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The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology. [ABSTRACT FROM AUTHOR]

Nowadays, the impact of the ships on the World economy is enormous, considering that every ship needs fuel to sail from source to destination. It requires a lot of fuel, and therefore, there is a need to monitor and predict a ship's average fuel consumption. However, although there are much models available to predict a ship's consumption, most of them rely on a uniform time set. Here we show the model of predicting external influences to ship's average fuel consumption based on a non-uniform time set. The model is based on the numeric fitting of recorded data. The first set of recorded data was used to develop the model, while the second set was used for validation. Statistical quality measures have been used to choose the optimal fitting function for the model. According to statistical measures, the Gaussian 7, Fourier 8, and smoothing spline fitting functions were chosen as optimal algorithms for model development. In addition to extensive data analysis, there is an algorithm for filter length determination for the preprocessing of raw data. This research is of interest to corporate logistics departments in charge of ensuring adequate fuel for fleets when and where required. [ABSTRACT FROM AUTHOR]