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One-step synthesis of triazine-based covalent organic frameworks at room temperature for efficient photodegradation of bisphenol A under visible light irradiation

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Frontiers of Materials Science 2023年第4期17卷 159-169页

作者： Pin Chen Siyuan Di Weixin XIE Zihan Li Shukui Zhu State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhan 430074China Huizhou Port Customs Huizhou 516080China

Herein,a novel visible-light-responsive photocatalyst with high efficiency was firstly synthesized at room temperature.The mild synthetic method resulted in a uniform spherical triazine-based covalent organic framework(TrCOF2)with ultra-high specific surface area as well as chemical stability.Due to the synergistic effect between the self-assembled uniform spherical structure and the abundant triazine-based structure,photoelectron–hole pairs were efficiently separated and migrated on the catalysts.On this basis,TrCOF2 was successfully applied to efficiently degrade bisphenol A(BPA).More than 98%of BPA was deraded after 60 min of visible light treatment,where the active specie of•O_(2)^(−)played a vital role during the degradation of BPA.The holes of TrCOF2 could produce O_(2)by direct reaction with water or hydroxide ions.Simultaneously,photoelectrons can be captured by O_(2)to generate•O_(2)^(−).Moreover,density functional theory(DFT)calculations proved the outstanding ability of the exciting electronic conductivity.Remarkably,a reasonable photocatalytic mechanism for TrCOF2 catalysts was proposed.This research can provide a facile strategy for the synthesis of TrCOFs catalysts at room temperature,which unfolds broad application prospects in the environmental field.

关键词： photocatalysis triazine-based covalent organic framework visible light pollutant degradation catalytic mechanism

Labeled Multi-Bernoulli Maneuvering Target Tracking Algorithm via TSK Iterative Regression Model

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Chinese Journal of Electronics 2022年第2期31卷 227-239页

作者： WANG Xiaoli xie weixin LI Liangqun Department of Electronics and Information Guangdong Polytechnic Normal University Automatic Target Recognition (ATR) Key Laboratory Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

Aiming at the problem that the existing labeled multi-Bernoulli(LMB) method has a single and fixed model set, an LMB maneuvering target tracking algorithm via Takagi-Sugeno-Kang(TSK) iterative regression multiple model is proposed. In the TSK iterative regression modeling, the feature information of the targets is analyzed and represented by multiple semantic fuzzy sets. Then the state is expanded to introduce model information, thereby the adaptive multi-model idea is incorporated into the framework of the LMB method to solve the uncertain maneuverability of moving targets. Finally,the simulation results show that the proposed algorithm can effectively achieve maneuvering target tracking in the nonlinear system.

关键词： Takagi-Sugeno-Kang Iterative regression model Labeled multi-bernoulli Maneuvering target tracking

Constrained Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking

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Chinese Journal of Electronics 2020年第3期29卷 501-507页

作者： ZHANG Hongwei xie weixin Sun Yat-sen University ATR Key Laboratory Shenzhen University

To track the bearings-only maneuvering target tracking accurately online, the soft measurement constraints are implemented into the Unscented Kalman filtering(UKF). To deal with the soft measurement constraints, the Lasso regularization is added as the obstacle function. In doing this, the sampled sigma points can be restricted into the feasible region. To enhance the sampling efficiency, the global optimal solution is acquired by a heuristic optimizer. To smooth the outliers,the posterior distribution is approximated by a Gaussian mixture consists of the original and the modified priors with the fuzzy weighted factor. Simulated results indicate the accuracy and the computational efficiency of the proposed method.

关键词： Bearings-only maneuvering target tracking Soft measurement constraints Optimal

A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

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Chinese Journal of Electronics 2023年第3期32卷 429-437页

作者： FAN Jiande xie weixin LIU Zongxiang ATR Key Laboratory Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we propose a low complexity distributed approach to address the multitarget detection/tracking problem in the presence of noisy and missing data. The proposed approach consists of two components: a distributed flooding scheme for measurements exchanging among sensors and a sampling-based clustering approach for target detection/tracking from the aggregated measurements. The main advantage of the proposed approach over the prevailing Markov-Bayes-based distributed filters is that it does not require any priori information and all the information required is the measurement set from multiple sensors. A comparison of the proposed approach with the available distributed clustering approaches and the cutting edge distributed multiBernoulli filters that are modeled with appropriate parameters confirms the effectiveness and the reliability of the proposed approach.

关键词： Sensor fusion Distributed multitarget tracking Constrained clustering Flooding Average consensus

Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

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Journal of Systems Engineering and Electronics 2019年第4期30卷 684-695页

作者： ZHANG Hongwei xie weixin Automatic Target Recognition Key Laboratory Shenzhen University

To track the nonlinear,non-Gaussian bearings-only maneuvering target accurately online,the constrained auxiliary particle filtering(CAPF)algorithm is presented.To restrict the samples into the feasible area,the soft measurement constraints are implemented into the update routine via the1 regularization.Meanwhile,to enhance the sampling diversity and efficiency,the target kinetic features and the latest observations are involved into the evolution.To take advantage of the past and the current measurement information simultaneously,the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors.As a result,the corresponding weights are more evenly distributed,and the posterior distribution of interest is approximated well with a heavier tailor.Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

