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Full-Text Articles in Physical Sciences and Mathematics

"Semiclassical Mastermind", Curtis Bair, Alexa S. Cunningham, Joshua Qualls Jan 2023

"Semiclassical Mastermind", Curtis Bair, Alexa S. Cunningham, Joshua Qualls

Posters-at-the-Capitol

Games are often used in the classroom to teach mathematical and physical concepts. Yet the available activities used to introduce quantum mechanics are often overwhelming even to upper-level students. Further, the "games" in question range in focus and complexity from superficial introductions to games where quantum strategies result in decidedly nonclassical advantages, making it nearly impossible for people interested in quantum mechanics to have a simple introduction to the topic. In this talk, we introduce a straightforward and newly developed "Semiclassical Mastermind" based on the original version of mastermind but replace the colored pegs with 6 possible qubits (x+, x-, …


Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant Jan 2023

Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant

Department of Electrical and Computer Engineering Faculty Publications

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them …


The Metaverse: A Virtual World In The Palm Of Your Hand, Ziad Doughan, Hadi Al Mubasher, Mustafa El Bizri, Ali Haidar Dec 2022

The Metaverse: A Virtual World In The Palm Of Your Hand, Ziad Doughan, Hadi Al Mubasher, Mustafa El Bizri, Ali Haidar

BAU Journal - Science and Technology

This paper explores the actual and future impact of the Metaverse as a virtual space. Thus, it focuses the probe on the technical challenges that face this everlasting emerging technology. Today, the Metaverse presents a digital environment to build collective architecture and historical heritage in a virtual space. In this digital world, the modeling and design methodology is based on individual archetypes that can puzzle new elements. Currently, traditional methods require change and adaptation in both the education and work market, especially due to the remote-work integration in the last few years. For example, many components are required to build …


Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn Dec 2022

Hybrid Life Cycles In Software Development, Eric Vincent Schoenborn

Culminating Experience Projects

This project applied software specification gathering, architecture, work planning, and development to a real-world development effort for a local business. This project began with a feasibility meeting with the owner of Zeal Aerial Fitness. After feasibility was assessed the intended users, needed functionality, and expected user restrictions were identified with the stakeholders. A hybrid software lifecycle was selected to allow a focus on base functionality up front followed by an iterative development of expectations of the stakeholders. I was able to create various specification diagrams that express the end projects goals to both developers and non-tech individuals using a standard …


Guaranteed Conformance Of Neurosymbolic Models To Natural Constraints, Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee Dec 2022

Guaranteed Conformance Of Neurosymbolic Models To Natural Constraints, Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee

Departmental Papers (CIS)

Deep neural networks have emerged as the workhorse for a large section of robotics and control applications, especially as models for dynamical systems. Such data-driven models are in turn used for designing and verifying autonomous systems. This is particularly useful in modeling medical systems where data can be leveraged to individualize treatment. In safety-critical applications, it is important that the data-driven model is conformant to established knowledge from the natural sciences. Such knowledge is often available or can often be distilled into a (possibly black-box) model M. For instance, the unicycle model for an F1 racing car. In this light, …


Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer Dec 2022

Actively Guided Cansats For Assisting Localization And Mapping In Unstructured And Unknown Environments, Cary Chun, M. Hassan Tanveer

Symposium of Student Scholars

When navigating in unknown and unstructured environments, Unmanned Arial Vehicles (UAVs) can struggle when attempting to preform Simultaneous Localization and Mapping (SLAM) operations. Particularly challenging circumstance arise when an UAV may need to land or otherwise navigate through treacherous environments. As the primary UAV may be too large and unwieldly to safely investigate in these types of situations, this research effort proposes the use of actively guided CanSats for assisting in localization and mapping of unstructured environments. A complex UAV could carry multiple of these SLAM capable CanSats, and when additional mapping and localization capabilities where required, the CanSat would …


