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arXiv 2022年

作者： Jadbabaie, Ali Makur, Anuran Shah, Devavrat The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Computer Science The Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

In this work, we study empirical risk minimization (ERM) within a federated learning framework, where a central server seeks to minimize an ERM objective function using n samples of training data that is stored across m clients and the server. The recent flurry of research in this area has identified the Federated Averaging (FedAve) algorithm as the staple for determining ϵ-approximate solutions to the ERM problem. Similar to standard optimization algorithms, e.g., stochastic gradient descent, the convergence analysis of FedAve and its variants only relies on smoothness of the loss function in the optimization parameter. However, loss functions are often very smooth in the training data too. To exploit this additional smoothness in data in a federated learning context, we propose the Federated Low Rank Gradient Descent (FedLRGD) algorithm. Since smoothness in data induces an approximate low rank structure on the gradient of the loss function, our algorithm first performs a few rounds of communication between the server and clients to learn weights that the server can use to approximate clients' gradients using its own gradients. Then, our algorithm solves the ERM problem at the server using an inexact gradient descent method. To theoretically demonstrate that FedLRGD can have superior performance to FedAve, we present a notion of federated oracle complexity as a counterpart to canonical oracle complexity in the optimization literature. Under some assumptions on the loss function, e.g., strong convexity and smoothness in the parameter, η-Hölder class smoothness in the data, etc., we prove that the federated oracle complexity of FedLRGD scales like (Equation presented) and that of FedAve scales like (Equation presented) (neglecting typically sub-dominant factors), where 1 is the ratio of client-to-server communication time to gradient computation time, p is the parameter dimension, and d is the data dimension. Then, we show that when d is small compared to n and the l

关键词： Optimization

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An Adaptive Direct-Discretized RNN With Fuzzy Factor for Handling Future Distinct-Layer Inequality and Equation System Applied to Robot Manipulator Control

An Adaptive Direct-Discretized RNN With Fuzzy Factor for Han...

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Intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Binbin Qiu Kehan Li Zihang Li Jinjin Guo School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Shenzhen China School of Computer Science Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This paper proposes an adaptive direct-discretized recurrent neural network (ADD-RNN) algorithm with fuzzy factor to address the problem of future distinct-layer inequality and equation system (FDLIES). For comparison purposes, a traditional indirect-discretized RNN (TID-RNN) algorithm is also developed to solve the same problem. The superior performance of the proposed ADD-RNN algorithm is validated through numerical experiment. The applicability and meliority of the proposed ADD-RNN algorithm are further demonstrated by applying it to tackle a motion planning and control problem of a robot manipulator with an additional constraint, which is formulated as a specific FDLIES problem.

关键词： Fuzzy control Recurrent neural networks Adaptive systems Accuracy Manipulators Planning Convergence

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Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding

arXiv

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arXiv 2022年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Junshan Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of California Davis United States The Department of Electrical and Computer Engineering University of Waterloo Canada

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data. © 2022, CC BY.

关键词： Semantics

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Solar Image Cloud Removal based on Improved Pix2Pix Network

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computers, Materials & Continua 2022年第12期73卷 6181-6193页

作者： Xukun Zhang Wei Song Ganghua Lin Yuxi Shi School of Information Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Solar Activity Chinese Academy of Sciences(KLSACAS)Beijing100101China National Language Resource Monitoring and Research Center of Minority Languages Minzu University of ChinaBeijing100081China National Astronomical Observatories Chinese Academy of Sciences(NAOCCAS)Beijing100101China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNew Jersey07102USA

In ground-based observations of the Sun,solar images are often affected by appearance of thin clouds,which contaminate the images and affect the scientific results from data *** this paper,the improved Pixel to Pixel Network(Pix2Pix)network is used to convert polluted images to clear images to remove the cloud shadow in the solar *** adding attention module to the model,the hidden layer of Pix2Pix model can infer the attention map of the input feature vector according to the input feature *** then,the attention map is multiplied by the input feature map to give different weights to the hidden features in the feature map,adaptively refine the input feature map to make the model pay attention to important feature information and achieve better recovery *** order to further enhance the model’s ability to recover detailed features,perceptual loss is added to the loss *** model was tested on the full disk H-alpha images datasets provided by Huairou Solar Observing Station,National Astronomical *** experimental results show that the model can effectively remove the influence of thin clouds on the picture and restore the details of solar *** peak signal-to-noise ratio(PSNR)reaches 27.3012 and the learned perceptual image patch similarity(LPIPS)reaches 0.330,which is superior to the existed dehaze algorithms.

