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Bi-layered coded metasurface for multi-functional hologram with broadband transmission and efficient reflection

Chinese Optics Letters 2024年第5期22卷 8-16页

作者：朱磊李绪胜董亮谢文静尚关玉 Shah Nawaz Burokur 丁旭旻 School of Computer and Control Engineering Qiqihar UniversityQiqihar 161006China Communication and Electronics Engineering Institute Qiqihar UniversityQiqihar 161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar 161000China Department of Microwave Engineering Harbin Institute of TechnologyHarbin 150001China LEME UPLUniversitéParis NanterreF92410 Ville d’AvrayFrance Advanced Microscopy and Instrumentation Research Center Harbin Institute of TechnologyHarbin 150080China Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

A multi-functional full-space metasurface based on frequency and polarization multiplexing is *** metasurface unit consists of metallic patterns printed on the two faces of a single-layered dielectric *** unit cell can control electromagnetic wavefronts to achieve a broadband transmission with amplitudes greater than 0.4 from 4.4 to 10.4 ***,at 11.7 GHz and 15.4 GHz,four high-efficiency reflection channels with a reflection amplitude greater than 0.8 are also *** illuminated by linearly polarized waves,five different functions can be realized at five different frequencies,which are demonstrated by theoretical calculations,full-wave simulations,and experimental measurements.

关键词： multi-functional metasurface broadband transmission efficient reflection full space

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Monocular Camera-Based Visual Truck Type Recognition

SSRN

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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Energy Efficiency Optimization for RF Energy Harvesting Relay System 11th

Energy Efficiency Optimization for RF Energy Harvesting Rela...

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11th EAI International Conference on Broadband Communications, networks, and Systems, BROADNETS 2020

作者： Liang, Guangjun Xin, Jianfang Wang, Qun Xia, Lingling Li, Meng Department of Computer Information and Network Security Jiangsu Police Institute Nanjing China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Nanjing China Electrical Engineering School Anhui Polytechnic University Wuhu China

ISBN: (纸本)9783030687366

This paper conducts research on the RF energy harvesting relay network, and proposes an improved energy harvesting relay protocol, which allows the energy harvesting source node to retransmit data to improve the system diversity gain, and constructs energy harvesting slot allocation, subcarrier pairing, and power Optimized model of distributed system energy efficiency. A resource allocation algorithm based on optimal energy efficiency is further proposed. The Dinkelbach method is used to transform the nonlinear programming problem into a linear programming problem. Then, the Hungarian algorithm and the sub-gradient method are used to obtain the iterative algorithm based on energy efficiency optimization. Simulation shows that the algorithm reduces the complexity of the algorithm and has good global convergence. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Energy harvesting

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FCDSB: A Fog Computing network Architecture Based on Dynamic Sharding Blockchain for Consumer Electronics in AIoT

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IEEE Transactions on Consumer Electronics 2024年

作者： Wei, Chenxiang Lin, Hui Que, Youxiong Wang, Xiaoding Fujian Normal University College of Computer and Cyber Security Fujian Fuzhou350117 China Fujian Province University Engineering Research Center of Cyber Security and Education Informatization Fujian Fuzhou350117 China Chinese Academy of Tropical Agricultural Sciences National Key Laboratory for Tropical Crop Breeding Institute of Tropical Bioscience and Biotechnology Sanya Research Institute Hainan Sanya572024 China Fujian Agriculture and Forestry University Key Laboratory of Sugarcane Biology and Genetic Breeding Ministry of Agriculture and Rural Affairs Fujian Fuzhou350002 China Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China

The Artificial Intelligence of Things (AIoT) is revolutionizing the consumer electronics sector by enabling intelligent interaction and real-time adjustments in consumer electronics products to provide personalized services. Fog computing extends computational services to the network edge, further enhancing the potential of AIoT. However, since fog layer devices dedicate most resources to executing core programs and data transmission, supporting additional security and privacy protections becomes highly challenging. Sharding blockchain, with its unique data-sharing mechanism, facilitates information flow in an open and trustless network, allowing secure and efficient management of large-scale AIoT data. Nevertheless, existing sharding blockchain systems face scalability issues due to low throughput and are susceptible to security risks from malicious attacks, limiting their application in AIoT. To address these challenges, this paper proposes a fog computing network architecture based on dynamic sharding blockchain, called FCDSB. The scheme optimizes sharding strategies by establishing a node reputation mechanism to reduce the impact of malicious nodes. The level of malice in the network is assessed by analyzing consensus inconsistencies, which helps determine the sharding boundaries under the current network conditions. Furthermore, deep reinforcement learning (DRL) is utilized to adaptively optimize system throughput and security levels to meet the evolving network conditions and demands. Simulation results indicate that this scheme significantly improves the scalability of sharding blockchain in AIoT applications while maintaining a high level of security. © 2024 IEEE.

关键词： Fog computing

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Lombard-VLD: Voice Liveness Detection Based on Human Auditory Feedback

Lombard-VLD: Voice Liveness Detection Based on Human Auditor...

