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Hybrid Cryptography on Wireless Sensor Network: A Systematic Literature Review 3

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Hybrid Cryptography on Wireless Sensor Network: A Systematic...

3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Gandara, R. Benny Wang, Gunawan Utama, Ditdit Nugeraha Mereu Buana University Master Program of Electrical Engineering Jakarta Indonesia Information Systems Management Department Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Jakarta11480 Indonesia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

Wireless technology gradual growth in the latest years has facilitated it to bridge its applications operated extensively in many types of industry. Wireless sensor network (WSN) technology brings several benefits over traditional wired technology by reason of the lower cost, immense flexibility escalation, and scalability which is able to be obtained from the old-style one. However, in its application WSN still experience some constraints on resource limitations in the form: processor, memory and energy, and any more challenges that related to availability, reliability and security. Based on its benefits, limitation, and challenges, and also three systematic stages, the review study was performed. The result of study was portrayed in this paper. It is regarding investigation result of hybrid cryptography (HC, as one of defense methods) realized for WSN to methodically increase the security level and decrease the resources usage. The result concluded that HC was proven in escalating the security defense level and downgrading the resources use on WSN compared to symmetric and asymmetric algorithms. © 2018 IEEE.

关键词： Wireless sensor networks

Observation of anti-parity-time-symmetry, phase transitions and exceptional points in an optical fibre

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arXiv 2020年

作者： Bergman, Arik Duggan, Robert Sharma, Kavita Tur, Moshe Zadok, Avi Alù, Andrea Faculty of Engineering Institute for Nano-Technology and Advanced Materials Bar-Ilan University Ramat-Gan5290002 Israel Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States School of Electrical Engineering Tel-Aviv University Tel-Aviv6997801 Israel Physics Program Graduate Center City University of New York New YorkNY10026 United States

The exotic physics emerging in non-Hermitian systems with balanced distributions of gain and loss has drawn a great deal of attention in recent years. These systems exhibit phase transitions and exceptional point singularities in their spectra, at which eigen-values and eigen-modes coalesce and the overall dimensionality is reduced. Among several peculiar phenomena observed at exceptional points, an especially intriguing property, with relevant practical potential, consists in the inherently enhanced sensitivity to small-scale perturbations. So far, however, these principles have been implemented at the expenses of precise fabrication and tuning requirements, involving tailored nano-structured devices with controlled distributions of optical gain and loss. In this work, anti-parity-time symmetric phase transitions and exceptional point singularities are demonstrated in a single strand of standard single-mode telecommunication fibre, using a setup consisting of entirely of off-the-shelf components. Two propagating signals are amplified and coupled through stimulated Brillouin scattering, which makes the process non-Hermitian and enables exquisite control over gain and loss. Singular response to small variations around the exceptional point and topological features arising around this singularity are experimentally demonstrated with large precision, enabling robustly enhanced spectral response to small-scale changes in the Brillouin frequency shift. Our findings open exciting opportunities for the exploration of non-Hermitian phenomena over a table-top setup, with straightforward extensions to higher-order Hamiltonians and applications in quantum optics, nanophotonics and sensing. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical fibers

Back propagation neural network experiment on team matchmaking MOBA game 1

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Back propagation neural network experiment on team matchmaki...

1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Tanuar, Evawaty Abbas, Bahtiar Saleh Trisetyarso, Agung Kang, Chul-Ho Gaol, Ford Lumban Suparta, Wayan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Sanata Dharma University Yogyakarta55282 Indonesia

ISBN: (纸本)9781538609545

Backpropagation is one of the famous method for learning, Implementing Backpropagation techniques in games is part of AI in game. This paper aim to analyzed the best team composition in a Moba Game type in learning using neural network. Mobile Legend: Bang Bang is one of MOBA game for mobile phone. In this experiment, the 2 hidden layers back-propagation neural network algorithm is used to analyze the Mobile Legend game. 5000 training steps was done to reach the stability. This also a way of learning to find the best composition in a team played. Research in area AI and game is very wide and open, a huge opportunity to be explore in future. The objective of this research is to produce the good combination of team which can be resulted in a greater winning chance. © 2018 IEEE.

关键词： Backpropagation

Training of Physical Neural Networks

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arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

Tunable nanophotonics enabled by chalcogenide phase-change materials

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arXiv 2020年

作者： Abdollahramezani, Sajjad Hemmatyar, Omid Taghinejad, Hossein Krasnok, Alex Kiarashinejad, Yashar Zandehshahvar, Mohammadreza Alù, Andrea Adibi, Ali School of Electrical and Computer Engineering Georgia Institute of Technology 778 Atlantic Drive NW AtlantaGA30332-0250 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Nanophotonics has garnered intensive attention due to its unique capabilities in molding the flow of light in the subwavelength regime. Metasurfaces (MSs) and photonic integrated circuits (PICs) enable the realization of mass-producible, cost-effective, and highly efficient flat optical components for imaging, sensing, and communications. In order to enable nanophotonics with multi-purpose functionalities, chalcogenide phase-change materials (PCMs) have been introduced as a promising platform for tunable and reconfigurable nanophotonic frameworks. Integration of non-volatile chalcogenide PCMs with unique properties such as drastic optical contrasts, fast switching speeds, and long-term stability grants substantial reconfiguration to the more conventional static nanophotonic platforms. In this review, we discuss state-of-the-art developments as well as emerging trends in tunable MSs and PICs using chalcogenide PCMs. We outline the unique material properties, structural transformation, electro-optic, and thermo-optic effects of well-established classes of chalcogenide PCMs. The emerging deep learning-based approaches for the optimization of reconfigurable MSs and the analysis of light-matter interactions are also discussed. The review is concluded by discussing existing challenges in the realization of adjustable nanophotonics and a perspective on the possible developments in this promising area. Copyright © 2020, The Authors. All rights reserved.