An Empirical Study On The Classification Of Python Language Features Using Eye-Tracking, Jigyasa Chauhan Dec 2022

An Empirical Study On The Classification Of Python Language Features Using Eye-Tracking, Jigyasa Chauhan

Computer Science and Engineering: Theses, Dissertations, and Student Research

Python, currently one of the most popular programming languages, is an object-
oriented language that also provides language feature support for other programming
paradigms, such as functional and procedural. It is not currently understood how
support for multiple paradigms affects the ability of developers to comprehend that
code. Understanding the predominant paradigm in code, and how developers classify
the predominant paradigm, can benefit future research in program comprehension as
the paradigm may factor into how people comprehend that code. Other researchers
may want to look at how the paradigms in the code interact with various code smells.
To investigate how …


Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal Dec 2022

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

Computer Science and Engineering: Theses, Dissertations, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …


Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan Dec 2022

Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan

Computer Science and Engineering: Theses, Dissertations, and Student Research

Deep neural networks (DNNs) can perform impressively in many natural language processing (NLP) tasks, but their black-box nature makes them inherently challenging to explain or interpret. Self-Explanatory models are a new approach to overcoming this challenge, generating explanations in human-readable languages besides task objectives like answering questions. The main focus of this thesis is the explainability of NLP tasks, as well as how attention methods can help enhance performance. Three different attention modules are proposed, SimpleAttention, CrossSelfAttention, and CrossModality. It also includes a new dataset transformation method called Two-Documents that converts every dataset into two separate documents required by the …


Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven Dec 2022

Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven

Computer Science and Engineering: Theses, Dissertations, and Student Research

Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …


Sequence-Based Bioinformatics Approaches To Predict Virus-Host Relationships In Archaea And Eukaryotes, Yingshan Li Dec 2022

Sequence-Based Bioinformatics Approaches To Predict Virus-Host Relationships In Archaea And Eukaryotes, Yingshan Li

Computer Science and Engineering: Theses, Dissertations, and Student Research

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …


Design Of Environment Aware Planning Heuristics For Complex Navigation Objectives, Carter D. Bailey Dec 2022

Design Of Environment Aware Planning Heuristics For Complex Navigation Objectives, Carter D. Bailey

All Graduate Theses and Dissertations

A heuristic is the simplified approximations that helps guide a planner in deducing the best way to move forward. Heuristics are valued in many modern AI algorithms and decision-making architectures due to their ability to drastically reduce computation time. Particularly in robotics, path planning heuristics are widely leveraged to aid in navigation and exploration. As the robotic platform explores and navigates, information about the world can and should be used to augment and update the heuristic to guide solutions. Complex heuristics that can account for environmental factors, robot capabilities, and desired actions provide optimal results with little wasted exploration, but …


Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu Dec 2022

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …


A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha Nov 2022

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

Computer Science and Engineering: Theses, Dissertations, and Student Research

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …


Exploring With Sticky Mittens: Reinforcement Learning With Expert Interventions Via Option Templates, Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Julia Parish-Morris Nov 2022

Exploring With Sticky Mittens: Reinforcement Learning With Expert Interventions Via Option Templates, Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Julia Parish-Morris

Departmental Papers (CIS)

Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging control tasks is that they often receive expert intervention that enables them to understand the high-level structure of the task before mastering low-level control actions. We propose a framework for leveraging expert intervention to solve long-horizon reinforcement learning tasks. We consider option templates, which are specifications encoding a potential option that can be trained using reinforcement learning. We formulate expert intervention as allowing the agent to execute option templates before learning an implementation. This …


Computer Engineering Education, Marilyn Wolf Nov 2022

Computer Engineering Education, Marilyn Wolf

CSE Conference and Workshop Papers

Computer engineering is a rapidly evolving discipline. How should we teach it to our students?

