检索结果-内蒙古大学图书馆

A Novel Multi-band EBG Structure Using Nested Square Grooves 5

学校读者我要写书评

暂无评论

A Novel Multi-band EBG Structure Using Nested Square Grooves

5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Sun, Guoxing Ren, Xingang Ju, Huping Xueyuan, Cai He, Shuping Xianliang, Wu Huang, Zhixiang Anhui University Key Laboratory of Target Recognition and Feature Extraction of Anhui Province Lu'an China Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Anqing Normal Unverisity Anqing China Ministry of Education Anhui University Key Laboratory of Intelligent Computing & Signal Processing Hefei China Anhui University Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Hefei China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A nested rectangle electromagnetic bandgap structure with dual broadband band is designed to enhance the performance of telecom/mobile 5G antennas operating at 3.3GHz and 4.9GHz, which has a short frequency spacing of about 1.6GHz. through loading EBG structure under dipole antenna, directional radiation, low profile and high gain performance have been achieved. The distance between electromagnetic bandgap (EBG) structure and antenna is only 0.067 wavelength, the first band and the second band of EBG are greatly expanded by etching nested square grooves around the EBG (NSGEBG) as compared with traditional etched ring groove EBG (ERGEBG). The bandwidth of the first band of 0.39GHz is twice wider than that of the ring groove of 0.2GHz, the second band has a bandwidth of 0.46GHz, which perfectly solves the problem that the spacing of the two operating frequency is greater than 2GHz. © 2022 IEEE.

关键词： Energy gap

Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Cao, Yice Liu, Chenchen Wu, Zhenhua Yao, Wenxin Xiong, Liu Chen, Jie Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China The 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

A Time-Aware Dynamic Service Quality Prediction Approach for Services

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of Service(QoS)prediction time-aware service recommendation

HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Tang, Bin Liu, Zhengyi Tan, Yacheng He, Qian School of Artificial Intelligence and Big Data Hefei University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

The High-Resolution Transformer (HRFormer) can maintain high-resolution representation and share global receptive fields. It is friendly towards salient object detection (SOD) in which the input and output have the same resolution. However, two critical problems need to be solved for two-modality SOD. One problem is two-modality fusion. The other problem is the HRFormer output’s fusion. To address the first problem, a supplementary modality is injected into the primary modality by using global optimization and an attention mechanism to select and purify the modality at the input level. To solve the second problem, a dual-direction short connection fusion module is used to optimize the output features of HRFormer, thereby enhancing the detailed representation of objects at the output level. The proposed model, named HRTransNet, first introduces an auxiliary stream for feature extraction of supplementary modality. Then, features are injected into the primary modality at the beginning of each multi-resolution branch. Next, HRFormer is applied to achieve forwarding propagation. Finally, all the output features with different resolutions are aggregated by intra-feature and inter-feature interactive transformers. Application of the proposed model results in impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T, and light field ***://***/liuzywen/HRTransNet Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

Log-Concave Sequences in Coding Theory

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Shi, Minjia Wang, Xuan An, Junmin Kim, Jon-Lark The Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of integrated Service Networks Xidian University Xi’an710071 China Department of Mathematics Sogang University Seoul Korea Republic of

We introduce the notion of logarithmically concave (or log-concave) sequences in Coding Theory. A sequence a0, a1, . . ., an of real numbers is called log-concave if a2i ≥ ai−1ai+1 for all 1 ≤ i ≤ n−1. A natural sequence of positive numbers in coding theory is the weight distribution of a linear code consisting of the nonzero values among Ai’s where Ai denotes the number of codewords of weight i. We call a linear code log-concave if its nonzero weight distribution is log-concave. Our main contribution is to show that all binary general Hamming codes of length 2r − 1 (r = 3 or r ≥ 5), the binary extended Hamming codes of length 2r (r ≥ 3), and the second order Reed-Muller codes R(2, m) (m ≥ 2) are all log-concave while the homogeneous and projective second order Reed-Muller codes are either log-concave, or 1-gap log-concave. Furthermore, we show that any MDS [n, k] code over Fq satisfying 3 ≤ k ≤ n/2 + 3 is log-concave if q ≥ q0(n, k) which is the larger root of a quadratic polynomial. Hence, we expect that the concept of log-concavity in coding theory will stimulate many interesting *** Codes 94B15 © 2024, CC BY.

关键词： Turbo codes

Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding

学校读者我要写书评

暂无评论

Detection Hub: Unifying Object Detection Datasets via Query ...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lingchen Meng Xiyang Dai Yinpeng Chen Pengchuan Zhang Dongdong Chen Mengchen Liu Jianfeng Wang Zuxuan Wu Lu Yuan Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft

Combining multiple datasets enables performance boost on many computer vision tasks. But similar trend has not been witnessed in object detection when combining multiple datasets due to two inconsistencies among detection datasets: taxonomy difference and domain gap. In this paper, we address these challenges by a new design (named Detection Hub) that is dataset-aware and category-aligned. It not only mitigates the dataset inconsistency but also provides coherent guidance for the detector to learn across multiple datasets. In particular, the dataset-aware design is achieved by learning a dataset embedding that is used to adapt object queries as well as convolutional kernels in detection heads. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding and leveraging the semantic coherence of language embedding. Detection Hub fulfills the benefits of large data on object detection. Experiments demonstrate that joint training on multiple datasets achieves significant performance gains over training on each dataset alone. Detection Hub further achieves SoTA performance on UODB benchmark with wide variety of datasets.

