Engineering Commons^™

Adaptive Beyond Von-Neumann Computing Devices And Reconfigurable Architectures For Edge Computing Applications, Mousam Hossain

Graduate Thesis and Dissertation 2023-2024

The Von-Neumann bottleneck, a major challenge in computer architecture, results from significant data transfer delays between the processor and main memory. Crossbar arrays utilizing spin-based devices like Magnetoresistive Random Access Memory (MRAM) aim to overcome this bottleneck by offering advantages in area and performance, particularly for tasks requiring linear transformations. These arrays enable single-cycle and in-memory vector-matrix multiplication, reducing overheads, which is crucial for energy and area-constrained Internet of Things (IoT) sensors and embedded devices.

This dissertation focuses on designing, implementing, and evaluating reconfigurable computation platforms that leverage MRAM-based crossbar arrays and analog computation to support deep learning and error …

Internet-Of-Things Privacy In Wifi Networks: Side-Channel Leakage And Mitigations, Mnassar Alyami

Graduate Thesis and Dissertation 2023-2024

WiFi networks are susceptible to statistical traffic analysis attacks. Despite encryption, the metadata of encrypted traffic, such as packet inter-arrival time and size, remains visible. This visibility allows potential eavesdroppers to infer private information in the Internet of Things (IoT) environment. For example, it allows for the identification of sleep monitors and the inference of whether a user is awake or asleep.

WiFi eavesdropping theoretically enables the identification of IoT devices without the need to join the victim's network. This attack scenario is more realistic and much harder to defend against, thus posing a real threat to user privacy. However, …

Addressing Challenges In Utilizing Gpus For Accelerating Privacy-Preserving Computation, Ardhi Wiratama Baskara Yudha

Graduate Thesis and Dissertation 2023-2024

Cloud computing increasingly handles confidential data, like private inference and query databases. Two strategies are used for secure computation: (1) employing CPU Trusted Execution Environments (TEEs) like AMD SEV, Intel SGX, or ARM TrustZone, and (2) utilizing emerging cryptographic methods like Fully Homomorphic Encryption (FHE) with libraries such as HElib, Microsoft SEAL, and PALISADE. To enhance computation, GPUs are often employed. However, using GPUs to accelerate secure computation introduces challenges addressed in three works.

In the first work, we tackle GPU acceleration for secure computation with CPU TEEs. While TEEs perform computations on confidential data, extending their capabilities to GPUs …

Privacy And Security Of The Windows Registry, Edward L. Amoruso

Graduate Thesis and Dissertation 2023-2024

The Windows registry serves as a valuable resource for both digital forensics experts and security researchers. This information is invaluable for reconstructing a user's activity timeline, aiding forensic investigations, and revealing other sensitive information. Furthermore, this data abundance in the Windows registry can be effortlessly tapped into and compiled to form a comprehensive digital profile of the user. Within this dissertation, we've developed specialized applications to streamline the retrieval and presentation of user activities, culminating in the creation of their digital profile. The first application, named "SeeShells," using the Windows registry shellbags, offers investigators an accessible tool for scrutinizing and …

Exploring The Diffusion Potential Of A Collaborative Mobile Platform For Disaster Management And Relief, Joao De Mendonca Salim

Honors Undergraduate Theses

This thesis describes the creation of a collaborative digital platform for disaster management and relief, focusing on the case study of the city of Petrópolis natural disaster in February 2022. The frequency and intensity of natural disasters are rising, necessitating efficient and timely disaster response efforts. This thesis details the development of a software application that fosters collaboration among governmental agencies, emergency services, non-governmental organizations (NGOs), and civil society to enhance logistical planning and situational awareness during disasters. The proposed platform harnesses the power of social networking and leverages the ubiquitous presence of smartphones equipped with cameras, GPS, and sensors …

A Unique Method Of Using Information Entropy To Evaluate The Reliability Of Deep Neural Network Predictions On Intracranial Electroencephalogram, Elakkat Dharmaraj Gireesh

