This article describes the manufacturing of a horn antenna using a 3D commercial printer. The horn antenna was chosen for its simplicity and practical versatility. The standardised horn antenna is one of the most widely used antennas in microwave technology. A standardised horn antenna can be connected to standardised waveguides. The horn antenna has been selected so that this antenna can be fabricated by 3D printing and thus obtain the equivalent of a standardised horn antenna. This 3D horn antenna can then be excited by a standardised waveguide. The 3Dprinted horn antenna with metallic layers has very good impedance characteristics, standing wave ratio and radiation patterns that are close to those of a standardised horn antenna. The 3D-based horn antenna is suitable for applications where low antenna weight is required, such as aerospace and satellite technologies. The article also describes a manufacturing procedure for a horn antenna (E-sector horn antenna) that is plated with galvanic layers of silver and gold. The design of the plated horn antenna in the Matlab application using the Antenna Toolbox extension is also described, including 3D printing procedures, post-processing procedures (plating) and practical testing of its functionality. The measured results are compared to simulations of the standardised horn antenna and then analysed.
Communication signals travelling in space with different modulation types and different 2 KEY FEATURE EXTRACTION frequencies fall in a very wide band. Usually, it is required to identify and monitor these signals for manyIn the proposed modulation classifiers (MC), the key applications. Some of these applications are for civilian features used are derived from three important sphere purposes such as signal confirmation, . interference identification and spectrum management. A qin g pharet -the instantaneous amliude,th new subsystem used in automatic wireless signal i modulation recognition based on ANN is described inThe first key feature, Ymax, is defined by [1], [2] this paper. The signal recognizer being developed consists of five subsystems: (1) adaptive antenna arrays, (2) pre-processing of EM signals, (3) key features Ymax =max |DFT(acn (i))| 2 NS (1 extraction, (4) modulation recognizer and (5) output stage. The choice of maximum value of spectral power N density of the normalized-centred amplitude, standard where s the number of samples per segment and deviation of the absolute value of the centred non-linear acn (i) is the value of the normalized-centred component of the instantaneous phase, standard instantaneous amplitude at time instants deviation of the absolute value of the normalizedcentred instantaneous amplitude, standard deviation of the absolute value of the normalized-centred t=-, (i=1,2, ..., Ns) instantaneous frequency, spectrum symmetry measure fs are used as key features for the analogue modulation recognizer based on the artificial neural networks Ymax represents the maximum value of the spectral (ANNs). The new original structure of the recognizer of power density of the normalized-centred instantaneous analogue signals is described. The modulation amplitude of the intercepted signal. recognizer using the two ANNs with two hidden layers. The results are summarized for real signals with noise The second key feature, C(ap, is defined by [1], [2] and fading.
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