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Soft transmission of 3D video for low power and low complexity scenario

Digital Communications and Networks 2023年第3期9卷 769-778页

作者： Ao Li Taihai Yang Wenxin Wu Lei Luo School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology East China Normal UniversityShanghai200062China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Thanks to the rapid development of naked-eye 3D and wireless communication technology,3D video related applications on mobile devices have attracted a lot of ***,the time-varying characteristics of the wireless channel is very challenging for conventional source-channel coding based transmission ***,the high complexity of source-channel coding based transmission scheme is undesired for low power mobile *** advanced transmission scheme named Softcast was proposed to achieve efficient transmission performance for 2D image/***,it cannot be directly applied to wireless 3D video transmission with high *** paper proposes a more efficient soft transmission scheme for 3D video with a graceful quality adaptation within a wide range of channel Signal-to-Noise Ratio(SNR).The proposed method first extends the linear transform to 4 dimensions with additional view dimension to eliminate the view redundancy,and then metadata optimization and chunk interleaving are designed to further improve the transmission ***,a synthesis distortion based chunk discard strategy is developed to improve the overall 3D video quality under the condition of limited *** experimental results demonstrate that the proposed method significantly improves the 3D video transmission performance over the wireless channel for low power and low complexity scenarios.

关键词： Softcast Low power 3D video Wireless

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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Red deer algorithm to detect the secret key of the monoalphabetic cryptosystem

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Soft Computing 2024年第17-18期28卷 10569-10582页

作者： Jain, Ashish Bansal, Sulabh Das, Nripendra Narayan Gupta, Shyam Sunder Department of Information Technology School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior474005 India

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Tabu search

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Blockchain-based internet of things security architecture and applications

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16703-16714页

作者： Chen, Hongsong Lei, Shi Zhang, Yiying Han, Xintong Cao, Yongrui Zhang, Yongpeng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

The Internet of Things (IoT) and blockchain are two emerging information technologies that will significantly impact the lives and production patterns of people. When they meet together, blockchain services can be used to solve some IoT challenges, however some new risks will be introduced. Therefore, how to use blockchain services in IoT environment and which key security and privacy challenges in IoT environment can be addressed by blockchain services are two critical issues. Thus, we propose a novel hybrid IoT architecture based on blockchain services for the first issue. To address the second issue, we design system architecture for implementing blockchain-as-a-service in the IoT environment, select the dimensions of fog computing, edge computing and software vulnerability to classify the challenges and solutions. Then practical application scenarios are summarized. The single on-chain time is between 15 and 20 s and the single off-chain in IOTA is about 2 s. The on-chain time is longer than the off-chain time. Finally, seven challenges and the related solution for blockchain-based IoT are proposed to promote future blockchain and IoT research. The proposed architecture can improve the structural security and functional security for IoT and Blockchain. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Chinese Relation Extraction Based on Cross-Attention and Multi-feature Perception

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1837-1853页

作者： Xu, Shiao Sun, Shuihua Zhang, Zhiyuan Zhou, Huan School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

Relation extraction aims at extracting semantic relations between given pairs of entities from unstructured textual data and is a critical task in information extraction. A relation extraction model based on cross-attention and multi-feature perception is proposed to address the limitations of Chinese relation extraction models that rely solely on characters, which struggle to capture rich semantic information, and the inability to ful ly utilize the textual structural information during neural network feature extraction. First, the model uses BERT to encode sentences and entities to obtain character vector sequences and word vector sequences of sentences, and uses word cross-attention mechanism to capture the key word and word information in sentences to form character and word cross-fused sentence sequences. Second, uses graph convolutional network and deep separable convolutional network to capture syntactic information between nodes and multi-granularity local semantic information in sentence sequences, respectively, and forms sentence features for classification by attention mechanism. Final ly, combines with Softmax classifier for relation classification. Experimental results on the Chinese SanWen dataset demonstrate the superiority of the proposed method over mainstream neural network-based relation extraction methods, with an F1-score of 71.57%. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Unknown Task Selection and Worker Recruitment Using Two-Stage Multi-Armed Bandit in Crowdsensing

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IEEE Internet of Things Journal 2025年第13期12卷 24441-24456页

作者： Song, Haoyuan Li, Peng Du, Rui Yu, Hai Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hubei Polytechnic University School of Computer Huangshi435003 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Mobile Crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This paper tackles these challenges by modeling the long-term multi-task, multi-worker selection problem as a Two-Stage Multi-Armed Bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a Combinatorial MAB (CMAB) problem. We propose an extended Upper Confidence Bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods. © 2014 IEEE.

关键词： Personnel selection

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An improved U-Net network for medical image segmentation 10

An improved U-Net network for medical image segmentation

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wang, Zhenzhen Zhang, Jia Liu, Zhihuan Chen, Shaomiao Lu, Danqing Central South University of Forestry and Technology College of Science Changsha Hunan China Central South University of Forestry and Technolog College of Computer and Information Engineering Changsha Hunan China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan China

ISBN: (纸本)9798350312461

In many computer-aided spinal imaging and disease diagnosis, automating the segmentation of the spine and cones from CT images is a challenging problem. Therefore, in this paper, we propose a triple channel expansion attention segmentation network based on U-Net for spinal CT images. We design a triple channel expansion attention to solve the problem of low accuracy caused by the loss of important feature information in the downsampling process of ordinary convolution, which uses different sizes of convolution set kernels to extract different features. Then through this attention, we output a feature image for each layer of the down-sampling, and finally skip connection with it during the up-sampling. Finally, many experimental results on VerSe 2019 and VerSe 2020 datasets show that our proposed network is superior to other prior art segmentation networks. © 2023 IEEE.

关键词： Image segmentation

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Generalized Extended Welch Bound for Multi-Cell Non-Orthogonal Sequence Sets

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IEEE Communications Letters 2025年第6期29卷 1176-1180页

作者： Shen, Bingsheng Liu, Kaiqiang Gu, Zhi Yang, Yang Zhou, Zhengchun Southwest Jiaotong University School of Information Science and Technology Chengdu China Southwest Jiaotong University School of Mathematics Chengdu China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Sequence sets with minimum total squared correlation (TSC) have important application in communications. In a multi-cell system, due to the difference in channel strength between the home cell and the neighboring cells, the TSC can be expressed as the sum of squares of weighted correlations among sequences. In this letter, we obtain a closed-form expression for the lower bound of weighted TSC under multi-cell systems which can be regarded as a generalization of the extended Welch bound, namely generalized extended welch bound (GEWB). Moreover, we find a class of sequence sets whose parameters can achieve the theoretical lower bound of the weighted TSC, indicating that the derived GEWB is tight. © 1997-2012 IEEE.

关键词： Plant cell culture

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SeaCap: Multi-Sight Embedding and Alignment for One-Stage Image Captioner

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IEEE Transactions on Multimedia 2025年 27卷 3411-3425页

作者： Wang, Bo Zhang, Zhao Zhao, Mingbo Jin, Xiaojie Xu, Mingliang Wang, Meng Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Yunnan Key Laboratory of Software Engineering Yunan China Donghua University School of Computer Science and Engineering Shanghai201620 China ByteDance Culver CityCA90230 United States Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

Recent mainstream image captioning methods usually adopt two-stage captioners, i.e., calculating the object features of the given image by a pre-trained detector and then feeding them into a language model to generate the descriptive sentences. However, such a two-stage procedure will lead to a task-based information gap that decreases the performance of the captioners, because the object features learned from the detection task are suboptimal representations and cannot provide all the necessary information for subsequent sentence generation. Besides, the object features are usually represented by the last pooling features of the detector that lose the local details of images. In this paper, we propose a novel One-Stage Image Captioner using dynamic multi-sight embedding and alignment, called SeaCap, which directly transforms input images into descriptive sentences in one stage to eliminate the information gap. Specifically, to obtain rich features, we use the Swin Transformer to capture multi-level features, followed by a sights alignment module to alleviate the vision confusion, and then feed them into a novel dynamic multi-sight embedding module to exploit both the global structure and local texture of input images. To enhance the global modeling capacity of the visual encoder, we propose a new dual-dimensional refining module to non-locally model the interaction of the embedded features. As a result, SeaCap can obtain rich and useful information to improve the performance of the captioner. Extensive comparisons on the benchmark MS-COCO, Flickr8K and Flickr30 K datasets verified the superior performance of our method. © 1999-2012 IEEE.

关键词： Embeddings

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A missing physical fitness test data classification method based on MLP 10

A missing physical fitness test data classification method b...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Che, Peng Peng, Zhenlian Cao, Buqing Liu, Jianxun Chen, Tieping Fan, Runqing Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %. © 2023 IEEE.

关键词： Classification (of information)

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