| 1. noise_filter(data, n): /parameter n refers to the packet size/ |
| 2. length <- length of data |
| 3. /group data/ |
| 4. group[ ] <- each group contains n consecutive signals |
| 5. /filter noise/ |
| 6. noise_filter_data = [] |
| 7. for goup_data in the group do |
| 8. low_num <- the factor with the ranking percentage of B% |
| 9. high_num <- the factor with the ranking percentage of A% |
| 10. temp_data = noise_filter_and_replace(goup_data, low_num, high_num) |
| 11. noise_filter_data.append(tem_data) /Merge all denoised groups/ |
| 12. return noise_filter_data |
| 13. |
| 14. /delete outliers and fill the blank places/ |
| 15. noise_filter_and_replace(data, low_num, high_num): |
| 16. /If the noise points exist in the first position or the last position, they need to be processed separately/ |
| 17. for index <-1 to len(data-1) do |
| 18. if data[index] is noise point then |
| 19. Find the nearest two non noise points data[first_weight] and data[second_weight] to the left and right |
| 20. Calculate the distance x, y to the point |
| 21. first_weight <- y / (x + y) |
| 22. second_weight <- x / (x + y) |
| 23. data[index] <- data[first_weight] first_weight + data[second_weight] second_weight |
| 24. return data |
