Topical pharmaceutical preparations containing betamethasone esters are widely prescribed for treatment of severe inflammatory skin conditions. Some betamethasone esters-containing preparations are formulated with either an antibacterial or an antifungal agent or a vitamin D3 derivative. A fast reversed-phase high-performance liquid chromatography method has been developed for the simultaneous determination of three betamethasone esters-containing binary mixtures along with the excipients of their dosage forms using clobetasone butyrate as internal standard. The first mixture was betamethasone valerate and fusidic acid (Mixture I) with chlorocresol as preservative. The second mixture was betamethasone dipropionate (BTD) and clotrimazole (Mixture II) with benzyl alcohol as preservative. The third mixture was BTD and calcipotriol monohydrate (Mixture III). Optimized chromatographic separation was achieved on a Discovery® C18 (4.6 × 250 mm, 5 μm) column, using water: acetonitrile (35:65, v/v) as mobile phase at flow rate of 1 mL/min with UV detection at 230 nm. The method was validated according to ICH guidelines. The regression coefficients were > 0.999 for all drugs. The method was successfully applied for the determination of the studied drugs in bulk, synthetic mixtures and dosage forms. The developed method is accurate, sensitive, selective and precise and can be used for routine analysis in quality control laboratories.
This paper presents a classification Convolutional Neural Network model for modulation recognition. The model is capable of classifying 11 different modulation techniques based on their In-phase and Quadrature components at baseband. The classification accuracy is higher than 80% for signals with a Signal-to-Noise Ratio higher than 2 dB. The model performance is evaluated using the same In-phase and Quadrature component data-sets used in the state of the art. Compared to previous work, the number of parameters and multiplications/additions is reduced by several orders of magnitude. The proposed Convolutional Neural Network is implemented on FPGA and achieves the same performance as the GPU model. Compared to other FPGA implementations of RF signal classifiers, the proposed implementation classifies twice as much modulation schemes while consuming only half the dynamic power.
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