과제정보
This study was supported by the BK21 FOUR project funded by the Ministry of Education, Korea (4199990113966, 10%), and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A6A1A03025109, 10%). This work was partly supported by an Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-00944, Metamorphic approach of unstructured validation/verification for analyzing binary code, 40%) and (No. 2022-0-00816, OpenAPI-based hw/sw platform for edge devices and cloud server, integrated with the on-demand code streaming engine powered by AI, 20%) and (No. 2022-0-01170, PIM Semiconductor Design Research Center, 20%).
참고문헌
- S. Lee, D. Lee, P. Choi, and D. Park, "Efficient Power Reduction Technique of LiDAR Sensor for Controlling Detection Accuracy Based on Vehicle Speed," IEMEK Journal of Embedded Systems and Applications, vol. 15, no. 5, pp. 215-225, Oct. 2020. https://doi.org/10.14372/IEMEK.2020.15.5.215
- S. Lee, K. H. Park, D. Park, "Communication-power overhead reduction method using template-based linear approximation in lightweight ecg measurement embedded device," IEMEK Journal of Embedded Systems and Applications, vol. 15, no. 5, pp. 205-214, Aug. 2020. https://doi.org/10.14372/IEMEK.2020.15.5.205
- J. Kim and S. Kim "Autonomous-flight Drone Algorithm use Computer vision and GPS," IEMEK Journal of Embedded Systems and Applications, vol. 11, no. 3, pp. 193-200, Jun. 2016. https://doi.org/10.14372/IEMEK.2016.11.3.193
- Y. Huang, Y. Li, X. Hu, and W. Ci, "Lane detection based on inverse perspective transformation and Kalman filter," KSII Transactions on Internet and Information Systems (TIIS), vol. 12, no. 2, pp. 643-661, Feb. 2018. https://doi.org/10.3837/tiis.2018.02.006
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas: NV, USA, pp. 779-788, 2016.
- K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask r-cnn," in Proceedings of the IEEE international conference on computer vision, Venice, Italy, pp. 2961-2969, 2017.
- P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell, "Understanding convolution for semantic segmentation," in 2018 IEEE winter conference on applications of computer vision (WACV), Lake Tahoe: NV, USA, pp. 1451-1460, Mar. 2018.
- J. Dai, Y. Li, K. He, and J. Sun, "R-fcn: Object detection via region-based fully convolutional networks," Advances in neural information processing systems, vol. 29, pp. 379-387, May. 2016.
- A. Paszke, A. Chaurasia, S. Kim, and E.Culurciello, "Enet: A deep neural network architecture for real-time semantic segmentation," arXiv preprint arXiv:1606.02147, 2016.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas: NV, USA, pp. 770-778, 2016.
- S. Kim, Y. Ji, and K.-B. Lee, "An effective sign language learning with object detection based roi segmentation," in Second IEEE International Conference on Robotic Computing (IRC), Laguna Hills: CA, USA, pp. 330-333, 2018.
- M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, "2d human pose estimation: New benchmark and state of the art analysis," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus: OH, USA, pp. 3686-3693, Jun. 2014.
- M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, "TensorFlow: A System for Large-Scale Machine Learning," in 12th USENIX symposium on operating systems design and implementation (OSDI 16), Savannah: GA, USA, pp. 265-283, 2016.
- D. G. Kim, Y. S. Park, L. J. Park, and T. Y. Chung, "Developing of new a tensorflow tutorial model on machine learning: focusing on the Kaggle titanic dataset," IEMEK Journal of Embedded Systems and Applications, vol. 14, no. 4, pp. 207-218, Aug. 2019. https://doi.org/10.14372/IEMEK.2019.14.4.207
- T. H. Trieu, Darkflow, GitHub Repository. 2018, [Online] Available: https://github.com/thtrieu/darkflow.(accessed on 14 February 2019)
- H. Yun and D. Park "Yolo-based Realtime Object Detection using Interleaved Redirection of Time-Multiplexed Streamline of Vision Snapshot for Lightweighted Embedded Processors," in 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Hualien City, Taiwan, pp. 1-2, Nov. 2021.
- NXP. Layerscape LS1028A Family of Industrial Applications Processors [Internet], Available: https://www.nxp.com/docs/en/fact-sheet/ls1028afs.pdf.
- J. T. Townsend, "Theoretical analysis of an alphabetic confusion matrix," Perception & Psychophysics, vol. 9, no. 1, pp. 40-50, Jan. 1971. https://doi.org/10.3758/BF03213026
- J. Ma, L. Chen, and Z. Gao, "Hardware implementation and optimization of tiny-YOLO network," in International Forum on Digital TV and Wireless Multimedia Communications, Shanghai, China, pp. 224-234, Nov. 2017.
- B. Stojanovic, O. Marques, A. Neskovic, and S. Puzovic, "Fingerprint roi segmentation based on deep learning," in 24th Telecommunications Forum (TELFOR), Belgrade, Serbia, pp. 1-4, 2016.
- W. Sun, B. Zheng, and W. Qian, "Automatic feature learning using multichannel roi based on deep structured algorithms for computerized lung cancer diagnosis," Computers in biology and medicine, vol. 89, pp. 530-539, Oct. 2017. https://doi.org/10.1016/j.compbiomed.2017.04.006
- A. Cerentinia, D. Welfera, M. C. d'Ornellasa, C. J. P. Haygertb, and G. N. Dotto, "Automatic identification of glaucoma using deep learning methods," in Proc. 16th World Congr. Med. Health Informat. Precision Healthcare Through Informat.(MEDINFO), Hangzhou, China, vol. 245, pp. 318-321, 2018.
- C. Oksuz, O. Urhan, and M. K. Gullu, "Covid-19 detection with severity level analysis using the deep features, and wrapper-based selection of ranked features," Concurrency and Computation: Practice and Experience, p. e6802, Dec. 2021.