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The Interplay between Artificial Intelligence and Fog Radio Access Networks

China Communications 2020年第8期17卷 1-13页

作者： Wenchao Xia Xinruo Zhang Gan Zheng Jun Zhang Shi Jin Hongbo Zhu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Department of Computer Science and Electronic Engineering University of EssexColchester CO43SQU.K. Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough UniversityLeicestershire LE113TUU.K. National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China

The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning(RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

关键词： artificial intelligence(AI) fog radio access network(F-RAN) machine learning network optimization

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R-MADDPG for partially observable environments and limited communication

arXiv

引用

arXiv 2020年

作者： Wang, Rose E. Everett, Michael How, Jonathan P. Department of Electrical Engineering and Computer Science Department of Mechanical Engineering Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States

There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars. The real world has challenging conditions for multiagent learning systems, such as its partial observable and nonstationary nature. Moreover, if agents must share a limited resource (e.g. network bandwidth) they must all learn how to coordinate resource use. (Hochreiter & Schmidhuber, 1997) This paper introduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for handling multiagent coordination under partial observable settings and limited communication. We investigate recurrency effects on performance and communication use of a team of agents. We demonstrate that the resulting framework learns timedependencies for sharing missing observations, handling resource limitations, and developing different communication patterns among agents. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Logic Gates Based on Synthetic Antiferromagnetic Bilayer Skyrmions

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Physical Review Applied 2021年第1期16卷 014040-014040页

作者： Mouad Fattouhi Kai Yu Mak Yan Zhou Xichao Zhang Xiaoxi Liu Mohamed El Hafidi Department of Applied Physics University of Salamanca 37008 Salamanca Spain School of Science and Engineering the Chinese University of Hong Kong Shenzhen Guangdong 518172 China Department of Electrical and Computer Engineering Shinshu University 4-17-1 Wakasato Nagano 380-8553 Japan Condensed Matter Physics Laboratory Hassan II University of Casablanca Faculty of Sciences Ben M’Sik 7955 Casablanca Morocco

Technologies based on magnetic skyrmions, such as computational devices that can operate at high speed or with low energy consumption, have been proposed by many researchers. Recently, synthetic antiferromagnetic (SAF) structures have been proposed to increase the stability and mobility of skyrmions by reducing or eliminating the skyrmion Hall effect. Here, we numerically study the current-induced dynamics of skyrmions on SAF bilayer structures. We demonstrate the effective control and manipulation of SAF skyrmions, including directional displacement and alignment. Furthermore, we design SAF-skyrmion-based logic gates, such as the and, or, xor, and not gates. Our design provides guidance for future development of spintronic computing devices that use topological nanoscale spin textures as information carriers.

关键词： Hall effect Skyrmions Antiferromagnets Devices Magnetic multilayers Nanostructures Landau-Lifschitz-Gilbert equation Micromagnetic modeling

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Enhanced weak force sensing through atom-based coherent noise cancellation in a hybrid cavity optomechanical system

arXiv

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arXiv 2022年

作者： Singh, S.K. Mazaheri, Mojtaba Peng, Jia-Xin Asjad, M. School of Physical Sciences Central University of Karnataka Kalaburagi585367 India Department of Basic Science Hamedan University of Technology Hamedan65169-1-3733 Iran State Key Laboratory of Precision Spectroscopy Quantum Institute for Light and Atoms Department of Physics East China Normal University Shanghai200062 China Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi59911 United Arab Emirates

We investigate weak force sensing based on coherent quantum noise cancellation in a nonlinear hybrid optomechanical system. The optomechanical cavity contains a moveable mechanical mirror, a fixed semitransparent mirror, an ensemble of ultracold atoms, and an optical parametric amplifier (OPA). Using the coherent quantum noise cancellation (CQNC) process, one can eliminate the back action noise at all frequencies. Also by tuning the OPA parameters, one can suppress the quantum shot-noise at lower frequencies than the resonant frequency. In the CQNC scheme, the damping rate of the mechanical oscillator matches the damping rate of the atomic ensemble, which is experimentally achievable even for a low-frequency mechanical oscillator with a high-quality factor. Elimination of the back action noise and suppression of the shot noise significantly enhance force sensing and thus overcome the standard quantum limit of weak force sensing. This hybrid scheme can play an essential role in the realization of quantum optomechanical sensors and quantum control. © 2022, CC0.

