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IEEE International Conference on Big Data

作者： Ganesh Arkanath Nishad Gupta Hasan Kurban Parichit Sharma K R Madhavan Elham Khorasani Buxton Mehmet M. Dalkilic Computer Science Department Indiana University Bloomington IN US Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Data Science Program Indiana University Bloomington IN US Computer Science Department University of Illinois Springfield IL US

Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA’s API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://***/fantasyleague/, and the source code can be found at https://***/gany-15/nbafan.

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Deep Learning Networks for Handwritten Bangla Character Recognition

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IAENG International Journal of Applied Mathematics 2023年第4期53卷 1页

作者： Begum, H. Islam, M.M. Eva, H.S. Emon, N.H. Siddique, F.A. Department of Electrical and Electronic Engineering East West University A/2 Aftabnagar Dhaka Bangladesh Department of Electrical Engineering and Computer Science South Dakota State University United States Engineering and Design Department Reverie Power and Automation Engineering Limited Bangladesh Program in Smart System Integrated Solutions Aalto University Finland

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Classification (of information)

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A Preliminary Study on the Collaborative Playing Piano Automatic with Deep Learning

A Preliminary Study on the Collaborative Playing Piano Autom...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Karuna Yampray Department of Electronic Engineering International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan College of Creative Design and Entertainment Technology Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

A great number of deep learning-based models have been recently proposed for automatic piano classification. In this paper, we describe our contribution to the challenge of automatic piano classification when the performer performs at the concert or stage. Among these models in deep learning, we use init-1D-WaveNet and init-2D-MLNet for comparison the accuracy in the piano beginning level of the Christmas song (Jingle bells). Our experimental results show that the assessment using the init-2D-MLNet still achieve high accuracy of 87.5%.

关键词： Deep learning Accuracy Instruments Collaboration Consumer electronics

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Application of 3D Printing and Design in Healthcare: A Focus on Undergraduate Research to Attract Students to bioengineering and Related Disciplines

Application of 3D Printing and Design in Healthcare: A Focus...

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Frontiers in Education (FIE) Conference

作者： Cindy M. Zelada-Quiroz Brian L. Reyes-Santiago Eduardo I. Ortiz-Rivera Bio-Engineering Graduate Program University of Puerto Rico at Mayagüez Mayagüez PR Department of Computer Science and Engineering University of Puerto Rico at Mayagüez Mayagüez PR Department of Electrical and Computer Engineering University of Puerto Rico at Mayagüez Mayagüez PR

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice paper describes developing and analyzing state-of-the-art smart boots created by combining CAD technology and advanced 3D printing techniques to attract students in bio-engineering and related fields. The primary objective of this innovative immobilization boot is to expedite fracture recovery phases through an ergonomic design to ensure optimal patient comfort during its use. Technological solutions are crucial in aiding the rehabilitation process for fractures caused by falls, heavy lifting, or rotational trauma. However, cost and comfort-related issues persist, underscoring the need for alternative approaches. This research addresses these challenges and delves into the broader implications of fracture treatment, catalyzing future projects and investigations in bioengineering. Additionally, this study serves as an educational tool that sparks the interest of high school and engineering students, promoting multidisciplinary collaboration in innovation. By involving students in specialized courses covering 3D design, human bone anatomy, biology, and materials science, this initiative empowers them to deepen their knowledge and develop new technologies to address bone injury problems. Material analyses include evaluating the type of material depending on the fracture site, such as PLA for printing and cotton and silicone gel for the midsection between the splint and the body. This research aims to advance our understanding of the type of fracture, the methods associated with their treatment, and tissue repair processes during bone callus formation. To summarize, this multidisciplinary approach drives advancements in bio-engineering and related fields, aiming to enhance patient outcomes and inspire students to pursue further research in bio-engineering and related fields. As part of this endeavor, a list of university-level courses based on the experience of the University of Puerto Rico at Mayaguez (UPRM), such as biology, bio-materials, 3D

关键词： Technological innovation Materials science and technology Three-dimensional displays Tissue engineering Medical services Three-dimensional printing Bones Mechanical factors Manufacturing Sparks

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A microwell-based impedance sensor on an insertable microneedle for real-time in vivo cytokine detection

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Microsystems & Nanoengineering 2021年第6期7卷 139-150页

作者： Naixin Song Pengfei Xie Wen Shen Hanju Oh Yejia Zhang Flavia Vitale Mehdi Javanmard Mark G.Allen Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPAUSA Department of Electrical and Computer Engineering Rutgers UniversityPiscatawayNJUSA Department of Physical Medicine and Rehabilitation University of PennsylvaniaPhiladelphiaPAUSA Department of Orthopedic Surgery University of PennsylvaniaPhiladelphiaPAUSA Corporal Michael J.Crescenz Veterans Affairs Medical Center PhiladelphiaPAUSA Department of Bioengineering School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaPAUSA Department of Neurology Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA

Impedance-based protein detection sensors for point-of-care diagnostics require quantitative specificity,as well as rapid or real-time ***,microfabrication of these sensors can lead to the formation of factors suitable for in vivo ***,we present microfabricated needle-shaped microwell impedance sensors for rapid-sample-to-answer,label-free detection of cytokines,and other *** microneedle form factor allows sensors to be utilized in transcutaneous or transvascular sensing *** vitro,experimental characterization confirmed sensor specificity and sensitivity to multiple proteins of *** characterization demonstrated sufficient microneedle robustness for transcutaneous insertion,as well as preserved sensor function *** further utilized these sensors to carry out real-time in vivo quantification of human interleukin 8(hIL8)concentration levels in the blood of transgenic mice that endogenously express *** assess sensor functionality,hIL8 concentration levels in serum samples from the same mice were quantified by *** agreement between real-time in vivo sensor readings in blood and subsequent ELISA serum assays was observed over multiple transgenic mice expressing hIL8 concentrations from 62 pg/mL to 539 ng/mL.

关键词： needle utilized insertion

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Long short-term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control

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Procedia computer science 2023年 216卷 213-220页

作者： Santo Wijaya Yaya Heryadi Yulyani Arifin Wayan Suparta Lukas Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta 55281 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance.

关键词： Model-based Reinforcement Learning Random-Shooting Policy LSTM MPC Nonlinear System

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Design and Optimization of the Porous Metamaterial EM Absorbers in X-band

Design and Optimization of the Porous Metamaterial EM Absorb...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Kanat Anurakparadorn Alan Taub Eric Michielssen Macromolecular Science and Engineering Program Department of Material Science and Engineering University of Michigan Ann Arbor Michigan USA Department of Mechanical Engineering University of Michigan Ann Arbor Michigan USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan USA

A strategy that combines experiment and simulation to design and optimize electromagnetic (EM) metamaterial absorbers containing a periodic porous structure is described. The approach provides the ability to produce absorbers that meet multiple user-specified objectives. Using the measured intrinsic properties of the baseline materials as an input to EM-field based computational modelling and optimization, absorption by the studied metamaterials measured by their reflection loss (RL) increases significantly. The resulting metamaterials have the potential for lower cost and lighter weight while providing greater protection than traditional metal gaskets and foams.

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Impact of fabrication methods on binder distribution and charge transport in composite cathodes of all-solid-state batteries

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Materials Futures 2023年第4期2卷 127-135页

作者： Benjamin Emley Chaoshan Wu Lihong Zhao Qing Ai Yanliang Liang Zhaoyang Chen Liqun Guo Tanguy Terlier Jun Lou Zheng Fan Yan Yao Materials Science and Engineering Program and Texas Center for Superconductivity at the University of Houston University of Houston4726 Calhoun RdHoustonTX 77204United States of America Department of Electrical and Computer Engineering University of Houston4726 Calhoun RdHoustonTX 77204United States of America Department of Materials Science and Nano Engineering Rice University6100 Main StreetHoustonTX 77005United States of America SIMS Laboratory Shared Equipment AuthorityRice UniversityHoustonTX 77005United States of America Department of Engineering Technology University of HoustonHoustonTX 77204United States of America

The manufacturing process of all-solid-state batteries necessitates the use of polymer ***,these binders,being ionic insulators by nature,can adversely affect charge transport within composite cathodes,thereby impacting the rate performance of the *** this work,we aim to investigate the impact of fabrication methods,specifically the solvent-free dry process versus the slurry-cast wet process,on binder distribution and charge transport in composite cathodes of solid-state *** the dry process,the binder forms a fibrous network,while the wet process results in binder coverage on the surface of cathode active *** difference in microstructure leads to a notable 20-fold increase in ionic conductivity in the dry-processed ***,the cells processed via the dry method exhibit higher capacity retention of 89%and 83%at C/3 and C/2 rates,respectively,in comparison to 68%and 58%for the wet-processed cells at the same *** findings provide valuable insights into the influence of fabrication methods on binder distribution and charge transport,contributing to a better understanding of the binder’s role in manufacturing of all-solid-state batteries.

关键词： solid-state battery solvent-free dry process polymer binder lithium thiophosphate electrolyte

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Multi-gate FeFET Discriminates Spatiotemporal Pulse Sequences for Dendrocentric Learning

Multi-gate FeFET Discriminates Spatiotemporal Pulse Sequence...

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International Electron Devices Meeting (IEDM)

作者： Hugo J.-Y. Chen Matthew Beauchamp Kasidit Toprasertpong Fei Huang Louis Le Coeur Thorgund Nemec H.-S. Philip Wong Kwabena Boahen Department of Electrical Engineering Stanford University Stanford CA USA GlobalFoundries Dresden Germany Department of Bioengineering Stanford University Stanford CA USA Department of Computer Science Stanford University Stanford CA USA

This paper presents a dendrite-like device that discriminates spatiotemporal patterns of pulses for parallel processing in 3D neuromorphic architectures. The device utilizes the ferroelectric layer in a segmented multi-gate FeFET design to detect a consecutive sequence of input pulses. Experimental results demonstrate successful emulation of highly selective sequence discrimination in dendrites of neurons in the cortex and highlight up to 100× signal-margin (output current differences). This nanodendrite design offers a neuromorphic solution to thermally scalable parallel processing in 3D systems.

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DeepCat: A Deep Learning Approach to Understand Your Cat's Body Language

DeepCat: A Deep Learning Approach to Understand Your Cat's B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Arwa Fawzy Ahmed Hassan Ahmed Gad Ahmed B. Zaky Haitham El-Hussieny Computer Science and Information Technology (CSIT) program Egypt-Japan University of Science and Technology Alexandria Egypt Department of Computer Science Engineering (CSE) Egypt-Japan University of Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Egypt Department of Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

Pet owners experience difficulty in understanding their pets' body language and its implications for animal welfare, given that animals cannot utilize human speech to communicate their emotions and health conditions. However, previous experiments for analyzing cat behavior have demonstrated that cats are precisely expressive. DeepCat, a deep-learning approach developed in this study, translates cats' body language signals, enabling owners to discern their feline companions' intended messages and emotional states. Our DeepCat model was trained on a dataset comprising 10,000 cat images, implementing automatic labeling to track key features, including the tail, eyes, and mouth. Presented as a Flutter application, DeepCat can function everywhere, allowing owners to easily monitor their cats and make informed decisions in situations that require caution. This paper discusses the potential benefits and limitations of DeepCat and provides suggestions for future research in this domain.

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