A method for the Computational Frequency Sweep Analysis of Nonlinear ODEs using GPU Acceleration

Computational sweep analysis of nonlinear ODEs (ordinary differential equations) is of importance in engineering system analysis and design. Sweep analyses usually demand intense computational power according to the number of points and the number of system parameters. This paper presents an efficient parallel algorithm for the sweep analysis of nonlinear ODEs based on graphical processing unit acceleration. The developed method preserves the jump phenomenon characteristics intrinsic to nonlinear ODEs and reduces the effects of irregular computational load. Experiments were realized using Duffing equation by sweeping frequency, amplitude, and equation coefficients. Directly, data parallel implementation and proposed implementations are compared to show the efficiency of the proposed method. Experimental results show that the new method provides significant reductions in the computational durations when compared to sequential implementation

Enhancing the image resolution in a single-pixel sub-THz imaging system based on compressed sensing

Compressed sensing (CS) techniques allow for faster imaging when combined with scan architectures, which typically suffer from speed. This technique when implemented with a subterahertz (sub-THz) single detector scan imaging system provides images whose resolution is only limited by the pixel size of the pattern used to scan the image plane. To overcome this limitation, the image of the target can be oversampled; however, this results in slower imaging rates especially if this is done in two-dimensional across the image plane. We show that by implementing a one-dimensional (1 -D) scan of the image plane, a modified approach to CS theory applied with an appropriate reconstruction algorithm allows for successful reconstruction of the reflected oversampled image of a target placed in standoff configuration from the source. The experiments are done in reflection mode configuration where the operating frequency is 93 GHz and the corresponding wavelength is lambda = 3.2 mm. To reconstruct the image with fewer samples, CS theory is applied using masks where the pixel size is 5 mm x 5 mm, and each mask covers an image area of 5 cm x 5 cm, meaning that the basic image is resolved as 10 x 10 pixels. To enhance the resolution, the information between two consecutive pixels is used, and over-sampling along 1-D coupled with a modification of the masks in CS theory allowed for oversampled images to be reconstructed rapidly in 20 x 20 and 40 x 40 pixel formats. These are then compared using two different reconstruction algorithms, TVAL3 and l(1)-MAGIC. The performance of these methods is compared for both simulated signals and real signals. It is found that the modified CS theory approach coupled with the TVAL3 reconstruction process, even when scanning along only 1-D, allows for rapid precise reconstruction of the oversampled target. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE

Preparing fMRI Data for Postprocessing: Conversion Modalities, Preprocessing Pipeline, and Parametric and Nonparametric Approaches

jaber, Hussain/0000-0002-4683-679X; ALGIN, Oktay/0000-0002-3877-8366WOS: 000487831900011The complexity of raw functional magnetic resonance imaging (fMRI) data with artifacts leads to significant challenges in multioperations with these data. FMRI data analysis is extensively used in neuroimaging fields, but the tools for processing fMRI data are lacking. A novel APP DESIGNER conversion, preprocessing, and postprocessing of fMRI (CPREPP fMRI) tool is proposed and developed in this work. This toolbox is intended for pipeline fMRI data analysis, including full analysis of fMRI data, starting from DICOM conversion, then checking the quality of data at each step, and ending in postprocessing analysis. The CPREPP fMRI tool includes 12 conversions of scientific processes that reflect all conversion possibilities among them. In addition, specific preprocessing order steps are proposed on the basis of data acquisition mode (interleaved and sequential modes). A severe and crucial comparison between statistical parametric and nonparametric mapping approaches of second-level analysis is presented in the same tool. The CPREPP fMRI tool can provide reports to exclude subjects with the extreme movement of the head during the scan, and a range of fMRI images are generated to verify the normalization effect easily. Real fMRI data are used in this work to prepare fMRI data tests. The experiment stimuli are chewing and biting, and the data are acquired from the National Magnetic Resonance Research (UMRAM) Center in Ankara, Turkey. A free dataset is used to compare the methods for postprocessing fMRI tests

Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining

AlJobouri, Hadeel/0000-0003-1792-9230; jaber, Hussain/0000-0002-4683-679X; ALGIN, Oktay/0000-0002-3877-8366WOS: 000433014300006PubMed: 29471065Background: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algorithms remains a problem. New method: A novel application of the robust unsupervised learning approach is proposed in the current study. Robust growing neural gas (RGNG) algorithm was fed into fMRI data and compared with growing neural gas (GNG) algorithm, which has not been used for this purpose or any other medical application. Learning algorithms proposed in the current study are fed with real and free auditory fMRI datasets. Results: The fMRI result obtained by running RGNG was within the expected outcome and is similar to those found with the hypothesis method in detecting active areas within the expected auditory cortices. Comparison with existing method(s): The fMRI application of the presented RGNG approach is clearly superior to other approaches in terms of its insensitivity to different initializations and the presence of outliers, as well as its ability to determine the actual number of clusters successfully, as indicated by its performance measured by minimum description length (MDL) and receiver operating characteristic (ROC) analysis. Conclusions: The RGNG can detect the active zones in the brain, analyze brain function, and determine the optimal number of underlying clusters in fMRI datasets. This algorithm can define the positions of the center of an output cluster corresponding to the minimal MDL value. (C) 2018 Elsevier B.V. All rights reserved