Low-cost smartphone-based LIBS combined with deep learning image processing for accurate lithology recognition

2021 ◽  
Author(s):  
Xu Wang ◽  
Sha Chen ◽  
Mengfan Wu ◽  
Ruiqin Zheng ◽  
Zhuo Liu ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Linear Array ◽  
Low Cost ◽  
Two Dimensional ◽  
Multichannel Detection

A low-cost and multi-channel smartphone-based spectrometer was developed for LIBS. As the CMOS detector is two-dimensional, simultaneous multichannel detection were achieved by coupling a linear array of fibres for light...

scholarly journals Detecção de buracos em pavimento asfáltico com base em Processamento Digital de Imagens e Deep Learning

2021 ◽  
Author(s):  
Bianka Tallita Passos ◽  
Wemerson Delcio Parreira ◽  
Anita Maria da Rocha Fernandes ◽  
Eros Comunello
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Laser Scanning ◽  
Low Cost ◽  
Two Dimensional ◽  
Road Maintenance ◽  
Operational Cost ◽  
The Road ◽  
On The Road ◽  
Cost Of Transportation

The road infrastructure conditions are directly related to the safetyand operational cost of transportation. Potholes are defects in thepaving that affect safety on the road. Therefore, identifying potholesis an important step in defining road maintenance and interventionstrategies. Among the approaches used to detect defects in roadsare vibration techniques, laser scanning and 3D reconstruction, andfinally methods that are vision-based. These vision-based methodsutilize image processing, considered low cost and that can be performedby common two-dimensional cameras. This research aimsto combine digital image processing and deep learning concepts facilitatingthe recognition of pothole-like defects in road images withasphalt paving. In order to carry out these experiments, differentnetwork architectures were used.

scholarly journals Measurement of Wastewater Discharge in Sewer Pipes Using Image Analysis

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1771
Author(s):  
Hyon Ji ◽  
Sung Yoo ◽  
Bong-Jae Lee ◽  
Dan Koo ◽  
Jeong-Hee Kang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Water Level ◽  
Low Cost ◽  
Water Levels ◽  
Boundary Line ◽  
Sewer Pipe ◽  
Flow Meters ◽  
Submerged Area ◽  
Boundary Lines

Generally, the amount of wastewater in sewerage pipes is measured using sensor-based devices such as submerged area velocity flow meters or non-contact flow meters. However, these flow meters do not provide accurate measurements because of impurities, corrosion, and measurement instability due to high turbidity. However, cameras have advantages such as their low cost, easy service, and convenient operation compared to the sensors. Therefore, in this study, we examined the following three methods for measuring the flow rate by capturing images inside of a sewer pipe using a camera and analyzing the images to calculate the water level: direct visual inspection and recording, image processing, and deep learning. The MATLAB image processing toolbox was used for analysis. The image processing found the boundary line by adjusting the contrast of the image or removing noise; a network to find the boundary line between wastewater and sewer pipe was created after training the image segmentation results and placing them into three categories using deep learning. From the recognized water levels, geometrical features were used to identify the boundary lines, and flow velocities and flow rates were calculated from Manning’s equation. Using direct inspection and image-processing techniques, boundary lines in images were detected at rates of 12% and 53%, respectively. Although the deep-learning model required training, it demonstrated 100% water-level detection, thereby proving to be the most advantageous method. Moreover, there is enough potential to increase the accuracy of deep learning, and it can be a possible replacement for existing flow measurement sensors.

scholarly journals From Auto-encoders to Capsule Networks: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01003
Author(s):  
Omaima El Alaoui-Elfels ◽  
Taoufiq Gadi
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Future Research ◽  
Two Dimensional ◽  
One Dimensional ◽  
New Generation ◽  
Open Issues

Convolutional Neural Networks are a very powerful Deep Learning structure used in image processing, object classification and segmentation. They are very robust in extracting features from data and largely used in several domains. Nonetheless, they require a large number of training datasets and relations between features get lost in the Max-pooling step, which can lead to a wrong classification. Capsule Networks(CapsNets) were introduced to overcome these limitations by extracting features and their pose using capsules instead of neurons. This technique shows an impressive performance in one-dimensional, two-dimensional and three-dimensional datasets as well as in sparse datasets. In this paper, we present an initial understanding of CapsNets, their concept, structure and learning algorithm. We introduce the progress made by CapsNets from their introduction in 2011 until 2020. We compare different CapsNets series architectures to demonstrate strengths and challenges. Finally, we quote different implementations of Capsule Networks and show their robustness in a variety of domains. This survey provides the state-of-theartof Capsule Networks and allows other researchers to get a clear view of this new field. Besides, we discuss the open issues and the promising directions of future research, which may lead to a new generation of CapsNets.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

Specific Labeling of Protein Domains with Antibody Fragments

1981 ◽  
Vol 39 ◽  
pp. 416-417
Author(s):  
U. Aebi ◽  
L.E. Buhle ◽  
W.E. Fowler
Keyword(s):  
Image Processing ◽  
Specific Protein ◽  
Antibody Fragments ◽  
Supramolecular Organization ◽  
Two Dimensional ◽  
Ordered Structures ◽  
Virus Capsids ◽  
Processing Techniques

Many important supramolecular structures such as filaments, microtubules, virus capsids and certain membrane proteins and bacterial cell walls exist as ordered polymers or two-dimensional crystalline arrays in vivo. In several instances it has been possible to induce soluble proteins to form ordered polymers or two-dimensional crystalline arrays in vitro. In both cases a combination of electron microscopy of negatively stained specimens with analog or digital image processing techniques has proven extremely useful for elucidating the molecular and supramolecular organization of the constituent proteins. However from the reconstructed stain exclusion patterns it is often difficult to identify distinct stain excluding regions with specific protein subunits. To this end it has been demonstrated that in some cases this ambiguity can be resolved by a combination of stoichiometric labeling of the ordered structures with subunit-specific antibody fragments (e.g. Fab) and image processing of the electron micrographs recorded from labeled and unlabeled structures.

Shifting perspectives: method, media and the complex image

1998 ◽  
Vol 10 (1-3) ◽  
pp. 100-108 ◽  
Author(s):  
Alicia Colson ◽  
Ross Parry
Keyword(s):  
Image Processing ◽  
Three Dimensional ◽  
Geographical Location ◽  
Spatial Relationship ◽  
Two Dimensional ◽  
Dimensional Approach ◽  
Complex Image ◽  
Cultural Resonance

This article argues that the analysis of a threedimensional image demanded a three-dimensional approach. The authors realise that discussions of images and image processing inveterately conceptualise representation as being flat, static, and finite. The authors recognise the need for a fresh acuteness to three-dimensionality as a meaningful – although problematic – element of visual sources. Two dramatically different examples are used to expose the shortcomings of an ingrained two-dimensional approach and to facilitate a demonstration of how modern (digital) techniques could sanction new historical/anthropological perspectives on subjects that have become all too familiar. Each example could not be more different in their temporal and geographical location, their cultural resonance, and their historiography. However, in both these visual spectacles meaning is polysemic. It is dependent upon the viewer's spatial relationship to the artifice as well as the spirito-intellectual viewer within the community. The authors postulate that the multi- faceted and multi-layered arrangement of meaning in a complex image could be assessed by working beyond the limitations of the two-dimensional methodological paradigm and by using methods and media that accommodated this type of interconnectivity and representation.

Developing an Inkjet Printer I: RGB Image to CMY Ink Amounts - Image Processing and Color Management

2020 ◽  
Vol 2020 (15) ◽  
pp. 350-1-350-10
Author(s):  
Yin Wang ◽  
Baekdu Choi ◽  
Davi He ◽  
Zillion Lin ◽  
George Chiu ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Cost ◽  
Experimental Results ◽  
Color Management ◽  
Finger Nail ◽  
Inkjet Printer ◽  
Rgb Image ◽  
Novel Algorithm

In this paper, we will introduce a novel low-cost, small size, portable nail printer. The usage of this system is to print any desired pattern on a finger nail in just a few minutes. The detailed pre-processing procedures will be described in this paper. These include image processing to find the correct printing zone, and color management to match the patterns’ color. In each phase, a novel algorithm will be introduced to refine the result. The paper will state the mathematical principles behind each phase, and show the experimental results, which illustrate the algorithms’ capabilities to handle the task.

Ti3C2-MXene/Bismuth Ferrite Nanohybrids for Efficient Degradation of Organic Dye and Colorless Pollutant

2019 ◽  
Author(s):  
Ayesha Tariq ◽  
M. Abdullah Iqbal ◽  
S. Irfan Ali ◽  
Muhammad Z. Iqbal ◽  
Deji Akinwande ◽  
...  
Keyword(s):  
Surface Area ◽  
Low Cost ◽  
Bismuth Ferrite ◽  
Organic Dye ◽  
Two Dimensional ◽  
Electron Hole ◽  
Photocatalytic Application ◽  
Electron Hole Pair ◽  
Pair Generation ◽  
Photocatalytic Applications

<p>Nanohybrids, made up of Bismuth ferrites/Carbon allotropes, are extensively used in photocatalytic applications nowadays. Our work proposes a nanohybrid system composed of Bismuth ferrite nanoparticles with two-dimensional (2D) MXene sheets namely, the BiFeO<sub>3</sub> (BFO)/Ti<sub>3</sub>C<sub>2</sub> (MXene) nanohybrid for enhanced photocatalytic activity. We have fabricated the BFO/MXene nanohybrid using simple and low cost double solvent solvothermal method. The SEM and TEM images show that the BFO nanoparticles were attached onto the MXene surface and in the inter-layers of two-dimensional (2D) MXene sheets. The photocatalytic application is tested for the visible light irradiation which showed the highest efficiency among all pure-BFO based photocatalysts, i.e. 100% degradation in 42 min for organic dye (Congo Red) and colorless aqueous pollutant (acetophenone) in 150 min, respectively. The present BFO-based hybrid system exhibited the large surface area of 147 m<sup>2</sup>g<sup>-1</sup>measured via Brunauer-Emmett-Teller (BET) sorption-desorption technique, and is found to be largest among BFO and its derivatives. Also, the photoluminescence (PL) spectra indicate large electron-hole pair generation. Fast and efficient degradation of organic molecules is supported by both factors; larger surface area and lower electron-hole recombination rate. The BFO/MXene nanohybrid presented here is a highly efficient photocatalyst compared to other nanostructures based on pure BiFeO<sub>3</sub> which makes it a promising candidate for many future applications.</p>

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