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Remote State Estimation for Complex Networks Under Probabilistic Bit Flips: A Transmission Power Allocation Scheme

IEEE Transactions on Automatic Control 2025年

作者： Song, Jiahao Wang, Zidong Liu, Qinyuan He, Xiao Tsinghua University Department of Automation Beijing100084 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Tongji University Key Laboratory of Embedded System and Service Computing Shanghai200092 China Shanghai Artificial Intelligence Laboratory Shanghai China

In this paper, the problem of remote state estimation is investigated for a class of complex networks with noisy wireless communication channels. The employment of the binary encoding scheme allows for the description of the influence of channel noises as probabilistic bit flips, where the probability of these bit flips is influenced by several factors with the signal-to-noise ratio (SNR) being a crucial one. In engineering practice, the method of adjusting the transmission power for each node in a complex network is commonly applied to modify the SNR so as to reduce the data distortion caused by bit flips. Furthermore, due to restricted communication resources, the total available transmission power is often limited particularly in large-scale systems such as complex networks. Consequently, the allocation of transmission power for all nodes under a total transmission power constraint becomes a concern. Utilizing the ultimately bounded filtering method, we devise a transmission-power-dependent state estimator, by which the combined effect of probabilistic bit flips and transmission power allocation on estimation performance is analyzed. Moreover, the task of co-designing the transmission power allocation scheme and estimator gains is modeled as an optimization problem, which is then addressed through a two-step optimization strategy. The existence of a unique optimal transmission power allocation scheme is also proven. Finally, numerical simulation examples are provided to demonstrate the effectiveness of the proposed co-design approach. © 1963-2012 IEEE.

关键词： Large scale systems

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Finite time cluster consensus of fractional-order multi-agent systems with directed topology

Finite time cluster consensus of fractional-order multi-agen...

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第33届中国控制与决策会议

作者： Weijin Liu Xiwei Liu Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education

In this paper,the finite time cluster consensus(FnTCC) of fractional-order multi-agent systems(FOMAS)with directed topology is *** fractional-order system is converted into an integer-order system by defining a neighborhood-based error variable,and suitable control rules are designed for the obtained first-order multi-agent *** to the exponential finite-time stability theorem,suitable Lyapunov functions are ***,the settling time function is *** simulation results prove the feasibility and validity of our theory.

关键词： Fractional-order Cluster consensus Exponential finite time Multi-agent systems Directed topology

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Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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Cross-Modal Representation Reconstruction for Zero-Shot Classification

Cross-Modal Representation Reconstruction for Zero-Shot Clas...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yu Wang Shenjie Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education P. R. China Mojie Technology (Ningbo)

ISBN: (纸本)9781728176055;9781728176062

Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CM-RR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.

关键词： Training Visualization Semantics Signal processing algorithms Signal processing Feature extraction Iterative algorithms

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Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment

Cross-Domain Semantic Segmentation of Urban Scenes via Multi...

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International Conference on Pattern Recognition

作者： Bin Zhang Shengjie Zhao Rongqing Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education China School of Software Engineering Tongji University Shanghai China

Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.

关键词： Image segmentation Solid modeling Semantics Neural networks Virtual reality Feature extraction Robustness

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Design and Implementation of DSP Cache 21

Design and Implementation of DSP Cache

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Liang, Kunkun Wu, Jun Ren, Haoqi Zhang, Zhifeng Tan, Bin Tongji University Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China School of Computer Science Fudan University Shanghai200438 China College of Electronics and Information Engineering Jinggangshan University Ji'an343009 China

ISBN: (纸本)9781665432061

The cache can improve the DSP processor's access speed to external memory and solve the 'Storage Wall' problem. Designing an efficient and flexible cache module plays an vital role in improving the memory access efficiency and overall performance of the DSP. Based on the analysis of DSP storage level design requirements, combined with the actual pipeline structure of the DSP processor SWIFT independently developed by our laboratory, in this paper, we design and implement first- level instruction and data cache. For L1 D-Cache, the relevant conflict detection and processing module is designed to support four parallel Load/Store access requests. The size of the cache can be flexibly configured according to actual application requirements to achieve a balance between power consumption and latency. To write back the modified data left in the cache before the cache size is changed, the Cacheclean instruction is designed. Finally, module-level functional verification and logic synthesis are carried out. The results show that the cache function meets expectations, and the critical path after logic synthesis optimization meets the requirement of 1GHz frenuency. © 2021 IEEE.

