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NOTE: The code in https://git.qoto.org/CoreRasurae/piv-image-generator could not be imported via git with CodeOcean and was manually inserted, after which a small change was made in exampleAllTestImagesMain to generate an example for each supported flow type. A generator of synthetic images of tracers in turbulent flows is described. Its main application is the benchmarking of Particle Image Velocimetry and Optical Flow algorithms. It can generate tracer images for different types of flows, namely: uniform; parabolic; flow with stagnation point; Rankine vor- tex and Rankine vortex with superimposed uniform flow. Control parameters are: Particle size, particle concentration, white gaussian image noise, particle velocity, image bit depth, particle out of plane movement (turbulence), and laser sheet thickness. Displacements, at specific instants, are deduced for any arbitrary position from the theoretical flow field. An example application is shown and discussed.
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C-CLCM is a continual learning classification method with constant-sized memory cells based on artificial immune system. It is inspired by the intelligent mechanism that memory cells of the biological immune system can recognize and eliminate previous invaders very fast and more efficiently when they attack again. Memory cells of C-CLCM are used as a classifier, and the new memory cells and new type memory cells can be continuously cultured by learning the testing data and new labeled data during the testing stage, thus realize the self-improvement of classification performance. Under certain conditions, C-CLCM will degenerate into a common supervised learning classification method.
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This capsule executes image reconstruction with neural reconstruction models (HamVN and FlowVN). Retrospective and prospective undersampled reconstructions will be stored in the `results` folder: `results/prospective/recon`: prospective CS reconstructions `results/volunteer/recon`: retrospective CS reconstructions for various acceleration factors
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This version of PIETOOLS has an input format compatible with the Differential Difference Formulation of networked control problems with delay. Options are given for heavy (slow), light (faster), stripped (faster) and extreme (fastest, but low accuracy) performance. This capsule uses the standard PIETOOLS executives for: stability analysis; a dual version of stability analysis; Hinf-gain analysis; A dual version of Hinf-gain analysis; Hinf-optimal controller synthesis; Hinf-optimal observer synthesis.
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Software for constructing MD-SC LDPC Codes with enhanced cycle properties
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Simulating datasets that satisfy linear relation of different intercepts [0 0.02 0.04 0.06 0.0] and different slopes [0 0.03 0.06 0.09]. For each of the possible (intercept, slope) pair 50 datasets were simulated. For each dataset Showing the overestimation of GLM and unbiased estimation of Bayesian,
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A new algorithm for computing Whittle index for an indexable restless bandit. In this capsule, the performance of Whittle index policy is compared with the optimal policy and the myopic policy.
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Vogel, D., & Willems, J. (2020). The effects of making public service employees aware of their prosocial and societal impact: A micro-intervention. Journal of Public Administration Research and Theory. https://doi.org/10.1093/jopart/muz044
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The codes are based on the research project A Deep Learning Framework for Assessing Physical Rehabilitation Exercises. The framework for automated quality assessment of physical rehabilitation exercises encompasses metrics for quantifying movement performance, scoring functions for mapping the performance metrics into numerical scores of movement quality, techniques for dimensionality reduction, and deep neural network models for regressing quality scores of input movements via supervised learning. The proposed framework employs an autoencoder network for dimensionality reduction, a performance metric based on the log-likelihood of a Gaussian mixture model, and a deep convolutional neural network for movement assessment.
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  • Software/Code
This program simulates the online design of physical watermark design and shows that the inferred covariance of the watermark converges to the one with known system parameters. Besides, this program computes the detection statistics during the whole detection period.
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  • Software/Code
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