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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Jupeng Ding Chih Lin I Jintao Wang Hui Yang Lili Wang Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region School of Information Science and Engineering Xinjiang University China China Mobile Research Institute Beijing China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China School of Information and Electrical Engineering Ludong University Yantai China

ISBN: (纸本)9781665405836

Optical wireless communication (OWC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. Due to the broadcast nature of OWC channel, physical layer security (PLS) techniques have been considered and explored to improve the transmission confidentiality of OWC links. However, almost all current schemes focus on scenarios including multiple transmitters and fail to match the scenarios with limited transmitters, even single transmitter. For addressing this issue, in this work, the dynamic optical beam based PLS enhancement scheme is proposed. Unlike conventional Lambertian beam based technique paradigm, the above scheme tentatively utilize the commercially available non-Lambertian beam to configure the secure OWC links. Numerical results show that, compared with about 1.10 bps/Hz average secrecy capacity (SC) of the conventional configuration, up to 1.93 bps/Hz average SC gain could be provided by the proposed dynamic beam scheme. Moreover, this potential gain will be increased to about 2.39 bps/Hz when the diversity combing is available for the legitimate user receiver.

关键词： Wireless communication Integrated optics Measurement Conferences Optical fiber networks Physical layer security Optical receivers

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Unambiguous signal reconstruction for GEO-LEO bistatic SAR with azimuth multichannel

Unambiguous signal reconstruction for GEO-LEO bistatic SAR w...

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IET International radar Conference 2018, IRC 2018

作者： Wang, Yuekun Lu, Zheng Xu, Mingming Zhu, Yu Li, Zhenfang Nanjing Research Institute of Electronics Technology Nanjing210039 China Beijing Institute of Spacecraft System Engineering Beijing100094 China National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

Spaceborne bistatic multichannel synthetic aperture radar (MC-SAR) system employed inclined geosynchronous Earth orbit (GEO) transmitter and low-Earth-orbit (LEO) receiver can provide fine spatial resolution and a vast area of surveillance, which presents great potentials for future Earth observation. Nevertheless, owing to special geometry and big differences of orbital characteristics, the conventional effective phase centre (EPC) operation for unambiguous SAR signal reconstruction cannot be directly used. To address the issue, starting from geometry model of GEO-LEO bistatic MC-SAR, the new effective positions of the receiving channels are obtained. Then, the time-variant characteristics of residual phase error after the presented EPC processing is analysed. Finally, the method of reconstructing unambiguous SAR signal is presented. Simulation results show the validity of the proposed method, which can efficiently reconstruct the unambiguous signal for GEO-LEO bistatic SAR. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： signal reconstruction

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On q-ary shortened-1-perfect-like codes

arXiv

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arXiv 2021年

作者： Shi, Minjia Wu, Rongsheng Krotov, Denis S. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematics Sciences Anhui University Anhui Hefei230601 China School of Mathematical Sciences University of Science and Technology of China Anhui Hefei230026 China Sobolev Institute of Mathematics pr. Akademika Koptyuga 4 Novosibirsk630090 Russia

We study codes with parameters of q-ary shortened Hamming codes, i.e., (n = (qm − q)/(q − 1), qn−m, 3)q. Firstly, we prove the fact mentioned in 1998 by Brouwer et al. that such codes are optimal, generalizing it to a bound for multifold packings of radius-1 balls, with a corollary for multiple coverings. In particular, we show that the punctured Hamming code is an optimal q-fold packing with minimum distance 2. Secondly, for every admissible length starting from n = 20, we show the existence of 4-ary codes with parameters of shortened 1-perfect codes that cannot be obtained by shortening a 1-perfect *** Codes 94B05, 94B65 Copyright © 2021, The Authors. All rights reserved.

关键词： Optimal systems

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Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation

arXiv

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arXiv 2022年

作者： Zhao, Yue Zhang, Lingming Liu, Yang Meng, Deyu Cui, Zhiming Gao, Chenqiang Gao, Xinbo Lian, Chunfeng Shen, Dinggang Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China The Chongqing Key Laboratory of Signal and Information Processing Chongqing400065 China Department of Orthodontics Stomatological Hospital of Chongqing Medical University Chongqing401147 China Chongqing Key Laboratory for Oral Diseases and Biomedical Sciences Chongqing401147 China Faculty of Information Technology Macau University of Science and Technology China School of Mathematics and Statistics Xi'an Jiaotong University Xian710049 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200030 China The School of Computer Science The University of Hong Kong 999077 Hong Kong School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation. Github: https://***/ZhangLingMing1/TSGCNet. © 2022, CC BY.

关键词： Scanning

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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & Intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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Speech Language Classification Algorithm Based on Multiple-instance Learning with Negative Sample Cleaning

Speech Language Classification Algorithm Based on Multiple-i...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Jianyi Zhang Wei Xu Jiaqi Yao Jikun Yan Xin Huang College of Information System Engineering PLA Strategic Support Force Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Speech language classification is an important direction in the field of machine learning. In order to further improve the effect of speech classification, this paper propose a new speech language classification algorithm based on multiple-instance learning with negative instance cleaning, namely NUMI-Biased SVM algorithm. Firstly, extract MFCC features from speech files, regarding each file as an instance bag, and every speech frame as an instance. Secondly, adopt PU learning algorithm to clean negative instances in positive bags to reduce their impact on classification. Finally, multiple-instance learning algorithm is used to classify the speech files. Experiments show that, compared with the SVM algorithm and multiple-instance SVM algorithm, NUMI-Biased-SVM has achieved significant improvement in classification performance.

关键词： Support vector machines Machine learning algorithms Feature extraction Telephone sets Cleaning Classification algorithms Mel frequency cepstral coefficient

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Evolving multi-resolution pooling CNN for monaural singing voice separation

arXiv

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arXiv 2020年

作者： Yuan, Weitao Dong, Bofei Wang, Shengbei Unoki, Masashi Wang, Wenwu Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin Polytechnic University Tianjin China School of Information Science Japan Advanced Institute of Science and Technology Japan Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

Monaural Singing Voice Separation (MSVS) is a challenging task and has been studied for decades. Deep neural networks (DNNs) are the current state-of-the-art methods for MSVS. However, the existing DNNs are often designed manually, which is time-consuming and error-prone. In addition, the network architectures are usually pre-defined, and not adapted to the training data. To address these issues, we introduce a Neural Architecture Search (NAS) method to the structure design of DNNs for MSVS. Specifically, we propose a new multi-resolution Convolutional Neural Network (CNN) framework for MSVS namely Multi-Resolution Pooling CNN (MRP-CNN), which uses various-size pooling operators to extract multi-resolution features. Based on the NAS, we then develop an evolving framework namely Evolving MRP-CNN (E-MRP-CNN), by automatically searching the effective MRP-CNN structures using genetic algorithms, optimized in terms of a single-objective considering only separation performance, or multi-objective considering both the separation performance and the model complexity. The multiobjective E-MRP-CNN gives a set of Pareto-optimal solutions, each providing a trade-off between separation performance and model complexity. Quantitative and qualitative evaluations on the MIR-1K and DSD100 datasets are used to demonstrate the advantages of the proposed framework over several recent baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Corrections to “An Improved Map-Drift Algorithm for Unmanned Aerial Vehicle SAR Imaging”

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IEEE Geoscience and Remote Sensing Letters 2022年 19卷

作者： Yan Huang Feiyang Liu Zhanye Chen Jie Li Wei Hong State Key Laboratory of Millimeter Waves Southeast University Nanjing China Purple Mountain Laboratory Nanjing China Satellite Signal Processing The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China School of Microelectronics and Communication Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion Chongqing University Chongqing China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

In the above article [1] , the corresponding authors should be Yan Huang and Jie Li.

关键词： Geoscience and remote sensing Autonomous aerial vehicles radar polarimetry

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Semantic knowledge transfer for semi-supervised medical image segmentation

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Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Shiwei Zhou Haifeng Zhao Leilei Ma Dengdi Sun Key Laboratory of Intelligent Computing & Signal Processing (ICSP) Ministry of Education Anhui University Hefei 230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei 230601 China School of Computer Science and Technology Anhui University Hefei 230601 China School of Artificial Intelligence Anhui University Hefei 230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei 230026 China

In semi-supervised medical image segmentation, due to possible differences in information content and distribution between labeled and unlabeled data, dealing with the two separately usually prevents knowledge transfer from labeled to unlabeled data. This prevents the model from effectively sharing learned information between the two types of data. To alleviate this problem, we train labeled and unlabeled data as a whole. Semantic mixing of labeled and unlabeled data is achieved by selecting and exchanging some of the region images of both through a mask to generate complementary input views. In addition, due to the limited labeled data, the unlabeled data has a weak ability to distinguish categories in the feature space. Traditional methods rely on pixel positions to generate positive and negative samples for contrastive learning to solve this problem, but relying on pixel position sampling can easily lead to semantic inconsistency, which affects the effect of feature learning; therefore, to address this problem, we propose an innovative labeled data-guided inter-class contrastive learning strategy, which extracts the category features from labeled and unlabeled data and exploits the accurate category information in the labeled data to guide contrastive learning, while introducing a similarity-based ranking weighting mechanism. Combining the two designs, we propose a new semantic knowledge transfer framework for semi-supervised medical image segmentation. Experiments demonstrate a significant improvement in our model compared to State of the Art (SOTA) on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset and the Left Atrium (LA) dataset.

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SPACE-MISSILE BORNE BISTATIC SAR GEOMETRY AND IMAGING PROPERTIES ANALYSIS

SPACE-MISSILE BORNE BISTATIC SAR GEOMETRY AND IMAGING PROPER...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Ping Guo Xiaoyang Jiao Anyi Wang Jing Wang Shiyang Tang Yang Liu College of Communication and Information Engineering Xi’an University of Science and Technology Xi’an China National Laboratory of Radar Signal Processing Xidian University Xi’an China

As a special bistatic configuration, space-missile borne bistatic synthetic aperture radar (SMB-BiSAR) is proposed, which has many potential applications in precision detection and guidance. Compared with traditional bistatic SAR, SMB bistatic SAR has complicated structure, and the imaging performance should be analyzed. In this paper, the geometric model of SMB-BiSAR is constructed in the reference target local (RTL) coordinate system firstly. Then, the range history is introduced with the vector notation. According to the range history, the Doppler properties and 2-D resolution are derived by using gradient method, and the forward-looking ability is demonstrated, which would guide the subsequent research. The simulations verify the validity of the analysis method.

关键词： Synthetic aperture radar Imaging Image resolution Azimuth Receivers Doppler effect Missiles

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