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IEEE International Conference on Radar

作者： Junseob Kim Sunghwan Cho Sunil Hwang Yeongyoon Choi Department of Electrical Engineering Korea Military Academy Republic of Korea Maneuver System Coordination and Contract Team Defense Acquisition Program Administration Republic of Korea Department of Electrical Engineering and Computer Science GIST Republic of Korea

This paper investigates the classification of low probability of intercept (LPI) radar signals by exploiting the intrinsic advantages of the Vision Transformer (ViT). Due to the characteristics of LPI radar signals, such as intrapulse modulation, wide frequency bands, and low transmission power, these signals are challenging to be detected and classified using traditional analytic methods. This has led to the adoption of various deep learning techniques to overcome these limitations. On the one hand, the ViT, originally developed for natural language processing, has demonstrated outstanding performance in computer vision by replacing the structure of the convolutional neural network (CNN) with the transformer, specifically leveraging self-attention. Therefore, this paper explores a method based on the ViT technique for classifying LPI signal images. The simulation results show that the proposed ViT method outperforms the traditional CNN method by 12.8% at −10dB SNR.

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Comprehensive Study on Cherenkov Radiation for Ultrafast PET Detectors, Considering Refractive Index and Wavelength

Comprehensive Study on Cherenkov Radiation for Ultrafast PET...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： H.S. Shim H. Kang J. Kim J.S. Lee Y. Jeong Interdisciplinary Program of Bioengineering Seoul National University Seoul Republic of Korea Integrated Major in Innovative Medical Science Seoul National University Seoul Republic of Korea Department of Electrical and Computer Engineering Seoul National University Seoul Republic of Korea

This study aims to investigate the characteristics of Cherenkov radiation with respect to wavelength and refractive index. Refractive index is a function of wavelength, and when it changes, not only does the gain of Cherenkov radiation increase, but also the photon loss in the medium increases. Therefore, we examine how photons of different wavelengths behave under changing refractive index conditions. We adjust the refractive index to maintain a constant emission angle of Cherenkov radiation based on the deceleration rate of electrons moving in the medium. When electrons generating Cherenkov radiation move a total of 200 μm, both long-wavelength and short-wavelength photons are emitted at the initial stage; however, only short-wavelength photons are emitted at the late stage. Although the shorter-wavelength photons arrive first among the photons that reach the detector, the longer-wavelength photons are detected more, forming a significant peak, which is due to the fact that the former experience more absorption-based photon loss in the medium than the latter.

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PAINDECOG: MACHINE LEARNING-BASED IDENTIFICATION OF PAIN BIOMARKERS FROM SEEG SIGNALS

arXiv

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arXiv 2025年

作者： Sidharth, Sidharth Sathish, Vishwas Bansal, Shweta Sun, Samantha Pham, Timmy Weaver, Kurt Rao, Rajesh P.N. Herron, Jeffrey Department of Electrical and Computer Engineering University of Washington Seattle United States Paul G. Allen School of Computer Science and Engineering University of Washington Seattle United States Department of Bioengineering University of Washington Seattle United States Department of Radiology University of Washington Seattle United States Department of Neurological Surgery University of Washington Seattle United States

This study presents a systematic machine-learning approach for classifying acute pain from raw electrophysiological signals. We address binary and ternary classification tasks, leveraging Power-In-Band (PIB) and signal coherence—a functional connectivity metric—as distinguishing features. Our method evaluates the effectiveness of traditional machine learning algorithms on a manually curated electrophysiological dataset obtained from intracranial electroencephalography (iEEG), offering valuable insights into model performance for pain detection. Furthermore, we identify critical electrode pairings associated with acute pain, providing a clearer understanding of the neural markers that differentiate pain states. This work highlights the potential of targeted feature engineering in advancing pain classification, setting the stage for future enhancements in real-time and personalized pain assessment tools. Additionally, these findings have promising applications in neuromodulation and Deep Brain Stimulation (DBS), where adaptive and closed-loop systems could leverage identified pain markers to modulate pain-related brain regions more precisely, offering improved therapeutic options for chronic pain management Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORING IL-6 BIOMARKER 25

A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORIN...

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25th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2021

作者： Laliberte, Kaitlyn E. Scott, Patrick Khan, Niazul I. Mahmud, Md Shaad Song, Edward Department of Electrical and Computer Engineering University of New Hampshire United States Materials Science Program University of New Hampshire United States

ISBN: (纸本)9781733419031

Graphene field-effect transistor (GFET) is becoming an increasingly popular biosensing platform for monitoring health conditions through biomarker detection. Moreover, the graphene's 2-dimensional geometry makes it ideal for implementing flexible wearable devices. In this work, we present a printable graphene transistor-based biosensor that can be used as a wearable sensor to detect a protein biomarker Interleukin-6 (IL-6). Our device contains a voltage control, a current meter, and a power source all integrated into a single miniaturized platform making it a truly skin-mounted wearable device. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Graphene transistors

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Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics

Novel NBA Fantasy League driven by Engineered Team Chemistry...

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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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High Rectification, Low Leakage p-Si/n-AlN Heterojunction PN Diode

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年

作者： Lu, Yi Zhou, Jie Gong, Jiarui Liu, Yang Cao, Haicheng Nong, Mingtao Liu, Zhiyuan Alam, Md.Tahmidul Gupta, Chirag Li, Xiaohang Ma, Zhenqiang University of Wisconsin-Madison Department of Electrical and Computer Engineering Madison 53706 WI United States King Abdullah University of Science and Technology (KAUST) CEMSE Division Advanced Semiconductor Laboratory Electrical and Computer Engineering Program Thuwal 23955-6900 Saudi Arabia

Ultrawide bandgap AlN is a highly attractive material for power and radio frequency electronics. Unipolar n-AlN Schottky barrier diodes have demonstrated their strengths, whereas further development of AlN-based bipolar devices is desired but lacked. In this letter, we report single-crystalline p-Si/n-AlN p-n diodes (PNDs) with superior performance, accomplished by grafting a p-type Si nanomembrane onto an n-AlN film. Improved Ohmic contact directly on n-AlN was obtained through high temperature annealing at 1100 °C with contact resistivity of 4.9×10^-3 Ω. cm². The PNDs exhibited remarkable uniformity across the wafer, excellent rectifying characteristics with a high ratio of ~3×10⁷ at ±10 V, low leakage current density of ~6.25×10^-9 A/cm² at -10 V, and hysteresis-free operation from -10 V to 30 V. Furthermore, the devices demonstrated superior thermal stability, with the on-resistance decreasing from 15.8 to 0.33 Ω. cm² as temperature increased from room temperature to 100 °C, revealing an activation energy of ~425 meV for deep donor state in AlN. No destructive breakdown of the devices was observed up to –894 V. These results highlight the Si/AlN PNDs’ potential for development of AlN-based electronic devices. © 1980-2012 IEEE.

关键词： Aluminum nitride p-n diode semiconductor grafting semiconductor heterojunction silicon

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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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