Abstract: A signal processing method is provided and involves iteratively deconvoluting at least one digital signal data set with respect to time. A signal processor is also provided that can perform a signal processing method for iteratively deconvoluting at least one digital signal data set. Also provided is an instruction set readable by a machine, tangibly embodying a program of instructions executable by a machine to perform a signal processing method of iteratively deconvoluting at least one digital signal data set. Also provided is a data set readable by a machine, tangibly embodying a data set computed by a signal processing method for iteratively deconvoluting at least one digital signal data set.

Abstract: A system and methods for evaluating genetic information and biological data applying a clustering approach which may be used for allele calling and genotyping. Statistical analysis of sample data is performed at various levels to develop a model which associates individual data points with selected genotyping clusters and provides a relative indication of the call confidence. The methods provide a unified framework for allele-calling in many different contexts and may be applied to the data acquired from various identification methodologies.

Abstract: The performance of electrochemical energy devices such as batteries, fuel cells, capacitors and sensors is enhanced by the use of electrically conducting ceramic materials in the form of fibers, powder, chips and substrates.

Abstract: An improved signal processing method in a signal processor for performing the deconvolution of a signal resulting from an analytical separation process is disclosed. In a first aspect, a signal representing a plurality of partially separated sample zones is measured, a point-spread-function of the signal is determined, and the Fourier transform of the signal and the point-spread-function is taken. Next, a noise component n of the signal is determined and the value of a result signal A(f) is calculated using the following filter ##EQU1## where D(f) is the Fourier transform of the signal, P(f) is the Fourier transform of the point-spread-function, and P*(f) is the complex conjugate of P(i). Finally, the inverse Fourier transform of the result A(f) is taken and reported as A(t). Preferably, the point-spread function is a Gaussian function having a standard deviation .sigma., where .sigma. is determined using either an .alpha..beta. tracker or the function .sigma.=(a+bt.sup.2).sup.