Projective rectification from the fundamental matrix

This paper describes a direct, self-contained method for planar image rectification of stereo pairs. The method is based solely on an examination of the Fundamental matrix, where an improved method is given for the derivation of two projective transformations that horizontally align all the epipolar projections. A novel approach is proposed to uniquely optimise each transform in order to minimise perspective distortions. This ensures the rectified images resemble the original images as closely as possible. Detailed results show that the rectification precision exactly matches the estimation error of the Fundamental matrix. In tests the remaining perspective distortion offers on average less than one percent viewpoint distortion. Both these factors offer superior robustness and performance compared with existing techniques

Rectification in one--dimensional electronic systems

Asymmetric current--voltage ($I(V)$) curves, known as the diode or rectification effect, in one--dimensional electronic conductors can have their origin from scattering off a single asymmetric impurity in the system. We investigate this effect in the framework of the Tomonaga--Luttinger model for electrons with spin. We show that electron interactions strongly enhance the diode effect and lead to a pronounced current rectification even if the impurity potential is weak. For strongly interacting electrons and not too small voltages, the rectification current, $I_r = [I(V)+I(-V)]/2$, measuring the asymmetry in the current--voltage curve, has a power--law dependence on the voltage with a negative exponent, $I_r \sim V^{-|z|}$, leading to a bump in the current--voltage curve.Comment: 9 pages; 3 figure

Artificial neural network prediction of weld distortion rectification using a travelling induction coil

An experimental investigation has been carried out to determine the applicability of an induction heating process with a travelling induction coil for the rectification of angular welding distortion. The results obtained from experimentation have been used to create artificial neural network models with the ability to predict the welding induced distortion and the distortion rectification achieved using a travelling induction coil. The experimental results have shown the ability to reduce the angular distortion for 8 mm and 10 mm thick DH36 steel plate and effectively eliminate the distortion on 6 mm thick plate. Results for 6 mm plate also show the existence of a critical induction coil travel speed at which maximum corrective bending occurs. Artificial neural networks have demonstrated the ability to predict the final distortion of the plate after both welding and induction heating. The models have also been used as a tool to determine the optimum speed to minimise the resulting distortion of steel plate after being subjected to both welding and induction heating processes

Thermal Conductivity and Thermal Rectification in Graphene Nanoribbons: a Molecular Dynamics Study

We have used molecular dynamics to calculate the thermal conductivity of symmetric and asymmetric graphene nanoribbons (GNRs) of several nanometers in size (up to ~4 nm wide and ~10 nm long). For symmetric nanoribbons, the calculated thermal conductivity (e.g. ~2000 W/m-K @400K for a 1.5 nm {\times} 5.7 nm zigzag GNR) is on the similar order of magnitude of the experimentally measured value for graphene. We have investigated the effects of edge chirality and found that nanoribbons with zigzag edges have appreciably larger thermal conductivity than nanoribbons with armchair edges. For asymmetric nanoribbons, we have found significant thermal rectification. Among various triangularly-shaped GNRs we investigated, the GNR with armchair bottom edge and a vertex angle of 30{\deg} gives the maximal thermal rectification. We also studied the effect of defects and found that vacancies and edge roughness in the nanoribbons can significantly decrease the thermal conductivity. However, substantial thermal rectification is observed even in the presence of edge roughness.Comment: 13 pages, 5 figures, slightly expanded from the published version on Nano Lett. with some additional note