Development of a mass model in estimating weight-wise particle size distribution using digital image processing

Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.

Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

Effect of time bin size on accuracy of streak tube imaging lidar

Streak tube imaging lidar （STIL） is an active imaging system that has a high range accuracy with tile use of a pulsed lapser transmitter and streak tube receiver to produce 3D range inlages. This work investigates the effect of tile time bin size oil the range accuracy of STIL systems based on the peak detection algorithm. The nunlerical simulation indicates that the time bin size has a significant effect on the range aceuracy, resulting in a modified analytical estimate of the range error. An indoor experilnent with a planar target is carried out to validate the theory that shows the linear relationship between tile range error and the time bin size. Finer 3D depth iinages of a fist model are acquired by using a smaller time bin size and the best range error of 0.003 In is achieved with the optimal time bin size of 0.07 ns.

On-line full scan inspection of particle size and shape using digital image processing

An on-line full scan inspection system is developed for particle size analysis. A particle image is first obtained through optical line scan technology and is then analyzed using digital image processing. The system is composed of a particle separation module, an image acquisition module, an image processing module, and an electric control module. Experiments are carried out using non-uniform 0.1 mm particles. The main advantage of this system consists of a full analysis of particles without any overlap or miss, thus improving the Area Scan Charge Coupled Device （CCD） acquisition problems. Particle size distribution, roundness, and sphericity can be obtained using the system with a deviation of repeated precision of around ±1%. The developed system is shown to be also convenient and versatile for any particle size and shape for academic and industrial users.

Ultrasound Imaging Signal Analysis of Underwater Topography in River Model Experiment

In this paper, we analyze the feature of ultrasonic image and investigate the effect of topography material, flow velocity and sediment concentration on the imaging of underwater topography by imaging experiments of model sands. These imaging experiments are conducted in river engineering physical model.The results show that the vertical distribution of pixel values is changed hugely at the position of imaging bright band of underwater topography. The imaging of underwater topography is not affected when flow velocity is below 40 cm/s and sediment concentration is below 5.0 ‰. The main influence factors of imaging signals are flow velocity and sediment concentration near the topographical bed. The resolution of ultrasound imaging signals is high, and the topography consisted of model sands with particle size smaller than 0.1 mm can be monitored well in the river model experiment.