scholarly journals PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform

2020 ◽  
Vol 10 (10) ◽  
pp. 3382
Author(s):  
Rahmat Ullah ◽  
Tughrul Arslan
Keyword(s):  
Image Reconstruction ◽  
High Performance Computing ◽  
High Performance ◽  
Reconstruction Algorithm ◽  
Apache Spark ◽  
Cloud Platform ◽  
Data Set ◽  
Image Reconstruction Algorithm ◽  
Performance Computing ◽  
Microwave Image

For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.

scholarly journals Viability of Cloud Computing for Real-Time Numerical Weather Prediction

2016 ◽  
Vol 31 (6) ◽  
pp. 1985-1996 ◽  
Author(s):  
David Siuta ◽  
Gregory West ◽  
Henryk Modzelewski ◽  
Roland Schigas ◽  
Roland Stull
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
High Performance ◽  
Virtual Machines ◽  
Weather Prediction ◽  
Cloud Platform ◽  
Numerical Weather ◽  
Performance Computing

Abstract As cloud-service providers like Google, Amazon, and Microsoft decrease costs and increase performance, numerical weather prediction (NWP) in the cloud will become a reality not only for research use but for real-time use as well. The performance of the Weather Research and Forecasting (WRF) Model on the Google Cloud Platform is tested and configurations and optimizations of virtual machines that meet two main requirements of real-time NWP are found: 1) fast forecast completion (timeliness) and 2) economic cost effectiveness when compared with traditional on-premise high-performance computing hardware. Optimum performance was found by using the Intel compiler collection with no more than eight virtual CPUs per virtual machine. Using these configurations, real-time NWP on the Google Cloud Platform is found to be economically competitive when compared with the purchase of local high-performance computing hardware for NWP needs. Cloud-computing services are becoming viable alternatives to on-premise compute clusters for some applications.

scholarly journals Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure

2021 ◽  
Vol 11 (4) ◽  
pp. 1741
Author(s):  
Hungjoo Kwon ◽  
Changbin Joh ◽  
Won Jong Chin
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Ultrasonic Pulse ◽  
Experimental Results ◽  
Ultrasonic Tomography ◽  
Data Set ◽  
Image Reconstruction Algorithm ◽  
Long Wavelength ◽  
Ultrasonic Array ◽  
Pulse Peak

An ultrasonic array device like the A1040 MIRA is used to non-destructively visualize the inside of concrete structures. A data set acquired by the ultrasonic array device is so unfocused that an image reconstruction algorithm is required to transform the data set into an understandable image. The image reconstruction algorithm like total focusing method exploits the time-of-flight of an ultrasonic pulse when focusing the image. While a high frequency ultrasonic pulse barely affects the accuracy of results, a low frequency ultrasonic pulse with a long wavelength causes an overall sagging of the resulting image around half wavelength of the pulse, which results in a poor quality of results. In this research, a modified total focusing method called pulse peak delay-total focusing method is proposed to calibrate the sagging in the resulting images due to the long wavelength of the pulse. The simulation of an ultrasonic array signal is implemented to validate the proposed method. The experimental results are compared with the simulation results to validate the proposed method. The simulation using the proposed method shows good agreement with experimental results. Analysis of results using potential damage curve and array performance indicator shows that the proposed method allows the higher accuracy, as well as the increased resolution of resulting images.

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