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Fully integrated and br.a.band Si-rich silicon nitride wavelength converter based on Bragg scattering intermodal four-wave mixing

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Photonics research 2024年第S1期12卷 I0001-I0010页

作者： VALErIO VITALI THALÍA DOMÍNGUEZ BUCIO HAO LIU JOSÉMANUEL LUQUE GONZÁLEZ FrANCISCO JUrADO-rOMErO ALEJANDrO OrTEGA-MOÑUX GLENN CHUrCHILL JAMES C.GATES JAMES HILLIEr NIKOLAOS KALFAGIANNIS DANIELE MELATI JENS H.SCHMID ILAr.a.CrISTIANI PAVEL CHEBEN J.GONZALO WANGÜEMErT-PÉrEZ ÑIGO MOLINA-FErNÁNDEZ FrEDErIC GArDES COSIMO LACAVA PErIKLIS PETrOPOULOS Optoelectronics Research Centre University of SouthamptonSouthampton SO171BJUK Electrical Computer and Biomedical Engineering DepartmentUniversity of PaviaPavia 27100Italy Telecommunication Research Institute(TELMA) Universidad de MálagaCEI Andalucı´a TECHE.T.S.I.Telecomunicación29010 MálagaSpain School of Science and Technology Nottingham Trent UniversityNottingham NG118NSUK Department of Electrical Engineering Eindhoven University of TechnologyEindhoven 5600 MBThe Netherlands Department of Materials Science and Engineering University of IoanninaIoannina 45110Greece Centre de Nanosciences et de Nanotechnologies UniversitéParis-SaclayCNRS91120 PalaiseauFrance Advanced Electronics and Photonics Research Center National Research Council CanadaOttawaOntario K1A 0R6Canada

Intermodal four-wave mixing(FWM)processes have recently attracted significant interest for.a.l-optical signal processing applications thanks to the possibility to control the propagation properties of waves exciting distinct spatial modes of the same *** allows,in principle,to place signals in different spectral regions and satisfy the phase matching condition over considerably larger bandwidths compared to intramodal ***,the demonstrations reported so far have shown a limited bandwidth and suffered from the lack of on-chip components designed for br.a.band manipulation of different *** demonstrate here a silicon-rich silicon nitride wavelength converter based on Bragg scattering intermodal FWM,which integrates mode conversion,multiplexing and de-multiplexing functionalities *** system enables wavelength conversion between pump waves and a signal located in different telecommunication bands(separated by 60 nm)with a 3 dB bandwidth exceeding 70 nm,which represents,to our knowledge,the widest bandwidth ever.a.hieved in an intermodal FWM-based system.

关键词： scattering pump modal

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IoT-Based Smart Cities: Challenges and Future Perspectives

IoT-Based Smart Cities: Challenges and Future Perspectives

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Inter.a.ional Conference on Science technology engineering and Management (ICONSTEM)

作者： Charanjeet Singh Shikha Chadha S. Bathrinath Ila Dixit Suganthi P T. Sathish Department of Electronics and Communication Engineering DeenbandhuChhotu Ram University of Science and Technology Haryana India Department of Computer Science and Engineering Sharda University Greater Noida Uttar Pradesh India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Management Maharaja Agarsen International College Raipur Chhattisgarh India Department of Mathematics R.M.K. Engineering College Tiruvallur Tamil Nadu India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The integration of Internet of Effects (IoT) technology into civic structure has heralded the arrival of intelligent metropolises, promising enhanced effectiveness, sustainability, and quality of life for citizens. still, the perpetration of IoT-grounded results in civic surroundings isn't devoid of challenges. This paper investigates the multifaceted challenges encountered in the development and deployment of IoT-driven innovative megacity enterprises. From issues of interoperability and security to enterprises regarding data sequestration and ethical counteraccusations, colorful hurdles stymie the flawless integration of IoT into civic geographies. Likewise, the paper explores implicit avenues for overcoming these challenges and presents unborn perspectives for the elaboration of IoT-enabled smart metropolises. By addressing these obstacles and visioning unborn directions, this exploration contributes to a deeper understanding of the complications girding IoTgrounded smart metropolises, paving the way for further flexible, sustainable, and inclusive civic surroundings in the times to come.

