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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach

A Platform for Monitoring Student Commuting in the Use of Sc...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Jessé Da Costa Rocha Marcela Alves de Souza Evelin Helena Silva Cardoso Nandamudi Vijaykumar Jasmine Priscyla Leite de Araújo Renato Lisboa Francês Graduate Program in Electrical Engineering Institute of Technology Federal University of Pará (UFPA) Belém Pará Brazil Information and Communication Technology Center Federal University of the South and Southeast of Para (Unifesspa) Marabá Pará Brazil Department of Computer Science Federal Rural University of the Amazon (UFRA) Capitão Poço Pará Brazil Coordination of Applied Research and Technological Development National Institute for Space Research (INPE) São Paulo Brazil

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics.

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Reduction of interface defects in gate-recessed GaN HEMTs by neutral beam etching

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Materials Today Advances 2024年 23卷

作者： Yu, Chia Hao Chiang, Wei Hsiang Chen, Yi-Ho Samukawa, Seiji Wuu, Dong Sing Chung, Chin-Han Hsiao, Ching-Lien Horng, Ray Hua Institute of Electronics National Yang Ming Chiao Tung University Department of Materials Science and Engineering National Chung Hsing University Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Institute of Comunications Engineering National Yang Ming Chiao Tung University Department of Applied Materials and Optoelectronic Engineering National Chi Nan University International College of Semiconductor Technology National Yang Ming Chiao Tung University Thin Film Physics Division Department of Physics Chemistry and Biology (IFM) Linköping University

This study investigates and compares the impact of different etching techniques on the fabrication of GaN high electron mobility transistors (HEMTs) between the inductively coupled plasma reactive ion etching (ICP-RIE) and the neutral beam etching (NBE) for the gate recess. By conducting direct current analysis, it was found that devices manufactured using the NBE exhibited superior electrical performance as compared with those produced using the ICP-RIE. These enhanced electrical characteristics include a transconductance of up to 100.4 mS/mm, a threshold voltage (V_th) of −2.3 V, an on/off current ratio of 1.1 × 10⁹, a subthreshold swing (S.S.) of 99.63 mV/dec, and a remarkably low gate leakage current. Additionally, we noted varying degrees of hysteresis in the I–V characteristics were related to process disparities possibly leading to interface defects. Multi-frequency capacitance-voltage (C–V) measurements were used to identify the interface defects at the oxide/AlGaN interface of the gate. The results revealed that devices fabricated using the NBE exhibited a lower interface defect density as compared with those fabricated using the ICP-RIE, thereby elucidating the reduced hysteresis observed in the I–V characteristics. These findings indicated the significant advantages of the NBE process in the fabrication of GaN HEMTs. © 2024 The Authors

关键词： GaN HEMTs Gate recess Interface defects Multi-frequency C–V Neutral beam etching

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Biophysics of amplitude-modulated giga-hertz electromagnetic waves stimulation

Biophysics of amplitude-modulated giga-hertz electromagnetic...

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Asilomar Conference on Signals, Systems & computers

作者： Fatima Ahsan Aravind C. Govindaraju Robert M. Raphael Taiyun Chi Sameer A. Sheth Wayne Goodman Behnaam Aazhang Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curly Institute Rice University Houston TX Department of Bioengineering Rice University Houston TX USA Department of Neurosurgery Baylor College of Medicine Houston TX USA Menninger Department of Psychiatry and Behavioral Sciences Baylor College of Medicine Houston TX USA

This paper presents the biophysical modeling for neuronal stimulation caused by amplitude-modulated giga-hertz (GHz) electromagnetic (EM) waves. We model a 1D cable equation for electrical signal propagation in a vestibular ganglion neuronal fiber and use Hodgkin-Huxley formalism to model voltage-dependent channel conductances. We find out that for a given stimulation signal strength, the GHz waveform amplitude-modulated at a low frequency of Δf causes neuronal firing with the frequency of Δf whereas the continuous wave GHz did not elicit any neuronal firing. Moreover, the voltage-dependent channel gating dynamics of the Δf amplitude-modulated GHz stimulation matches that of a simple low frequency stimulation at Δf. Additionally, we find that the modulation depth controls the firing rate of the neurons. The significance of this property is that the amplitude modulation caused by the interference of two similar high frequency signals could be leveraged to focally excite neurons at depth without stimulating overlying cortical regions. These theoretical predictions based on the Hodgkin-Huxley model can later be tested experimentally.

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Terahertz Landau Polaritons in Nano-slots: Ultrastrong Coupling under Extreme Spatial Confinement

Terahertz Landau Polaritons in Nano-slots: Ultrastrong Coupl...

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International Conference on Infrared and Millimeter Waves

作者： Dasom Kim Sunghwan Kim Dukhyung Lee Shuang Liang Fuyang Tay Michael J. Manfra Dai-Sik Kim Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory and Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea Department of Physics and Astronomy Purdue University West Lafayette IN USA Birck Nanotechnology Center Purdue University West Lafayette IN USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Materials Engineering Purdue University West Lafayette IN USA Department of Physics and Astronomy Rice University Houston TX USA Department of Material Science and NanoEngineering Rice University Houston TX USA

We report ultrastrong coupling of the cyclotron resonance of a two-dimensional electron gas in a GaAs quantum well to terahertz cavity photons under extreme confinement to the nanometer scale. This was realized by an array of nano-slots fabricated close to the quantum well layer on the GaAs substrate. Our terahertz magnetospectroscopic measurements revealed two polariton branches as a function of applied magnetic field. While the photon-like part of the two polariton branches was clearly observed, the matter-like part was generally elusive. Our electromagnetic simulations quantitatively reproduce our observation for the upper-polariton branch. However, for the lower-polariton branch, our simulations predict a pronounced resonance, which was absent in experimental spectra. This discrepancy may be a consequence of the breakdown of the dipole approximation in this extreme confinement situation.

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Competition between Hot Carriers and Surface Electrochemistry in Gold Nanorod Dissolution

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The Journal of Physical Chemistry C 2025年第22期129卷 10154-10162页

作者： Jiamu Lin Katsuya Shiratori Claire A. West Alexander Al-Zubeidi Zhenyang Jia Hyuncheol Oh Stephan Link Christy F. Landes Department of Chemistry University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Department of Chemistry Rice University Houston Texas 77005 United States Department of Physics University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Applied Physics Graduate Program Rice University Houston Texas 77005 United States Department of Chemistry University of Illinois Urbana−Champaign Urbana Illinois 61801 United States Department of Chemistry Rice University Houston Texas 77005 United States Department of Chemical and Biomolecular Engineering University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Department of Chemical and Biomolecular Engineering Rice University Houston Texas 77005 United States Department of Chemistry University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Department of Chemistry Rice University Houston Texas 77005 United States；Department of Physics University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Applied Physics Graduate Program Rice University Houston Texas 77005 United States；Department of Electrical and Computer Engineering University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Department of Electrical and Computer Engineering Rice University Houston Texas 77005 United States；Materials Research Laboratory University of Illinois Urbana−Champaign Urbana Illinois 61801 United States Department of Chemistry University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Department of Chemistry Rice University Houston Texas 77005 United States；Department of Chemical and Biomolecular Engineering University of Illinois Urbana−Champaign Urbana Illinois 61801 United States；Department of Chemical and Biomolecular Engineering Rice University Houston Texas 77005 United States；Department of Electrical and Computer Engineering University of Illinois Urbana−Champaign Urbana Illinois 61801 United

Plasmonic nanostructures have the potential to revolutionize photocatalysis by harnessing hot carriers to drive novel chemical reactions. Precise control of reaction sites on nanoparticles remains crucial for advancing catalyst design. The influence of hot carriers, particularly in the interplay with surface electrochemistry, needs further exploration. Employing single-particle spectroelectrochemical methods, we identify the conditions that lead to tip-preferred versus isotropic dissolution. We investigate how the applied potential, excitation laser power density, and wavelength of illumination directly direct gold nanorod dissolution. There is a competition between hot carrier localization and electrochemistry in determining the dissolution anisotropy. We observe that higher potentials favor isotropic dissolution, whereas higher laser power densities drive tip-specific dissolution. The results provide new insights into the control of tuning the gold nanoparticle dissolution anisotropy.

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Loss tangent fluctuations due to two-level systems in superconducting microwave resonators

arXiv

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arXiv 2024年

作者： Vallières, A. Russell, M.E. You, X. Garcia-Wetten, D.A. Goronzy, D.P. Walker, M.J. Bedzyk, M.J. Hersam, M.C. Romanenko, A. Lu, Y. Grassellino, A. Koch, J. McRae, C.R.H. Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Electrical Computing and Energy Engineering Department University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal quality factor Qi as a function of power or temperature. In this work, we investigate large temporal fluctuations of Qi at low powers over periods of 12 to 16 hours (relative standard deviation σQi/Qi = 13%). These fluctuations are ubiquitous across multiple resonators, chips and cooldowns. We are able to attribute these fluctuations to variations in the TLS loss tangent due to two main indicators. First, measured fluctuations decrease as power and temperature increase. Second, for interleaved measurements, we observe correlations between low- and medium-power Qi fluctuations and an absence of correlations with high-power fluctuations. Agreement with the TLS loss tangent mean is obtained by performing measurements over a time span of a few hours. We hypothesize that, in addition to decoherence due to coupling to individual near-resonant TLS, superconducting qubits are affected by these observed TLS loss tangent fluctuations. © 2024, CC BY.

关键词： Qubits

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Multiscale Numerical Modeling for Near-Field Microwave Impedance Microscopy

Multiscale Numerical Modeling for Near-Field Microwave Imped...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Diego Tami Douglas A. A. Ohlberg Jhonattan C. Ramirez Cássio Gonçalves Do Rego Gilberto Medeiros-Ribeiro Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Brazil Microscopy Center Universidade Federal de Minas Gerais Brazil Department of Electronic Engineering Universidade Federal de Minas Gerais Brazil Computer Science Department Universidade Federal de Minas Gerais Brazil

In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Microscopy Masers Lasers and electrooptics Laser modes Numerical models Impedance Electrooptical waveguides

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