Superconducting Properties of YBa2Cu3O7-δ with a Multiferroic Addition Synthesized by a Capping Agent-Aided Thermal Treatment Method.

A bulk YBa2Cu3O7-δ (Y-123) superconductor synthesized by a thermal treatment method was added with different weight percentages (x = 0.0, 0.2, 1.0, 1.5, and 2.0 wt.%) of BiFeO3 (BFO) nanoparticle. X-ray diffraction (XRD), alternating current susceptibility (ACS), and field emission scanning electron microscopy (FESEM) were used to determine the properties of the samples. From the XRD results, all samples showed an orthorhombic crystal structure with a Pmmm space group. The sample x = 1.0 wt.% gave the highest value of Y-123. The high amounts of BFO degraded the crystallite size of the sample, showing that the addition did not promote the grain growth of Y-123. From ACS results, the Tc-onset value was shown to be enhanced by the addition of the BFO nanoparticle, where x = 1.5 wt.% gave the highest Tc value (91.91 K). The sample with 1.5 wt.% showed a high value of Tp (89.15 K). The FESEM analysis showed that the average grain size of the samples decreased as BFO was introduced. However, the small grain size was expected to fill in the boundary, which would help in enhancing the grain connectivity. Overall, the addition of the BFO nanoparticles in Y-123 helped to improve the superconducting properties, mainly for x = 1.5 wt.%.

Comparison of Adaptive Spectral Estimation for Vehicle Speed Measurement with Radar Sensors.

Vehicle speed-over-ground (SoG) radar offers significant advantages over conventional speed measurement systems. Radar sensors enable contactless speed measurement, which is free from wheel slip. One of the key issues in SoG radar is the development of the Doppler shift estimation algorithm. In this paper, we compared two algorithms to estimate a mean Doppler frequency accurately. The first is the center-of-mass algorithm, which based on spectrum center-of-mass estimation with a bandwidth-limiting technique. The second is the cross-correlation algorithm, which is based on a cross-correlation technique by cross-correlating Doppler spectrum with a theoretical Gaussian curve. Analysis shows that both algorithms are computationally efficient and suitable for real-time SoG systems. Our extensive simulated and experimental results show both methods achieved low estimation error between 0.5% and 1.5% for flat road conditions. In terms of reliability, the cross-correlation method shows good performance under low Signal-to-Noise Ratio (SNR) while the center-of-mass method failed in this condition.