关键词： Geography Ethics Smart cities Mathematics Security Internet of Things Sustainable development

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Clifford circuit based heuristic optimization of fermion-to-qubit mappings

arXiv

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arXiv 2025年

作者： Yu, Jeffery Liu, Yuan Sugiura, Sho Van Voorhis, Troy Zeytinŏglu, Sina Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Joint Quantum Institute NIST University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27606 United States Department of Computer Science North Carolina State University RaleighNC27606 United States Department of Physics North Carolina State University RaleighNC27606 United States Blocq Inc. 717 Market Street San FranciscoCA94103 United States Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Theoretical Physics TU Wien Wiedner Hauptstraße 8-10/136 ViennaA-1040 Austria Physics and Informatics Laboratory NTT Research Inc. SunnyvaleCA94085 United States

Simulation of interacting fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analysing fermionic systems with a quantum computer hinges on the efficiency of fermion-to-qubit mappings that encode non-local fermionic degrees of freedom in local qubit degrees of freedom. While recent works have highlighted the impor.a.ce of designing fermion-to-qubit mappings that are tailored to specific problem Hamiltonians, the methods proposed so far.a.e either restricted to a narrow class of mappings or they use computationally expensive and unscalable brute-force search algorithms. Here, we address this challenge by designing a heuristic numerical optimization framework for fermion-to-qubit mappings. To this end, we first translate the fermion-to-qubit mapping problem to a Clifford circuit optimization problem, and then use simulated annealing to optimize the average Pauli weight of the problem Hamiltonian. For.a.l fermionic Hamiltonians we have considered, the numerically optimized mappings outperform their conventional counter.a.ts, including ter.a.y-tree-based mappings that are known to be optimal for single cr.a.ion and annihilation operators. We find that our optimized mappings yield between 15% to 40% improvements on the average Pauli weight when the simulation Hamiltonian has an intermediate level of complexity. Most remar.a.ly, the optimized mappings improve the average Pauli weight for 6×6 nearest-neighbor hopping and Hubbard models by more than 40% and 20%, respectively. Surprisingly, we also find specific interaction Hamiltonians for which the optimized mapping outperform any ter.a.y-tree-based mapping. Our results establish heuristic numerical optimization as an effective method for obtaining mappings tailored for specific fermionic Hamiltonian. © 2025, CC BY.

关键词： Hamiltonians

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Coherent VCSEL Network Computing 27

Coherent VCSEL Network Computing

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27th OptoElectronics and Communications Conference/Inter.a.ional Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Chen, Zaijun Sludds, Alexander Davis, ronald Christen, Ian Ateshian, Lamia Bernstein, Liane Heuser, Tobias Heermeier, Niels Lott, James reitzenstein, Stephan Hamerly, r.a. Englund, Dirk Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Fakultät Ii Institut für Festkörperphysik Sekretariat Technische Universität Berlin Berlin10623 Germany Ntt Research Inc. Physics & Informatics Laboratories SunnyvaleCA94085 United States

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Deep learning

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Assessment of Low Temperature Configurations on Energy, Exergy, Exergoeconomic and Exergoenvironmenal Perspectives

SSRN

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SSrN 2022年

作者： Ganesh, N. Shankar Maheswari, G. Uma Srinivas, T. reddy, B.V. Department of Computer Applications School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mechanical Engineering Kingston Engineering College Tamil Nadu Vellore632059 India Department of Mechanical Engineering Dr.B.R.Ambedkar National Institute of Technology Jalandhar N.I.T. Post Office Punjab Jalandhar144011 India OshawaON Canada

