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International Conference on Communication technology (ICCT)

作者： Qinwen Ji Chunhua Zhu Xinying Guo Key Laboratory of Grain Information Processing and Control School of Information Science and Engineering Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China

ISBN: (纸本)9781665432078

In mmWave MIMO systems relying on lens antenna arrays, beamspace sparse channel estimation is one of the key technologies to improve system performance. One new beamspace sparse channel estimation scheme with deep residual learning is proposed, which introduces the channel prior information following the Gaussian mixture distribution and the residual processing unit. Firstly, the prior-aided GM-LAMP network is employed to obtain a preliminary estimation; then the deep residual learning network is designed to reduce the impact of channel noise. Compared with existing methods, the proposed algorithm can improve estimation accuracy with the lower computational complexity.

关键词： System performance Conferences Channel estimation Estimation Sparse matrices Computational complexity MIMO communication

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Tunable elastic topological insulators based on bicomponent perforated structures

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Waves in Random and Complex Media 2023年

作者： Cai, Chengxin He, Guangchen Sun, Qing Zhang, Boqiang Qin, Yao Ministry of Education Zhengzhou China Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China College of Information Science and Engineering Henan University of Technology Zhengzhou China Mechanical and Electrical Engineering Henan University of Technology Zhengzhou China

In this paper, a tunable elastic topological insulator based on bicomponent is designed. We rotate the perforated structure of the mixed components in two different ways, local and global, to reduce the structural symmetry of the lattice and obtain the elastic topological insulator. By introducing a mixed-component perforated structure with a certain rotation angle into the primitive cell, the symmetry is incomplete and the quadruple degenerate state is constructed. When locally rotating mixed component perforated structure, the quadruple degenerate state is separated and reversed at (Formula presented.) point, and we can get a structure with topological properties. In addition, without re-constructing the perforated structure parameters of the unit cell, when the mixed component perforated structure is rotated as a whole, we can also realize the elastic topological insulator, and its non-trivial band gap is widened by about three times. Finally, we verify the transport properties of the topological boundary states of the elastic topological insulator by both numerical simulations and experimental tests. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Topological insulators

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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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An Efficient Particle YOLO Detector for Urine Sediment Detection 4th

An Efficient Particle YOLO Detector for Urine Sediment Dete...

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

作者： Chen, Zejian Hu, Rong Chen, Fukun Fan, Haoyi Ching, Fum Yew Li, Zuoyong Su, Shimei 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 School of Electrical Engineering Zhengzhou University Henan Zhengzhou450001 China

ISBN: (纸本)9783031201011

Urine sediment detection is an essential aid in assessing kidney health. Traditional machine learning approaches treat urine sediment particle detection as an image classification task, segmenting particles for detection based on information such as edges or thresholds. However, the segmentation of sediment particles is complex due to the low contrast and weak edge characteristics of urine sediment images. In this paper, we consider urine sediment particle detection as a object detection task and propose the YOLOv5s-CBL, a detector dedicated to particle detection. Specifically, to mitigate the impact of background noise on detection accuracy, we inherit CBAM on the YOLOv5s model to help the network filter useless noise information and find regions of interest to extract target features. Then, we expand the original three-scale feature layer to improve the sensitivity of the model to larger-scale target sediment particles. Finally, we use the BiFPN structure instead of the original PANet combined with the FPN structure, which can more effectively fuse multiple different scales of information to improve the detection performance of the model. We compared state-of-the-art methods on two real-world datasets, and the experimental results demonstrated the effectiveness of YOLOv5s-CBL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Optimization Model for Predicting Stored grain Temperature Using Deep Learning LSTMs

Optimization Model for Predicting Stored Grain Temperature U...

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International Conference on Networking and Network Applications (NaNA)

作者： Koomson Patrick Weidong Yang Erbo Shen College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461320

With reliable and accurate grain temperature forecasting models, granary operators could easily make the right decisions to avoid food spoilage. In this study, an analysis of a single hidden layer long short-term memory model, a multi-layer (stacked) long short-term memory model, and its evaluation is presented to determine how accurate it is for forecasting stored grain's temperature from past data. Using temperature sensors, the data is collected over three years in a warehouse in Yunnan, China. There are two datasets: a training dataset and a test dataset. About 40 percent of the data is set aside as a test dataset, while the remaining 60 percent is used as a training dataset. There are several hyper-parameters included in the analysis. By computing the root of the mean square error (RMSE), we can compare the two models. We also use the mean absolute error assessment tool (MAE) and the correlation between predicted and actual values (R2) to evaluate the prediction. In addition to optimizing the number of hidden layers and neurons in each hidden layer, the two models are improved by comparing the actual and predicted models. The experiments we conducted confirm that a single hidden layer can achieve the same or better results than the multilayer (stacked) LSTM when the hyper-parameters are chosen and tuned appropriately, considering the size of the data and the goal.

关键词： Training Temperature sensors Analytical models Temperature distribution Predictive models Data models Reliability

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Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： Brain computer interface

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RTI-grain: A method for detecting the foreign body of granary based on RSS 6

RTI-Grain: A method for detecting the foreign body of granar...

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6th International Symposium on System and Software Reliability, ISSSR 2020

作者： Zhu, Chunhua Tian, Jiake Shi, Zhen Yang, Jing Key Laboratory of Grain Information Processing Control College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9780738104973

This paper proposes a non-destructive, fast, low-cost, non-contact method for detecting stored grain foreign body based on received signal strength (RSS) information, RTI- grain. The feasibility of stored grain foreign body detection was verified by using RSS data. On this basis, the RTI-grain detection method was designed. Firstly, the RSSI data is subjected to data normalization, noise elimination and averaging preprocessing, and then ellipse weight model was used to compute the attenuation coefficients in each pixel, finally the Tikhonov regularization was adopted to reconstruct the image of the abnormal area. The experimental verification shows that the foreign body of empty hole, metal and water in granary can be detected, and the detection accuracy is 98.1% at maximum. © 2020 IEEE.

关键词： Component Image reconstruction Radio tomography imaging(RTI) Received signal strength information (RSSI) Stored grain

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Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis 4th

Domain Adversarial Interaction Network for Cross-Domain Fau...

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

作者： Lu, Weikai Chen, Jian Zheng, Hao Fan, Haoyi Wei, Eng Yee Cao, Xinrong Zhang, Deyang School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Henan Provincial Institute of Scientific and Technical Information Zhengzhou450003 China

ISBN: (纸本)9783031200984

Intelligent fault diagnosis has been widely used in the industry and plays a crucial role in the health management of machinery. In recent years, unsupervised domain adaptation (UDA) has been applied to fault diagnosis, showing excellent performance under variable working conditions. However, most existing UDA-based methods do not consider the temporal relations in the fault signal, resulting in sub-optimal performance. In this paper, we proposed a domain adversarial interaction network (DAIN) to solve this problem. By downsampling sub-sequences of fault signals and interacting with their features, DAIN can obtain feature representations containing the temporal relations. In addition, domain adversarial learning and maximum mean discrepancy (MMD) are applied to DAIN to align the domain discrepancy and distribution discrepancy of source and target domains. We conducted extensive experiments on the public Paderborn university (PU) dataset, and the results demonstrate that the proposed method can achieve higher cross-domain fault diagnosis accuracy than the existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Failure analysis

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Highly efficient multi-frequency terahertz coding metasurfaces

Highly efficient multi-frequency terahertz coding metasurfac...

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

作者： Yangyang Zhou Yinfei Li Chengxin Cai Bin Li School of Microelectronics South China University of Technology Guangzhou China China Electronic Produce Reliability and Environmental Testing Research Institute Guangzhou China Ministry of Education Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Zhengzhou People’s Republic of China

Coding terahertz metasurfaces devices are crucial in scientific research and engineering applications. However, these devices usually operate at a single frequency point and are sensitive to polarization. To overcome this limitation, we designed a reflective coding metasurface with “0” and “1” cells. The metasurface exhibits highly efficient, multi-frequency, x-polarized and circularly polarized independent characteristics. The proposed structure achieves over 97% reflection with 180° of phase difference at the frequencies of 0.7 THz and 1.5 THz. Notably, at the frequency of 1.234 THz, the difference of reflection phase and amplitude of the “0” and “1” cells are 180° and more than 90%, respectively. Based on these advantages, we constructed two 1-bit encoding metasurface devices realizing the different reflective beam-splitting functions at three frequency points.

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WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equivalent Permittivity

WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equi...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Pengming Hu Weidong Yang Xuyu Wang Shiwen Mao Erbo Shen Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou College of Information Science and Engineering Henan University of Technology Zhengzhou China Knight Foundation School of Computing and Information Sciences Florida International University FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL

Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper, according to the dielectric properties of wheat, we propose a wheat moisture content detection system with commercial Wi-Fi devices, termed WiWm-EP. First, we introduce the relationship between the moisture content of wheat and its dielectric constant. Then, we establish an equivalent permittivity (EP) model to characterize wheat moisture content, where the EP can be calculated from channel state information (CSI) of the dual antenna model. Besides, we build the fitting function between the EP and the moisture content as the wheat moisture detection model. Finally, we evaluate the performance of the system through different experiments. The average relative error of the detection results of five wheat samples with different moisture contents is less than 3%.

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