Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Department of Computer Science (18)
- Department of Applied Computing (8)
- Deep learning (5)
- Department of Mechanical Engineering-Engineering Mechanics (4)
- Accessibility (2)
-
- Analogy (2)
- Artificial intelligence (2)
- Classification (2)
- College of Computing (2)
- College of Forest Resources and Environmental Science (2)
- Department of Materials Science and Engineering (2)
- Engagement (2)
- Human-computer interaction (2)
- Machine learning (2)
- Metaphor (2)
- Text entry (2)
- AI/ML (1)
- Active power filter (1)
- Analogical reasoning (1)
- Associative learning (1)
- Automatic segmentation (1)
- Biochemistry (1)
- Biological sciences (1)
- Blind (1)
- CNN (1)
- Cardiac wall (1)
- Cell biology (1)
- Chemical diversity (1)
- Cognition (1)
- Cognitive Tutorial (1)
- Publication
- Publication Type
Articles 1 - 30 of 41
Full-Text Articles in Entire DC Network
Race: An Efficient Redundancy-Aware Accelerator For Dynamic Graph Neural Network, Hui Yu, Yu Zhang, Jin Zhao, Yujian Liao, Zhiying Huang, Donghao He, Lin Gu, Hai Jin, Xiaofei Liao, Haikun Liu, Bingsheng He, Jianhui Yue
Race: An Efficient Redundancy-Aware Accelerator For Dynamic Graph Neural Network, Hui Yu, Yu Zhang, Jin Zhao, Yujian Liao, Zhiying Huang, Donghao He, Lin Gu, Hai Jin, Xiaofei Liao, Haikun Liu, Bingsheng He, Jianhui Yue
Michigan Tech Publications, Part 2
Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph …
Regulation Of Regeneration In Arabidopsis Thaliana, Md Khairul Islam, Sai Teja Mummadi, Sanzhen Liu, Hairong Wei
Regulation Of Regeneration In Arabidopsis Thaliana, Md Khairul Islam, Sai Teja Mummadi, Sanzhen Liu, Hairong Wei
Michigan Tech Publications, Part 2
We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. …
Multi-View Information Fusion Using Multi-View Variational Autoencoder To Predict Proximal Femoral Fracture Load, Chen Zhao, Joyce H. Keyak, Xuewei Cao, Qiuying Sha, Li Wu, Zhe Luo, Lan Juan Zhao, Qing Tian, Michael Serou, Chuan Qiu, Kuan Jui Su, Hui Shen, Hong Wen Deng, Weihua Zhou
Multi-View Information Fusion Using Multi-View Variational Autoencoder To Predict Proximal Femoral Fracture Load, Chen Zhao, Joyce H. Keyak, Xuewei Cao, Qiuying Sha, Li Wu, Zhe Luo, Lan Juan Zhao, Qing Tian, Michael Serou, Chuan Qiu, Kuan Jui Su, Hui Shen, Hong Wen Deng, Weihua Zhou
Michigan Tech Publications, Part 2
Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or “strength”) and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to …
Integrating Embeddings From Multiple Protein Language Models To Improve Protein O-Glcnac Site Prediction, Suresh Pokharel, Pawel Pratyush, Hamid D. Ismail, Junfeng Ma, Dukka Kc
Integrating Embeddings From Multiple Protein Language Models To Improve Protein O-Glcnac Site Prediction, Suresh Pokharel, Pawel Pratyush, Hamid D. Ismail, Junfeng Ma, Dukka Kc
Michigan Tech Publications, Part 2
O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we …
S-Net: A Multiple Cross Aggregation Convolutional Architecture For Automatic Segmentation Of Small/Thin Structures For Cardiovascular Applications, Nan Mu, Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Cassie Bonifas, Jordan Gosnell, Marcus Haw, Joseph Vettukattil, Jingfeng Jiang
S-Net: A Multiple Cross Aggregation Convolutional Architecture For Automatic Segmentation Of Small/Thin Structures For Cardiovascular Applications, Nan Mu, Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Cassie Bonifas, Jordan Gosnell, Marcus Haw, Joseph Vettukattil, Jingfeng Jiang
Michigan Tech Publications, Part 2
With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network …
Hydrogen Peroxide-Dependent Oxidation Of Erk2 Within Its D-Recruitment Site Alters Its Substrate Selection, Anthony E. Postiglione, Laquaundra L. Adams, Ese S. Ekhator, Anuoluwapo E. Odelade, Supriya Patwardhan, Meenal Chaudhari, Avery S. Pardue, Anjali Kumari, William A. Lefever, Olivia P. Tornow, Tamer S. Kaoud, Johnathan Neiswinger, Jun Seop Jeong, Derek Parsonage, Kimberly J. Nelson, Dukka Kc, Cristina M. Furdui, Heng Zhu, Andrew J. Wommack, Kevin N. Dalby, Ming Dong, Leslie B. Poole, Jeremiah D. Keyes, Robert H. Newman
Hydrogen Peroxide-Dependent Oxidation Of Erk2 Within Its D-Recruitment Site Alters Its Substrate Selection, Anthony E. Postiglione, Laquaundra L. Adams, Ese S. Ekhator, Anuoluwapo E. Odelade, Supriya Patwardhan, Meenal Chaudhari, Avery S. Pardue, Anjali Kumari, William A. Lefever, Olivia P. Tornow, Tamer S. Kaoud, Johnathan Neiswinger, Jun Seop Jeong, Derek Parsonage, Kimberly J. Nelson, Dukka Kc, Cristina M. Furdui, Heng Zhu, Andrew J. Wommack, Kevin N. Dalby, Ming Dong, Leslie B. Poole, Jeremiah D. Keyes, Robert H. Newman
Michigan Tech Publications, Part 2
Extracellular signal-regulated kinases 1 and 2 (ERK1/2) are dysregulated in many pervasive diseases. Recently, we discovered that ERK1/2 is oxidized by signal-generated hydrogen peroxide in various cell types. Since the putative sites of oxidation lie within or near ERK1/2’s ligand-binding surfaces, we investigated how oxidation of ERK2 regulates interactions with the model substrates Sub-D and Sub-F. These studies revealed that ERK2 undergoes sulfenylation at C159 on its D-recruitment site surface and that this modification modulates ERK2 activity differentially between substrates. Integrated biochemical, computational, and mutational analyses suggest a plausible mechanism for peroxide-dependent changes in ERK2-substrate interactions. Interestingly, oxidation decreased ERK2’s …
Evolution Of Glassy Carbon Derived From Pyrolysis Of Furan Resin, Josh Kemppainen, Ivan Gallegos, Aaron Krieg, Jacob R. Gissinger, Kristopher E. Wise, Margaret Kowalik, Julia A. King, S. Gowtham, Adri Van Duin, Gregory Odegard
Evolution Of Glassy Carbon Derived From Pyrolysis Of Furan Resin, Josh Kemppainen, Ivan Gallegos, Aaron Krieg, Jacob R. Gissinger, Kristopher E. Wise, Margaret Kowalik, Julia A. King, S. Gowtham, Adri Van Duin, Gregory Odegard
Michigan Tech Publications, Part 2
Glassy carbon (GC) material derived from pyrolyzed furan resin was modeled by using reactive molecular dynamics (MD) simulations. The MD polymerization simulation protocols to cure the furan resin precursor material are validated via comparison of the predicted density and Young's modulus with experimental values. The MD pyrolysis simulations protocols to pyrolyze the furan resin precursor is validated by comparison of calculated density, Young's modulus, carbon content, sp carbon content, the in-plane crystallite size, out-of-plane crystallite stacking height, and interplanar crystallite spacing with experimental results from the literature for furan resin derived GC. The modeling methodology established in this work can …
The Influence Of Arm Positions On Mechanical Dyssynchrony Measured By Gated Myocardial Perfusion Imaging, Zhongqiang Zhao, Cheng Wang, Zeyu Peng, Ju Bu, Chunxiang Li, Dianfu Li, Weihua Zhou, Rongsheng Lu, Lijun Tang, Yong Li
The Influence Of Arm Positions On Mechanical Dyssynchrony Measured By Gated Myocardial Perfusion Imaging, Zhongqiang Zhao, Cheng Wang, Zeyu Peng, Ju Bu, Chunxiang Li, Dianfu Li, Weihua Zhou, Rongsheng Lu, Lijun Tang, Yong Li
Michigan Tech Publications, Part 2
Background : In routine procedures, patient's arms are positioned above their heads to avoid potential attenuation artifacts and reduced image quality during gated myocardial perfusion imaging (G-MPI). However, it is difficult to achieve this action in the acute period following pacemaker implantation. This study aimed to explore the influence of arm positioning on myocardial perfusion imaging (MPI) in different types of heart disease. Methods: This study was conducted retrospectively. A total of 123 patients were enrolled and underwent resting G-MPI using a standard protocol with arms positioned above their heads and again with their arms at their sides. All individuals …
A Usability Study Of Nomon: A Flexible Interface For Single-Switch Users, Nicholas Bonaker, Emli Mari Nel, Keith Vertanen, Tamara Broderick
A Usability Study Of Nomon: A Flexible Interface For Single-Switch Users, Nicholas Bonaker, Emli Mari Nel, Keith Vertanen, Tamara Broderick
Michigan Tech Publications, Part 2
Many individuals with severe motor impairments communicate via a single switch - which might be activated by a blink, facial movement, or puff of air. These switches are commonly used as input to scanning systems that allow selection from a 2D grid of options. Nomon is an alternative interface that provides a more flexible layout, not confined to a grid. Previous work suggests that, even when options appear in a grid, Nomon may be faster and easier to use than scanning systems. However, previous work primarily tested Nomon with non-motor-impaired individuals, and evaluation with potential end-users was limited to a …
