scholarly journals Image processing methods for in situ estimation of cohesive sediment floc size, settling velocity, and density

2015 ◽  
Vol 13 (5) ◽  
pp. 250-264 ◽  
Author(s):  
S. Jarrell Smith ◽  
Carl T. Friedrichs
Keyword(s):  
Image Processing ◽  
Settling Velocity ◽  
Cohesive Sediment ◽  
Processing Methods ◽  
Floc Size

scholarly journals A Methodology for Estimating the Parameters in Three-Dimensional Cohesive Sediment Transport Models by Assimilating In Situ Observations with the Adjoint Method

2017 ◽  
Vol 34 (7) ◽  
pp. 1469-1482 ◽  
Author(s):  
Daosheng Wang ◽  
Jicai Zhang ◽  
Ya Ping Wang ◽  
Xianqing Lv ◽  
Yang Yang ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Adjoint Method ◽  
Settling Velocity ◽  
Transport Model ◽  
Three Dimensional ◽  
Temporal Variations ◽  
Cohesive Sediment ◽  
Cohesive Sediment Transport ◽  
Resuspension Rate

AbstractThe model parameters in the suspended cohesive sediment transport model are quite important for the accurate simulation of suspended sediment concentrations (SSCs). Based on a three-dimensional cohesive sediment transport model and its adjoint model, the in situ observed SSCs at four stations are assimilated to simulate the SSCs and to estimate the parameters in Hangzhou Bay in China. Numerical experimental results show that the adjoint method can efficiently improve the simulation results, which can benefit the prediction of SSCs. The time series of the modeled SSCs present a clear semidiurnal variation, in which the maximal SSCs occur during the flood tide and near the high water level due to the large current speeds. Sensitivity experiments prove that the estimated results of the settling velocity and resuspension rate, especially the temporal variations, are robust to the model settings. The temporal variations of the estimated settling velocity are negatively correlated with the tidal elevation. The main reason is that the mean size of the suspended sediments can be reduced during the flood tide, which consequently decreases the settling velocity according to Stokes’s law, and it is opposite in the ebb tide. The temporal variations of the estimated resuspension rate and the current speeds have a significantly positive correlation, which accords with the dynamics of the resuspension rate. The temporal variations of the settling velocity and resuspension rate are reasonable from the viewpoint of physics, indicating the adjoint method can be an effective tool for estimating the parameters in the sediment transport models.

Parallel in-situ measurement of heliostat aim points in central receiver systems by image processing methods

Solar Energy ◽  
2019 ◽  
Vol 180 ◽  
pp. 648-663 ◽  
Author(s):  
Gregor Bern ◽  
Peter Schöttl ◽  
De Wet van Rooyen ◽  
Anna Heimsath ◽  
Peter Nitz
Keyword(s):  
Image Processing ◽  
In Situ Measurement ◽  
Processing Methods ◽  
Central Receiver

scholarly journals Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 221 ◽  
Author(s):  
Ido Cohen ◽  
Eli David ◽  
Nathan Netanyahu
Keyword(s):  
Image Processing ◽  
Gene Ontology ◽  
Deep Learning ◽  
In Situ Hybridization ◽  
High Resolution ◽  
State Of The Art ◽  
Processing Methods ◽  
Previous State ◽  
Traditional Image

In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Traditional image processing methods are typically applied for learning functional representations of genes, based on their expressions in these brain images. In this paper, we describe a novel end-to-end deep learning-based method for generating compact representations of in situ hybridization (ISH) images, which are invariant-to-translation. In contrast to traditional image processing methods, our method relies, instead, on deep convolutional denoising autoencoders (CDAE) for processing raw pixel inputs, and generating the desired compact image representations. We provide an in-depth description of our deep learning-based approach, and present extensive experimental results, demonstrating that representations extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Our methods improve the previous state-of-the-art classification rate (Liscovitch, et al.) from an average AUC of 0.92 to 0.997, i.e., it achieves 96% reduction in error rate. Furthermore, the representation vectors generated due to our method are more compact in comparison to previous state-of-the-art methods, allowing for a more efficient high-level representation of images. These results are obtained with significantly downsampled images in comparison to the original high-resolution ones, further underscoring the robustness of our proposed method.

A formula for the settling velocity of cohesive sediment flocs in water

2019 ◽  
Vol 19 (5) ◽  
pp. 1422-1428
Author(s):  
Zhongfan Zhu
Keyword(s):  
Simple Formula ◽  
Settling Velocity ◽  
Basic Structure ◽  
Cohesive Sediment ◽  
Data Sets ◽  
Floc Size ◽  
Median Size ◽  
Primary Particles ◽  
Fractal Architecture ◽  
Good Agreement

Abstract A simple formula is developed to relate the size and settling velocity of cohesive sediment flocs in both the viscous and inertial settling ranges. This formula maintains the same basic structure as the existing formula but is amended to incorporate the fact that the flocculated sediment has an internal fractal architecture and is composed of different-sized primary particles. The input parameters needed for calculating the settling velocity include the median size and size distribution of the primary particles, the fractal dimension of the floc, the density of the sediment, and two calibrated coefficients that incorporate the effects of floc shape, permeability, and flow separation on drag. The proposed formula is compared with four data sets of settling velocity–floc size collected from the published literature, and a good agreement between the model and these data can be found.

Otoconia perfect/imperfect crystals

1994 ◽  
Vol 52 ◽  
pp. 42-43
Author(s):  
César D. Fermin ◽  
Dale Martin
Keyword(s):  
Image Processing ◽  
Room Temperature ◽  
Phosphotungstic Acid ◽  
Gallus Domesticus ◽  
Crystal Matrix ◽  
Organic Templates ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

Comparison of Different Image Processing Methods with Spatial Information in Clinical Brain MRI

2020 ◽  
Author(s):  
Iza Sazanita Isa ◽  
Mohamad Khairul Faizi Mat Saad ◽  
Muhammad Haris Khusairi Mohmad Kadir ◽  
Ahmad Afifi Ahmad Afandi ◽  
Noor Khairiah A. Karim ◽  
...  
Keyword(s):  
Image Processing ◽  
Spatial Information ◽  
Brain Mri ◽  
Processing Methods

scholarly journals Image Processing Methods of LANDSAT TM Data for Lineament Analysis

1989 ◽  
Vol 1989 (14B) ◽  
pp. 25-39
Author(s):  
Katsuaki KOIKE ◽  
Hiroyuki ITOH ◽  
Michito OHMI
Keyword(s):  
Image Processing ◽  
Landsat Tm ◽  
Processing Methods ◽  
Lineament Analysis

scholarly journals Fast Transforms in Image Processing: Compression, Restoration, and Resampling

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Leonid P. Yaroslavsky
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Image Restoration ◽  
Compressive Sensing ◽  
Adaptive Filters ◽  
Processing Methods ◽  
Fast Transforms ◽  
Image Transforms

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

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