关键词： Pix2Pix decontamination full-disk H-alpha images

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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Secure Microwave QR Code Communication Using Pseudo-Random Constellation Rotation

Secure Microwave QR Code Communication Using Pseudo-Random C...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chunpeng Guo Beiyuan Liu Zeyang Sun Chen Chen Sai Xu School of Cybersecurity Northwestern Polytechnical University Xi’an China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In contrast to the intelligent reflecting surface (IRS) as a pure reflection device in the past, in this letter, from a more micro perspective, we propose to employ pseudo-random constellation rotation to secure microwave quick response (QR) code communication. Specifically, confidential information is encoded into a QR code, which is displayed on an IRS. Pseudo-random constellation rotation is applied to each element of the IRS to encrypt the QR code. This ensures that only the authenticated user (Bob) can decode and access the confidential information, while an eavesdropper (Eve) is unable to correctly interpret the QR code. We derive a closed-form expression for the bit error rate (BER) and analyze the impact of various factors on the BER for both Bob and Eve, including the distance between Bob and the IRS, Bob’s transmit power, and the Rician factor. Simulation results demonstrate the proposed scheme’s effectiveness in achieving secure communication.

关键词： Privacy Closed-form solutions Simulation Bit error rate Rician channels QR codes Microwave devices Microwave communication Reflection Encryption

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A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage

arXiv

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arXiv 2024年

作者： Yang, Ziyuan Kang, Ming Teoh, Andrew Beng Jin Gao, Chengrui Chen, Wen Zhang, Bob Zhang, Yi The College of Computer Science Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China The School of Cyber Science and Engineering Sichuan University Chengdu610065 China The School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of The College of Computer Science Sichuan University Chengdu610065 China The Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China The School of Cyber Science and Engineering Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China

In recent years, palmprints have been widely used for individual verification. The rich privacy information in palmprint data necessitates its protection to ensure security and privacy without sacrificing system performance. Existing systems often use cancelable technologies to protect templates, but these technologies ignore the potential risk of data leakage. Upon breaching the system and gaining access to the stored database, a hacker could easily manipulate the stored templates, compromising the security of the verification system. To address this issue, we propose a dual-level cancelable palmprint verification framework in this paper. Specifically, the raw template is initially encrypted using a competition hashing network with a first-level token, facilitating the end-to-end generation of cancelable templates. Different from previous works, the protected template undergoes further encryption to differentiate the second-level protected template from the first-level one. The system specifically creates a negative database (NDB) with the second-level token for dual-level protection during the enrollment stage. Reversing the NDB is NP-hard and a fine-grained algorithm for NDB generation is introduced to manage the noise and specified bits. During the verification stage, we propose an NDB matching algorithm based on matrix operation to accelerate the matching process of previous NDB methods caused by dictionary-based matching rules. This approach circumvents the need to store templates identical to those utilized for verification, reducing the risk of potential data leakage. Extensive experiments conducted on public palmprint datasets have confirmed the effectiveness and generality of the proposed framework. Upon acceptance of the paper, the code will be accessible at https://***/Deep-Imaging-Group/NPR. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Cognitive Disentanglement for Referring Multi-Object Tracking

