A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core system

Kartheeswaran, Subramanian; Durairaj, Daniel Dharmaraj Christopher

doi:10.1016/j.imu.2017.05.001

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…ANN finds its wide application in different streams such as engineering, science, pharmaceutics. Some of the important fields include sound and pattern detection, the prediction of the market trends, the bankrupt-ion, military targets, and mineral exploration sites [34][35][36][37]. Neural networks fend off the need for costly and impractical physical models, complex mathematical formulas, and computer models.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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“…As a result, it achieves a quicker and more effective MBIR than traditional CNNs. Wu et al [ 40 ] presented a deep CNN for CT image reconstruction. This study aims to decrease the memory and time usage of CT reconstruction network training to make it realistic for new processors while preserving the quality of the images reconstructed.…”

Section: Soft Computingmentioning

confidence: 99%

“…AI techniques like deep learning and neural network have created a novel framework with novel approaches in inverse problems which could change the area. [40] developed a sequential and parallel data decomposition technique based on PSO-ANN (particle swarm optimization with artificial neural networks). Generally, ANN training takes a long time; therefore, the author decomposes the dataset into a subset and assigned the weight of each subset optimized by PSO.…”

Section: Neural Networkmentioning

confidence: 99%

A Survey of Soft Computing Approaches in Biomedical Imaging

Devi

Singh

Tiwari

et al. 2021

Journal of Healthcare Engineering

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Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clinics. Soft computing plays a major role in the recent advances in medical imaging. It handles uncertainties and improves the qualities of an image. Until now, various soft computing approaches have been proposed for medical applications. This paper discusses various medical imaging modalities and presents a short review of soft computing approaches such as fuzzy logic, artificial neural network, genetic algorithm, machine learning, and deep learning. We also studied and compared each approach used for other imaging modalities based on the certain parameter used for the system evaluation. Finally, based on comparative analysis, the possible research strategies for further development are proposed. As far as we know, no previous work examined this issue.

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“…Создание этих сигналов с одинаковой длиной выборки называется нормализацией периода. Он может быть выполнен с помощью интерполяции или процесса децимации [8]. В контексте ЭКГ каждый удар сердца должен быть периодически нормализован, чтобы выполнить над ними любую математическую операцию.…”

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The Analysis of Approximation Chebyshev for Filters With Finite and Infinite Impulse Characteristics

Goryaev¹,

Mashtykov²,

Bembitov³

et al. 2021

СНТ (MHT)

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Современная биомедицинская аппаратура может быть невосприимчива к различным типам шумов, но только не к базовому шуму и помехам электропитания. Эти источники шума имеют частотные спектры, которые загрязняют соответствующие спектры электрокардиограммы (ЭКГ). Алгоритмы цифровой обработки сигналов в модулях предварительной обработки электрокардиографических устройств играют ключевую роль в обеспечении высококачественных сигналов ЭКГ для анализа, интерпретации и представления. Из-за перекрытия спектра мощности сигнала и шума в полученных данных ЭКГ, в частотно-селективных фильтрах стоит проблема между удалением шума и возможной деформацией полезного сигнала. Что наиболее важно, результат фильтрации не должен отрицательно влиять на последующую диагностику и интерпретацию. В работе было проведено количественное исследование оценки результатов между фильтрами Ремеза и Чебышева на основе минимальной среднеквадратичной ошибки, более высокой значения отношения сигнал/помеха и пикового отношения сигнал/шум в среде Python с использованием режекторного фильтра. Результаты показали, что шумовой сигнал с использованием фильтра Ремеза имеет лучший баланс между плавностью и точностью, чем фильтр Чебышева. Однако фильтр Чебышева отличается минимальной вычислительной нагрузкой, соответственно, такой тип фильтра найдет применение в мониторах аритмии на базе микрокомпьютеров.

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A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core system

Cited by 9 publications

References 24 publications

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

A Survey of Soft Computing Approaches in Biomedical Imaging

The Analysis of Approximation Chebyshev for Filters With Finite and Infinite Impulse Characteristics

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