Beamforming with an array of sensors is an advanced signal processing technique for directional signal transmission or reception. This directionality is achieved by phase shifts of received signals of each sensor for the constructive interference of wavefronts, resulting in the amplification of the signal from a particular direction. In this study, the use of an asymmetric sensor array is proposed to reduce the effects of ‘spatial aliasing’, which is typically encountered in the structural health monitoring (SHM) practice when employing beamforming. In this technique, a sensor array is asymmetrically and closely deployed for robust beamforming based source localization. This sensor deployment has a great effect on suppressing the phenomenon that wave signals are constructively shifted even on other directions. The proposed asymmetric sensor array is to reduce the spatial aliasing error without using any advanced signal processing techniques. In order to demonstrate the proposed sensor array technique, several simulation and experimental investigations are carried out with various symmetric and asymmetric sensor arrays. For the experiments, a complicated composite structure including honeycomb core, spars, and holes is used. The performance comparison is then made to demonstrate the performance of the proposed sensor array technique. The superior robustness of the asymmetric sensor array is confirmed by both simulation and experiment results in complicated structures.
Strabismus is an eye movement disorder that the eyes do not properly align with each other when looking at an object. This disorder is usually caused by muscle malfunctions, nerve problems or injuries. Currently, the ophthalmic prism with two nonparallel planes is used to diagnose the strabismus angle. The light into one eye is refracted when passing through the prism, which adjusts both eyes to looking forward. The strabismus angle is then identified after checking the parameter of the prism. However, the whole process is operated depending on the doctors’ experience which shows somewhat low efficiency and low accuracy. In this study, an automated strabismus diagnosis technique using VR device is developed. A specially-designed VR is built to simulate the normal strabismus diagnosis steps, in which screens are controlled to change alternately between on and off. The eye motions are tracked by two IR cameras by an image-processing based pupil tracking technique. After tracking the motion of the pupil, the position information is converted to the strabismus angle by considering the eyeball diameter. With this process, the strabismus angle is accurately and automatically identified using a unique feature recognition technique. To demonstrate the performance of this technique, experiments are carried out on various persons, including strabismus patients. The results are compared to the doctor’s diagnosis. The results show that this technique could identify the strabismus angle with high accuracy and high efficiency.
Crack detection during the manufacturing process of pressed panel products is an important aspect of quality management. Tradition approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the press-forming process.
In this study, we performed automated crack detection by integrating two image processing techniques with a multi-view-camera system. The first technique is based on evaluation of the edge lines which are extracted from a percolated object image. This technique could detect a crack without a reference image. Almost all of the edge lines of the panels show smooth variances of angle on the edges. When a crack occurs in panel products, an angle higher than 140 degree by the edge lines would appear, which could be used as an indication of crack presence. Another technique applies local image amplitude mapping (LAM) and compares a test image with the reference image. LAM is used to alleviate the problem associated with that the captured images during the manufacturing stage are not aligned against the reference image. The features created by LAM subtraction between the reference and test image are used to identify a crack.
Before crack detection, multi-view images of a panel product are captured using multiple cameras. Afterwards, cracks are detected using both crack detection techniques based on image processing. The proposed technique is demonstrated in an actual manufacturing lines with real panel products. Experimental results clearly show that proposed technique could effectively improve the detection rate and speed for pressed panel products.
Crack detection on pressed panel during the press forming process is an important step to ensure the quality of panel products. Traditional crack detection technique has been generally performed by experienced human inspectors, which is subjective and expensive. Therefore, the implementation of automated and accurate crack detection is necessary during the press forming process. In this study, we performed an optimal camera positioning and automated crack detection using two image processing techniques with multi-view-camera system. The first technique is based on evaluation of the panel edge lines which are extracted from a percolated object image. This technique does not require a reference image for crack detection. Another technique is based on the comparison between a reference and a test image using the local image amplitude mapping. Before crack detection, multi-view images of a panel product are captured using multiple cameras and 3D shape information is reconstructed. Optimal camera positions are then determined based on the shape information. Afterwards, cracks are automatically detected using two crack detection techniques based on image processing. In order to demonstrate the capability of the proposed technique, experiments were performed in the laboratory and the actual manufacturing lines with the real panel products. Experimental results show that proposed techniques could effectively improve the crack detection rate with improved speed.
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