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IEEE International Wireless Symposium (IWS)

作者： Changqing Liu Liang Zhang Shijie Huang Chao Wang Xingchuan Yu Xianliang Wu Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei China

ISBN: (数字)9781728167039

ISBN: (纸本)9781728167046

A low-profile circularly polarized Metasurfaces antenna based on compact coplanar waveguide (CPW) feed was proposed. The Metasurfaces consists of 16 periodic square-cut metal patches. An hourglass coupled slot was adopted to achieve broadband impedance matching. The antenna has a simple structure and a low cross section, and it can be implemented by a layer of a printed circuit substrate. A circular polarized wave is generated by changing the size of the cut angle of the Metasurfaces, and at the same time, the impedance bandwidth can be widened. To verify the simulation results, a 1λ 0 ×1λ 0 ×0.05λ 0 (at 10 GHz) model antenna was fabricated and measured. The simulation and measurement results show good consistency. The test results show that the -10dB return loss bandwidth reaches 45.3 %, and the circular polarization axis ratio(AR) is less than 3dB with a bandwidth of 18.5%.

关键词： Antenna measurements Polarization Bandwidth Metasurfaces Radar antennas Antenna feeds Coplanar waveguides

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12-Port Hybrid MIMO Antenna Array for 5G Smartphone Applications

12-Port Hybrid MIMO Antenna Array for 5G Smartphone Applicat...

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IEEE International Wireless Symposium (IWS)

作者： Peng Liu Yufa Sun Bingbing Chen Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Anhui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal University Hefei China

ISBN: (数字)9781728167039

ISBN: (纸本)9781728167046

A hybrid twelve-antenna array operating in the 3.3-3.6 GHz and 4.8-5.0 GHz band for multiple input multiple output (MIMO) applications in 5G smartphones is proposed. The proposed hybrid antenna array elements are symmetrically placed along the long edges of the smartphone, and they are composed of two inverted-F antennas (IFA) with different structural parameters. Simulated antenna isolation is better than -14.5 dB without any decoupling structures. Moreover, simulated radiation patterns, antenna efficiencies, and envelop correlation coefficients are also given in the paper.

关键词： Wireless communication 5G mobile communication Structural engineering Smart phones MIMO communication Antenna arrays Antenna radiation patterns

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Half adder and half subtractor logic gates based on nicking enzymes

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Molecular Systems design and Engineering 2019年第6期4卷 1103-1113页

作者： Zhao, Yunbin Liu, Yuan Zheng, Xuedong Wang, Bin Lv, Hui Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China College of Computer Science Shenyang Aerospace University Shenyang 110136 China

The excellent specificity and predictability of DNA pairing and its natural ability to interact with other biomolecules make DNA an ideal material for building molecular logic devices (MLDs). However, there are still many challenges in the process of building such devices, including their complex structures, potentially harsh reaction conditions, long reaction times and so on. Thus, the parallel nature of DNA and other biomolecules needs to be developed before MLDs based on DNA can be exploited to a greater extent. In order to solve this problem, herein, we carefully selected two enzymes, *** and ***, that were both persistent and compatible, and used them to build a nicking enzyme platform. Based on this enzyme platform, we constructed a novel XOR logic gate with flexible internal signaling. Furthermore, AND and INHIBIT logic gates were also modified to use the same enzymes as the XOR logic gate as their inputs. As a result, the algorithm process of half adder and half subtractor was realized by this work. This study provides a new approach for typical DNA-based arithmetic operations and promotes the development of advanced MLDs. © 2019 The Royal Society of Chemistry.

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Supervised Deep Second-Order Covariance Hashing for Image Retrieval

Supervised Deep Second-Order Covariance Hashing for Image Re...

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2020国际计算机前沿大会

作者： Qian Wang Yue Wu Jianxin Zhang Hengbo Zhang Chao Che Lin Shan Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University School of Computer Science and Engineering Dalian Minzu University School of Economic and Management Dalian University of Science and Technology

Recently, deep hashing methods play a pivotal role in image retrieval tasks by combining advanced convolutional neural networks(CNNs) with efficient hashing. Meanwhile, second-order representations of deep convolutional activations have been established to effectively improve network performance in various computer vision applications. In this work, to obtain more compact hash codes, we propose a supervised deep second-order covariance hashing(SDSo CH) method by combining deep hashing with second-order statistic model. SDSo CH utilizes a powerful covariance pooling to model the second-order statistics of convolutional features, which is naturally integrated into the existing point-wise hashing network in an end-to-end manner. The embedded covariance pooling operation well captures the interaction of convolutional features and produces global feature representations with more discriminant capability, leading to the more informative hash codes. Extensive experiments conducted on two benchmarks demonstrate that the proposed SDSoCH outperforms its first-order counterparts and achieves superior retrieval performance.

关键词： Deephashing Point-wisemanner Covariancepooling Imageretrieval

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A hybrid algorithm based on artificial bee colony and pigeon inspired optimization for 3D protein structure prediction

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Journal of Bionanoscience 2018年第1期12卷 100-108页

作者： Li, Ting Zhou, Changjun Wang, Bin Xiao, Boxiang Zheng, Xuedong Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China Beijing Key Laboratory of Digital Plant Beijing100097 China

With the accomplishment of the Human Genome Project, the significance of protein structure research becomes more outstanding. The prediction of protein structure is aimed at the study of amino acid molecules, and belongs to the field of Bio-Nano research. There are two kinds of methods to study protein structure: Experiment and theory. This paper adopts ab-initio prediction method which is one of theoretical methods. Based on the "thermodynamic hypothesis," there are many different models applied to the field. This paper selects 3D AB off-lattice model to solve this problem. ABC-PIO combines the characteristics of the diversity generated by artificial bee colony algorithm and the fast convergence speed of pigeon inspired optimization. At the end of this algorithm, Cauchy perturbation strategy is added to optimize the local optimum. Two functions are introduced to dynamically control two operators of the hybrid algorithm. In order to test the quality of ABC-PIO, ten real protein sequences from the Protein Data Bank have been selected. Compared with other four algorithms, the algorithm gets better result. To calculate the error between the predicted and the true protein structure RMSD is set. Comparisons are made not only in the convergence curves but also in the backbone drawings. © 2018 American Scientific Publishers.

关键词： Optimization

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Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay

arXiv

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arXiv 2023年

作者： Wang, Guankun Bai, Long Wu, Yanan Chen, Tong Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Electrical and Information Engineering The University of Sydney Sydney Australia Department of Biomedical Engineering National University of Singapore Singapore Suzhou Research Institute National University of Singapore Suzhou China Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works have been developed for automated diagnosis or processing of endoscopic view. However, existing DL models may suffer from catastrophic forgetting. When new target classes are introduced over time or cross institutions, the performance of old classes may suffer severe degradation. More seriously, data privacy and storage issues may lead to the unavailability of old data when updating the model. Therefore, it is necessary to develop a continual learning (CL) methodology to solve the problem of catastrophic forgetting in endoscopic image segmentation. To tackle this, we propose a Endoscopy Continual Semantic Segmentation (EndoCSS) framework that does not involve the storage and privacy issues of exemplar data. The framework includes a mini-batch pseudo-replay (MB-PR) mechanism and a self-adaptive noisy cross-entropy (SAN-CE) loss. The MB-PR strategy circumvents privacy and storage issues by generating pseudo-replay images through a generative model. Meanwhile, the MB-PR strategy can also correct the model deviation to the replay data and current training data, which is aroused by the significant difference in the amount of current and replay images. Therefore, the model can perform effective representation learning on both new and old tasks. SAN-CE loss can help model fitting by adjusting the model’s output logits, and also improve the robustness of training. Extensive continual semantic segmentation (CSS) experiments on public datasets demonstrate that our method can robustly and effectively address the catastrophic forgetting brought by class increment in endoscopy scenes. The results show that our framework holds excellent potential for real-world deployment in a streaming learning manner. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Second-order Attention Guided Convolutional Activations for Visual Recognition

Second-order Attention Guided Convolutional Activations for ...

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International Conference on Pattern Recognition

作者： Shannan Chen Qian Wang Qiule Sun Bin Liu Jianxin Zhang Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.

关键词： Visualization Tensors Limiting Network topology Computational modeling Image representation Pattern recognition

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DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification

DCET-Net: Dual-Stream Convolution Expanded Transformer for B...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ying Zou Shannan Chen Qiule Sun Bin Liu Jianxin Zhang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Engineering Dalian Minzu University Dalian China

ISBN: (纸本)9781665429825

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task.

关键词： Limiting Biological system modeling Streaming media Transformers Feature extraction Breast cancer Convolutional neural networks

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Holographic capture and projection system of real object based on tunable zoom lens

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PhotoniX 2020年第1期1卷 271-285页

作者： Di Wang Chao Liu Chuan Shen Yan Xing Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing 100191China Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230039China.

In this paper, we propose a holographic capture and projection system of real objectsbased on tunable zoom lenses. Different from the traditional holographic system, aliquid lens-based zoom camera and a digital conical lens are used as key parts to reachthe functions of holographic capture and projection, respectively. The zoom camera isproduced by combing liquid lenses and solid lenses, which has the advantages of fastresponse and light weight. By electrically controlling the curvature of the liquid-liquidsurface, the focal length of the zoom camera can be changed easily. As anothertunable zoom lens, the digital conical lens has a large focal depth and the opticalproperty is perfectly used in the holographic system for adaptive projection, especiallyfor multilayer imaging. By loading the phase of the conical lens on the spatial lightmodulator, the reconstructed image can be projected with large depths. With theproposed system, holographic zoom capture and color reproduction of real objects canbe achieved based on a simple structure. Experimental results verify the feasibility ofthe proposed system. The proposed system is expected to be applied to microprojectionand three-dimensional display technology.

关键词： Spatial light modulator Holographic system Zoom lenses Display technology

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ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification

arXiv

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arXiv 2021年

作者： Wang, Jinping Tan, Xiaojun Lai, Jianhuang Li, Jun Xiang, Canqun School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing School of Computer Science and Engineering Sun Yatsen University Ministry of Education Guangzhou510006 China Zhuhai519082 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

Previous studies have shown the great potential of capsule networks for the spatial contextual feature extraction from hyperspectral images (HSIs). However, the sampling locations of the convolutional kernels of capsules are fixed and cannot be adaptively changed according to the inconsistent semantic information of HSIs. Based on this observation, this paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels on the basis of an enlarged receptive field. Note that this unit can learn more discriminative representations of HSIs with fewer parameters. Specifically, two cascaded ASP-based convolution operations (ASPConvs) are applied to input images to learn relatively high-level semantic features, transmitting hierarchical structures among capsules more accurately than the use of the most fundamental features. Furthermore, the semantic features are fed into ASP-based conv-capsule operations (ASPCaps) to explore the shapes of objects among the capsules in an adaptive manner, further exploring the potential of capsule networks. Finally, the class labels of image patches centered on test samples can be determined according to the fully connected capsule layer. Experiments on three public datasets demonstrate that ASPCNet can yield competitive performance with higher accuracies than state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectroscopy

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