关键词： Phase change materials

THz Generation Control in Fe/X Spintronic Multilayers by chemical bonding at epitaxial Fe/GaAs(001) interfaces

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THz Generation Control in Fe/X Spintronic Multilayers by che...

Conference on Lasers and Electro-Optics (CLEO)

作者： Roman Adam Genyu Chen Daniel E. Bürgler Derang Cao Sarah Heidtfeld Debamitra Chakraborty Jing Cheng Ivan Komissarov Hilde Hardtdegen Martin Mikulics Claus M. Schneider Roman Sobolewski Research Centre Jülich Peter Grünberg Institute (PGI-6) Jülich Germany Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA College of Physics Center for Marine Observation and Communications Qingdao University Qingdao China Department of Electrical and Computer Engineering University of Rochester Rochester New York USA Research Centre Jülich Ernst Ruska Centre for Microscopy and Spectroscopy with Electrons Jülich Germany Department of Physics University of California Davis Davis California USA

We report an additional degree of control of THz transients in Fe/X spintronic emitters grown on GaAs(001). We ascribe the field-independent enhancement of the THz amplitude to specific chemical bonding at the epitaxi... 详细信息

ISBN: (纸本)9781943580910

关键词： Electromagnetic radiation Free electron lasers Gallium arsenide Magnetic fields Terahertz generation Ultrafast lasers

Biphenylene monolayer as a two-dimensional nonbenzenoid carbon allotrope: A first-principles study

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arXiv 2021年

作者： Bafekry, Asadolah Faraji, M. Fadlallah, M.M. Jappor, H.R. Karbasizadeh, S. Ghergherehchi, M. Gogova, D. Department of Radiation Application Shahid Beheshti University Tehran*** Iran Micro and Nanotechnology Graduate Program TOBB University of Economics and Technology Sogutozu Caddesi No 43 Sogutozu Ankara06560 Turkey Department of Physics Faculty of Science Benha University Benha13518 Egypt Department of Physics College of Education for Pure Sciences University of Babylon Hilla Iraq Department of Physics Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Physics University of Oslo P.O. Box 1048 Blindern Oslo Norway

In a very recent accomplishment, the two-dimensional form of Biphenylene network (BPN) has been successfully fabricated [Fan et al., science, 372, 852-856 (2021)]. Motivated by this exciting experimental result on 2D layered BPN structure, herein we perform detailed density functional theory-based first-principles calculations, for the first time, in order to gain insight into the structural, electronic and optical properties of this promising nanomaterial. Our theoretical results reveal the BPN structure is constructed from three rings of tetragon, hexagon and octagon, meanwhile the electron localization function shows very strong bonds between the C atoms in the structure. The dynamical stability of BPN is verified via the phonon band dispersion calculations. The mechanical properties reveal the brittle behavior of BPN monolayer. The Young's modulus has been computed as 0.1 TPa, which is smaller than the corresponding value of graphene, while the Poisson's ratio determined to be 0.26 is larger than that of graphene. The band structure is evaluated to show the electronic features of the material;determining the BPN monolayer as metallic with a band gap of zero. The optical properties (real and imaginary parts of the dielectric function, and the absorption spectrum) uncover BPN as an insulator along the zz direction, while owning metallic properties in xx and yy directions. We anticipate that our discoveries will pave the way to the successful implementation of this new 2D allotrope of carbon in advanced nanoelectronics. © 2021, CC BY.

关键词： Monolayers

Diffusive topological transport in spatiotemporal thermal lattices

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arXiv 2021年

作者： Xu, Guoqiang Yang, Yihao Zhou, Xue Chen, Hongsheng Alù, Andrea Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Kent Ridge117583 Singapore Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Zhejiang University Hangzhou310027 China International Joint Innovation Center ZJU-UIUC Institute The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center of the City University of New York New YorkNY10016 United States

Topological insulating phases are usually found in periodic lattices stemming from collective resonant effects, and it may thus be expected that similar features may be prohibited in thermal diffusion, given its purely dissipative and largely incoherent nature. We report the diffusion-based topological states supported by spatiotemporally-modulated advections stacked over a fluidic surface, thereby imitating a periodic propagating potential in effective thermal lattices. We observe edge and bulk states within purely nontrivial and trivial lattices, respectively. At interfaces between these two types of lattices, the diffusive system exhibits interface states, manifesting inhomogeneous thermal properties on the fluidic surface. Our findings establish a framework for topological diffusion and thermal edge/bulk states, and it may empower a distinct mechanism for flexible manipulation of robust heat and mass transfer. © 2021, CC BY.

关键词： Interface states

Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone