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作者： Pandey, Suman Choi, Minji Yoo, Jae-Hyoung Hong, James Won-Ki School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of Graduate School of Artificial Intelligence Pohang University of Science and Technology Pohang Korea Republic of Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

This paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) called gated recurrent unit (GRU) for resource demand prediction. Then, considering these predictions, we built an intuitive scale in/out algorithm. We also developed an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place these scaled-out VNFs in appropriate locations. The integrated algorithm that combines prediction, scaling, and placement are called RNN-EdgeQL. RNN-EdgeQL (v2) is further improved to achieve application agnostic group level elasticity in the chain, independent of applications installed on the VNFs. We tested our algorithm on two realistic temporal dynamic load models including Internet traffic (Abilene) and an application specific traffic (Wiki) on an OpenStack testbed. The contribution of this article is threefold. First, prediction model prepares the target SFC for the upcoming load. Second, an application agnostic characteristics of the algorithm achieves the group-level elasticity in SFC. Finally, the EdgeQL placement model minimizes the end-to-end path of an SFC in multi-access edge computing (MEC) environment. As a result, RNN-EdgeQL (v2) gives the lowest overall latency, lowest SLA violations, and lowest VNFs requirement, compared to RNN-EdgeQL (v1) and Threshold-Openstack default placement. © 2022 John Wiley & Sons Ltd.

关键词： Recurrent neural networks

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Millimeter-Wave Gap Waveguide-Based Broadband Slant-Polarized Slot Array Antenna

Millimeter-Wave Gap Waveguide-Based Broadband Slant-Polarize...

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European Conference on Antennas and Propagation, EuCAP

作者： Davood Zarifi Ali Farahbakhsh Michal Mrozowski Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland School of Electrical and Computer Engineering University of Kashan Kashan Iran Department of Electrical and Computer Engineering Graduate University of Kerman Kerman Iran

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This research deals with the design of a broadband, slant-polarized antenna array for 26–40 GHz frequency band. The design employs gap waveguide technology to minimize unwanted energy leakage, ensuring high gain and efficiency. In this configuration, the radiating slots are angled at 45°, and a two-layer feed network, utilizing compact folded E-plane groove gap waveguide power dividers, is used to energize the slots. Key advantages of the proposed antenna array include its low profile, low loss, wide bandwidth, and suitability for millimeter-wave applications.

关键词： Power dividers Slot antennas Millimeter wave technology Optical device fabrication Gap waveguide Bandwidth Millimeter wave propagation Broadband communication Broadband antennas Antenna arrays

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Transforming Fading Channel From Fast to Slow: Intelligent Refracting Surface Aided High-Mobility Communication

Transforming Fading Channel From Fast to Slow: Intelligent R...

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作者： Huang, Zixuan Zheng, Beixiong Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National University of Singapore Nus Graduate School 119077 Singapore

Intelligent reflecting/refracting surface (IRS) has recently emerged as a promising solution to reconfigure wireless propagation environment for enhancing the communication performance by tuning passive signal reflection or refraction. In this paper, we study a new IRS-aided high-mobility communication system by employing the intelligent refracting surface with a high-speed vehicle to aid its passenger's communication with a remote base station (BS). Due to the environment's random scattering and vehicle's high mobility, a rapidly time-varying channel is typically resulted between the static BS and fast-moving IRS/user, which renders the channel estimation for IRS with a large number of passive refracting elements more challenging, as compared to that for the conventional slow fading IRS channels with low-mobility users. In order to reap the high IRS passive beamforming gain with low channel training overhead, we propose a new and efficient two-stage transmission protocol to achieve both IRS channel estimation and refraction optimization for data transmission. Specifically, by exploiting the quasi-static channel between the IRS and user both moving at the same high speed as well as the line-of-sight (LoS) dominant channel between the BS and IRS, the user first estimates the LoS component of the cascaded BS-IRS-user channel in Stage I, based on which IRS passive refraction is designed to maximize the corresponding IRS-refracted channel gain. Then, the user estimates the resultant IRS-refracted channel as well as the non-IRS-refracted channel in Stage II for setting an additional common phase shift at all IRS refracting elements so as to align these two channels for maximizing the overall channel gain for data transmission. Simulation results show that the proposed design can efficiently achieve the full IRS passive beamforming gain in the high-mobility communication scenario, which also converts the overall BS-user channel from fast to slow fading for more reliable t

关键词： Fading channels

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Multi-Resonant Class-E2 DC-DC Converter based on Voltage-Controlled Oscillator

Multi-Resonant Class-E2 DC-DC Converter based on Voltage-Con...

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IEEE Kansas Power and Energy Conference

作者： Lucas Sangoi Mendonça Valmir de Oliveira Academic Department of Electronics Federal University of Technology-Paraná Curitiba Brazil Academic Department of Electronics Graduate Program in Electrical and Computer Engineering Federal University of Technology-Paraná Curitiba Brazil

ISBN: (数字)9798350372403

ISBN: (纸本)9798350372410

This paper shows the design and implementation of a multi-resonant Class-E 2 DC-DC converter based on voltage- controlled oscillator. By cascading a Class-E inverter to a Class-E rectifier, a double soft-switching converter is obtained, in which, the active switch turns-on with zero voltage switching and the diode turns-off with zero derivative voltage switching. This work relies on design equations based on normalized charts that relate the resonant frequency to the current transfer function, quality factor and peak values for the switch variables. In order to activate the switch, a circuit scheme is proposed based on a voltage-controlled oscillator connected to a low-side gate-driver. Therefore, it is not necessary to use a microcontroller neither auxiliary power sources. Experimental results are presented to show the feasibility of the proposed converter.

关键词： Q-factor Voltage-controlled oscillators Transfer functions Resonant frequency Rectifiers Switches DC-DC power converters

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Accuracy Improvement of CNN MobileNet-V1 and Residual Network 50 Layers Models using Adam setting for Car Type Classification

Accuracy Improvement of CNN MobileNet-V1 and Residual Networ...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Taqiyuddin, Muhammad Windarto, Yudi Eko Syafei, Wahyul Amien Windasari, Ike Pertiwi Prasetijo, Agung Budi Diponegoro University Graduate School of Information System Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Diponegoro University Department of Electrical Engineering Semarang Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

The industry 4.0 era is coming. This causes companies and agencies are competing in building a system that integrates cyber technology and automation technology. One example is the system for recognizing, detecting, and sorting cars according to their type as the number of cars dramatically increases. For the case when calculating certain types of cars passing the road, instant and accurate sorting of car types is required. This certainly influences the development process of related agencies. This paper presents an accuracy improvement of convolutional neural network in MobileNet-V1 and ResNet-50 models using Adam setting for car type classification. Maximum results are obtained by varying different parameters, the use of the optimizer Adam and RMSprop, employing ImageNet as transfer learning, as well as implementing dropout layers and 2 kinds of learning rate. This system is trained using 10,708 pictures of City Car, Pick Up, Sedan, Sport ultra-vehicle, and Van. Accuracy of car type classification reaches 92,1% for MobileNet-V1 and 88,2% for ResNet-50 models. © 2022 IEEE.

关键词： Convolutional neural networks

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The Influence of Overcharging and Over-Discharging on the Capacity Degradation of Lithium-Ion Batteries

The Influence of Overcharging and Over-Discharging on the Ca...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Joelton Deonei Gotz Marco Antonio Simões Teixeira Fernanda Cristina Corrêa Emilson Ribeiro Viana Alceu André Badin Milton Borsato Graduate Program in Electrical and Computer Engineering Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil Graduate Program in Informatics (PPGIa) Pontifical Catholic University of Paraná (PUC-PR) Curitiba Brazil Graduate Program in Electrical Engineering Federal Technological University of Paraná (UTFPR-PG) Ponta Grossa Brazil Physics Department Graduate Program in Physics and Astronomy Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil Graduate Program in Mechanical Engineering Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

Lithium-ion batteries (LIB) are the best technology for supplying and storing energy for electric mobility systems. Despite that, this technology is sensitive to abuses that can compromise its lifetime and cause fire and explosion risks. In this context, if LIB is not correctly controlled, some abuses, such as overcharging (OC) and over-discharging (OD), can be observed and provoke a loss of capacity. Therefore, this work presented the application of several experiments in cells, of the NMC type, under OC and the combination of OC and OD. The results indicated that cells charged until 4.4 and 4.5 V and discharged until 3.0 V do not lose capacity for twenty-five cycles, but more cycles should be done to understand the impact over time. On the other side, the cell charged until 4.6 and 4.8 V had its security protection romped in the fourth charging cycle, and the cell charged until 5.0 V had its safety compromised in the first charging cycle. Conversely, cells with a combination of OC and OD were more compromised. In this context, while a cell is submitted only with OC and does not lose capacity, a cell under OC and then under OD had its protection romped in the third charging cycle. Cells under 4.6 V and 4.8 V lost capacity during the second charging cycle. In conclusion, OC provokes a high level of degradation in the cell, but the combination of OC and OD causes even more degradation.

