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arXiv 2019年

作者： Chien, Ching-Hang Wu, Shang-Hsuan Ngo, Trong Huynh-Buu Chang, Yia-Chung Research Center for Applied Sciences Academia Sinica Taipei115 Taiwan Nano Science and Technology Program TIGP Academia Sinica Taipei115 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu300 Taiwan Department of Electrical and Computer Engineering University of Texas at Austin United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2V4 Canada

A theoretical model for describing the emission spectra of microsphere cavities is presented, and its predictions of detailed lineshapes of emission spectra associated with whispering gallery modes (WGMs) of various orders in ZnO microspheres (MSs) are verified experimentally by photoluminescence (PL) spectroscopy. The interplay of Purcell effect, quality factor, and leaky modes in spontaneous and stimulated emission spectra related to WGMs of all orders is revealed. The key success of the theory is based on the expansion of the full Green function of the MS in terms of all possible resonance modes in complex frequency space, which allows incorporation of contributions from leaky modes, stimulated emission processes, and Purcell effect. We show that the spontaneous emission spectrum calculated according to Mie theory (without Purcell effect) is dominated by the contribution of leaky modes, while the spontaneous and stimulated emission enhanced by Purcell effect are responsible for the main WGM resonance peaks observed experimentally. It is found that the stimulated emission peaks are doubly enhanced by their respective mode quality factor Q: one factor from the Purcell effect and the other factor from the photon number derived from the rate equation. After combining all these effects the theory can provide a quantitative description of fine features of both TE and TM modes (including higher-order modes) observed in the PL spectra of ZnO MSs. Surprisingly, it is found that for ZnO MS with diameter larger than 5 µm, the PL emission spectrum is dominated by higher-order modes. The quantitative understanding of the interplay of these emission mechanisms should prove useful for optimizing the performance of light-emitting devices based on micro resonators. Copyright © 2019, The Authors. All rights reserved.

关键词： Microspheres

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ELM vs. WiSARD: A performance comparison 25

ELM vs. WiSARD: A performance comparison

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25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Oliveira, Luiz Fernando R. França, Felipe M.G. Systems Engineering and Computer Science Program COPPE Universidade Federal Do Rio de Janeiro Brazil

ISBN: (纸本)9782875870391

The extreme learning machine (ELM) is known for being a fast learning neural model. This work presents a performance comparison between ELM and the WiSARD weightless neural network model, regarding training and testing times, and classification accuracy as well. The two models were implemented in the same programming language and experiments were carried out on the same hardware environment. By using a group of datasets from the public repositories UCI and Statlog, experimental results shows that the WiSARD presented training times approximately one order of magnitude smaller than ELM, while classification accuracy varied according the number of classes involved. However, while WiSARD's architecture setups were not exhaustively searched, architecture setups for ELM were kept the same as the ones found in the literature as the best for each given dataset. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Classification (of information)

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OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

arXiv

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arXiv 2022年

作者： Babier, Aaron Mahmood, Rafid Zhang, Binghao Alves, Victor G.L. Barragán-Montero, Ana Maria Beaudry, Joel Cardenas, Carlos E. Chang, Yankui Chen, Zijie Chun, Jaehee Diaz, Kelly Eraso, Harold David Faustmann, Erik Gaj, Sibaji Gay, Skylar Gronberg, Mary Guo, Bingqi He, Junjun Heilemann, Gerd Hira, Sanchit Huang, Yuliang Ji, Fuxin Jiang, Dashan Giraldo, Jean Carlo Jimenez Lee, Hoyeon Lian, Jun Liu, Shuolin Liu, Keng-Chi Marrugo, José Miki, Kentaro Nakamura, Kunio Netherton, Tucker Nguyen, Dan Nourzadeh, Hamidreza Osman, Alexander F.I. Peng, Zhao Muñoz, José Darío Quinto Ramsl, Christian Rhee, Dong Joo Rodriguez, Juan David Shan, Hongming Siebers, Jeffrey V. Soomro, Mumtaz H. Sun, Kay Hoyos, Andrés Usuga Valderrama, Carlos Verbeek, Rob Wang, Enpei Willems, Siri Wu, Qi Xu, Xuanang Yang, Sen Yuan, Lulin Zhu, Simeng Zimmermann, Lukas Moore, Kevin L. Purdie, Thomas G. McNiven, Andrea L. Chan, Timothy C.Y. Department Of Mechanical And Industrial Engineering University Of Toronto TorontoON Canada Vector Institute TorontoON Canada Department Of Radiation Oncology University Of Virginia Health System CharlottesvilleVA United States Department Of Molecular Imaging Radiation Oncology UCLouvain Louvain-la-Neuve Belgium Department Of Radiation Oncology Memorial Sloan Kettering Cancer Center New YorkNY United States Department Of Radiation Oncology The University Of Alabama At Birmingham BirminghamAL United States Department Of Engineering And Applied Physics University Of Science And Technology Of China Hefei China Shenying Medical Technology Co. Ltd. Guangdong Shenzhen China Department Of Radiation Oncology Yonsei University Seoul Korea Republic of Department Of Physics National University Of Colombia Medellín Colombia Atominstitut Vienna University Of Technology Vienna Austria Department Of Biomedical Engineering Cleveland Clinic ClevelandOH United States Department Of Radiation Physics The University Of Texas MD Anderson Cancer Center HoustonTX United States Department Of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department Of Radiation Oncology Medical University Of Vienna Vienna Austria Department Of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Department Of Radiation Oncology Peking University Cancer Hospital And Institute Beijing China Department Of Electrical Engineering And Automation Anhui University Hefei China Department Of Radiation Oncology Massachusetts General Hospital BostonMA United States Department Of Radiation Oncology University Of North Carolina At Chapel Hill Chapel HillNC United States Department Of Medical Imaging Taiwan AI Labs Taipei Taiwan Department Of Biomedical And Health Sciences Hiroshima University Hiroshima Japan Laboratory Department Of Radiation Oncology The University Of Texas Southwestern Medical Center DallasTX United States Department Of Radiatio

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved;the deviation in dosevolume histogram (DVH) criterion;and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBPgenerated plans performed significantly better (P © 2022, CC BY.

