论文列表

期刊论文

[2021a] Xinshan Zhu, Shuoshi Li, Yongdong Gan, Yun Zhang, Biao Sun*. “Multi-stream Fusion Network with Generalized Smooth L1 Loss for Single Image Dehazing.” IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2021.3108022 [paper]

[2021b] Jiaqi Hu, Yanqiu Zou, Biao Sun*, Xinyao Yu, Ziyang Shang, Jie Huang, Shangzhong Jin, Pei Liang*. “Raman spectrum classification based on transfer learning by a convolutional neural network: Application to pesticide detection.” Spectrochimica Acta Part A: Molecular andBiomolecular Spectroscopy, 265 (2022) 120366. [paper]

[2021c] Biao Sun, Jia-Jun Lv, Lin-Ge Rui, Yu-Xuan Yang, Yun-Gang Chen, Chao Ma*, Zhong-Ke Gao. “Seizure prediction in scalp EEG based channel attention dual-input convolutional neural network.” Physica A: Statistical Mechanics and its Applications, 126376 [paper]

[2021d] Biao Sun, and Wenfeng Zhao*. “Compressed Sensing of Extracellular Neurophysiology Signals: A Review.” Frontiers in Neuroscience 15 (2021): 682063. [paper]

[2021e] Zhu, Xinshan, Jiayu Wang, Biao Sun*, Chao Ren, Ting Yang, and Jie Ding. “An efficient ensemble method for missing value imputation in microarray gene expression data.” BMC bioinformatics 22, no. 1 (2021): 1-25. [paper]

[2021f] Sun, Biao, Chaoxu Mu, Zexu Wu, and Xinshan Zhu*. “Training-Free Deep Generative Networks for Compressed Sensing of Neural Action Potentials.” IEEE Transactions on Neural Networks and Learning Systems (2021). [paper]

[2021g] Hu, Jiaqi, De Zhang, Hantao Zhao, Biao Sun, Pei Liang*, Jiaming Ye, Zhi Yu, and Shangzhong Jin. “Intelligent spectral algorithm for pigments visualization, classification and identification based on Raman spectra.” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 250 (2021): 119390. [paper]

[2021h] Sun, Biao, Han Zhang, Yunyan Zhang, Zexu Wu, Botao Bao, Yong Hu, and Ting Li*. “Compressed sensing of large-scale local field potentials using adaptive sparsity analysis and non-convex optimization.” Journal of Neural Engineering 18, no. 2 (2021): 026007. [paper]

[2021i] Zhang, Han, Xing Zhao, Zexu Wu, Biao Sun, and Ting Li*. “Motor imagery recognition with automatic EEG channel selection and deep learning.” Journal of Neural Engineering 18, no. 1 (2021): 016004. [paper]

[2021j] Sun, Biao, Han Zhang, Zexu Wu, Yunyan Zhang, and Ting Li*. “Adaptive Spatiotemporal Graph Convolutional Networks for Motor Imagery Classification.” IEEE Signal Processing Letters 28 (2021): 219-223. [paper]

[2020a] Lu, Tong, Tingting Chen, Feng Gao, Biao Sun, Vasilis Ntziachristos, and Jiao Li*. “LV‐GAN: A deep learning approach for limited‐view optoacoustic imaging based on hybrid datasets.” Journal of Biophotonics 14, no. 2 (2021): e202000325. [paper]

[2020b] Sun, Biao, Xing Zhao, Han Zhang, Ruifeng Bai, and Ting Li*. “EEG Motor Imagery Classification With Sparse Spectrotemporal Decomposition and Deep Learning.” IEEE Transactions on Automation Science and Engineering (2020). [paper]

[2019a] Hou, Huirang, Biao Sun*, and Qinghao Meng. “Slow cortical potential signal classification using concave–convex feature.” Journal of neuroscience methods 324 (2019): 108303. [paper]

[2018a] Ni, Yuming, and Biao Sun*. “A remote free-head pupillometry based on deep learning and binocular system.” IEEE Sensors Journal 19, no. 6 (2018): 2362-2369. [paper]

[2018b] Zhu, Xinshan, Yongjun Qian, Xianfeng Zhao, Biao Sun*, and Ya Sun. “A deep learning approach to patch-based image inpainting forensics.” Signal Processing: Image Communication 67 (2018): 90-99. [paper]

