Compression, estimation, and analysis of ultrasonic signals

Ultrasonic imaging of materials often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data size and to facilitate the analysis and remote access of ultrasonic information. Hence, the locally obtained ultrasonic signals can be transferred efficiently through wireless or wired communication channels to the remotely located experts. In this research, we analyze different signal processing techniques to compress and denoise ultrasonic signals. We also developed a reconfigurable hardware architecture implementation of an ultrasonic signal processor that achieves high speed, high data volume, and reconfigurability.; The precise ultrasonic data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. We introduce a successive parameter estimation technique that identifies echoes, compresses and denoises ultrasonic signals. This technique uses a modified version of the continuous wavelet transform to decompose the ultrasonic signal in Gaussian shaped echoes. Furthermore, a chirplet transform is employed to decompose the ultrasonic signal in chirp-shaped echoes. These techniques provide a high resolution and accurate estimation of the echo parameters.; Ultrasonic data is often embedded in noise. Hence, we introduce a technique to estimate an adaptive thresholding function that uses the statistical parameters of the noise embedded in the signal. Then, the statistical parameters are used to generate a thresholding function based on the probability distribution function of the noise. We analyze the performance of adaptive and classical thresholding techniques when applied to the discrete wavelet transform (DWT), discrete cosine transform (DCT), and Walsh-Hadamard transform (WHT) coefficients. The results show that the adaptive thresholding technique is a very powerful method that allows the detection of low SNR ultrasonic backscattered echoes.; We also developed subband and transform coding techniques to compress ultrasonic signals. In particular, the data compression performance of the DCT, WHT, and DWT are examined using simulated and experimental ultrasonic data. The results obtained show that the DWT is better in the representation of broadband signals, while the DCT and the WHT are more suitable in the representation of narrowband signals.