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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

Chinese Journal of Electronics 2023年第2期21卷 202-208页

作者： Huihui Li Xuanqin Mou Zhen Ji Hang Yu Yan Li Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Embedded System Design Shenzhen China

A novel wideband 5.8GHz CPW-fed antenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numerically using the Method of moment (MOM) and the Finite element method (FEM). With the antenna size limited to $30\times 30 \text{mm}^{2}$ , the −10dB bandwidth obtained by MOM is 3.235GHz (5.765∼9GHz) and the −9.5dB band-width obtained by FEM is 2.74GHz (5.32∼8.06GHz), corresponding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured −10dB bandwidth is 2.68GHz (5.63GHz∼8.31GHz), 46.2% of the 5.8GHz frequency. Furthermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than −10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： Antenna measurements Resonant frequency Loss measurement Frequency measurement Finite element analysis Broadband antennas RFID tags

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Interactive Semantic Segmentation With Weak Supervision 22

Interactive Semantic Segmentation With Weak Supervision

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Gong, Lei Wang, Da-Han Wu, Yun Ye, Hai-Li Zhu, Chen-Yan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Medical Diagnostic Systems Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781450396110

At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and expensive, especially for special applications that require expert annotation. The weakly-supervised segmentation method using the point-level supervision information has been investigated which however has great problems that the supervision information is quite limited and the performance is far from fully supervised methods. In this paper, we proposes an novelty interactive image segmentation method based on weak supervision, which allows multiple feedbacks of easily obtained weakly supervised information and improves the efficiency of utility of the supervision information. In the downstream task (interactive image segmentation), supervised information at the point level is used for many times, which makes the connection between pixels in the upstream task become closer and improves the segmentation accuracy. First, image-level tags are used to train the classification network. Then the pseudo-semantic labels are generated and put into the interactive segmentation network for training, and an almost completely supervised CNN is obtained, which further improves the performance and provides operability for human-computer interaction. The proposed method achieves promising semantic segmentation results that are close to those obtained by strongly supervised segmentation methods on the PASCAL VOC 2012 datasets. © 2022 ACM.

关键词： Semantic Segmentation

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： image reconstruction

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A New Method for recognition Partially Occluded Curved Objects under Affine Transformation

A New Method for Recognition Partially Occluded Curved Objec...

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International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： GuiMei Zhang JiYuan Xu JianXin Liu Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Province China School of Mechanical Engineering and Automation Xihua University Chengdu Sichuan China

A new method dealing with recognition of partially occluded and affine distortion objects is presented. The method is designed for objects with smooth curved boundary. It divides an object into affine-invariant parts based on the feature point. And a new approach for matching each part is presented in this paper. Robust Hausdorff distance (RHD) is introduced to measure the similarity between feature points set of model and that of target. In terms of the new RHD, the optimal affine transform can be estimated. And then the sub-curve match pairs are calculated based on the optimal affine transformation. The experimental results show proposed algorithm are capable of coping with partial occlusion and affine transformation.

关键词： Transforms Shape Robustness pattern recognition image recognition Mathematical model Distortion

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Detection of Billet Character in Complex Production Line Scene

Detection of Billet Character in Complex Production Line Sce...

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第三届数字制造与自动化国际会议

作者： Hong Hanyu Yu Zhejun Zhang Xiuhua Laboratory for Image Processing and Intelligent Control Wuhan Institute of Technology Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmented method can be used in a wide variety of billet scenes. According to high temperature and complex scene in the rolling line, we use an effective clustering and projection characteristics to determine the terminal condition of recursive segmentation. Then we can label character candidate regions in turn by this effective characteristics, and select the regions we want to achieve. The experiments show that this method makes full use of the characteristics of region and clustering. It can improve the quality of detection, and the detection result meets the need of practical application.

关键词： image processing production line billet character

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Handwritten Mathematical Expression recognition with Self-Attention

Handwritten Mathematical Expression Recognition with Self-At...

