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SSRN 2023年

作者： Gai, Shan Huang, Xiang School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang330063 China

A reduced biquaternion neural network (RQNN) is a new type of neural network framework that has achieved significant success in machine learning. However, as the reduced biquaternion algebra system contains infinite zero divisors, the RQNN is easily trapped in a local minimum and overfitted. We propose a new regularization scheme for the RQNN to address these issues. First, we propose a new operation in the reduced biquaternion domain—the reduced biquaternion complex modulus (RQCM). Second, we analyse the algebraic properties and obtain the geometric meaning of the RQCM. Finally, we design an improved weight decay method using the RQCM. Unlike conventional weight decay methods with matrix norm constraints, the proposed method can better project the reduced biquaternion in terms of the scale and phase. Colour image classification and denoising tasks are used to demonstrate the superior performance of the RQCM over state-of-the-art weight decay methods. The source code can be found at https://***/tasteimage/Reg4RQNN . © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Theoretical Analysis of Solvent Effect on NAPBr Dye's Two-photon Absorption Ability and Non-Radiative Transition in Lipid Droplets Detection

arXiv

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arXiv 2024年

作者： Wang, Hongyang Wang, Xiaofei Zhou, Yong Fan, Jianzhong Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean Energy School of Physics and Electronics Shandong Normal University Jinan250014 China Guangdong Provincial Key Laboratory of Luminescence from Molecular Aggregates South China University of Technology Guangzhou510640 China

Two-photon fluorescence imaging has shown a promising application in biomedical imaging due to its outstanding advantages such as large penetration depth, low photo-damage, and photo-bleaching, etc. Among them, the two-photon fluorescent dye NAPBr, which can effectively select and monitor lipid droplets in living cells and biological tissues, has attracted extensive attention because of its excellent fluorescent properties. However, the research on the fluorescent abilities of two-photon fluorescent dyes in solvent environment is not sufficient. In our work, theoretical analysis reveals the internal mechanism of the solvent effect on geometric structure and photophysical properties of two-photon fluorescent dyes, especially non-radiative transition process, and holes-electrons distribution and transfer. This can provide a reference for the development of efficient two-photon absorption (TPA) molecules with aggregation-induced emission (AIE) characteristics. Related data also showed good regularity. Moreover, dye in four solvents have excellent photophysical properties: high fluorescence quantum efficiency (up to 66.60%), large Stokes shift (up to 108696 cm-1), and two-photon absorption cross section (up to 3658 GM). The medium dielectric constant solution environment can achieve a balance between two-photon absorption and fluorescence emission capabilities better, which lays a solid foundation for the study of TPA molecules with AIE functions in terms of solvent effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum efficiency

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Graph Convolutional Network-based Functional-structural Sub-region Framework for Head and Neck Cancer Prognosis with PET/CT Imaging

Graph Convolutional Network-based Functional-structural Sub-...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Zhou, Zidong Peng, Junyi Lin, Guoyu Wu, Huiqin Lv, Wenbing Lu, Lijun Southern Medical University School of Biomedical Engineering Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Guangzhou510515 China Yunnan University School of Information Science and Engineering Kunming650504 China Pazhou Lab Guangzhou510330 China

ISBN: (纸本)9781665488723

In order to improve the behavior of the prognostic model for patients with head and neck cancer (HNC), we constructed a graph convolutional network (GCN), characterizing the interaction of functional and structural information among different sub-regions of tumors in both PET and CT images. Sub-regions were obtained by using the Watershed-based method and were regarded as vertexes in the graph. The element of 1 in the adjacency matrix indicates corresponding two vertices are spatially contiguous, while the element of 0 indicates they are separated. The selected radiomics features were used to characterize each vertex. 642 HNC patients from 8 different centers were used. The best C-index of 0.767 was achieved by the combination of GCN and clinical parameters compared to the radiomics model (0.747) or GCN model (0.702). the proposed approach has great potential in aiding treatment decision-making. © 2022 IEEE.

关键词： Graph convolutional neural network Head and neck cancer Prognosis Sub-region

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Multi-task Guided Blind Omnidirectional image Quality Assessment with Feature Interaction

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liu, Yun Li, Sifan Duan, Huiyu Zhou, Yu Fan, Daoxin Zhai, Guangtao Liaoning University Faculty of Information Shenyang110036 China Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai200240 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People’s Hospital Xuzhou221116 China

Omnidirectional image quality assessment (OIQA) has become an increasingly vital problem in recent years. Most previous blind OIQA methods only extract local features from the distorted viewports, or extract global features from the entire distorted image, lacking the interaction and fusion between local and global features. Moreover, the lack of reference information also limits their performance. Thus, we propose a blind OIQA model which consists of three novel modules, including a bidirectional pseudo-reference module, a Mamba-based global feature extraction module, and a multi-scale local-global feature aggregation module. Specifically, by considering the image distortion degradation process, a bidirectional pseudo-reference module capturing the error maps on viewports is first constructed to refine the multi-scale local visual features, which can supply rich quality degradation reference information without the reference image. To well complement the local features, the VMamba module is adopted to extract the representative multi-scale global visual features. Inspired by human hierarchical visual perception characteristics, a novel multi-scale aggregation module is built to strengthen the feature interaction and effective fusion which can extract deep semantic information. Finally, motivated by the multi-task managing mechanism of human brain, a multi-task learning module is introduced to assist the main quality assessment task by digging the hidden information in compression type and distortion degree. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the blind OIQA task compared to other models. © 1991-2012 IEEE.

关键词： image quality

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Donor Engineering Construction for Red and Near-Infrared Thermally Activated Delayed Fluorescence Molecules

SSRN

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SSRN 2024年

作者： Zhao, Jiaqiang Fan, Jianzhong Liu, Songsong School of Physics and Electronic Information Weifang University Weifang261061 China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean Energy School of Physics and Electronics Shandong Normal University Jinan250014 China

The advancement of red and near-infrared (NIR) thermally activated delayed fluorescence (TADF) molecules holds considerable promise for applications in electroluminescence and bio-medical fields, but they remain a formidable challenge due to the lack of effective molecular design strategies. Herein, the excited state properties and energy consumption process of donor-acceptor-donor (D-A-D) type red and NIR TADF molecules are theoretically studied based on density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations coupled with the thermal vibration correlation function (TVCF) method. Through investigating the photophysical properties of two reported molecules, the experimental measurements are reasonable explained. Moreover, by employing donor engineering strategy, seven novel TADF molecules are theoretically proposed. Results indicate that small energy gaps and large spin-orbit coupling (SOC) constants between the singlet and triplet excited states are obtained, which are associated with their frontier molecular orbital properties. Consequently, remarkable reverse intersystem crossing (RISC) process and emission process are obtained, efficient red TADF features for the designed molecules MeFAC-PCNCF3, spAcO-PCNCF3, BFDMAC-PCNCF3 and BTDMAC-PCNCF3 as well as NIR TADF features for the designed molecules PTZ-PCNCF3, tBUPTZ-PCNCF3 and PXZ-PCNCF3 are achieved. Our work reveals the relationships between molecular structures and luminescence properties, providing valuable insights to facilitate the development of new efficient red and NIR TADF emitters. © 2024, The Authors. All rights reserved.

关键词： Fluorescence

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Spatial-Temporal Constrained Pseudo-labeling for Unsupervised Person Re-identification via GCN Inference 18th

Spatial-Temporal Constrained Pseudo-labeling for Unsupervis...

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18th International Forum of Digital Multimedia communication, IFTC 2021

作者： Ling, Sen Yang, Hua Liu, Chuang Chen, Lin Zhao, Hongtian The Institute of Image Communication and Network Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789811922657

Most existing unsupervised person re-identification (Re-ID) methods primarily depend on the cluster distance, and merely exploit the available source labeled data to assign pseudo labels for the unannotated data. Whereas, the cluster distance usually fails to adapt to different datasets due to the domain gap. Besides, learning exclusively from the source data can not generate accurate pseudo labels for the lack of the target data information. To address this problem, we propose to exploit the spatial-temporal constraints to facilitate the pseudo label generation process. Specifically, graphs for the labeled source data are constructed and the graph convolution network (GCN) is used to learn graph embeddings. Based on these graph embeddings, the likelihood of linkages between graph nodes is estimated and utilized to assign pseudo labels for the unlabeled data. Then, with the pseudo labels, a smoothed spatial-temporal probability distribution model is generated to amend the likelihood of linkages between graph nodes as well as correct the visual similarity scores for person Re-ID. Finally, we optimize the pseudo label assignment, feature extraction networks, and spatial-temporal model alternatively and iteratively to improve the person Re-ID performance. Comprehensive experiments demonstrate that the proposed method outperforms state-of-the-art methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Graph theory

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A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization

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Chinese Journal of Electronics 2019年第4期28卷 764-772页

作者： CHEN Guoyu LI Junhua Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University

The development of algorithms to solve Many-objective optimization problems(MaOPs) has attracted significant research interest in recent *** various types of Pareto front(PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm(RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector *** concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.

