Publications

Complementary split ring resonators (CSRRs) have been extensively studied as planar sensors in the last two decades. However, their practical use remains limited to controlled environments and classification problems. Their performance relies on high-end vector network analyzers (VNAs), highly repeatable laboratory conditions, and special sample holders or microfluidic circuits hinders its regular use in chemistry laboratories as an analytical tool. Temperature drifts and humidity variations during measuring, uncertainties in the electromagnetic properties of the sample containers, and careless sample handling introduce significant uncertainties in measurements, leading to unreliable results. Therefore, the prediction of target compounds concentration in samples has been out of the research focus up to now. Machine learning (ML) algorithms can help to mitigate these uncertainties and open the applicability of CSRR sensors to quantification problems, where it is necessary to determine the amount of a substance in a liquid (or solid) sample. This work presents a novel approach that tackles this issue, combining a CSRR sensor with well-stabilized ML algorithms that enhance its quantification performance. For illustration purposes, a low-cost, benchtop CSRR-based system is proposed to predict ethanol concentration in water solutions. Ethanol samples from 10% to 96% concentration were prepared in commercial vials, generating 450 randomized measurements. Principal component analysis (PCA) was employed for data exploration, while a partial least squares (PLS) regression model, tuned with leave-one-group-out cross validation (LOGO-CV), was trained for ethanol concentration prediction. No feature extraction technique or noise reduction strategy was applied. Although this straightforward workflow is well known in the chemical sensing field, it has not been applied to data acquired with CSRR sensors. The trained model achieved a root mean square error in prediction (RMSEP) of 3.7% . Compared with 23.4% RMSEP when using univariate calibration at optimized frequencies, it presents a prediction performance reduced by a factor of 6. No evidence of underfitting or overfitting was observed during the test of the trained model. The low RMSEP achieved by the presented setup demonstrates the potential of CSRR-based sensors when combined with ML techniques for concentration prediction working in realistic, uncontrolled conditions. This pushes forward the applicability of CSRR sensors in the chemical analysis field, which might lead to benchtop, low-cost, and reliable analysis devices for many laboratories.

JTD Keywords: Chemical analysis, Chemical sensors, Complementary, Complementary split ring resonator (csrr) sensors, Concentration prediction, Design, Electronic mail, Ethanol, Feature extraction, Filters, Machine learning (ml), Metamaterials, Principal component analysis, Resonators, Rf sensors, Sensor phenomena and characterization, Sensors, Split-ring resonators, Temperature measurement, Transmission, Transmission line measurements, Uncertainty, Variable selection

Fuzzy theory was motivated by the need to create human-like solutions that allow representing vagueness and uncertainty that exist in the real-world. These capabilities have been recently further enhanced by deep learning since it allows converting complex relation between data into knowledge. In this paper, we present a novel Deep-Neuro-Fuzzy strategy for unsupervised estimation of the interaction forces in Robotic Assisted Minimally Invasive scenarios. In our approach, the capability of Neuro-Fuzzy systems for handling visual uncertainty, as well as the inherent imprecision of real physical problems, is reinforced by the advantages provided by Deep Learning methods. Experiments conducted in a realistic setting have demonstrated the superior performance of the proposed approach over existing alternatives. More precisely, our method increased the accuracy of the force estimation and compared favorably to existing state of the art approaches, offering a percentage of improvement that ranges from about 35% to 85%.

JTD Keywords: Estimation, Force, Machine learning, Robots, Three-dimensional displays, Uncertainty, Visualization