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arXiv 2023年

作者： Wang, Yanfang Xue, Shibei Song, Hongbin Jiang, Min Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China General Education Division Shenzhen Key Lab of Semiconductor Lasers The Chinese University of Hong Kong Guang Dong Shenzhen518116 China School of Electronics and Information Engineering Soochow University Suzhou215006 China

In this paper, we propose a non-Markovian quantum channel approach to mitigating the degradation of the average fidelity in continuous-variable quantum teleportation. The non-Markovian quantum channel is modeled by an augmented system, where ancillary systems are introduced to represent the internal modes of non-Markovian environments. With a proper non-Markovianity, enhanced effects of the channels on continuous-variable quantum teleportation are observed. Also, the logarithmic negativity of entangled states can be effectively maintained so that the decay of the average fidelity of quantum teleportation is mitigated. In addition, the analysis on different teleported quantum states shows that coherent states exhibit stronger robustness than those of squeezed states and cat states in quantum teleportation with a non-Markovian channel. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum entanglement

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The Computational Complexity of Tissue P Systems with Evolutional Symport/Antiport Rules

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COMPLEXITY 2018年第1期2018卷

作者： Pan, Linqiang Song, Bosheng Valencia-Cabrera, Luis Perez-Jimenez, Mario J. Huazhong Univ Sci & Technol Sch Automat Educ Minist China Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China Univ Seville Dept Comp Sci & Artificial Intelligence Res Grp Nat Comp Avda Reina Mercedes S-N E-41012 Seville Spain

Tissue P systems with evolutional communication (symport/antiport) rules are computational models inspired by biochemical systems consisting of multiple individuals living and cooperating in a certain environment, where objects can be modified when moving from one region to another region. In this work, cell separation, inspired from membrane fission process, is introduced in the framework of tissue P systems with evolutional communication rules. The computational complexity of this kind of P systems is investigated. It is proved that only problems in class P can be efficiently solved by tissue P systems with cell separation with evolutional communication rules of length at most (n, 1), for each natural number n >= 1. In the case where that length is upper bounded by (3, 2), a polynomial time solution to the SAT problem is provided, hence, assuming that P not equal NP a new boundary between tractability and NP-hardness on the basis of the length of evolutional communication rules is provided. Finally, a new simulator for tissue P systems with evolutional communication rules is designed and is used to check the correctness of the solution to the SAT problem.

关键词： Tissue

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mDixon-based synthetic CT generation via transfer and patch learning

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PATTERN RECOGNITION LETTERS 2020年 138卷 51-59页

作者： Song, Xin Qian, Pengjiang Zheng, Jiamin Jiang, Yizhang Xia, Kaijian Traughber, Bryan Wu, Dongrui Muzic, Raymond F., Jr. Jiangnan Univ Sch Artificial Intelligence & Comp Sci 1800 Lihu Ave Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi 214122 Jiangsu Peoples R China Soochow Univ Changshu Hosp Changshu Peoples Hosp 1 Changshu 215500 Jiangsu Peoples R China Case Western Reserve Univ Case Ctr Imaging Res Cleveland OH 44106 USA Case Western Reserve Univ Dept Radiat Oncol Cleveland OH 44106 USA Univ Hosp Seidman Canc Ctr Dept Radiat Oncol Cleveland OH 44106 USA Louis Stokes Cleveland VA Med Ctr Dept Radiat Oncol Cleveland OH 44106 USA Huazhong Univ Sci & Technol Sch Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China Case Western Reserve Univ Univ Hosp Dept Radiol Cleveland OH 44106 USA Case Western Reserve Univ Univ Hosp Case Ctr Imaging Res Cleveland OH 44106 USA

