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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yue Zhang Yuefei Liu Guohui Yang Kuang Zhang Yingsong Li School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

A control circuit for active metasurface based on varactor diode is designed in this paper. By varying the reverse bias voltage on the varactor diode, the designed circuit can regulate the additional phase of the active meta-atoms. The circuit adopts a form of one master and multiple slaves, which can simultaneously control the bias voltage of 960 meta-atoms. Each slave can control up to 64 channels. The control accuracy is 0.04V. The power consumption of a single slave is 160mW. Though experiment verification, the circuit has a good effect in controlling the beam deflection of the metasurface and generating OAM beams.

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Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under Non-Gaussian Noises 5

Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under...

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

作者： Hadi, Abdul Huang, Xinqi Ali, Burhan Li, Yingsong College of Information and Communications Engineering Harbin Engineering University Harbin150001 China College of Intelligent System and Control Science Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Two sparse adaptive filtering (AF) algorithms based on Andrew's sine estimator (ASE) are presented to achieve improved performance for identifying sparse systems, where the ASE is derived within the least-square framework. Furthermore, zero-attracting (ZA) scheme is used in ASE to construct ZA-ASE and its re-weighting form (RZA-ASE) to combat non-Gaussian noises and use the sparse characteristics of the system. Their performance is investigated via simulations and compared with the least-mean square (LMS) and the maximum correntropy criterion (MCC) algorithms to show their superior performance. © 2022 IEEE.

关键词： Adaptive filtering

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A Study on the Recognition of Single-Word Tones of Yunnan Zhaotong Dialect Based on Speech signal Analysis

A Study on the Recognition of Single-Word Tones of Yunnan Zh...

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Language Technology and Digital Humanities (LTDH), International Conference on

作者： Hanyun Qi Shiliang Lyu Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Key Laboratory of Minzu Language and Cultures Intelligent Information Processing Gansu Province Northwest Minzu University

ISBN: (数字)9798350380187

ISBN: (纸本)9798350380194

Zhaotong City is located in the northeast of Yunnan Province and used to be called “Zhuti County”. Due to the influence of population migration, the Zhaotong dialect has the characteristics of the northern dialect as well as its own unique features. Early research on the tones of Zhaotong dialect relied on traditional language survey methods. In this paper, we use modern methods of speech signal analysis to reanalyze the tones of Zhaotong dialect by means of spectral analysis of sound waveforms, resonance peak analysis, fundamental frequency extraction, etc. combined with the knowledge of experimental phonetics. It is concluded that the Zhaotong dialect consists of four tone categories, namely, Tone1, Tone2, Tone3, and Tone4. Among the single character tones, Tonel is characterized by a flat tonal pattern; Tone2 is characterized by a descending tonal pattern; Tone3 is characterized by a descending tonal pattern; and Tone4 is characterized by a zigzag tonal pattern.

关键词： Surveys Hands Fluctuations Urban areas Speech recognition Turning Registers Digital preservation Speech processing Spectral analysis

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Acoustic Analysis of Long and Short Vowels of Eastern Yugur Language for Low Resource Speech Synthesis

Acoustic Analysis of Long and Short Vowels of Eastern Yugur ...

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Language Technology and Digital Humanities (LTDH), International Conference on

作者： Shiliang Lyu Hanyun Qi Yuwen Min Qianqian Ma Key Laboratory of Minzu Language and Cultures Intelligent Information Processing Northwest Minzu University Gansu Province Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University

ISBN: (数字)9798350380187

ISBN: (纸本)9798350380194

The articulation of long and short vowels is not only a change in duration, but also includes changes in the shape of the oral cavity to comprehensively affect the timbre. Since the low-resource phonetic data is very limited, especially the pronunciation of long and short vowels will be affected by the surrounding phonemes or syllables, which makes it difficult for the speech synthesis model to adequately learn and capture the differences of vowels, thus affecting the accuracy and naturalness of the synthesis of long and short vowels. The eastern Yugur language in China belongs to the Mongolian group of the Altaic language family, with an endangered language and limited phonetic resource data. If this endangered language is extinct, it can be restored through speech synthesis. In this article, we analyze the acoustics of long and short vowels in the Eastern Yugur language, and find out the differences in the distribution of acoustic parameters, as well as the differences in the parameters of males and females, so as to provide a reference for the synthesis of the Eastern Yugur language.

关键词： Time-frequency analysis Shape Resonant frequency Training data Phonetics Rhythm Resonance Acoustics Speech synthesis Timbre

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A CLASSIFICATION OF TWO-DISTANCE-TRANSITIVE CAYLEY GRAPHS OVER THE GENERALIZED QUATERNION GROUPS

arXiv

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arXiv 2025年

作者： Jin, Wei Li, Ping Shan Tan, Li School of Mathematics and Computational Science Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China School of Statistics and Data Science Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China School of Mathematics and Statistics Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

A non-complete graph is 2-distance-transitive if, for i = 1, 2 and for any two vertex pairs (u1, v1) and (u2, v2) with the same distance i in the graph, there exists an element of the graph automorphism group that maps (u1, v1) to (u2, v2). This is a generalization concept of the classical well-known distance-transitive graphs. In this paper, we completely determine the family of 2-distance-transitive Cayley graphs over the generalized quaternion groups. Copyright © 2025, The Authors. All rights reserved.

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Performer: A High-Performance Global-Local Model-Augmented with Dual Network Interaction Mechanism

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Tan, Dayu Hao, Rui Hua, Linfeng Xu, Qi Su, Yansen Zheng, Chunhou Zhong, Weimin Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

In deep learning, Convolutional Neural Networks (CNNs) focus on local information through convolutional kernels, while transformers attend to global information using self-attention mechanisms. The union of these distinct approaches enables a more comprehensive extraction of image features. However, the feature map dimensions of CNN and Transformer differ, leading to dimension mismatch issues when combining these architectures. Additionally, the parameter size of the hybrid model integrating both architectures remains large, making it difficult to train. To further augmenting the interpretation of complex image patterns, we present Performer, a dual-network architecture that seamlessly combines CNNs and transformers, resulting in a novel and efficient representation learning model. In the Performer model, we innovate by devising a unique interaction methodology for CNN and transformer architectures to enhance the image feature extraction capabilities mutually. To counteract issue of dimensionality mismatch, we also introduce a refined transformer block, a advancement over the transformer block of ViT. To validate the effectiveness of Performer, we conduct extensive experiments on both classification and segmentation tasks. Performer achieve an accuracy of 83.37% on the ImageNet-200 dataset. For semantic segmentation, Performer excels on the CamVid and Hippocampus datasets. On CamVid, our model achieves a mean Intersection over Union (mIoU) of 63.27% and Pixel Accuracy of 92.11%, demonstrating superior performance in capturing fine details and handling complex scenes effectively. The code is available at https://***/hlfthh/ Performer. © 2016 IEEE.

关键词： Semantic Segmentation

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No-Reference Image Quality Assessment Based on Active Reasoning Module 6

No-Reference Image Quality Assessment Based on Active Reason...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Qi, Junwei Deng, Yuhao Wang, Qingchun Yang, Zhen Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

We present a no-reference image-quality - assessment algorithm based on active reasoning module. This algorithm has three modules: the feature extraction module, the active reasoning module, and the quality assessment module. The active reasoning module incorporates the generator component of the generative adversarial network and enhances its structure with the Res2Net architecture. By integrating this module into the backbone feature extraction network, we improve the receptive field of each convolutional layer, enabling the network to capture information at different scales of the image. To preserve the texture information of the image, we input the gradient map of the distorted image, the distorted image itself, and the image features generated by the generator into the quality assessment module, which has a multi-feature regression networks. This module establishes a mapping model from image-feature to image-quality scores. We conducted experimental analysis of this algorithm on three widely used public datasets, confirming the excellent performance and superiorities of the presented algorithm. The results validate the effectiveness of the designed algorithm and its ability to assess image quality accurately. © 2023 IEEE.

关键词： Image quality

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TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering 38

TFGDA: Exploring Topology and Feature Alignment in Semi-supe...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Dan, Jun Liu, Weiming Xie, Chunfeng Yu, Hua Dong, Shunjie Tan, Yanchao Zhejiang University China Queen Mary University of London United Kingdom Dalian University of Technology China Shanghai Jiao Tong University China Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China Fujian Key Laboratory of Network Computing and Intelligent Information Processing China

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Double-Local Conditional Probability Based Fast Calculation Method for Approximation Regions of Probabilistic Rough Sets

SSRN

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SSRN 2023年

作者： Xu, Yi Zhou, Meng Sun, Weikang School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

As an extended rough set model, the probabilistic rough sets can effectively process data with noise. How to effectively calculate three approximation regions, is a crucial issue of probability rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O(|U||U||C|). In order to improve the computing efficiency of three approximation regions of probabilistic rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of probabilistic rough sets is presented, and the time complexity is O(MAX(X||U||C|, |XC||U||C|)). Experimental results based on 12 datasets from UCI demonstrate the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Equivalence classes

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Blockchain-Assisted Revocable Cross-Domain Authentication for Vehicular Ad-Hoc Networks

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Ru Cui, Jie Zhang, Jing Wei, Lu Zhong, Hong He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Wuhan University School of Cyber Science and Engineering 430072 China

With the rapid development of vehicular ad-hoc networks (VANETs) and the increasing diversification of user demands, interactions between different management domains have become more frequent. Identity authentication is an effective way to establish cross-domain trust and secure communication. However, the existing cross-domain authentication schemes of VANETs are limited to the same management or authentication technology for each domain and rely on centralized cross-domain identity management. Even distributed management solutions encounter latency sensitivity, security and privacy challenges. To address these challenges, we propose a blockchain-assisted revocable cross-domain authentication scheme for VANETs. The proposed scheme can establish trust between domain entities by deploying different authentication methods and using distributed management to avoid single-point failures. In addition, the scheme can revoke the identity of malicious vehicles by updating the group public key, thereby ensuring the security and privacy of cross-domain Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. This design avoids the additional impacts of blockchain technology constraints on the high mobility and real-time requirements of VANETs. Security analysis and performance evaluation show that our scheme can resist more attacks and has better security than other related schemes while also achieving a better balance between communication and computational cost. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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