Electronic Theses and Dissertations, 2020-2023

Deep Neural networks (DNN) are fundamentally information processing machines, which synthesize the complex patterns in input to arrive at solutions, with applications in various fields. One major question when working with the DNN is, which features in the input lead to a specific decision by DNN. One of the common methods of addressing this question involve generation of heatmaps. Another pertinent question is how effectively DNN has captured the entire information presented in the input, which can potentially be addressed with complexity measures of the inputs. In the case of patients with intractable epilepsy, appropriate clinical decision making depends on …

Micro-Credentialing With Fuzzy Content Matching: An Educational Data-Mining Approach, Paul Amoruso

Electronic Theses and Dissertations, 2020-2023

There is a growing need to assess and issue micro-credentials within STEM curricula. Although one approach is to insert a free-standing academic activity into the students learning and degree path, herein the development and mechanism of an alternative approach rooted in leveraging responses on digitized quiz-based assessments is developed. An online assessment and remediation protocol with accompanying Python-based toolset was developed to engage undergraduate tutors who identify and fill knowledge gaps of at-risk learners. Digitized assessments, personalized tutoring, and automated micro-credentialing scripts for Canvas LMS are used to issue skill-specific badges which motivate the learner incrementally, while increasing self-efficacy. This …

Leveraging Signal Transfer Characteristics And Parasitics Of Spintronic Circuits For Area And Energy-Optimized Hybrid Digital And Analog Arithmetic, Adrian Tatulian

Electronic Theses and Dissertations, 2020-2023

While Internet of Things (IoT) sensors offer numerous benefits in diverse applications, they are limited by stringent constraints in energy, processing area and memory. These constraints are especially challenging within applications such as Compressive Sensing (CS) and Machine Learning (ML) via Deep Neural Networks (DNNs), which require dot product computations on large data sets. A solution to these challenges has been offered by the development of crossbar array architectures, enabled by recent advances in spintronic devices such as Magnetic Tunnel Junctions (MTJs). Crossbar arrays offer a compact, low-energy and in-memory approach to dot product computation in the analog domain by …

Advanced Deep Learning Methodologies For Deepfake Detection, Aminollah Khormali

Electronic Theses and Dissertations, 2020-2023

The recent advances in the field of Artificial Intelligence (AI), particularly Generative Adversarial Networks (GANs) and an abundance of training samples along with robust computational resources have significantly propelled the field of AI-generated fake information in all kinds, e.g., deepfakes. Deepfakes are among the most sinister types of misinformation, posing large-scale and severe security and privacy risks targeting critical governmental institutions and ordinary people across the world. The fact that deepfakes are AI-generated digital content and not actual events captured by a camera implies that they still can be detected using advanced AI models. Although the deepfake detection task has …

Mixed-Criticality System Design For Real-Time Scheduling And Routing Upon Platforms With Uncertainties, Sudharsan Vaidhun Bhaskar

Electronic Theses and Dissertations, 2020-2023

Unlike typical computing systems, applications in real-time systems require strict timing guarantees. In the pursuit of providing guarantees, the complex dynamic behaviors of these systems are simplified using models to keep the analysis tractable. In order to guarantee safety, such models often involve pessimistic assumptions. While the amount of pessimism was reasonable for simple computing platforms, for modern platforms the pessimism involves ignoring features that improve performance such as cache usage, instruction pipelines, and more. In this work, we explore routing and scheduling problems in real-time systems, where the uncertainties in the operation are carefully accounted for by complex models …

Big Data Processing Attribute Based Access Control Security, Anne Tall

Electronic Theses and Dissertations, 2020-2023

The purpose of this research is to analyze the security of next-generation big data processing (BDP) and examine the feasibility of applying advanced security features to meet the needs of modern multi-tenant, multi-level data analysis. The research methodology was to survey of the status of security mechanisms in BDP systems and identify areas that require further improvement. Access control (AC) security services were identified as priority area, specifically Attribute Based Access Control (ABAC). The exemplar BDP system analyzed is the Apache Hadoop ecosystem. We created data generation software, analysis programs, and posted the detailed the experiment configuration on GitHub. Overall, …

Biomimetic Design, Modeling, And Adaptive Control Of Robotic Gripper For Optimal Grasping, Mushtaq Al-Mohammed

