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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Li, Wei He, Haobo Li, Guang Kuang, Lidan Zhang, Jin Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410076 China Zhejiang University School of Control Science and Engineering Hangzhou310027 China

ISBN: (纸本)9798350383706

Biomimetic olfaction models have been applied to various fields such as image recognition, text classification, electroencephalography (EEG) recognition, and spatio-temporal sequence prediction. However, the challenge of obtaining olfactory data complicates the construction of olfactory datasets, ultimately resulting in a scarcity of applications for biomimetic olfaction models in olfactory tasks. In this work, we begin by conducting a comprehensive comparison of existing biomimetic olfactory models from various aspects such as structure, degree of biomimicry, and performance. Following this, prefrontal lobe olfactory data from ten subjects were collected for three different odors using functional Near-Infrared Spectroscopy (fNIRS). Finally, the data underwent denoising through the use of various data processing methods, and the quantity of the olfactory dataset was expanded. The olfactory dataset constructed in this study can be used to evaluate the performance of biomimetic olfactory models in processing olfactory tasks, which may aid in advancing the development of these models in real-world olfactory applications. © 2023 IEEE.

关键词： Image recognition

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EEG-based comparative measurement of visual fatigue caused by stereoscopic depth perception

EEG-based comparative measurement of visual fatigue caused b...

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The 2023 International Conference on Display Technology, (ICDT 2023)

作者： Jia, Lixiu Huang, Sheng Ding, Yiquan Feng, Lihang Huang, Xiaohua School of Computer Engineering Nanjing Institute of Technology Jiangsu Nanjing China School of Electrical Engineering and Control Science Nanjing Tech University Jiangsu Nanjing China

In this study, we investigated the influence of stereoscopic depth on visual fatigue based on subjective and EEG measurement. 19 channels of EEG data of 17 subjects before and after watching 3D movie with different depth ranges were recorded, and the power of δ, θ, α, β, power spectral parameters (gravity frequency, power spectral entropy) were calculated and analyzed. The results showed that visual fatigue caused by 3D enhanced movies with larger negative disparity was more significantly severe than that of 3D normal movies. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Stereo image processing

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Research on Optimization Method of Visual Odometer Based on Point Line Feature Fusion 23

Research on Optimization Method of Visual Odometer Based on ...

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7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Zhang, Guoyin Yuan, Quande Liu, Yifan School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China School of Computer Technology and Engineering Changchun Institute of Technology Jilin Changchun China

ISBN: (纸本)9781450399883

In low-texture-scenes, point feature based simultaneous location and map (SLAM) building algorithms are difficult to extract enough valid feature points, so that the SLAM system cannot work properly. To solve such problems, this paper proposes a visual SLAM algorithm for fusion of point and line features. On the basis of ORB point feature extraction, the LSD line feature algorithm is introduced and the extracted line segments are represented by the Plücker coordinate method. In the online feature matching stage, the LBD line segment descriptor is introduced, and the quadtree algorithm is used to avoid the concentration problem of point feature extraction. The progressive sampling uniformity (PROSAC) algorithm and geometric constraints are used to filter out incorrect matching of point line features, thereby improving positioning accuracy. Then, in the process of feature fusion, the idea of point line feature weighting is introduced to construct a new error function for point line fusion, minimizing the error function to obtain more accurate positioning information. Finally, the publicly available dataset was used to conduct experiments. © 2023 ACM.

关键词： Feature extraction

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Research of Extended Dimension Embedded CKF-SLAM Base on Truncated Singular Value Decomposition 8

Research of Extended Dimension Embedded CKF-SLAM Base on Tru...

