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Abstract. The paper gives a review of continuous wave optical devices called THz photomixers used for excitation and detection of the terahertz radiation. Possible structures of the terahertz photomixers are classified and described.

This paper reviews machine-learning methods that are nowadays the most frequently used for the supervised classification of spectral signals in laser-induced breakdown spectroscopy (LIBS). We analyze and compare various statistical classification methods, such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), partial least-squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), support vector machine (SVM), naive Bayes method, probabilistic neural networks (PNN), and K-nearest neighbor (KNN) method. The theoretical considerations are supported with experiments conducted for real soft-solder-alloy spectra obtained using LIBS. We consider two decision problems: binary and multiclass classification. The former is used to distinguish overheated soft solders from their normal versions. The latter aims to assign a testing sample to a given group of materials. The measurements are obtained for several laser-energy values, projection masks, and numbers of laser shots. Using cross-validation, we evaluate the above classification methods in terms of their usefulness in solving both classification problems.

Basic mechanisms influencing the resonant frequency of the RF-discharged slab-waveguide CO2 laser head are described. An algorithm of calculations to design a laser head resonant frequency is given. The algorithm is experimentally confirmed.

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