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Accelerated reconstruction of ZIF-67 with significantly enhanced glucose detection sensitivity

Nano Research 2024年第6期17卷 4737-4743页

作者： Huihui Jin Weihao Zeng Wei Qian Lun Li Pengxia Ji Zhengying Li Daping He School of Information Engineering Wuhan University of TechnologyWuhan 430070China Hubei Engineering Research Center of RF-Microwave Technology and Application Wuhan University of TechnologyWuhan 430070China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China

Research on metal-organic framework(MOF)-based non-enzymatic glucose sensors usually ignores the impact of the surface reconstruction degree of MOF on the activity of catalyzing glucose *** this work,we choose zeolitic imidazolate framework-67(ZIF-67),which is commonly used in glucose sensing,as a representative to investigate the influence of reconstruction degree on its structure and glucose catalytic *** employing the electrochemical activation strategy,the activity of ZIF-67 in catalyzing glucose gradually increased with the prolongation of the activation time,reaching the optimum after 2 h *** detection sensitivity of the activated ZIF-67 was 19 times higher than that of the initial ZIF-67,and the limit of detection(LOD)was lowered from 7 to 0.4μ*** findings demonstrate that the oxidation degree of ZIF-67 deepened rapidly with continuously activation and was basically reconstructed to CoOOH after 2 h activation,accompanied by a morphological change from cuboctahedral to ***,theoretical investigation revealed that ZIF-67 is not suitable as a stable glucose sensor electrode since the adsorbed glucose molecules hasten the dissociation of ligands and the breaking of Co-N bond in ***,our work has important implications for the rational design of next-generation MOF-based glucose sensors.

关键词： zeolitic imidazolate framework-67(ZIF-67) glucose oxidation electrochemical activation reconstruction CoOOH

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DTCC:Multi-level dilated convolution with transformer for weakly-supervised crowd counting

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Computational Visual Media 2023年第4期9卷 859-873页

作者： Zhuangzhuang Miao Yong Zhang Yuan Peng Haocheng Peng Baocai Yin Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial IntelligenceFaculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Taiji Computer Corporation Ltd. China

Crowd counting provides an important foundation for public security and urban *** to the existence of small targets and large density variations in crowd images,crowd counting is a challenging *** methods usually apply convolution neural networks(CNNs)to regress a density map,which requires annotations of individual persons and ***-supervised methods can avoid detailed labeling and only require counts as annotations of images,but existing methods fail to achieve satisfactory performance because a global perspective field and multi-level information are usually *** propose a weakly-supervised method,DTCC,which effectively combines multi-level dilated convolution and transformer methods to realize end-to-end crowd *** main components include a recursive swin transformer and a multi-level dilated convolution regression *** recursive swin transformer combines a pyramid visual transformer with a fine-tuned recursive pyramid structure to capture deep multi-level crowd features,including global *** multi-level dilated convolution regression head includes multi-level dilated convolution and a linear regression head for the feature extraction *** module can capture both low-and high-level features simultaneously to enhance the receptive *** addition,two regression head fusion mechanisms realize dynamic and mean fusion *** on four well-known benchmark crowd counting datasets(UCF_CC_50,ShanghaiTech,UCF_QNRF,and JHU-Crowd++)show that DTCC achieves results superior to other weakly-supervised methods and comparable to fully-supervised methods.

关键词： crowd counting transformer dilated convolution global perspective field pyramid

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A lightweight object detection network based on YOLOv5 for SAR image 2

A lightweight object detection network based on YOLOv5 for S...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Shen, Aijia Zhao, Liangbo Xu, Fanyun Wang, Guoqing Liu, Wenchao Shen, Zimeng Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (纸本)9798331515669

Remote sensing image target detection is one of the key technologies in the field of intelligent interpretation of remote sensing images, and it has significant application value in various areas, including military defense. When performing remote sensing image object detection on airborne and spaceborne platforms, the vast amount of remote sensing data processing and limited computational resources impose high real-time requirements on the object detection algorithms. This paper designs a lightweight object detection network model named YOLOv5-tiny, based on the existing deep learning network detection model YOLOv5s, and deploys it on the Jetson TX2 development board for training and testing. Experimental results show that the proposed YOLOv5-tiny model, when tested on the SAR-AIRcraft-1.0 with an input image size of 640x640, is 10 times smaller than YOLOv5s;it has a computational cost of 5.2 GFLOPs, which is 1/5 of YOLOv5s, and the processing time for a single image is reduced to half that of YOLOv5s, with only a 0.1% decrease in accuracy. © 2024 IEEE.

关键词： Object tracking

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Prototype-Guided Representation Distribution Optimization for Change Detection in High-Resolution Remote Sensing Imagery 2

Prototype-Guided Representation Distribution Optimization fo...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wang, Guoqing Chen, He Wei, Xin Shi, Junqing Chen, Liang Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

Change detection is the process of automatically extracting change areas from bi-temporal remote sensing images. The key of deep learning-based change detection methods is to extract features that represent changes/non-changes. Most of the existing methods only enhance the representation ability of the changed regions through the model architecture design, but lack further constraints in the representation space. This paper introduces a prototype-guided representation distribution optimization method. By comparing pixel features with prototypes, confusing pixels can be determined, and these pixels are adaptively weighted during learning, so that similar representations are closer and different representations are farther apart. In addition, the representation distribution is also applied as transferred knowledge to knowledge distillation for lightweight CD model. Experimental results on two public datasets show the effectiveness of the proposed method. © 2024 IEEE.

关键词： Change detection

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From First-Order to Second-Order Rationality: Advancing Game Convergence with Dynamic Weighted Fictitious Play 21st

From First-Order to Second-Order Rationality: Advancing Gam...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Ju, Qi Hei, Falin Liu, Yuxuan Fang, Zhemei Luo, Yunfeng Huazhong University of Science and Technology Wuhan China National Key Laboratory of Science and Technology on Multispectral Information Processing Wuhan China

ISBN: (纸本)9789819601271

Constructing effective algorithms to converge to Nash Equilibrium (NE) is a important problem in algorithmic game theory. Prior research generally posits that the upper bound on the convergence rate for games is OT-1/2. This paper introduces a novel perspective, positing that the key to accelerating convergence in game theory is "rationality". Based on this concept, we propose a Dynamic Weighted Fictitious Play (DW-FP) algorithm. We demonstrate that this algorithm can converge to a NE and exhibits a convergence rate of O(T-1) in experimental evaluations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Consensus algorithm

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Three-Dimensional Fixed-Time Cooperative Guidance law with Impact time Constraints

Three-Dimensional Fixed-Time Cooperative Guidance law with I...

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2023 China Automation Congress, CAC 2023

作者： Li, Yajun Liu, Lei Fan, Huijin Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology Wuhan430074 China

ISBN: (纸本)9798350303759

In this article, we propose fixed-time cooperative guidance laws for achieving simultaneous attacks on a maneuvering target by multiple missiles at desired terminal angles. The 3-D guidance commands encompass radial acceleration along the line-of-sight (LOS) direction to enable coordinated attacks, as well as normal acceleration perpendicular to the LOS direction to ensure interception with terminal angle constraints. Firstly, the radial acceleration is designed from a nonlinear fixed-time cooperative protocol to achieve the fixed-time consensus of impact times for multiple missiles. Secondly, based on the fixed-time sliding manifold and fixed-time reaching law, the normal acceleration is designed for each missile to converge the LOS angle to the desired value within a fixed time. Furthermore, the unknown target acceleration is treated as a system disturbance, estimated by fixed-time observers, and compensated in the guidance commands, ensuring effective handling of uncertainties introduced by the target's acceleration. Finally, simulations are conducted to demonstrate the performance of the guidance law in the 3-D guidance scenario. © 2023 IEEE.

关键词： Acceleration

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DFIMat: Decoupled Flexible Interactive Matting in Multi-person Scenarios 17th

DFIMat: Decoupled Flexible Interactive Matting in Multi-per...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Jiao, Siyi Zeng, Wenzheng Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609710

Interactive portrait matting refers to extracting the soft portrait from a given image that best meets the user’s intent through their inputs. Existing methods often underperform in complex scenarios, mainly due to three factors. (1) Most works apply a tightly coupled network that directly predicts matting results, lacking interpretability and resulting in inadequate modeling. (2) Existing works are limited to a single type of user input, which is ineffective for intention understanding and also inefficient for user operation. (3) The multi-round characteristics have been under-explored, which is crucial for user interaction. To alleviate these limitations, we propose DFIMat, a decoupled framework that enables flexible interactive matting. Specifically, we first decouple the task into 2 sub-ones: localizing target instances by understanding scene semantics and the flexible user inputs, and conducting refinement for instance-level matting. We observe a clear performance gain from decoupling, as it makes sub-tasks easier to learn, and the flexible multi-type input further enhances both effectiveness and efficiency. DFIMat also considers the multi-round interaction property, where a contrastive reasoning module is designed to enhance cross-round refinement. Another limitation for multi-person matting task is the lack of training data. We address this by introducing a new synthetic data generation pipeline that can generate much more realistic samples than previous arts. A new large-scale dataset SMPMat is subsequently established. Experiments verify the significant superiority of DFIMat. With it, we also investigate the roles of different input types, providing valuable principles for users. Our code and dataset will be public. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

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Global information Edge Augmentation Network for Remote Sensing Image Continuous Super-Resolution 2

Global Information Edge Augmentation Network for Remote Sens...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Gao, Shize Lei, Yong Zhang, Jingya Wang, Guoqing Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The continuous-scale super-resolution (SR) method for remote sensing (RS) images has the potential to achieve flexible scale SR with a single network, representing a significant research area in the field of RS. However, the majority of prevailing methods learn features from localization, thereby neglecting the global semantic coherence of RS images. This results in unbalanced SR outcomes. Furthermore, the optimization strategy based on the multilayer perceptron (MLP) utilized in the implicit neural representation (INR) method results in blurred reconstructed image edges. To address these issues, a novel continuous-scale SR method for RS images, a global information edge augmentation network (GIEAN), is proposed. Initially, the global state space model (GSSM) aggregates the global information of the image from diverse perspectives and learns the contextual interactions at disparate locations of the image globally. Subsequently, the dual edge enhancement module (DEEM) learns the image body and edges independently through the main branch and edge branch, respectively, with the objective of enhancing the edge component of the SR results. Extensive experimental evaluation on the UCMerced dataset demonstrates the superiority of GIEAN over existing continuous-scale SR techniques. © 2024 IEEE.

关键词： Image enhancement

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Chen, Yangdong Chen, Mohan Zhang, Yuejie Feng, Rui Zhang, Tao Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (纸本)9798350368741

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research. © 2025 IEEE.

关键词： Diffusion Models Point Cloud Generation Self-supervised Contrastive Learning Sketch

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DiriNet:An Estimation Network for Spectral Response Function and Point Spread Function

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Journal of Beijing Institute of technology 2024年第4期33卷 287-297页

作者： Ting Hu Siyuan Cheng Chang Liu Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Space Star Technology Co. LtdBeijing 100086China Research Institute of Intelligent Wire-less Communication Network Technology Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China

Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread ***,few works have been payed on the estimation of the two degra-dation *** learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly ***,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread *** the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation *** image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.

关键词： Dirichlet network point spread function spectral response function hyper-spectralimage multi-spectral image

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