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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Manni Chen Kwok L. Chung Zhining Huang Jiating Wen Yingsong Li School of Computer Science and Engineering Huizhou University Guangdong China Glorious Sun Guangdong School of Fashion Huizhou University Guangdong China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

This paper presents a hybrid method for determination of continuous dielectric properties of clothing materials. The dielectric constant and loss tangent of three types of materials are firstly investigated using open-ended coaxial probe from 0.5 to 20 GHz. The average measured values are input into the CST simulation of two CPW lines to obtain the two-port S-parameters. Finally, more reasonable yet reliable results are obtained by using two retrieval algorithms (TRACE and ABCD) for validation and comparative purposes.

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Research on patent classification based on hierarchical label semantics

Research on patent classification based on hierarchical labe...

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Education, Knowledge and Information Management (ICEKIM), International Conference on

作者： Jie Chen Shu Zhao Fan Lu Feng Liu Yangping Zhang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9781665416672

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model.

关键词： Representation learning Patents Correlation Semantics Text categorization Education Information management

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A Novel Sparse MIMO Array with Higher Uniform DOF and Sensor Separation

A Novel Sparse MIMO Array with Higher Uniform DOF and Sensor...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Wanlu Shi Yingsong Li Zhixiang Huang College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

In this paper, a novel sparse multiple-input and multiple-output (MIMO) array (SMA) is proposed with closed-form expression. The proposed SMA is devised using the difference-coarray of the sum-coarray concept and the uniform linear array (ULA) fitting (UF) principle. More specifically, the transmitting and receiving arrays of the proposed SMA are sparse arrays designed via the UF principle. Compared with the existing SMAs, the proposed SMA has augmented uniform degrees of freedom (uDOF) and sensor separation. Numerical simulations verify the good capacity of the proposed SMA in terms of uDOF, mutual coupling, and direction finding accuracy.

关键词： Mutual coupling Direction-of-arrival estimation Closed-form solutions Fitting Estimation Numerical simulation MIMO communication

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Robust DOA Estimation in RIS-Assisted MISO Systems

Robust DOA Estimation in RIS-Assisted MISO Systems

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Canping Yu Wudang Xiao Yingsong Li Dingcheng Zou Nikola Zlatanov Rodrigo C. de Lamare Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia Centre for Telecommunications Research (CETUC) Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Gávea Brazil

ISBN: (数字)9798350362510

ISBN: (纸本)9798350362527

Reconfigurable intelligent Surfaces (RIS) are recognized for their ability to actively control signal propagation, presenting a wide array of applications in 5G and the forthcoming 6G networks. In this paper, we design a RIS-assisted multi-input single-output (MISO) model and propose a corresponding direction of arrival (DOA) estimation algorithm. The proposed DOA estimation algorithm leverages the non-convex Ekblom norm to serve as the error criterion, ensuring robustness against impulsive noises, while the smoothed L1 norm is implemented as the regularization constraint to further enhance the performance of the algorithm. Distinct from prior research, the system under consideration operates with a single receive antenna, facilitating DOA estimation through the adjustment of RIS reflection coefficients, thereby markedly reducing system hardware costs. Moreover, we devise an alternating minimization strategy to obtain the optimal solution to the problem. A series of simulation results indicate that the proposed algorithm surpasses existing methods in terms of accuracy and robustness.

关键词： Wireless communication Direction-of-arrival estimation Accuracy Simulation Noise Estimation Reconfigurable intelligent surfaces

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Performer: A High-Performance Global-Local Model-Augmented with Dual Network Interaction Mechanism

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Tan, Dayu Hao, Rui Hua, Linfeng Xu, Qi Su, Yansen Zheng, Chunhou Zhong, Weimin Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

In deep learning, Convolutional Neural Networks (CNNs) focus on local information through convolutional kernels, while transformers attend to global information using self-attention mechanisms. The union of these distinct approaches enables a more comprehensive extraction of image features. However, the feature map dimensions of CNN and Transformer differ, leading to dimension mismatch issues when combining these architectures. Additionally, the parameter size of the hybrid model integrating both architectures remains large, making it difficult to train. To further augmenting the interpretation of complex image patterns, we present Performer, a dual-network architecture that seamlessly combines CNNs and transformers, resulting in a novel and efficient representation learning model. In the Performer model, we innovate by devising a unique interaction methodology for CNN and transformer architectures to enhance the image feature extraction capabilities mutually. To counteract issue of dimensionality mismatch, we also introduce a refined transformer block, a advancement over the transformer block of ViT. To validate the effectiveness of Performer, we conduct extensive experiments on both classification and segmentation tasks. Performer achieve an accuracy of 83.37% on the ImageNet-200 dataset. For semantic segmentation, Performer excels on the CamVid and Hippocampus datasets. On CamVid, our model achieves a mean Intersection over Union (mIoU) of 63.27% and Pixel Accuracy of 92.11%, demonstrating superior performance in capturing fine details and handling complex scenes effectively. The code is available at https://***/hlfthh/ Performer. © 2016 IEEE.

关键词： Semantic Segmentation

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A further study on the mass formula for linear codes with prescribed hull dimension

arXiv

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arXiv 2024年

作者： Li, Shitao Shi, Minjia Li, Yang Ling, San Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China School of Mathematics Hefei University of Technology Hefei230601 China School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore

Finding a mass formula for a given class of linear codes is a fundamental problem in combinatorics and coding theory. In this paper, we consider the action of the unitary (resp. symplectic) group on the set of all Hermitian (resp. symplectic) linear complementary dual (LCD) codes, prove that all Hermitian (resp. symplectic) LCD codes are on a unique orbit under this action, and determine the formula for the size of the orbit. Based on this, we develop a general technique to obtain a closed mass formula for linear codes with prescribed Hermitian (resp. symplectic) hull dimension, and further obtain some asymptotic results. Copyright © 2024, The Authors. All rights reserved.

关键词： Hulls (ship)

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GenRec: Unifying Video Generation and Recognition with Diffusion Models

arXiv

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arXiv 2024年

作者： Weng, Zejia Yang, Xitong Xing, Zhen Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Department of Computer Science University of Maryland United States

Video diffusion models are able to generate high-quality videos by learning strong spatial-temporal priors on large-scale datasets. In this paper, we aim to investigate whether such priors derived from a generative process are suitable for video recognition, and eventually joint optimization of generation and recognition. Building upon Stable Video Diffusion, we introduce GenRec, the first unified framework trained with a random-frame conditioning process so as to learn generalized spatial-temporal representations. The resulting framework can naturally supports generation and recognition, and more importantly is robust even when visual inputs contain limited information. Extensive experiments demonstrate the efficacy of GenRec for both recognition and generation. In particular, GenRec achieves competitive recognition performance, offering 75.8% and 87.2% accuracy on SSV2 and K400, respectively. GenRec also performs the best on class-conditioned image-to-video generation, achieving 46.5 and 49.3 FVD scores on SSV2 and EK-100 datasets. Furthermore, GenRec demonstrates extraordinary robustness in scenarios that only limited frames can be observed. Code will be available at https://***/wengzejia1/GenRec. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Metasurface-Loaded Circularly Polarized Dual-Band MIMO Cellphone Frame Antenna for Mobile Communication Applications

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Meng, Yanfeng Ren, Aidi Wang, Chao Xu, Guanghui Zhao, Luyu Li, Yingsong Yang, Lixia Huang, Zhi-Xiang Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230039 China Anhui University School of Electronic and Information Engineering Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230039 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui237010 China Anhui University Key Laboratory of Electromagnetic Environment al Sensing of Anhui Higher Education Institutes Hefei230039 China

In this letter, a set of circularly polarized (CP) multiple-input multiple-output (MIMO) cellphone frame antennas for mobile communication applications is proposed. The planar inverted-F antenna (PIFA) and monopole antenna operate at frequencies of 3.5 GHz and 5.5 GHz with impedance and axial ratio (AR) bandwidths overlapping at 11.4% (3.3-3.7 GHz) and 7.3% (5.2-5.6 GHz), respectively. To reduce the electromagnetic coupling between the two antennas, the V-shaped mental fence is added and different rectangular metasurface (RMS) arrays are adopted to enhance the CP radiation performances. This design not only covers 5G communication, LTE, and the 5 GHz Wi-Fi band. It also provides a communication strategy for future direct satellite communication from cellphones, enhancing efficient switching between satellite and cellular networks. © 2002-2011 IEEE.

关键词： Monopole antennas

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model

Structure-constrain Source Free Domain Adaptation with Regul...

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Data Driven Control and Learning Systems (DDCLS)

作者： Dan Yang Xin Peng Cheng Su Dayu Tan Chaoyang Chen Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness.

关键词： Fault diagnosis Learning systems Adaptation models Data privacy Transfer learning Prototypes Predictive models

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MotionEditor: Editing Video Motion via Content-Aware Diffusion

MotionEditor: Editing Video Motion via Content-Aware Diffusi...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shuyuan Tu Qi Dai Zhi-Qi Cheng Han Hu Xintong Han Zuxuan Wu Yu-Gang Jiang Shanahai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Research Asia Camegie Mellon University Huya Inc.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing diffusion-based video editing models have made gorgeous advances for editing attributes of a source video over time but struggle to manipulate the motion information while preserving the original protagonist's appearance and background. To address this, we propose MotionEditor, the first diffusion model for video motion editing. MotionEditor incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence. While ControlNet enables direct generation based on skeleton poses, it encounters challenges when modifying the source motion in the inverted noise due to contradictory signals between the noise (source) and the condition (reference). Our adapter complements Control-Net by involving source content to transfer adapted control signals seamlessly. Further, we build up a two-branch ar-chitecture (a reconstruction branch and an editing branch) with a high-fidelity attention injection mechanism facilitating branch interaction. This mechanism enables the editing branch to query the key and value from the reconstruction branch in a decoupled manner, making the editing branch retain the original background and protagonist appearance. We also propose a skeleton alignment algorithm to address the discrepancies in pose size and position. Experiments demonstrate the promising motion editing ability of MotionEditor, both qualitatively and quantitatively. To the best of our knowledge, MotionEditor is the first to use diffusion models specifically for video motion editing, considering the origin dynamic background and camera movement.

关键词： Adaptation models Computer vision Heuristic algorithms Noise Dynamics Diffusion models Controllability

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