Conventional optical analysis tools containing an integrated computational element may have an operational profile that is too large for convenient use within confined locales. Optical analysis tools having a miniaturized operational profile can comprise: an electromagnetic radiation source that provides electromagnetic radiation to an optical train; and an optical computing device positioned within the optical train. The optical computing device comprises a planar array detector having at least two optical detection regions. At least one of the at least two optical detection regions has an integrated computational element disposed thereon. The planar array detector and the integrated computational element are in a fixed configuration with respect to one another.

An object recognition apparatus includes a first spectrometer configured to obtain a first type of spectrum data from light scattered, emitted, or reflected from an object; a second spectrometer configured to obtain a second type of spectrum data from the light scattered, emitted, or reflected from the object, the second type of spectrum data being different from the first type of spectrum data; an image sensor configured to obtain image data of the object; and a processor configured to identify the object using data obtained from at least two from among the first spectrometer, the second spectrometer, and the image sensor and using at least two pattern recognition algorithms.

A hybrid spectral imager apparatus has an imaging head arrangement (IHA), a control and processing unit (CPU), and a display. The IHA includes an optical imager, multiband filtering optics (MBFO), and a sensor arrangement. The optical imager collects and focuses an image of a target scene or object along an imaging path. The multiband optics includes a beam divider for generating at least two replica images of the target image, and a multiband filter (MF) interposed into the imaging path and effecting multi-bandpass filtering in the image replicas. The sensor arrangement has at least one Mosaic filter array (MFA) focal plane array (FPA) sensor onto which the multiband filtered image replicas are focused, and a focal plane array masked, in a pixelized manner, with at least three wide-band primary color-type filters, with each primary color-type response separating and capturing one single-band. The CPU is coupled to the IHA and is configured to execute program instructions for calibrating image acquisition processes, controlling and synchronizing the acquisition/capturing of the image replicas by the MFA sensor arrangement, and spectrally purifying the MFA sensor arrangement responses to compensate band cross-talking between the MFA and the MF. The display is configured to display on a user interface at least the acquired single-band images. The CPU is further configured to reconstruct and display, on the display, a set of at least three different single-band images per MFA-FPA sensor employed. The IHA is configured to capture sets of different single-band images for video snapshot spectral imaging at desired spectral bands within the FPA sensor arrangement spectral sensitivity range.

A totagram is produced by an iterative spectral phase recovery process resulting in complete information recovery using special masks, without a reference beam. Using these special masking systems reduce computation time, number of masks, and number of iterations. The special masking system is (1) a unity mask together with one or more bipolar binary masks with elements equal to 1 and −1, or (2) a unity mask together with one or more phase masks, or (3) a unity mask together with one pair of masks or more than one pair of masks having binary amplitudes of 0's and 1's, in which the masks in the pair are complementary to each other with respect to amplitude, or (4) one or more pairs of complementary masks with binary amplitudes of 0's and 1's without a unity mask.

The disclosed embodiments include thin film multivariate optical element and detector combinations, thin film optical detectors, and downhole optical computing systems. In one embodiment, a thin film multivariate optical element and detector combination includes at least one layer of multivariate optical element having patterns that manipulate at least one spectrum of optical signals. The thin film multivariate optical element and detector combination also includes at least one layer of detector film that converts optical signals into electrical signals. The thin film optical detector further includes a substrate. The at least one layer of multivariate optical element and the at least one layer of detector film are deposited on the substrate.

In some implementations, a device may determine that an unknown sample is an outlier sample by using an aggregated classification model. The device may determine that one or more spectroscopic measurements are not performed accurately based on determining that the unknown sample is the outlier sample. The device may cause one or more actions based on determining the one or more spectroscopic measurements are not performed accurately.

A method for optimizing an output result of a spectrometer and an electronic device using the method are provided. The method includes the following. First spectral data and second spectral data are obtained. A plurality of pipelines including a first pipeline and a second pipeline are obtained. The first pipeline is selected from the plurality of pipelines as a selected pipeline. The output result corresponding to the second spectral data is generated according to the selected pipeline. A performance of the first pipeline is calculated according to the first spectral data, and a first instruction is generated according to the performance. The selected pipeline is changed into the second pipeline according to the first instruction to update the output result.

The present disclosure provides a spectral imaging chip and apparatus, an information processing method, a fingerprint living body identification device and a fingerprint module. The spectral imaging chip can obtain spectral information of a captured object without affecting the spatial resolution and imaging quality of the resulting image, which is convenient for grasping more comprehensive information of the object to be imaged. The fingerprint living body identification device and fingerprint module can realize fingerprint living body identification through the spectral imaging chip, which is advantageous to improve the stability of the component performance, while reducing the volume, weight and cost of the spectral components, greatly improving the anti-counterfeiting ability of the fingerprint identification system.

In some implementations, a device may receive spectroscopic data associated with a dynamic process. The device may generate a principal component analysis (PCA) model based on a first block of spectra from the spectroscopic data. The device may project a second block of spectra from the spectroscopic data to the PCA model generated based on the first block of spectra. The device may determine a value of a metric associated with the second block based on projecting the second block of spectra to the PCA model. The device may determine whether the dynamic process has reached an end point based on the value of the metric associated with the second block.

DCS analyzer including a memory to store autocorrelation values, model parameters, fitting parameters, and simulated correlation values from a DCS model; a mean square error (MSE) module to compute MSE between theoretical autocorrelation values computed from the model parameters and measured autocorrelation values; a sorting module to sort three latest MSE values obtained from the MSE module and generate indexes of largest, medium, and smallest MSE values; a convergence checking module to determine whether convergence is reached in solving an autocorrelation equation; a search module to calculate αD.sub.B and β values at reflection, extension, contraction, and shrink locations; a comparison module to compare two latest MSE values and find new αD.sub.B and β values to replace values associated with a largest MSE; a state controller coupled with the memory and the modules to control an operation thereof; and an output buffer to present a fitted solution of the autocorrelation equation.