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- Machine Learning (2)
- Artificial Intelligence; Catastrophic Forgetting; Continual Learning; Deep Learning; Machine Learning; Neuroscience (1)
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- Autoencoder; BotNets; Cyber Security; Ensemble Learning; Intrusion Detection; Machine Learning (1)
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- Bound states in the continuum (1)
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Articles 1 - 16 of 16
Full-Text Articles in Physical Sciences and Mathematics
Design And Implementation Of Truly Random Number Generation Using Memristors For In-Memory Computing, Nick Felker
Design And Implementation Of Truly Random Number Generation Using Memristors For In-Memory Computing, Nick Felker
Theses and Dissertations
This paper proposes a new security module based on non-volatile memory. The module uses a memristor-based true random number generator to generate random numbers which can be used for cryptography. The module is implemented in software using a modified RISC-V instruction set architecture. The paper evaluates the performance of the module using the RISC-V simulator Gem5. The results show that the module can generate random numbers at a rate of 63 microseconds per number, which is faster than the standard C library’s random number generator. The module can also be used to scramble strings of characters and generate hashes of …
Hardware Acceleration Of Numerical Methods For Solving Ordinary Differential Equations, Soham Bhattacharya
Hardware Acceleration Of Numerical Methods For Solving Ordinary Differential Equations, Soham Bhattacharya
Theses and Dissertations
Along with the advancement in technology, the role of hardware accelerators is increasing consistently, delivering advancements in scientific simulations and data analysis in scientific computing, signal processing tasks in communication systems, matrix operations, and neural network computations in artificial intelligence and machine learning models. On the other hand, several high-speed computer applications in this era of high-performance computing often depend on ordinary differential equations (ODEs); however, their nonlinear nature can present a challenge to obtaining analytic solutions. Consequently, numerical approaches prove effective in delivering only approximate solutions to these equations. This research discusses the implementation of a customized hardware accelerator …
Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo
Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo
Theses and Dissertations
New technologies are being introduced at a rate faster than ever before and smaller in size. Due to the size of these devices, security is often difficult to implement. The existing solution is a firewall-segmented “IoT Network” that only limits the effect of these infected devices on other parts of the network. We propose a lightweight unsupervised hybrid-cloud ensemble anomaly detection system for malware detection. We perform transfer learning using a generalized model trained on multiple IoT device sources to learn network traffic on new devices with minimal computational resources. We further extend our proposed system to utilize federated learning …
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco
Theses and Dissertations
Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …
Brain-Inspired Continual Learning: Rethinking The Role Of Features In The Stability-Plasticity Dilemma, Hikmat Khan
Brain-Inspired Continual Learning: Rethinking The Role Of Features In The Stability-Plasticity Dilemma, Hikmat Khan
Theses and Dissertations
Continual learning (CL) enables deep learning models to learn new tasks sequentially while preserving performance on previously learned tasks, akin to the human's ability to accumulate knowledge over time. However, existing approaches to CL face the challenge of catastrophic forgetting, which occurs when a model's performance on previously learned tasks declines after learning the new task. In this dissertation, we focus on the crucial role of input data features in determining the robustness of CL models to mitigate catastrophic forgetting. We propose a framework to create CL-robustified versions of standard datasets using a pre-trained Oracle CL model. Our experiments show …
Towards A Practical Method For Monitoring Kinetic Processes In Polymers With Low-Frequency Raman Spectroscopy, Robert Vito Chimenti
Towards A Practical Method For Monitoring Kinetic Processes In Polymers With Low-Frequency Raman Spectroscopy, Robert Vito Chimenti
Theses and Dissertations
Unlike liquids and crystalline solids, glassy materials exist in a constant state of structural nonequilibrium. Therefore, a comprehensive understanding of material kinetics is critical for understanding the structure-property-processing relationships of polymeric materials. Amorphous materials universally display low-frequency Raman features related to the phonon density of states resulting in a broad disorder band for Raman shifts below 100 cm-1, which is related to the conformational entropy and the modulus. This disorder band is dominated by the Boson peak, a feature due to phonon scattering because of disorder and can be related to the transverse sound velocity of the material, and a …
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Theses and Dissertations
Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …
Adversary Aware Continual Learning, Muhammad Umer
Adversary Aware Continual Learning, Muhammad Umer
Theses and Dissertations
Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …
Using Dielectric Scatters To Selectively Excite Embedded Eigenstates In Cavity Resonators, Olugbenga Joshua Gbidi
Using Dielectric Scatters To Selectively Excite Embedded Eigenstates In Cavity Resonators, Olugbenga Joshua Gbidi
Theses and Dissertations
Bound states in the continuum (BICs) are waves that remain in the continuous spectrum of radiating waves that carry energy, however, still localized within the spectrum. BICs, also embedded eigenmodes, exhibit high quality factors that have been observed in optical and acoustic waveguides, photonic structures, and other material systems. Presently, there are limited means to select these BICs in terms of the quality factor and their excitation. In this work, we show that a different type of BIC, Quasi-BICs (Q-BICs), in open resonators can have their quality attuned by introducing embedded scatters. Using microwave cavities and dielectric scatters as an …
Investigation Of Adhesion, Deformation Mechanics, And Electrical Properties Of Ag/Sio2/Pdms Tri-Layers For Stretchable Electronic Applications, Rhandy Joe Paladines
Investigation Of Adhesion, Deformation Mechanics, And Electrical Properties Of Ag/Sio2/Pdms Tri-Layers For Stretchable Electronic Applications, Rhandy Joe Paladines
Theses and Dissertations
The motivation behind this research is to improve the interfacial layer bonding of metallic thin films to PDMS substrates with the aid of a buffer layer. The physical vapor deposition (PVD) technique of sputtering was used to deposit bilayer thin films of silver (Ag) and silicon dioxide (SiO2) on PDMS. Two chamber pressures were used in this work, 5 and 20 mTorr, to investigate the role of this parameter in determining the interfacial adhesion and the role in determining the resistance sensitivity. Studies of the surface energy and increased bonding strength for metallization are carried out. Surface characterization using atomic …
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
Theses and Dissertations
Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …
Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel
Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel
Theses and Dissertations
Infrastructure is a key component in the well-being of our society that leads to its growth, development, and productive operations. A well-built infrastructure allows the community to be more competitive and promotes economic advancement. In 2021, the ASCE (American Society of Civil Engineers) ranked the American infrastructure as substandard, with an overall grade of C-. The overall ranking suffers when key infrastructure categories are not maintained according to the needs of the population. Therefore, there is a need to consider alternative methods to improve our infrastructure and make it more sustainable to enhance the overall grade. One of the challenges …
Rebalancing Shared Mobility Systems By User Incentive Scheme Via Reinforcement Learning, Matthew Brian Schofield
Rebalancing Shared Mobility Systems By User Incentive Scheme Via Reinforcement Learning, Matthew Brian Schofield
Theses and Dissertations
Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, leading to users being unable to receive service. If such imbalance problems are not mitigated some users will not be serviced. There is an increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to produce an effective user incentivization policy scheme to encourage users of a shared mobility system to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior …
Heterogeneous Anisotropy Index And Scaling In Multiphase Random Polycrystals, Muhammad Ridwan Murshed
Heterogeneous Anisotropy Index And Scaling In Multiphase Random Polycrystals, Muhammad Ridwan Murshed
Theses and Dissertations
Under consideration is the finite-size scaling of elastic properties in single and two-phase random polycrystals with individual grains belonging to any crystal class (from cubic to triclinic). These polycrystals are generated by Voronoi tessellations with varying grain sizes and volume fractions. By employing variational principles in elasticity, we introduce the notion of a 'Heterogeneous Anisotropy Index' and investigate its role in the scaling of elastic properties at finite mesoscales. The index turns out to be a function of 43 variables, 21 independent components for each phase and the volume fraction of either phase. Furthermore, the relationship between Heterogeneous Anisotropy Index …
Data Analysis And Processing Techniques For Remaining Useful Life Estimations, John Scott Bucknam
Data Analysis And Processing Techniques For Remaining Useful Life Estimations, John Scott Bucknam
Theses and Dissertations
In the field of engineering, it is important to understand different engineering systems and components, not only in how they currently perform, but also how their performance degrades over time. This extends to the field of prognostics, which attempts to predict the future of a system or component based on its past and present states. A common problem in this field is the estimation of remaining useful life, or how long a system or component functionality will last. The well-known datasets for this problem are the PHM and C-MAPSS datasets. These datasets contain simulated sensor data for different turbofan engines …
Quad General Tree Drawing Algorithm And General Trees Characterization: Towards An Environment For The Experimental Study On General Tree Drawing Algorithms, Chu Yao
Theses and Dissertations
Information visualization produces (interactive) visual representations of abstract data to reinforce human cognition and perception; thus enabling the viewer to gain knowledge about the internal structure of the data and causal relationships in it. The visualization of information hierarchies is concerned with the presentation of abstract hierarchical information about relationships between various entities. It has many applications in diverse domains such as software engineering, information systems, biology, and chemistry. Information hierarchies are typically modeled by an abstract tree, where vertices are entities and edges represent relationships between entities. The aim of visualizing tree drawings is to automatically produce drawings of …