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International Conference on Networking, Architecture, and Storage (NAS)

作者： Hao Lv You Zhou Fei Wu Weijun Xiao Xubin He Zhonghai Lu Changsheng Xie Shenzhen research institute of Huazhong University of Science and Technology Shenzhen China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Ministry of Education Key Laboratory of Data Storage System Wuhan China Department of Electrical and Computer Engineering Virginia Commonwealth University Richmond USA College of Science and Technology Temple University Philadelphia USA School of Information and Communication Technology KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781538683682

Pushing NAND flash memory to higher density, manufacturers are aggressively enlarging the flash page size. However, the sizes of I/O requests in a wide range of scenarios do not grow accordingly. Since a page is the unit of flash read/write operations, traditional flash translation layers (FTLs) maintain the page mapping regularity. Hence, small random write requests become common, leading to extensive partial logical page writes. This write inefficiency significantly degrades the performance and increases the write amplification of flash storage. In this paper, we first propose a configurable mapping layer, called minipage, whose size is set to match I/O request sizes. The minipage-level mapping provides better flexibility in handling small writes at the cost of sequential read performance degradation and a larger mapping table. Then, we propose a new FTL, called PM-FTL, that exploits the minipage-level mapping to improve write efficiency and utilizes the page-level mapping to reduce the costs caused by the minipage-level mapping. Finally, trace-driven simulation results show that compared to traditional FTLs, PM-FTL reduces the write amplification and flash storage response time by an average of 33.4% and 19.1%, up to 57.7% and 34%, respectively, under 16KB flash pages and 4KB minipages.

关键词： Flash memories Random access memory Metadata Electronic mail Time factors Memory management Market research

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Accelerating Surrogate Assisted Evolutionary Algorithms Via Explainable Machine Learning

SSRN

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SSRN 2024年

作者： Li, Bingdong Yang, Yanting Liu, Dacheng Zhang, Yan Zhou, Aimin Yao, Xin Shanghai Institute of AI for Education East China Normal University Shanghai200062 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Space Microwave Remote Sensing System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100090 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100039 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the quality of the solution. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the DTLZ test suite and three real-world optimization problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and systems 2024年 915卷 v-vii页

作者： Marriwala, Nikhil Kumar Dhingra, Sunil Jain, Shruti Kumar, Dinesh Department of Electronics and Communication Engineering University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Solan India Department of Electrical and Computer System Engineering RMIT University MelbourneVIC Australia

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APPLICATIONS OF A SUB-OPTIMAL DISCONTINUOUS CONTROL ALGORITHM FOR UNCERTAIN SECOND ORDER systemS

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International Journal of Robust and Nonlinear Control 1998年第4期7卷

作者： G. Bartolini A. Ferrara E. Usai Department of Electrical and Electronic Engineering University of Cagliari Piazza d'Armi 09123 Cagliari Italy Department of Communication Computer and System Sciences University of Genova Via Opera Pia 13 Genova Italy

In this paper the authors present a couple of algorithms, one for the case of analogue devices and one for the case of digital devices, that realize a nonlinear controller performing a second order sliding mode. These algorithms allow the finite time stabilization of uncertain second order nonlinear systems with incomplete state measurements. Two different applications of this technique are presented. In the first we deal with the problem of chattering avoidance in sliding mode control of uncertain nonlinear systems without using any observer. The second application shows that the developed algorithms allow the hybrid force/position continuous control of a two arms constrained planar manipulator even when the accelerations are not available. © 1997 by John Wiley & Sons, Ltd.

