Abstract: A method for training a neural network (NN), the method includes: receiving a training dataset including: (a) multiple pairs of: (i) a diffraction image indicative of X-ray photons diffracted from structures formed in a sample responsively to directing an incident X-ray beam at an angle relative to the sample, and (ii) a label, including: a first parameter indicative of at least a first property of the structures, and a second parameter indicative of at least a second property of the incident X-ray beam, and (b) multiple predefined outputs for the multiple pairs, respectively. The NN is trained to obtain the predefined outputs by: (i) applying the NN to at least a given pair of the pairs, and (ii) responsively to receiving from the NN an estimated output of the given pair, providing the NN with a given predefined output of the predefined outputs that corresponds to the given pair.

Abstract: The present disclosure provides a device and method for producing low-frequency magnetic-field signals to enable long-range wireless communication through conductive media. The magnetic-field signals can be generated by mechanically moving permanent magnets. In some examples, transmitters are capable of transmitting signals in the ultra-low and very-low frequency ranges (e.g., 100 Hz-30 kHz), utilizing a small amount of power.

Abstract: The present disclosure provides a device and method for producing low-frequency magnetic-field signals to enable long-range wireless communication through conductive media. The magnetic-field signals can be generated by mechanically moving permanent magnets. In some examples, transmitters are capable of transmitting signals in the ultra-low and very-low frequency ranges (e.g., 100 Hz-30 kHz), utilizing a small amount of power.