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LoBee: Bidirectional Communication between LoRa and ZigBee based on Physical-Layer CTC

IEEE Transactions on Wireless Communications 2025年

作者： Gao, Demin Wang, Haoyu Chen, YongRui Ye, Qiaolin Wang, Weizheng Guo, Xiuzhen Wang, Shuai Liu, Yunhuai He, Tian Nanjing Forestry University College of Information Science and Technology Artificial Intelligence Nanjing210037 China University of Minnesota The Department of Computer Science and Engineering 55414 United States University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China Southeast University The School of Computer Science and Engineering Nanjing211189 China City University of Hong Kong Department of Computer Science 999077 Hong Kong College of Control Science and Engineering Zhejiang 310027 China Peking University The Department of Computer Science and Engineering Beijing100871 China University of Minnesota The Department of Computer Science and Engineering Minneapolis55414 United States

LoRa networks operating in a star topology, this configuration creates a single point of failure and may limit scalability and reliability in areas that are large and geographically dispersed. In order to improve the overall transmission capabilities of the network, recent studies show that adding LoRa to the ZigBee devices effectively disseminates network management. By doing so, the strengths of both technologies can be leveraged, with LoRa serving as the long-range transmitter and ZigBee functioning as the mesh network. In this study, we present LoBee, a novel bidirectional communication method between LoRa and ZigBee that relies on Physical-Layer Cross-Technology Communication. Despite the fact that LoRa and ZigBee utilize different modulation techniques, ZigBee devices can detect and recognize LoRa chirps through the process of sampling the received signal strength. For the transmissions from ZigBee to LoRa devices, we carefully select the input chips to generate specific waveforms, where LoBee detects the preamble of a ZigBee frame based on the locations of the repeated peaks. Our evaluation, which was conducted using USRP and commodity devices, demonstrates that LoBee is capable of achieving concurrent bidirectional wireless communications, with a data rate of approximately 639.38 bits per second from LoRa to ZigBee and from ZigBee to LoRa with more than 90% frame reception rate in the 2.4 GHz frequency band. © 2002-2012 IEEE.

关键词： Zigbee

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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Hierarchical Retrieval of High-Resolution Fingerprints Based on Pore Feature

Hierarchical Retrieval of High-Resolution Fingerprints Based...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Ma Yuanrong Xu Suyu Dong Wei Wang School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Weihai China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system’s effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time.

关键词： Accuracy Image recognition Attention mechanisms Image matching Fingerprint recognition Feature extraction Robustness Computational efficiency Bioinformatics

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Deep Adaptively Feature Extracting Network for Cervical Squamous Lesion Cell Detection 4th

Deep Adaptively Feature Extracting Network for Cervical Squ...

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

作者： Peng, Zhonghua Hu, Rong Wang, Fuen Fan, Haoyi Eng, Yee Wei Li, Zuoyong Zhou, Liwei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China Department of Nutrition The First Affiliated Hospital of Zhengzhou University Henan Zhengzhou450052 China

ISBN: (纸本)9783031200984

Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where cells are previously stacked in clusters. To address this problem, we utilize the YOLOv5 as the baseline and build on YOLOv5 by using the simple Transformer Block which only combines with multi-head self attention layers and MLP to better extract cell features as well as to obtain global information. In addition, we allow the model to refine features adaptively to assist with detection by using Convolutional Block Attention Module (CBAM), an attention module being simple and effective for feed-forward convolutional neural networks, in the complex background information. Finally, we compare the model with YOLOv5 as baseline. In CDetector dataset, our model obtains 52.5% mAP@.5, which is 6% better than baseline. In transfer learning, it is 62.2%, which outperforms baseline by 3.2%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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Optimized Algebraic Decoding Algorithm for (47, 24, 11) QR Code

Optimized Algebraic Decoding Algorithm for (47, 24, 11) QR C...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Chunlan Luo Xiaoxia Zhu College of Computer and Control Engineering Minjiang University Fuzhou Fujian China

A fast decoding algorithm scheme is proposed for the quadratic residue code with code length of 47 and large error-correcting capacity of 5 errors in this paper, called optimized algebraic decoding algorithm (OADA). The main contributions of this article are to deduce a method to quickly compute the related unknown syndrome of (47,24,11) QR code and a simpler discriminant condition to detect whether four errors occurred in the received word. The decoding algorithm scheme needs lower memory requirement compared with the algorithm based on the full hard decision algebraic decoding algorithm. For exhaustive simulation test of all the error patterns shows that the proposed algorithm of (47, 24, 11) QR code improves the decoding performance and decoding efficiency.

