Abstract: An optical processor is presented for applying optical processing to a light field passing through a predetermined imaging lens unit. The optical processor comprises a pattern in the form of spaced apart regions of different optical properties. The pattern is configured to define a phase coder, and a dispersion profile coder. The phase coder affects profiles of Through Focus Modulation Transfer Function (TFMTF) for different wavelength components of the light field in accordance with a predetermined profile of an extended depth of focusing to be obtained by the imaging lens unit. The dispersion profile coder is configured in accordance with the imaging lens unit and the predetermined profile of the extended depth of focusing to provide a predetermined overlapping between said TFMTF profiles within said predetermined profile of the extended depth of focusing.

Abstract: A system, a method, and a non-transitory computer readable for generating a profile of one or more data objects comprising determining a format of the at least one data object and selecting a data transformation policy based on the format of the at least one data object and generating a model descriptive of the non-structured data contained in the at least one data object based on the data transformation policy and selecting at least a portion of the model indicative of a portion of the non-structured data and generating a profile of the portion of the non-structured data contained in all of the one or more data objects.

Abstract: A system, a method, and a non-transitory computer readable for generating a profile of one or more data objects comprising determining a format of the at least one data object and selecting a data transformation policy based on the format of the at least one data object and generating a model descriptive of the non-structured data contained in the at least one data object based on the data transformation policy and selecting at least a portion of the model indicative of a portion of the non-structured data and generating a profile of the portion of the non-structured data contained in all of the one or more data objects.

Abstract: A method for radical linear compression of datasets where the data are dependent on some number M of parameters. If the noise in the data is independent of the parameters, M linear combinations of the data can be formed, which contain as much information about all the parameters as the entire dataset, in the sense that the Fisher information matrices are identical; i.e. the method is lossless. When the noise is dependent on the parameters, the method, although not precisely lossless, increases errors by a very modest factor. The method is general, but is illustrated with a problem for which it is well-suited: galaxy spectra, whose data typically consist of about 1000 fluxes, and whose properties are set by a handful of parameters such as age, brightness and a parameterized star formation history. The spectra are reduced to a small number of data, which are connected to the physical processes entering the problem.