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Communication delay-aware cooperative adaptive cruise control with dynamic network topologies——A convergence of communication and control

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Digital Communications and Networks 2025年第1期11卷 191-199页

作者： Jihong Liu Yiqing Zhou Ling Liu State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190China Zhongke Nanjing Mobile Communication&Computing Innovation Institute Nanjing211100China

Wireless communication-enabled Cooperative Adaptive Cruise Control(CACC)is expected to improve the safety and traffic capacity of vehicle *** CACC considers a conventional communication delay with fixed Vehicular Communication Network(VCN)***,when the network is under attack,the communication delay may be much higher,and the stability of the system may not be *** paper proposes a novel communication Delay Aware CACC with Dynamic Network Topologies(DADNT).The main idea is that for various communication delays,in order to maximize the traffic capacity while guaranteeing stability and minimizing the following error,the CACC should dynamically adjust the VCN network topology to achieve the minimum inter-vehicle *** this end,a multi-objective optimization problem is formulated,and a 3-step Divide-And-Conquer sub-optimal solution(3DAC)is *** results show that with 3DAC,the proposed DADNT with CACC can reduce the inter-vehicle spacing by 5%,10%,and 14%,respectively,compared with the traditional CACC with fixed one-vehicle,two-vehicle,and three-vehicle look-ahead network topologies,thereby improving the traffic efficiency.

关键词： Communication delay Cooperative adaptive Cruise control Network topology String stability

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Secure Computation Efficiency Resource Allocation for Massive MIMO-Enabled mobile Edge computing Networks

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China Communications 2024年第11期21卷 150-162页

作者： Sun Gangcan Sun Jiwei Hao Wanming Zhu Zhengyu Ji Xiang Zhou Yiqing School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Beijing Key Laboratory of Mobile Computing and Pervasive Device and the Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)*** first derive the secure transmission rate based on the mMIMO under imperfect channel state *** on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit *** to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach ***,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the ***,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.

关键词： eavesdropping massive multiple input multiple output mobile edge computing partial offloading secure computation efficiency

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Advancing Multi-grained Alignment for Contrastive Language-Audio Pre-training 24

Advancing Multi-grained Alignment for Contrastive Language-A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Yiming Guo, Zhifang Wang, Xiangdong Liu, Hong Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

Recent advances have been witnessed in audio-language joint learning, such as CLAP, that shows much success in multi-modal understanding tasks. These models usually aggregate uni-modal local representations, namely frame or word features, into global ones, on which the contrastive loss is employed to reach coarse-grained cross-modal alignment. However, frame-level correspondence with texts may be ignored, making it ill-posed on explainability and fine-grained challenges which may also undermine performances on coarse-grained tasks. In this work, we aim to improve both coarse- and fine-grained audio-language alignment in large-scale contrastive pre-training. To unify the granularity and latent distribution of two modalities, a shared codebook is adopted to represent multi-modal global features with common bases, and each codeword is regularized to encode modality-shared semantics, bridging the gap between frame and word features. Based on it, a locality-aware block is involved to purify local patterns, and a hard-negative guided loss is devised to boost alignment. Experiments on eleven zero-shot coarse- and fine-grained tasks suggest that our model not only surpasses the baseline CLAP significantly but also yields superior or competitive results compared to current SOTA works. © 2024 Owner/Author.

关键词： Semantics

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Query-Aware Semantic Encoder-Based Resource Allocation in Task-Oriented Communications

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IEEE Transactions on mobile computing 2025年第7期24卷 6413-6429页

作者： Cai, Qing Zhou, Yiqing Liu, Ling Yu, Hanxiao Wu, Yihao Shi, Ningzhe Shi, Jinglin University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China

Task-oriented communications with semantic encoders are promising to enhance the communication efficiency, by selecting and transmitting valuable data according to task requirements/queries. However, existing semantic encoders lack the capability to track the changing in queries, leading to biased data selection. This paper proposes a query-aware semantic encoder, i.e., Query-Data Cross (QDC) encoder for task-oriented communications. By consistently focusing on data features that are most relevant to the current query at the transmitter, QDC can adapt to changing queries. Based on the dynamic semantic relevance obtained by QDC, a relevance-based data selection and bandwidth allocation optimization (RDSBA) problem is formulated, considering a multi-device task-oriented communication system, where devices should transmit valuable data with high relevance to the queries broadcasted by the base station (BS). RDSBA aims to maximize the data profit of all devices, which is defined as the difference between the relevance of data selected for the BS and the cost of obtaining the data. Then, a DRL-based data selection and bandwidth allocation (DRL-DB) algorithm is proposed to solve the NP-hard optimization problem. Simulation results demonstrate that QDC can smartly track the changing in queries and achieve an accuracy of at least 85% in relevance evaluation, more than 8% higher than existing schemes. Based on the relevance provided by QDC, the proposed RDSBA scheme with DRL-DB can increase the data profit by at least 18%, comparing to existing schemes. © 2002-2012 IEEE.

关键词： Bandwidth

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FedBone:Towards Large-Scale Federated Multi-Task Learning

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Journal of Computer Science & Technology 2024年第5期39卷 1040-1057页

作者： Yi-Qiang Chen Teng Zhang Xin-Long Jiang Qian Chen Chen-Long Gao Wu-Liang Huang Beijing Key Laboratory of Mobile Computing and Pervasive Devices Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China

Federated multi-task learning(FMTL)has emerged as a promising framework for learning multiple tasks simultaneously with client-aware personalized *** the majority of studies have focused on dealing with the non-independent and identically distributed(Non-IID)characteristics of client datasets,the issue of task heterogeneity has largely been *** with task heterogeneity often requires complex models,making it impractical for federated learning in resource-constrained *** addition,the varying nature of these heterogeneous tasks introduces inductive biases,leading to interference during aggregation and potentially resulting in biased global *** address these issues,we propose a hierarchical FMTL framework,referred to as FedBone,to facilitate the construction of large-scale models with improved *** leverages server-client split learning and gradient projection to split the entire model into two components:1)a large-scale general model(referred to as the general model)on the cloud server,and 2)multiple task-specific models(referred to as client models)on edge clients,accommodating devices with limited compute *** enhance the robustness of the large-scale general model,we incorporate the conflicting gradient projection technique into FedBone to rectify the skewed gradient direction caused by aggregating gradients from heterogeneous *** proposed FedBone framework is evaluated on three benchmark datasets and one real ophthalmic *** comprehensive experiments demonstrate that FedBone efficiently adapts to the heterogeneous local tasks of each client and outperforms existing federated learning algorithms in various dense prediction and classification tasks while utilizing off-the-shelf computational resources on the client side.

关键词： federated learning multi-task learning split learning heterogeneous task

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Realization of a computational efficient BBU cluster for cloud RAN

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 849-859页

作者： Sah Tyagi, Sumarga Kumar Zhou, Yiqing Lin, Tian Marahatta, Avinab Shi, Jinglin Key Laboratory of Mobile Computing and Pervasive Device Beijing100190 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Computational efficiency, CE, of a baseband unit, BBU, has become a key issue for a cloud radio access network, CRAN. The CE is defined as the sum of computational power, CP and computing resources, CR. Where CP and CR can be defined as the amount of power and processing resources of the core computing unit, CCU, of the BBU consumed during execution of traffic requests from remote radio heads, RRHs, respectively. A CCU is nothing but a general-purpose processor. To optimize CR utilization, less number of BBUs has to execute the same amount of RRH-requests. This causes dramatic rise in CCU’s operating temperature and imposes CP efficiency problem, because leakage power of a CCU, which is a wasted power, increases with its operating temperature. On the other hand, a CCU can reduce its CP remarkably by executing the RRH-requests slowly at lower frequency. However, the execution has to meet deadline of the RRH-request that is equivalent to a circuit delay in CCU, which depends on supply voltage, CCU-temperature, and frequency. Therefore, it is very challenging to minimize CP of GPP while optimizing CR subject to RRH-request’s deadline. Previous approaches, including our previously proposed TADCRA scheme, tries to turn off as many GPPs as possible by integrating traffic load on fewer GPPs to maximize the CR utilization while working at allowable temperature. Compared to TADCRA, it is observed that significant CP can be saved from the active GPP by dynamically adjusting its voltage and frequency (DVFS) while executing RRH-requests. Thus, given optimal number of GPPs obtained from TADCRA that maximizes the CR efficiency, a computational efficiency problem is formulated subject to minimum operating frequency, voltage and RRH-request’s deadline constraints. To address this problem, we propose computational efficient allocation (CEA) algorithm, where Lagrange multiplier tool is used for CP efficiency problem, while our proposed heuristic, Win–Win, solves the CR optimization p

关键词： Computational efficiency

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AvatarWild:Fully controllable head avatars in the wild

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Visual Informatics 2024年第3期8卷 96-106页

作者： Shaoxu Meng Tong Wu Fang-Lue Zhang Shu-Yu Chen Yuewen Ma Wenbo Hu Lin Gao Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Victoria University of Wellington WellingtonNew Zealand ByteDance Ltd. BeijingChina

Recent advancements in the field have resulted in significant progress in achieving realistic head reconstruction and manipulation using neural radiance fields(NeRF).Despite these advances,capturing intricate facial details remains a persistent ***,casually captured input,involving both head poses and camera movements,introduces additional difficulties to existing methods of head avatar *** address the challenge posed by video data captured with camera motion,we propose a novel method,AvatarWild,for reconstructing head avatars from monocular videos taken by consumer ***,our approach decouples the camera pose and head pose,allowing reconstructed avatars to be visualized with different poses and expressions from novel *** enhance the visual quality of the reconstructed facial avatar,we introduce a view-dependent detail enhancement module designed to augment local facial details without compromising viewpoint *** method demonstrates superior performance compared to existing approaches,as evidenced by reconstruction and animation results on both multi-view and single-view ***,our approach stands out by exclusively relying on video data captured by portable devices,such as *** not only underscores the practicality of our method but also extends its applicability to real-world scenarios where accessibility and ease of data capture are crucial.

