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A Comparative Analysis of Adaptive IIR Filtering Techniques using LabVIEW
Divya Sharma,Rashpinder Kaur,Gurjinder Singh 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.8
Removal of noises from real-time speech signal is a typical problem. The signal interference initiated by background noise is a major problem in voice communication systems. Adaptive Filtering methods have emerged as an important technology for communication systems. This technique has been employed to improve the quality of the speech signal by cancelling the undesirable phenomenon such as acoustic noise. In this paper, for the removal of additional noise from speech signal an adaptive filter has been designed using LMS, NLMS, SLMS and VSS-LMS algorithms. This paper presents the instigation of Least Mean Square algorithm (LMS), Normalized Least Mean Square algorithm (NLMS), Sign Least mean square algorithm (SLMS) and Variable step size (VSS) algorithm on an infinite impulse response (IIR) filter using adaptive filter toolkit of LabVIEW software. User interface is designed using LabVIEW to obtain the learning curves for these adaptive algorithms. The final results show the comparison of the performance of the entire proposed algorithms with each other. The complete performance of the designed system in terms of stability and convergence rate has been observed.
Paramvir Singh,Rashpinder Kaur,Gurjinder Singh 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.7
In this recent era of technological development, innovation advances in every aspect of life can be easily seen. Automatic systems were developing at very fast pace. Automatic license late recognition is one of the systems that help in developing intelligent transport system in a city or country. This also finds application in the areas of vehicle surveillance, border security, toll tax management etc. This paper evaluates the performance analysis for recognition of image with alphanumeric characters under different environmental conditions. The overall performance recognition rate was 97.57%