We present the design of a strain gauge embedded in polydimethylsiloxane (PDMS) that could be implanted and used for monitoring strain on surfaces of bones with high resolution. Our ultimate goal is to design and fabricate the device such that it could be used to provide real-time data of strain development in live subject. We have simulated the mechanical characteristics of thin-film metal strain gauges embedded in a flexible substrate made from polydimethyl-siloxane (PDMS) with various loading conditions using ANSYS/spl reg/ finite element analysis tool. Various gauge designs were subjected to stresses from several different directions. Linear relationships between fractional change of resistance and nominal resistance were found for both tensile and compressive stress applied on the gauges. Most significantly, the simulation demonstrated that external stresses were effectively transmitted through the PDMS layer to the thin-film metal, validating our approach.

The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-lambda, where r is display visual resolution in pixels/degree, and lambda is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.