Spin filtering implemented through Rashba and weak magnetic modulations

We present two theoretical schemes for spin filters in one-dimensional semiconductor quantum wires with spatially modulated Rashba spin-orbit coupling (SOC) as well as weak magnetic potential. For case I, the SOC is periodic and the weak magnetic potential is applied uniformly along the wire. Full spin polarizations with opposite signs are obtained within two separated energy intervals. For case II, the weak magnetic potential is periodic while the SOC is uniform. An ideal negative/positive switching effect for spin polarization is realized by tuning the strength of SOC. The roles of SOC, magnetic potential, and their coupling on the spin filtering are analyzed.Comment: 4 pages, 4 figure

General covariant geometric momentum, gauge potential and a Dirac fermion on a two-dimensional sphere

For a particle that is constrained on an ($N-1$)-dimensional ($N\geq2$) curved surface, the Cartesian components of its momentum in $N$-dimensional flat space is believed to offer a proper form of momentum for the particle on the surface, which is called the geometric momentum as it depends on the mean curvature. Once the momentum is made general covariance, the spin connection part can be interpreted as a gauge potential. The present study consists in two parts, the first is a discussion of the general framework for the general covariant geometric momentum. The second is devoted to a study of a Dirac fermion on a two-dimensional sphere and we show that there is the generalized total angular momentum whose three cartesian components form the $su(2)$ algebra, obtained before by consideration of dynamics of the particle, and we demonstrate that there is no curvature-induced geometric potential for the fermion.Comment: 8 pages, no figure. Presentation improve

Analysis of synaptic weight distribution in an Izhikevich network

Izhikevich network is a relatively new neuronal network, which consists of cortical spiking model neurons with axonal conduction delays and spike-timingdependent plasticity (STDP) with hard bound adaptation. In this work, we use uniform and Gaussian distributions respectively to initialize the weights of all excitatory neurons. After the network undergoes a few minutes of STDP adaptation, we can see that the weights of all synapses in the network, for both initial weight distributions, form a bimodal distribution, and numerically the established distribution presents dynamic stability

Calibration of miniature inertial and magnetic sensor units for robust attitude estimation

Attitude estimation from miniature inertial and magnetic sensors has been used in a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation devices, to biomotion analysis. However, appropriate sensor calibrations for accurate sensor measurements are essential to the performance of attitude estimation algorithms. In this paper, we present a robust sensor calibration method for accurate attitude estimation from three-axis accelerometers, gyroscopes, and magnetometer measurements. The proposed calibration method only requires a simple pan-tilt unit. A unified sensor model for inertial and magnetic sensors is used to convert the sensor readings to physical quantities in metric units. Based on the sensor model, a cost function is constructed, and a two-step iterative algorithm is then proposed to calibrate the inertial sensors. Due to the difficulties of acquiring the ground-truth of the Earth magnetic field, a simplified pseudomagnetometer calibration method is also presented based on an ellipsoid fitting algorithm. The calibration method is then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration method