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Articles 61 - 76 of 76
Full-Text Articles in Other Electrical and Computer Engineering
Online Laboratory Course Using Low Tech Supplies To Introduce Digital Logic Design Concepts, Dhanya Nair
Online Laboratory Course Using Low Tech Supplies To Introduce Digital Logic Design Concepts, Dhanya Nair
Engineering Faculty Articles and Research
This paper describes a Digital Logic Design Laboratory Course developed to engage students with hardware systems within an online setting. This is a junior level core course for students from Computer Science (CS), Computer Engineering (CE) and Electrical Engineering (EE). Hence, the laboratories are designed to provide the hands-on experience of breadboarding, testing and debugging essential to CE and EE while accommodating CS students with no prior hardware experience. Commercially available low-cost electronic trainers (portable workstations) are loaned to the students in addition to basic electronic components. To ensure a strong foundation in debugging, prior to utilizing these workstations, students …
Implementing Inverse Design Tools For Plasmonic Digital Logic Devices, Krishna Narayan, Mark C. Harrison
Implementing Inverse Design Tools For Plasmonic Digital Logic Devices, Krishna Narayan, Mark C. Harrison
Engineering Faculty Articles and Research
Despite the benefits that optics and photonics have brought to improving communications, there remains a lack of commercialized optical computing devices and systems, which reduces the benefits of using light as an information-carrying medium. We are developing architectures and designs of photonic logic gates for creating larger-scale functional photonic logic circuits. In contrast to other approaches, we are focusing on the development of logic devices which can be cascaded in arbitrary ways to allow for more complex photonic integrated circuit design. Additionally, optical computing often uses on-off keying, which fails to take advantage of denser encoding schemes often used to …
Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary
Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …
Circus In Motion: A Multimodal Exergame Supporting Vestibular Therapy For Children With Autism, Oscar Peña, Franceli L. Cibrian, Monica Tentori
Circus In Motion: A Multimodal Exergame Supporting Vestibular Therapy For Children With Autism, Oscar Peña, Franceli L. Cibrian, Monica Tentori
Engineering Faculty Articles and Research
Exergames are serious games that involve physical exertion and are thought of as a form of exercise by using novel input models. Exergames are promising in improving the vestibular differences of children with autism but often lack of adaptation mechanisms that adjust the difficulty level of the exergame. In this paper, we present the design and development of Circus in Motion, a multimodal exergame supporting children with autism with the practice of non-locomotor movements. We describe how the data from a 3D depth camera enables the tracking of non-locomotor movements allowing children to naturally interact with the exergame . A …
Admittance Method For Estimating Local Field Potentials Generated In A Multi-Scale Neuron Model Of The Hippocampus, Clayton S. Bingham, Javad Paknahad, Christopher Bc Girard, Kyle Loizos, Jean-Marie C. Bouteiller, Dong Song, Gianluca Lazzi, Theodore W. Berger
Admittance Method For Estimating Local Field Potentials Generated In A Multi-Scale Neuron Model Of The Hippocampus, Clayton S. Bingham, Javad Paknahad, Christopher Bc Girard, Kyle Loizos, Jean-Marie C. Bouteiller, Dong Song, Gianluca Lazzi, Theodore W. Berger
Engineering Faculty Articles and Research
Significant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric fields are a first-order approximation that may be suitable for model validation, but they are computationally expensive and cannot accurately capture boundary conditions in heterogeneous tissue. While there are many appropriate numerical methods of solving electric fields in neural tissue models, there isn't an established standard for mesh geometry nor a well-known rule for handling any mismatch in spatial resolution. Moreover, the …
A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead
A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead
Engineering Faculty Articles and Research
Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Engineering Faculty Articles and Research
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …
Supporting Self-Regulation Of Children With Adhd Using Wearables: Tensions And Design Challenges, Franceli L. Cibrian, Kimberley D. Lakes, Arya Tavakoulnia, Kayla Guzman, Sabrina Schuck, Gillian R. Hayes
Supporting Self-Regulation Of Children With Adhd Using Wearables: Tensions And Design Challenges, Franceli L. Cibrian, Kimberley D. Lakes, Arya Tavakoulnia, Kayla Guzman, Sabrina Schuck, Gillian R. Hayes
Engineering Faculty Articles and Research
The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch …
Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi
Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi
Engineering Faculty Articles and Research
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization …
Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead
Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead
Engineering Faculty Articles and Research
Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …
Multiple-Change-Point Modeling And Exact Bayesian Inference Of Degradation Signal For Prognostic Improvement, Yuxin Wen, Jianguo Wu, Qiang Matthew Zhou, Tzu-Liang Bill Tseng
Multiple-Change-Point Modeling And Exact Bayesian Inference Of Degradation Signal For Prognostic Improvement, Yuxin Wen, Jianguo Wu, Qiang Matthew Zhou, Tzu-Liang Bill Tseng
Engineering Faculty Articles and Research
Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to …
Degradation Modeling And Rul Prediction Using Wiener Process Subject To Multiple Change Points And Unit Heterogeneity, Yuxin Wen, Jianguo Wu, Devashish Das, Tzu-Liang Bill Tseng
Degradation Modeling And Rul Prediction Using Wiener Process Subject To Multiple Change Points And Unit Heterogeneity, Yuxin Wen, Jianguo Wu, Devashish Das, Tzu-Liang Bill Tseng
Engineering Faculty Articles and Research
Degradation modeling is critical for health condition monitoring and remaining useful life prediction (RUL). The prognostic accuracy highly depends on the capability of modeling the evolution of degradation signals. In many practical applications, however, the degradation signals show multiple phases, where the conventional degradation models are often inadequate. To better characterize the degradation signals of multiple-phase characteristics, we propose a multiple change-point Wiener process as a degradation model. To take into account the between-unit heterogeneity, a fully Bayesian approach is developed where all model parameters are assumed random. At the offline stage, an empirical two-stage process is proposed for model …
Application Of Real Field Connected Vehicle Data For Aggressive Driving Identification On Horizontal Curves, Arash Jahangiri, Vincent Berardi, Sahar Ghanipoor Machiani
Application Of Real Field Connected Vehicle Data For Aggressive Driving Identification On Horizontal Curves, Arash Jahangiri, Vincent Berardi, Sahar Ghanipoor Machiani
Psychology Faculty Articles and Research
The emerging technology of connected vehicles generates a vast amount of data that could be used to enhance roadway safety. In this paper, we focused on safety applications of a real field connected vehicle data on a horizontal curve. The database contains connected vehicle data that were collected on public roads in Ann Arbor, Michigan with instrumented vehicles. Horizontal curve negotiations are associated with a great number of accidents, which are mainly attributed to driving errors. Aggressive/risky driving is a contributing factor to the high rate of crashes on horizontal curves. Using basic safety message data in connected vehicle data …
Multiple-Phase Modeling Of Degradation Signal For Condition Monitoring And Remaining Useful Life Prediction, Yuxin Wen, Jianguo Wu, Yuan Yuan
Multiple-Phase Modeling Of Degradation Signal For Condition Monitoring And Remaining Useful Life Prediction, Yuxin Wen, Jianguo Wu, Yuan Yuan
Engineering Faculty Articles and Research
Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies.
Bendablesound: An Elastic Multisensory Surface Using Touch-Based Interactions To Assist Children With Severe Autism During Music Therapy, Franceli L. Cibrian, Oscar Peña, Deysi Ortega, Monica Tentori
Bendablesound: An Elastic Multisensory Surface Using Touch-Based Interactions To Assist Children With Severe Autism During Music Therapy, Franceli L. Cibrian, Oscar Peña, Deysi Ortega, Monica Tentori
Engineering Faculty Articles and Research
Neurological Music Therapy uses live music to improve the sensorimotor regulation of children with severe autism. However, they often lack musical training and their impairments limit their interactions with musical instruments. In this paper, we present our co-design work that led to the BendableSound prototype: an elastic multisensory surface encouraging users to practice coordination movements when touching a fabric to play sounds. We present the results of a formative study conducted with 18 teachers showing BendableSound was perceived as “usable” and “attractive”. Then, we present a deployment study with 24 children with severe autism showing BendableSound is “easy to use” …
Spatiotemporal Fluorescent Detection Measurements Using Embedded Waveguide Sensors, Mark C. Harrison, Andrea M. Armani
Spatiotemporal Fluorescent Detection Measurements Using Embedded Waveguide Sensors, Mark C. Harrison, Andrea M. Armani
Engineering Faculty Articles and Research
Integrated waveguide biosensors, when combined with fluorescent labeling, have significantly impacted the field of biodetection. While there are numerous types of waveguide sensors, the fundamental excitation method is fairly consistent: the evanescent field of the waveguide excites a fluorophore whose emission is detected, either directly via imaging or indirectly via a decrease in power transfer. Recently, a sensor device was demonstrated which is able to back-couple the emitted light into the waveguide, allowing the signal to be detected directly. However, this previous work focused on the development of an empirical model, leaving many theoretical questions unanswered. Additionally, the results from …