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IEEE Symposium on security and Privacy

作者： Hongcheng Zhu Zongkun Sun Yanzhen Ren Kun He Yongpeng Yan Zixuan Wang Wuyang Liu Yuhong Yang Weiping Tu School of Cyber Science and Engineering Wuhan University Wuhan China State Grid Wuhan Electric Power Supply Company Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Voice Liveness Detection (VLD) aims to protect speaker authentication from speech spoofing by determining whether speeches come from live speakers or loudspeakers. Previous methods mainly focus on their differences at the signal level. In this paper, we propose the first VLD that uses the human auditory feedback mechanism (i.e., the Lombard effect), called Lombard-VLD. The key idea is that live speakers can physiologically and involuntarily adjust their speaking patterns in a noisy background but loudspeakers cannot. Moreover, we design a reference-based dual input mode and a differential SE-ResBlock to model the acoustic differences caused by the Lombard effect. Experimental results show that Lombard-VLD achieves 0% and 0.24% EER in two datasets, outperforming the state-of-the-art methods. It is robust to various environmental factors, including different distances, postures of the speaker, and environmental noise, with an average accuracy of over 98.51%. It also has a good generalization to unseen speakers, genders, and datasets, with EER lower than 2.68%, 3.44%, and 7.32%, respectively. This work shows the advantages of the Lombard effect in VLD, which has fewer user limitations and better detection performance.

关键词： Loudspeakers Privacy Accuracy Working environment noise Authentication Physiology Environmental factors Acoustics security Noise measurement

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Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design 23

Semi-Supervised Federated Learning with non-IID Data: Algori...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Zhe Ma, Shiyao Nie, Jiangtian Wu, Yi Yan, Qiang Xu, Xiaoke Niyato, Dusit School of Data Science and Technology Heilongjiang University Harbin150080 China Cryptology and Network Security Laboratory of Heilongjiang University Harbin150080 China College of Information and Communication Engineering Dalian Minzu University China School of Computer Science and Engineering Nanyang Technological University Singapore WeBank Co. Ltd. China

ISBN: (纸本)9781665494571

Federated Learning (FL) allows edge devices (or clients) to keep data locally while simultaneously training a shared high-quality global model. However, current research is generally based on an assumption that the training data of local clients have ground-truth. Furthermore, FL faces the challenge of statistical heterogeneity, i.e., the distribution of the client's local training data is non-independent identically distributed (non-IID). In this paper, we present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL. In particular, this paper focuses on studying the labels-at-server scenario where there is only a limited amount of labeled data on the server and only unlabeled data on the clients. In our system design, we propose a novel method to tackle the problems, which we refer to as Federated Mixing (FedMix). FedMix improves the naive combination of FL and semi-supervised learning methods and designs parameter decomposition strategies for disjointed learning of labeled, unlabeled data, and global models. To alleviate the non-IID problem, we propose a novel aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation algorithm, which can adjust the weight of the corresponding local model according to this frequency. Extensive evaluations conducted on CIFAR-10 dataset show that the performance of our proposed method is significantly better than those of the current baseline. It is worth noting that our system is robust to different non-IID levels of client data. © 2021 IEEE.

关键词： Training Smart cities Design methodology Training data Semisupervised learning Collaborative work Data models

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Exploration and practice on the construction of computer first-class major 16

Exploration and practice on the construction of computer fir...

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16th IEEE International Conference on computer Science and Education, ICCSE 2021

作者： Li, Wei Hei, Xinhong Wang, Lei Wang, Xiaofan Xi'An University of Technology School of Computer Science and Engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Shaanxi University of Technology School of Mathematics and Computer Science Hanzhong China

ISBN: (纸本)9781665414685

This paper analyzes the current situation of the construction of the first-class computer major, and puts forward the construction scheme of the first-class computer major in local colleges based on the concept of outcome based education (OBE). The reform scheme of the construction of the first-class computer major is elaborated from four aspects: training objectives, curriculum system, teaching mode, and evaluation mechanism. © 2021 IEEE.

关键词： Training computer science Knowledge engineering Problem-solving System analysis and design

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A Triangular Stable Node network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection 39

Each Fake News is Fake in its Own Way: An Attribution Multi-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defense Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

ISBN: (纸本)157735897X

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering

CoG-DQA: Chain-of-Guiding Learning with Large Language Model...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shaowei Wang Lingling Zhang Longji Zhu Tao Qin Kim-Hui Yap Xinyu Zhang Jun Liu School of Computer Science and Technology Xi'an Jiaotong University China Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Ministry of Education Xi'an Shaanxi China School of Electrical & Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diagram Question Answering (DQA) is a challenging task, requiring models to answer natural language questions based on visual diagram contexts. It serves as a crucial basis for academic tutoring, technical support, and more practical applications. DQA poses significant challenges, such as the demand for domain-specific knowledge and the scarcity of annotated data, which restrict the applicability of large-scale deep models. Previous approaches have explored external knowledge integration through pretraining, but these methods are costly and can be limited by domain disparities. While Large Language Models (LLMs) show promise in question-answering, there is still a gap in how to cooperate and interact with the diagram parsing process. In this paper, we introduce the Chain-of-Guiding Learning Model for Diagram Question Answering (CoG-DQA), a novel framework that effectively addresses DQA challenges. CoG-DQA leverages LLMs to guide diagram parsing tools (DPTs) through the guiding chains, enhancing the precision of diagram parsing while introducing rich background knowledge. Our experimental findings reveal that CoG-DQA surpasses all comparison models in various DQA scenarios, achieving an average accuracy enhancement exceeding 5% and peaking at 11% across four datasets. These results underscore CoG-DQA's capacity to advance the field of visual question answering and promote the integration of LLMs into specialized domains.

关键词： Bridges Visualization computer vision Large language models Computational modeling Natural languages Question answering (information retrieval)

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