This virtual roundtable on computer engineering education was conducted in summer 2022 over a combination of email and virtual meetings. The panel considered what topics are of importance to the computer engineering curriculum, what distinguishes computer engineering from related disciplines, and how computer engineering concepts should be taught.


Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte Nov 2022

Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte

LSU Doctoral Dissertations

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), …


Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro Nov 2022

Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro

The Journal of Purdue Undergraduate Research

No abstract provided.


Applications Of Blockchain In Business Processes: A Comprehensive Review, Wattana Viriyasitavat, Li Xu, Dusit Niyato, Zhuming Bi, Danupol Hoonsopon Nov 2022

Applications Of Blockchain In Business Processes: A Comprehensive Review, Wattana Viriyasitavat, Li Xu, Dusit Niyato, Zhuming Bi, Danupol Hoonsopon

Information Technology & Decision Sciences Faculty Publications

Blockchain (BC), as an emerging technology, is revolutionizing Business Process Management (BPM) in multiple ways. The main adoption is to serve as a trusted infrastructure to guarantee the trust of collaborations among multiple partners in trustless environments. Especially, BC enables trust of information by using Distributed Ledger Technology (DLT). With the power of smart contracts, BC enforces the obligations of counterparties that transact in a business process (BP) by programming the contracts as transactions. This paper aims to study the state-of-the-art of BC technologies by (1) exploring its applications in BPM with the focus on how BC provides the trust …


Mechatronics Bachelor Curriculum Development In Light Of Industry 4.0 Technology Needs: Contrasting Us And German University Curricula, Paniz Hazaveh, Aleksandr Sergeyev, Nathir Rawashdeh Nov 2022

Mechatronics Bachelor Curriculum Development In Light Of Industry 4.0 Technology Needs: Contrasting Us And German University Curricula, Paniz Hazaveh, Aleksandr Sergeyev, Nathir Rawashdeh

Michigan Tech Publications

This study compares Mechatronics bachelor curricula at universities in the United States of America and German universities. Mechatronics education is relatively new in the United States, but has been common in Germany for over a decade. With the multidisciplinary nature of technologies required by the 4’th industrial revolution, a.k.a. Industry 4.0, composing an appropriate Mechatronics curriculum becomes a challenge and an opportunity. This paper studies how Mechatronics education can address the future needs of industry, while building on a specific university’s strengths and industry links. We have also analyzed the new undergraduate Mechatronics program at Michigan Technological University (MTU) and …


Operation Of A Controllable Force-Sensing Industrial Pneumatic Parallel Gripper System, Brian Piechocki, Chelsey Spitzner, Namratha Karanam, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh Nov 2022

Operation Of A Controllable Force-Sensing Industrial Pneumatic Parallel Gripper System, Brian Piechocki, Chelsey Spitzner, Namratha Karanam, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh

Michigan Tech Publications

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project was performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and application of a force-programmable and sensing pneumatic parallel gripper system. Force sensing is a critical part of many systems in modern automation systems. Applications such as prosthetics, robotic surgery, or basic manufacturing systems may rely on the ability to properly read and control forces applied to an object. This work evaluates the basic operation of the pneumatic force-sensing gripper …


A Smart Parallel Gripper Industrial Automation System For Measurement Of Gripped Work Piece Thickness, Erik Kocher, Chukwuemeka George Ochieze, Ahmat Oumar, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh Nov 2022

A Smart Parallel Gripper Industrial Automation System For Measurement Of Gripped Work Piece Thickness, Erik Kocher, Chukwuemeka George Ochieze, Ahmat Oumar, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh

Michigan Tech Publications

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project is performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and ladder programming of the smart parallel gripper system to measure the width of components grasped with the gripper. In addition, details of the system’s components, operation, more advanced uses are discussed. On the automation line, this smart gripper can be used to measure the thickness of work pieces while handling them and classifying these as either acceptable, too large …


