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31. In our discussion, classification is a significant component of the pattern recognition system, but unsupervised learning may also play a role there.

32. A speaker dependent, isolated word and small vocabulary embedded speech recognition system is designed and implemented.

33. The paper introduces system configuration, recognition principle, detection procedure, key technology and solutions of LED shape recognition system established on the SGI workstation.

34. A speech recognition system is designed to illustrate the twin rotor helicopter speech control function.

35. Face Recognition System code integrity, and can be used directly.

36. This paper briefly introduces the flying spot scanner of the "789" model of Optical Character Recognition system developed in Futan University.

37. In order to make the speech recognition system maintain the good performance under these noise conditions, we must use various methods to enhance the robustness of system.

38. In a mandarin tone recognition system, the parameter is usually pitch contour. But the tone can also be partly characterized by pitch difference, energy and energy difference.

39. In the SVM-based speaker recognition system study, it is important to obtain a ideal recognition rate.

40. The main goal of this subject is realization of the automatic face recognition system.

41. A novel method to account for dynamic speaker characteristic properties in a speech recognition system is presented.

42. Voice recognition system using neural network complete source code, has been tested.

43. This segmentation algorithm that has promising results (the segmentation correct rate reach to 86.3%) and is then incorporated into a complete bank check character recognition system.

44. Feature extraction of targets radiated - noise is the key technique of passive sonar target recognition system.

45. A. Net speech recognition system complete source code can be directly used by the test.

46. It is essential to realize the automatic face recognition system.

47. Fifthly, the design and realization of doorplate recognition system based on DM642 is presented, including program migration from PC to DSP, program optimization on DM642 platform.

48. This paper discusses pattern recognition system based on artificial neural networks, which uses mathematics software MATLAB.

49. The location of the vehicle license plate is the first step in the automotive vehicle license plate recognition system and the accuracy of locating plays an important role in this system.

50. Prompts that expect a response can either provide a list of valid words that are accepted and recognized by the voice recognition system, or you can ask for input through the telephone keypad.

51. In that case, one could argue that this glitch in our patter - recognition system is helpful.

52. Fingerprint classification(FC) which has been the hot point and hard point of researchers in national or aboard is an important composing of Automatic Fingerprint Recognition System(AFRS).

53. Through experiment investigation, this paper proposed future work for improving pattern recognition system based on VHM.

54. Instead, you must specify the words and phrases that you expect to receive so that the voice recognition system is more accurate.

54. Sentencedict.com try its best to gather and build good sentences.

55. The infrared pattern recognition system consists of image preprocessing, feature extraction and pattern classification.

56. Objective To evaluate the accuracy of computerized automatic recognition system of cephalometry.

57. The visual features can improve the performance of the speech recognition system under noisy environment.

58. Cervical smear recognition system bases on neural network is designed and developed. The system can be used to merge, classify and standardize files of the cell feature parameters automatically.

59. According to this algorithm, a numerical recognition system based on BP neural network was designed. This system could be put into the application of numerical recognition such as bill system.

60. A iris recognition system is based on four parts: iris image sampling, iris image preprocessing, feature extraction, iris image matching and recognition.