This article describes a method of an off-line signature recognition by using hough transform to detect stroke lines from signature image. The hough transform is used to extract the parameterized hough space from signature skeleton as unique characterisitic feature of signatures. In the experiment, the Back Propagation Neural Network is used as a tool to evaluate the performance of the proposed method. The system has been tested with 70 test signatures from different persons. The experimental results reveal the recognition rate 95.24 YO.

-This paper describes a novel method of rotodynamic machine condition monitoring using a wavelet transform and a neural network. A continuous wavelet transform is applied to the signals coUected from accelerometer. The transformed images are then extracted as unique characteristic features relating to the various types of machine conditions. In the experiment, four types of machine operating conditions have been investigated: a balanced shaft, an unbalanced shaft, a misaligned shaft and a defective bearing. The back propagation neural network (BPNN) is used as a tool to evaluate the pegormance of the proposed method. The experimental results result in a recognition rate of 90percent.Kevwords -Wavelet transform, neural network, rotodynamic machinery.

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Figure 1 illustrates a systematic diagram of the proposed bearing condition monitoring technique.

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