Establishing Physical And Chemical Mechanisms Of Polymerization And Pyrolysis Of Phenolic Resins For Carbon-Carbon Composites, Ivan Gallegos, Josh Kemppainen, Jacob R. Gissinger, Malgorzata Kowalik, Adri Van Duin, Kristopher E. Wise, S. Gowtham, Gregory Odegard
Establishing Physical And Chemical Mechanisms Of Polymerization And Pyrolysis Of Phenolic Resins For Carbon-Carbon Composites, Ivan Gallegos, Josh Kemppainen, Jacob R. Gissinger, Malgorzata Kowalik, Adri Van Duin, Kristopher E. Wise, S. Gowtham, Gregory Odegard
Michigan Tech Publications, Part 2
The complex structural and chemical changes that occur during polymerization and pyrolysis critically affect material properties but are difficult to characterize in situ. This work presents a novel, experimentally validated methodology for modeling the complete polymerization and pyrolysis processes for phenolic resin using reactive molecular dynamics. The polymerization simulations produced polymerized structures with mass densities of 1.24 ± 0.01 g/cm3 and Young's moduli of 3.50 ± 0.64 GPa, which are in good agreement with experimental values. The structural properties of the subsequently pyrolyzed structures were also found to be in good agreement with experimental X-ray data for the phenolic-derived carbon …
Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue
Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue
Michigan Tech Publications, Part 2
Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …
Cytochrome P450 Gene Families: Role In Plant Secondary Metabolites Production And Plant Defense, Panchali Chakraborty, Ashok Biswas, Susmita Dey, Tuli Bhattacharjee, Swapan Chakrabarty
Cytochrome P450 Gene Families: Role In Plant Secondary Metabolites Production And Plant Defense, Panchali Chakraborty, Ashok Biswas, Susmita Dey, Tuli Bhattacharjee, Swapan Chakrabarty
Michigan Tech Publications, Part 2
Cytochrome P450s (CYPs) are the most prominent family of enzymes involved in NADPH- and O2-dependent hydroxylation processes throughout all spheres of life. CYPs are crucial for the detoxification of xenobiotics in plants, insects, and other organisms. In addition to performing this function, CYPs serve as flexible catalysts and are essential for producing secondary metabolites, antioxidants, and phytohormones in higher plants. Numerous biotic and abiotic stresses frequently affect the growth and development of plants. They cause a dramatic decrease in crop yield and a deterioration in crop quality. Plants protect themselves against these stresses through different mechanisms, which are accomplished by …
Deep Learning-Based Diagnosis Of Disease Activity In Patients With Graves’ Orbitopathy Using Orbital Spect/Ct, Ni Yao, Longxi Li, Zhengyuan Gao, Chen Zhao, Yanting Li, Chuang Han, Jiaofen Nan, Zelin Zhu, Yi Xiao, Fubao Zhu, Min Zhao, Weihua Zhou
Deep Learning-Based Diagnosis Of Disease Activity In Patients With Graves’ Orbitopathy Using Orbital Spect/Ct, Ni Yao, Longxi Li, Zhengyuan Gao, Chen Zhao, Yanting Li, Chuang Han, Jiaofen Nan, Zelin Zhu, Yi Xiao, Fubao Zhu, Min Zhao, Weihua Zhou
Michigan Tech Publications
Purpose: Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. Materials and methods: GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO …
Identification Of Dialect For Eastern And Southwestern Ojibwe Words Using A Small Corpus, Kalvin Hartwig, Evan Lucas, Timothy C. Havens
Identification Of Dialect For Eastern And Southwestern Ojibwe Words Using A Small Corpus, Kalvin Hartwig, Evan Lucas, Timothy C. Havens
Michigan Tech Publications, Part 2
The Ojibwe language has several dialects that vary to some degree in both spoken and written form. We present a method of using support vector machines to classify two different dialects (Eastern and Southwestern Ojibwe) using a very small corpus of text. Classification accuracy at the sentence level is 90% across a five-fold cross validation and 72% when the sentence-trained model is applied to a data set of individual words. Our code and the word level data set are released openly at https://github.com/evanperson/OjibweDialect.
Coordinating Tethered Autonomous Underwater Vehicles Towards Entanglement-Free Navigation, Abhishek Patil, Myoungkuk Park, Jungyun Bae
Coordinating Tethered Autonomous Underwater Vehicles Towards Entanglement-Free Navigation, Abhishek Patil, Myoungkuk Park, Jungyun Bae
Michigan Tech Publications
This paper proposes an algorithm that provides operational strategies for multiple tethered autonomous underwater vehicle (T-AUV) systems for entanglement-free navigation. T-AUVs can perform underwater tasks under reliable communication and power supply, which is the most substantial benefit of their operation. Thus, if one can overcome the entanglement issues while utilizing multiple tethered vehicles, the potential applications of the system increase including ecosystem exploration, infrastructure inspection, maintenance, search and rescue, underwater construction, and surveillance. In this study, we focus on developing strategies for task allocation, path planning, and scheduling that ensure entanglement-free operations while considering workload balancing among the vehicles. We …
Neuroevolution Application To Collaborative And Heuristics-Based Connected And Autonomous Vehicle Cohort Simulation At Uncontrolled Intersection, Frederic Jacquelin, Jungyun Bae, Bo Chen, Darrell Robinette
Neuroevolution Application To Collaborative And Heuristics-Based Connected And Autonomous Vehicle Cohort Simulation At Uncontrolled Intersection, Frederic Jacquelin, Jungyun Bae, Bo Chen, Darrell Robinette
Michigan Tech Publications, Part 2
Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go …
Pedagogical Prisms: Toward Domain Isomorphic Analogy Design For Relevance And Engagement In Computing Education, Briana Christina Bettin, Linda Ott
Pedagogical Prisms: Toward Domain Isomorphic Analogy Design For Relevance And Engagement In Computing Education, Briana Christina Bettin, Linda Ott
Michigan Tech Publications, Part 2
Analogy is a frequently leveraged pedagogical tool used across many disciplines, with computing being no exception. Computing education researchers, however, have raised concerns regarding the limitations of analogy. One obvious concern is the relevance of any given analogy to learners. Designing relevant analogies can greatly increase student engagement with the problem space by centering examples on their lived experiences. Relevant analogies can also facilitate learners in building appropriate connections as they explore novel concepts. Designing relevant analogies is an ongoing process which requires understanding the learners' context. It is unlikely that any given analogy will be "universally"relevant across learners, problems, …
More (Sema|Meta)Phors: Additional Perspectives On Analogy Use From Concurrent Programming Students, Briana Christina Bettin, Linda Ott, Julia Hiebel
More (Sema|Meta)Phors: Additional Perspectives On Analogy Use From Concurrent Programming Students, Briana Christina Bettin, Linda Ott, Julia Hiebel
Michigan Tech Publications, Part 2
A concurrent computing course is filled with challenges for upper-level programming students. Understanding concurrency provides deeper insight into many modern computing and programming language behaviors, but the subject matter can be difficult even for relatively proficient students. It can be a challenge to help students navigate and understand these unfamiliar topics. While there is a difference in general programming familiarity, teaching this novel material is not unlike some challenges faced when engaging introductory students with first programming concepts. In this work, we explore the use of analogy by students while learning a novel programming methodology. We investigate perceptions of the …
Two Kinds Of Average Approximation Accuracy, Qingzhao Kong, Wanting Wang, Dongxiao Zhang, Wenbin Zhang
Two Kinds Of Average Approximation Accuracy, Qingzhao Kong, Wanting Wang, Dongxiao Zhang, Wenbin Zhang
Michigan Tech Publications
Rough set theory places great importance on approximation accuracy, which is used to gauge how well a rough set model describes a target concept. However, traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model. To overcome this, two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed. The first is the relative average approximation accuracy, which is based on all sets in the universe and has several basic properties. The second is …
