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Progress in Electromagnetic Research Symposium (PIERS)

作者： Qing Guo Yuting Chen Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781665409889

An X-band CMOS low noise amplifier (LNA) with flat gain and noise Figure (NF) is proposed in this paper. To achieve sufficient gain, the LNA adopts two-stage topology. A feed-back transformer is used at the input terminal to implement noise and wideband input matching. Transformer-based inter-stage and output matching networks are adopted to achieve well isolation and compact size. By introducing inductive peaking technique, the proposed LNA obtain lower NF and extend the bandwidth. Post simulation results show that the LNA achieves a gain of 14.6± 2dB and NF of 2. 5-3.3dB across a bandwidth from 8.5 GHz to 12.5 GHz. With the supply voltage of 1 V at the first stage and 1.8 V at the second stage, the dc power dissipation is 20.8 mW. The simulated input 1dB compression point ($\mathrm{P}_{\mathrm{l}\mathrm{d}\mathrm{B}}$) is -27.3dBm. The core chip area is only 0.41× 0.87mm 2 .

关键词： Low-noise amplifiers Impedance matching Simulation Voltage Transformer cores Transformers CMOS technology

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Electromagnetic Scattering from One-dimensional Gaussian Rough Surface Based on Stochastic Integral Equation

Electromagnetic Scattering from One-dimensional Gaussian Rou...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： An Qi Wang Meng Nan Zhu Zhi Xiang Huang Key Laboratory of intelligent Computing & Signal Processing Anhui University Hefei China Key Laboratory of intelligent Computing & Signal Processing Anhui University

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

In this paper, the stochastic integral equation method (SIEM) is proposed to calculate the electromagnetic scattering from the one-dimensional Gaussian rough surface. In the SIEM, the statistical parameters of the Gaussian rough surface are firstly included in the Green's function. And the new Green's function is used to get the new integral equation. Then the method of moments is applied to solve the new integral equation. The accuracy of the SIEM is shown by comparing with the statistical average of multiple samples through the Monte Carlo method. And, the sample of the Gaussian rough surface is not needed to simulate in the SIEM.

关键词： Integral equations Electromagnetic scattering Stochastic processes Surface roughness Rough surfaces Numerical models Optimization

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UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

UHRSNet: A Semantic Segmentation Network Specifically for Ul...

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International Conference on Pattern Recognition

作者： Lianlei Shan Minglong Li Xiaobin Li Yang Bai Ke Lv Bin Luo Si-Bao Chen Weiqiang Wang University of Chinese Academy of Sciences Beijing China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei China

Semantic segmentation is a basic task in computer vision, but only limited attention has been devoted to the ultra-high-resolution (UHR) image segmentation. Since UHR images occupy too much memory, they cannot be directly put into GPU for training. Previous methods are cropping images to small patches or downsampling the whole images. Cropping and downsampling cause the loss of contexts and details, which is essential for segmentation accuracy. To solve this problem, we improve and simplify the local and global feature fusion method in previous works. Local features are extracted from patches and global features are from downsampled images. Meanwhile, we propose one new fusion called local feature fusion for the first time, which can make patches get information from surrounding patches. We call the network with these two fusions ultra-high-resolution segmentation network (UHRSNet). These two fusions can effectively and efficiently solve the problem caused by cropping and downsampling. Experiments show a remarkable improvement on Deepglobe dataset [1].

关键词： Training Image segmentation Computer vision Semantics Graphics processing units Feature extraction Pattern recognition

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RGB-T Saliency Detection via Robust Graph Learning and Collaborative Manifold Ranking 14th

RGB-T Saliency Detection via Robust Graph Learning and Colla...

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14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Li, Sheng Ding, Zhuanlian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

Visual saliency detection inspired by brain cognitive mechanisms is an important component of computer vision, aiming at automatically highlight salient visual objects from the image background. In complex real scenarios, integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting saliency detection performance. In this paper, we propose a novel collaborative algorithm for saliency detection by adaptively incorporating information from grayscale and thermal images. Specifically, we first construct an affinity graph model via robust graph learning to characterize the feature similarities and location relationships between two different image patches under each modal. Based on these affinity graphs, a collaborative manifold ranking method cross-modality consistent constraints is designed to effectively and efficiently infer the salient score for each patch. Moreover, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking scheme to achieve an adaptive weighted fusion of different source data. For optimization, we propose some iterative algorithms to efficiently solve the models with several subproblems. Experiments on the grayscale-thermal datasets demonstrate the superior performance of the new method over the state-of-the-art approaches. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Graphic methods

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A Novel Tri-band Frequency Selective Surface 2

A Novel Tri-band Frequency Selective Surface

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Wu, Shenbing Chen, Mingsheng Wang, Chao Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing and Signal Processing Anhui University Hefei Anhui230601 China School of Electronic Engineering Huainan Normal University Huainan Anhui232001 China

This paper presents a novel tri-band frequency miniaturized frequency selection surface (FSS). The proposed FSS consists of single-node folding and single-node folding bending. The single-layer FSS exhibits three stop bands centered at 2.45, 3.61 and 5.79 GHz, with bandwidths of 0.42, 0.48 and 0.70 GHz, respectively. The proposed FSS has better miniaturization characteristics, and its cell size is 0.126 ?×0.126 ?. The three stop band frequencies of the FSS can be independently controlled by simply changing the length of the corresponding branch. In addition, the proposed FSS is symmetrical about the center and exhibits good resonance stability in both TE and TM polarization. The prototype of the proposed FSS was fabricated and tested, compared with the simulation results, the FSS showed stable performance. This method provides ideas for designing multiband FSS in the future. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Frequency selective surfaces

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Dual-Graph Regularized Sparse Low-Rank Matrix Recovery for Tag Refinement 14th

Dual-Graph Regularized Sparse Low-Rank Matrix Recovery for T...

