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Improved Uniform Linear Array Fitting Scheme with Increased Lower Bound on Uniform Degrees of Freedom for DOA Estimation

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TechRxiv 2022年

作者： Shi, Wanlu Li, Yingsong The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

Recently, uniform linear array (ULA) fitting (UF) principle is proposed for sparse array (SA) design using pseudo-polynomial equations. Typically, it is verified that the designed SAs via UF enjoy a lower bound on uniform degrees of freedom (uDOF), which is approximately 0.5N2 with N sensors. Herein, an improved UF (IUF) scheme is proposed to significantly increase the lower bound on uDOF, which is realized by introducing two sub-ULAs, with name of adjoint transfer arrays (ATAs), on both sides of a transfer sub-ULA. The ATAs together with the transfer sub-ULA serve as a new layer to construct an adjoint transfer layer (ATL) that has improved aperture in comparison with traditional transfer layer to improve the lower bound on uDOF, which is derived in detail. Furthermore, two novel SAs are developed based on the ATL to verify the effectiveness of the proposed IUF scheme to increase the uDOF. Numerical simulations verify the superiority of devised SAs for direction-of-arrival (DOA) estimations. © 2022, CC BY.

关键词： Polynomials

Compact Broadband Rectifier With Wide Input Power Range Based on Adaptive Input Power Distribution

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TechRxiv 2022年

作者： Liu, Xin Li, Yingsong Zhang, Dawei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

This paper presents a novel compact rectifier array with both broadband and wide input power range characteristics. The rectifier array consists of two rectifier units operating at low-power and high-power levels, respectively, and an adaptive input power distribution network (AIPDN) enabling automatic distribution of radio frequency (RF) energy at different power levels. In addition, a matching inductance is introduced to counteract the capacitance of the diode. As a result, the high RF-DC power conversion efficiency (PCE) is achieved in both broadband and wide input power range. For demonstration, a prototype of the presented rectifier array is fabricated and measured. The measured results are in good accordance with the simulated results. With an input power range of 8 – 25 dBm and a bandwidth of 1.82 – 2.75 GHz (fractional bandwidth of 40.7%), the PCE is greater than 50%. At 2.4 GHz, the input power ranges from -5.5 to 27.5 dBm with PCE greater than 50%. Besides, a peak PCE of 72.1% is obtained at an input power of 25 dBm. This work can lay the foundation for the development of a new RF energy harvest systems with wide frequency band and dynamic input power. © 2022, CC BY.

关键词： Horn antennas

Transient Convergence Analysis of Least Lncosh Algorithm

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TechRxiv 2022年

作者： Sun, Zeyang Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

Lncosh function has been used for constructing a cost function for devising an adaptive filter algorithm to provide a desired performance under non-Gaussian noise, naming as least Lncosh algorithm (LLA). However, its transient behavior analysis has not been analyzed and investigated. As an important characteristic for the LLA, the transient behavior affects its performance in real-world applications. Herein, we present the theoretical model for evaluating the mean weight behavior and the transient excess mean square error (EMSE) behavior of the LLA. In addition, the proposed models can also be used to predict the transient behavior of the adaptive filtering algorithms with error nonlinearities under non-Gaussian noises. The experiment results have verified the validity of the presented models. © 2022, CC BY.

关键词： Adaptive filters

Design of 0.8-3.7 GHz Broadband asymmetric multi-node directional coupler

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Design of 0.8-3.7 GHz Broadband asymmetric multi-node direct...

International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Ruikang Wang Bo Wu Meng Kong Jingyang Wang Yue Ma Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal University Hefei China

ISBN: (纸本)9781665482271

In this paper, a 15 dB asymmetric broadband directional coupler for 4G and 5G communication is designed. The physical size of the coupler is $13\ \text{mm}\times 60\ \text{mm}$ . The dielectric constant is 4.4 and the thickness of single layer plate is 2 mm. The couplers operate in the 0.8-3.7 GHz band. The coupler has the advantages of bandwidth width, convenient processing and installation. ANSYS HFSS 2019 simulation results shows that in the 0.8-3.7 GHz band, the return loss (S 11 ) is more than 16.4 dB, the insertion loss (S 21 ) is from 0.14 dB to 1.18 dB, the coupling degree (S 31 ) is about 15 dB, and the isolation degree (S 41 ) is more than 29 dB. The method of multi-section coupling line cascade is adopted, to achieve the requirements of bandwidth width. The directivity of the coupler is increased by loading the branches to meet the design requirements of the 15 dB coupler.