Electronic Theses and Dissertations, 2020-2023

Grasping is an essential skill for almost every assistive robot. Variations in shape and/or weight of different objects involved in Activities of Daily Living (ADL) lead to complications, especially, when the robot is trying to grip novel objects for which it has no prior information –too much force will deform or crush the object while too little force will lead to slipping and possibly dropped objects. Thus, successful grasping requires the gripper to immobilize an object with the minimal force. In Chapter 2, we present the design, analysis, and experimental implementation of an adaptive control to facilitate 1-click grasping of …

Energy And Area Efficient Machine Learning Architectures Using Spin-Based Neurons, Hossein Pourmeidani

Electronic Theses and Dissertations, 2020-2023

Recently, spintronic devices with low energy barrier nanomagnets such as spin orbit torque-Magnetic Tunnel Junctions (SOT-MTJs) and embedded magnetoresistive random access memory (MRAM) devices are being leveraged as a natural building block to provide probabilistic sigmoidal activation functions for RBMs. In this dissertation research, we use the Probabilistic Inference Network Simulator (PIN-Sim) to realize a circuit-level implementation of deep belief networks (DBNs) using memristive crossbars as weighted connections and embedded MRAM-based neurons as activation functions. Herein, a probabilistic interpolation recoder (PIR) circuit is developed for DBNs with probabilistic spin logic (p-bit)-based neurons to interpolate the probabilistic output of the neurons …

Examining Cooperative System Responses Against Grid Integrity Attacks, Alexander D. Parady

Honors Undergraduate Theses

Smart grid technologies are integral to society’s transition to sustainable energy sources, but they do not come without a cost. As the energy sector shifts away from a century’s reliance on fossil fuels and centralized generation, technology that actively monitors and controls every aspect of the power infrastructure has been widely adopted, resulting in a plethora of new vulnerabilities that have already wreaked havoc on critical infrastructure. Integrity attacks that feedback false data through industrial control systems, which result in possible catastrophic overcorrections and ensuing failures, have plagued grid infrastructure over the past several years. This threat is now at …

Synthesis Methodologies For Robust And Reconfigurable Clock Networks, Necati Uysal

Electronic Theses and Dissertations, 2020-2023

In today's aggressively scaled technology nodes, billions of transistors are packaged into a single integrated circuit. Electronic Design Automation (EDA) tools are needed to automatically assemble the transistors into a functioning system. One of the most important design steps in the physical synthesis is the design of the clock network. The clock network delivers a synchronizing clock signal to each sequential element. The clock signal is required to be delivered meeting timing constraints under variations and in multiple operating modes. Synthesizing such clock networks is becoming increasingly difficult with the complex power management methodologies and severe manufacturing variations. Clock network …

Optimal Feature Learning For Facial Expression Recognition, Kamran Ali

Electronic Theses and Dissertations, 2020-2023

A great deal of research has been done to improve the performance of Facial Expression Recognition (FER) algorithms, but extracting optimal features to represent expressions remains a challenging task. The biggest drawback is that most work on FER ignores the inter-subject variations in facial attributes of individuals present in data. Hence, the representation extracted for the recognition of expressions is polluted by identity-related features that negatively affect the generalization capability of a FER technique on unseen identities. To overcome the effect of subject-identity bias, previous research shows the effectiveness of extracting identity-invariant expression features for FER. However, most of those …

Self Adaptive Reinforcement Learning For High-Dimensional Stochastic Systems With Application To Robotic Control, Sayyed Jaffar Ali Raza

Electronic Theses and Dissertations, 2020-2023

A long standing goal in the field of artificial intelligence (AI) is to develop agents that can perceive richer problem space and effortlessly plan their activity in minimal duration. Several strides have been made towards this goal over the last few years due to simultaneous advances in compute power, optimized algorithms, and most importantly evident success of AI based machines in nearly every discipline. The progress has been especially rapid in area of reinforcement learning (RL) where computers can now plan-ahead their activities and outperform their human rivals in complex problem domains like chess or Go game. However, despite encouraging …

Performance Enhancement Of Time Delay And Convolutional Neural Networks Employing Sparse Representation In The Transform Domains, Masoumeh Kalantari Khandani