Low temperature power generation system suitable to recover waste heat from renewable source is investigated in the present work. First law analysis assess the perfor.a.ce of the system considered for investigation in energy aspect. The system adopted to work at hot sink conditions and investigation on perfor.a.ce assessment is carried out in exergy aspect. Exergy analysis in both conventional and advanced methods propose the system components which need improvements on itself and in association with other components in more appropr.a.e manner. Exergoenvironmental analysis using Life cycle assessment (LCA) method is examined in the system at the same sink conditions. Exergy analysis reveals that the component with high source will yield more losses resulting higher irreversibility. Hence turbine and evaporator needs investigation in improving the system’s perfor.a.ce. Exergoenvironmental investigation suggests that the highest impact is resulted in the low temperature regenerator (LTrGN). The improvement in these components designs and limitations in the characteristics of mater.a. chosen and the manufacturing methods involved will favor.a.higher perfor.a.ce of the system. © 2022, The Authors. All rights reserved.

关键词： Exergy

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An Innovative Method for Improving Speech Intelligibility in Automatic Sound Classification Based on relative-CNN- rNN

An Innovative Method for Improving Speech Intelligibility in...

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Sustainable Communication Networks and Application (ICSCNA), Inter.a.ional Conference on

作者： Alkawati Magadum Monica Goud Gopinath V P. Prabbu Sankar B. Sivadharshini M. Ananthi MCA Dept MIT ADT - University College of Management Pune Maharastra India Department of MCA MIT ADT-University College of Management Pune Maharastra India Department of CSE K S R Institute for Engineering and Technology Tiruchengode Tamil Nadu India Department of Computer Science and Engineering Vel Tech multi tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Information Technology KGISL Institute of Technology Coimbatore Tamil Nadu India

Sound engineers and producers establish the speech-to-background ratio (SBr) during mixing depending on the rules of thumb and their own ears. However, there is no assurance that the general public will be able to understand the voice content. This research introduces a method for.a.tomatically selecting the optimal SBr for.a.scenario based on an objective intelligibility metric. For several types of ambient noise, the SBr estimated by the model is necessary to achieve a minimal intelligibility level when compared to the SBr selected by listeners. The model estimation was found to benefit from an additional gain even for nor.a.ly hearing listeners. The proposed method involves three stages: preprocessing, feature extraction, and model training. It employs a FIr filter for preprocessing and further the spectral centroid, roll-off, flux, and ZCr for feature extraction. r-CNN -rNN is used for training the model. The proposed method outperforms the CNN and CNN-rNN models.

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Comparative Analysis on Sediment Classification using Convolutional Neural Network

Comparative Analysis on Sediment Classification using Convol...

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Inter.a.ional Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： r Kumudham Sathish Kumar Leo L Megalan S Sivakumar T Venkateshkanna V. Velmurugan Department of ECE School of Engineering Vels Institute of Science and Technology and Advanced Studies (VISTAS) Chennai India Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of ECE School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Bharath Institute of Higher Education and Research (BIHER) Chennai India Department of Electronics and Communication Engineering JAYA Engineering college Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9781665462761

In this proposed work, we trained the neural network models for feature extraction and categorize the under.a.er seafloor sonar images into sediments, sands, mines, rocks, mud, gravels, etc using Convolutional Neural Network such as resNet50, Inceptionv3 and VGG-19. Moreover, the perfor.a.ce of neural network is estimating by metrics measures such as validation accuracy, accuracy, loss, and accuracy loss upto 10 epochs. Hence assessment had performed among all three neural network models. The experimental output signifies that resNet50 method achieves 94.4% appreciable accuracy with loss as 0.01 in distinguishing sonar imagery into sediments, sands, mud

关键词： Sonar measurements Sea floor Sonar rocks Feature extraction Loss measurement Sediments

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Machine Learning Appr.a.hes for Efficient Traffic Flow in Smart Cities

Machine Learning Approaches for Efficient Traffic Flow in Sm...

