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

作者： Jiaxiong Fang Juan Liu Hua Chen Wei Liu Ye Tian Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

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SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation for Histopathology Image

SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiatai Lin Guoqiang Han Jingxuan Zhou Zhenwei Shi Zaiyi Liu Chu Han South China University of Technology Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Semantic segmentation for histopathology images is one of the fundamental tasks in computational pathology. Due to the high cost of pixel-level annotation acquisition, the weakly supervised semantic segmentation (WSSS) attempts to achieve information-intensive segmentation task for histopathology images to reduce the labeling effort of pathologists by leveraging image-level labels. However, traditional WSSS requires a large-scale training set with image-level labels, which still imposes considerable labeling costs on pathologists. To this end, this work proposes a Small-Scale Weakly Supervised Semantic Segmentation (SS-WSSS) approach to achieve the comparable performance only with small-scale weakly-labeled data to further reduce pathologist’s labeling effort. Since histopathology images can easily generate massive unlabeled data, our SS-WSSS aims to learn with the unlabeled data to bridge the information gap. First, we propose a Single-to-Multi Prototype Similarity (S2M-PS) method to generate reliable pseudo-labels for unlabeled data by measuring the similarity between single-label prototypes and multi-label feature maps. Then, we introduce a Cross-Task CoTraining (CT 2 ) method for pseudo-supervision of models with pseudo-labels self-refinement to avoid overfitting to noisy labels. We conduct the experiment on two public datasets to demonstrate the effectiveness of our SS-WSSS. In the experiment, our method achieves comparable performance with SOTA methods only using 30% labeled data.

关键词： Training Costs Histopathology Semantic segmentation Semantics Pipelines Prototypes Labeling Noise measurement Reliability

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Design of P Band Filter Antenna with High Out-of-Band Suppression

Design of P Band Filter Antenna with High Out-of-Band Suppre...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiangling Dou Yinsu Yuan Jian Song Qingwang Wang Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Graduate School Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

P-band antennas offer tremendous potential in various fields, including remote sensing, communication, and radar applications. However, It is crucial to enhance their effectiveness in these domains that the current challenges in the P-band, particularly wide bandwidth and exceptional out-of-band suppression be achieved. This paper aims to introduce an efficient design scheme for a high-efficiency, high out-of-band suppression P-band filter antenna. The antenna structure comprises a Y-shaped feeding network, upper and lower parallel dielectric plates, a vertical dielectric plate, a central metal plate, and a pair of microstrip patches. The bottom feeding network employs a Y-shaped feeding network. The coupling of the antenna was effectively controlled and the good bandwidth and impedance matching have been achieved. In order to achieve a wider bandwidth and lower return loss for the antenna, slotting technology was employed on the semi-circular radiation patch. The introduction of a vertical metal plate enhances filtering performance of antenna. Additionally, there are radiation zeros at 0.68 GHz and 1.43 GHz, high-frequency selectivity is imparted and signal reflection losses are effectively reduced. key performance indicators include a filter antenna center frequency of 0.75 GHz, a −10 dB impedance bandwidth of 32.9% (0.89 GHz–1.24 GHz). In the upper stopband (0.5 GHz–0.8 GHz), the suppression level exceeds 20 dB, while in the lower stopband (1.1 GHz–1.8 GHz), suppression level exceeds 15 dB. Significant suppression characteristics are observed in the 0.5 GHz–0.8 GHz and 1.1 GHz–1.8 GHz frequency ranges. Design elements such as the semi-circular radiating patch, slotting technology, and rectangular slots on the substrate have been employed in the antenna. The suppression level, frequency selectivity had been enhance of antenna. Simulation results indicate a gain of 7.06 dB, overall efficiency beyond 95%, co-pol superior to cross-pol by more than 35 dB, and antenna

关键词： Slot antennas Filtering Simulation Bandwidth Microstrip antennas Directive antennas Dielectrics

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

arXiv

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arXiv 2023年

作者： Yang, Yong Chen, Qiong Feng, Yuan Huang, Tianlin School of Computer Science and Engineering South China University of Technology China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet. © 2023, CC BY-NC-SA.

关键词： Embeddings

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Stabilization of Boolean Networks: A Model-free Constrained State-flipped Control Approach

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 15662-15670页

作者： Liu, Zejiao Yerudkar, Amol Liu, Yang Cao, Jinde School of Mathematical Sciences Zhejinag Normal University Jinhua 321004 China East China University of Science and Technology School of Mathematics Shanghai 200237 China School of Computer Science & Technology Zhejiang Normal Univerity Jinhua 321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application School of Mathematical Sciences Jinhua 321004 China Zhejiang Normal University Hangzhou School of Automation Hangzhou 311231 China Southeast University School of Mathematics Nanjing 210096 China Yonsei University Yonsei Frontier Lab Seoul 03722 South Korea

In this paper, we explore the stabilization of Boolean Networks (BNs) under state-flipped control, incorporating three key constraints to address the challenges of disease treatment response times and the practical limitations of therapeutic interventions. Our approach includes: (i) a feasible flipped nodes constraint to ensure a subset of nodes can be realistically manipulated;(ii) a transient period constraint to align treatment with time-sensitive windows of therapeutic opportunity;and (iii) a constraint on the number of flip transitions to minimize resource consumption and potential side effects. Furthermore, an evolution selection rule is set for the BNs. Then, a necessary and sufficient condition is given for the stabilization under the above constraints. We present a modified Q-learning (QL) algorithm to design a policy for stabilization under constraints in the model-free case. Finally, BN models of metastatic melanoma network and T-cell large granular lymphocyte (T-LGL) network are considered to show the applicability of our results. © 2004-2012 IEEE.

关键词： Boolean networks (BNs) constrained state-flipped control Q-learning (QL) semi-tensor product (STP) stabilization

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Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis

arXiv

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arXiv 2025年

作者： Wu, Wenyi Zhang, Hao Wei, Zhisen Jing, Xiao-Yuan Zhang, Qinghua Wu, Songsong Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis School of Computer Guangdong University of Petrochemical Technology Maoming525000 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Province Zhangzhou363000 China

Source-free domain adaptation (SFDA) has been exploited for cross-domain bearing fault diagnosis without access to source data. Current methods select partial target samples with reliable pseudo-labels for model adaptation, which is sub-optimal due to the ignored target samples. We argue that every target sample can contribute to model adaptation, and accordingly propose in this paper a novel SFDA-based approach for bearing fault diagnosis that exploits both reliable and unreliable pseudo-labels. We develop a data-augmentation-based label voting strategy to divide the target samples into reliable and unreliable ones. We propose to explore the underlying relation between feature space and label space by using the reliable pseudo-labels as ground-truth labels, meanwhile, alleviating negative transfer by maximizing the entropy of the unreliable pseudo-labels. The proposed method achieves well-balance between discriminability and diversity by taking advantage of reliable and unreliable pseudo-labels. Extensive experiments are conducted on two bearing fault benchmarks, demonstrating that our approach achieves significant performance improvements against existing SFDA-based bearing fault diagnosis methods. Our code is available at https://***/BdLab405/SDALR Copyright © 2025, The Authors. All rights reserved.

关键词： Failure analysis

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Evolution of AVS video coding standards: twenty years of innovation and development

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Science China(Information Sciences) 2022年第9期65卷 50-73页

作者： Siwei MA Li ZHANG Shiqi WANG Chuanmin JIA Shanshe WANG Tiejun HUANG Feng WU Wen GAO Institute of Digital Media School of Computer Science Peking University Bytedance Inc. Department of Computer Science City University of Hong Kong CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China

Ever since the founding of the Audio and Video Coding Standard(AVS) Workgroup of China in 2002, it has been dedicated to advancing and innovating the digital audio-video industry with highly efficient and economical encoding/decoding technologies. Three representative generations of video coding standards have been finalized and published, consistently improving the coding performance in the past two *** series of AVS standards establish solid foundations for ubiquitous video applications in the areas including acquisition, coding, production, delivery, integrated system, public service, general screen content, and mixed reality media. Along with the standardization process, an extensive amount of studies have been carried out on efficiency-aware designation, algorithm optimization, and hardware implementation of these innovative video coding techniques. This paper explains how those developed techniques provide a lasting impact on the video coding community, extensively, technologically, and systematically. In particular, we provide a comprehensive survey of the three generations of the standards, and timely and in-depth summarize the efforts of the AVS video coding standards in the twenty years. The rate-distortion performance comparisons,in particular in terms of the 8K ultra-high-definition(UHD) contents, reflect the elegant design of the state-of-the-art AVS3 standards. We have also elaborated on a variety of well-established and promising applications, including commercial level real-time 8K encoder, high-frame-rate decoder chip for cell phone,and live streaming solution for sports. In addition, the China Central Television(CCTV) of China Media Group(CMG), the state television of China, has officially launched the first 8K broadcasting channel(CCTV-8K) since 2021 using AVS3. Given the significant success realized by the AVS standards, it is envisioned that a new era of 8K UHD video is arriving.

