應用於人體姿勢辨識與機器人之可重組深度神經網路引擎－子計畫二：應用於強化式學習之可重組深度神經網路技術(3/3)

Mathematics

1. Electrocardiography 100%
2. Spectrogram 81%
3. Neural Networks 65%
4. Preprocessing 40%
5. Real-time 39%
6. Power Consumption 28%
7. Field Programmable Gate Array 27%
8. Short-time Fourier Transform 26%
9. Cycle 24%
10. Latency 22%
11. Hardware 21%
12. Detector 20%
13. Classifier 19%
14. Eliminate 17%
15. Two Dimensions 15%
16. Convolution 14%
17. kernel 11%
18. Demonstrate 10%

Engineering & Materials Science

1. Reinforcement learning 70%
2. Electrocardiography 69%
3. Convolutional neural networks 63%
4. Deep neural networks 45%
5. Particle accelerators 23%
6. Electric power utilization 21%
7. Wearable technology 19%
8. Intelligent computing 17%
9. Fourier transforms 17%
10. Convolution 17%
11. Computer hardware 17%
12. Reconfigurable architectures 15%
13. Engines 14%
14. Clocks 14%
15. Field programmable gate arrays (FPGA) 14%
16. Data storage equipment 14%
17. Detectors 13%
18. Classifiers 12%
19. Supervised learning 12%
20. System-on-chip 11%
21. Artificial intelligence 10%
22. Mobile computing 9%
23. Throughput 8%
24. Neural networks 7%