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Identification of aerosol type and chemical composition may help to trace their origin and estimate their impact on land and people. Aerosols chemical composition, size distribution and particles shape, manifest themselves in their spectral scattering cross-section. In order to make a reliable identification, comprehensive spectral analysis of aerosol scattering should be carried out. Usually, spectral LIDAR measurements of aerosols are most efficiently performed using an Nd:YAG laser transmitter in the fundamental frequency and its 2^nd, 3^rd and 4^th harmonics. In this paper we describe automatic detection and identification of several aerosol types and size distributions, using a multi-spectral lidar system operating in the IR, NIR and UV spectral regions. The LIDAR transmitter is based on a single Nd:YAG laser. In addition to the 3^rd and 4^th harmonics in the UV, two optical parametric oscillator units produce the eye-safe 1.5 µm wavelength in the near IR and up to 40 separable spectral lines in the 8-11 µm IR. The combination of a wide spectral coverage required for backscattering analysis combined with fluorescence data, enable the generation of a large spectral data set for aerosols identification. Several natural and anthropogenic aerosol types were disseminated in controlled conditions, to test system capabilities. Reliable identification of transient and continuous phenomena demands fast and efficient control and detection algorithms. System performance, using the specially designed algorithms, is described below.

In this paper, we consider the use of blind deconvolution for optoacoustic (photoacoustic) imaging and investigate the performance of the method as means for increasing the resolution of the reconstructed image beyond the physical restrictions of the system. The method is demonstrated with optoacoustic measurement obtained from six-day-old mice, imaged in the near-infrared using a broadband hydrophone in a circular scanning configuration. We find that estimates of the unknown point spread function, achieved by blind deconvolution, improve the resolution and contrast in the images and show promise for enhancing optoacoustic images.

Proteins constitutively function within networks. Concurrent detection of multiple proteins is crucial to clinical diagnoses and multidimensional drug profiling. Fluorescence microscopy is capable of multicolor imaging, and has the capability to quantify essentially any physiological changes that occur at the single-cell level and in the context of live single cells, and thus provides an alternative to flow cytometry for multiplexed live single-cell assay. The staining of cells with multiple labels is still a technical challenge while multiplexed assays are complicated by spectral emission overlaps and measurement errors. In this study, we applied emission fingerprinting technique provided by Zeiss LSM 510 META detector, and achieved concurrent detection of ten proteins expressed on the same endothelial cell sample. This approach can be further applied to real-time measurement of multiple proteins expressed on live single cell surface, and therefore will enable a novel approach of multiplexed live single cell detection.

Identification of aerosol type and chemical composition may help to trace their origin and estimate their impact on land and people. Aerosols chemical composition, size distribution and particles shape, manifest themselves in their spectral scattering cross-section. In order to make a reliable identification, comprehensive spectral analysis of aerosol scattering should be carried out. Usually, spectral LIDAR measurements of aerosols are most efficiently performed using an Nd:YAG laser transmitter in the fundamental frequency and its 2^nd, 3^rd and 4^th harmonics. In this paper we describe automatic detection and identification of several aerosol types and size distributions, using a multispectral lidar system operating in the IR, NIR and UV spectral regions. The LIDAR transmitter is based on a single Nd:YAG laser. In addition to the 3^rd and 4^th harmonics in the UV, two optical parametric oscillator units produce the eye-safe 1.5 μm wavelength in the near IR and up to 40 separable spectral lines in the 8-11 μm IR. The combination of a wide spectral coverage required for backscattering analysis combined with fluorescence data, enable the generation of a large spectral data set for aerosols identification. Several natural and anthropogenic aerosol types were disseminated in controlled conditions, to test system capabilities. Reliable identification of transient and continuous phenomena demands fast and efficient control and detection algorithms. System performance, using the specially designed algorithms, is described below.

Machine learning (ML) approaches as part of the artificial intelligence domain are becoming increasingly important in multispectral and hyperspectral remote sensing analysis. This is due to the fact that there is a significant increase in the quality and quantity of the remote sensing sensors that produce data of higher spatial and spectral resolutions. With higher resolutions, more information can be extracted from the data, which require more complex and sophisticated techniques compared to the traditional approaches of data analysis. Machine learning approaches are able to analyse remote sensing (RS) data more effectively and give higher classification accuracy. This review will discuss and demonstrate some applications of machine learning techniques in the processing of multispectral and hyperspectral remote sensing data. Future recommendations will also be given to highlight the way forward in the use of machine learning approaches in optical remote sensing data analysis.