Electronic Theses and Dissertations, 2020-2023

Deep neural networks are quickly advancing and increasingly used in many applications; however, these networks are often extremely large and require computing and storage power beyond what is available in most embedded and sensor devices. For example, IoT (Internet of Things) devices lack powerful processors or graphical processing units (GPUs) that are commonly used in deep networks. Given the very large-scale deployment of such low power devices, it is desirable to design methods for efficient reduction of computational needs of neural networks. This can be done by reducing input data size or network sizes. Expectedly, such reduction comes at the …

Energy-Efficient In-Memory Architectures Leveraging Intrinsic Behaviors Of Embedded Mram Devices, Shadi Sheikhfaal

Electronic Theses and Dissertations, 2020-2023

For decades, innovations to surmount the processor versus memory gap and move beyond conventional von Neumann architectures continue to be sought and explored. Recent machine learning models still expend orders of magnitude more time and energy to access data in memory in addition to merely performing the computation itself. This phenomenon referred to as a memory-wall bottleneck, is addressed herein via a completely fresh perspective on logic and memory technology design. The specific solutions developed in this dissertation focus on utilizing intrinsic switching behaviors of embedded MRAM devices to design cross-layer and energy-efficient Compute-in-Memory (CiM) architectures, accelerate the computationally-intensive operations …

Robust Acceleration Of Data-Centric Applications Using Resistive Computing Systems, Baogang Zhang

Electronic Theses and Dissertations, 2020-2023

With the accessible data reaching zettabyte level, CMOS technology is reaching its limit for the data hungry applications. Moore's law has been reaching its depletion in recent studies. On the other hand, von Neumann architecture is approaching the bottleneck due to the data movement between the computing and memory units. With data movement and power budgets becoming the limiting factors of today's computing systems, in-memory computing using emerging non-volatile resistive devices has attracted an increasing amount of attention. A non-volatile resistive device may be realized using memristor, resistive random access memory (ReRAM), phase change memory (PCM), or spin-transfer torque magnetic …

Energy-Aware Real-Time Scheduling On Heterogeneous And Homogeneous Platforms In The Era Of Parallel Computing, Ashik Ahmed Bhuiyan

Electronic Theses and Dissertations, 2020-2023

Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform's computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption. Motivated by the facts mentioned above, this thesis studies the approach to integrating the dynamic voltage and frequency scaling (DVFS) policy with real-time …

Fpga-Augmented Secure Crash-Consistent Non-Volatile Memory, Yu Zou

Electronic Theses and Dissertations, 2020-2023

Emerging byte-addressable Non-Volatile Memory (NVM) technology, although promising superior memory density and ultra-low energy consumption, poses unique challenges to achieving persistent data privacy and computing security, both of which are critically important to the embedded and IoT applications. Specifically, to successfully restore NVMs to their working states after unexpected system crashes or power failure, maintaining and recovering all the necessary security-related metadata can severely increase memory traffic, degrade runtime performance, exacerbate write endurance problem, and demand costly hardware changes to off-the-shelf processors. In this thesis, we summarize and expand upon two of our innovative works, ARES and HERMES, to design …

Long Short-Term Memory With Spin-Based Binary And Non-Binary Neurons, Meghana Reddy Vangala

Electronic Theses and Dissertations, 2020-2023

Research in the field of neural networks has shown advancement in the device technology and machine learning application platforms of use. Some of the major applications of neural network prominent in recent scenarios include image recognition, machine translation, text classification and object categorization. With these advancements, there is a need for more energy-efficient and low area overhead circuits in the hardware implementations. Previous works have concentrated primarily on CMOS technology-based implementations which can face challenges of high energy consumption, memory wall, and volatility complications for standby modes. We herein developed a low-power and area-efficient hardware implementation for Long Short-Term Memory …

Optimizing Peer Selection Among Internet Service Providers (Isps), Shahzeb Mustafa

Electronic Theses and Dissertations, 2020-2023

Connections among Internet Service Providers (ISPs) form the backbone of the Internet. This enables communications across the globe. ISPs are represented as Autonomous Systems (ASes) in the global Internet and inter-ISP traffic exchange takes place via inter-AS links, which are formed based on inter-ISP connections and agreements. In addition to customer-provider agreements, a crucial type of inter-ISP agreement is peering. ISP administrators use various platforms like AP-NIC and NANOG networking events for establishing new peering connections in accordance with their business and technical needs. Such methods are often inefficient and slow, potentially resulting in missed opportunities or sub-optimal routes. The …