We propose a practicable method for generating synthetic CT images from modified Dixon (mDixon) MR data for the challenging body section of the abdomen and extending into the pelvis. Attenuation correction is necessary to make quantitatively accurate PET but is problematic withPET/MR scanning as MR data lack the information of photon attenuation. Multiple methods were proposed to generate synthetic CT from MR images. However, due to the challenge to distinguish bone and air in MR signals, most existing methods require advanced MR sequences that entail long acquisition time and have limited availablity. To address this problem, we propose a voxel-oriented method for synthetic CT generation using both the transfer and patch learning (SCG-TPL). The overall framework of SCG-TPL includes three stages. Stage I extracts seven-dimensional texture features from mDixon MR images using the weighted convolutional sum;Stage II enlists the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) clustering as well as the patch learning-oriented semi-supervised LapSVM classification to train multiple candidate fourtissue-type-identifiers (FM);Stage III synthesizes CT for new patients' mDixon images using the candidate FITIs and voting principle. The significance of our method is threefold: (1) As the global model for patch learning, guiding by the referenced knowledge, KL-TFCM can credibly initialize MR data with overcoming the individual diversity. As the local complement, LapSVM can adaptively model each patch with low time and labor costs. (2) Jointly using the transfer KL-TFCM clustering and patch learning-oriented LapSVM classification, SCG-TPL is able to output accurate synthetic CT in the abdomen. (3) SCG-TPL synthesizes CT only using easily-obtainable mDixon MR images, which greatly facilitates its clinical practicability. Experimental studies on ten subjects' mDixon MR data verified the superiority of our proposed method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Synthetic CT mDixon MR Abdomen Transfer learning Patch learning

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Fast weighted K-view-voting algorithm for image texture classification

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OPTICAL ENGINEERING 2012年第2期51卷 027004-027004页

作者： Liu, Hong Lan, Yihua Wang, Qian Jin, Renchao Song, Enmin Hung, Chih-Cheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China Zhongnan Univ Econ & Law Informat Sch Wuhan 430073 Hubei Peoples R China So Polytech State Univ Sch Comp & Software Engn Marietta GA 30060 USA

We propose an innovative and efficient approach to improve K-view-template (K-view-T) and K-view-datagram (K-view-D) algorithms for image texture classification. The proposed approach, called the weighted K-view-voting algorithm (K-view-V), uses a novel voting method for texture classification and an accelerating method based on the efficient summed square image (SSI) scheme as well as fast Fourier transform (FFT) to enable overall faster processing. Decision making, which assigns a pixel to a texture class, occurs by using our weighted voting method among the "promising" members in the neighborhood of a classified pixel. In other words, this neighborhood consists of all the views, and each view has a classified pixel in its territory. Experimental results on benchmark images, which are randomly taken from Brodatz Gallery and natural and medical images, show that this new classification algorithm gives higher classification accuracy than existing K-view algorithms. In particular, it improves the accurate classification of pixels near the texture boundary. In addition, the proposed acceleration method improves the processing speed of K-view-V as it requires much less computation time than other K-view algorithms. Compared with the results of earlier developed K-view algorithms and the gray level co-occurrence matrix (GLCM), the proposed algorithm is more robust, faster, and more accurate. (c) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/***.51.2.027004]

关键词： image texture classification gray level co-occurrence matrix fast Fourier transform K-view algorithms

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Predicting pathologic tumor response to chemoradiotherapy with histogram distances characterizing longitudinal changes in ¹⁸F-FDG uptake patterns

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MEDICAL PHYSICS 2013年第10期40卷 101707-101707页

作者： Tan, Shan Zhang, Hao Zhang, Yongxue Chen, Wengen D'Souza, Warren D. Lu, Wei Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ China Sch Automat Wuhan 430074 Peoples R China Univ Maryland Sch Med Dept Radiat Oncol Baltimore MD 21201 USA Huazhong Univ Sci & Technol Tongji Med Coll Dept Nucl Med Wuhan 430022 Peoples R China Univ Maryland Sch Med Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA

Purpose: A family of fluorine-18 (F-18)-fluorodeoxyglucose (F-18-FDG) positron-emission tomography (PET) features based on histogram distances is proposed for predicting pathologic tumor response to neoadjuvant chemoradiotherapy (CRT). These features describe the longitudinal change of FDG uptake distribution within a tumor. Methods: Twenty patients with esophageal cancer treated with CRT plus surgery were included in this study. All patients underwent PET/CT scans before (pre-) and after (post-) CRT. The two scans were first rigidly registered, and the original tumor sites were then manually delineated on the pre-PET/CT by an experienced nuclear medicine physician. Two histograms representing the FDG uptake distribution were extracted from the pre- and the registered post-PET images, respectively, both within the delineated tumor. Distances between the two histograms quantify longitudinal changes in FDG uptake distribution resulting from CRT, and thus are potential predictors of tumor response. A total of 19 histogram distances were examined and compared to both traditional PET response measures and Haralick texture features. Receiver operating characteristic analyses and Mann-Whitney U test were performed to assess their predictive ability. Results: Among all tested histogram distances, seven bin-to-bin and seven crossbin distances outperformed traditional PET response measures using maximum standardized uptake value (AUC = 0.70) or total lesion glycolysis (AUC = 0.80). The seven bin-to-bin distances were: L-2 distance (AUC = 0.84), chi(2) distance (AUC = 0.83), intersection distance (AUC = 0.82), cosine distance (AUC = 0.83), squared Euclidean distance (AUC = 0.83), L-1 distance (AUC = 0.82), and Jeffrey distance (AUC = 0.82). The seven crossbin distances were: quadratic-chi distance (AUC = 0.89), earth mover distance (AUC = 0.86), fast earth mover distance (AUC = 0.86), diffusion distance (AUC = 0.88), Kolmogorov-Smirnov distance (AUC = 0.88), quadratic form dis

关键词： F-18-FDG PET tumor response esophageal cancer histogram distance

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A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期75卷

作者： Li, Lianghao He, Cheng Cheng, Ran Li, Hongbin Pan, Linqiang Jin, Yaochu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Bielefeld Univ Fac Technol D-33619 Bielefeld Germany

With their complexity and vast search space, large-scale multiobjective optimization problems (LSMOPs) chal-lenge existing multiobjective evolutionary algorithms (MOEAs). Recently, several large-scale multiobjective evolutionary algorithms have been developed to tackle LSMOPs. Unlike conventional MOEAs that concentrate on selection operations in the objective space, large-scale MOEAs emphasize operations in the decision space, such as offspring generation, to tackle the large number of decision variables. Nevertheless, most present large-scale MOEAs experience difficulty effectively and efficiently solving LSMOPs with tens of thousands or more decision variables or exhibit poor versatility in solving different LSMOPs. We propose a fast large-scale MOEA framework with reference-guided offspring generation, named FLEA, aiming at these issues. Generally, FLEA constructs several reference vectors in the decision space to steer the sampling of promising solutions during offspring generation. A parameter is used to allocate computation resources between the convergence and diversity of the offspring population adaptively. Without computationally expensive problem reformulation or decision variable analysis techniques, the proposed method can significantly accelerate the search speed of conventional MOEAs in solving LSMOPs. FLEA is examined on various LSMOPs with up to 1.6 million decision variables, demonstrating its superior effectiveness, efficiency, and versatility in large-scale multiobjective optimization.

关键词： Evolutionary algorithm Multiobjective optimization Offspring generation Large-scale optimization Reference vector

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Directed epileptic network from scalp and intracranial EEG of epileptic patients

Directed epileptic network from scalp and intracranial EEG o...

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IEEE Workshop on Machine Learning for Signal processing

作者： L. Amini C. Jutten S. Achard O. David H. Soltanian-Zadeh G.A. Hossein-Zadeh P. Kahane L. Minotti L. Vercueil Grenoble INP GIPSA Laboratory Grenoble France Grenoble INP GIPSA-LAB Grenoble Cedex France Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Tehran Iran CHUG Neurology Department GIN U836 INSERM Grenoble France Neurology Department CHUG Grenoble France Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA

We proposed recently the computation of epileptic connectivity graphs based on wavelet correlation coefficients between EEG signals. The suspected epileptiform electrodes are recognized using the clustering of the topological properties of the graph that can be useful for pre-surgical studies. Here, we present a method for comparing epileptic networks estimated from scalp and intracranial EEG (IEEG) in partial epilepsy patients. The results are presented for a patient with left temporal epilepsy. Good spatial correspondence between the IEEG and the scalp EEG epileptic graphs is obtained. These results are consistent with the patient's clinical diagnosis.

