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Landau Polaritons in the Ultrastrong and Superstrong Coupling Regime in a Multimode Terahertz Photonic-Crystal Cavity

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Landau Polaritons in the Ultrastrong and Superstrong Couplin...

International Conference on Infrared and Millimeter Waves

作者： Fuyang Tay Ali Mojibpour Stephen Sanders Shuang Liang Hongjing Xu Geoff C. Gardner Andrey Baydin Michael J. Manfra Alessandro Alabastri David Hagenmüller 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 and Astronomy Purdue University West Lafayette IN USA Birck Nanotechnology Center Purdue University West Lafayette IN USA Department of Physics and Astronomy Rice University Houston TX USA Smalley–Curl Institute Rice University Houston TX USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Materials Engineering Purdue University West Lafayette IN USA ISIS (UMR 7006) Université de Strasbourg and CNRS Strasbourg France Department of Materials Science and NanoEngineering Rice University Houston TX USA

We have demonstrated ultrastrong and superstrong coupling of a Landau-quantized two-dimensional electron gas in GaAs with a multiple-mode three-dimensional terahertz photonic-crystal cavity. The cavity had an asymmetric design such that the real-space profiles of photonic modes depended on the electric field polarization. Terahertz magnetospectroscopy experiments with polarized radiation clearly showed that the degree of hybridization of multiple cavity modes is a function of the spatial overlap of the photonic modes. These findings can be utilized for the dynamical tuning of multimode light–matter interactions, which can lead to the development of novel terahertz devices.

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

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

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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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

A Deep Learning Model with Radiomics Analysis Integration for Glioblastoma post-Resection Survival Prediction

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arXiv 2022年

作者： Hu, Zongsheng Yang, Zhenyu Zhang, Haozhao Vaios, Eugene Lafata, Kyle Yin, Fang-Fang Wang, Chunhao Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan215316 China Department of Radiation Oncology Duke University DurhamNC27710 United States Department of Radiology Duke University DurhamNC27710 United States Department of Electrical and Computer Engineering Duke University DurhamNC27710 United States

Purpose: To develop a novel deep learning model that integrates radiomics analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM) post-resection survival prediction Methods: A cohort of 235 GBM patients with complete surgical resection was divided into short-term/long-term survival groups with 1-yr survival time threshold. Each patient received a pre-surgery multi-parametric MRI exam with 4 scans: T1, contrast-enhanced T1 (T1ce), T2, and FLAIR. Three tumor subregions were segmented by neuroradiologists, and the whole dataset was divided into training, validation, and test groups following a 7:1:2 ratio. The developed model comprises three data source branches: in the 1st radiomics branch, 456 radiomics features (RF) were calculated from the three tumor subregions of each patient’s MR images;in the 2nd deep learning branch, an encoding neural network architecture was trained for survival group prediction using each single MR modality, and high-dimensional parameters from the last two network layers were extracted as deep features (DF). The extracted radiomics features and deep features were processed by a feature selection procedure to reduce the dimension size of each feature space. In the 3rd branch, patient-specific clinical features (PSCF), including patient age and three tumor subregions volumes, were collected from the dataset. Finally, data sources from all three branches were fused as an integrated input for a supporting vector machine (SVM) execution for survival group prediction. Different strategies of model design were investigated in comparison studies, including 1) 2D/3D-based image analysis, 2) different radiomics feature space dimension reduction methods, and 3) different data source combinations in SVM input design. Results: The model achieved 0.638 prediction accuracy in the test set when using patient-specific clinical features only, which was higher than the results using radiomics features/deep features as sole input of SVM

关键词： Magnetic resonance imaging

More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science Graduate Program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages fractional exponents to capture long-memory effects in optimization. However, its utility is often limited by the difficulty of tuning and stabilizing these exponents. We propose 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), which integrates the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to adapt the fractional exponent in a data-driven manner. By tracking model sensitivity and effective dimensionality, 2SEDFOSGD dynamically modulates the exponent to mitigate oscillations and hasten convergence. Theoretically, for onoconvex optimization problems, this approach preserves the advantages of fractional memory without the sluggish or unstable behavior observed in naïve fractional SGD. Empirical evaluations in Gaussian and α-stable noise scenarios using an autoregressive (AR) model highlight faster convergence and more robust parameter estimates compared to baseline methods, underscoring the potential of dimension-aware fractional techniques for advanced modeling and estimation tasks. © 2025, CC BY.

关键词： Convex optimization

Terahertz Time-Domain Spectroscopy for Probing DC Conductivity of Single-Layer Graphene

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Terahertz Time-Domain Spectroscopy for Probing DC Conductivi...

Western New York Image Processing Workshop (WNYIPW)

作者： Debamitra Chakraborty Declan Walden Jing Cheng Julia Wallace Kiryl A. Niherysh Andrei V. Felsharuk Donats Erts Ivan Komissarov Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics Rochester New York USA Department of Physics and Astronomy University of Rochester Rochester New York USA Department of Biomedical Engineering University of Rochester Rochester New York USA Institute of Chemical Physics University of Latvia Riga Latvia Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering University of Rochester Rochester New York USA

We report preliminary results of extracting electrical parameters like DC conductivity, mobility, and carrier concentration of single-layer graphene using Terahertz time-domain spectroscopy (THz-TDS) in transmission geometry. We utilize a simple Drude model and its extension, the Drude-Smith model, capable of accommodating charge carrier scattering by optical and acoustic phonons to describe the complex conductivity in graphene. We observed that a significant area of the sample scanned followed the Drude-Smith behavior, while at a few locations, the simple Drude behavior prevailed. We identified possible reasons that might contribute to this observation, like defects, the presence of polymer residue on the sample, or the sample strain. This research sheds new light on our understanding of graphene's electrical properties and lays the foundation for future investigations addressing defects and contaminants in graphene samples.

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Neuromuscular fatigue detection by mechanomyography in people with complete spinal cord injury

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Research on Biomedical engineering 2020年第3期36卷 203-212页

作者： Krueger, Eddy Popović-Maneski, Lana Neto, Guilherme Nunes Nogueira Mendonça Scheeren, Eduardo Fiusa, Jessika Mehret Nohama, Percy Neural Engineering and Rehabilitation Laboratory Universidade Estadual de Londrina LondrinaPR Brazil Graduate Program in Rehabilitation Sciences UEL-UNOPAR Campus Universitário s/n zipcode LondrinaPR86051-990 Brazil Institute of Technical Sciences of the Serbian Academy of Sciences and Arts KnezMihailova 35/IV zipcode Belgrade11000 Serbia Graduate Program in Health Technology Pontifícia Universidade Católica do Paraná Rua Imaculada Conceição 1155 Prado Velho zipcode CuritibaPR80215-901 Brazil Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Av. Sete de Setembro 3165 zipcode CuritibaPR80230-901 Brazil

Purpose: Functional electrical stimulation (FES) is a method of activating paralyzed muscles. During FES application, fast muscle fatigue can occur (the inability of stimulated muscles to generate force). Therefore, it is beneficial to estimate the muscle fatigue for FES closed-loop control for walking to prevent unexpected muscle collapse and adapt the FES strategy in real time. Mechanomyography (MMG) is a noninvasive technique for registering myofiber vibrations, representing an ideal candidate for the provision of feedback. The hypothesis was that MMG signals could effectively detect muscle fatigue and, thus, provide feedback. Methods: We tested this hypothesis by analyzing the wavelet transform of signals from an MMG sensor positioned over the rectus femoris muscle during electrically evoked contractions in subjects with spinal cord injury (SCI). The signals were collected from a total of 24 lower limb muscles. We investigated both legs of 15 participants with spinal cord injury (male, YOA = 27.13 ± 5.05, M = 75.8 ± 10.35 kg, and H = 1.78 ± 0.07 m, American Spinal Injury Impairment Scale (AIS) A and B). All MMG signals were analyzed in 12 frequency bands from 5 to 53 Hz. Results: We found different trends in the magnitudes in different frequency bands. The magnitude decreased in 13, 16, 20, 25, and 35 Hz bands in correlation with fatigue. The greatest statistical difference was found at 20 Hz and 25 Hz. Conclusion: This result suggests that processed MMG signals indicate muscle fatigue and can, thus, be used as the feedback in FES systems. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Muscle

Photonic Terahertz Phased Array

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arXiv 2024年

作者： Niu, Li Feng, Xi Zhang, Xueqian Lu, Yongchang Wang, Qingwei Xu, Quan Chen, Xieyu Ma, Jiajun Qiu, Haidi Sha, Wei E.I. Zhang, Shuang Alù, Andrea Zhang, Weili Han, Jiaguang Center for Terahertz Waves College of Precision Instrument and Optoelectronics Engineering The Key Laboratory of Optoelectronics Information and Technology Tianjin University Tianjin300072 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical & Electronic Engineering University of Hong Kong 999077 Hong Kong Photonics Initiative Advanced Science Research Center City University of New York New York10031 United States Physics Program Graduate Center City University of New York New York10016 United States School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China

Phased arrays are crucial in various technologies, such as radar and wireless communications, due to their ability to precisely control and steer electromagnetic waves. This precise control improves signal processing and enhances imaging performance. However, extending phased arrays to the terahertz (THz) frequency range has proven challenging, especially for high-frequency operation, broadband performance, two-dimensional (2D) phase control with large antenna arrays, and strong phase modulation. Here, we introduce a photonic platform to realize a THz phased array that bypasses the above challenges. Our method employs 2D phase coding with 2-bit across a broad THz frequency range from 0.8 to 1.4 THz. The core of our design is a pixelated nonlinear Pancharatnam-Berry metasurface driven by a spatially modulated femtosecond laser, allowing precise phase control of THz signals. We showcase the effectiveness of our method through four proof-of-concept applications: single beamforming, dual beamforming, imaging and vortex beam generation. The realized photonic platform provides a promising pathway for developing broadband phased arrays in the THz regime. Copyright © 2024, The Authors. All rights reserved.

关键词： Terahertz waves

Novel diamond shuttle to deliver flexible neural probe with reduced tissue compression

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Microsystems & Nanoengineering 2020年第1期6卷 903-915页

作者： Kyounghwan Na Zachariah J.Sperry Jiaao Lu Mihaly Voroslakos Saman S.Parizi Tim M.Bruns Euisik Yoon John P.Seymour Electrical Engineering&Computer Science Department University of MichiganAnn ArborMI 48105USA Biomedical Engineering Department University of MichiganAnn ArborMI 48105USA Biointerfaces Institute University of MichiganAnn ArborMI 48105USA The Neuroscience Institute New York UniversityNew YorkNY 10016USA Center for Nanomedicine Institute for Basic Science(IBS)Seoul 03722Korea Graduate Program of Nano Biomedical Engineering(Nano BME) Yonsei-IBS InstituteYonsei UniversitySeoul 03722Korea Vivian L Smith Department of Neurosurgery University of Texas Health Science CenterHoustonTX 77030USA Department of Electrical and Computer Engineering Rice UniversityHoustonTX 77005USA

The ability to deliver flexible biosensors through the toughest membranes of the central and peripheral nervous system is an important challenge in neuroscience and neural *** devices implanted through dura mater and thick epineurium would ideally create minimal compression and acute damage as they reach the neurons of *** demonstrate that a three-dimensional diamond shuttle can be easily made with a vertical support to deliver ultra-compliant polymer microelectrodes(4.5-µm thick)through dura mater and thick *** diamond shuttle has 54%less cross-sectional area than an equivalently stiff silicon shuttle,which we simulated will result in a 37%reduction in blood vessel *** also discovered that higher frequency oscillation of the shuttle(200Hz)significantly reduced tissue compression regardless of the insertion speed,while slow speeds also independently reduced tissue *** and recording performance are demonstrated in rat and feline models,but the large design space of these tools are suitable for research in a variety of animal models and nervous system targets.

关键词： sectional neural thick