In this Letter we propose an adaptive scene-based nonuniformity correction method for fixed-pattern noise removal in imaging arrays. It is based on the minimization of the total variation of the estimated irradiance, and the resulting function is optimized by an isotropic total variation approach making use of an alternating minimization strategy. The proposed method provides enhanced results when applied to a diverse set of real IR imagery, accurately estimating the nonunifomity parameters of each detector in the focal-plane array at a fast convergence rate, while also forming fewer ghosting artifacts.

Gold nanoparticles (AuNPs) are currently under intense investigation for biomedical and biotechnology applications, thanks to their ease in preparation, stability, biocompatibility, multiple surface functionalities, and size-dependent optical properties. The most commonly used method for AuNP synthesis in aqueous solution is the reduction of tetrachloroauric acid (HAuCl₄) with trisodium citrate. We have observed variations in the pH and in the concentration of the gold colloidal suspension synthesized under standard conditions, verifying a reduction in the reaction yield by around 46% from pH 5.3 (2.4 nM) to pH 4.7 (1.29 nM). Citrate-capped AuNPs were characterized by UV-visible spectroscopy, TEM, EDS, and zeta-potential measurements, revealing a linear correlation between pH and the concentration of the generated AuNPs. This result can be attributed to the adverse effect of protons both on citrate oxidation and on citrate adsorption onto the gold surface, which is required to form the stabilization layer. Overall, this study provides insight into the effect of the pH over the synthesis performance of the method, which would be of particular interest from the point of view of large-scale manufacturing processes.

A novel adaptive scene-based nonuniformity correction technique is presented. The technique simultaneously estimates detector parameters and performs the nonuniformity correction based on the retina-like neural network approach. The proposed method includes the use of an adaptive learning rate rule in the gain and offset parameter estimation process. This learning rate rule, together with a reduction in the averaging window size used for the parameter estimation, may provide an efficient implementation that should increase the original method's scene-based ability to estimate the fixed-pattern noise. The performance of the proposed algorithm is then evaluated with infrared image sequences with simulated and real fixed-pattern noise. The results show a significative faster and more reliable fixed-pattern noise reduction, tracking the parameters drift, and presenting a good adaptability to scene changes and nonuniformity conditions.

In this article we present the development of a biosensor system that integrates nanotechnology, optomechanics and a spectral detection algorithm for sensitive quantification of antibiotic residues in raw milk of cow. Firstly, nanobiosensors were designed and synthesized by chemically bonding gold nanoparticles (AuNPs) with aptamer bioreceptors highly selective for four widely used antibiotics in the field of veterinary medicine, namely, Kanamycin, Ampicillin, Oxytetracycline and Sulfadimethoxine. When molecules of the antibiotics are present in the milk sample, the interaction with the aptamers induces random AuNP aggregation. This phenomenon modifies the initial absorption spectrum of the milk sample without antibiotics, producing spectral features that indicate both the presence of antibiotics and, to some extent, its concentration. Secondly, we designed and constructed an electro-opto-mechanic device that performs automatic high-resolution spectral data acquisition in a wavelength range of 400 to 800 nm. Thirdly, the acquired spectra were processed by a machine-learning algorithm that is embedded into the acquisition hardware to determine the presence and concentration ranges of the antibiotics. Our approach outperformed state-of-the-art standardized techniques (based on the 520/620 nm ratio) for antibiotic detection, both in speed and in sensitivity.

In this paper, we report on the spectral detection of wustite, Fe(II) oxide (FeO), and magnetite, Fe(II, III) oxide (Fe₃O₄), molecular emissions during the combustion of pyrite (FeS₂), in a laboratory-scale furnace operating at high temperatures. These species are typically generated by reactions occurring during the combustion (oxidation) of this iron sulfide mineral. Two detection schemes are addressed: the first consisting of measurements with a built-in developed spectrometer with a high sensitivity and a high spectral resolution. The second one consisting of spectra measured with a low spectral resolution and a low sensitivity commercial spectrometer, but enhanced and analyzed with post signal processing and multivariate data analysis such as principal component analysis (PCA) and a multivariate curve resolution—the alternating least squares method (MCR-ALS). A non-linear model is also proposed to reconstruct spectral signals measured during pyrite combustion. Different combustion conditions were studied to evaluate the capacity of the detection schemes to follow the spectral emissions of iron oxides. The results show a direct correlation between FeO and Fe₃O₄ spectral features intensity, and non-linear relations with key combustion variables such as flame temperature, and the combusted sulfide mineral particle size.

In this paper a low-cost, practical pixel-based flame spectrum and temperature estimation system based on flame color images is proposed. A spectral resolution of $\sim 0.4\mathrm{nm}$ is achieved with an optical system formed by a color camera, a linear model, a flame’s spectral training data, and a spectral reconstruction procedure. As a proof of concept, the estimated spectra are compared to local measurements performed with a commercial spectrometer. In order to estimate the absolute flame-temperature maps, two radiometric images at different wavelengths are reconstructed and the two-color pyrometry method is applied. Experiments show errors of about 2.0% over the estimated temperature, making this system a practical tool for flame sensing in combustion-process monitoring.