Architecting for Artificial Intelligence with Emerging Nanotechnology

Kulkarni, Sourabh; Bhat, Sachin; Moritz, Csaba Andras

doi:10.1145/3445977

Cited by 4 publications

(1 citation statement)

References 126 publications

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“…Image processing (20,21) is the process by which one can obtain useful information about any image. Image processing was used in engineering application like cyphering (22,23) chemistry (24,25), materials (26), biomedical (27)(28)(29), and nanotechnology (30)(31)(32), Telcomunications (33), Covid19 detection (34). Image processing has become a radical, fast, and accurate solution for measuring the size of nanoparticles (4,35).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Particle Size in Composite Materials Using Image Processing

Alabdali,

Alkalid

2024

IJE

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Composite materials are the most important materials in materials science and engineering, which contain two or more materials. In materials engineering, the scanning electron microscopy (SEM) technique is an approach to measure the material's particle size. A new procedure was used instead of SEM is called Artificial Intelligence (AI). Artificial Intelligence (AI) is an interdisciplinary science and branch of computer science that involves solving problems that require human intelligence and capabilities. The computer vision is a subfield of AI, which uses some algorithms to detect the details of images by using computer called image processing. Detecting the particles and measuring the size of materials scanned by SEM is an essential task that helps to describe their feature, traditionally, the size is calculated manually by adding mesh to an SEM image or by drawing a diagonal line in an arbitrary particle. In this paper, a new model based on Artificial Intelligence (AI) is proposed using computer vision to analyze the size of all particles. This model is used to detect the particle size of additives in composite materials like graphene flakes and measure the size of them depending on the reference size fixed on the scanning electron microscope (SEM). The model was used based on the Open-source Computer Vision (OpenCV) library, utilizing multi-layers of canny edge detection, Sobel filter, Brightness and contrast algorithms, using Python 3. The results have achieved very satisfied indication with a very low process time = 0.2 mili-seconds.

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Section: Introductionmentioning

confidence: 99%

Analysis of Particle Size in Composite Materials Using Image Processing

Alabdali,

Alkalid

2024

IJE

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Nanotechnology and Computer Science: Trends and advances

Taha¹,

Barzinjy²,

Hussain³

et al. 2022

Memories - Materials, Devices, Circuits and Systems

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A Conditional Generative Adversarial Network and Transfer Learning-Oriented Anomaly Classification System for Electrospun Nanofibers

Ieracitano

Mammone

Paviglianiti

et al. 2022

Int. J. Neur. Syst.

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This paper proposes a generative model and transfer learning powered system for classification of Scanning Electron Microscope (SEM) images of defective nanofibers (D-NF) and nondefective nanofibers (ND-NF) produced by electrospinning (ES) process. Specifically, a conditional-Generative Adversarial Network (c-GAN) is developed to generate synthetic D-NF/ND-NF SEM images. A transfer learning-oriented strategy is also proposed. First, a Convolutional Neural Network (CNN) is pre-trained on real images. The transfer-learned CNN is trained on synthetic SEM images and validated on real ones, reporting accuracy rate up to 95.31%. The achieved encouraging results endorse the use of the proposed generative model in industrial applications as it could reduce the number of needed laboratory ES experiments that are costly and time consuming.

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Architecting for Artificial Intelligence with Emerging Nanotechnology

Cited by 4 publications

References 126 publications

Analysis of Particle Size in Composite Materials Using Image Processing

Analysis of Particle Size in Composite Materials Using Image Processing

Nanotechnology and Computer Science: Trends and advances

A Conditional Generative Adversarial Network and Transfer Learning-Oriented Anomaly Classification System for Electrospun Nanofibers

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