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arXiv 2024年

作者： Xiang, Wenzhao Liu, Chang Su, Hang Yu, Hongyang Beijing100190 China Peng Cheng Laboratory Shenzhen10587 China University of the Chinese Academy of Sciences Beijing100049 China Institute of Image Communication and Networks Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Masked image modeling (MIM) has gained significant traction for its remarkable prowess in representation learning. As an alternative to the traditional approach, the reconstruction from corrupted images has recently emerged as a promising pretext task. However, the regular corrupted images are generated using generic generators, often lacking relevance to the specific reconstruction task involved in pre-training. Hence, reconstruction from regular corrupted images cannot ensure the difficulty of the pretext task, potentially leading to a performance decline. Moreover, generating corrupted images might introduce an extra generator, resulting in a notable computational burden. To address these issues, we propose to incorporate adversarial examples into masked image modeling, as the new reconstruction targets. Adversarial examples, generated online using only the trained models, can directly aim to disrupt tasks associated with pre-training. Therefore, the incorporation not only elevates the level of challenge in reconstruction but also enhances efficiency, contributing to the acquisition of superior representations by the model. In particular, we introduce a novel auxiliary pretext task that reconstructs the adversarial examples corresponding to the original images. We also devise an innovative adversarial attack to craft more suitable adversarial examples for MIM pre-training. It is noted that our method is not restricted to specific model architectures and MIM strategies, rendering it an adaptable plug-in capable of enhancing all MIM methods. Experimental findings substantiate the remarkable capability of our approach in amplifying the generalization and robustness of existing MIM methods. Notably, our method surpasses the performance of baselines on various tasks, including ImageNet, its variants, and other downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Mechanical Vibrations on a Deployable Nanosatellite Antenna: SAR Performance Analysis

Mechanical Vibrations on a Deployable Nanosatellite Antenna:...

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European Conference on Antennas and Propagation, EuCAP

作者： Andrew C. M. Austin Timothy Pelham Simone Mencarelli Annalisa Tresoldi Mohammed Dabboor Guglielmo Aglietti Michael J. Neve Communication Systems and Networks Group University of Bristol Bristol UK Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea—Auckland Space Institute University of Auckland Auckland New Zealand Science and Technology Branch Environment and Climate Change Canada Dorval QC Canada

The mechanical vibrations of a high-strain glass-fibre composite deployable X-band synthetic aperture radar (SAR) antenna structure are investigated using a finite element method. The antenna is approximately $\mathrm{2}\ \mathrm{m}\times \mathrm{0}.\mathrm{3}\ \mathrm{m}$ , and exhibits natural frequencies at 2.8 Hz and 7.6 Hz. The radiation patterns for a reflectarray formed from the structure are calculated using a ray-tracing approach for the nominal and mechanically deformed cases. The mechanically deformed antenna pattern is defocused, and a SAR system model shows this reduces the signal-to-noise ratio of a point scattering target by approximately 6 dB.

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Cybersecurity Using Hybrid Type Model for Classification Through SCO Optimization Technique

Cybersecurity Using Hybrid Type Model for Classification Thr...

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Computing, Power and communication Technologies (IC2PCT), IEEE International Conference on

作者： Jothi Prabha Appadurai Lekshmi S Raveendran R Latha E. Gangadevi M.Lawanya Shri Shilpa Gite Department of CSE(Networks) Kakatiya Institute of Technology and Science Warangal Telangana India Sathyabama Institute of Science and Technology Chennai India Electronics & Communication Engineering Saveetha Engineering College Saveetha Nagar Thandalam Chennai India Department of Computer Science Loyola College Chennai India SITE Vellore Institute of Technology Vellore India AIML department Symbiosis Institute of Technology Pune. Symbiosis International (Deemed University) Pune India

