Machine Learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring (OPM) and cross-layer network optimizations for software-defined networks (SDNs). However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This review article aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.

In long-haul optical communication systems, compensating nonlinear effects through digital signal processing (DSP) is difficult due to intractable interactions between Kerr nonlinearity, chromatic dispersion (CD) and amplified spontaneous emission (ASE) noise from inline amplifiers. Optimizing the standard digital back propagation (DBP) as a deep neural network (DNN) with interleaving linear and nonlinear operations for fiber nonlinearity compensation was shown to improve transmission performance in idealized simulation environments. Here, we extend such concepts to practical single-channel and polarization division multiplexed wavelength division multiplexed experiments. We show improved performance compared to state-of-the-art DSP algorithms and additionally, the optimized DNNbased DBP parameters exhibit a mathematical structure which guides us to further analyze the noise statistics of fiber nonlinearity compensation. This machine learning-inspired analysis reveals that ASE noise and incomplete CD compensation of the Kerr nonlinear term produce extra distortions that accumulates along the DBP stages. Therefore, the best DSP should balance between suppressing these distortions and inverting the fiber propagation effects, and such trade-off shifts across different DBP stages in a quantifiable manner. Instead of the common 'black-box' approach to intractable problems, our work shows how machine learning can be a complementary tool to human analytical thinking and help advance theoretical understandings in disciplines such as optics.

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Recent developments in Fourier transform infrared spectroscopy-partial least squares (FTIR-PLSs) extend the application of this strategy to the field of the edible oils and fats research. In this work, FT-IR spectroscopy was used as an effective analytical tool to determine the peroxide value of virgin walnut oil (VWO) samples undergone during heating. The spectra were recorded from a film of pure oil between two disks of KBr for each sample at frequency regions of 4000–650 cm−1. Changes in the values of the frequency of most of the bands of the spectra were observed and used to build the calibration model. PLS model correlates the actual and FT-IR estimated value of peroxide value with a correlation coefficient of 0.99, and the root mean square error of the calibration (RMSEC) value is 0.4838. The methodology has potential as a fast and accurate way for the quantification of peroxide value of the edible oils.

Quantum dots (QDs) have great promise in biological imaging, and as this promise is realized, there has been increasing interest in combining the benefits of QDs with those of other materials to yield composites with multifunctional properties. One of the most common materials combined with QDs is magnetic materials, either as ions (e.g. gadolinium) or as nanoparticles (e.g. superparamagnetic iron oxide nanoparticles, SPIONs). The fluorescent property of the QDs permits visualization, whereas the magnetic property of the composite enables imaging, magnetic separation, and may even have therapeutic benefit. In this review, the synthesis of fluorescent-magnetic nanoparticles, including magnetic QDs is explored; and the applications of these materials in imaging, separations, and theranostics are discussed. As the properties of these materials continue to improve, QDs have the potential to greatly impact biological imaging, diagnostics, and treatment.

Quantum dots (QDs) have tremendous potential for biomedical imaging, including super-resolution techniques that permit imaging below the diffraction limit. However, most QDs are produced via organic methods, and hence require surface treatment to render them water-soluble for biological applications. Previously, we reported a micelle-templating method that yields nanocomposites containing multiple core/shell ZnS-CdSe QDs within the same nanocarrier, increasing overall particle brightness and virtually eliminating QD blinking. Here, this technique is extended to the encapsulation of Mn-doped ZnSe QDs (Mn-ZnSe QDs), which have potential applications in super-resolution imaging as a result of the introduction of Mn(2+) dopant energy levels. The size, shape and fluorescence characteristics of these doped QD-micelles were compared to those of micelles created using core/shell ZnS-CdSe QDs (ZnS-CdSe QD-micelles). Additionally, the stability of both types of particles to photo-oxidation was investigated. Compared to commercial QDs, micelle-templated QDs demonstrated superior fluorescence intensity, higher signal-to-noise ratios, and greater stability against photo-oxidization,while reducing blinking. Additionally, the fluorescence of doped QD-micelles could be modulated from a bright 'on' state to a dark 'off' state, with a modulation depth of up to 76%, suggesting the potential of doped QD-micelles for applications in super-resolution imaging.

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