Remotely Sensed Seasonal Shoreward Intrusion of the East Australian Current: Implications for Coastal Ocean Dynamics

For decades, the presence of a seasonal intrusion of the East Australian Current (EAC) has been disputed. In this study, with a Topographic Position Index (TPI)-based image processing technique, we use a 26-year satellite Sea Surface Temperature (SST) dataset to quantitatively map the EAC off northern New South Wales (NSW, Australia, 28–32°S and ~154°E). Our mapping products have enabled direct measurement (“distance” and “area”) of the EAC’s shoreward intrusion, and the results show that the EAC intrusion exhibits seasonal cycles, moving closer to the coast in austral summer than in winter. The maximum EAC-to-coast distance usually occurs during winter, ranging from 30 to 40 km. In contrast, the minimum distance usually occurs during summer, ranging from 15 to 25 km. Further spatial analyses indicate that the EAC undergoes a seasonal shift upstream of 29°40′S and seasonal widening downstream. This is the first time that the seasonality of the EAC intrusion has been confirmed by long-term remote-sensing observation. The findings provide new insights into seasonal upwelling and shelf circulation previously observed off the NSW coast.

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High-resolution snow depth prediction using Random Forest algorithm with topographic parameters and an ecosystem map: a case study in the Greiner Watershed, Nunavut

10.22541/au.163777510.05550930/v1 ◽

2021 ◽

Author(s):

Julien Meloche ◽

Alexandre Langlois ◽

Nick Rutter ◽

Don McLennan ◽

Alain Royer ◽

...

Keyword(s):

Random Forest ◽

Snow Cover ◽

Snow Depth ◽

The Arctic ◽

Topographic Position Index ◽

Depth Prediction ◽

Position Index ◽

First Time ◽

Topographic Parameters

Increased surface temperatures (0.7℃ per decade) in the Arctic affects polar ecosystems by reducing the extent and duration of annual snow cover. Monitoring of these important ecosystems needs detailed information on snow cover properties (depth and density) at resolutions (< 100 m) that influence ecological habitats and permafrost thaw. As arctic snow is strongly influenced by vegetation, an ecotype map at 10 m resolution was added to a method with the Random Forest (RF) algorithm previously developed for alpine environments and applied here over an arctic landscape for the first time. The topographic parameters used in the RF algorithm were Topographic Position Index (TPI) and up-wind slope index (Sx), which were estimated from the freely available Arctic DEM at 2 m resolution. Ecotypes with taller vegetation with moister soils were found to have deeper snow because of the trapping effect. Using feature importance with RF, snow depth distributions were predicted from topographic and ecosystem parameters with a root mean square error = 8 cm (23%) (R² = 0.79) at 10 m resolution for an arctic watershed (1 500 km²) in western Nunavut, Canada.

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Identification of Machining Chatter Marks on Surfaces of Automotive Valve Seats

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4004331 ◽

2011 ◽

Vol 133 (4) ◽

Cited By ~ 6

Author(s):

Hagay Bamberger ◽

Saikrishnan Ramachandran ◽

En Hong ◽

Reuven Katz

Keyword(s):

Processing Technique ◽

Image Processing Technique ◽

Inspection System ◽

Circle Fitting ◽

Machining Processes ◽

Robust Identification ◽

Machining Chatter ◽

Occurrence Matrix ◽

First Time ◽

Chatter Marks

In this paper, we propose a method for inspecting machining chatter marks on automotive valve seats using image processing technique. Chatter or self-excited tool-workpiece relative vibration is unacceptable in the machining processes of valve seats, because it causes poor surface finish and can create internal leaks in the engine. Robust identification of chatter marks on machined surfaces is one of the main challenges in the realization of an automatic in-line inspection system. We suggest several methods for calculating the chatter marks severity, using circle fitting and gray level co-occurrence matrix technique, which was for the first time implemented on circular images.

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A New Image Processing Technique for STEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100052171 ◽

1974 ◽

Vol 32 ◽

pp. 432-433

Author(s):

Yasushi Kokubo ◽

Hirotami Koike ◽

Teruo Someya

Keyword(s):

Electron Microscopy ◽

Image Processing ◽

Scanning Electron Microscopy ◽

Elastic Scattering ◽

Field Emission ◽

Processing Technique ◽

Image Processing Technique ◽

Energy Analyzer ◽

Scanning Microscope ◽

Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

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A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183863 ◽

2018 ◽

pp. 194-200

Author(s):

J. Magelin Mary ◽

Chitra K. ◽

Y. Arockia Suganthi

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Medical Image ◽

Medical Image Processing ◽

Processing Technique ◽

Input Image ◽

Image Processing Technique ◽

Computational Time ◽

Image Mining ◽

The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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Predicting changes in distribution of a large coastal shark in the face of the strengthening East Australian Current

Marine Ecology Progress Series ◽

10.3354/meps13322 ◽

2020 ◽

Vol 642 ◽

pp. 163-177 ◽

Cited By ~ 1

Author(s):

Y Niella ◽

AF Smoothey ◽

V Peddemors ◽

R Harcourt

Keyword(s):

Austral Summer ◽

Conservation Strategies ◽

Limiting Factor ◽

Commercial Fisheries ◽

East Australian Current ◽

Spatial And Temporal Patterns ◽

Marine Animals ◽

Factors Affecting ◽

Southeastern Australia ◽

The Face

In the face of accelerating climate change, conservation strategies will need to consider how marine animals deal with forecast environmental change as well as ongoing threats. We used 10 yr (2009-2018) of data from commercial fisheries and a bather protection program along the coast of New South Wales (NSW), southeastern Australia, to investigate (1) spatial and temporal patterns of occurrence in bull sharks and (2) environmental factors affecting bull shark occurrence along the coast of NSW. Predicted future distribution for this species was modelled for the forecast strengthening East Australian Current. Bull sharks were mostly harvested in small to larger estuaries, with average depth and rainfall responsible for contrasting patterns for each of the fisheries. There was an increase in the occurrence of bull sharks over the last decade, particularly among coastal setline fisheries, associated with seasonal availability of thermal gradients >22°C and both westward and southward coastal currents stronger than 0.15 and 0.60 m s-1, respectively, during the austral summer. Our model predicts a 3 mo increase in the availability of favourable water temperatures along the entire coast of NSW for bull sharks by 2030. This coastline provides a uniquely favourable topography for range expansion in the face of a southerly shift of warmer waters, and habitat is unlikely to be a limiting factor for bull sharks in the future. Such a southerly shift in distribution has implications for the management of bull sharks both in commercial fisheries and for mitigation of shark-human interactions.

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An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0107 ◽

2017 ◽

Vol 7 (7) ◽

pp. 408

Author(s):

Yashpal Jitarwal ◽

Tabrej Ahamad Khan ◽

Pawan Mangal

Keyword(s):

Image Processing ◽

Texture Features ◽

Automatic Classification ◽

Processing Technique ◽

Image Processing Technique ◽

Advance Technique ◽

Time Required ◽

Fruit Sorting ◽

Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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