关键词： bearings-only maneuvering target tracking soft measurement constraints constrained auxiliary particle filtering(CAPF)

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TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images

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Chinese Journal of Electronics 2020年第5期29卷 916-925页

作者： LI Yanshan FAN Leidong xie weixin ATR National Key Lab.of Defense Technology Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering Shenzhen University

This paper proposes a robust sparse descriptor based on tensor theory by using the spatial and spectral information synthetically, namely the Tensor gradient SIFT(TGSIFT), for Hyperspectral image(HSI).TGSIFT integrates both spatial and spectral information and considers the natural vector feature of HSIs. Based on the HSI Gaussian scale space, a new tensor model for HSI is proposed which takes the vectorial nature of HSI into consideration and preserves all the necessary structural information distributed over all the bands.The TGSIFT descriptor is formed based on the model proposed. Experimental results of HSI matching show that the TGSIFT descriptor achieves better matching performance than other SIFT descriptors under different transformations, including illumination change, sensor noise, image rotation, viewpoint change, and scale change.

关键词： Hyperspectral image (HSI) Interest points Scale-invariant feature transform (SIFT) Sparse descriptor Tensor gradient SIFT (TGSIFT)

Track-Oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking

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Chinese Journal of Electronics 2023年第5期32卷 1106-1119页

作者： DU Haocui xie weixin LIU Zongxiang LI Liangqun ATR Key Laboratory Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we derive and propose a track-oriented marginal Poisson multi-Bernoulli mixture(TO-MPMBM) filter to address the problem that the standard random finite set filters cannot build continuous trajectories for multiple extended targets. First, the Poisson point process model and the multi-Bernoulli mixture(MBM) model are used to establish the set of birth trajectories and the set of existing trajectories, respectively.Second, the proposed filter recursively propagates the marginal association distributions and the Poisson multiBernoulli mixture(PMBM) density over the set of alive trajectories. Finally, after pruning and merging process,the trajectories with existence probability greater than the given threshold are extracted as the estimated target trajectories. A comparison of the proposed filter with the existing trajectory filters in two classical scenarios confirms the validity and reliability of the TO-MPMBM filter.

关键词： Extended target tracking Random finite set Poisson multi-Bernoulli mixture Poisson point process Marginal distribution Target trajectory

Low-power STED nanoscopy based on temporal and spatial modulation

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Nano Research 2022年第4期15卷 3479-3486页

作者： Luwei Wang Yue Chen Yong Guo Weixin XIE Zhigang Yang Xiaoyu Weng Wei Yan JunleQu Center for Biomedical Photonics&College of Physics and Optoelectronic Engineering Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceShenzhen 518060China College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518060China

Stimulated emission depletion(STED)nanoscopy enables the visualization of subcellular organelles in unprecedented detail.However,reducing the power dependency remains one of the greatest challenges for STED imaging in living cells.Here,we propose a new method,called modulated STED,to reduce the demand for depletion power in STED imaging by modulating the information from the temporal and spatial domains.In this approach,an excitation pulse is followed by a depletion pulse with a longer delay;therefore,the fluorescence decay curve contains both confocal and STED photons in a laser pulse period.With time-resolved detection,we can remove residual diffraction-limited signals pixel by pixel from STED photons by taking the weighted difference of the depleted photons.Finally,fluorescence emission in the periphery of an excitation spot is further inhibited through spatial modulation of fluorescent signals,which replaced the increase of the depletion power in conventional STED.We demonstrate that the modulated STED method can achieve a resolution of<100 nm in both fixed and living cells with a depletion power that is dozens of times lower than that of conventional STED,therefore,it is very suitable for long-term superresolution imaging of living cells.Furthermore,the idea of the method could open up a new avenue to the implementation of other experiments,such as light-sheet imaging,multicolor and three-demensional(3D)super-resolution imaging.

关键词： fluorescence microscopy super-resolution imaging stimulated emission depletion fluorescence lifetime signal modulation

Action Status Based Novel Relative Feature Representations for Interaction Recognition

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Chinese Journal of Electronics 2022年第1期31卷 168-180页

作者： LI Yanshan GUO Tianyu LIU Xing LUO Wenhan xie weixin ATR National Key Laboratory of Defense Technology Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Tencent

Skeleton-based action recognition has always been an important research topic in computer vision. Most of the researchers in this field currently pay more attention to actions performed by a single person while there is very little work dedicated to the identification of interactions between two people. However, the practical application of interaction recognition is actually more critical in our society considering that actions are often performed by multiple people. How to design an effective scheme to learn discriminative spatial and temporal representations for skeleton-based interaction recognition is still a challenging problem. Focusing on the characteristics of skeleton data for interactions, we first define the moving distance to distinguish the action status of the participants. Then some view-invariant relative features are proposed to fully represent the spatial and temporal relationship of the skeleton sequence. Further, a new coding method is proposed to obtain the novel relative feature representations. Finally, we design a three-stream CNN model to learn deep features for interaction recognition. We evaluate our method on SBU dataset, NTU RGB+D 60 dataset and NTU RGB+D 120 dataset. The experimental results also verify that our method is effective and exhibits great robustness compared with current state-of-the-art methods.

关键词： Human action analysis Interaction recognition Action status Multi-stream network Relative feature representations