Biologically inspired vector fields to generate hair directions

This paper describes how biologically inspired vector fields can be used to partially automate the manual and time-consuming process of specifying hair directions. This approach replicates the consequence of stretching of skin from natural hair development process, in contrast to replicating the appearance of hair. The direction of each hair on the surface of an arbitrary 3D model is determined by interpolating the solution vector field that satisfies a set of user-defined constraints describing the stretching of skin. Results found that the generated hair directional pattern closely resembles that found naturally. Further investigation revealed that the presence of naturally occurring hair types and the varying distribution of hair directions induced by the calculated vector field enhanced the realism of hair coats generated using this approach. Aside from hair or fur, this approach can also be applied to hair-like masses such as grass, feathers, or scales.

On rapid vector alignment in permanent magnet brushless motor

A software replacement for the commutation signals of a permanent magnet brushless motor is presented. The feedback observed acceleration loop or equivalently the high-order position polynomial controller allows finding the initial relative orientation between the two magnetic fields of the motors within a fraction of a second. Also, using the proposed method allows a considerable cost saving, since the transducer that is usually used for this purpose can be eliminated. The cost saving is most obvious in the case of linear motors and angle motors with large diameters. The way the problem is posed is an essential part of this work and it is the reason behind the apparent simplicity of the solution. The method has been tested when a relative encoder was used and the motor current was regulated.

A novel monotonic fixed-point algorithm for l1-regularized least square vector and matrix problem

Least square problem with l₁ regularization has been proposed as a promising method for sparse signal reconstruction (e.g., basis pursuit de-noising and compressed sensing) and feature selection (e.g., the Lasso algorithm) in signal processing, statistics, and related fields. These problems can be cast as l₁-regularized least-square program (LSP). In this paper, we propose a novel monotonic fixed point method to solve large-scale l₁-regularized LSP. And we also prove the stability and convergence of the proposed method. Furthermore we generalize this method to least square matrix problem and apply it in nonnegative matrix factorization (NMF). The method is illustrated on sparse signal reconstruction, partner recognition and blind source separation problems, and the method tends to convergent faster and sparser than other l₁-regularized algorithms.