关键词： Quantum noise

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Goos-Hänchen shift at a temporal boundary

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Physical Review A 2022年第6期106卷 L061501-L061501页

作者： Sergey A. Ponomarenko Junchi Zhang Govind P. Agrawal Department of Electrical and Computer Engineering Dalhousie University Halifax Nova Scotia Canada B3J 2X4 Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia Canada B3H 4R2 The Institute of Optics University of Rochester Rochester New York 14627 USA Laboratory for Laser Energetics University of Rochester Rochester New York 14623 USA

We discover a pronounced temporal shift of the peak of an optical pulse upon total internal reflection of the pulse from a sharp temporal boundary propagating in a homogeneous, isotropic, weakly dispersive linear medium. We derive an analytical expression for this shift and juxtapose the discovered effect to the spatial Goos-Hänchen shift occurring on reflection of a beam from an interface separating two homogeneous, isotropic, conservative linear media. In particular, we show that, in contrast to the spatial shift, the sign of the temporal shift is dictated by that of the group-velocity mismatch between the pulse and the temporal boundary, implying the possibility of a delay or advancement of the pulse upon reflection. Our analytical results, which are in excellent agreement with our numerical simulations, shed light on the fundamental aspects of the interaction of wave packets with temporal boundaries in material media.

关键词： Classical optics Metamaterials Optics & lasers Photonics

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Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”

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IEEE Access 2025年 13卷 31659-31659页

作者： Khaoula Taji Ali Sohail Tariq Shahzad Bilal Shoaib Khan Muhammad Adnan Khan Khmaies Ouahada Electronic Systems Information Processing Mechanics and Energy Laboratory Kenitra Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science Minhaj University Lahore Lahore Pakistan Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Computer Science Green International University Lahore Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si Republic of Korea

Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.

关键词： Plant diseases Metaheuristics

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Active Microwave Thermographic Measurement of In-Plane Thermal Diffusivity

Active Microwave Thermographic Measurement of In-Plane Therm...

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Ali Mirala Kristen M. Donnell Microwave Sensing (μSense) Laboratory Department of Electrical and Computer Engineering Missouri University of Science & Technology Rolla MO USA

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

This research shows the efficacy of an Active Microwave Thermography (AMT) based thermal materials characterization approach for measuring in-plane thermal diffusivity. To this end, two independent approaches, space-resolved and iterative, are utilized to extract the in-plane thermal diffusivity from AMT measurements conducted on two quasi-isotropic CFRP laminates. The experimental results obtained from the two approaches yield consistent values of the in-plane thermal diffusivity, thus supporting the validity of the results and showing the applicability of the AMT-based materials characterization technique.

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A Hybrid Credit Scoring Model Using Neural Networks and Logistic Regression 15th

A Hybrid Credit Scoring Model Using Neural Networks and Logi...

引用

15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Munkhdalai, Lkhagvadorj Lee, Jong Yun Ryu, Keun Ho Database/Bioinformatics Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Department of Computer Science Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789811397134

Credit scoring is one of important issues in banking to control a loss due to debtors who fail to meet their credit payment. Hence, the banks aim to develop their credit scoring model for accurately detecting their bad borrowers. In this study, we propose a hybrid credit scoring model using deep neural networks and logistic regression to improve its predictive accuracy. Our proposed hybrid credit scoring model consists of two phases. In the first phase, we train several neural network models and in the second phase, those models are merged by logistic regression. In experimental part, our model outperformed baseline models on over three benchmark datasets in terms of H-measure, area under the curve (AUC) and accuracy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Automatic Modulation Classification of Frequency-Hopping Signals Using High-Dimensional Phase Diagrams

Automatic Modulation Classification of Frequency-Hopping Sig...

引用

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qibo Chen Kun Yan Hsiao-Chun Wu Shih Yu Chang Xiao Yan Yiyan Wu Haonan Chang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Department of Applied Data Science San Jose State University San Jose CA USA School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu P. R. China Communications Research Centre Canada Ottawa Canada Haonan Chang is with Xi'an University Xi'an P. R. China

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM); the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise.

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STRESS: Super-resolution for dynamic fetal MRI using self-supervised learning

arXiv

引用

arXiv 2021年

作者： Xu, Junshen Turk, Esra Abaci Grant, P. Ellen Golland, Polina Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Fetal-Neonatal Neuroimaging Developmental Science Center Boston Children's Hospital BostonMA United States Harvard Medical School BostonMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal function, is limited to fast imaging techniques with compromises in image quality and resolution. Super-resolution for dynamic fetal MRI is still a challenge, especially when multi-oriented stacks of image slices for oversampling are not available and high temporal resolution for recording the dynamics of the fetus or placenta is desired. Further, fetal motion makes it difficult to acquire high-resolution images for supervised learning methods. To address this problem, in this work, we propose STRESS (Spatio-Temporal Resolution Enhancement with Simulated Scans), a self-supervised super-resolution framework for dynamic fetal MRI with interleaved slice acquisitions. Our proposed method simulates an interleaved slice acquisition along the high-resolution axis on the originally acquired data to generate pairs of low- and high-resolution images. Then, it trains a super-resolution network by exploiting both spatial and temporal correlations in the MR time series, which is used to enhance the resolution of the original data. Evaluations on both simulated and in utero data show that our proposed method outperforms other self-supervised super-resolution methods and improves image quality, which is beneficial to other downstream tasks and evaluations. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical resolving power

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