关键词： Analytical models Codes Simulation Computational modeling Memory management Signal processing algorithms QR codes

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UniQuad: A Unified and Versatile Quadrotor Platform Series for UAV Research and Application

arXiv

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arXiv 2024年

作者： Zhang, Yichen Chen, Xinyi Liu, Peize Wang, Junzhe Zou, Hetai Pan, Neng Gao, Fei Shen, Shaojie The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

As quadrotors take on an increasingly diverse range of roles, researchers often need to develop new hardware platforms tailored for specific tasks, introducing significant engineering overhead. In this article, we introduce the UniQuad series, a unified and versatile quadrotor platform series that offers high flexibility to adapt to a wide range of common tasks, excellent customizability for advanced demands, and easy maintenance in case of crashes. This project is fully open-source at https://***/UniQuad. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Detection of False Data Injection Attacks Based on Spatio-Temporal Characterization in Smart Grids

Detection of False Data Injection Attacks Based on Spatio-Te...

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Chinese control and Decision Conference, CCDC

作者： Shouxuan Jiao Qiang Zhao Yinghua Han Jinkuan Wang Hantong Feng College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

With the increasing popularization of information and communication technologies, the development of smart grids has also emerged. However, the high reliance on information technology puts the grid at risk of malicious attacks. False Data Injection Attack (FDIA)have become a serious threat to the smart grid. While most of the current methods for detecting FDIA focus on determining whether the grid has been subjected to such attacks, they often overlook the importance of considering the topology and temporal correlations within the grid data and are unable to localize their attacks. To address this limitation, we have proposed a novel multi-label attack localization detection method based on spatio-temporal features. Our method considers both time and space and enables the localisation of attacks. Spatial feature extraction is conducted using a Convolutional Neural Network (CNN), which is subsequently subjected to feature filtering through an Attention Mechanism. The filtered features are then channeled into both Long-Term and Short-Term Memory network (LSTM) via a channel fusion process. This intricate process allows for the extraction of spatio-temporal features, ultimately facilitating the accurate localization of attacks. To evaluate the efficacy of our approach, we conducted extensive simulation experiments using the IEEE 14bus system and the IEEE 118-bus system. The results showcase remarkable performance with an overall accuracy of 99.6% for the former and 98.7% for the latter, underscoring the effectiveness of our proposed model.

关键词： Location awareness Correlation Attention mechanisms Accuracy Feature extraction Smart grids Topology

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Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： Optimization algorithms

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Model Free Adaptive Consensus control for Multiagent Systems with Actuator Faults

Model Free Adaptive Consensus Control for Multiagent Systems...

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Data Driven control and Learning Systems (DDCLS)

作者： Yuan Wang Zhenbin Du Yanming Wu School of Computer and Control Engineering Yantai University Yantai China College of Automation Shenyang Aerospace University Shenyang China

The model free consensus control issue of multiagent systems under actuator faults is investigated in this paper. In order to address the actuator faults, an adaptive estimation law is developed adaptively to estimate the fault information online on the basis of the neighboring output tracking error. Then, a distributed model free adaptive fault-tolerant control approach is constructed to realize consensus tracking. Compared with previous methods, the dependence on constructing RBFNN to handle faults is avoided. Moreover, only input/output data information are employed instead of the known system model. To prove the effectivenss of established scheme, a simulation is offered at the end.

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Robot path guidance algorithm based on compound collaborative filtering 6

Robot path guidance algorithm based on compound collaborativ...

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6th IEEE International Conference on Intelligent Computing and Signal Processing, ICSP 2021

作者： Liu, WenLin Cheng, JingLong Lu, XingHua Pan, YiFu Gao, YuNing Huali College Guangdong University of Technology Computer and Information Engineering Guangzhou China Institute of Computer Control Technology Huali College Guangdong University of Technology Guangzhou China

ISBN: (纸本)9780738143705

In order to improve the stability of control and path guidance of rigid-soft hybrid snake-like robot, a crawling path guidance method of rigid-soft hybrid snake-like robot based on composite collaborative filtering is proposed. The physical spatial grid distribution model of rigid-soft hybrid snake-like robot crawling is constructed, the SLAM guidance method is adopted to carry out grid regionalization matching in the obstacle avoidance process of rigid-soft hybrid snake-like robot, the optimization constraint parameters of obstacle avoidance path distribution of rigid-soft hybrid snake-like robot are established, and the path distribution characteristic variables in the obstacle avoidance process of rigid-soft hybrid snake-like robot are analyzed based on the kinematic characteristic analysis method. Parameter optimization design of rigid-soft hybrid snake-like robot crawling path guidance is carried out by local spatial feature distribution detection and recognition method, and optimization control is carried out by composite collaborative filtering algorithm. Adaptive inertial parameter optimization and accurate location are carried out by multi-sensor parameter positioning method. The simulation results show that this method has better adaptive optimization ability and higher robot positioning accuracy, which improves the obstacle avoidance and guidance ability of the rigid-soft hybrid snake robot. © 2021 IEEE.

关键词： Collaborative filtering

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