关键词：

Learning from rich semantics and coarse locations for long-tailed object detection 23

学校读者我要写书评

暂无评论

Learning from rich semantics and coarse locations for long-t...

Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Lingchen Meng Xiyang Dai Jianwei Yang Dongdong Chen Yinpeng Chen Mengchen Liu Yi-Ling Chen Zuxuan Wu Lu Yuan Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University and Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft

Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to explore extra data with image-level labels, yet it produces limited results due to (1) semantic ambiguity—an image-level label only captures a salient part of the image, ignoring the remaining rich semantics within the image; and (2) location sensitivity—the label highly depends on the locations and crops of the original image, which may change after data transformations like random cropping. To remedy this, we propose RichSem, a simple but effective method, which is robust to learn rich semantics from coarse locations without the need of accurate bounding boxes. RichSem leverages rich semantics from images, which are then served as additional "soft supervision" for training detectors. Specifically, we add a semantic branch to our detector to learn these soft semantics and enhance feature representations for long-tailed object detection. The semantic branch is only used for training and is removed during inference. RichSem achieves consistent improvements on both overall and rare-category of LVIS under different backbones and detectors. Our method achieves state-of-the-art performance without requiring complex training and testing procedures. Moreover, we show the effectiveness of our method on other long-tailed datasets with additional experiments. Code is available at https://***/MengLcool/RichSem.

关键词：

Exploring Invariance Matters for Domain Generalization

学校读者我要写书评

暂无评论

IEEE Transactions on Image processing 2025年 34卷 3336-3351页

作者： Wang, Shanshan He, Houmeng Yang, Xun Liu, Zhipu Zhong, Yuanhong Zhang, Xingyi Wang, Meng Anhui University National Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology Institutes of Physical Science and Information Technology Hefei230601 China University of Science and Technology of China School of Information Science and Technology Department of Electronic Engineering and Information Science Hefei230026 China Chongqing University of Technology School of Computer Science and Engineering Chongqing400054 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400030 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Domain generalization (DG) aims to solve the problem of significant performance degradation when target domain data collected from the Out-Of-Distribution (O.O.D). Previous efforts try to exploit invariant features in the source domain through CNN networks. However, inspired by causal mechanisms, we find that the complex spurious-invariant information is still hidden in this view invariant features, and the impact of domain and class discrepancies on extracting invariance has not been effectively mitigated. To alleviate these issues, we propose a self-weighted multi-view mining invariance domain generalization framework (SMIDG). On the one hand, to make up for the insufficiency of traditional single-view convolutional feature extraction networks, we propose to mine features from another frequency view and use the self-adaptive adversarial masks to eliminate some spurious correlations, ensuring causal invariance in the coarse-grained generalization. However, due to inconsistencies in discriminative information between interdomain and intra-domain samples, as well as inter-class and intra-class samples, the coarse-grained elimination of spurious associations does not fully resolve this issue. On the other hand, we also consider the fine-grained generalization from two aspects. Firstly, to better tackle the domain discrepancies, we propose a novel progressive contrastive learning strategy that learns the underlying specific features of samples while gradually mitigating domain discrepancies, thereby ensuring domain invariance in fine-grained generalization. Secondly, due to the issue of feature inconsistency, we adopt a self-adaptive hard sample mining method with information gain to ensure that the model pays more attention on hard disentangled samples, thus maintaining feature invariance. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art approaches. © 1992-2012 IEEE.

关键词： Contrastive learning Feature extraction Frequency-domain analysis Training Data models Data mining Correlation Data augmentation Convolutional neural networks Computer science

Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wen, Zhuofan Gui, Shangtong Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of Al Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base LLM verifies the draft for acceptance or rejection. In this framework, the final inference speed is decided by the decoding speed of the draft model and the acceptance rate of the draft provided by the draft model. Currently the widely used draft models usually generate draft tokens for the next several positions in a non-autoregressive way without considering the correlations between draft tokens. Therefore, it has a high decoding speed but an unsatisfactory acceptance rate. In this paper, we focus on how to improve the performance of the draft model and aim to accelerate inference via a high acceptance rate. To this end, we propose a CTC-based draft model which strengthens the correlations between draft tokens during the draft phase, thereby generating higher-quality draft candidate sequences. Experiment results show that compared to strong baselines, the proposed method can achieve a higher acceptance rate and hence a faster inference speed. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding