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16th International Conference on Wireless Communications and Signal processing, WCSP 2024

作者： Wang, Yan Li, Qiang Li, Yingsong Li, Liping Li, Xingwang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China School of Physics and Electronic Information Engineering Henan Polytechnic University China

ISBN: (纸本)9798350390643

In faster-than-Nyquist (FTN) transmission over frequency-selective fading channels, it is essential to eliminate both FTN-induced intersymbol-interference (ISI) (FTN-ISI) and channel-induced ISI (channel-ISI). Although frequency domain equalization (FDE) techniques can mitigate channel-ISI at the receiver, their performance is often inadequate. In contrast, cyclic prefix/suffix (CPS)-G-to-minus-half (GTMH) precoding shows superior effectiveness in mitigating channel-ISI. However, existing CPS-GTMH precoding techniques assume the transmitter obtains perfect channel state information (CSI) estimated at the receiver. This is impractical due to the high latency in satellite communication links. Therefore, this paper explores precoding for FTN signaling under imperfect CSI (ICSI) and proposes CPS-minimum mean squared error (MMSE) and linear pre-equalization (LPE)-MMSE precoding schemes based on the MMSE criterion. Both schemes employ a cyclic prefix to transform the channel-ISI matrix into the circular matrix, thereby avoiding inter-block interference. The primary distinction is that CPS-MMSE precoding handles FTN-ISI and channel-ISI together using MMSE precoding. In contrast, LPE-MMSE precoding addresses FTN-ISI with LPE and channel-ISI with MMSE precoding separately. Simulation results indicate that CPS-MMSE and LPE-MMSE precodings outperform CPS-FDE and LPE-FDE in mitigating channel-ISI when the estimation error is 0.005 and 0.01. For instance, with a packing ratio of 0.9, a roll-off factor of 0.25, and an estimation error of 0.01, CPS-MMSE and LPE-MMSE precodings achieve bit error rate (BER) gains of approximately 0.8 dB and 1 dB at a BER of 1e4, respectively. However, when the estimation error increases to 0.1, the BER performance of CPS-FDE and LPE-FDE surpasses that of the CPS-MMSE and LPE-MMSE precodings, indicating the proposed precoding schemes' unsuitability for larger channel estimation errors. © 2024 IEEE.

关键词： Bit error rate

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4th International Conference on Computer Science and Blockchain, CCSB 2024

作者： Huang, Guimin Shi, Canjun School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350356502

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance. © 2024 IEEE.

关键词： Convolutional neural networks

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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Optical and Electrical Analysis of an Advanced 4T All-perovskite Tandem Solar Cell

Optical and Electrical Analysis of an Advanced 4T All-perovs...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Xia, Zhaosheng Ren, Xingang Wei, Shengyang Huang, Zhixiang Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Hefei230039 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9798350375909

The investigation of the optoelectronic characteristics of all-perovskite tandem solar cells holds pivotal significance in surpassing the Shockley-Queisser limit of single-junction perovskite solar cells. Initially, we established two distinct four-terminal tandem solar cells (4T TSCs) composed of different perovskite materials: FA0.8Cs0.2Pb(I0.7Br0.3)3 (Eg = 1.75 eV) and MASnI3 (Eg = 1.3 eV). Through numerical simulations of the optoelectronic characteristics of 4T TSCs, we evaluated the performance of the TSCs at varying perovskite layer thicknesses. Following optimization, the power conversion efficiency (PCE) of the 4T TSCs increased from the initial 30.34% to 33.54%. Through an in-depth analysis of the cell and energy distribution in TSCs, it was observed that the light-coupling layer significantly influenced the PCE of the 4T TSCs. Upon optimizing the light-coupling layer, the PCE of the TSC increased from the initial 30.34% to 34.7%. This study provides crucial theoretical and experimental foundations for optimizing the performance of all-perovskite TSCs. © 2024 IEEE.

关键词： Perovskite solar cells

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Relative Position-based Distributed Fixed-time Formation Control of Multiple UAVs under GNSS-denied environment 43

Relative Position-based Distributed Fixed-time Formation Con...

