We took advantage of the large anomalous Hall effect (AHE) in Fe–Pt ferromagnetic alloys and fabricated magnetic sensors for low-frequency applications. We characterized the low-frequency electronic noise and the field detectability of the Fe_xPt_100-x system with various thin film thicknesses and Fe concentrations. The noise source consisted of 1/f and Johnson noise. A large current density increased the 1/f noise but not the Johnson noise. We found that the field detectability of the optimized Fe–Pt thin film offers much better low-frequency performance than a highly sensitive commercial semiconductor Hall sensor. Anomalous Hall effect sensors are, therefore, good candidates for magnetic sensing applications.

We systematically study the magnetic properties, electron transport, and the anomalous Hall effect (AHE) in Fe_xPt_100-x solids over a wide composition range. These solids are characterized by strong spin-orbit interaction and enhanced magnetic moments from both elements. In our study, we vary the thickness and the composition of the thin films, the temperature of measurement, and the magnitude of the magnetic field in various directions. We have determined the Hall angle, the spin diffusion length, and the electron mean free path in this alloy system. The AHE is more than 3 orders of magnitude larger than the normal Hall effect, making Fe_xPt_100-x a good candidate for spintronic applications. The Hall angle determined in the Fe_xPt_100-x solids is comparable to that in pure Pt. From the analysis on the correlation between the AHE and electron transport, we find that the AHE is dominated by both the intrinsic Karplus-Luttinger (Berry phase) mechanism and the extrinsic side-jump mechanism.

Outdoor imaging often suffers from the negative effects of the rain streaks, which would lead to the distortion of image contents. Most of the existing deraining approaches propose to build prior models to formulate the appearance of rain streaks and image features. However, these works deal with the rain streaks removal on the whole image regions. This may lead to the image regions without rain streaks distorted. In this paper, we proposed a new method for simultaneously investigating the candidates of rain streaks and image restoration. We use the L0 sparsity regularization for rain region estimation , and apply the framelet with L0 norm to protect the sharpen images. Our method only processes the regions with rain streaks and leaves other regions free. Experimental results demonstrate the effectiveness of our approach on rain streak removal and image restoration.