关键词： Couplings Microwave technology Power transmission lines 5G mobile communication Simulation Millimeter wave technology Broadband communication

DMNER: Biomedical Named Entity Recognition by Detection and Matching

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DMNER: Biomedical Named Entity Recognition by Detection and ...

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation

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A Joint Learning Sentiment Analysis Method Incorporating Emo...

IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Jie Chen Luping Luo Bojing Ji Shu Zhao Yanping Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University China School of Big Data and Artificial Intelligence Chizhou University China

ISBN: (纸本)9781665456579

Social media is the platform for most people to share their opinions, emojis are also widely used to express moods, emotions, and feelings on social media. There have been many researched on emojis and sentiment analysis. However, existing methods mainly face two limitations. First, since deep learning relies on large amounts of labeled data, the training samples of emoji are not enough to achieve the training effect. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this paper, we propose a joint learning sentiment analysis method incorporating emoji-augmentation, and the method has two advantages compared with the existing work. First, We optimize the easy data augmentation method so that the newly generated sentences can also preserve the semantic information of emojis, which relieves the problem of insufficient training data with emojis. Second, it fuses emojis and text features to allow the model to better learn the mutual emotional semantics between text and emojis, jointly training emojis and words to obtain the sentence representations containing more semantic information of both emojis and text. Our experimental results show that the proposed method can significantly improve the performance compared with several baselines on two datasets.

关键词： Training Sentiment analysis Social networking (online) Fuses Text recognition Mood Semantics

Nonnegative Maximum Correntropy Adaptive Algorithm and Its Statistical Analysis

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TechRxiv 2022年

Dynamic system modeling methods have become a hot topic for stationary and nonstationary signal processing. Nonnegativity is a desired constraint that usually exerts on to be estimated parameters, and its generation usually based on the inherent physical characteristics of unknown system. Moreover, non-Gaussian noise is present in many practical system identification situations. In this paper, an adaptive nonnegative maximum correntropy criterion (NNMCC) algorithm is proposed for system identification under non-negativity constraints. We derive the NNMCC algorithm based on the Karush-Kuhn-Tucker conditions and a fixed-point iteration scheme. The first-order and second-order moments of the NNMCC algorithm adaptive weights are theoretically analyzed. Experimental results validate the theoretical analysis and illustrate the superior performance of NNMCC in non-Gaussian noise environments. © 2022, CC BY.

关键词： Adaptive filtering

Identifying Disease Modules Based on Connectivity and Semantic Similarities 14th

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Identifying Disease Modules Based on Connectivity and Semant...

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

作者： Su, Yansen Zhu, Huole Zhang, Lei Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789811534140

The identification of disease modules has attracted increasing attention due to the importance in comprehending pathogenesis of complex diseases. Most of the existing methods were based on the protein-protein interaction (PPI) networks with the incompleteness and incorrectness of the interactome, which results in many disease-related proteins and pathways not within their disease modules. In this paper, we propose a method named IDMCSS to effectively identify disease modules by adding some potential interactions and removing some incorrect interactions in the existing human PPI network. Firstly, the connective similarity and semantic similarity are calculated between the known disease proteins and each of their neighbors. Then, the network is adjusted by adding the interactions with large connective and semantic similarity, and removing those with small connective and semantic similarity. Further, the proteins are sorted in the descending order according to the similarity to the known disease proteins, and the sorted proteins are added into a candidate node set one by one until a certain biological information is not enriched in the candidate node set. Finally, the connected subnetwork with the largest number of nodes in the candidate node set is selected as the disease module. The disease modules identified by IDMCSS involve crucial biological processes of related diseases and can predict 12 targets for drug intervention. The experimental results on asthma demonstrate the effectiveness of the proposed method in comparison to existing algorithms for disease module identification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Proteins

Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data