CV

Research experience

• Power Constrained Image Contrast Enhancement:
  Working with the Qualcomm Technologies, Inc., San Diego, USA
  • Developed an image-quality-lossless end-to-end learning network to achieve power savings in emissive displays.
  • Developed a mixed-norm PCSR-based approach for perceptible image representation on OLED displays while improving power saving.
  • Developed a saliency-guided deep framework for power consumption suppressing on mobile devices.
• High dynamic range (HDR) image generation:
  Working with the Qualcomm Technologies, Inc., San Diego, USA
  • Developed a prior guided deep network to enhance the image details for HDR image generation.
  • Developed a reinforcement learning based approach to explore the intermediate image generation in two-exposure fusion for HDR image.
• Image Reconstruction and Restoration:
  Working with with the Multimedia Big Data System Lab, Yuan Ze University
  • Developed a semisupervised learning based model for removing multimodal noise from big imaging dataset.
  • Developed a color transferred convolutional neural network for image dehazing.
  • Developed a unsupervised learning model for single image haze removal.
• Maintenance Efficiency: False Trigger Detection:
  Working with with the Far Eastern Electronic Toll Collection Co, Ltd.
  • Developed a machine learning-based framework for detecting and analyzing the false triggers in electronic toll collection system.
  • Developed data visualization tools for visualizing the framework in electronic toll collection system.
• Advanced Driver-Assistance System:
  Working with the Innovation Center for Big Data and Digital Convergence, Yuan Ze University
  • Developed an automatic dangerous driving intensity analysis system for intelligent vehicle.
  • Developed an advanced driver risk measurement system for usage-Based insurance on big driving data.
  • Developed a deep convolutional neural network for impaired driving detection problem.
  • Developed a danger-level analysis framework for dealing with high variety and high volume problems of multisourced driving data.
• Sound Event Detection System:
  Working with the Fujian Provincial Key Laboratory of Information Security and Network Systems, Fuzhou University
  • Developed a bird sound event detection system by using devised random forest classifier.

Education

• Ph.D. in computer science, Yuan Ze University,2020, ranked 1 out of 4
• M.S. in YuanZe University, 2017 (Joint-M.S. Program), ranked 2 out of 50
• M.S. in Fuzhou University, 2018 (Gauranteed accpeted), ranked 1 out of 6
• B.E. in Fuzhou University, 2015, ranked 2 out of 39

Honors and Awards

• Best Ph.D. Thesis Award from Taiwanese Association for Consumer Electronics (TACE), 2020
• Best Thesis Award for Outstanding Ph.D. Thesis from Chinese Image Processing and Pattern Recognition Society (IPPR), 2020
• Golden Medal Award, Taiwan Innotech Expo Invention Contest, 2019
• Special prize schloarship from Fuzhou University, 2018
• Best Thesis Award for Outstanding M.S. Thesis from Fuzhou University, 2018
• Best Thesis Award for Outstanding M.S. Thesis from Taiwan Institute of Information \& Computing Machinery, 2017
• Best Thesis Award for Outstanding M.S. Thesis from Taiwan Institute of Electrical and Electronic Engineering (TIEEE), 2017
• The third prize, Chinese National Electronic Design Competition for Graduate Students, 2015
• Outstanding League Cadre from Fuzhou University, 2014

Journal articles

• J. L. Yin, B. Chen, Y. Peng and C. Tsai, ``Deep Battery Saver: End-to-end Learning for Power Constrained Contrast Enhancement,’’ IEEE Transactions on Multimedia. (In press)
• J. L. Yin, B. Chen, and K. Lai, ``Driver Danger-Level Monitoring System Using Multi-Sourced Big Driving Data,’’ IEEE Transactions on Intelligent Transportation Systems. (In press)
• J. L. Yin, Y. Huang, B. Chen, and S. Ye, ``Color Transferred Convolutional Neural Networks for Image Dehazing,’’ IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 11, pp. 3957-3967, Nov. 2020.
• J. L. Yin, B. Chen, E. Lai, and L. Shi, ``Power-constrained Image Contrast Enhancement through Sparse Representation by Joint Mixed-norm Regularization,’’ IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 8, pp. 2477-2488, Aug. 2020.
• J. L. Yin and B. Chen, ``An Advanced Driver Risk Measurement System for Usage-Based Insurance on Big Driving Data,’’ IEEE Transactions on Intelligent Vehicles, vol. 3, no. 4, pp. 585–594, Dec. 2018.
• J. L. Yin, B. Chen, and Y. Li, ``Highly Accurate Image Reconstruction for Multimodal Noise Suppression Using Semisupervised Learning on Big Data,’’ IEEE Transactions on Multimedia, vol. 20, no. 11, pp. 3045–3056, Nov. 2018.
• J. L. Yin, B. Chen, K. Lai, and Y. Li, ``An Advanced Driver Assistance System for Dangerous Intensity Analysis from Multimodal Driving Signals,’’ IEEE Sensors Journal, vol. 18, no. 12, pp. 4785–4794, Jun. 2018.

Conference Proceedings

• J. L. Yin, B. Chen, Y. Peng, and CC. Tsai, ``Deep Prior Guided Network for High-quality Image Fusion,’’ IEEE International Conference on Multimedia and Expo (ICME), London, UK, July. 2020. (In press)
• J. L. Yin, B. Chen, Y. Peng, and Y. Lin, ``Color Shifting-Aware Image Dehazing,’’ IEEE International Symposium on Multimedia (ISM), San Diego, USA, pp.128-1283 Dec. 2019.
• J. L. Yin, T. Peng, J. Kuan and B. Chen, ``Towards Perspective-Free Pavement Distress Detection via Deep Learning,’’ IEEE Global Conference on Consumer Electronics (GCCE), Osaka, Janpan, Oct. pp.661-662, 2019.
• L. Huang, J. L. Yin, B. Chen and S. Ye, ``Towards Unsupervised Single Image Dehazing With Deep Learning,’’ IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, pp. 2741–2745, Sept. 2019.
• J. Su, Y. Huang, J. L. Yin, B. Chen, and S. Qu, ``Saliency-Guided Deep Framework for Power Consumption Suppressing on Mobile Devices,’’ IEEE International Conference on Knowledge Innovation and Invention (ICKII), Jeju Island, South Korea, pp. 191–194, Jul. 2018.
• Y. Huang, J. L. Yin, B. Chen, and S. Ye, ``Impaired Driving Detection Based on Deep Convolutional Neural Network Using Multimodal Sensor Data,’’ IEEE International Conference on Applied System Innovation (IEEE ICASI), Chiba, Tokyo, Japan, pp. 19–22, Apr. 2018.
• B. Chen, J. L. Yin, and Y. Li, ``Image Noise Removing Using Semi-supervised Learning on Big Image Data,’’ IEEE International Conference on Multimedia Big Data (BigMM), Laguna Hills, California, USA, pp. 338–345, Apr. 2017.

Patents

• J. L. Yin, B. Chen, E. Lai, and L. Shi, Method, ``Image Processing Device, and Display System for Power-Constrained Image Enhancement,’’ U.S. Patent, Grant No. US20190066629A1, Aug. 2019.