<p>Volatile organic compounds (VOCs) are endogenous byproducts of metabolic pathways that can be altered by a disease or condition, leading to an associated and unique VOC profile or signature. Current methodologies for VOC detection include canines, gas chromatography-mass spectrometry (GC-MS), and electronic nose (eNose). Some of the challenges for canines and GC-MS are cost-effectiveness, extensive training, expensive instrumentation. On the other hand, a significant downfall of the eNose is low selectivity. This thesis proposes to design a breathalyzer using chemiresistive gas sensors that detects VOCs from human breath, and subsequently create an interface to process and deliver the results via Bluetooth Low Energy (BLE). Breath samples were collected from patients with hypoglycemia, COVID-19, and healthy controls for both. Samples were processed, analyzed using GC-MS and probed through statistical analysis. A panel of 6 VOC biomarkers distinguished between hypoglycemia (HYPO) and Normal samples with a training AUC of 0.98 and a testing AUC of 0.93. For COVID-19, a panel of 3 VOC biomarkers distinguished between COVID-19 positive symptomatic (COVID-19) and healthy Control samples with a training area under the curve (AUC) of receiver operating characteristic (ROC) of 1.0 and cross-validation (CV) AUC of 0.99. The model was validated with COVID-19 Recovery samples. The discovery of these biomarkers enables the development of selective gas sensors to detect the VOCs. </p><p><br></p><p>Polyethylenimine-ether functionalized gold nanoparticle (PEI-EGNP) gas sensors were designed and fabricated in the lab and metal oxide (MOX) semiconductor gas sensors were obtained from Nanoz (Chip 1: SnO₂ and Chip 2: WO₃). These sensors were tested at different relative humidity (RH) levels, and VOC concentrations. Contact angle which measures hydrophobicity, was 84° and the thickness of the PEI-EGNP coating was 11 µ m. The PEI-EGNP sensor response at RH 85% had a signal 10x higher than at RH 0%. Optimization of the MOX sensor was performed by changing the heater voltage and concentration of VOCs. At RH 85% and heater voltage of 2500 mV, the performance of the sensors increased. Chip 2 had higher sensitivity towards VOCs especially for one of the VOC biomarkers identified for COVID-19. PCA distinguished VOC biomarkers of HYPO, COVID-19, and healthy human breath using the Nanoz. A sensor interface was created to integrate the PEI-EGNP sensors with the printed circuit board (PCB) and Bluno Nano to perform machine learning. The sensor interface can currently process and make decisions from the data whether the breath is HYPO (-) or Normal (+). This data is then sent via BLE to the Hypo Alert app to display the decision.</p>

As all genres of popular music, classical music consists of many different subgenres. The aim of this work is to recognize those subgenres from orchestral recordings. It is focused on the time period from the very end of 16th century to the beginning of 20th century, which means that Baroque era, Classical era and Romantic era are researched. The Music Information Retrieval (MIR) method was used to classify chosen subgenres. In the first phase of MIR method, parameters were extracted from musical recordings and were evaluated. Only the best parameters were used as input data for machine learning classifiers, to be specific: kNN (K-Nearest Neighbor), LDA (Linear Discriminant Analysis), GMM (Gaussian Mixture Models) and SVM (Support Vector Machines). In the final chapter, all the best results are summarized. According to the results, there is significant difference between the Baroque era and the other researched eras. This significant difference led to better identification of the Baroque era recordings. On the contrary, Classical era ended up to be relatively similar to Romantic era and therefore all classifiers had less success in identification of recordings from this era. The results are in line with music theory and characteristics of chosen musical eras.