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14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Bao, Yuanyuan Ge, Meiling Ding, Zhuanlian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

In recent years, extremely large amounts of images with manual tags are easily available in many social websites such as Twitter, Flickr, and Instagram. However, these user-provided tags are often imprecise and incomplete, which inevitably limits the performances of image retrieval and other related applications. To this end, tag refitment technology aims at improving the quality of images tags automatically, and has been a fundamental challenge in Internet era. In this paper, we propose a novel dual-graph regularized sparse low-rank matrix recovery method, referred to as DGSLR briefly, to infer and improve the manual tags. Specifically, our DGSLR model first suppress the sparse noisy tags by norm, and decompose simultaneously the residual low-rank matrix to learning the low-dimensional vector representations of images and tags. Moreover, the visual similarities and the tag pairwise correlation are fully exploited to smooth the decomposition results by using the dual lapalcian graph regularization terms. For optimization, an improved alternative iteration strategy is designed to solve the resulting objective function. Extensive experiments on the tasks of tag refinement demonstrate the superior performance of the proposed algorithms over several representative methods, especially when the manual tag data is highly noisy and sparse. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Cross-Modal Object Tracking via Modality-Aware Fusion Network and A Large-Scale Dataset

arXiv

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arXiv 2023年

作者： Liu, Lei Zhang, Mengya Li, Cheng Li, Chenglong Tang, Jin The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China The Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing The Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China The School of Computer Science and Technology Anhui University Hefei230601 China

Visual tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multi-modal imaging platforms are complex and lack real-world applicability. In contrast, near-infrared (NIR) imaging, commonly used in surveillance cameras, can switch between RGB and NIR based on light intensity. However, tracking objects across these heterogeneous modalities poses significant challenges, particularly due to the absence of modality switch signals during tracking. To address these challenges, we propose an adaptive cross-modal object tracking algorithm called Modality-Aware Fusion Network (MAFNet). MAFNet efficiently integrates information from both RGB and NIR modalities using an adaptive weighting mechanism, effectively bridging the appearance gap and enabling a modality-aware target representation. It consists of two key components: an adaptive weighting module and a modality-specific representation module. The adaptive weighting module predicts fusion weights to dynamically adjust the contribution of each modality, while the modality-specific representation module captures discriminative features specific to RGB and NIR modalities. MAFNet offers great flexibility as it can effortlessly integrate into diverse tracking frameworks. With its simplicity, effectiveness, and efficiency, MAFNet outperforms state-of-the-art methods in cross-modal object tracking. To validate the effectiveness of our algorithm and overcome the scarcity of data in this field, we introduce CMOTB, a comprehensive and extensive benchmark dataset for cross-modal object tracking. CMOTB consists of 61 categories and 1000 video sequences, comprising a total of over 799K frames. We believe that our proposed method and dataset offer a strong foundation for advancing cross-modal object tracking research. The dataset, toolkit, a

关键词： Large dataset

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Hand and gesture

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Virtual Reality & intelligent Hardware 2021年第3期3卷 I0001-I0002页

作者： Xilin CHEN Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China

Hands play an important role in our daily *** use our hands for manipulation in working,emphasis in speaking,communication in non-verbal environment,*** gesture not only uses for simple commands in traffic control,but also extends as a kind of language-sign *** the areas of VR/AR and HCI,understanding hand and its action can greatly improve user *** covers a broad topics related to hands,including hand detection,tracking,hand pose estimation,gesture recognition,and sign language *** papers are collected in this *** cover different topics related to hand and gesture.

关键词： verbal language. hands

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Multi-Granularity Prosodic Speech Synthesis with Grammar Information

SSRN

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SSRN 2024年

作者： Zhou, Jian Liu, Jiahui Fan, Cunhang Zheng, Wenming Lv, Zhao Tao, Liang Kwan, Hon Keung The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering University of Windsor WindsorN9B 3P4 Canada

Personalized speech synthesis involves learning an individual's unique speaking patterns from a reference audio to generate speech that mimics the speaker's habitual rhythm. However, the one-to-many mapping between text and speech, combined with the dynamic nature of personalized speech style characteristics in the reference audio, poses significant challenges for accurate personalized speech synthesis. In this paper, we propose a Grammar Infused Multi-granularity Prosody Network (GMG ProsodyNet) for personalized speech synthesis. Specifically, 1) We model prosodic features hierarchically at the levels of utterance, content syntax, word, and phoneme. We utilize the utterance-level prosodic feature to guide the prediction of fine-grained prosodic features according to the hierarchical property of prosodic features. We introduce a context syntax encoder to improve the prediction accuracy of duration, pitch, and energy characteristics in the synthesized audio. The proposed word-level prosodic encoder can efficiently extract valuable pitch dynamic and speech continuity features from the word spectrum. For controlling subtle prosodic nuances, we employ phoneme-level prosodic modeling. 2) We extract syntax level prosodic features from the syntax graph constructed by Graph Neural Network (GNN) with word as node, exploiting grammatical dependencies between distant words. Experimental results demonstrate that the proposed GMG ProsodyNet can effectively encode delicate and personalized prosodic features, leading to improved speech synthesis quality, fluency, and naturalness. © 2024, The Authors. All rights reserved.

关键词： Speech synthesis

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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