检索结果-内蒙古大学图书馆

Control of space-dependent four-wave mixing in a four-level atomic system

学校读者我要写书评

暂无评论

Physical Review A 2020年第3期102卷 033516-033516页

作者： Jing Qiu Zhiping Wang Dongsheng Ding Zhixiang Huang Benli Yu Key Laboratory of Opto-Electronic Information Acquisition and Manipulation Ministry of Education Anhui University Hefei 230601 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui 230026 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China

We propose a scheme to demonstrate the manipulation of space-dependent four-wave mixing (FWM) in a four-level atomic system. By adjusting the detuning of the control field, one can effectively control the FWM output field transferred from a pump beam carrying orbital angular momentum. More interestingly, by appropriate choice of the intensity of the control field, the FWM field can be significantly enhanced and phase twist is almost completely suppressed. Furthermore, the superposition modes created by the interference between the FWM field and a same-frequency Gaussian beam are also discussed, showing many interesting properties. Our results may open some possibilities for phase imprinting in Bose-Einstein condensates or atom manipulation with optical tweezers.

关键词： Angular momentum of light Atom optics Coherent control Optical coherence

Secure Optical Wireless Links with Dynamic Beam and Diversity Receiver

学校读者我要写书评

暂无评论

Secure Optical Wireless Links with Dynamic Beam and Diversit...

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

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

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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

Evolving multi-resolution pooling CNN for monaural singing voice separation

学校读者我要写书评

暂无评论

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

Semantic knowledge transfer for semi-supervised medical image segmentation

学校读者我要写书评

暂无评论

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.

关键词：

Multi-interactive dual-decoder for RGB-thermal salient object detection

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Tu, Zhengzheng Li, Zhun Li, Chenglong Lang, Yang Tang, Jin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGB-thermal salient object detection (SOD) aims to segment the common prominent regions of visible image and corresponding thermal infrared image that we call it RGBT SOD. Existing methods don’t fully explore and exploit the potentials of complementarity of different modalities and multi-type cues of image contents, which play a vital role in achieving accurate results. In this paper, we propose a multi-interactive dual-decoder to mine and model the multi-type interactions for accurate RGBT SOD. In specific, we first encode two modalities into multi-level multi-modal feature representations. Then, we design a novel dual-decoder to conduct the interactions of multi-level features, two modalities and global contexts. With these interactions, our method works well in diversely challenging scenarios even in the presence of invalid modality. Finally, we carry out extensive experiments on public RGBT and RGBD SOD datasets, and the results show that the proposed method achieves the outstanding performance against state-of-the-art algorithms. The source code has been released at: https://***/lz118/Multi-interactiveDual-decoder. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

Challenge-aware rgbt tracking

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Li, Chenglong Liu, Lei Lu, Andong Ji, Qing Tang, Jin Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking. In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking. In particular, we design several parameter-shared branches in each layer to model the target appearance under the modality-shared challenges, and several parameter-independent branches under the modality-specific ones. Based on the observation that the modality-specific cues of different modalities usually contains the complementary advantages, we propose a guidance module to transfer discriminative features from one modality to another one, which could enhance the discriminative ability of some weak modality. Moreover, all branches are aggregated together in an adaptive manner and parallel embedded in the backbone network to efficiently form more discriminative target representations. These challenge-aware branches are able to model the target appearance under certain challenges so that the target representations can be learnt by a few parameters even in the situation of insufficient training data. From the experimental results we will show that our method operates at a real-time speed while performing well against the state-of-the-art methods on three benchmark datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning