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Disruptive Technologies (ICDT), International Conference on

作者： Senthil Kumar Nageswara Rao Atyam Khadar Nawas K Shashank Awasthi M. Kotteeswaran K. S. Thirunavukkarasu KL School of Architecture Koneru Lakshmanan Education Foundation Guntur Andhra Pradesh India Electrical and Electronics Department CMR Institute of Technology Bengaluru Karnataka India School of Computing Science and Engineering Vellore Institute of Technolog Chennai Tamil Nadu India Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India School of Management and Commerce Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India School of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards.

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High-performance UV polarization sensitive photodetector for a graphene(2D)/GaN(3D) junction with a non-centrosymmetric electric field

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Photonics Research 2025年第6期13卷 1544-1553页

作者： Zou, Can Liu, Qing Zhang, Lu Tang, Xiao Li, Xiaohang Li, Shuti Gao, Fangliang Guangdong Engineering Research Center of Optoelectronic Functional Materials and Devices School of Electronic Science and Engineering School of Microelectronics South China Normal University Guangzhou510631 China Advanced Semiconductor Laboratory Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology Thuwal239556900 Saudi Arabia

This study pioneers a high-performance UV polarization-sensitive photodetector by ingeniously integrating non-centrosymmetric metal nanostructures into a graphene (Gr)∕Al2O3∕GaN heterojunction. Unlike conventional approaches constrained by graphene’s intrinsic isotropy or complex nanoscale patterning, our design introduces asymmetric metal architectures (E-/T-type) to artificially create directional anisotropy. These structures generate plasmon-enhanced localized electric fields that selectively amplify photogenerated carrier momentum under polarized UV light (325 nm), synergized with Fowler-Nordheim tunneling (FNT) across an atomically thin Al2O3 barrier. The result is a breakthrough in performance: a record anisotropy ratio of 115.5 (E-type, −2 V) and exceptional responsivity (97.7 A/W), surpassing existing graphene-based detectors by over an order of magnitude. Crucially, by systematically modulating metal geometry and density, we demonstrate a universal platform adaptable to diverse 2D/3D systems. This study provides a valuable reference for developing and practically applying photodetectors with higher anisotropy than ultraviolet polarization sensitivity. © 2025 Chinese Laser Press.

关键词： Nanoparticles

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Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

arXiv

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arXiv 2024年

作者： Xu, Yucheng Yang, Jia-Qi Fan, Kebin Wang, Sheng Wu, Jingbo Zhang, Caihong Zhan, De-Chuan Padilla, Willie J. Jin, Biaobing Chen, Jian Wu, Peiheng Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE School of Electronic Science and Engineering Nanjing University Nanjing210023 China Purple Mountain Laboratories Nanjing211111 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering Duke University Box 90291 DurhamNC27708 United States

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. © 2024, CC0.

关键词： Tuning

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DNA serial dilution: an investigation toward accurate broadband plasmonic signature 22

DNA serial dilution: an investigation toward accurate broadb...

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Plasmonics in Biology and Medicine XXII 2025

作者： Dehzangi, Arash Ghasemi, Masih Choudhury, P.K. Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX United States Laser and Plasma Research Institute Shahid Beheshti University Daneshju Blvd.Evin Tehran Iran College of Optical Science and Engineering International Research Center for Advanced Photonics Zhejiang University Building 1A 718 East Haizhou Rd. Zhejiang Haining China

ISBN: (纸本)9781510684225

Serial dilution of Balb/c rat DNA in the visible broadband plasmonic spectroscopy was explored as a promising technique to reduce this genomic substance concentration in the nanoscale. The physical DNA of a living creature, after extraction from the resources (such as blood, nail, hair, tissue, and other possible organs) assumes amorphous pile of strands in the microscale or larger. The serial dilution technique was exploited to exponentially decrease the DNA concentration of BALB/c rat - the target living mammal - in the nanoscale, and the diluted DNA was deposited over a thin film of gold (for fixturing and removing moisture). Plasmonic surface waves of different frequencies were found to promote wave propagation more efficiently when lower thickness of unstructured-nanoscale-DNA layer was deposited (over gold thin film). The results manifest that the most diluted DNA sample reveals distinguishable plasmonic conditions. The sensitivity of the configuration was enhanced in the presence of a DNA sample (as compared to the case of non-existing measurand), which was further increased for larger incidence angles. © 2025 SPIE.

