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Image Search Results
Journal: Visual Computing for Industry, Biomedicine and Art
Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation
doi: 10.1186/s42492-018-0002-5
Figure Lengend Snippet: Overall flowchart of a MEXCUDA function
Article Snippet: In details,
Techniques:
Journal: Visual Computing for Industry, Biomedicine and Art
Article Title: Optimizing photoacoustic image reconstruction using cross-platform parallel computation
doi: 10.1186/s42492-018-0002-5
Figure Lengend Snippet: Example process flow of the heterogeneous computing. In this figure, we create a sample MEXCUDA function for calculating the radius matrix of a scanned region to each transducer elements. The host code (CPU) is in charge of initialization and finalization blocks such as reading the input from MATLAB, performing condition checks, and allocating memory for input and output data. Device code in GPU is responsible for computing the radius matrix in parallel from required inputs (parallel computation block)
Article Snippet: In details,
Techniques: Blocking Assay
Journal: Neuroinformatics
Article Title: Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting
doi: 10.1007/s12021-020-09489-1
Figure Lengend Snippet: Overview over hardware and software requirements of the different functions for estimating L1-regularized linear models. The lasso function is part of the Statistics and Machine Learning Toolbox and only included here for benchmarking purposes (see main text). The lasso_mex, lasso_mexcuda and lasso_gpu functions introduced here are specifically optimized for the mass-univariate analysis approach and depend on different hardware and software configurations
Article Snippet: While the underlying coordinate descent algorithm is implemented in C++, the
Techniques: Software