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IEEE International Conference on Communications (ICC)

作者： Ouwen Huan Mingzhe Chen Tao Luo Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one base station (BS), and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design a hard expectation-maximization (EM) based deep learning (DL) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the log-likelihood function as a maximum matching problem. Subsequently, the model parameters are updated according to the maximum matching between unlabeled CSI and position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 60% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning.

关键词： Training Deep learning Base stations Simulation Fingerprint recognition Cameras Data models

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Millimeter-sized, ultra-sensitive planar Hall effect gradiometer

Millimeter-sized, ultra-sensitive planar Hall effect gradiom...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Hariharan Nhalil Moty Schultz Shai Amrusi Asaf Grosz Lior Klein Department of Physics Institute of Nanotechnology and Advanced Materials Bar-Ilan University Ramat-Gan Israel Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (数字)9798350338362

ISBN: (纸本)9798350338379

We present a miniature, ultra-sensitive magnetic field gradiometer in the form of a single elliptical planar Hall effect sensor that allows measuring magnetic field at 9 different locations on a 4 mm length scale [1]. The gradiometer detects magnetic field gradients with equivalent gradient magnetic noise levels of $\sim 958, \sim 192, \sim 51$ , and $\sim 26\ \text{nT}/\mathrm{m}\surd$ Hz at 0.1, 1, 10, and 50 Hz, respectively, and tested under ambient conditions by measuring the field gradient produced by an electric current flowing through a straight wire. The compact size, low noise level, versatility, simple design, and low cost of this gradiometer makes it a suitable choice for detecting magnetic field gradients in small, confined spaces such as current probes or wearable electronic medical devices.

关键词： Magnetic field measurement Current measurement Magnetic confinement Wires Magnetic noise Hall effect Magnetic fields

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A Survey of the AVOA Metaheuristic Algorithm and its Suitability for Power System Optimization and Damping Controller Design

A Survey of the AVOA Metaheuristic Algorithm and its Suitabi...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Aliyu Sabo Theophilus Ebuka Odoh Samuel Habu Hossien Shahinzadeh Farshad Ebrahimi Department of Electrical andElectronic Engineering Advanced Lightning Power and EnergyResearch (ALPER) Universiti Putra Malaysia Serdang Malaysia Department of Electrical Engineering Water Resource Institute Kaduna Nigeria Department of Electrical Engineering Amikabir University of Technology Tehran Iran Dept. of Electrical and Computer Engineering University of Houston Houston TX USA

The performance of metaheuristic optimization techniques is system-dependent, it is essential to ascertain the suitability of an algorithm before its application. This study surveys the suitability of AVOA in damping controller design by reviewing the performance of AVOA in used cases of power system optimization problems and damping controller design. Based on the findings, AVOA is recommended as a good optimizer in power system optimization and damping controller design.

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Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric 4th

Highsimb: A Concrete Blockchain High Simulation with Contra...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Pengfei Jie, Wanqing Voundi Koe, Arthur Sandor Hou, Ruitao Yan, Hongyang Nouioua, Mourad Thien, Phan Duc Mbous Ikong, Jacques Lancine, Camara Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Pazhou Lab Guangzhou510330 China Faculty of Mathematics and Computer Science University of Mohamed Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Faculty of Information Technology Nam Dinh University of Technology Education Nam Dinh420000 Viet Nam Department of Electrical and Telecommunications Engineering National Advanced School of Engineering - University of Yaounde I Yaounde337 Cameroon Department of Computer Science University of Social Sciences and Management Bamako2575 Mali

ISBN: (纸本)9783031200984

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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African Vultures Optimization Algorithm for Optimal Damping Controllers Design in the electrical Power Grid System

African Vultures Optimization Algorithm for Optimal Damping ...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Aliyu Sabo Theophilus Ebuka Odoh Samuel Habu Hossien Shahinzadeh Farshad Ebrahimi Department of Electrical and Electronic Engineering Advanced Lightning Power and Energy Research (ALPER) Universiti Putra Malaysia Serdang Malaysia Department of Electrical Engineering Water Resource Institute Kaduna Nigeria Department of Electrical Engineering Amikabir University of Technology Tehran Iran Dept. of Electrical and Computer Engineering University of Houston Houston TX USA

Electromechanical oscillation affects power system stability and hinders the improvement of the generating capacity of the electrical power grid system. This is because if electromechanical oscillation is not controlled, it leads to the loss of synchronism in generators and total power disruption in the power grid system. To solve this problem, this study introduces Power System Stabilizer (PSS) damping controllers in the IEEE standard power test systems of Single Machine Infinite Bus (SMIB), Western System Coordinating Council (WSCC), and the New England Power Test Systems for electromechanical oscillation damping and control. The African Vultures Optimization Algorithm (AVOA) was used to obtain the PSS damping controller parameters to ensure optimal performance. Results showed that the introduced PSS damping controllers can dampen and control electromechanical oscillation.

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FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?

