主持参与完成的项目：主持完成国家自然科学基金、甘肃省自然科学基金、高等学校博士学科点专项科研基金等多项，包括：

1.国家自然科学基金面上项目：乳腺癌诊断中乳腺钼靶X线影像处理与分析关键技术研究，批准号：61175012;

2. 甘肃省自然科学基金：基于深度神经网络的乳腺钼靶X线图像数据信息提取与病理研究，批准号：18JR3RA288.

目前在研项目：

1. 国家自然科学基金：PCNN点火机制完善行探索极其在医学图像处理中的应用研究，批准号：620610231.

2. 国家自然科学基金项目：PCNN在图像处理中的时、频域特性研究，批准号：61961037.

3. 横向合作项目：主动性氢钟精密控温及伺服控制系统研制.

已在IEEE Transactions on Neural Networks and Learning Systems、Neural Computation、Pattern Recognition、Signal Processing、电子学报、科学通报等学术期刊和国际学术会议发表论文300余篇，其中SCI检索60余篇，SCI一区8篇，二区18篇(Google学术h指数36，引用量6043，Researchgate的h指数为30，阅读量67860)，EI索引100余篇，包括如下部分SCI论文：

[1] Y Qi, Z Yang, W Sun, M Lou, J Lian, W Zhao, X Deng, Y Ma. A comprehensive overview of image enhancement techniques. Archives of Computational Methods in Engineering, 2022, 29 (1), 583-607.

[2] M Lou, J Meng, Y Qi, X Li, Y Ma. MCRNet: Multi-level context refinement network for semantic segmentation in breast ultrasound imaging. Neurocomputing, 2022, 470, 154-169.

[3] Y Qi, Z Yang, J Lian, Y Guo, W Sun, J Liu, R Wang, Y Ma. A new heterogeneous neural network model and its application in image enhancement. Neurocomputing, 2021, 440, 336-350.

[4] J Pi, Y Qi, M Lou, X Li, Y Wang, C Xu, Y Ma. FS-UNet: Mass segmentation in mammograms using an encoder-decoder architecture with feature strengthening. Computers in Biology and Medicine, 2021, 137, 104800.

[5] C Xu, Y Qi, Y Wang, M Lou, J Pi, Y Ma. ARF-Net: An Adaptive Receptive Field Network for breast mass segmentation in whole mammograms and ultrasound images. Biomedical Signal Processing and Control, 2022, 71, 103178.

[6] C Xu, M Lou, Y Qi, Y Wang, J Pi, Y Ma. Multi-scale attention-guided network for mammograms classification. Biomedical Signal Processing and Control, 2021, 68, 102730.

[7] W Zhao, M Lou, Y Qi, Y Wang, C Xu, X Deng, Y Ma. Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms. Applied Intelligence, 2021, 51 (12), 8810-8827.

[8] M Lou, Y Qi, J Meng, C Xu, Y Wang, J Pi, Y Ma. DCANet: Dual contextual affinity network for mass segmentation in whole mammograms. Medical Physics, 2021, 48 (8), 4291-4303.

[9] X Gong, Z Yang, D Wang, Y Qi, Y Guo, Y Ma. Breast density analysis based on glandular tissue segmentation and mixed feature extraction. Multimedia Tools and Applications, 2019, 78 (22), 31185-31214.

[10] Y Wang, Y Qi, C Xu, M Lou, Y Ma. Learning multi-frequency features in convolutional network for mammography classification. Medical & biological engineering & computing, 2022, 1-12.

[11] Wang R, Ma Y, Sun W, et al. Multi-level nested pyramid network for mass segmentation in mammograms[J]. Neurocomputing, 2019, 363: 313-320.

[12] Yuli Chen, Sung-Kee Park, Yide Ma, and Rajeshkanna Ala. A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation. IEEE Transactions on Neural Networks. 2011, 22(6):880-892.

[13] Kun Zhan, Hongjuan Zhang, and Yide Ma. New Spiking Cortical Model for Invariant Texture Retrieval and Image Processing. IEEE Transactions on Neural Networks. 2009, 20(12): 1980-1986.

[14] Yuli Chen, Yide Ma, Dong Hwan Kim, and Sung-Kee Park. Region-based Object Recognition by Color Segmentation Using a Simplified PCNN. IEEE Transactions on Neural Networks and Learning Systems. 2014. (In Press)

[15] Deng X, Yan C, Ma Y. PCNN mechanism and its parameter settings[J]. IEEE transactions on neural networks and learning systems, 2019.

[16] Dong Min, Zhang Jiuwen, and Ma Yide. Image Denoising via Bivariate Shrinkage Function Based on a New Structure of Dual Contourlet Transform. Signal Processing. 2015, 209: 25-37.

[17] Songlin Du, Yaping Yan, and Yide Ma. Quantum-Accelerated Fractal Image Compression: An Interdisciplinary Approach, IEEE Signal Processing Letters. 2015, 22(4): 499-503.

[18] Yang, Z., Lian, J., Guo, Y., Li, S., Wang, D., Sun, W., & Ma, Y. An overview of PCNN model’s development and its application in image processing. Archives of Computational Methods in Engineering, 2019, 26(2), 491-505.

[19] Yang, Z., Lian, J., Li, S., Guo, Y., Qi, Y., & Ma, Y. Heterogeneous SPCNN and its application in image segmentation. Neurocomputing, 2018, 285, 196-203.

[20] Ya nan Guo., Yang Z, Ma Y, Lian J, et al. Saliency motivated improved simplified PCNN model for object segmentation[J]. Neurocomputing, 2018, 275: 2179-2190.

[21] Deng Xiangyu and Ma Yide. PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection. Chinese Journal of Electronics. 2014, 23(1): 97-103.

出版著作及教材10部，包括：

[1]专著：Applications of Pulse Coupled-neural Networks. Springer&High Education Press,2010.

[2]专著：脉冲耦合神经网络与数字图像处理.科学出版社，2008年.

[3]译著：脉冲耦合神经网络与图像处理(第2版).高等教育出版社,2008年.

[4]译著：图像处理与脉冲耦合神经网络：基于Python的实现(第3版).国防工业出版社,2017年.

[5]教材：微型计算机原理及其应用(第4版).高等教育出版社，2004年.

[6]教材：非线性电路-基础分析与设计.高等教育出版社,2011年.

[7]教材：MSP430单片机原理与应用.清华大学出版社，2017年.

项目组与美国乔治梅森大学、瑞典皇家理工学院、日本早稻田大学、意大利特伦托大学等科研院所相关学者保持着长期的合作交流，此外，与兰大一院等保持长期合作关系。

1.宝钢优秀教师奖

2.甘肃省教学名师奖

3.“嵌入式系统课程群建设与创新人才培养”---甘肃省教学成果一等奖。

4.“嵌入式系统课程群教学团队建设”---甘肃省教学团队荣誉称号。

5.教育部新世纪优秀人才

IEEESenior Member

IEEE Computer Society Member

MEMBER of ACM

中国计算机学会会员

中国图形图像处理协会高级会员

《兰州大学学报(自然版)》编委

《甘肃科学学报》编委

担任IEEE TIP, TNNLS,TYB, TIM, TBMIP, PAMI, PR等期刊审稿人