisions. In this dissertation, we have focused on fairness and accuracy …
Domain Adaptive Transfer Learning For Visual Classification, Ashiq Imran
Domain Adaptive Transfer Learning For Visual Classification, Ashiq Imran
Computer Science and Engineering Dissertations
Deep Neural Networks have made a significant impact on many computer vision applications with large-scale labeled datasets. However, in many applications, it is expensive and time-consuming to gather large-scale labeled data. With the limited availability of labeled data, it is challenging to obtain great performance. Moreover, in many real-world problems, transfer learning has been applied to cope with limited labeled training data. Transfer learning is a machine learning paradigm where pre-trained models on one task can be reused for another task. This dissertation investigates transfer learning and related machine learning techniques such as domain adaptation on visual categorization applications. At …
Low-Dose Ct Image Denoising Using Deep Learning Methods, Zeheng Li
Low-Dose Ct Image Denoising Using Deep Learning Methods, Zeheng Li
Computer Science and Engineering Theses
Low-dose computed tomography (LDCT) has raised highly attention since the counterpart, full-dose computed tomography (FDCT), brings potential ionizing radiation influence to patients. However, LDCT still suffers from several issues such as relatively higher noise level, which limits its uses in practical applications. To improve LDCT image quality, conventional denoising methods, such as KSVD and BM3D, are first introduced to suppress noise in low-dose images. These methods, however, works under assumptions that are not robust to various data. In this paper, we conduct an extensive research on deep learning based denoising method in LDCT images. We mainly base on Generative-Adversarial Network …
Deep Learning Methods For Image Restoration And Reconstruction, Zahra Anvari
Deep Learning Methods For Image Restoration And Reconstruction, Zahra Anvari
Computer Science and Engineering Dissertations
The problem of image reconstruction and restoration refers to recovering the clean images from corrupted ones. Corruption or degradation can occur due to atmospheric conditions such as rain, fog, mist, snow, dust, and air pollution or technical drawbacks of imaging devices such as motion blurriness, compression noise, low-resolution, etc. Image reconstruction algorithms aim at reducing these artifacts and degradation and generate clear images. Scenes captured under bad weather conditions such as rain, fog, mist, and haze suffer from visibility issues thus introduce obstacles for computer vision applications, e.g. object detection, recognition, tracking, and segmentation. In this dissertation, we focus on …
Resource Allocation And Capacity In Wireless Communications And Networks, Zikai Wang
Resource Allocation And Capacity In Wireless Communications And Networks, Zikai Wang
Electrical Engineering Dissertations
How to allocate resources in the era of Big Data in telecommunications becomes a new issue. Smartphone data could be a function of personality, as the smartphone supports interpersonal interaction, and the data collected from the smartphone usage often contains rich customer opinion and behavioral information. A bandwidth allocation method based on smartphone users' personality traits and channel condition is studied in a unified mathematical framework in this dissertation. Personalizing bandwidth allocation could be done by analyzing smartphone users' personality traits, resulting in business intelligence, a smarter and more efficient usage of the limited bandwidth, while taking channel fading conditions …
Road Map Generation And Feature Extraction Algorithms From Gps Trajectories And Trajectories Data Warehousing, Tariq Alsahfi
Road Map Generation And Feature Extraction Algorithms From Gps Trajectories And Trajectories Data Warehousing, Tariq Alsahfi
Computer Science and Engineering Dissertations
Advanced technologies in location acquisition allow us to track the movement of moving objects (people, planes, vehicles, animals, ships, ..) in geographical space. These technologies generate a vast amount of trajectory data (TD). Several applica- tions in different fields can utilize such trajectory data, for example, traffic control management, social behavior analysis, wildlife migrations and movements, ship tra- jectories, shoppers behavior in a mall, facial nerve trajectory, location-based services (LBS) and many others. Fortunately, there are now many trajectory data sets avail- able that collected from moving objects such as cars with enabled GPS devices. Two main challenges arise when …
Semi-Supervised Deep Learning With Applications In Surgical Video Analysis And Bioinformatics, Sheng Wang
Semi-Supervised Deep Learning With Applications In Surgical Video Analysis And Bioinformatics, Sheng Wang
Computer Science and Engineering Dissertations
In the current era of big data, deep learning has been the state-of-the-art model for various applications. Image-based applications such as image classification, object detection, image segmentation, benefit most from deep learning networks. One reason for the successful applications of deep learning is that there are a large number of labeled training samples for the model to learn from. People are interested in reducing the cost of getting labeled training samples, and there are various research going on with unsupervised, semi-supervised, and self-supervised deep learning. The cost of health-related data is even higher. Labeling the surgical videos with tools being …