The effectiveness of Network Intrusion Detection Systems (NIDS) in recognizing complex attacks is hindered by evasion strategies like polymorphic malware and encrypted traffic. Vulnerabilities in NIDS algorithms are regularly exploited by cybercriminals, highlighting the dynamic relationship between changing attack tactics and cybersecurity defenses. An enhanced approach to strengthen Network Intrusion Detection Systems is suggested by this study. The methodology begins with a rigorous preprocessing phase that includes normalization functions to progress the accuracy and consistency of the input data. Optimizing the NIDS’s efficacy is the aim of the Single Candidate Optimization (SCO) algorithm for feature selection. We utilize a hybrid model that incorporates ANN, Wavelet Transform, and Long Short-Term Memory (LSTM) for the classification phase. This model is designed to identify sequential dependencies in the network traffic data that we have. A second iteration of the SCO is devoted to hyperparameter optimization in order to attain optimal model performance and to further optimize the model. For our review, we opted to use the BoT-IoT dataset because it is a gold standard in the field. The suggested approach demonstrates how SCO can improve hyperparameter optimization and feature selection, leading to a more accurate and cyberattack-resistant NIDS. Consequences from experiments and evaluations of performance demonstrate that the proposed strategy is effective; it achieves a remarkable 99.6 percent accuracy. This proves how successful it is in judgement to current representations, which is a significant step towards consolidation cybersecurity defenses against changing threat landscapes.

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer’s Di...

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Energy, Materials and communication Engineering (ICEMCE), International Conference on

作者： D. Karthikeyan R. Nivasini Kiruba Thangam Raja D. Madhivadhani B. Sivakumar A.T.R. Krishna Priya Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Guvi Geek Networks IITM Research Park Chennai Tamilnadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Department of Electronics and Communication Engineering Kings Engineering College Sriperumbudur Chennai Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari India

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer’s disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper.

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UAV-Assisted Downlink-and-Uplink communication in the Presence of Multiple Malicious Jammers

UAV-Assisted Downlink-and-Uplink Communication in the Presen...

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IEEE Conference on Wireless communications and Networking

作者： Zhiyu Huang Zhichao Sheng Ali A. Nasir Cheng Yin Antonino Masaracchia Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai China Department of Electrical Engineering and Center for Communication Systems and Sensing KFUPM Dhahran Saudi Arabia Institute for Communication Systems University of Surrey U.K. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast U.K.

This paper investigates the unmanned aerial vehicle (UAV)-assisted communication network with multiple downlink users (DLUs) and uplink users (ULUs) in the presence of multiple malicious jammers. To guarantee fairness among the users and their uplink and downlink communication throughput, we aim to maximize the minimum average throughput by jointly optimizing the scheduling of ULUs/DLUs, three dimensional (3D) trajectory and the UAV transmission power. Although the optimization problem is computationally intractable due to its non-convexity, we develop an iterative algorithm based on the block coordinate descend approach and the successive convex approximation technique to solve the problem efficiently. Numerical outcomes show that our proposed algorithm can improve throughput significantly over several benchmark schemes.

关键词： Three-dimensional displays Processor scheduling Benchmark testing Throughput Autonomous aerial vehicles Downlink Scheduling

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DOLPHIN: Phonics based Detection of DGA Domain Names

DOLPHIN: Phonics based Detection of DGA Domain Names

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2021 IEEE Global communications Conference (GLOBECOM)

作者： Dan Zhao Hao Li Xiuwen Sun Yazhe Tang School of Computer Science and Technology Xi'an Jiaotong University Xi'an University of Finance and Economics Anhui University Science and Technology on Communication Networks Laboratory

ISBN: (纸本)9781728181059

Botnets are the machines that increasingly controlled by cybercriminals to perform various attacks. They use Domain Generation Algorithm (DGA) to frequently generate their illegitimate domains for preventing detection. To overcome such dynamics, existing solutions try to capture the characteristics of domain names, such that the automatically generated domains can be identified. However, those solutions are not conformed to the linguistic conventions of reading and writing. For a comprehensive understanding of strings of domain names, we present DOmain Linguistic PHonIcs detectioN (DOLPHIN), a novel method that can detect the illegitimate domain names generated by DGAs. Considering the correspondence between pronunciations and spellings, we design the DOLPHIN patterns. They are the classification of vowels and consonants in variable lengths as follow the principles of phonics. DOLPHIN recognizes strings of domain names and reconstructs them with the components of variable-length vowels and consonants following the DOLPHIN patterns. We implement the features used DOLPHIN in supervised learning methods and compare them to the fore-most method FANCI. Experimental results show that, compared to FANCI with RFs, DOLPHIN can achieve higher detection accuracy of 0.0238 in average with lower FPR without much overhead.

关键词： Radio frequency Virtual assistants Heuristic algorithms Supervised learning Linguistics Writing Feature extraction

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Scheduling of wireless edge networks for feedback-based interactive applications

arXiv

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arXiv 2021年

作者： Zoppi, Samuele Champati, Jaya Prakash Gross, James Kellerer, Wolfgang Department of Communication Networks Technical University of Munich Germany IMDEA Networks Institute Spain School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Interactive applications with automated feedback will largely influence the design of future networked infrastructures. In such applications, status information about an environment of interest is captured and forwarded to a compute node, which analyzes the information and generates a feedback message. Timely processing and forwarding must ensure the feedback information to be still applicable;thus, the quality-of-service parameter for such applications is the end-to-end latency over the entire loop. By modelling the communication of a feedback loop as a two-hop network, we address the problem of allocating network resources in order to minimize the delay violation probability (DVP), i.e. the probability of the end-to-end latency exceeding a target value. We investigate the influence of the network queue states along the network path on the performance of semi-static and dynamic scheduling policies. The former determine the schedule prior to the transmission of the packet, while the latter benefit from feedback on the queue states as time evolves and reallocate time slots depending on the queue's evolution. The performance of the proposed policies is evaluated for variations in several system parameters and comparison baselines. Results show that the proposed semi-static policy achieves close-to-optimal DVP and the dynamic policy outperforms the state-of-the-art algorithms. © 2021, CC BY.

关键词： Interactive devices

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Edge-Cloud Collaboration for Low-Latency, Low-Carbon, and Cost-Efficient Operations

SSRN

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SSRN 2024年

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping School of Computer and Communication Engineering University of Science and Technology Beijing China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing China School of Economics and Management Beijing University of Posts and Telecommunications China No.30 Xueyuan Road Haidian District Beijing100083 China No.10 Xitucheng Road Haidian District Beijing100876 China

The growing demand for low-latency services and the increasing impact of carbon emissions pose challenges to traditional cloud computing architectures. Hence, to address the high latency limitations of traditional cloud computing and leverage the advantages of abundant renewable energy sources (RESs) and low-priced electricity of remote clouds, we design an edge-cloud collaboration system to distribute mixed workloads, aiming at meeting delay requirements while reducing carbon emissions and improving operating profits. Specifically, delay-sensitive workloads are allocated to nearby edge clouds, while delay-tolerant workloads are assigned to remote core clouds. Additionally, a multi-level scheduling strategy is proposed to flexibly allocate delay-tolerant workloads. Beyond responding to RES generation and electricity price signals, this strategy prioritizes workloads and reduces the supply of high-priced electricity to low-priority workloads, further decreasing electricity costs. Finally, we use Alibaba workload traces to evaluate the proposed strategy. Simulation results demonstrate that the proposed edge-cloud collaboration system can reduce the average response delay of delay-sensitive workloads by 33.42 times compared to the traditional cloud system. Additionally, compared to the effective energy storage systems (ESSs)-based algorithm, the proposed strategy not only reduces carbon emissions by 3.14% but also increases operating profits by 18.77%. These results highlight its potential to enhance environmental sustainability, economic benefits, and user experience. © 2024, The Authors. All rights reserved.

关键词： Renewable energy

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Kerr effect in ultra-compact hybrid plasmonic metal-insulator-metal nano-focusing structure

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Chinese Optics Letters 2022年第3期20卷 106-110页

作者： Kejian Zhu Pengfei Sun F|engfei Xu Xingpeng Liu Tangyou Sun Haiou Li Zhiping Zhou State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic TechnologyGuilin 541004China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China

Nano-focusing structures based on hybrid plasmonic waveguides are likely to play a key role in strong nonlinear optical *** the insertion loss is considerable,a significant nonlinear phase shift may be achieved by decreasing the nano-focusing device footprint and careful parameter ***,we study the Kerr effect in hybrid plasmonic waveguides by analyzing the mode effective area,energy velocity,and insertion ***,by utilizing plasmonics to manipulate the effective index and mode similarity,the TM mode is reflected and absorbed,while the TE mode passes through with relatively low propagation *** providing a deep understanding of hybrid plasmonic waveguides for nonlinear applications,we indicate pathways for their future optimization.

关键词： silicon photonics plasmonics nonlinear optics nano-focusing

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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