萬能科技大 學資工系 助理教授 徐旺興 Email: kimble@vnu.tw

1. Computational Intelligence and its Applications • 計算智慧及其應用 萬能科技大學資工系 助理教授 徐旺興 Email: kimble@vnu.edu.tw

2. Outline • Motivation • Computational Intelligence • Fuzzy, ANFIS, SVM, HMM and GMM • Frequency Calibration based on the ANFIS • Handwriting Recognition on Handheld Devices using Accelerometers • Conclusion • Future work

3. Motivation (1/2) • Studied in past years • QoS: Simulation • NGN: Framework improvement • SIP: multimedia (client/server) • Handoff: BS, Agent or Broker and Mobile Device • Time & Frequency: Control system • 3D Handwriting recognition

4. Motivation (2/2) • Issue of time series

5. What is Computational Intelligence • CI related to other branches of computer science, such as artificial intelligence (AI), classification, data mining, graphical methods, intelligent agents and intelligent systems, machine intelligence, machine learning, natural computing, parallel distributed processing, pattern recognition, probabilistic methods, soft computing, multivariate statistics, optimization and operation research.

6. Frequency Calibration based on the ANFIS • Implement a control system • Normal mode and holdover mode • Technology of CI • Fuzzy - a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values in interval [0,1], in contrast to classical or digital logic, which operates on discrete values of either 0 or 1 (true or false). • ANFIS - learns features in the data set and adjusts the system parameters according to a given error criterion.

7. The requirements of time and frequency accuracy for the dominant wirelesstechnologies.

8. OCXO (Oven-Controlled Crystal Oscillator) • The stability of OCXO base on environmental effects such as vibration, temperature, pressure and humidity.

9. System architecture (1/2) Slave Clock 1pps 1pps Voltage: +/-10V Frequency offset 1pps=one plus per second

10. System architecture (2/2) • The frequency offset with respect to time • Their change are the input variables of the fuzzy controller An incremental voltage generated by the fuzzy controller is used to update the voltage for steering the oscillator below.

11. Fuzzy rule table Ri: ifyis Ai1and yisAi2 thenu is Bi,for i 1,2…n Ai2 The input space is divided into five sets: negative big (NB), negative small (NS), zero (ZE), positive small (PS) and positive big (PB) for a frequency offset or its change. Ai1 Bi

12. Component of the system (1/2) • Cesium (HP5071A) • 10 MHz of a cesium atomic clock (10-14) • OCXO (FTS1130) • 10 MHz of oven-controlled crystal oscillator (10-8) • TIC (SR620) • time interval counter • Time interval and frequency counter

13. Component of the system (1/2) • D/A • ADLINK PCI-6208 • 16-bit resolution with the bi-polar • Voltage: 10V to +10V • Fuzzy controller • Software coding by C/C++, Matlab • ANFIS controller • Software coding by C/C++, Matlab

14. Two mode in this system • Normal Mode • Fuzzy controller • Collecting the control signal • To train the ANFIS controller simultaneously • Handover Mode • ANFIS controller • When the signal of the primary clock is lost • The voltage (control signal) is predicted by the ANFIS controller

15. Experimental setup • The OCXO is steered every 10s by the fuzzy controller or the ANFIS controller to syntonize with the primary clock. • Choice about five-day input-output data pairs: • The first four-day pairs were used for training the ANFIS. • The remaining about one-day pairs were used for validating the identified model.

16. Experimental results and analysis The desired data The predicted data The Prediction error

17. Frequency stability

18. Conclusion of this chapter • The frequency stability of the OCXO could be improved from a few parts in 10-9 to 10-12over a measurement period of one day. (Normal Mode) • Holdover Mode shows the frequency stability of the OCXO could be maintained within a few parts in 10-11 for an averaging time of on day.

19. 3D Handwriting Recognition • Accelerometer • 3D gesture • Pattern Recognition • Mobile Device’s Accelerometer (30Hz~70Hz) • Device • HTC G1 • Software component • Collectors (Java code) • Training and recognition (matlab code), off-line.

20. Proposed method • WLCS + SVM • HMM + GMM

21. The architecture of the proposed 3D handwriting recognition system

22. Three axes acceleration data of pattern ‘Kimble’.

23. Data preprocessing

24. Data Training

25. Longest Common Subsequence（LCS） • Example of LCS • s1: 2 5 7 9 3 1 2 • s2: 3 5 3 2 8 • LCS: 5 3 2

26. Weight LCS

27. Data Classification – SVM (1/3) To Find the Hyper-plane (e.g. 2D’s hyper-plane is a line)

28. SVM (2/3) To Find the optimal Hyper-plan H

29. SVM (3/3)

30. Experimental setup • We collect a set of 26 gestures (alphabet), 20 samples per gesture from 3 different persons, totaling 1560 gestures samples. • 50 samples for training and 10 samples for testing.

31. The average length of the WLCS between letters Models Test data

32. Performance Criteria • The classification performance can be evaluated using mis-classification rate such as apparent error rate and/or graphical representation tools such as the receiver operating characteristic (ROC) curve.

33. Terms associated ROC curve

34. An example of ROC • The table shows 20 data and the score assigned to each by a scoring classifier Sorting by score

35. ROC curve of the example 10 positive points at y-axis 10 positive points at x-axis

36. Max. and Min. AUC (Area under curve) The Min. AUC is alphabet ’G’ The Max. AUC is alphabet ’C’.

37. List of AUC from ‘A’ to ‘Z’ • the alphabet such ‘C’, ‘L’, ‘P’, ‘S’, ‘U’, ‘V’ and ‘Z’ is good instance and • ’G’ is a randomly chosen negative instance.

38. Summary about LCS +SVM • LCS + SVM is the lite-computing algorithm, the average accuracy is 86.85%

39. Hidden Markov Model (HMM) • HMMs allow you to estimate probabilities of unobserved events. • Given plain text, which underlying parameters generated the surface. • E.g., in speech recognition, the observed data is the acoustic signal and the words are the hidden parameters.

40. HMMs and their Usage • HMMs are very common in Computational Linguistics: • Speech recognition (observed: acoustic signal, hidden: words) • Handwriting recognition (observed: image, hidden: words)

41. Parameters of an HMM • States: A set of states S=s1,…,sn • Transition probabilities: A= a1,1,a1,2,…,an,nEach ai,j represents the probability of transitioning from state si to sj. • Emission probabilities: A set B of functions of the form bi(ot) which is the probability of observation ot being emitted by si • Initial state distribution: is the probability that si is a start state

42. The Three Basic HMM Problems (1/2) • Problem 1 (Evaluation): Given the observation sequence O=o1,…,oT and an HMM model , how do we compute the probability of O given the model? • Problem 2 (Decoding): Given the observation sequence O=o1,…,oT and an HMM model , how do we find the state sequence that best explains the observations?

43. The Three Basic HMM Problems (2/2) • Problem 3 (Learning): How do we adjust the model parameters , to maximize ?

44. Example of HMM The states The observations

45. An Example model, the semi-code of HMM

46. The dynamic programming computation

47. Diary data and reconstructed weather

48. In this work • Given the observation sequence O=o1,…,oT, • e.g. 5555222233344433377001111111…. • Build 26 Model HHMA, HHMB, … HMMz • Training each model by EM algorithm. (Problem 3) • Recognition, compute the probability of O given the model. (Problem 1) (Forward-Backward Algorithm)

49. GMM – Gaussian Mixture model

50. Experimental setup • We collect a set of 26 gestures (alphabet), 20 samples per gesture from 3 different persons, totaling 1560 gestures samples.