周冬明，男，博士，雲南大學信息學院教授、博士生導師。
基本介紹
人物經歷,受教育經歷,研究工作經歷,研究方向,研究成果,教學課程,發表論文,
人物經歷
受教育經歷
2001/09—2004/06，復旦大學，信息學院電子工程系，博士。
1981/09—1985/06，華中理工大學（華中科技大學），自動控制工程系，學士。
研究工作經歷
2005/12— ，雲南大學，信息學院通信工程系，教授。
2008/03—2008/09，加拿大約克大學，訪問學者。
2001/09—2004/06，復旦大學，信息學院電子工程系，攻讀博士。
1999/11—2005/11，雲南大學，信息與電子科學系，副教授。
1990/11—1999/10，雲南大學，信息與電子科學系，講師。
1988/06—1990/10，雲南大學，信息與電子科學系，助教
研究方向
神經網路的動力學機制研究，基於視覺皮層神經元模型的圖像處理，基於視覺皮層神經元模型的路徑最佳化計算，基於視覺皮層神經元模型的生物信息處理。
研究成果
主持參與的研究項目：3個國家自然科學基金項目：視覺皮層神經元模型的脈衝同步振盪相關理論及套用研究（61065008），2011.01-2013.12，已結題；視覺皮層神經元脈衝同步振盪信息的圖像融合技術研究（61365001），2014.01-2017-12；視感知模型脈衝耦合神經網路的圖像特徵提取及套用研究（61463052），2015.01-2018-12。4個省級項目：脈衝耦合神經網路動力學機制、目標識別和學習系統研究（省自然科學基金項目，2005F0010M），已結題；脈衝耦合神經網路的圖像處理及目標識別系統研究（省自然科學基金項目，2007F174M），已結題；視感知模型PCNN的圖像特徵提取與套用研究（省自然科學基金青年項目，2012FD003），已結題；視覺皮層模型的視感知模型理論與套用研究（省教育廳項目，2010Y247），已結題。2個校級項目：脈衝耦合神經網路動力學機制、目標識別和學習系統研究（校級重點項目，2004Z007C），已結題；脈衝耦合神經網路的動力學參數估計研究（校級青年項目，2007Q024C），已結題。發表論文100餘篇，申請發明專利7項。
教學課程
《電路分析基礎》本科生；《電子與電路基礎》本科生；《數據通信與計算機網路》本科生。
《高等電路與系統導論》研究生。
發表論文
代表性SCI論文
1. Multi-focus image fusion combining focus-region-level partition and pulse-coupled neural network, Soft Computing (2019), 23:4685–4699,
2. Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model, Medical & Biological Engineering & Computing (2019) 57:887–900
3. Analysis of pulse period for passive neuron in pulse coupled neural network,Mathematics and Computers in Simulation 155 (2019) 277–289,
4. Infrared and visible image fusion based on convolutional neural network model and saliency
detection via hybrid l 0 - l 1 layer decomposition,Journal of Electronic Imaging 27(6), 063036 (2018),DOI: 10.1117/1.JEI.27.6.063036
5. Multi-focus: Focused region finding and multi-scale transform for image fusion,Neurocomputing 320 (2018) 157–170,
6. A Color Multi-Exposure Image Fusion Approach Using Structural Patch Decomposition, IEEE Access, VOLUME 6, 2018, 42877-42885, Digital Object Identifier 10.1109/ACCESS.2018.2859355
7. Fully Convolutional Network-Based Multifocus Image Fusion,Neural Computation 30, 1775–1800 (2018),doi:10.1162/neco_a_01098
8. Infrared and visible image fusion combining interesting region detection and nonsubsampled contourlet transform, Journal of Sensors, Volume 2018, Article ID 5754702, 15 pages, (2018)
9. Infrared and v isible images fusion using v isual saliency and optimized spiking cortical model in non-subsampled shearlet transform domain,Multimedia Tools and Applications,.(2018)
10. A lightweight scheme for multi-focus image fusion, Multimed Tools Appl (2018), 77:23501–23527
11. A regularized locality projection-based sparsity discriminant analysis for face recognition, International Journal of Pattern Recognition and Artificial Intelligence (2018), 32(5) 1856006,
12. Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain, Infrared Physics & Technology (2018) 88: 1–12.
13. Multi-focus image fusion method using S-PCNN optimized by particle swarm optimization,Soft Comput (2018), 22:6395–6407
14. Similarity/dissimilarity calculation methods of DNA sequences: A survey, Journal of Molecular Graphics and Modelling (2017), 76: 342–355.
15. A survey of infrared and visual image fusion methods, Infrared Physics & Technology (2017) 85: 478–501.
16. Similarity/dissimilarity calculation methods of DNA sequences: A survey,Journal of Molecular Graphics and Modelling 76 (2017) 342–355,
17. Global asymptotic stability by complex-valued inequalities for complex-valued neural networks with delays on period time scales，Neurocomputing 219 (2017) 494–501.
18. Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain,J.Appl. Remote Sens. 11(1), 015011 (2017)
19. A novel DNA sequence similarity calculation based on simplified pulse-coupled neural network and Huffman coding, Physica A 461 (2016) 325–338.
20. Remote sensing image fusion method in CIELab color space using nonsubsampled shearlet transform and pulse coupled neural networks,Journal of Applied Remote Sensing, 10(2), 025023(2016),doi:10.1117/1.JRS.10.025023.
21. Multifocus color image fusion based on NSST and PCNN,Journal of Sensors,Vol.2016, 8359602(2016), doi.org/10.1155/2016/8359602.
22. Facial feature extraction using frequency map series in PCNN,Journal of Sensors, Vol.2016, 5491341(2016), doi.org/10.1155/2016/5491341.
23. New LMI-based conditions for global exponential stability to a class of Cohen-Grossberg BAM networks with delays, Neurocomputing, 121 (2013) 512-522.
24. Analysis of autowave characteristics for competitive pulse coupled neural network and its application. Neurocomputing, 72 (2009)2331–2336.
25. Novel LMI-based condition on global asymptotic stability for a class of Cohen-Grossberg BAM networks with the extended activation functions,IEEE Trans.on Neural Networks and Learning Systems,Vol.25(6)(2014),1161-1172.
26. Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays，Neural Networks, 25 (2012) 94-105.
27. Periodic solution to Cohen-Grossberg BAM neural networks with delays on time scales, Journal of the Franklin Institute, 348(10) (2011),2759-2781.
28. An analytic model for enhancing IEEE 802.11 point coordination function media access control protocol, European Transactions on Telecommunications, 22(6)(2011) 332-338.
29. Passivity-based adaptive hybrid synchronization of a new hyperchaotic system with uncertain parameters, The Scientific World Journal, 2012 (2012).
30. Existence and global exponential stability of a periodic solution for a discrete-time interval general BAM neural networks，Journal of the Franklin Institute, 347卷，5期, pp 763-780, 2010.
代表性EI期刊及會議論文
2.基於Unit-Linking PCNN和圖像熵的圖像分割新方法，系統仿真學報，20（1）（2008），222-227.
3.Cognitive radio multi-channel routing algorithm based on a modified PCNN,2012 International Conference on Advanced Computational Intelligence, 2012/10/18-2012/10/20, pp 549-552, Nanjing, 2012/10/18,會議論文.
4.QoS routing algorithm using competitive PCNN,Applied Mechanics and Materials, 229-231卷, pp 1908-1912, 2012/7/24.
5.Face detection method using PCNN and skin color model,Advanced Materials Research, 562-564卷, pp 1377-1381, 2012/4/27.
6.Multi-focus image fusion based on PCNN model,2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics(IHMSC 2012), 2012/8/26-2012/8/27, Nanchang, 2012/8/26,會議論文.
7.Target face detection using pulse coupled neural network and skin color model,The 2th International Conference on Computer Science and Service System, 2012/8/11-2012/8/13, pp 1310-1313, Nanjing, 2012/8/11,會議論文.
8.An image segmentation method using image enhancement and PCNN with adaptive parameters,Advanced Materials Research, 490-495卷, pp 1251-1255, 2012/5/18.
9.Facial expression recognition algorithm based on PCNN,International Conference of Electrical, Automation and Mechanical Engineering (EAME 2015),會議論文.
10.Block medical image fusion based on adaptive PCNN,978-1-4799-8353-7 /15/$31.00 ©2015 IEEE,會議論文。
代表性中文核心期刊論文
[1]S-PCNN與二維靜態小波相結合的遙感圖像融合研究，雷射與光電子學進展，52,101004(2015)，101004-1-6.
[2]多目標粒子群最佳化PCNN參數的圖像融合算法,中國圖象圖形學報,2016,21(10):1298-1306.
[3]基於S-PCNN與DDCT相結合的多感測器圖像融合,雷射與紅外,45(9) 1123-1128,2015.
[4]基於簡化脈衝耦合神經網路的噪聲人臉識別,雲南大學學報（自然科學版），2015,37(5):687-694.
[5]一種基於PCNN的改進型虹膜識別算法,計算機科學,41(11A):110-114,2014.
[6]PCNN 的周期特性分析,雲南大學學報( 自然科學版)，2015，37(1):26-30.
[7]基於局部控制核的彩色圖像目標檢測方法，電子技術套用，2016，42（12）：89-92.
[8]基於簡化PCNN與拉普拉斯金字塔分解的彩色圖像融合，計算機套用，2016，36（S1）：133-137.
[9]基於拉普拉斯金字塔與 PCNN － SML的圖像融合算法，計算機科學,2016,43(6A):122-124.