FDD Massive MIMO: How to Optimally Combine UL Pilot and Limi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jungyeon Kim Jinseok Choi Jeonghun Park Ahmed Alkhateeb Namyoon Lee Department of Electrical Engineering Pohang University of Science and Technology Pohang Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea School of Electrical and Electronic Engineering Yonsei University Seoul Republic of Korea School of Electrical Computer and Energy Engineering Arizona State University AZ USA School of Electrical Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) becomes challenging due to the tremendous feedback overhead that increases with the number of antennas. Using uplink pilots to estimate downlink CSI is a promising approach that can eliminate the need for CSI feedback, but its accuracy decreases significantly as the number of channel paths increases. In this paper, we propose a mean square error (MSE)-optimal downlink channel reconstruction method that jointly utilizes uplink CSI and limited downlink CSI. With the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for channel phase quantization and show the optimal feedback bit allocation method to minimize the MSE. Harnessing robust downlink precoding, we demonstrate that the proposed downlink channel reconstruction is sufficient to achieve a sum-spectral efficiency comparable to that with perfect downlink CSI.

关键词： Accuracy Quantization (signal) Spectral efficiency Precoding Bit rate Channel estimation Massive MIMO Reconstruction algorithms Downlink Uplink

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Reed-Solomon Codes Over Ring with Lower Computational Complexity

Reed-Solomon Codes Over Ring with Lower Computational Comple...

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IEEE International Conference on Big Data

作者： Shicheng Tan Hanxu Hou Leilei Yu School of Computer Science & Technology Dongguan University of Technology Dongguan China School of Electrical Engineering & Intelligentization Dongguan University of Technology Dongguan China Shenzhen Institute for Advanced Study University of Electronic Science & Technology of China

Reed-Solomon (RS) codes are widely used in storage systems to provide high data reliability. The existing RS codes are constructed over finite field with field size larger than the code length that have high encoding/decoding complexity. In this paper, we propose a new construction for RS codes over a cyclic ring instead of finite field. Our new RS codes only incurs XOR and cyclic-shift operation in the encoding/decoding processes, and therefore have lower encoding/decoding complexity than the existing RS codes. In addition, we show that we can employ the efficient Reed-Muller (RM) transform in our new RS codes that reduces the encoding/decoding complexity. Moreover, we implement our new RS codes by C++ and show that our new RS codes have better encoding/decoding performance than the existing RS codes for the evaluated parameters.

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Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO with Lens Antenna Array

Intelligent Analog Beam Selection and Beamspace Channel Trac...

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作者： Zarini, Hosein Mili, Mohammad Robat Rasti, Mehdi Andreev, Sergey Nardelli, Pedro H. J. Bennis, Mehdi Sharif University of Technology Department of Computer Engineering Tehran*** Iran Pasargad Institute for Advanced Innovative Solutions Tehran199163361 Iran Oulu90014 Finland Tampere University Faculty of Information Technology and Communication Sciences Tampere33100 Finland Lappeenranta-Lahti University of Technology School of Energy Systems Department of Electrical Engineering Lappeenranta53850 Finland University of Oulu Center for Wireless Communications Oulu90014 Finland

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts. © 2015 IEEE.

关键词： Learning systems

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A New Combined PV Output Power Forecasting Model Based on Optimized LSTM Network

A New Combined PV Output Power Forecasting Model Based on Op...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Azim Heydari Farshid Keynia Afef Fekih Lina Bertling Tjernberg Grazia Barchi David Moser Institute for Renewable Energy Eurac Research Bolzano Italy Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette LA USA School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

One of the most challenging problems in designing and implementing effective management strategies and demand responses in renewable-rich grids is the uncertainty associated with the power output (PO) of solar photovoltaic (PV) systems. The exact and trustworthy prediction of PV power can provide substantial decision support for planning and operating power systems. This paper develops an intelligent PV output power (PV-OP) forecasting model. The proposed PV-OP forecasting model consists of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) signal decomposition model to decompose the original PV output power signal to different frequencies, LSTM neural network as a main forecaster engine, and multi-objective NSGA-II optimization algorithm as a hyperparameters optimizer. The developed PV power forecasting model was validated using the PV power datasets of an off-grid village located in Chalokwa, Zambia. The obtained results confirmed the performance and accuracy of the proposed PV output power forecasting model.

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Balanced Self-Paced Learning for AUC Maximization

arXiv

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arXiv 2022年

作者： Gu, Bin Zhang, Chenkang Xiong, Huan Huang, Heng MBZUAI United Arab Emirates School of Computer & Software Nanjing University of Information Science & Technology China Institute for Advanced Study in Mathematics Harbin Institute of Technology China Department of Electrical & Computer Engineering University of Pittsburgh PA United States

Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide a statistical objective for self-paced AUC. Based on this, we propose our self-paced AUC maximization formulation, where a novel balanced self-paced regularization term is embedded to ensure that the selected positive and negative samples have proper proportions. Specially, the sub-problem with respect to all weight variables may be non-convex in our formulation, while the one is normally convex in existing self-paced problems. To address this, we propose a doubly cyclic block coordinate descent method. More importantly, we prove that the sub-problem with respect to all weight variables converges to a stationary point on the basis of closed-form solutions, and our BSPAUC converges to a stationary point of our fixed optimization objective under a mild assumption. Considering both the deep learning and kernel-based implementations, experimental results on several large-scale datasets demonstrate that our BSPAUC has a better generalization performance than existing state-of-the-art AUC maximization methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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