关键词： Pipelines

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HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detection in High-Speed Networks

HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detect...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Jiakun Yu Yu-E Sun He Huang Yang Du Guoju Gao Hongli Xu Shigang Chen School of Computer Science and Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China School of Computer Science and Technology University of Science and Technology of China Hefei China

Detecting heavy hitters that constitute the majority of network traffic is a critical task in network measurement. However, the highly skewed network traffic and the size-limited on-chip memory pose great challenges for heavy-hitter detection. The prior arts either use sketches to track all flows or maintain a fix-sized tracking list for recording elephant flows, resulting in limited detection precision or time-consuming tracking list exchanges. This paper presents HeavyTracker, an efficient algorithm that detects heavy hitters based on a count-with-threshold strategy. A two-dimensional tracker unit array is employed in our design to capture the heavy hitters, where each flow is randomly mapped to one unit in all rows. The tracker unit is designed to accept many flows and precisely report the largest two with frequencies that reach a predefined threshold. This design eliminates the frequent and time-consuming exchanges for maintaining the tracking list, allowing us to use a hash table to track the reported elephant flows. Experimental results based on real Internet traces show that HeavyTracker achieves a high $\mathbf{F}_{\beta}$-Score of threshold-t detection (0.97) and over 99% precision of top-k detection under a tight memory size. Besides, it reduces the frequency estimation error by 97.4% compared to the state-of-the-art.

关键词： Art High-speed networks Measurement uncertainty Telecommunication traffic Size measurement Frequency estimation Internet

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Synchronization and control for multi-weighted and directed complex networks

arXiv

引用

arXiv 2021年

作者： Liu, Xiwei Department of Computer Science and Technology Tongji University China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

The study of complex networks with multi-weights has been a hot topic recently. For a network with a single weight, previous studies have shown that they can promote synchronization. But for complex networks with multi-weights, there are no rigorous analysis to show that synchronization can be reached faster. In this paper, the complex network is allowed to be directed, which will make the synchronization analysis difficult for multiple couplings. In virtue of the normalized left eigenvectors (NLEVec) corresponding to the zero eigenvalue of coupling matrices, we prove that if the Chebyshev distance between NLEVec is less than some value, which is defined as the allowable deviation bound, then the synchronization and control will be realized with sufficiently large coupling strengths, i.e., all coupling matrices do accelerate synchronization. Moreover, adaptive rules are also designed for the coupling strength. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

arXiv

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arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Response of the Yellow and East China seas low-trophic ecosystems to two typhoons at different translational speeds

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Journal of Oceanology and Limnology 2025年 1-21页

作者： Zhu, Lei Zhang, Jing Shi, Changcen Yang, Wei Zhang, Haiyan Wang, Yucheng Liu, Guangliang Bian, Changwei Zhao, Liang College of Marine and Environmental Science Tianjin University of Science and Technology Tianjin China Key Laboratory of Marine Resource Chemistry and Food Technology (TUST) Ministry of Education Tianjin China Key Laboratory of Ocean Observation and Information of Hainan Province Sanya China Tianjin Key Laboratory for Marine Environmental Research and Service School of Marine Science and Technology Tianjin University Tianjin China Laoshan Laboratory Qingdao China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory Ocean University of China Qingdao China

Frequent typhoons can significantly change the temperature, nutrient availability, and phytoplankton biomass in marginal seas. The oceanic response to typhoons is usually influenced by the features of the typhoon, among which the translational speed is critically important. By using a highresolution coupled physical-biological model, we investigated the response of the Yellow and East China seas (YECS) to two typhoons at different translational speeds, Muifa in August 2011 and Bolaven in August 2012. The model well reproduced the spatial and temporal variations of temperature, chlorophyll-a concentration over the YECS. Results show that typhoons with slower translational speeds uplift more deep water, leading to a more significant oceanic response. Divergence and convergence caused nutrient fluxes in opposite directions in the surface and bottom layers. Moreover, the nutrient flux in the bottom layer was greater than that in the surface layer. These phenomena are closely related to the spatial distribution of nutrients. Further studies show that the degree of ocean response to typhoons is highly correlated with the initial conditions of physical and biological elements of the upper ocean before the typhoon, as well as with ocean structure. Pretyphoon initial conditions of oceanic physical and ecological elements, mixed layer depth, and potential energy anomalies can all alter the degree of typhoon-induced oceanic response. This study emphasizes the important roles of the translational speed of typhoons and the initial oceanic conditions in the oceanic response to typhoons.

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