Flat field concave holographic grating with broad spectral region and moderately high resolution.

In order to deal with the conflicts between broad spectral region and high resolution in compact spectrometers based on a flat field concave holographic grating and line array CCD, we present a simple and practical method to design a flat field concave holographic grating that is capable of imaging a broad spectral region at a moderately high resolution. First, we discuss the principle of realizing a broad spectral region and moderately high resolution. Second, we provide the practical method to realize our ideas, in which Namioka grating theory, a genetic algorithm, and ZEMAX are used to reach this purpose. Finally, a near-normal-incidence example modeled in ZEMAX is shown to verify our ideas. The results show that our work probably has a general applicability in compact spectrometers with a broad spectral region and moderately high resolution.

Dwell time algorithm in ion beam figuring.

To get a high-precision optical surface, the deconvolved process of dwell time was transferred to a matrix equation in which the damped factor and the extra removal amount were introduced to expand the freedom of solution. A path weight factor and a surface error weight factor were used to take the scanning path and the initial surface error into account. Combined with the Gerchberg bandlimited extrapolation algorithm for initial surface error map extension, a high-precision final surface could be obtained within a factual aperture. Two surface error maps were calculated to rms = 0.1 nm from rms = 130.23 nm and to rms = 0.08 nm from rms = 82.74 nm. The simulations show that a perfect dwell time solution could be obtained by the revised matrix equation and initial surface error map extension with the help of the least squares QR (LSQR) algorithm.

Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery.

Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors.