@article {343,
	title = {Design of a Novel Very-Low Input Capacitance High-Bandwidth Buffer for a Biomass Sensor},
	journal = {IEEE Sensors Journal},
	volume = {25},
	year = {2025},
	pages = {655-662},
	abstract = {<p>In this work, a high-bandwidth very-low input capacitance original topology buffer was designed. The proposed buffer circuitry is conceived as part of a novel noninvasive nonimmersed real-time dielectric spectroscopy biomass sensor. Both a circuital-based simulation model and an experimental printed circuit board (PCB) prototype were implemented and successfully tested. The buffer effective parasitic input capacitance was lowered to a 2\% of its original value by means of power supply bootstrapping. This improvement will allow performing differential voltage measurements with the required accuracy and without compromising the necessary bandwidth.</p>
},
	keywords = {Bandwidth, Biosensors, Capacitance, Capacitance measurement, Dielectrics, Hands, High-bandwidth buffer, Mathematical models, nonimmersed biomass sensor, noninvasive dielectric spectroscopy, power supply bootstrapping, Printed circuits, Topology, very-low input capacitance buffer, voltage measurement},
	doi = {10.1109/JSEN.2024.3486534},
	author = {Amor, Fernando and Haberman, Marcelo A. and Jamilis, Mart{\'\i}n and Guerrero, Federico N. and De Battista, Hern{\'a}n}
}
@article {345,
	title = {Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {72},
	year = {2025},
	month = {June},
	pages = {2001-2012},
	abstract = {<p>Objective: A common challenge in model identification with clinical data is incomplete and sometimes imprecise information. In this work, we provide a method to reconstruct the corrupted input data in a clinical dataset and, jointly identify the person-specific parameters of a metabolic model describing meal-insulin-glucose-dynamics for people with type 1 diabetes (T1D). Method: The proposed method is an algorithm that iterates between nonlinear least-squares and mixed-integer quadratic programming to optimize model parameters in conjunction with sparse corrections to the input data. In order to handle long stretches of data, the optimization problem is designed to be i) computationally tractable, and ii) robust against the potential presence of significant inaccuracies corrupting a data portion. Moreover, since the pattern of the inaccuracies is specific to each person, we propose a personalized hyperparameter tuning approach. The method is applied on clinical data from 13 people with T1D. Identified model performance is compared to the performance of model identified with standard least squares (LS) method. Results: Compared to LS, identifying corrections in conjunction with model parameters on training data lead to an improvement in the model prediction capabilities on unseen data with an average 2.2\% improvement in MARD for two-hour prediction horizon (p-value = 0.0006). Conclusions: The proposed method is effective in model identification for clinical data with unknown inaccuracies in the inputs. Significance: Personalized models with high accuracy can inform treatment decisions and lead to better glucose control outcomes in people with T1D.</p>
},
	keywords = {Adaptation models, Data models, Diabetes, Glucose, Incomplete data, insulin, Mathematical models, mixed-integer optimization, model identification, Postal services, Predictive models, Standards, Training data, type 1 diabetes},
	issn = {1558-2531},
	doi = {10.1109/TBME.2025.3530711},
	author = {Ozaslan, Basak and Aiello, Eleonora M. and Fushimi, Emilia and Doyle, Francis J. and Dassau, Eyal}
}