关键词： Epilepsy Scalp Electroencephalography Electrodes Surgery Time frequency analysis Indium phosphide Radio control Process control control systems

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Global-Local Discriminative Representation Learning Network for Viewpoint-Aware Vehicle Re-Identification in intelligent Transportation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Chen, Xiaobo Yu, Haoze Zhao, Feng Hu, Yu Li, Zuoyong Shandong Technol & Business Univ Sch Comp Sci & Technol ogy Yantai 264005 Shandong Peoples R China Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Shandong Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China Minjiang Univ Coll Comp & Control Engn Fujian Prov Key Lab Informat Proc & Intelligent Co Fuzhou 350121 Peoples R China

Vehicle re-identification (Re-ID) that aims at matching vehicles across multiple nonoverlapping cameras is prevalently recognized as an important application of computer vision in intelligent transportation. One of the major challenges is to extract discriminative features that are resistant to viewpoint variations. To address this problem, this article proposes a novel vehicle Re-ID model from the perspectives of effective feature fusion and adaptive part attention (APA). First, we put forward a channel attention-based feature fusion (CAFF) module that can learn the significance of features from different layers of the backbone network. In such a way, our model can leverage complementary features for vehicle Re-ID. Then, to address the viewpoint variation problem, we present an APA module that evaluates the significance of local vehicle parts based on the visible areas and the extracted features. By doing so, our model can concentrate more on the vehicle parts with rich discriminative information while paying less attention to the parts with limited distinctive capability. Finally, the whole model is trained by simultaneous classification and metric learning. Experiments on two large-scale vehicle Re-ID datasets are carried out to evaluate the proposed model. The results show that our model achieves competing performance compared with other state-of-the-art approaches.

关键词： ~Attention mechanism feature fusion global and local features vehicle re-identification (Re-ID)

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Point Set Registration With a Hybrid Structure Constraint

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IEEE ACCESS 2019年 7卷 164246-164255页

作者： Sun, Jing Chen, Xia Sun, Zhan-Li Lam, Kin-Man Zeng, Zhigang Anhui Univ Sch Elect Engn & Automat Hefei 230039 Anhui Peoples R China Hefei ZC Optoelect Technol Ltd Anhui Prov Key Lab Nondestruct Evaluat Hefei 230031 Anhui Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Due to some unfavorable factors, how to accurately register point sets is still a challenging task. In this paper, an effective point set registration approach is proposed based on a hybrid structure constrain. In the proposed method, a composite weight coefficient is determined based on the amplitudes of the vector and the corresponding projection of neighbor points. Given the composite weight coefficient, a local structure constraint is constructed as a linear combination of the vectors of neighbor points. A Gaussian mixture model is established by utilizing the local structure constraint and a global structure constraint based on the motion coherence theory. In addition, an expectation-maximization algorithm is derived to solve the unknown variables in the proposed model. For the constraint terms, an update strategy is utilized to obtain the approximately optimal weight coefficients. Compared to the state-of-the-art approaches, the proposed model is more robust due to the use of multiple effective constraints. Experimental results on some widely used data sets demonstrate the effectiveness of the proposed model.

关键词： Point set registration Gaussian mixture model structure constraint expectation-maximization algorithm

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BK Channel C-Linker and AC Region are Crucial for Auxiliary β2 Subunit Regulations

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BIOPHYSICAL JOURNAL 2016年第3期110卷 280A-280A页

作者： Yan, Zhenzhen Hu, Bin Guo, Xiying Weng, Anxi Zhong, Ling Xiao, Feng Ding, Jiuping Hou, Panpan Huazhong Univ Sci & Technol Minist Educ Coll Life Sci & Technol Key Lab Mol Biophys Wuhan 430074 Peoples R China Washington Univ Ctr Invest Membrane Excitabil Dis Dept Biomed Engn St Louis MO USA Huazhong Univ Sci & Technol Dept Biomed Engn Key Lab Image Proc & Intelligent Control Minist EducColl Life Sci & Technol Wuhan 430074 Peoples R China

1380-Pos

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