关键词： Neural radiance fields Head avatar synthesis Face reconstruction Face reenactment Facial animation

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Class-imbalanced Time Series Adaptation via Multi-Expert Consistency Entropy Minimization

Class-imbalanced Time Series Adaptation via Multi-Expert Con...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Yuan Gu, Yang Guo, Shuai Weng, Weining Wen, Shijie Chen, Yiqiang Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350386226

Wearable-based time series recognition aims to infer behavioral classes based on time series signals collected by sensors. Domain Adaptation (DA) methods have significantly improved the performance on out-of-domain test classification tasks. However, highly imbalanced label distribution in real-world tasks hinders the practicality of DA methods. In this paper, we propose a Multi-Expert Consistency Entropy Minimization (MECEM) method for class-imbalanced domain adaptation. Through the incorporation of a multi-expert committee mechanism, we recalibrate decision boundaries in response to imbalanced source distributions, thereby mitigating bias towards minority classes and promoting fairness in committee decisions. Furthermore, we introduce a time-frequency dynamic augmentation mechanism, which simultaneously addresses domain shift on the target domain and dynamically adapts the augmentation library for the classification task. The MECEM method facilitates the propagation of consistency entropy during backpropagation, conveying both the reliability of pseudo-labels and the classification task relevance of augmentation techniques. Experiments across various tasks on activity recognition and Parkinson's tremor grading demonstrate the superiority of our method. © 2024 IEEE.

关键词： Wearable sensors

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KGViT: Knowledge Graph Integrated Vision Transformer for EEG-Based Emotion Recognition 10

KGViT: Knowledge Graph Integrated Vision Transformer for EEG...

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10th IEEE Smart World Congress, SWC 2024

作者： Chen, Qianzhong Weng, Weining Shen, Jianfei Chen, Yiqiang Gu, Yang Institute of Computing Technology Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331520861

Emotion recognition using electroencephalography (EEG) has attracted considerable attention and achieved significant advancements in the brain-computer interface field. Although many deep learning methods have shown superior performance over traditional approaches in emotion recognition tasks, existing studies neglect auxiliary information, such as participants' details and descriptions of stimuli, focusing solely on EEG data. This oversight may lead to poor generalization across different participants. To address this issue, we propose a knowledge graph integrated with a vision transformer (KGViT). The KGViT model utilizes the vision transformer to capture effective features from the 3D temporal-spectral-spatial representations of EEG data. Innovatively, we design a knowledge graph feature extraction module using a graph convolutional network. This module processes node and relation matrices constructed from the EEG dataset, enabling the model to leverage prior knowledge for improved performance. The hybrid feature fusion module is introduced to calculate dynamic weights for knowledge graph features based on EEG features and integrate them into a unified representation. Extensive experiments were conducted on the DEAP and DREAMER datasets to assess the effectiveness of KGViT, with outcomes surpassing the state-of-the-art baseline. Accuracy rates of 9 7. 7 8 %, 9 8. 0 6 %, 9 8. 8 9 %, and 9 7. 4 7 % were achieved on DEAP-Valence, DEAP-Arousal, DREAMER-Valence, and DREAMER-Arousal, respectively. © 2024 IEEE.

关键词： Knowledge graph

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InfraTag: Customizable Infrastructure Interaction Activity Recognition on Resource-Constrained Hardware 10

InfraTag: Customizable Infrastructure Interaction Activity R...

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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Qingyu Shen, Jianfei Fan, Feiyi Gu, Yang Chen, Yiqiang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China

ISBN: (纸本)9798331520861

Recognition of infrastructure interaction activity can provide environmental context, which is essential for achieving human interaction with ubiquitous computing environments. However, there are different types of infrastructure and user habits, and existing methods generally rely on special-purpose sensing hardware or training models in situ, which are task-specific with a high user burden for open-set recognition tasks. To address this challenge, we created InfraTag, through our proposed customizable method to conduct one-shot customization that combines generalizability and flexibility. This method utilizes prototype encoding by introducing domain knowledge to learn a generic motion posture feature embedding and with a similarity-matching design to achieve one-shot customization on resource-constrained hardware. InfraTag can be attached to infrastructures, and after one-shot learning of the new infrastructure interaction activity sample during the initial deployment, ongoing recognition can be well performed. For example, we apply InfraTag to in-home infrastructures, such as doors, windows, waterpipes, etc., that can augment everyday experiences with context-aware functionality. Overall, our work demonstrates a more general approach to recognizing infrastructure interaction activities in ubiquitous scenarios, and its resource-efficient and easy-to-deploy make it a more feasible option for future consumer devices. © 2024 IEEE.

关键词： Ubiquitous computing

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