Binary data compression by linear transformation

TL;DR: This paper proposes a novel methodology for detecting systematic spatial failure patterns at wafer level for yield learning by taking the testing results of a number of dies over different wafers, cluster all these wafer according to their failures, and eventually identify the underlying spatialFailure patterns.

TL;DR: Three algorithms are developed for the compression of one-dimensional data by loosely coupled cubic splines obtained from NOAA radiosonde/radiometric data and the results are compared with compression by finite-length discrete Fourier series computed by FFT procedures.

TL;DR: A high-speed computational algorithm, similar to the fast Fourier transform algorithm, which performs the Hadamard transformation has been developed, which provides a potential toleration to channel errors and the possibility of reduced bandwidth transmission.

TL;DR: The coding theory can be extended to include the minimization of the expectation of any positive definite quadratic function of the differences between the original and reconstructed signals.