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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Kang, Meijun Feng, Jian Rong, Mandi Fang, Ming The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230039 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China State Key Laboratory of Millimeter Waves Southeast University Nanjing214135 China

ISBN: (纸本)9781665452366

In order to obtain an absorber with high absorption efficiency and wide absorption band, a metamaterial structure by Metal-ENZ-Insulator-Metal (MEIM) is designed, and its absorption in the 130-230 THz band is calculated using the simulation software frequency-domain solver. By rotating the upper metal layer and the lower hollow layer, the simulation results can be slightly disturbed. In addition, the introduction of the epsilon-near-zero (ENZ) media will also affect the bandwidth and absorption of the absorber. Therefore, the high absorption and wide bandwidth of the absorber are achieved by the size of the structure, the rotation angle of the corresponding structure,and the clever application of the ITO layer. © 2022 IEEE.

关键词： Metamaterials

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A miniaturized double-sided UWB-MIMO antenna with four-notched band

A miniaturized double-sided UWB-MIMO antenna with four-notch...

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

作者： Qitao Chen Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University HeFei China

A $\boldsymbol{40}\times\boldsymbol{40}\times \boldsymbol{1.6}\mathbf{mm}^{3}$ ultra-wideband MIMO antenna is proposed in this paper. The antenna realizes four-notched band characteristics by embedding different types of slits in the radiating element. The antenna has good UWB performance, with four-band rejection to avoid interference caused by four narrow frequency bands WiMAX(3.3-3.8GHz), WLAN(S.1-S.8GHz), INSAT(6.7-7.3GHz) and ITV 8GHz (8.13-8.38GHz). It achieves better than - 15dB of isolation, and impedance bandwidth from 3.06-15.5GHz.

关键词： Antenna measurements Bandwidth Interference Size measurement Ultra wideband antennas Impedance Antennas

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Design of Ultra-Wideband Terahertz Wavelength Division Multiplexer Based on Inverse Design Algorithm

Design of Ultra-Wideband Terahertz Wavelength Division Multi...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Xiao, Zhicheng Song, Kaihong Fang, Ming The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230039 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China State Key Laboratory of Millimeter Waves Southeast University Nanjing214135 China

ISBN: (纸本)9781665452366

Integrated photonic gadgets play a key function in lots of fields. The process of designing silicon optical devices mostly relies on experience to manually adjust the subtle parameters, which is laborious and ineffective. The proposed inverse design algorithms can simplify the design process of silicon optical devices, improve the design efficiency, and realize multifunctional compact devices. In this paper, two inverse design based methods are proposed to design terahertz band wavelength division multiplexer(WDM). One is based on topology optimization to design a 4-channel ultra-wideband WDM and the other employs a direct binary search technique to create a 3-channel ultra-wideband terahertz WDM, both of which have high transmission, low interference and high integration. Multifunctional photonics devices are predicted to be increasingly used in modern photonic integrated circuits due to these advantages. © 2022 IEEE.

关键词： Optical devices

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A Wideband Circularly Polarized Folded Transmitarray Antenna With Linearly Polarized Feed 7

A Wideband Circularly Polarized Folded Transmitarray Antenna...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Sun, Xiangdong Guo, Xulong Li, Yin Zhang, Yongliang Li, Yingsong Inner Mongolia University School of Electronic Information Engineering Hohhot010021 China Inner Mongolia University Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Coordination Center of China Hohhot010020 China Peng Cheng Laboratory Shenzhen518000 China Inner Mongolia University School of Transportation Institute Hohhot010021 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350384437

A low-profile, high-gain, wideband circularly polarized folded transmitarray antenna (CPFTA) is proposed, and a polarization-selective linear-to-circular polarization (LP-CP) conversion element is introduced. The CPFTA consists of a linearly polarized (LP) magnetoelectric dipole antenna and a circularly polarized (CP) helical patch antenna. The CPFTA demonstrates excellent performance with a 3dB gain bandwidth of 29.5% and a 3dB axial ratio (AR) bandwidth of 40.9%. © 2024 IEEE.

关键词： Circular polarization

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A Communication Protocol Identification Method Based on Time-Frequency Domain Combination for Internet of Things

A Communication Protocol Identification Method Based on Time...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhiwei Zheng Hui Zhi Kui Tie Xianpeng Wang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Zhongdian Kesiyi Technology (Anhui) Co. Ltd Bengbu China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

At present, Internet of Things (IoT) testing instruments and meters are unable to identify the communication protocol of communication data when all characteristic parameters of communication data are unknown. Regarding this problem, this paper utilizes the K-Nearest Neighbors (KNN) model in machine learning, designs communication protocol identification methods and proposes a communication protocol identification method based on time-frequency domain combination (CPI- TFC). First, we generate time-domain waveforms and frequency-domain waveforms from sampling communication data sequences, and attempt only time-domain identification method and only frequency-domain identification method based on KNN model. Then, we compare the results, design a communication protocol identification method based on the combination of time-domain identification and frequency domain identification, and propose CPI-TFC. Experimental results show that CPI-TFC can achieve higher identification accuracy than only time-domain identification method and only frequency-domain identification method, so it can effectively identify the communication protocol for a IoT communication data. Moreover, CPI- TFC can provide prior information for subsequent configuring parameters in IoT testing instruments, improve the parameter configuration efficiency.

关键词： Meters Time-frequency analysis Protocols Accuracy Simulation Machine learning Nearest neighbor methods Internet of Things Time-domain analysis Testing

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Sparsity-aware Subband Adaptive Filtering in Impulsive Interferences

Sparsity-aware Subband Adaptive Filtering in Impulsive Inter...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhikai Gong Yingsong Li Baohe Pang Xueyan Li Wudang Xiao Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In this paper, we propose two new subband adaptive filtering (SAF) algorithms based on the cost function of the logistic distance metric cost function and use the proportionate proximal gradient algorithm to exploit the sparsity of the system fully. Compared with prior works, the proposed algorithms can handle many different situations better. Through computer simulations, we prove that the algorithm performs excellently in sparse systems disturbed by impulse noises. The algorithm can be used in areas like system identifications.

关键词： Measurement Adaptive systems Computer simulation Noise Filtering algorithms Cost function Information filters

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SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks

SES: Bridging the Gap Between Explainability and Prediction ...

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International Conference on Data Engineering

作者： Zhenhua Huang Kunhao Li Shaojie Wang Zhaohong Jia Wentao Zhu Sharad Mehrotra Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Amazon Research Seattle USA University of California Irvine Irvine USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Despite the Graph Neural Networks' (GNNs) pro-ficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the ex-planations to compute a triplet loss and enhance the node representations by contrastive learning. Extensive experiments demonstrate the superiority of SES on multiple datasets and tasks. SES outperforms baselines on real-world node classification datasets by notable margins of up to 2.59% and achieves state-of-the-art (SOTA) performance in explanation tasks on synthetic datasets with improvements of up to 3.0%. Moreover, SES delivers more coherent explanations on real-world datasets, has a fourfold increase in Fidelity+ score for explanation quality, and demonstrates faster training and expla-nation generating times. To our knowledge, SES is a pioneering GNN to achieve SOTA performance on both explanation and prediction tasks.

关键词： Training Bridges Accuracy Reliability engineering Data engineering Graph neural networks Generators

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model

Structure-constrain Source Free Domain Adaptation with Regul...

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Data Driven Control and Learning Systems (DDCLS)

作者： Dan Yang Xin Peng Cheng Su Dayu Tan Chaoyang Chen Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness.

关键词： Fault diagnosis Learning systems Adaptation models Data privacy Transfer learning Prototypes Predictive models

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Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

arXiv

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arXiv 2023年

作者： Yang, Shangshang Ma, Haiping Zhen, Cheng Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230039 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230601 China The Faculty of Technology Bielefeld Unversity Bielefeld33619 Germany

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students’ proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multiobjective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task’s search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models’ variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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