关键词： Surface plasmon resonance

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Electron Confinement-Induced Plasmonic Breakdown in Metals

arXiv

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arXiv 2024年

作者： Das, Prasanna Rudra, Sourav Rao, Dheemahi Banerjee, Souvik Indiradevi, Ashalatha Pillai, Kamalasanan Garbrecht, Magnus Boltasseva, Alexandra Bondarev, Igor V. Shalaev, Vladimir M. Saha, Bivas Chemistry and Physics of Materials Unit International Centre for Materials Science Jawaharlal Nehru Centre for Advanced Scientific Research Bangalore560064 India Sydney Microscopy and Microanalysis The University of Sydney CamperdownNSW2006 Australia Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Department of Mathematics & Physics North Carolina Central University DurhamNC27707 United States School of Advanced Materials and Sheikh Saqr Laboratory Jawaharlal Nehru Centre for Advanced Scientific Research Bangalore560064 India

Plasmon resonance in metals represents the collective oscillation of the free electron gas density and enables enhanced light-matter interactions in nanoscale dimensions 1–4. Traditionally, the classical Drude model describes the plasmonic excitation, wherein the plasma frequency exhibits no spatial dispersion 5. Here, we show conclusive experimental evidence of the breakdown of the plasmon resonance and a consequent photonic metal-insulator transition in an ultrathin archetypal refractory plasmonic material, hafnium nitride (HfN). Epitaxial HfN thick films exhibit a low-loss and high-quality Drude-like plasmon resonance in the visible spectral range 6,7. However, as the film thickness is reduced to nanoscale dimensions, the Coulomb interaction among electrons increases due to the electron confinement, leading to the spatial dispersion of the plasma frequency 8,9. Importantly, with the further decrease in thickness, electrons lose their ability to shield the incident electric field, turning the medium into a dielectric. The breakdown of the plasmon resonance in epitaxial ultrathin metals could be useful for fundamental physics studies in transdimensional regimes 10 and novel photonic device applications. © 2024, CC BY.

关键词： Electrons

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A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

arXiv

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arXiv 2024年

作者： Hickert, Cameron Yan, Zhongxia Wu, Cathy The Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems The Institute for Data Systems and Society The Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a 'scalable supervision' concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs - which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied - and (ii) aggregation across larger regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Optimization for Deep Learning:An Overview

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Journal of the Operations Research Society of China 2020年第2期8卷 249-294页

作者： Ruo-Yu Sun Department of Industrial and Enterprise Systems Engineering and affiliated to Coordinated Science LaboratoryUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Optimization is a critical component in deep *** think optimization for neural networks is an interesting topic for theoretical research due to various ***,its tractability despite non-convexity is an intriguing question and may greatly expand our understanding of tractable ***,classical optimization theory is far from enough to explain many ***,we would like to understand the challenges and opportunities from a theoretical perspective and review the existing research in this ***,we discuss the issue of gradient explosion/vanishing and the more general issue of undesirable spectrum and then discuss practical solutions including careful initialization,normalization methods and skip ***,we review generic optimization methods used in training neural networks,such as stochastic gradient descent and adaptive gradient methods,and existing theoretical ***,we review existing research on the global issues of neural network training,including results on global landscape,mode connectivity,lottery ticket hypothesis and neural tangent kernel.

关键词： Deep learning Non-convex optimization Neural networks Convergence Landscape

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Chiral, Topological, and Knotted Colloids in Liquid Crystals

arXiv

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arXiv 2024年

作者： Yuan, Ye Smalyukh, Ivan I. Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan Department of Physics Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program Soft Materials Research Center University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

The geometric shape, symmetry, and topology of colloidal particles often allow for controlling colloidal phase behavior and physical properties of these soft matter systems. In liquid crystalline dispersions, colloidal particles with low symmetry and nontrivial topology of surface confinement are of particular interest, including surfaces shaped as handlebodies, spirals, knots, multi-component links, and so on. These types of colloidal surfaces induce topologically nontrivial three-dimensional director field configurations and topological defects. Director switching by electric fields, laser tweezing of defects, and local photo-thermal melting of the liquid crystal host medium promote transformations among many stable and metastable particle-induced director configurations that can be revealed by means of direct label-free three-dimensional nonlinear optical imaging. The interplay between topologies of colloidal surfaces, director fields, and defects is found to show a number of unexpected features, such as knotting and linking of line defects, often uniquely arising from the nonpolar nature of the nematic director field. This review article highlights fascinating examples of new physical behavior arising from the interplay of nematic molecular order and both chiral symmetry and topology of colloidal inclusions within the nematic host. Furthermore, the article concludes with a brief discussion of how these findings may lay the groundwork for new types of topology-dictated self-assembly in soft condensed matter leading to novel mesostructured composite materials, as well as for experimental insights into the pure-math aspects of low-dimensional topology. © 2024, CC BY.

关键词： Crystal symmetry

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ComBat harmonization for multicenter MRI based radiomics features

ComBat harmonization for multicenter MRI based radiomics fea...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Elisavet Stamoulou Georgios C. Manikis Manolis Tsiknakis Kostas Marias Computational Biomedicine Laboratory Institute of Computer Science FORTH-Hellas Heraklion Greece Department of Electrical & Computer Engineering Computational Biomedicine Laboratory Institute of Computer Science FORTHHellas Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9781728173719

ISBN: (纸本)9781728173726

Radiomics, the high-throughput feature extraction from medical images, has gained momentum in radiology and the noninvasive assessment of the tumor, treatment response and therapy monitoring. A significant effort has been put to incorporate radiomics in the clinical setting, towards developing radiomics-based machine learning models from a large amount of images across multiple centers. However, multicenter radiomics has been proven to be sensitive to different scanner models, acquisition protocols and reconstruction settings, deriving imaging features of increased variability that hamper robustness and generalizability of the models. Recent literature has stressed the importance of utilizing harmonization techniques to compensate for multicenter effects. These include traditional image-based techniques and harmonization at the feature level using ComBat. The latter has been shown to have increased performance in several radiomics studies. To this end, this study investigated the capability of ComBat and M-ComBat to eliminate the “center-effect” of multicenter images from an heterogeneous TCIA public dataset. Both methods were tested on 102 patients with WHO grade IV glioblastomas and 64 patients with WHO grade II and III glioma and performance was assessed using a machine learning pipeline comprising combinations of different feature selection, data resampling and machine learning models. A significant increase in the performance was obtained using M-ComBat, yielding a Matthew Correlation Coefficient of $0.91 \pm 0.05$ compared to $0.76 \pm 0.08$ that was obtained with the absence of any harmonization prior to model development.

关键词： Protocols Pipelines Medical treatment Machine learning Radiology Feature extraction Robustness

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Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing

arXiv

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arXiv 2022年

作者： Ma, Haoming Yuan, Xiaojun Ding, Zhi Fan, Dian Fang, Jun The National Key Laboratory of Science and Technology on Communication The University of Electronic Science and Technology of China Chengdu China The Department of Electrical and Computer Engineering University of California at Davis DavisCA95616 United States

To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). In OA-FMTL, the local updates of edge devices are sparsified, compressed, and then sent over the uplink channel in a superimposed fashion. The ES employs over-the-air computation in the presence of inter-task interference. More specifically, the model aggregations of all the tasks are reconstructed from the channel observations concurrently, based on a modified version of the turbo compressed sensing (Turbo-CS) algorithm (named as M-Turbo-CS). We analyze the performance of the proposed OA-FMTL framework together with the M-Turbo-CS algorithm. Furthermore, based on the analysis, we formulate a communication-learning optimization problem to improve the system performance by adjusting the power allocation among the tasks at the edge devices. Numerical simulations show that our proposed OA-FMTL effectively suppresses the inter-task interference, and achieves a learning performance comparable to its counterpart with orthogonal multi-task transmission. It is also shown that the proposed inter-task power allocation optimization algorithm substantially reduces the overall communication overhead by appropriately adjusting the power allocation among the tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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