关键词： Forecasting

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DEA and Fuzzy Simple Additive Weighting for Benchmarking Qualitative Data 1

DEA and Fuzzy Simple Additive Weighting for Benchmarking Qua...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Abdullah, Dahlan Hartono, H. Ongko, Erianto Erliana, Cut Ita Irwansyah, Defi Iskandar, Akbar Sari, Ayu Esteka Abdussakir Yuniningsih Siregar, Nurintan Asyiah Kahar, Muhammad Syahrul Waliulu, Raditya Faisal Amalia, Ainna Kurniasih, Nuning Arifin, Muhammad Muzid, Syafiul Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Economic and Business Universitas Pembangunan Nasional Veteran Surabaya Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Mathematics Education Department Universitas Muhammadiyah Sorong Sorong Indonesia Informatics Engineering Department Universitas Muhammadiyah Sorong Sorong Indonesia Islamic Institut of Miftahul Ula Kertosono Nganjuk East Java Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Information System Faculty of Engineering Universitas Muria Kudus Indonesia Information System Department Universitas Muria Kudus Indonesia

Data Envelopment Analysis (DEA) is one method that is often used in the benchmarking process. The benchmarking process with DEA is often faced with problems where there is a Decision Making Unit (DMU) both Input and Output which is not in the form of qualitative data but in the form of qualitative data. Qualitative data requires an assessment from the appraiser so that it can become quantitative data. This study will use the Fuzzy Simple Additive Weighting method to convert qualitative data into quantitative. The results of this study will produce a model that integrates DEA with Fuzzy Simple Additive Weighting. © Published under licence by IOP Publishing Ltd.

关键词： Data envelopment analysis

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The Application of Fuzzy Logic Method in the Debtors Eligibility Assessment system of Microfinance Institution

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IOP Conference Series: Materials science and engineering 2020年第1期879卷

作者： S L B Ginting M R M Rizky Y R Ginting Sutono Computer System Study Program Faculty of Engineering and Computer Science Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung 40132 Indonesia. Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 125 Simpang Baru Pekanbaru 28293 Indonesia.

Decision-making processes in determining loan eligibility are often subjective which leads to imprecise credit predictions. Due to inaccurate inquiry on prospective customers done by field survey officers of Bank Perkreditan Rakyat (BPR) Bandung City, it has experienced credit complications such as bad credits. Therefore, this study aims to help decision-makers in determining creditworthiness and preventing bad credits from occurring. To realize this solution, the study uses the Fuzzy Logic method to calculate the creditworthiness of each prospective loaner based on the inquiries done in the field survey. Fuzzy Logic is known to be a 'counting' methodology with varying words. In addition, it can implement human expertise into machine language with ease and adequately. Based on numerous testing performed, the results demonstrate a level of 90% in accuracy when inputting within the valid ranges of each fuzzy set and membership function. However, the level of accuracy is only based on the clarification result which is determined by a researcher and BPR director, not a general level of accuracy for other microfinance institutions. Nevertheless, the findings of this study prove the method has a high enough accuracy to support decision-makers in determining the loan eligibility of prospective loaners and through this application in the surveying process, survey workers can work more efficiently. Hence, in future has a higher chance of predicting bad credits from potential loaners.

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Closed-loop sparse channel estimation for wideband millimeter-wave full-dimensional MIMO systems

arXiv

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arXiv 2019年

作者： Liao, Anwen Gao, Zhen Wang, Hua Chen, Sheng Alouini, Mohamed-Slim Yin, Hao School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom King Abdulaziz University Jeddah21589 Saudi Arabia Electrical Engineering Program Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Makkah Province Saudi Arabia Institute of China Electronic System Engineering Corporation Beijing100141 China

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Millimeter waves

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Spoof-Fluid-Spoof Acoustic Waveguide and its Applications for Sound Manipulation

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Physical Review Applied 2019年第5期12卷 054014-054014页

作者： Norbert Cselyuszka Andrea Alù Nikolina Janković BioSense Institute University of Novi Sad Novi Sad Serbia Silicon Austria Labs Sensor System Microsystem Technologies Villach Austria Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10026 USA Department of Electrical Engineering City College of New York New York New York 10031 USA

We explore the implementation of a spoof–fluid-spoof (SFS) acoustic waveguide, which is the acoustic analogue of a metal-insulator–metal waveguide in plasmonics. The proposed structure consists of two corrugated rigid surfaces separated by a fluid layer. The properties of the guided acoustic modes between corrugated rigid parallel plates are analytically and numerically studied by deriving their dispersion relation and through a careful analysis of symmetric and antisymmetric modes supported by this structure. Similar to plasmonic waveguides, it is shown that the dispersion relation of the proposed SFS acoustic waveguide, and the supported group and phase velocities, can be largely controlled by changes in the geometrical parameters of the waveguide, opening the possibility for application of this technology for acoustic delay lines, modulators, and sensors. The analytical and numerical results are validated through experiments in a controlled environment.

关键词： Acoustic measurements Acoustic metamaterials Acoustic modeling Acoustic wave phenomena Plasmonics Surface plasmon polariton Waveguides Surface acoustic wave

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