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41st computer Graphics International Conference, CGI 2024

作者： Cao, Mingwei Nian, Siqi Yi, Jun School of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9783031820205

Many multi-view stereo networks employing a cascade structure can efficiently estimate depth while conserving memory. However, the accuracy of the depth map in the fine stage heavily relies on the depth map estimated in the coarse stage. Additionally, the multi-stage depth maps generated by the cascaded structure are simply used to calculate losses without further utilization, which results in the loss of inter-stage differentiation information. To address these issues, we propose a method called DUE-MVSNet. We use dual uncertainty estimation to mitigate the adverse effects introduced by the cascade structure. Specifically, we design an adjacent stage uncertainty (ASU) module and a pair-wise stage uncertainty (PSU) module. The ASU module dynamically adjusts the depth hypothesis range for the current stage by learning the uncertainty of the previous stage. The PSU module estimates the uncertainty between each pair of stages, mitigating the adverse effects of high uncertainty regions. We evaluated our method on the DTU and Tanks & Temples datasets. Experimental results show that our method achieves superior reconstruction results compared to the state-of-the-art methods. Code is available at https://***/caomw/due-mvsnet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stereo image processing

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Robust Stability of Discrete-time Genetic Regulatory Networks with Reaction Diffusion

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IAENG International Journal of Applied Mathematics 2024年第9期54卷 1756-1764页

作者： Zou, Chengye Yang, Yongwei Li, Haifeng Shang, Yubao Liu, Yunong Information Science and Engineering Yanshan University Qinhuangdao066004 China School of Mathematics and Computer Science Tongling University Anhui Tongling244000 China Computer and Information Science Qinghai Institute of Technology Xining810016 China Information Science and Engineering Yanshan University Qinhuangdao066004 China

In the realm of discrete-time modeling for gene regulatory networks, significant focus has been placed on addressing the time lags inherent in the process of DNA transcription to RNA and the subsequent translation of mRNA to proteins. These temporal delays have been consistently incorporated into discrete gene regulatory network models. However, true gene regulatory networks are also subject to spatial variables, due to the uneven distribution of protein and mRNA concentrations. The integration of reaction-diffusion terms is thus essential to fully represent the impact of spatial dynamics on gene regulatory networks. In such networks, reaction-diffusion dynamics highlight the complex interactions between neighboring spatial regions, where closeness leads to mutual influences on their functional activities. Based on this conceptual groundwork, this study introduces a discrete-time gene regulatory network model that includes the mutual interconnections between spatial areas. To guarantee the model's robust stability, we have established delay-dependent stability criteria using carefully designed Lyapunov-Krasovskii functions, framed within the context of linear matrix inequalities. The robustness and effectiveness of our approach are demonstrated through a numerical example presented in this work. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Stability criteria

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TransFuseNetS: A new Image Medical Segmentation Method based on TransFuse Architecture 24

TransFuseNetS: A new Image Medical Segmentation Method based...

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Chen, Wei Wang, Zhiheng School of Computer and Information Engineering Heilongjiang University of Science and Technology Heilongjiang China School of Computer and Information Engineering Heilongjiang University of Science and Technology Anhui China

ISBN: (纸本)9798400718199

In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmentation is still a difficult problem. In view of the above problems, this project intends to use TransFuseNetS under the framework of Transfuse for image medical segmentation. TransFuseNetS further optimizes and strengthens the Fusion architecture. First of all, this project intends to design a more complex coding architecture and propose an overall attention strategy at the channel level to improve the feature representation and understandability of the image. Secondly, the linear fusion method of two directions is used to make the multi-level images more comprehensive and rich, so as to improve the identification and segmentation of multi-scale images. Through a large number of experiments, we find that TransFuseNetS can effectively improve the recognition rate of CT images. Compared with the traditional artificial neural network, the average error of IOU and ACC of artificial neural network is reduced by 0.1571, and the average error is reduced by 0.0029. The research results of this project will lay a foundation for the research and application in the fields of medical image processing and medical image processing. © 2024 ACM.

关键词： Image segmentation

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Lysosome-targeted carbon dots with a light-controlled nitric oxide releasing property for enhanced photodynamic therapy

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Chinese Chemical Letters 2024年第4期35卷 420-425页

作者： Hao Cai Xiaoyan Wu Lei Jiang Feng Yu Yuxiang Yang Yan Li Xian Zhang Jian Liu Zijian Li Hong Bi School of Materials Science and Engineering Anhui UniversityHefei 230601China School of Chemistry and Chemical Engineering Anhui UniversityHefei 230601China School of Food and Biological Engineering Hefei University of TechnologyHefei 230009China

The reactive oxygen species(ROS) generation efficiency is always limited by the extreme tumor microenvironment(TME), leading to unsatisfactory antitumor effects in photodynamic therapy(PDT). As a promising gas therapy molecule, nitric oxide(NO) is independent of oxygen and could even synergize ROS to enhance the therapeutic effect. However, the short half-life, instability, and uncontrollable release of exogenous NO limited the application of tumor synergistic therapy. Herein, we reported a novel kind of red-emissive carbon dots(CDs) that was capable of lysosome-targeted and light-controlled NO delivery. The CDs were synthesized by using metformin and methylene blue(MB) via a hydrothermal *** obtained metformin-MB CDs(MMCDs) exhibited a higher1O2quantum yield and NO generation efficiency under light emitting diode(LED) light irradiation. Noteworthily, the1O2could further in situ oxidize NO into peroxynitrite anions(ONOO-), which own the higher cytotoxicity against cancer *** experiments indicate that MMCDs could destruct lysosome membrane integrity and kill almost 80%of Hep G2 cells under light irradiation while very low cytotoxicity in the dark. Moreover, MMCDs significantly decreased tumor volume and weight after phototherapy in hepatoma Hep G2-bearing mice. Our study provides a new strategy for light-controlled NO generation as well as precise lysosome-targeting for enhancement of PDT efficiency.

关键词： Carbon dots Nitric oxide Lysosome-targeting Photodynamic therapy Cancer

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Social Force Embedded Mixed Graph Convolutional Network for Multi-class Trajectory Prediction

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-11页

作者： Du, Quancheng Wang, Xiao Yin, Shouguo Li, Lingxi Ning, Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Artificial Intelligence Anhui University Hefei China School of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indianapolis IN USA

Accurate prediction of agent motion trajectories is crucial for autonomous driving, contributing to the reduction of collision risks in human-vehicle interactions and ensuring ample response time for other traffic participants. Current research predominantly focuses on traditional deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These methods leverage relative distances to forecast the motion trajectories of a single class of agents. However, in complex traffic scenarios, the motion patterns of various types of traffic participants exhibit inherent randomness and uncertainty. Relying solely on relative distances may not adequately capture the nuanced interaction patterns between different classes of road users. In this paper, we propose a novel multi-class trajectory prediction method named the social force embedded mixed graph convolutional network (SFEM-GCN). The primary goal is to extract social interactions among agents more accurately. SFEM-GCN comprises three graph topologies: the semantic graph (SG), position graph (PG), and velocity graph (VG). These graphs encode various of social force relationships among different classes of agents in complex scenes. Specifically, SG utilizes one-hot encoding of agent-class information to guide the construction of graph adjacency matrices based on semantic information. PG and VG create adjacency matrices to capture motion interaction relationships between different classes agents. These graph structures are then integrated into a mixed graph, where learning is conducted using a spatio-temporal graph convolutional neural network (ST-GCNN). To further enhance prediction performance, we adopt temporal convolutional networks (TCNs) to generate the predicted trajectory with fewer parameters. Experimental results on publicly available datasets demonstrate that SFEM-GCN surpasses state-of-the-art methods in terms of accuracy and robustness. IEEE

关键词： Recurrent neural networks

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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BA-GNN: Behavior-aware graph neural network for session-based recommendation

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Frontiers of computer science 2023年第6期17卷 135-144页

作者： Yongquan LIANG Qiuyu SONG Zhongying ZHAO Hui ZHOU Maoguo GONG School of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China School of Electronic Engineering Xidian UniversityXi’an 710071China

Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous *** existing studies mainly focus on making predictions by considering users’single interactive *** recent efforts have been made to exploit multiple interactive behaviors,but they generally ignore the influences of different interactive behaviors and the noise in interactive *** address these problems,we propose a behavior-aware graph neural network for session-based ***,different interactive sequences are modeled as directed ***,the item representations are learned via graph neural ***,a sparse self-attention module is designed to remove the noise in behavior ***,the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session *** results on two public datasets show that our proposed method outperforms all competitive *** source code is available at the website of GitHub.

关键词： session-based recommendation multiple interactive behaviors graph neural networks

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2D-MOF/2D-MOF heterojunctions with strong hetero-interface interaction for enhanced photocatalytic hydrogen evolution

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Rare Metals 2023年第12期42卷 3993-4004页

作者： Yan Ma Hui-Xue Fang Rong Chen Qian Chen Sheng-Jun Liu Kui Zhang Hai-Jin Li School of Materials Science and Engineering Anhui University of TechnologyMa'anshan 243032China School of Chemistry and Chemical Engineering Anhui University of TechnologyMa'anshan 243032China School of Energy and Environment Anhui University of TechnologyMa'anshan 243002China

Metal organic framework(MOF) shows great potential in the research field of photocatalysis,and it is a big challenge to achieve efficient photocatalytic *** this work,we have successfully grown two-dimensional MOF(2D-MOF) nanosheets on 2D-MOF nanosheets for the first time using a homometallic nodal strategy,and successfully prepared ultrathin nanosheets with tightly bound 2D/2D heterojunctions.2D Ni-BDC nanosheets were used as carriers to grow 2D Ni-TCPP nanosheets on top of ***-TCPP has a high light absorption capacity,thus extending the light absorption range of 2D/2D *** tight coupling of the heterojunction effectively shortens the electron transfer distance,promotes the separation of interracial charges,and improves the photocatalytic ***,Ni-BDC/Ni-TCPP-3can achieve to a hydrogen production rate of428.0 μmol·g^(-1),approximately 5.75 times higher than NiBDC and 5.24 times higher than Ni-TCPP,***,2D-MOF/2D-MOF heterojunctions provide a promising strategy for enhancing photocatalytic performance through rational heterostructure design with homometallic node strategy.

关键词： Two-dimensional metal organic framework(2D MOF) Homometallic node strategy Heterojunction Photocatalytic

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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An Efficient Procedure for Removing Salt and Pepper Noise in Images

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Informatica (Slovenia) 2022年第2期46卷 197-203页

作者： Xu, Guangyu Aminu, Muhammad Jibril School of Computer Science and Engineering Anhui University of Science and Technology Huainan China

In this paper, we propose an efficient algorithm for removing salt and pepper noise in images. The process of denoising is implemented in two stages: noise detection followed by noise removal. For noise detection, two extreme intensity values in an image are used to detect possible "noise pixels". For noise removal, the switching mechanism only selects "noise pixels" for processing to avoid altering any fine image details, and only the identified noise-free pixels are used to achieve better denoising performance. Two filtering techniques, the edge-preserving filtering (EPF) and the extremum-compressing median filtering (ECMF), are employed for edge-preserving and noise removal. The EPF provides higher correlation between the corrupted pixel and neighborhood pixel, which gives rise to better edge preservation. The ECMF can yield an appropriate estimation by selecting the median pixel from the noise-free pixels of current filtering window. The proposed algorithm is tested on different images and provides a better restoration performance over some of the salt and pepper noise filters. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Salt and pepper noise

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