Improving Performance And Flexibility Of Fabric-Attached Memory Systems, Vamsee Reddy Kommareddy

Electronic Theses and Dissertations, 2020-2023

As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such a system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, diverse applications run on HPC systems with different memory requirements and memory utilization can fluctuate widely from one application to another. Since memory modules are private for a corresponding computing node, a large percentage …

Extracting Data-Level Parallelism In High-Level Synthesis For Reconfigurable Architectures, Juan Andres Escobedo Contreras

Electronic Theses and Dissertations, 2020-2023

High-Level Synthesis (HLS) tools are a set of algorithms that allow programmers to obtain implementable Hardware Description Language (HDL) code from specifications written high-level, sequential languages such as C, C++, or Java. HLS has allowed programmers to code in their preferred language while still obtaining all the benefits hardware acceleration has to offer without them needing to be intimately familiar with the hardware platform of the accelerator. In this work we summarize and expand upon several of our approaches to improve the automatic memory banking capabilities of HLS tools targeting reconfigurable architectures, namely Field-Programmable Gate Arrays or FPGA's. We explored …

Pervasive Spectrum Sharing For Improved Wireless Experience, Mostafizur Rahman

Electronic Theses and Dissertations, 2020-2023

Spectrum sharing among cellular users has been a promising approach to attain better efficiency in the use of the limited spectral bands. The existing dynamic spectrum access techniques include sharing of the licensed spectrum bands by allowing other 'secondary' users to use the bands if the licensee 'primary' user is idle. This primary-secondary spectrum sharing is limited in terms of design space, and may not be sufficient to meet the ever-increasing demand of connectivity and high signal quality to improve the end-users' wireless experience. The next step to increase spectrum efficiency is to design markets where sharing takes place pervasively …

Investigations On The Use Of Hyperthermia For Breast Cancer Treatment, Sreekala Suseela

Electronic Theses and Dissertations, 2020-2023

Hyperthermia using electromagnetic energy has been proven to be an effective method in the treatment of cancer. Hyperthermia is a therapeutic procedure in which the temperature in the tumor tissue is raised above 42°C without causing any damage to the surrounding healthy tissue. This method has been shown to increase the effectiveness of radiotherapy and chemotherapy. Radio frequencies, microwave frequencies or focused ultrasound can be used to deliver energy to the tumor tissue to attain higher temperatures in the tumor region for hyperthermia application. In this dissertation the use of a near field focused (NFF) microstrip antenna array for the …

Modeling Site Specific Urban Propagation Using A Variable Terrain Radiowave Parabolic Equation - Vertical Plane Launch (Vtrpe-Vpl) Hybrid Technique, Pierre Cadette

Electronic Theses and Dissertations, 2020-2023

The development of efficient algorithms for calculating propagation loss in site specific urban environments has been an active area of research for many years. This dissertation demonstrates that, for particular scenarios, a hybrid approach that combines the Variable Terrain Radiowave Parabolic Equation (VTRPE) and Vertical Plane Launch (VPL) models can be used to produce accurate results for a downrange region of interest. The hybrid approach consists of leveraging the 2-D parabolic equation method in the initial propagation region, where backscatter and out of plane energy can be neglected, then transitioning to the more computationally intensive 3-D ray launching method for …

Distributed Multi-Agent Optimization And Control With Applications In Smart Grid, Towfiq Rahman

Electronic Theses and Dissertations, 2020-2023

With recent advancements in network technologies like 5G and Internet of Things (IoT), the size and complexity of networked interconnected agents have increased rapidly. Although centralized schemes have simpler algorithm design, in practicality, it creates high computational complexity and requires high bandwidth for centralized data pooling. In this dissertation, for distributed optimization of networked multi-agent architecture, the Alternating Direction Method of Multipliers (ADMM) is investigated. In particular, a new adaptive-gain ADMM algorithm is derived in closed form and under the standard convex property to greatly speed up the convergence of ADMM-based distributed optimization. Using the Lyapunov direct approach, the proposed …