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43rd Chinese Control Conference, CCC 2024

作者： Yang, Jiahao Zhi, Yongran Xue, Chenlong Liu, Lei Fan, Huijin Wang, Bo Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

In this paper, a novel fixed-time formation control algorithm is proposed for multiple unmanned aerial vehicles (UAVs) in environments where global navigation satellite system(GNSS) is denied, where the absolute positions of UAVs are unmeasurable, and only relative positions are accessible. To address this challenge, firstly, a composite observers is designed with one estimating relative positions between leader and followers and the other being a distributed fixed-time velocity observer. The integration of these observers enables each follower to obtain relative position and velocity of the leader within a fixed time. Subsequently, based on the composite observers and the desired formation matrix, a fixed-time controller is formulated aiming to achieve the formation task within a fixed time. Furthermore, Lyapunov stability theory demonstrates that the designed observers and controller can successfully accomplish the formation task within a fixed time. Finally, numerical simulations validate the effectiveness of the analytical results. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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Lightweight Mask Wearing Detection Algorithm Based on Improved YOLOv5

Lightweight Mask Wearing Detection Algorithm Based on Improv...

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作者： Su, Xiaodong Xu, Hongyan Zhang, Yurong Zhao, Jiayuan Zhang, Fengchun Chen, Xu Harbin University of Commerce Harbin China Heilongjiang Key Laboratory of E-Commerce and Intelligent Information Processing Harbin China

To prevent the spread of the epidemic, a lightweight mask-wearing detection algorithm based on the modified YOLOv5 is proposed. The three categories of mask-wearing, mask-less, and non-standard mask-wearing were studied, and images of faces covered with hands and clothing were added as interference items to make the study more realistic. Using YOLOv5 as the base model, the modified deformable convolution is introduced into the CSP-Darknet53 module of the backbone network, which effectively enhances the model's ability to extract key information such as faces and masks and improves the detection accuracy of the model. In addition, image defogging based on dark channel prior is introduced to make the model adapt to detect whether pedestrians wear a mask correctly in special weather. Finally, the knowledge distillation method is used to perform light-weight compression on the model to solve the problem of slow detection speed in practical applications. Training and testing on the self-made T-Mask dataset, the mAP of the model reaches 93.6%, which is about 3% points higher than the original YOLOv5m algorithm, and the number of parameters is reduced to 1/3 of the original one, which has a higher practical value. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Unpaired Thick Cloud Removal with Multiscale SAR-optical Data Fusion 5

Unpaired Thick Cloud Removal with Multiscale SAR-optical Dat...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Xiang, Xuanyu Tan, Yihua Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9798350373820

Cloud removal is vital for the analysis of optical satellite images. To alleviate the impact of thick clouds, recent advances integrate deep learning withmultimodal data. To relax the requirements of paired training samples, existing methods utilized cycle-consistent architecture to learn the relation between cloudy and cloud-free images based on unpaired training samples. When considering thick cloud removal based on unpaired training data, the relevant studies are insufficient and they face two challenges: 1) The information in non-cloudy areas is not preserved well after cloud removal;2) The recovered information lacks consistency with the global textures and structures. Based on optical-SAR fusion and cycle-consistent training, this paper proposes Multiscale Cycle-consistent Fusion (MCF) model. MCF designs preservation loss to overcome the first challenge, and proposes global-local discriminator combined with Parallel Dilated Channel-weighted Module (PDCM) for the second challenge. MCF is evaluated both on a simulated dataset and a real dataset, which demonstrates its effectiveness. © 2024 IEEE.

关键词： Image fusion

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A shape and texture feature blending network for bone marrow cells classification using auxiliary learning 12

A shape and texture feature blending network for bone marrow...

