Saavedra Ruiz, Andrés

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    Rapid, portable and low-cost water quality device using machine learning
    (2025-05-08) Saavedra Ruiz, Andrés; Resto Irizarry, Pedro J.; College of Engineering; Serrano Rivera, Guillermo J.; Juan García, Eduardo J.; Tarafa Vélez, Pedro J.; Bioengineering Program; Ortiz Rodríguez, Marcos E.
    Maintaining high water quality standards is essential for preventing the spread of waterborne diseases that pose significant risks to public health, such as cholera, dysentery, hepatitis, and typhoid fever. While conventional methods, including membrane filtration (MF), multiple tube fermentation (MTF), and enzyme-based assays, offer high sensitivity and specificity in detecting bacterial indicators like Escherichia coli (E. coli) and Enterococcus faecalis (E. faecalis), these techniques are hindered by lengthy processing times, reliance on specialized laboratory facilities, and the need for trained personnel. To address these limitations, this thesis presents the development of a novel, portable, and cost-effective UV-LED/RGB sensor system for rapid bacteriological water quality assessment. The system incorporates a multi-well, self-loading microfluidic device, UV-LEDs for sample excitation, and RGB sensors (devices capable of detecting light intensity in red, green and blue wavelengths ranges) for fluorescence data acquisition, alongside a defined substrate assay and portable incubation system for automated bacterial quantification. The microfluidic device autonomously loads water samples, eliminating the need for sample preparation, and enables bacterial enumeration via most probable number (MPN) analysis. Fluorescence signals from individual wells are processed using machine learning (ML) algorithms, including Multilayer Perceptron Neural Networks (MLPNN), Random Forest (RF), Logistic Regression (LR), and Support Vector Machines (SVM). These algorithms classify well as positive or negative within 30 minutes. The results indicate that the best-performing algorithm was MLPNN, achieving evaluation metrics of up to 100% for E. faecalis detection. For this study, E. coli was not utilized as an indicator. Comparisons with the Quanti-TrayTM/2000 system revealed greater dispersion and limitations in the detection range for high bacterial concentrations. However, the rapid response time, the reduced sample volume required, and the integration of automation and ML-based analysis tools present a promising alternative for bacteriological detection and quantification.
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    Digital signal analysis for detection of air bubbles on artificial thigh vessels
    (2015) Saavedra Ruiz, Andrés; León Colón, Leyda V.; College of Engineering; Morales Tirado, Lizdabel; Cancelos Mancini, Silvina; Department of Electrical and Computer Engineering; Sundaram, Paul A.
    Decompression sickness (DCS) occurs when divers rise to the surface exposing the body to sudden changes in pressure, generating nitrogen bubbles in tissues, causing serious bodily injury and even death. To prevent this risk, tables indicating divers’ ascent rates, descent rates, and waiting time in between decompression stops have been developed. Even with the help of such tables, decompression sickness still occurs in individuals who follow the instructions in dive tables. Therefore, prevention of DCS may be viable with a method that detects the presence of bubbles in real time. II In this thesis, we show a new method for bubble detection using a simplified human thigh prototype constructed with a piezoelectric ring (PZT) placed around it. In order to test this new method, we use two high-speed cameras, to record the bubbles produced in a bubble generator system, and pill microphones (PM) to measure disturbances in the prototype when it is in resonance. The electrical signals from the piezoelectric ring (PZT) and microphones (PM) are the inputs to a pattern recognition algorithm. In the classification stage of the pattern recognition, three classifiers are tested; the choice of classifiers are determined by the best accuracy. A neural network based classifier performed the best detection of bubbles for five classes of different diameter ranges. The detection accuracy was 98%.