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Electrical Power engineering, Communication and Computing technology(ODICON), Odisha Inter.a.ional Conference on

作者： Aniket Kulkarni P. Anitha John Yesudas Valluri M V Sunena rose U Hemavathi Omar M. Hussein Software QA Manager Drata Inc San Diego CA USA Department of Computer Applications Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Faculty of Management Studies CMS Business School Jain (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Medical Technical College Medical Instrumentation Engineering Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of r.a.-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic str.a. prediction; this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accor.a.ce with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are cr.a.ed using data from four of the six junctions. The excellent perfor.a.ce metrics obtained by all ML and DL computations suggest that their.a.plication to intelligent traffic light management is feasible.

关键词： Measurement Power engineering Machine learning algorithms Statistical analysis Smart cities Transpor.a.ion Machine learning Prediction algorithms Timing Junctions

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Lightning-Fast Dual-Layer Lossless Coding for radiance For.a. High Dynamic range Images

arXiv

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arXiv 2023年

作者： Suzuki, Taizo Yukikata, Sara Yang, Kai Yoshida, Taichi The Institute of Engineering Information and Systems University of Tsukuba Ibaraki305-8573 Japan Sky Co. Ltd. Tokyo108-0075 Japan Da-Jiang Innovations Science and Technology Japan Co. Ltd. Tokyo108-0075 Japan The Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan

This paper proposes a fast dual-layer lossless coding for high dynamic range images (HDrIs) in the radiance for.a.. The coding, which consists of a base layer.a.d a lossless enhancement layer, provides a standard dynamic range image (SDrI) without requiring an additional algorithm at the decoder.a.d can losslessly decode the HDrI by adding the residual signals (residuals) between the HDrI and SDrI to the SDrI, if desired. To suppress the dynamic range of the residuals in the enhancement layer, the coding directly uses the mantissa and exponent infor.a.ion from the radiance for.a.. To further reduce the residual energy, each mantissa is modeled (estimated) as a linear function, i.e., a simple linear regression, of the encoded-decoded SDrI in each region with the same exponent. This is called simple linear regressive mantissa estimator. Experimental results show that, compared with existing methods, our coding reduces the average bitrate by approximately 1.57-6.68 % and significantly reduces the average encoder implementation time by approximately 87.13-98.96 %. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Generative Adver.a.ial Networks for Heterogeneous Unsupervised Domain Adaptation Detection

Generative Adversarial Networks for Heterogeneous Unsupervis...

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IEEE Inter.a.ional Conference on Intelligent engineering Systems (INES)

作者： Homayoun Safarpour Motealegh Mahalegi Amir.a.had Far.a.i György Molnár Enikő Nagy Institute of Mechatronics and Vehicle Engineering Óbuda University Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Domain adaptation, a subset of transfer learning, involves generating examples from two related but differently distributed source and target domains. This paper introduces an incremental adver.a.ial learning method for unsupervised domain adaptation, where the source data is labeled, and the target data remains unlabeled. We employ a discriminative adver.a.ial strategy as the primary technique for unsupervised domain adaptation to minimize the significant distributional var.a.ce between the source and target domains. Traditional domain adaptation methods typically train the source domain to align with a fixed target domain distribution. However, in many r.a.-world scenarios, the target domain's distribution continuously evolves, leading to a lack of generalizability in the trained model for subsequent domains. To tackle this issue, we suggest using a continuous sequential domain adaptation method. This involves including instances from several related target domains in the training process in a sequential and continuous manner. This process ensures that all receiving domains are correctly aligned with their respective sources, and to maintain sequence continuity, the source domain is incrementally updated by incorporating the most reliable data from the current target domain. This enhancement aims to improve the network's training for forthcoming domain data. Leveraging the effectiveness of deep neural networks, our method utilizes deep GANs for domain adaptation. The appr.a.h enhances domain matching by augmenting the source with selected target data and introduces a novel strategy for continuous sequential domain matching. This involves a progressive update of source domain samples with reliable data from the current target domain, facilitating seamless adaptation to successive domain shifts. The proposed method demonstrated a high classification accuracy, achieving an impressive 99% and 95% in different settings. Furthermore, it outperforms previous studies rega

关键词： Training Adaptation models Machine learning algorithms Accuracy Transfer learning Clustering algorithms Sensitivity and specificity

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