关键词： Many-objective optimization Reference vector Research mode Evolutionary algorithm

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LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models

arXiv

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arXiv 2024年

作者： Ge, Qihang Sun, Wei Zhang, Yu Li, Yunhao Ji, Zhongpeng Sun, Fengyu Jui, Shangling Min, Xiongkuo Zhai, Guangtao The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Huawei Technologies Shanghai200240 China

The explosive growth of videos on streaming media platforms has underscored the urgent need for effective video quality assessment (VQA) algorithms to monitor and perceptually optimize the quality of streaming videos. However, VQA remains an extremely challenging task due to the diverse video content and the complex spatial and temporal distortions, thus necessitating more advanced methods to address these issues. Nowadays, large multimodal models (LMMs), such as GPT-4V, have exhibited strong capabilities for various visual understanding tasks, motivating us to leverage the powerful multimodal representation ability of LMMs to solve the VQA task. Therefore, we propose the first Large Multi-Modal based Video Quality Assessment (LMM-VQA) model, which introduces a novel spatiotemporal visual modeling strategy for quality-aware feature extraction. Specifically, we first reformulate the quality regression problem into a question and answering (Q&A) task and construct Q&A prompts for VQA instruction tuning. Then, we design a spatiotemporal vision encoder to extract spatial and temporal features to represent the quality characteristics of videos, which are subsequently mapped into the language space by the spatiotemporal projector for modality alignment. Finally, the aligned visual tokens and the quality-inquired text tokens are aggregated as inputs for the large language model (LLM) to generate the quality score as well as the quality level. Extensive experiments demonstrate that LMM-VQA achieves state-of-the-art performance across five VQA benchmarks, exhibiting an average improvement of 5% in generalization ability over existing methods. Furthermore, due to the advanced design of the spatiotemporal encoder and projector, LMM-VQA also performs exceptionally well on general video understanding tasks, further validating its effectiveness. Our code will be released at https://***/Sueqk/LMM-VQA. © 2024, CC BY.

关键词： Video streaming

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

引用

WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

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Identification of various food residuals on denim based on hyperspectral imaging system and combination optimal strategy

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Artificial Intelligence in Agriculture 2021年第1期5卷 125-132页

作者： Yuzhen Chen Ziyi Xu Wencheng Tang Menghan Hu Douning Tang Guangtao Zhai Qingli Li Shanghai Key Laboratory of Multidimensional Information Processing School of Communication&Electronic EngineeringEast China Normal UniversityShanghai 200062China School of Statistics East China Normal UniversityShanghai 200062China Institute of Image Communication and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China McCormick School of Engineering Northwestern UniversityEvanstonILUnited States

As the science and technology develop,crime methods and scenes have become increasingly complex and *** evidence analysis has become amore and more important criminal investigation technology and liquid is the main form of trace *** can provide not only energy,but clues to solve *** this study,we build a hyperspectral imaging system to detect liquid residue traces,including apple juice,coffee,cola,milk and tea,on denims with light,middle and dark *** obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data ***,Partial Least Squares(PLS)is applied to select the informative wavelengths from the preprocessed *** modeling phase,the combination optimal strategy,support vector machine(SVM)combined with random forest(RF),is developed to establish classification *** experimental results demonstrate that the combination optimal model can achieve TPR,FPR,Precision,Recall,F1,and AUC of 83.5%,2.30%,79.7%,83.5%,81.6%,and 94.7%for classifying fabrics contaminated by various food *** respect to the classification of liquid and fabric types,the combination optimalmodel also yields satisfactory classification *** future work,wewill expand the types of liquid,and make appropriate adjustment to algorithms for improving the robustness of classification *** research may play a positive role in the construction of a harmonious society.

关键词： Hyperspectral imaging Food residual on denim Combination optimal strategy Variable selection Forensic application

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