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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Lulu Meng Sipei Min Zhixiang Huang Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

This paper proposes and designs a microstrip antenna that can work in the B1 band for Beidou with a double-layer substrate, and realizes the circularly polarized radiation characteristics. The antenna consists of a ground plane, two substrates, and two metal patches. The first layer of the patch is a rectangular structure as well as the second layer of the patch is a T-shaped structure with two vertical branches which are to achieve circular polarization. This structure adopts a simple coaxial feeding method, with the probe passing through the second layer of the substrate to connect the ground plane and the T-shaped strip. The simulation optimization results using the HFSS 2021 show that the axial ratio (AR) beam width in both planes exceeds 160°, and the AR at the design frequency is 0.18 dB. The impedance bandwidth is 70 MHz, which can meet the requirements of Beidou's operation.

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Boundary Cue Guidance and Contextual Feature Mining for Glass Segmentation 48

Boundary Cue Guidance and Contextual Feature Mining for Glas...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Xiao, Qiquan Zhang, Yuan Li, Xuanya Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9781728163277

Glass is ubiquitous in the real world, and its perception has many applications, including robot navigation and drone tracking. However, due to the transparent property of glass, the interior of a glass area can be any surrounding scene or object, which brings challenges for computer vision. Inspired by the human senses, boundary cues are one of the crucial factors for people to judge the location of glass contours. Hence, we propose a boundary cue guidance and contextual feature mining network (BCNet) to accurately and efficiently segment glass. Specifically, we first design a multi-branch boundary extraction module (MBEM) for learning accurate boundary cues combined with multi-level encoded features. Second, we propose a boundary cue guidance module (BCGM), inject the boundary cues into the representation learning, and provide constraints with object structure semantics to guide feature extraction. Besides, we design a contextual feature mining module (CFMM) to dynamically capture the contextual information of different receptive fields for the detection of different sizes and shapes of the glass. Finally, extensive experiments on two benchmark glass datasets, GDD and GSD. The results demonstrate that our BCNet achieves state-of-the-art segmentation performance against existing methods. © 2023 IEEE.

关键词： Boundary cue guidance Contextual feature Convolutional neural network Glass segmentation

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A Band-pass FSS with Wide Angular Stability and High Polarization Stability

A Band-pass FSS with Wide Angular Stability and High Polariz...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yunshuai Gao Shuying Ding Zhixiang Huang Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A mimic multi-layer polarization-independent FSS with accessible angular-stability is reported using a three-layer structure, where the top layer and the bottom layer are realized based the metal rings that are same, and the middle is constructed by a slotted metal structure. The mentioned three layers are aimed to separate the used two dielectric substrates. For a well illustration, the constructed dimensions of the installed FSS are $\boldsymbol{5\times 5}\mathbf{mm}^{2}$ . As a case of the FSS cell design, here the dimension is limited as $\boldsymbol{0.15}\lambda\times \boldsymbol{0.15}\lambda$ . Since two different structure is used, the FSS has double resonace, making a wide operation. From the numerical evaluations, the FSS can reject the signal from C-and X-band.

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A RIS-Supported DOA Estimation Using Combined Norm Constraint in Non-Gaussian Noise Environments

A RIS-Supported DOA Estimation Using Combined Norm Constrain...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Lei He Liping Li Run Ying Canping Yu Qiang Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Reconfigurable intelligent Surface (RIS) is a revolutionary technology in modern wireless communication systems, since it can adjust the wireless communication environment with passive components. In this paper, we consider the system environment in the presence of Base Station (BS) interference and implement the estimation of the target Direction-of-Arrival (DOA). To improve the estimation performance, we propose a DOA estimation algorithm based on RIS. The proposed algorithm consists of an error term for the lorentzian bound function and a regular term for the atomic norm (termed as LBF-AN), which are implemented to eliminate system noise and signal interference, respectively. The simulation results show that the DOA estimation performance of the proposed algorithm is better than existing algorithms.

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Classification of Cervical Images Based On Deep Neural Network With Attention Mechanism

Classification of Cervical Images Based On Deep Neural Netwo...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yun Feng Lan Chen Xia Zhang Yingsong Li Liping Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

In this paper, we propose a DenseNet-CBAM model that utilizes DenseNet121 as the backbone network for feature extraction. The features are then weighted using the Convolutional Block Attention Module (CBAM), which incorporates both channel and spatial attention mechanisms. The features are further processed with ReLU activation and adaptive average pooling operations. Finally, a linear classifier is applied to output the final category predictions. Experiment results show that the proposed model performs the best in terms of the ability in detecting patients with high-grade squamous intraepithelial lesions (HSIL).

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Machine Learning Based FDTD Method for 3D Electromagnetic Simulation

Machine Learning Based FDTD Method for 3D Electromagnetic Si...

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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Dai, Jinpeng Xie, Guoda Hou, Guilin Zhang, Liyuan Huang, Zhixiang Anhui University The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Institute of Energy Hefei Comprehensive National Science Center Hefei230031 China Institute of Plasma Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9798350383317

This article introduces an innovative and efficient deep learning-assisted Finite-Difference Time-Domain (DL-FDTD) method in the field of computational electromagnetics. This method ingeniously integrates the Gated Recurrent Unit (GRU) network and Particle Swarm Optimization (PSO) algorithm into the traditional FDTD framework, termed as the PSO-GRU-FDTD model. A key innovation of this model is the application of the particle swarm algorithm, which significantly simplifies the parameter tuning process, thereby accelerating model development. This advancement represents a notable breakthrough from the complex parameter adjustment process typical in traditional neural network models. Moreover, compared to the traditional Long Short-Term Memory (LSTM) networks, the GRU network excels in simplicity, convenience, and efficiency, while also ensuring accuracy. Ultimately, this method is applied to three-dimensional electromagnetic simulation and emulation. Numerical results demonstrate that this approach exhibits outstanding performance in both simulation accuracy and efficiency. © 2024 IEEE.

关键词： Electromagnetic simulation

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A method for generating Dunhuang dance instrumental images based on optimized diffusion model

A method for generating Dunhuang dance instrumental images b...

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2024 International Conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Wang, Jiaxin He, Xiangzhen Suzuki, Kenneth M. Northwest Minzu University Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou730030 China Northwest Minzu University Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Lanzhou730030 China

ISBN: (纸本)9781510685840

Dunhuang culture is a treasure house of human history and culture. As an integral part of Dunhuang culture, Dunhuang dance is famous for its unique national characteristics and artistic characteristics. In the form of Dunhuang dance, instrumental music and props are used to greatly enrich the stage expression, forming a unique dance form of "holding music while dancing, dancing with instruments". However, the phenomenon of homogenization of instrumental props occurs frequently, which hinders the overall presentation effect and beauty of Dunhuang dance. The development based on the generative diffusion model provides new ideas for the design of Dunhuang dance instrumental props. First, the role played by musical instruments and props in the presentation of Dunhuang dance art was sorted out, and the Dunhuang dance instrumental props were digitally collected and processed, and a special data set was constructed. Secondly, based on the recognition of related instruments, a diffusion model instrumental prop image generation that can be used in the Chinese context is trained. Experimental results show that the instrumental props generated by this method not only conform to realistic logic but also have strong applicability. © 2024 SPIE.

关键词： Photons

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Enhanced Symplectic Geometric Mode Decomposition via K-means Clustering for LFM signal Denoising

Enhanced Symplectic Geometric Mode Decomposition via K-means...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Jincheng Ni Jin Zhao Xianliang Wu Ping Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University Hefei China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This article presents a Symplectic Geometric Mode Decomposition (SGMD) method incorporating K-means clustering, coupled with wavelet denoising, for mitigating noise in Linear Frequency Modulation (LFM) signals. This method substitutes the original component recombination approach in SGMD with the K-means clustering algorithm, thus rendering the signal grouping more logically coherent. In the experiment, we compare the method proposed in this article with other denoising methods. The experimental findings demonstrate substantial advantages of the proposed method in mitigating noise in LFM signals, thereby enhancing both signal-to-Noise Ratio (SNR) and signal quality. The research outcomes underscore the feasibility and substantial potential of this method in mitigating noise in signals. In subsequent studies, additional investigation into its applicability across diverse domains can be pursued alongside the refinement of clustering and denoising algorithms.

关键词： Electric potential Chirp modulation Noise reduction Refining Clustering algorithms Rendering (computer graphics) Information and communication technology

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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

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IEEE Transactions on Information Theory 2025年第6期71卷 4785-4796页

作者： Tian, Liying Song, Xiaoshi Liu, Zilong Li, Yubo Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the "modulating" zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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Pseudo Multi-Source Domain Extension and Selective Pseudo-Labeling for Unsupervised Domain Adaptive Medical Image Segmentation 48

Pseudo Multi-Source Domain Extension and Selective Pseudo-La...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Liu, Xiaokang Wang, Zhiqiang Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Xiangnan University College of Medical Imaging Laboratory and Rehabilitation Chenzhou423000 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

Unsupervised domain adaptation (UDA) attracts extra attention in medical image processing because no additional labels are required when adapting to different distributions. In this work, we propose a novel unsupervised domain adaptation framework named as Domain Expansion and PseudoLabeling (DEPL). We extend the domain of a labeled source domain data to four different distributed domains and use adversarial learning to align the image appearance level and feature level from the four different domains to the unlabeled target domain. In addition, we propose a selective pseudolabeling mechanism, namely using strong confidence pseudolabeling to boost model performance. We evaluate our model for the MR to CT adaptation segmentation task on the public dataset MMWHS. Compared to seven other state-of-the-art segmentation methods, our DEPL achieves the best Dice similarity coefficient by 82.4%, which is at least 3.9% higher than the other UDA segmentation methods. © 2023 IEEE.

关键词： Adversarial learning domain extension Image segmentation Selective pseudo-labeling Unsupervised domain adaptive

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