Index Catalog // Arch : Northwestern University Institutional Repository

Enhancing Safety and Robustness for Mission-critical Systems with Formal Methods

Description:: Mission-critical systems are those imperative systems whose failures can result in catastrophic consequences. Traditional techniques, such as manual investigation and testing, cannot ensure the absence of errors and security vulnerabilities within these systems. This dissertation leverages formal methods to comprehensively examine several mission-critical systems and their essential components. For each...
Subject:: Electrical engineering, Computer engineering, and Computer science
Creator:: Li, You
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 08/23/2023
Date Created:: 2023-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:16748 and etdadmin_upload_1013532

Geometric deep learning in neuroimaging and human reward behavior

Description:: In recent years, machine learning on graphs (or networks) has gone from a niche topic with only a few active researchers worldwide, to a heavily invested field with novel use cases for dealing with relationships and/or interactions within complex systems in the natural and social sciences. Traditionally, choosing the right...
Keyword:: cognitive science, graph neural networks, machine learning, neuroimaging, image processing, and artificial intelligence
Subject:: Artificial intelligence, Electrical engineering, and Computer science
Creator:: Azcona, Emanuel Aquiles
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 09/22/2022
Date Created:: 2022-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_922507 and http://dissertations.umi.com/northwestern:16184

Analytical Methods and Deep Priors on X-ray Ptychography, Computed Tomography, and Computed Laminography

Description:: X-ray imaging at nano and micro-scale is of great importance for the material science and defense industry. Large penetration depth and low wavelength of x-rays offer an important potential to image objects at high resolution and in a non-invasive process. While the ever-growing community is pursuing novel applications and looking...
Keyword:: Computed Tomography, Deep Image Priors, X-ray Ptychography, Deep Learning, Computational Imaging, and Computed Laminography
Subject:: Electrical engineering, Computational physics, and Computer science
Creator:: Barutcu, Semih
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 09/22/2022
Date Created:: 2022-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_927076 and http://dissertations.umi.com/northwestern:16218

Deep Reinforcement Learning based Wireless Resource Management

Description:: Next generation cellular networks are expected to support a massive data traffic volume and satisfy a vast number of users that have latency-critical quality-of-service expectations. Towards serving this demand, it is envisaged that the interference management problem will be the main bottleneck due to the likeliness of a heavily interfering...
Keyword:: power control, deep Q-learning, reinforcement learning, actor critic learning, radio resource management, and Jakes fading model
Subject:: Electrical engineering, Computer science, and Engineering
Creator:: Nasir, Yasar Sinan
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/02/2022
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:15788 and etdadmin_upload_849279

Auditory-inspired Approaches to Audio Representation and Analysis for Machine Hearing

Description:: The study and design of machines that are able to analyze the auditory scene and organize sound into parts that are perceptually meaningful to humans is referred to as machine hearing. Such machines are expected to distinguish between different sound categories (e.g., speech, music, background noise), focus on a sound...
Keyword:: Audio Source Separation, Common Fate, Machine Hearing, Audio Signal Processing, Audio Classification, and Audio Representation
Subject:: Electrical engineering and Computer science
Creator:: Pishdadian, Fatemeh
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/01/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_780859 and http://dissertations.umi.com/northwestern:15404

Deep Perceptual Losses and Self-supervised Fine-tuning for Image and Video Super-resolution

Description:: Super-resolution (SR) has become one of the most critical problems in image and video processing. In Chapter 2 of this thesis, a detailed review of existing Deep Learning (DL) techniques for addressing the SR task, with an emphasis on how DL and analytical techniques can be combined, is provided. Chapter...
Keyword:: Image Processing, Video Processing, Deep Learning, and Super-Resolution
Subject:: Electrical engineering and Computer science
Creator:: Lucas, Alice Marie
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 01/21/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:15076 and etdadmin_upload_741221

Unified Compositional Models for Visual Recognition

Description:: A core problem in many computer vision applications is visual recognition (including object classification, detection and localization). Recent advances in artificial neural networks (aka ”deep learning”) have significantly pushed forward the state-of-the-art visual recognition performances. However, due to the lack of semantic structure modeling, most current deep learning approaches do...
Keyword:: visual recognition, deep learning, computer vision, and compositional
Subject:: Electrical engineering and Computer science
Creator:: Tang, Wei
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/12/2020
Date Created:: 2019-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_670616 and http://dissertations.umi.com/northwestern:14725

Neural Network Approaches for Video Transmission, Motion Estimation and Error Concealment

Description:: Abstract The work presented in this dissertation addresses three broad areas of video signal processing: video transmission, motion estimation and error concealment. In the first category, focused on the source-side, we present two machine learning models for efficient content-aware resource allocation and packet prioritization for video transmission over shared/constrained, lossy...
Keyword:: CNN, Optical Flow, Video Transmission, Neural Networks, ConvLSTM, Motion Estimation, Error Concealment, and Capsule Networks
Subject:: Electrical engineering, Computer engineering, and Computer science
Creator:: Sankisa, Arun
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/12/2020
Date Created:: 2019-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:14822 and etdadmin_upload_685993

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Enhancing Safety and Robustness for Mission-critical Systems with Formal Methods

Geometric deep learning in neuroimaging and human reward behavior

Analytical Methods and Deep Priors on X-ray Ptychography, Computed Tomography, and Computed Laminography

Deep Reinforcement Learning based Wireless Resource Management

Auditory-inspired Approaches to Audio Representation and Analysis for Machine Hearing

Deep Perceptual Losses and Self-supervised Fine-tuning for Image and Video Super-resolution

Unified Compositional Models for Visual Recognition

Neural Network Approaches for Video Transmission, Motion Estimation and Error Concealment

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