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SPIE 12th International Symposium on Multispectral Image processing and pattern recognition, MIPPR 2023

作者： Guo, Zhengyang Yang, Zengbiao Xiang, Xuanyu Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510674981

ISBN: (纸本)9781510674974

Morphological examination of bone marrow cells is crucial for diagnosing blood diseases. However, manual classification of bone marrow cells is time-consuming and subjective. Therefore, it is necessary to develop an autoclassification method for bone marrow cells. Although deep learning methods are commonly used for cell classification like Resnet50, they don't take advantage of the features of bone marrow cells such as the shape features of cells. However, the shape of cell and nucleus plays a significant role in distinguishing between different cell types. In this paper, we proposed a Shape and Texture Feature Blending Network (STFB-Net) for bone marrow cells classification based on auxiliary learning. We used ResNext50 as the backbone network for STFB-Net due to its exceptional ability to extract texture features from cells. In addition, we proposed the Shape Feature Extraction Module (SFEM) to enhance the backbone network's ability for capturing shape features. SFEM shares a part of parameters with the backbone network. SFEM extracts features at multiple scales and up-samples them, then fuses multi-scale features to predict the shape of cells. We performed experiments on two bone marrow cell datasets. The results show that the proposed STFB-Net can effectively extract texture and shape features, which brings better performance than other cell classification methods. By using the Grad-CAM method to visualize the features extracted by STFB-Net, we proved the reliability and the effectiveness of the STFB-Net in extracting cell shape features. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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Improving millimeter-wave imaging quality using the vortex phase method

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Defence Technology（防务技术） 2024年第9期39卷 193-202页

作者： Nairui Hu Feng Qi Yelong Wang Zhaoyang Liu Pengxiang Liu Weifan Li Key Laboratory of Terahertz Imaging and Sensing Shenyang 110169China Shenyang Aerospace University Shenyang 110136China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of SciencesShenyang 110169China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110169China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China

This paper investigates a new vortex wave imaging approach to improve the imaging quality of small metal targets of size less than 1.5 *** with different spiral phase plates are designed to efficiently transmit vortex beams with orbital angular momentums(OAMs).By analyzing the OAM spectrum of the target,it was discovered that the predominant reflection contains a particular OAM mode that carries abundant azimuthal *** can be explained by the OAM selectivity of the target and the guidance of the vortex transmitting beam.A simple reflection vortex imaging system was designed to capture the phase *** results show that the high image contrast reaches 14.9%,which is twice as high as that of the imaging without *** of simulations and experiments demonstrate that the vortex phase imaging approach proposed in this paper can effectively improve the imaging quality at 80 *** approach is suitable for other millimeter wave imaging systems and is helpful to improve the resolution in anti-terrorism security checks.

关键词： Orbital angular momentums(OAMs)beams Focal plane imaging Spiral phase plates(SPPs) Vortex spectrum

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A New Elimination Based Multi-Material Reconstruction for Spectral CT Imaging 3

A New Elimination Based Multi-Material Reconstruction for Sp...

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3rd International Conference on Optics and Image processing, ICOIP 2023

作者： Yu, Xiaohuan Cai, Ailong Li, Lei Yan, Bin Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781510667426

Spectral computed tomography (spectral CT) is an emerging imaging technology that is capable of distinguishing material properties. However, the difficulty of decomposition process is intensified by the nonlinearity of the measurements and ill-conditioned problem, particularly when the number of materials and energies do not match. Therefore, the key issue in spectral CT is to design an improved algorithm in the accurate material decomposition. This study proposes a one-step multi-material algorithm that combines a statistical reconstruction model with a gradient-sparsity based prior. In our approach, the elimination based method under volume conservation constrained condition is designed for the inconsistent scanning of number of materials and energies. Newton descent method is adopted to efficiently solve the optimization problem based on a simple surrogate function. Through simulated experiments, the proposed method achieves a significantly higher peak signal-to-noise ratio (PSNR) compared to other algorithms, with an increase of approximately 23.988 dB and 23.462 dB. Numerical experiments have confirmed the efficiency of our proposed method in reconstructing the material distributions while reducing noise compared to state-of-the-art methods. © 2023 SPIE. All rights reserved.

关键词： Signal to noise ratio

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