[2018c] Zhao, Wenfeng, Biao Sun, Tong Wu, and Zhi Yang*. “On-chip neural data compression based on compressed sensing with sparse sensing matrices.” IEEE transactions on biomedical circuits and systems 12, no. 1 (2018): 242-254. [paper]

[2017a] Hou, Hui-Rang, Qing-Hao Meng, Ming Zeng, and Biao Sun*. “Improving classification of slow cortical potential signals for BCI systems with polynomial fitting and voting support vector machine.” IEEE Signal Processing Letters 25, no. 2 (2017): 283-287. [paper]

[2017b] Liu, Ying-Jie, Qing-Hao Meng*, Pei-Feng Qi, Biao Sun, and Xin-Shan Zhu. “Using spike-based bio-inspired olfactory model for data processing in electronic noses.” IEEE Sensors Journal 18, no. 2 (2017): 692-702. [paper]

[2017c] Qi, Pei-Feng, Ming Zeng, Zhi-Hua Li, Biao Sun, and Qing-Hao Meng*. “Design of a portable electronic nose for real-fake detection of liquors.” Review of Scientific Instruments 88, no. 9 (2017): 095001. [paper]

[2017d] Hui, Feng, Sun Biao*, and Ma Shu-Gen. “One-bit compressed sensing reconstruction for block sparse signals.” ACTA PHYSICA SINICA 66, no. 18 (2017). [paper]

[2017e] Sun, Biao*, and Hui Feng. “Efficient compressed sensing for wireless neural recording: A deep learning approach.” IEEE Signal Processing Letters 24, no. 6 (2017): 863-867. [paper]

[2017f] Sun, Biao*, and Yuming Ni. “A training-free one-bit compressed sensing framework for wireless neural recording.” IEEE Communications Letters 21, no. 8 (2017): 1775-1778. [paper]

[2017g] Sun, Biao, Wenfeng Zhao, and Xinshan Zhu*. “Training-free compressed sensing for wireless neural recording using analysis model and group weighted-minimization.” Journal of neural engineering 14, no. 3 (2017): 036018. [paper]

[2016a] Zhu, Xin-Shan, Ya Sun, Qing-Hao Meng, Biao Sun*, Ping Wang, and Ting Yang. “Optimal watermark embedding combining spread spectrum and quantization.” EURASIP Journal on Advances in Signal Processing 2016, no. 1 (2016): 1-12. [paper]

[2016b] Biao Sun*, Hui Feng, and Xinxin Xu. “History: An efficient and robust algorithm for noisy 1-bit compressed sensing.” IEICE TRANSACTIONS on Information and Systems 99, no. 10 (2016): 2566-2573. [paper]

[2016c] Sun, Biao, Hui Feng, Kefan Chen, and Xinshan Zhu*. “A deep learning framework of quantized compressed sensing for wireless neural recording.” IEEE Access 4 (2016): 5169-5178. [paper]

[2015a] Sun, Biao, and Zhilin Zhang*. “Photoplethysmography-based heart rate monitoring using asymmetric least squares spectrum subtraction and bayesian decision theory.” IEEE Sensors Journal 15, no. 12 (2015): 7161-7168. [paper]

[2014a] Meng-Li Cao, Qing-Hao Meng*, Ming Zeng, Biao Sun, Wei Li, and Cheng-Jun Ding. “Distributed least-squares estimation of a remote chemical source via convex combination in wireless sensor networks.” Sensors 14, no. 7 (2014): 11444-11466. [paper]

[2014b] Xu, Xinxin, Qun Wang, Zhanghong Tang, and Biao Sun*. “Optimal design of non-magnetic metamaterial absorbers using visualization method.” IEICE Electronics Express 11, no. 18 (2014): 20140676-20140676. [paper]

[2013a] Sun, Biao, Qian Chen, Xinxin Xu, Yun He, and Jianjun Jiang*. “Permuted&filtered spectrum compressive sensing.” IEEE Signal Processing Letters 20, no. 7 (2013): 685-688. [paper]

[2013b] Sun, Biao, Qian Chen, Xinxin Xu, Li Zhang, and Jianjun Jiang*. “A fast and accurate two-stage algorithm for 1-bit compressive sensing.” IEICE transactions on information and systems 96, no. 1 (2013): 120-123. [paper]