arXiv

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arXiv 2025年

作者： Liang, Shaofeng Guan, Runwei Lian, Wangwang Liu, Daizong Sun, Xiaolou Wu, Dongming Yue, Yutao Ding, Weiping Xiong, Hui Qingdao China Guangzhou China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China Wangxuan Institute of Computer Technology Peking University Beijing China School of Automation Southeast University Nanjing China School of Computer Science Beijing Institute of Technology Beijing China School of Artificial Intelligence and Computer Science Nantong University Nantong China Guangzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tracking specific objects in video sequences based on language references. However, existing RMOT approaches often treat language descriptions as holistic embeddings and struggle to effectively integrate the rich semantic information contained in language expressions with visual features. This limitation is especially apparent in complex scenes requiring comprehensive understanding of both static object attributes and spatial motion information. In this paper, we propose a Cognitive Disentanglement for Referring Multi-Object Tracking (CDRMT) framework that addresses these challenges. It adapts the "what" and "where" pathways from the human visual processing system to RMOT tasks. Specifically, our framework first establishes cross-modal connections while preserving modality-specific characteristics. It then disentangles language descriptions and hierarchically injects them into object queries, refining object understanding from coarse to fine-grained semantic levels. Finally, we reconstruct language representations based on visual features, ensuring that tracked objects faithfully reflect the referring expression. Extensive experiments on different benchmark datasets demonstrate that CDRMT achieves substantial improvements over state-of-the-art methods, with average gains of 6.0% in HOTA score on Refer-KITTI and 3.2% on Refer-KITTI-V2. Our approach advances the state-of-the-art in RMOT while simultaneously providing new insights into multi-source information fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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A Fuzzy-Power Direct-Discretization RNN Algorithm for Solving Discrete Multilayer Dynamic systems With Robotic Applications

A Fuzzy-Power Direct-Discretization RNN Algorithm for Solvin...

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Intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Binbin Qiu Zihang Li Kehan Li Jinjin Guo School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Shenzhen China School of Computer Science Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This work proposes a new recurrent neural network (RNN) algorithm for solving discrete multilayer dynamic systems (DMDSs). First, by utilizing the direct-discretization technique, a direct-discretization RNN (DDRNN) algorithm for solving the DMDSs is introduced. On this basis, a fuzzy-power direct-discretization RNN (FDDRNN) algorithm is proposed, which integrates a fuzzy control system (FCS) within the DDRNN framework. This integration dynamically modulates the time step τ by exerting a fuzzy-power factor r, which is determined based on the logarithm of the residual, as τ r . Finally, comparative evaluations among the gradient-based RNN (GBRNN), the DDRNN, and the FDDRNN algorithms are conducted through numerical experiments and robotic application experiments involving both planar and spatial robots. The results indicate that the proposed FDDRNN algorithm outperforms the GBRNN and DDRNN algorithms in terms of accuracy, convergence speed, and stability.

关键词： Recurrent neural networks Accuracy Heuristic algorithms Dynamics Nonhomogeneous media Stability analysis Dynamical systems Robots Numerical stability Convergence

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Implementational Aspects for the Characterization of Low-Inertia in Power systems

Implementational Aspects for the Characterization of Low-Ine...

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Annual North-American Power Symposium

作者： M. D. Baquedano-Aguilar Nader Aljohani Sean P. Meyn Arturo Bretas Department of Electrical & Computer Engineering University of Florida Gainesville Florida Pacific Northwest National Laboratory Distributed Systems Group Richland WA

ISBN: (纸本)9781665499224

The replacement of conventional generating units with non-conventional renewable energy resources impacts the reliability of power systems. With high integration of renewable-based generation resources, there is a reduction in total system inertia, which could lead to systems with less time to react for frequency restoration in case of unexpected unit failures. Though several power system testbeds are available for studying and analysing different stability phenomena, none are suitable for characterizing, or analyzing low-inertia conditions. In this paper, a power system simulation environment is introduced for investigating control architectures for power grids, with special attention to the impact of high penetration of inverter-based generation resources (IBRs). The testbed is applied to the WSCC 9- bus test system, modified so that a range of IBR penetration levels can be analyzed (from zero to 80%). The testbed is suitable for studying both fast and slow electromechanical phenomena. A conclusion from numerical experiments is that high levels of IBRs do not necessarily result in low-inertia. This is due in part to the layers of control in the grid already in place to avoid frequency excursions.

关键词： Renewable energy sources Time-frequency analysis System dynamics Simulation Power system stability Power grids Stability analysis

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