大学旷计算机课,旷利丹讲师

国际期刊论文(全部SCI期刊)：

[1]Kuang LD, Lin QH, Gong XF, Cong F, Wang YP, Calhoun VD. “Shift-Invariant  Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with  Phase Sparsity Constraint,” IEEE Transactions on Medical Imaging. DOI:  10.1109/TMI.2019.2936046. (IF: 7.816)

[2]Kuang LD, Lin QH, Gong XF, Cong F, Sui J, Calhoun VD. “Model order  effects onICA of resting-state complex-valued fMRI data: Application to schizophrenia,” Journal  of Neuroscience Methods, vol. 304, pp. 24−38, 2018. (IF: 2.785)

[3]Kuang LD, Lin QH, Gong XF, Cong F, Calhoun VD. “Adaptive independent  vector analysis for multi-subject complex-valued fMRI data,” Journal of  Neuroscience Methods, vol. 281, pp. 49−63, 2017.(IF: 2.785)

[4]Kuang LDLin QH, Gong XF, Cong F, Sui J, Calhoun VD. “Multi-subject fMRI  analysis via combined independent component analysis and shift-invariant  canonical polyadic decomposition,” Journal of Neuroscience Methods,  vol. 256, pp. 127–140, 2015. (IF: 2.785)

[5]Qiu Y, Lin QH, Kuang LD, Gong XF, Cong F, Wang YP,  Calhoun VD. “Spatial source phase: A new feature for identifying spatial  differences based on complex-valued resting-state fMRI data,” Human Brain  Mapping,vol.40,  pp.2662–2676, 2019.(IF: 4.927)

[6]Yu  MC, Lin QH, Kuang LD, Gong XF, Cong F, Calhoun VD. “ICA of full  complex-valued fMRI data using phase information of spatial maps,” Journal  of Neuroscience Methods, vol. 249, pp. 75−91, 2015.(IF: 2.785)

[7]Cong  F, Lin QH, Kuang LD, Gong XF, Astikanen P, Ristaniemi T. “Tensor decompoistion  of EEG signals: A brief review, ” Journal of  Neuroscience Methods, vol. 248, pp. 59–69, 2015.(IF: 2.785)

国际会议论文(全部EI检索)：

[8]Kuang LD, Lin QH, Gong XF, Cong F, Calhoun VD. “Post-ICA phase  de-noising for resting-state complex-valued FMRI data,” in Proc. 42nd IEEE  Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP2017),  New Orleans, USA,pp. 856–860,2017.(信号处理领域顶级会议，CCF推荐会议B类，口头报告)

[9]Kuang LD, Lin QH, Gong XF, Chen YG, Cong F, Calhoun VD. “Model order  effects on independent vector analysis applied to complex-valued fMRI data,” in  Proc. 14th IEEE Int. Symposium on Biomedical Imaging (ISBI 2017),  Melbourne, Australia, pp. 81–84, 2017.(生物医学图像处理领域顶级会议)

[10]Kuang LDLin QH, Gong XF, Cong F,  Calhoun VD. “An adaptive fixed-point IVA algorithm applied to multi-subject  complex-valued fMRI data,” in Proc. 41st IEEE Int. Conf. Acoustics, Speech  and Signal Processing (ICASSP2016), Shanghai, China,pp. 714–718,2016.(信号处理领域顶级会议，CCF推荐会议B类，口头报告)

[11]Kuang LD, Lin QH, Gong XF, J.  Fan, Cong F, Calhoun VD. “Multi-subject fMRI data analysis: Shift-invariant  tensor factorization vs. group independent component analysis,” in Proc.  1st IEEE China Summit and Int. Conf. Signal and Information Processing (ChinaSIP2013),  Beijing, China, pp. 269–272, 2013.(IEEE会议，口头报告)

[12]Qiu Y, Lin  QH, Kuang LD, Zhao WD, Gong XF, Cong F, Calhoun VD. “Classification of  schizophrenia patients and healthy controls using ICA of complex-valued fmri  data and convolutional neural networks,”in Proc. 16thInternational Symposium on Neural Networks (ISNN 2019), Moscow, Russia,  2019.(IEEE会议，口头报告)

中国发明专利：

[13]林秋华,邝利丹,龚晓峰,丛丰裕.一种用于多被试fMRI数据分析的分组张量方法,专利号201410126455.8,  2017.1.18,已授权.

[14]林秋华,邝利丹,龚晓峰,丛丰裕.一种结合独立成分分析与移不变规范多元分解的多被试功能核磁共振成像数据分析方法,专利号201510510622.3,  2018.1.16,已授权.

[15]林秋华,邝利丹,龚晓峰,丛丰裕.一种适于多被试复数fMRI数据分析的自适应定点IVA算法,专利号201610165248.2, 2016.6.8,已授权.

[16]林秋华,邝利丹,龚晓峰,丛丰裕.对静息态复数fMRI数据进行ICA后处理消噪的相位精确范围检测方法,专利号201710116707.2, 2017.3.1,已授权.

[17]邝利丹,林秋华,龚晓峰,丛丰裕.一种适于多被试fMRI数据分析的快速移不变CPD方法,专利号201811510882.0,  2018.12.11,已受理.

[18]邝利丹,林秋华,张经宇,龚晓峰,丛丰裕.一种引入空间源相位稀疏约束的多被试复数fMRI数据移不变CPD分析方法,专利号201910168387.4, 2019.03.06,已受理.

科研奖励：

[19]邝利丹,林秋华,龚晓峰,丛丰裕.2017年辽宁省优秀论文三等奖, 2017.09.19.

[20]邝利丹,林秋华,龚晓峰,丛丰裕.2016年辽宁省优秀论文三等奖, 2016.07.29.

[21]邝利丹,林秋华,龚晓峰,丛丰裕.2016年大连市优秀论文三等奖, 2016.07.26.