会议论文

[2019a] Zhao, Wenfeng, Biao Sun, Jian Chen, and Yajun Ha. “AxC-CS: Approximate Computing for Hardware Efficient Compressed Sensing Encoder Design.” In 2019 32nd IEEE International System-on-Chip Conference (SOCC), pp. 479-483. IEEE, 2019. [paper]

[2019b] Su, Yunzhi, Xinshan Zhu, Biao Sun, and Chao Ren. “A Steganographic Scheme based on Perceptual Loss and Feature Fusion.” In 2019 Chinese Control Conference (CCC), pp. 8701-8706. IEEE, 2019. [paper]

[2018a] Sun, Lei, Biao Sun, and Yunxue Huang. “A Hardware Platform for Wireless Structural Health Monitoring Based on Compressed Sensing.” In 2018 13th World Congress on Intelligent Control and Automation (WCICA), pp. 159-164. IEEE, 2018. [paper]

[2018b] Han, Xu, Yongiun Qian, Xinshan Zhu, Biao Sun, Chao Ren, and Mingshuang Wang. “Probability Model Based Clustering for Gene Activity States.” In 2018 13th World Congress on Intelligent Control and Automation (WCICA), pp. 1691-1696. IEEE, 2018. [paper]

[2016a] Zhao, Wenfeng, Biao Sun, Tong Wu, and Zhi Yang. “Hardware efficient, deterministic QCAC matrix based compressed sensing encoder architecture for wireless neural recording application.” In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 212-215. IEEE, 2016. [paper]

[2016b] Sun, Biao, Yuming Ni, and Wenfeng Zhao. “Training-free compressed sensing for wireless neural recording.” In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 18-21. IEEE, 2016. [paper]

[2016c] Li, Ji-Gong, Biao Sun, Fan-Lin Zeng, Jia Liu, Jing Yang, and Li Yang. “Experimental study on multiple odor sources mapping by a mobile robot in time-varying airflow environment.” In 2016 35th Chinese Control Conference (CCC), pp. 6032-6037. IEEE, 2016. [paper]

[2016d] Zeng, Ming, Erhong Wang, Zaixin Yang, Qinghao Meng, Biao Sun, and Jiaying Wang. “Superfamilies of networks for analyzing the correlations of different flow fields.” In 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 2626-2631. IEEE, 2016. [paper]

[2016e] Zeng, Ming, Wenxin Ma, Qinghao Meng, Biao Sun, Zhanxie Wu, and Jing Lu. “Noise resistance ability analysis of the visibility graph and the limited penetrable visibility graph.” In 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 2648-2653. IEEE, 2016. [paper]

[2016f] Zeng, Ming, Jing Lu, Zaixin Yang, Qinghao Meng, Biao Sun, and Jiaying Wang. “Multivariate order recurrence network for analyzing cross-correlation of the wind field and the gas concentration field.” In 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 2654-2659. IEEE, 2016. [paper]

[2016g] Zeng, Ming, Mingyuan Zhao, Qinghao Meng, and Biao Sun. “Multivariate directed weighted complex network for characterizing 3D wind speed signals in indoor and outdoor environments.” In 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 2666-2671. IEEE, 2016. [paper]

[2016h] Sun, Biao, Hui Feng, and Zhilin Zhang. “A new approach for heart rate monitoring using photoplethysmography signals contaminated by motion artifacts.” In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 809-813. IEEE, 2016. [paper]

[2015a] Bing, Luo, Meng Qing-Hao, Xu Fei, Wang Jia-Ying, and Sun Biao. “Development of an on-board single-frequency GNSS RTK system for MAVs.” In 2015 34th Chinese Control Conference (CCC), pp. 6005-6010. IEEE, 2015. [paper]

[2015b] Jia-Ying, Wang, Meng Qing-Hao, Luo Bing, Zeng Ming, and Sun Biao. “Multiple-fan active control wind tunnel for outdoor near-surface airflow simulation.” In 2015 34th Chinese Control Conference (CCC), pp. 5959-5964. IEEE, 2015. [paper]

[2015c] Bing, Luo, Meng Qing-Hao, Wang Jia-Ying, Sun Biao, and Wang Ying. “Three-dimensional gas distribution mapping with a micro-drone.” In 2015 34th Chinese Control Conference (CCC), pp. 6011-6015. IEEE, 2015. [paper]


部分研究生的毕业论文

[2020] 侯惠让,基于嗅觉脑电的气味与情绪识别方法,博士论文,暂未上网

论文摘要:嗅觉作为生物进化史上最古老的感觉,与人类的记忆、学习和情绪等密切相关。大脑皮层是最高级的神经中枢,能够评估来自各感官的刺激。研究大脑对不同气味的识别能力在嗅觉功能障碍的评估与诊断、抑郁症等精神类疾病患者的情绪调控等方面具有重要的意义。本文在课题组自行构建的嗅觉脑电数据集基础上,围绕气味种类识别、气味浓度识别以及嗅觉诱发情绪识别三个问题,从EEG信号的特征提取及分类两个方面展开了较为深入的研究。

[2020] 张晗,基于深度学习的运动想象脑电信号分类方法研究,硕士论文(获评天津大学优秀硕士学位论文),暂未上网

论文摘要:使用运动想象脑电信号作为输入的脑机接口搭建了大脑和外部电子设备之 间交流的通道,可以通过识别脑电信号来控制外部设备。目前神经网络在运动 想象领域获得了广泛的关注,然而该技术目前存在诸多局限,限制了其发展。 主要问题包括:1.不相关的通道会降低分类算法的性能;2.没有考虑通道之间的 相互影响;3.基于运动想象的 EEG 信号分类准确率还不够高。为解决以上问题, 本文从深度学习出发,展开了对运动想象脑电信号分类任务的研究。

[2019] 孙磊,基于FPGA的压缩感知硬件优化与系统实现研究,硕士论文,下载地址

论文摘要:压缩感知理论利用信号稀疏性直接采样压缩后的信号,具有信号采样率低、数据存储压力小、硬件简单等优点,在无线传感器网络、生物电信号采集、多输入多输出系统设计以及变换域采样系统设计等领域具有广泛的应用前景。如何设计更高效、更便于硬件实现的信号编码器以及如何设计复杂度低、信号重建质量高的算法一直是该领域的重点研究方向。本论文对压缩感知理论中的观测矩阵优化设计、重建算法设计以及压缩感知硬件实现等问题进行了系统研究,并在心电信号数据集上进行了实验验证。

[2018] 倪宇明,基于深度学习和双目视觉的头部自由遥测式瞳孔计研究,硕士论文,下载地址

论文摘要:瞳孔直径的变化与神经系统活跃性直接关联,可以反应出许多重要的人体信息状态,应用在疲劳驾驶、疾病诊断以及人机交互等各个领域。远程头部自由式瞳孔计具有非接触、快捷等特点,具有广阔的研究与应用前景。然而,当前的远程头部自由式瞳孔计容易受到环境光的干扰、人眼追踪速度慢、测量瞳孔直径时会产生畸变误差而且使用前需要繁琐的校正流程。本文针对这些问题建立了基于深度学习的双目视觉测量模型,提高了系统的测量速度和精度,提高了系统的抗环境光干扰能力,同时无需每次使用前进行校正。

[2017] 陈科帆,基于压缩感知的模拟信息转换器设计与研究,硕士论文,下载地址

论文摘要:压缩感知(Compressed Sensing, CS)是近年来发展起来的信号采样的新理论和新方法,引起了多个学科领域内研究人员的广泛关注。在无线传感器网络领域,CS也具有重要的理论价值和潜在应用前景。譬如,CS可以替代传统的奈奎斯特采样器件,减少数据采集、存储、传输及处理所需的能量,从而延长无线传感器网络的续航时间。本文基于压缩感知理论及随机解调硬件架构,设计了一种新型模拟信息转换器(Analog to Information Convertor, AIC),并将其应用于桥梁结构健康监测无线传感器网络中,进行了数值模拟仿真及实验验证。

[2017] 丰卉,面向气体信号量化压缩感知的贝叶斯重建方法研究,硕士论文,下载地址

论文摘要:在社会经济飞速发展的今天,环境问题日益突出,有害气体的监测成为一项非常重要的内容。当前使用的气体传感器普遍存在较长的反应和恢复时间,其采样率存在瓶颈,在采样过程中容易丢失气体浓度信号的细节信息,因而难以精确的对有害气体信号的分布情况进行估计。压缩感知理论有望解决气体传感器的采样率瓶颈,提高对气体信号估计的精确度。本文在压缩感知的基础上,提出了基于分块稀疏贝叶斯学习的量化压缩感知重建算法,并且对气体信号进行了实验验证其优越性。