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8th International Conference on control, Robotics and Cybernetics, CRC 2023

作者： Zheng, Shibo He, Kai Meng, Shuo Liu, Fangchencan Guo, Qiang Shenyang Jianzhu University School of Electrical and Control Engineering Shenyang City China Shenyang Jianzhu University School of Computer Science and Engineering Shenyang City China

ISBN: (纸本)9798350330588

In response to the problem of the filtering error of the CKF algorithm increasing linearly with the dimensionality of the state space, resulting in difficulty in propagating multiplicative noise, and the instability of the system caused by outliers in the system state driven model and observation data, this paper proposes an Extended Dimensional Embedded Cubature Kalman Filter algorithm based on the Truncated Singular Value Decomposition (TSVD-AECKF). Firstly, Singular Value Decomposition (SVD) is used instead of Cholesky Decomposition in the CKF algorithm to suppress the non-positivity of the system state covariance matrix;Considering the impact of small singular values on stability, this paper adopts the truncation method and provides a method for determining the truncation threshold;Secondly, the system noise is added to the state variable, and the embedded cubature criterion is used to improve the traditional CKF while expanding its dimensions;Finally, through simulation experiments, TSVD-AECKF was compared with other SLAM methods, and the results showed that this method can effectively suppress positioning errors that increase with the dimension of the state space, enhance the filter's ability to resist noise data interference, and thereby improve the robustness and stability of the mapping. © 2024 IEEE.

关键词： Singular value decomposition

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Dynamic Feature Point Elimination Algorithm Based on YOLOv5 and Optical Flow Method 23

Dynamic Feature Point Elimination Algorithm Based on YOLOv5 ...

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7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Liu, Yifan Yuan, Quande Zhang, Guoyin School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China School of Computer Technology and Engineering Changchun Institute of Technology Jilin Changchun China

ISBN: (纸本)9781450399883

In traditional visual simultaneous localization and mapping (vSLAM) systems, feature points are critical for feature matching and pose estimation. However, visual odometry keyframes often contain many dynamic points, which can adversely affect the accuracy of the SLAM system. To address this issue, this paper proposes a dynamic feature point elimination algorithm based on the YOLOv5 target detection network and optical flow method using image processing techniques. Specifically, the YOLOv5 network is used to identify regions in the image that may contain dynamic objects, which are then divided into sub-regions. Depth information from the depth image and feature points from the dynamic regions are used to obtain feature points with depth information. These feature points are then clustered using the GMM clustering algorithm, and dynamic feature points are eliminated based on the clustering results. Experimental results show that the proposed algorithm effectively eliminates dynamic points in the image, thus improving the accuracy of the SLAM system. © 2023 ACM.

关键词： Optical flows

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ASLiquid: Non-Intrusive Liquid Counterfeit Identification with Your Earphones 24

ASLiquid: Non-Intrusive Liquid Counterfeit Identification wi...

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22nd ACM Conference on Embedded Networked Sensor Systems, SenSys 2024

作者： Li, Zhaohui Luo, Wei Zhang, Yongmin Chen, Jianxi Shu, Yuanchao Zhang, Yaoxue School of Computer Science and Engineering Central South University China College of Control Science and Engineering Zhejiang University China Department of Computer Science and Technology BNRist Tsinghua University China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798400706974

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density. © 2024 Copyright is held by the owner/author(s).

关键词： Earphones

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Tensor Canonical Correlation Analysis Networks for Multi-view Remote Sensing Scene Recognition (Extended Abstract) 39

Tensor Canonical Correlation Analysis Networks for Multi-vie...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Yang, Xinghao Liu, Weifeng Liu, Wei College of Control Science and Engineering Qingdao China University of Technology Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350322279

Remote sensing (RS) images are frequently observed from multiviews. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle the multiview RS recognition problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition. © 2023 IEEE.

关键词： Convolution

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ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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Domain Adapted Object Detection with Intermediate Domain Generation 21

Domain Adapted Object Detection with Intermediate Domain Gen...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yuhang, He Yangyang, Sun Zhijun, Xue Shijiao, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798331519254

Autonomous driving detection technology in real-world road scenarios faces numerous challenges, including variable weather conditions and complex road environments. Therefore, developing an object detection model with robust domain adaptation is crucial. In this paper, we investigate the use of image style transfer techniques to leverage target domain images for enhancing model performance. Experimental results show that our proposed approach significantly improves detection efficacy. Notably, our method outperforms the Oracle results in tasks such as transitioning from Cityscapes to Foggy Cityscapes, highlighting its effectiveness in addressing domain adaptation challenges. © 2024 IEEE.

关键词： Image enhancement

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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