An Industrial Pneumatic And Servo Four-Axis Robotic Gripper System: Description And Unitronics Ladder Logic Programming, Zongguang Liu, Chrispin Johnston, Aleksi Leino, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh Nov 2022

An Industrial Pneumatic And Servo Four-Axis Robotic Gripper System: Description And Unitronics Ladder Logic Programming, Zongguang Liu, Chrispin Johnston, Aleksi Leino, Travis Winter, Aleksandr Sergeyev, Mark Gauthier, Nathir Rawashdeh

Michigan Tech Publications

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project is performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and ladder programming of a four-axis robot enclosed in a cage with one side guarded by an optical fence. The robot has pneumatically actuated X-Y linear motion and a pneumatic gripper. Furthermore, the Z-axis motion and gripper wrist rotation are controlled by servo motors. A human machine interface (HMI) is also present, and it allows for easy manipulation and programming …


Gesture Controlled Collaborative Robot Arm And Lab Kit, Abel A. Reyes, Skylar Reinhardt, Tony Wise, Nathir Rawashdeh, Sidike Paheding Nov 2022

Gesture Controlled Collaborative Robot Arm And Lab Kit, Abel A. Reyes, Skylar Reinhardt, Tony Wise, Nathir Rawashdeh, Sidike Paheding

Michigan Tech Publications

In this paper, a mechatronics system was designed and implemented to include the subjects of artificial intelligence, control algorithms, robot servo motor control, and human-machine interface (HMI). The goal was to create an inexpensive, multi-functional robotics lab kit to promote students’ interest in STEM fields including computing and mechtronics. Industrial robotic systems have become vastly popular in manufacturing and other industries, and the demand for individuals with related skills is rapidly increasing. Robots can complete jobs that are dangerous, dull, or dirty for humans to perform. Recently, more and more collaborative robotic systems have been developed and implemented in the …


An Evaluation Framework For Digital Image Forensics Tools, Zainab Khalid, Sana Qadir Oct 2022

An Evaluation Framework For Digital Image Forensics Tools, Zainab Khalid, Sana Qadir

Journal of Digital Forensics, Security and Law

The boom of digital cameras, photography, and social media has drastically changed how humans live their day-to-day, but this normalization is accompanied by malicious agents finding new ways to forge and tamper with images for unlawful monetary (or other) gains. Disinformation in the photographic media realm is an urgent threat. The availability of a myriad of image editing tools renders it almost impossible to differentiate between photo-realistic and original images. The tools available for image forensics require a standard framework against which they can be evaluated. Such a standard framework can aid in evaluating the suitability of an image forensics …


Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger Oct 2022

Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify …


Virtual Sensor Middleware: Managing Iot Data For The Fog-Cloud Platform, Fadi Almahamid, Hanan Lutfiyya, Katarina Grolinger Oct 2022

Virtual Sensor Middleware: Managing Iot Data For The Fog-Cloud Platform, Fadi Almahamid, Hanan Lutfiyya, Katarina Grolinger

Electrical and Computer Engineering Publications

This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent an IoT application and distributed sensor data processing across multiple fog nodes. Furthermore, the virtual sensor deals with the heterogeneous nature of IoT devices and the various communication protocols using different adapters to communicate with the IoT devices and the underlying protocol. VSM uses the publish-subscribe design pattern …


Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth Oct 2022

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …


Software Protection And Secure Authentication For Autonomous Vehicular Cloud Computing, Muhammad Hataba Oct 2022

Software Protection And Secure Authentication For Autonomous Vehicular Cloud Computing, Muhammad Hataba

Dissertations

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC.

In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our …


Towards Qos-Based Embedded Machine Learning, Tom Springer, Erik Linstead, Peiyi Zhao, Chelsea Parlett-Pelleriti Oct 2022

Towards Qos-Based Embedded Machine Learning, Tom Springer, Erik Linstead, Peiyi Zhao, Chelsea Parlett-Pelleriti

Engineering Faculty Articles and Research

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the …