Multiscale Modeling And Simulation Of Clutter In Isar Imaging, Jon Mitchell
Multiscale Modeling And Simulation Of Clutter In Isar Imaging, Jon Mitchell
Electrical Engineering Dissertations
Clutter is common in applications of radar imaging and can adversely impact target imaging by contributing scattered energy that is not accounted for in target signal models. One potential source of clutter is moving foliage in the vicinity of the target, such as a target embedded in a forest. ISAR imaging of moving clutter results in an equivalent current image that changes over each imaging sample. The stochastic nature of this clutter equivalent current presents challenges in detecting and imaging a weak embedded target using traditional algorithms. This dissertation proposes a multiscale model and analysis method to characterize the multiscale …
Efficient Network Design For High Dimensional Data, Xin Miao
Efficient Network Design For High Dimensional Data, Xin Miao
Computer Science and Engineering Dissertations
Due to the powerful feature representation capabilities, deep learning has became a powerful tool in the field of computer vision. Especially in the aspect of high-dimensional images, deep learning can achieve fast inference compared with most traditional methods. This paper focuses on how to design an efficient neural network and apply it to two high-dimensional images application, video facial landmarks detections and compressive imaging system. In this first part of this paper, we focus on landmarks detection for video facial images. Existing methods for facial landmarks detection mainly rely on cascaded regression. It is an indirect method and progressively estimates …
Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi, Sheheryar Arshad
Feature Extraction In Noise-Diverse Environments For Human Activities Recognition Using Wi-Fi, Sheheryar Arshad
Computer Science and Engineering Dissertations
With the rapid development of 802.11 standard and Internet of Things (IoT) applications, Wi-Fi (IEEE 802.11) has emerged as the most widely used wireless communication technology. Wi-Fi based sensing has found widespread use cases involving activity recognition, indoor localization, design of smart spaces and in healthcare applications. This dissertation presents the study of human activities’ sensing and recognition using channel state information (CSI) of Wi-Fi. We highlight the limitations of existing methods and consequently design the frameworks for collecting stable CSI and monitoring different indoor and outdoor environments for human activities. Specifically, this dissertation provide means to define and extract …
Comprehensive Study Of Generative Methods On Drug Discovery, Siyu Xiu
Comprehensive Study Of Generative Methods On Drug Discovery, Siyu Xiu
Computer Science and Engineering Theses
Observing the recent success of the deep learning (DL) technology in multiple life-changing application areas, e.g., autonomous driving, image/video search and discovery, natural language processing, etc., many new opportunities have presented themselves. One of the biggest ones lies in applying DL in accelerating the drug discovery, where millions of human lives could potentially be saved. However, applying DL into the drug discovery task turns out to be non-trivial. The most successful DL methods take fix-sized tensors/matrices, e.g., images, or sequences of tokens, e.g., sentences with variant numbers of words, as their inputs. However, none of these registers with the inputs …
Multivariate Time Series Pattern Recognition Using Machine Learning And Deep Learning Methods, Sai Abhishek Devar
Multivariate Time Series Pattern Recognition Using Machine Learning And Deep Learning Methods, Sai Abhishek Devar
Industrial, Manufacturing, and Systems Theses
In this research work, we have implemented machine learning & deep-learning algorithms on real-time multivariate time series datasets in the manufacturing & health care fields. The research work is organized in two case-studies. The case study-1 is about rare event classification in multivariate time series in a pulp and paper manufacturing industry, data was collected of multiple sensors at each stage of production line, the data contains a rare event of paper break that commonly occurs in the industry. For preprocessing we have implemented sliding window approach for calculating first order difference method to capture the variation in the data …
Deep Representation Learning For Clustering And Domain Adaptation, Mohsen Kheirandishfard
Deep Representation Learning For Clustering And Domain Adaptation, Mohsen Kheirandishfard
Computer Science and Engineering Dissertations
Representation learning is a fundamental task in the area of machine learning which can significantly influence the performance of the algorithms used in various applications. The main goal of this task is to capture the relationships between the input data and learn feature representations that contain the most useful information of the original data. Such representations can be further leveraged in many machine learning applications such as clustering, natural language analysis, recommender systems, etc. In this dissertation, we first present a theoretical framework for solving a broad class of non-convex optimization problems. The proposed method is applicable to various tasks …
