Image deconvolution for optical small satellite with deep learning and real-time GPU acceleration

Ngo, Tan D.; Bui, Tuyen T.; Tuấn, Phạm Minh; Thai, Hong T. B.; Giang, Nguyễn Long; Nguyen, Tu N.

doi:10.1007/s11554-021-01113-y

Cited by 47 publications

(13 citation statements)

References 13 publications

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“…Concerning particular real-world activities, AI applications begin to address and exceed human-level skills. In the growth of new businesses around increasing reality, biophotonics, autonomic cars, and IoT, AI innovation develops and matures IoT (Gao et al, 2020a;Ngo et al, 2021). In a survey performed by scholars Siemens and Forbes, the growth and acceptance of AI have shown that by 2035, the global gross domestic product (GDP) would grow by 16%.…”

Section: Introduction To Risk Analysis In E-businessmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

Lakshmanan

Maheswari

2021

Ann Oper Res

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The main job of the e-business professional is to produce appropriate and trustworthy financial data. The solid internal controlling systems and the ethics and integrity of administration and staff depend significantly on the accuracy and relevancy of financial reports. This article demonstrates how Artificial Intelligence interacts with internal controlling systems creatively to assist managers in creating quality financial data by minimizing the risk of loss. A risk analysis approach for e-business is proposed in this article. Even though various kinds of research suggested in accountancy utilizing artificial intelligence, none of these proved explicitly how data risks were reduced via AI technology. The analysis benefits organizations save considerable expenses and losses by providing reliable financial data, helping managers decide effectively, and enhancing firms' efficiency. This article offers a concept of utilizing artificial intelligence to automate the removal of the vulnerability of internal monitoring systems by all types of businesses. It decreases the risk control, identification, and reporting quality through managing the risk of information reporting by offering the highest risk prediction accuracy of 89% and the precision ratio of 91% compared to the existing models.

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Section: Introduction To Risk Analysis In E-businessmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

Lakshmanan

Maheswari

2021

Ann Oper Res

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“…Generally, the architecture of DCNN models comprises a large number of layers, including convolutional, pooling, dropout, fully connected layers, and so forth. AlexNet and GoogLeNet are the two most effective CNN frameworks that are widely used to address the problems of image classification (Hulstaert, 2018;Krizhevsky et al, 2012;Maeda-Gutiérrez et al, 2020;Ngo et al, 2021;Oğuz & Yağanoğlu, 2022).…”

Section: Selection Of Modelmentioning

confidence: 99%

AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

Chakraborty

Subeesh

Potdar

et al. 2023

Journal of Field Robotics

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The modernization of postharvest operations and penetration of emerging technologies in horticultural processing have provided intelligent solutions for reducing postharvest losses. Work environmental and occupational health issues require immediate attention as the awkward posture and continuous drudgeryprone on-farm sorting and grading activities may lead to musculoskeletal disorders.The main objective of this study was to develop an automatic farm-friendly machine for real-time citrus fruit washing, image-based sorting, and weight grading; designed optimally and equipped with an embedded system comprising a lightweight convolutional neural network (CNN) model. Also included in this study was a thorough ergonomic assessment of the developed machine in a real work environment. The parametric choice of the fruit washing and singulation system was performed by employing computational fluid dynamics modeling and response surface methodology designed optimization. It was observed that under steady-state conditions, the water jet would arrive at a velocity of 11.36 m/s which would eventually suit a singulation conveyor with a slope of 25°. A noninvasive grading and sorting approach for citrus fruits is presented in this paper that leverages deep learning to classify the fruits into "accept" and "reject" classes. The custom lightweight CNN model "SortNet" has shown excellent classification results with an overall accuracy of 97.6%. The ergonomic evaluation shows that the average body part discomfort score in case of operating an automatic fruit grading machine was much lower (12.3 ± 2.0) than the traditional method (30.9 ± 3.3). Further, in the case of machine operation, the percentage load on the muscles ranged from 28.67 to 34.31 reflecting that subjects can work for longer duration on the machine without fatigue as compared with the traditional manual operation.

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“…In the method proposed in [ 15 , 16 , 17 ], MobileNetV2 pre-trained classifier is used for the purpose of classification. A Kaggle dataset with real world persons with masks is used.…”

Section: Related Workmentioning

confidence: 99%

Face mask recogniser using image processing and computer vision approach

Sharadhi

Gururaj

Shankar

et al. 2022

Global Transitions Proceedings

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Image deconvolution for optical small satellite with deep learning and real-time GPU acceleration

Cited by 47 publications

References 13 publications

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment

Face mask recogniser using image processing and computer vision approach

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