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A new microbend fiber-optic sensor has been developed and applied to static and, dynamic fracture problems and impact detection. The static and dynamic calibration of this sensor is described. The sensor was made using a multimode fiber and the response of the sensor was studied and optimized. It has a strain sensitivity (gage factor) of over 28 with wide dynamic range and high frequency response. A single edge notched specimen was used with this sensor to determine both the static and dynamic stress intensity factors. The experimental results match well with the theoretical predictions and with the results obtained using electrical strain gages.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, SUPPLEMENTARY NOTES ABSTRACTImage compression is performed to reduce the bit rate for image transmission and the required storage in the case of image archival. Typically, it involves a transformation of the image to reduce the correlation and redundancy between pixels. The wavelet transform has gained wide acceptance as a tool to decorrelate most image sources. The transformed image is decomposed at different scales of details and approximation. Such decomposition facilitates image compression and progressive transmission. In this study, a system and method for image compression is presented based on the wavelet transformation. A threshold is determined adaptively based on the desired compression ratio and only significant coefficients are retained. . SUBJECT TERMS . Image Data Compression by Adaptive Thresholding of Wavelet CoefficientsMahmoud A. Abdallah Ram-Nandan P. Singh ABSTRACTImage compression is performed to reduce the bit rate for image transmission and the required storage m the case of image archival. Typically, it involves a transformation of the image to reduce the correlation and redundancy between pixels. The wavelet transform has gained wide acceptance as a tool to decorrelate most image sources. The transformed image is decomposed at different scales of details and approximation. Such decomposition facilitates image compression and progressive transmission. In this study, a system and method for image compression is presented based on the wavelet transformation. A threshold is determined adaptively based on the desired compression ratio and only significant coefficients are retained.

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