Learning For Clinical Outcome Prediction From Big Medical Data, Jiawen Yao
Learning For Clinical Outcome Prediction From Big Medical Data, Jiawen Yao
Computer Science and Engineering Dissertations
With the advance of recent technological innovations, nowadays scientists can easily capture and store tremendous amounts of different types of medical data such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), big pathological images and high dimensional cell profiling data. Developing deep learning and machine learning to analyze such large-scale medical data sets for patient health care is an interesting but challenging problem. Inspired by the trend, in this dissertation, we focus on solving real-world problems, like survival analysis on image-omics data and reducing uncertainty from undersampled MRI. Survival analysis is a crucial tool in the clinical study of cancer …
Towards Automated Understanding Of Laparoscopic Videos, Babak Namazi
Towards Automated Understanding Of Laparoscopic Videos, Babak Namazi
Electrical Engineering Dissertations
Despite the advantages of minimally invasive surgeries, the indirect access and lack of the 3D field of view of the area of interest introduce complications in the procedures. Fortunately, the recorded videos from the operation offer the opportunity for intra-operative and post-operative analyses of the procedures, to improve future performance and safety. Such analysis is essential to provide the tools for evaluation and assessment of the surgeries. In this dissertation, we investigate the potential of deep learning techniques in understanding the videos captured during laparoscopic surgeries. To this end, we describe new methods for identifying the surgical instruments and the …
Convex And Non-Convex Optimization Methods For Machine Learning, Fariba Zohrizadeh
Convex And Non-Convex Optimization Methods For Machine Learning, Fariba Zohrizadeh
Computer Science and Engineering Dissertations
This dissertation is concerned with modeling fundamental and challenging machine learning tasks as convex/non-convex optimization problems and designing a mechanism that could solve them in a cost and time-effective manner. Extensive theoretical and practical studies are carried out to give deeper insights into the robustness and effectiveness of the formulated problems. In what follows, we investigate some well-known tasks that frequently arise in machine learning applications. Image Segmentation: Image segmentation is a fundamental and challenging task in computer vision with diverse applications in various areas. One of the major challenges in image segmentation is to determine the optimal number of …
Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar
Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar
Computer Science and Engineering Theses
Machine Learning is at the forefront of every field today. The subfields of Machine Learning called Reinforcement Learning and Deep Learning, when combined have given rise to advanced algorithms which have been successful at reaching or surpassing the human-level performance at playing Atari games to defeating multiple times champion at Go. These successes of Machine Learning have attracted the interest of the financial community and have raised the question if these techniques could also be applied in detecting patterns in the financial markets. Until recently, mathematical formulations of dynamical systems in the context of Signal Processing and Control Theory have …
Ultra-Context: Maximizing The Context For Better Image Caption Generation, Ankit Khare
Ultra-Context: Maximizing The Context For Better Image Caption Generation, Ankit Khare
Computer Science and Engineering Theses
Several combinations of visual and semantic attention have been geared towards developing better image captioning architectures. In this work we introduce a novel combination of word-level semantic context with image feature-level visual context, which provides a more holistic overall context for image caption generation. This approach does not require training any explicit network structure, using any external resource for training semantic attributes, or supervision during any training step. The proposed architecture addresses the significance of learning to find context at three levels to achieve a better trade-off as well as a balance between the two lines of attentiveness (word-level and …
From Body To Brain: Using Artificial Intelligence To Identify User Skills & Intentions In Interactive Scenarios, Michalis Papakostas
From Body To Brain: Using Artificial Intelligence To Identify User Skills & Intentions In Interactive Scenarios, Michalis Papakostas
Computer Science and Engineering Dissertations
Artificial Intelligence has probably been the most rapidly evolving field of science during the last decade. Its numerous real-life applications have radically altered the way we experience daily-living with great impact in some of the most basic aspects of human lives including but not limited to health and well-being, communication and interaction, education, driving, daily, and entertainment. Human-Computer Interaction (HCI) is the field of Computer Science lying in the epicenter of this evolution and is responsible for transforming rudimentary research findings and theoretical principles into intuitive tools, responsible for enhancing human performance, increasing productivity and ensuring safety. Two of the …