Experimental Study: Deep Learning-Based Fall Monitoring Among Older Adults With Skin-Wearable Electronics, Yongkuk Lee, Suresh Pokharel, Asra Al Muslim, Dukka Kc, Kyoung Hag Lee, Woon Hong Yeo
Experimental Study: Deep Learning-Based Fall Monitoring Among Older Adults With Skin-Wearable Electronics, Yongkuk Lee, Suresh Pokharel, Asra Al Muslim, Dukka Kc, Kyoung Hag Lee, Woon Hong Yeo
Michigan Tech Publications
Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults. The cost-effective skin-wearable motion monitoring device is designed and fabricated using thin copper films. It includes a six-axis motion sensor and is directly laminated on the skin …
Multi-Modal Knowledge Graph Inference Via Media Convergence And Logic Rule, Feng Lin, Dongmei Li, Wenbin Zhang, Dongsheng Shi, Yuanzhou Jiao, Qianzhong Chen, Yiying Lin, Wentao Zhu
Multi-Modal Knowledge Graph Inference Via Media Convergence And Logic Rule, Feng Lin, Dongmei Li, Wenbin Zhang, Dongsheng Shi, Yuanzhou Jiao, Qianzhong Chen, Yiying Lin, Wentao Zhu
Michigan Tech Publications
Media convergence works by processing information from different modalities and applying them to different domains. It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective. To address the issue, an inference method based on Media Convergence and Rule-guided Joint Inference model (MCRJI) has been proposed. The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction. First, a multi-headed self-attention approach …
Understanding Adoption Barriers To Dwell-Free Eye-Typing: Design Implications From A Qualitative Deployment Study And Computational Simulations, Per Ola Kristensson, Morten Mjelde, Keith Vertanen
Understanding Adoption Barriers To Dwell-Free Eye-Typing: Design Implications From A Qualitative Deployment Study And Computational Simulations, Per Ola Kristensson, Morten Mjelde, Keith Vertanen
Michigan Tech Publications
Eye-typing is a slow and cumbersome text entry method typically used by individuals with no other practical means of communication. As an alternative, prior HCI research has proposed dwell-free eye-typing as a potential improvement that eliminates time-consuming and distracting dwell-timeouts. However, it is rare that such research ideas are translated into working products. This paper reports on a qualitative deployment study of a product that was developed to allow users access to a dwell-free eye-typing research solution. This allowed us to understand how such a research solution would work in practice, as part of users' current communication solutions in their …
Flextype: Flexible Text Input With A Small Set Of Input Gestures, Dylan Gaines, Mackenzie M. Baker, Keith Vertanen
Flextype: Flexible Text Input With A Small Set Of Input Gestures, Dylan Gaines, Mackenzie M. Baker, Keith Vertanen
Michigan Tech Publications
In many situations, it may be impractical or impossible to enter text by selecting precise locations on a physical or touchscreen keyboard. We present an ambiguous keyboard with four character groups that has potential applications for eyes-free text entry, as well as text entry using a single switch or a brain-computer interface. We develop a procedure for optimizing these character groupings based on a disambiguation algorithm that leverages a long-span language model. We produce both alphabetically-constrained and unconstrained character groups in an offline optimization experiment and compare them in a longitudinal user study. Our results did not show a significant …
Challenges, Choice, & Change: Experiences And Reflections From The First Semester Of A Technology And Human Futures Course, Briana Christina Bettin
Challenges, Choice, & Change: Experiences And Reflections From The First Semester Of A Technology And Human Futures Course, Briana Christina Bettin
Michigan Tech Publications
Society's rapidly increasing reliance on technology has created urgency for future leaders to understand these technologies' potential impacts. News of technical and algorithmic concerns in society are frequent, highlighting the real impacts to real people. Learners may approach computing in order to feel prepared for careers and life in modern society. These learners benefit from understanding the many ways technology may be part of their future careers, their lives, and the lives of others. Beyond this, many wish to use computing to solve problems - but complex societal problems may feel too overwhelming to solve. Greater understanding of the technologies …