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2021年第四届算法、计算和人工智能国际会议

作者： Xueke Chi Da-Han Wang Yuefeng Wu Yun Wu Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology

Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the local attention mechanism based on RNN,the high similarity between coding features can cause attention confusion. To settle this problem, we propose an encoder-decoder model with self-attention, which captures the global information of the feature map and fuses the local information of the CNN as complementary features. Experiments are conducted on the CROHME2014 and CROHME 2016 competition datasets. The experimental results show that, when only using the official training dataset, the proposed method achieves recognition accuracies of51.98% and 50.74% on the CROHME2014 and CROHME2016 competition datasets, respectively, which outperforms the other methods significantly. The improvements demonstrate the effectiveness of the self-attention module.

关键词： offline recognition handwritten mathematical expression selfattention non-local

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A Robust Algorithm for Reading recognition of Pointer Instrument with Local Matching 12

A Robust Algorithm for Reading Recognition of Pointer Instru...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Niu, Tun Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

The inspection and recognition of readings on pointer instruments are important tasks in power inspection. Due to the diversity and the complex dial information of pointer instruments, the adaptability and low accuracy of existing methods are not enough. In this paper, we proposed a pointer instrument reading recognition algorithm based on local fast matching. Firstly, YOLOv8 is used to locate and segment the instrument in the inspection image. The positions and values of key scale are marked in the template image. Based on the key local information, a mapping relationship is established between the instrument location region in the inspection image and the template image. Then, the Hough transform is used to detect the position of the pointer, map it to the template image, and finally calculate the instrument reading based on the relative positions of the key graduations on the template image and the mapped pointer position. Experimental results demonstrate that the proposed method achieves a relative error within 5% for pointer instrument reading recognition in indoor substations. © 2024 SPIE.

关键词： Inspection

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Research on the Detection of Abnormal Cervical Cells Based on Improved YOLOv7

Research on the Detection of Abnormal Cervical Cells Based o...

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

作者： Xuewei Wang Shuang Yuan Lidong Yang Dawei Niu Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. The algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. The experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively.

关键词： Accuracy Sensitivity Shape Prevention and mitigation Signal processing algorithms Signal processing Feature extraction Convolutional neural networks Detection algorithms Cervical cancer

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Adaptive nonlinear auto-associative modeling through manifold learning

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9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005

作者： Zhang, Junping Li, Stan Z. Intelligent Information Processing Laboratory Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences China National Laboratory of Pattern Recognition Center for Biometrics and Security Research Institute of Automation Beijing 100080 China

ISBN: (纸本)3540260765

We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separately and recognition thereby. Unlike traditional supervised manifold learning algorithm, the proposed ANAM algorithm has several advantages: 1) it implicitly embodies discriminant information because the suboptimal parameters of ANAM are determined based on error rate of the validation set. 2) it avoids the curse of dimensionality without loss accuracy because recognition is completed in the original space. Experiments on character and digit databases show that the advantages of the proposed ANAM algorithm. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Learning systems

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Dual-Channel SAR Ground Moving Target Detection and Parameters Estimation

Dual-Channel SAR Ground Moving Target Detection and Paramete...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： H. Shabana Liu Fang Shuyu Xiang National Key Laboratory image perception and recognition Xidian University National Key Laboratory of Radar Signal Processing Xidian University

ISBN: (纸本)9781479929948

This paper proposes a new algorithm for imaging moving targets via estimating their motion parameters with dual channel synthetic aperture radar. Displaced phase center array (DPCA) antennas are used for clutter cancelation through which moving targets are detected. The range motion of a moving target induces a Doppler centroid shift and additional range walk, while the along-track motion changes the Doppler modulation rate. The Doppler centroid shift dislocates the moving target from its actual azimuth position, and the additional range walk deteriorates the imaging resolution. The slope angle of the range walk trajectory is estimated via Hough transform, then the range velocity can be obtained. Doppler parameters of the moving targets are estimated with the fractional Fourier transform (FrFT). In this way, both target range and azimuth velocities are acquired. Next, the moving targets are focused with one uniform imaging algorithm. Simulated data processing results are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： Synthetic Aperture Radar (SAR) Ground moving target identification (GMTI) Hough transform Motion parameter estimation Displaced phase center antenna (DPCA) Fractional Fourier transform (FrFT)

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