关键词： VSS uncertain systems chattering avoidance plane manipulators

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and systems 2023年 588卷 v-vii页

作者： Marriwala, Nikhil Tripathi, C.C. Jain, Shruti Kumar, Dinesh Kurukshetra University Kurukshetra India NITTTR Bhopal India Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Solan India Department of Electrical and Computer System Engineering RMIT University MelbourneVIC Australia

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Master equations and quantum trajectories for squeezed wave packets

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Physical Review A 2022年第2期105卷 023721-023721页

作者： Jonathan A. Gross Ben Q. Baragiola T. M. Stace Joshua Combes Institut Quantique Université de Sherbrooke Sherbrooke Quebec Canada J1K 2R1 Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131-0001 USA Yukawa Institute for Theoretical Physics Kyoto University Kitashirakawa Oiwakecho Sakyo-ku Kyoto 606-8502 Japan Centre for Quantum Computation and Communication Technology School of Science RMIT University Melbourne Victoria 3000 Australia ARC Centre for Engineered Quantum System Department of Physics University of Queensland Brisbane Queensland 4072 Australia Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA

The interaction between matter and squeezed light has mostly been treated within the approximation that the field-correlation time is small. Methods for treating squeezed light with more general correlations currently involve explicitly modeling the systems producing the light. We develop a general purpose input-output theory for a particular form of narrowband squeezed light, a squeezed wave-packet mode, that only concerns the statistics of the squeezed field and the shape of the wave packet. This formalism allows us to derive the input-output relations and the master equation. We also consider detecting the scattered field using photon counting and homodyne measurements, which necessitates the derivation of the stochastic master equation. The non-Markovian nature of the field manifests itself in the master equation as a coupled hierarchy of equations. We illustrate these with consequences for the decay and resonance fluorescence of two-level atoms in the presence of such fields.

关键词： Open quantum systems Squeezing of quantum noise Stochastic processes

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EEG Neurofeedback Training Among Adult with Attention Deficit: A Review Article

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IOP Conference Series: Materials Science and engineering 2021年第1期1077卷

作者： Eshrak Alaros Dini Oktarina Dwi Handayani Hamwira Yaacob Muharman Lubis School of Computer Science & Engineering Faculty of Innovation & Technology Taylor's University Selangor Malaysia Computer Science Department Kulliyyah of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia Information System Faculty of Industrial Engineering Telkom University Indonesia

Neurofeedback is considered a promising therapy for boosting cognitive skills, which 10% of the US medical sector is using as a new training method among patients. Attention is a significant mechanism for learning and is essential for life in general. As the person grows up the level of responsibilities also increases, which needs high concentration in doing normal activities. To succeed in any activity, neurofeedback therapy needs to check brain waves to locate the problem in the brain and train it. This paper aims to review the attention deficit treatment based on neurofeedback using a systematic review method. The results found that the common features are the area of EEG (Frontal, central) in the brain, type of tools and software, time of training and the impact upon the trainer. Neurofeedback showed improvement of brain attention-ability for a longer time as well as reduction in recovery time in case of a disturbance interruption.

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Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with Applications in Automotive Radars

arXiv

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arXiv 2023年

作者： Gao, Silin Zhang, Zhe Wang, Muhan Zhang, Yan Zhao, Jie Zhang, Bingchen Wang, Yue Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China The Beijing Autoroad Tech Co. Ltd China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization (ANM) is a promising gridless sparse recovery algorithm under the Toeplitz model and solved by convex relaxation, thus it is only applicable to uniform linear arrays (ULAs) with array manifolds having a Vandermonde structure. In automotive applications, it is essential to apply the gridless DoA estimation to NLAs with arbitrary geometry with efficiency. In this paper, a fast ANM-based gridless DoA estimation algorithm for NLAs is proposed, which employs the array manifold separation technique and the accelerated proximal gradient (APG) technique, making it applicable to NLAs without losing of efficiency. Simulation and measurement experiments on automotive multiple-input multiple-outpt (MIMO) radars demonstrate the superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Intelligent spectrum learning for wireless networks with reconfigurable intelligent surfaces

arXiv

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arXiv 2021年

作者： Yang, Bo Cao, Xuelin Huang, Chongwen Yuen, Chau Qian, Lijun Renzo, Marco Di Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University No.38 Zheda Road Hangzhou310007 China Department of Electrical and Computer Engineering CREDIT Center Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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