Fast fringe pattern phase demodulation using FIR Hilbert transformers

This paper suggests the use of FIR Hilbert transformers to extract the phase of fringe patterns. This method is computationally faster than any known spatial method that produces wrapped phase maps. Also, the algorithm does not require any parameters to be adjusted which are dependent upon the specific fringe pattern that is being processed, or upon the particular setup of the optical fringe projection system that is being used. It is therefore particularly suitable for full algorithmic automation. The accuracy and validity of the suggested method has been tested using both computer-generated and real fringe patterns. This novel algorithm has been proposed for its advantages in terms of computational processing speed as it is the fastest available method to extract the wrapped phase information from a fringe pattern

A spatial algorithm to reduce phase wraps from two dimensional signals in fringe projection profilometry

© 2015 Elsevier Ltd. All rights reserved. In this paper, we present a novel algorithm to reduce the number of phase wraps in two dimensional signals in fringe projection profilometry. The technique operates in the spatial domain, and achieves a significant computational saving with regard to existing methods based on frequency shifting. The method works by estimating the modes of the first differences distribution in each axial direction. These are used to generate a tilted plane, which is subtracted from the entire phase map. Finally, the result is re-wrapped to obtain a phase map with fewer wraps. The method may be able to completely eliminate the phase wraps in many cases, or can achieve a significant phase wrap reduction that helps the subsequent unwrapping of the signal. The algorithm has been exhaustively tested across a large number of real and simulated signals, showing similar results compared to approaches operating in the frequency domain, but at significantly lower running times

Single-beam three-dimensional optical trapping at extremely low insertion angles via optical fiber optimization

Employing optical fiber to deliver the trapping laser to the sample chamber significantly reduces the size and costs of optical tweezers (OT). The utilization of fiber decouples the OT from the microscope, providing scope for system portability, and the potential for uncomplicated integration with other advanced microscopy systems. For use with an atomic force microscope, the fiber must be inserted at an angle of 10 deg to the plane of the sample chamber floor. However, the literature states that optical trapping with a single fiber inserted at an angle ≤20 deg is not possible. This paper investigates this limitation and proposes a hypothesis that explains it. Based on this explanation, a tapered-fiber optical tweezer system is developed. This system demonstrates that such traps can indeed be made to function in three-dimensions (3-D) at insertion angles of ≤10 deg using relatively low optical powers, provided the fiber taper is optimized. Three such optimized tapered fiber tips are presented, and their ability to optically trap both organic and inanimate material in 3-D is demonstrated. The near-horizontal insertion angle introduced a maximum trapping range (MTR). The MTR of the three tips is determined empirically, evaluated against simulated data, and found to be tunable through taper optimization

An artificial neural network approach to payload estimation in four wheel drive loaders

ABSTRACT Estimation of the manipulated payload mass in offhighway machines is made non-trivial by the nonlinearities associated with the hydraulic systems used to actuate the linkage of the machine in addition to the nonlinearity of the kinematics of the linkage itself. Hydraulic cylinder friction, hydraulic conduit compressibility, linkage machining variation and linkage joint friction all make this a complex task under even ideal (machine static) conditions. This problem is made even more difficult when the linkage is mobile as is often the case with off-highway equipment such as four-wheel-drive loaders, cranes, and excavators. The rigid body motion of this type of equipment affects the gravitational loads seen in the linkage and impacts the payload estimate. The commercially available state-of-the-art load estimation solutions rely on the mobile machine becoming pseudo-static in order to maintain accuracy. This requirement increases the time required to move the material and decreases the productivity of the machine. An artificial neural network solution to this problem that enables the machine to remain dynamic and still accurately estimate the payload is discussed in this paper. Development and implementation on an actual four-wheel-drive loader is shown