Abstract. Magnetic skyrmions are topologically protected states that are very promising for the design of the next generation of ultralow-power electronic devices. In this letter, we propose a magnetic tunnel junction based spin-transfer torque diode with a magnetic skyrmion as ground state and a perpendicular polarizer patterned as nanocontact for a local injection of the current. The key result is the possibility to achieve sensitivities (i.e. detection voltage over input microwave power) larger than 2000V/W for optimized contact diameters. Our results can be very useful for the identification of a new class of spintorque diodes with a non-uniform ground state.

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A modified version of the PNN (probabilistic neural network) learning phase which allows a considerable simplification of network structure by including a vector quantization of learning data is proposed. It can be useful if large training sets are available. The procedure has been successfully tested in two synthetic data experiments. The proposed network has been shown to improve the classification performance of the LVQ (learning vector quantization) procedure.

This paper investigates various types of coded waveforms that could be used for air-coupled ultrasound, using a pulse compression approach to signal processing. These are needed because of the low signal-to-noise ratios that are found in many air-coupled ultrasonic nondestructive evaluation measurements, due to the large acoustic mismatch between air and many solid materials. The various waveforms, including both swept-frequency signals and those with binary modulation, are described, and their performance in the presence of noise is compared. It is shown that the optimum choice of modulation signal depends on the bandwidth available and the type of measurement being made.

Neural networks found significant applications in microwave CAD. In this paper, after providing a brief description of neural networks employed so far in this context, we illustrate some of their most significant applications and typical issues arising in practical implementation. We also summarize current research tendencies and introduce use of self‐organizing maps enhancing model accuracy and applicability. We conclude considering some future developments and exciting perspectives opened from use of neural networks in microwave CAD. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 158–174, 1999.