Verification of the in-line PS function. حسب Raul Castañeda (13932971)

Available functions in the utility package. حسب Raul Castañeda (13932971)

The performance of the contralateral temporo-frontal setup across virtual time. حسب Janne J. Luppi (17804327)

"…Reversing the polarity of the stimulation reversed the effect, with cathodal stimulation further shifting the mesures from healthy values. (C) Total power was very strongly reduced by the ADD algorithm, but anodal stategies resulted in a shift towards healthy values, while cathodal setups caused a slight improvement. …"

The performance of the contralateral parieto-frontal setups across virtual time. حسب Janne J. Luppi (17804327)

"…Reversing the polarity of the stimulation reversed the effect, with cathodal stimulation further shifting the mesures from healthy values. (C) Total power was very strongly reduced by the ADD algorithm, but anodal stategies resulted in a sizable shift towards healthy values, while cathodal setups had a slight worsening effect. …"

The recurrent T3D algorithm. حسب Neythen J. Treloar (8698251)

Sensitivity results from the Morris screening algorithm. حسب Mitchel J. Colebank (7390403)

Available functions in the phase-shifting package. حسب Raul Castañeda (13932971)

Available functions in the numerical propagation package. حسب Raul Castañeda (13932971)

Available functions in the phase reconstruction package. حسب Raul Castañeda (13932971)

Convergence graphs of the ten algorithms for the five functions from the CEC 2014 suite. حسب Juan Yao (293170)

Example of landmark registration to align ‘peaks’ of high density in the probability density function (PDF) of CD4 expression on blood T cells for a target distribution and chosen... حسب Ross J. Burton (10941910)

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation حسب Kyriacos Yiannacou (10292766)

"…We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …"

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation حسب Kyriacos Yiannacou (10292766)

"…We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …"

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation حسب Kyriacos Yiannacou (10292766)

"…We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …"

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation حسب Kyriacos Yiannacou (10292766)

"…We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …"

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation حسب Kyriacos Yiannacou (10292766)

"…We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …"

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation حسب Kyriacos Yiannacou (10292766)

"…We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …"

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation حسب Kyriacos Yiannacou (10292766)

"…We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. …"

Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). حسب Solomon Shiferaw Beyene (9143178)

Accuracy, sensitivity, specificity and F-score. حسب Solomon Shiferaw Beyene (9143178)