关键词： video coding AVS standards video applications intelligent coding

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FrDiff: Framelet-based Conditional Diffusion Model for Multispectral and Panchromatic Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Junkang Fang, Faming Wang, Tingting Zhang, Guixu Song, Haichuan East China Norm University School of Computer Science and Technology Shanghai200062 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China

The process of fusing low-resolution multispectral (LRMS) and high-resolution panchromatic (PAN) imagery, commonly referred to as pansharpening, is intended to generate high-resolution multispectral (HRMS) imagery. Typically, most pre-existing pansharpening frameworks mainly emphasize the straightforward learning of the mapping relationship among PAN and LRMS images to HRMS images. However, a key limitation of these frameworks is their potential overemphasis on spatial information, particularly the enhancement of low-frequency components. As a result, such an oversight potentially hinders the model's ability to simultaneously restore both spectral and spatial details. To address this issue, we propose a novel pansharpening model based on the denoising diffusion probabilistic model (DDPM), dubbed FrDiff. Specifically, we build a framelet-based conditional diffusion model that leverages the generative power of diffusion models to produce more refine results. Different from conventional methods directly inferring HRMS images, our strategy is designed to project their framelet coefficients, utilizing the available PAN and LRMS images as resources. This approach enables the separation of high-frequency and low-frequency components through framelet transformation, which are subsequently recombined to create a novel set of conditional embeddings that feed into the diffusion process. At the same time, the powerful predictive power of the diffusion model is exploited to simultaneously recover the high-frequency and low-frequency components of the HRMS. Moreover, we introduce a framelet-oriented cross-attention module dedicated to honing spectral fidelity. This module is crucial for improving the spectral precision of the HRMS images, ensuring a balanced emphasis on both spatial and spectral enhancements. Quantitative and qualitative experiments on multiple benchmark datasets demonstrate that the proposed method achieves more robustness and high-quality results than other sta

关键词： Convolutional neural networks

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Radioactive Waste Repackaging Methods Based on Genetic Algorithms and Simulation for Reactor Decommissioning

Radioactive Waste Repackaging Methods Based on Genetic Algor...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Tong Wu Deying Wang Fengjuan Chen Nan Zhang Fan Yang Jiaqi Wang Meng Xu Computer Application Institute of Nuclear Industry China National Nuclear Corporation (CNNC) Beijing China Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education Beijing University of Technology Beijing China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

After going through the lifecycle of design, construction, and operation, nu-clear reactors need go to decommissioning phase according to requirement of nuclear regulator or need of environmental protection. Decommissioning, as the last phase of a nuclear facility's lifecycle, is characterized by its difficulty, long duration, and high cost. During the decommissioning process, various types of waste are generated, such as solid, liquid, gas waste, and mixed waste. In order to protect the surrounding environment from radioactive contamination and prevent excessive radiation exposure to workers and residents, radioactive waste needs to be processed, prepared, transported, stored, and disposed of according to standards. In response to the low space utilization of steel containers due to the irregular shapes of waste when packing decommissioned reactor components, this paper optimizes the process using genetic algorithm to determine the optimal number of cut chunks for reactor components and the best placement of cut chunks in the steel container. First, the complex three-dimensional irregular part packing problem is projected onto a two-dimensional plane for study to simplify the problem's difficulty; second, genetic algorithm is used for individual selection, crossover, and mutation to obtain the optimal layout of individuals; finally, the optimal number of cut chunks in different areas is determined based on the optimization results of individuals, and a global packing solution for all cut chunks is obtained. The results indicate that the proposed method not only provides the optimal cutting solutions for different cutting areas of reactor components and the best placement of cut chunks in the steel container but also satisfies all cutting constraints, potentially providing insights for future reactor decommissioning work

关键词： Radioactive waste Shape Loading Containers Solids Steel Inductors Optimization Standards Genetic algorithms

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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