Plmsnosite: An Ensemble-Based Approach For Predicting Protein S-Nitrosylation Sites By Integrating Supervised Word Embedding And Embedding From Pre-Trained Protein Language Model, Pawel Pratyush, Suresh Pokharel, Hiroto Saigo, Dukka Kc
Plmsnosite: An Ensemble-Based Approach For Predicting Protein S-Nitrosylation Sites By Integrating Supervised Word Embedding And Embedding From Pre-Trained Protein Language Model, Pawel Pratyush, Suresh Pokharel, Hiroto Saigo, Dukka Kc
Michigan Tech Publications
Background: Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results: Using an …
The Effects Of Robot Voices And Appearances On Users' Emotion Recognition And Subjective Perception, Sangjin Ko, Jaclyn A. Barnes, Jiayuan Dong, Chung Hyuk Park, Ayanna Howard, Myounghoon Jeon
The Effects Of Robot Voices And Appearances On Users' Emotion Recognition And Subjective Perception, Sangjin Ko, Jaclyn A. Barnes, Jiayuan Dong, Chung Hyuk Park, Ayanna Howard, Myounghoon Jeon
Michigan Tech Publications
As the influence of social robots in people's daily lives grows, research on understanding people's perception of robots including sociability, trust, acceptance, and preference becomes more pervasive. Research has considered visual, vocal, or tactile cues to express robots' emotions, whereas little research has provided a holistic view in examining the interactions among different factors influencing emotion perception. We investigated multiple facets of user perception on robots during a conversational task by varying the robots' voice types, appearances, and emotions. In our experiment, 20 participants interacted with two robots having four different voice types. While participants were reading fairy tales to …
A Non-Reference Evaluation Of Underwater Image Enhancement Methods Using A New Underwater Image Dataset, Ashraf Saleem, Sidike Paheding, Nathir Rawashdeh, Ali Awad, Navjot Kaur
A Non-Reference Evaluation Of Underwater Image Enhancement Methods Using A New Underwater Image Dataset, Ashraf Saleem, Sidike Paheding, Nathir Rawashdeh, Ali Awad, Navjot Kaur
Michigan Tech Publications
The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our …
Harmonic Distortion Reduction Of Transformer-Less Grid-Connected Converters By Ellipsoidal-Based Robust Control, Hisham M. Soliman, Ashraf Saleem, Ehab H.E. Bayoumi, Michele De Santis
Harmonic Distortion Reduction Of Transformer-Less Grid-Connected Converters By Ellipsoidal-Based Robust Control, Hisham M. Soliman, Ashraf Saleem, Ehab H.E. Bayoumi, Michele De Santis
Michigan Tech Publications
A photovoltaic generator connected to a large network and supplying a nonlinear load (source of harmonics) injects distorted current into the grid. This manuscript presents an invariant-ellipsoid set design of a robust controlled active power filter to inject current into the large grid with minimum total harmonic distortion (THD). The nonlinear load current is considered an external disturbance to minimize its effect on the injected grid current. Moreover, the large grid is modeled as a fixed voltage source in a series with a Thevenin impedance whose value changes within an interval. Using the invariant-ellipsoid technique, the problem is cast as …
Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa
Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa
Dissertations, Master's Theses and Master's Reports
An Invasive species is a species that is alien or non-native to the ecosystem which causes harm to economic, environmental, or human health (E.O. 13112 of Feb 3, 1999). Invasive species have posed a serious threat to ecosystems across the globe. These invasive species have impacts on the biodiversity and productivity of invaded forests. Remotely sensed data is a valuable resource for understanding and addressing issues related to invasive species. This study presents a novel approach for mapping the distribution of two invasive plant species, Common and Glossy Buckthorn, using unmanned aerial vehicles (UAVs), machine learning algorithms, geographic information systems …
Neuromorphic Computing Applications In Robotics, Noah Zins
Neuromorphic Computing Applications In Robotics, Noah Zins
Dissertations, Master's Theses and Master's Reports
Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, …