关键词： Degradation Lithium-ion batteries Battery management systems Propulsion Security Reliability Protection Stress Monitoring Predictive maintenance

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks

AdapterBias: Parameter-efficient Token-dependent Representat...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Fu, Chin-Lun Chen, Zih-Ching Lee, Yun-Ru Lee, Hung-Yi Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9781955917766

Transformer-based pre-trained models with millions of parameters require large storage. Recent approaches tackle this shortcoming by training adapters, but these approaches still require a relatively large number of parameters. In this study, AdapterBias, a surprisingly simple yet effective adapter architecture, is proposed. AdapterBias adds a token-dependent shift to the hidden output of transformer layers to adapt to downstream tasks with only a vector and a linear layer. Extensive experiments are conducted to demonstrate the effectiveness of AdapterBias. The experiments show that our proposed method can dramatically reduce the trainable parameters compared to the previous works with a minimal decrease in task performances compared with fine-tuned pretrained models. We further find that Adapter- Bias automatically learns to assign more significant representation shifts to the tokens related to the task in consideration. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Computational linguistics

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Kernel-Based Embedded Feature Selection for Motor Imagery Based BCI

Kernel-Based Embedded Feature Selection for Motor Imagery Ba...

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Iranian Conference on electrical engineering (ICEE)

作者： Mehdi Kamandar Electrical and Computer Engineering Department Graduate University of Advanced Technology Kerman Iran

Brain-computer interface (BCI) based on motor imagery (MI) classification using scalp recorded multi-channel EEG signal play a major role in the control of artificial limbs and machines by people with severe disabilities. The most popular features are band power of EEG signals in frequency sub-bands in a relatively wide frequency band (e. g., 8–30 Hz) in different time windows of imagination period. The effectiveness of these features is highly dependent on the sub-bands and time windows adopted, since the optimal sub-bands and time windows is generally subject-specific. The nonlinear support vector machine (SVM) with Gaussian kernel is an excellent classifier for MI classification. In this paper, the SVM with an anisotropic Gaussian kernel with a scaling coefficient for each feature is used, instead of one scale factor for all features. The scaling coefficients are tuned by maximizing the kernel-target alignment criterion with $l_{l}$ regularization. Some of the scaling coefficients will be zero after maximization due to $l_{l}$ regularization, that is equivalent to removing corresponding irrelevant features from classification processes. Therefore, an embedded feature selection is also done to remove the destructive effect of irrelevant and redundant features. The average accuracy of the SVM classifier with anisotropic Gaussian kernel for four subjects in BCI IV-1 dataset is about 10% more than the SVM with isotropic Gaussian kernel.

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DEEP-LEARNING-BASED MAGNETIC RESONANCE SIMULTANEOUS MULTISLICE IMAGING USING HOLOGRAPHIC IMAGE DECODING

arXiv

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arXiv 2024年

作者： Ito, Satoshi Sato, Yuki Endo, Naoya Ouchi, Shohei Graduate Program in Information Electrical and Electronic Systems Engineering Utsunomiya University Japan Department of Innovative Electrical and Electronic Engineering National Institute of Technology Oyama Japan

Simultaneous multislice (SMS) imaging is a one of the acceleration technique of magnetic resonance imaging. SMS requires accurate sensitivity distributions in the slice plane for each receiving coil. This requirement is difficult to satisfy in practice, limiting the applications of this imaging technique. Here, images are reconstructed by applying deep learning and amplitude modulation to each slice image. Simulation experiments show that image reconstruction can be achieved for both real- and complex-valued images. Image quality tends to decrease with increasing number of simultaneously acquired images. It is also shown that a larger difference in the phase modulation coefficients between slices tends to increase the quality of the reconstructed images. Simulation experiments and initial MR imaging experiments show promising results for this method. Copyright © 2024, The Authors. All rights reserved.

关键词： Holography

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