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

Electromagnetic Scattering from One-dimensional Gaussian Rou...

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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Design of Miniaturized UWB Low Noise Amplifier Based on 65nm CMOS Technology

Design of Miniaturized UWB Low Noise Amplifier Based on 65nm...

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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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Capacitive Flexible Three-Axis Force Tactile Sensor Based on Porous Composite Dielectric Layer for Dexterous Robotic Manipulation

SSRN

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SSRN 2022年

作者： Guo, Xiaohui Li, Cuicui Zheng, Guoqing Wang, Jingyi Tang, Guopeng Hong, Weiqiang Wang, Yifan Pan, Mingqi Xu, Yaohua Hong, Qi Zhao, Yunong Zhang, Hongwei Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Lu’an237010 China East China Institute of Photo-electron ICs Jiangsu Suzhou215163 China

Flexible tactile sensors that can detect both normal and tangential forces with good flexibility, high sensitivity, wide detection range and rapid dynamic response are imminently needed for electronic skin (e-skin). Thus, we innovatively demonstrated a flexible capacitive three-axis tactile sensor with satisfactory flexibility, high sensitivity (0.693 N -1 at 0-5 N and 0.483 N -1 at 5-14 N for normal force, as well as 0.551 N -1 at 0-2 N and 0.481 N -1 at 2-6 N for tangential force), fast response time (30 ms) and a low hysteresis (6.8%), which was fabricated by combining advanced sensor structure with porous composite dielectric layer in a simple and low-cost three-dimensional (3D) printing method and layer-by-layer (LBL) self-assembly method. The working principle and sensing characteristics of the sensor were elaborated by establishing the theoretical mechanism, and further simulation verification was carried out by the finite element modeling (FEM). Our sensor was employed in soft manipulator grasping and gamepad rotation, suggestive of the application potential of the three-axis force tactile sensor as flexible e-skin in the field of tactile sensing. Our work may pave a way to softness haptic perception strategy, which could be widely utilized in e-skin, soft robot, dexterous robotic manipulation, and intelligent prosthesis. © 2022, The Authors. All rights reserved.

关键词： Composite materials

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Super-Broadband Rectifier With Wide-band Resistance Compression Network and Harmonic Cycling for RF-Harvesting

TechRxiv

引用

TechRxiv 2022年

作者： Liu, Xin Zhang, Dawei Li, Yingsong Huang, Zhixiang College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Lu'An230088 China

In this paper, a super-broadband (SBB) rectifier using a wideband resistance compression network (WRCN) and harmonic cycling is proposed, simulated, fabricated, measured and analyzed in detail for the harvesting of ambient Radio frequency (RF) energy. Meanwhile, impedance compression and matching are achieved to implement the SBB using WRCN and impedance operation networks. Additionally, harmonic cycling structure and bending technology are employed to improve the power conversion efficiency (PCE) and reduce the size, respectively. For demonstration, a prototype of the proposed rectifier is fabricated and measured. The measured results are in good accordance with the simulated ones, indicating that the rectifier can provide high efficiency and ultra-broadband rectification characteristics. A PCE over 50% is realized at 3.5 GHz with an input power range of 6–24 dBm, and a PCE of 75% is obtained at an input power of 18 dBm. The PCE is greater than 50% at 17 dBm with an input frequency of 2.7–4.6 GHz. This work can find significant applications in the development of RF-harvesting. © 2022, CC BY.

关键词： Harvesting

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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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DSPE: An End-to-End Drug Synergy Combination Prediction Algorithm for Echinococcosis

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Current Bioinformatics 2025年

作者： Li, Haitao Jiang, Liyuan Chu, Yuanyuan Liu, Yuansheng Zheng, Chunhou Su, Yansen Key Laboratory of Intelligent Computing and Signal Processing School of Artificial Intelligence Anhui University 111 Jiulong Road Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center 5089 Wangjiang West Road Anhui Hefei230088 China College of Computer Science and Electronic Engineering Hunan University China

Background: Echinococcosis, a parasitic disease caused by the larvae of the Echinococcus parasite, poses a serious threat to human health. Medication is an indispensable means of treatment for Echinococcosis;however, due to the less satisfactory efficacy of single drugs, identifying effective drug combinations for the treatment of Echinococcosis is essential. Yet, current predictive models for drug synergy in Echinococcosis face accuracy challenges due to data scarcity, method limitations, and insufficient feature representation. Objective: This work aims to design an end-to-end method to predict drug synergistic combinations, which enables efficient and accurate identification of drug combinations against Echinococcosis. Methods: In this work, an end-to-end method, named DSPE, is proposed for predicting anti-Echinococcosis drug synergistic combinations. In DSPE, a dataset of Echinococcosis drug synergistic combinations is constructed by retrieving and extracting information from related scientific articles. Further, DSPE employs a residual graph attention network to deeply analyze drug characteristics and their interactions, thereby enhancing the performance of deep learning models. It also explores the protein-protein interaction network related to Echinococcosis, using node2vec combined with an attention mechanism to efficiently encode disease features. Finally, it predicts the synergy of drug combinations based on the Bliss score by integrating drug combinations and disease features. Results: Experimental evidence shows that DSPE outperforms five state-of-the-art algorithms in predicting drug combination effects by leveraging disease-target information and single-agents for the treatment. Conclusion: DSPE improves prediction accuracy and addresses the issue of data scarcity for new diseases, offering new insights and methods for the development of treatment plans for parasitic diseases in the future. © 2025 Bentham Science Publishers.

关键词： Diseases

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A quaternary analogue of Tang-Ding codes

arXiv

引用

arXiv 2023年

作者： Shi, Minjia Tao, Sihui Kim, Jon-Lark Solé, Patrick 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 Aix Marseille Univ CNRS Centrale Marseille I2M Marseille France

In a recent paper, Tang and Ding introduced a class of binary cyclic codes of rate close to one half with a designed lower bound on their minimum distance. The definition involves the base 2 expansion of the integers in their defining set. In this paper we propose an analogue for quaternary codes. In addition, the performances of the subfield subcode and of the trace code (two binary cyclic codes) are investigated. Copyright © 2023, The Authors. All rights reserved.

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A Dual-Polarized Antenna For 5G N78 Application

A Dual-Polarized Antenna For 5G N78 Application

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiakang Pan Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University China College of Computer Science and Technology Harbin Engineering University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A compact dual polarization dipole antenna for the application to 5G base station is investigated, which is composed of radiator, baluns, and metal reflector, having an impendence bandwidth of 23.6% with VSWR<1.5. The dual-polarized antenna has a high ports isolation, high cross-polarization and high gain in operating bandwidth. The results indicate that this work can operate in entire N78 band and provide potential application for 5G base station.

关键词： Base stations 5G mobile communication Dipole antennas Baluns Metals Bandwidth Reflector antennas

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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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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

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

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-LiDARfusion 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.

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