Real-Time Volumetric Synthetic Aperture Software Beamforming of Row-Column Probe Data.

Two delay-and-sum beamformers for 3-D synthetic aperture imaging with row-column addressed arrays are presented. Both beamformers are software implementations for graphics processing unit (GPU) execution with dynamic apodizations and third-order polynomial subsample interpolation. The first beamformer was written in the MATLAB programming language and the second was written in C/C++ with the compute unified device architecture (CUDA) extensions by NVIDIA. Performance was measured as volume rate and sample throughput on three different GPUs: a 1050 Ti, a 1080 Ti, and a TITAN V. The beamformers were evaluated across 112 combinations of output geometry, depth range, transducer array size, number of virtual sources, floating-point precision, and Nyquist rate or in-phase/quadrature beamforming using analytic signals. Real-time imaging defined as more than 30 volumes per second was attained by the CUDA beamformer on the three GPUs for 13, 27, and 43 setups, respectively. The MATLAB beamformer did not attain real-time imaging for any setup. The median, single-precision sample throughput of the CUDA beamformer was 4.9, 20.8, and 33.5 Gsamples/s on the three GPUs, respectively. The throughput of CUDA beamformer was an order of magnitude higher than that of the MATLAB beamformer.

Autoencoder-aided measurement of concentration from a single line of speckle.

We demonstrate that a single 6mm line sample of simulated near-field speckle intensity suffices for accurate estimation of the concentration of dielectric micro-particles over a range from 104 to 6⋅106 particles per ml. For this estimation, we analyze the speckle using both standard methods (linear principal component analysis, support vector machine (SVM)) and a neural network, in the form of a sparse stacked autoencoder (SSAE) with a softmax classifier or with an SVM. Using an SSAE with SVM, we classify line speckle samples according to particle concentration with an average accuracy of over 78%, with other methods close behind.

A matlab framework for estimation of NLME models using stochastic differential equations: applications for estimation of insulin secretion rates.

The non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.