College of Engineering

Theses and dissertations submitted to the College of Engineering

Items in this Collection

Virtual Reality (VR) therapy has gained interest in healthcare, particularly for Parkinson’s Disease (PD) patients. Unfortunately, not all PD patients have access to this effective therapy. Exploring phone-based VR as a complementary tool could broaden accessibility, especially in regions like the Philippines where smartphone ownership is common. Focus Group Discussions (FGD) were conducted to identify key outcome measures for a VR therapy application. Usability and simplicity were tested through user experience and quality evaluation. The pilot application received positive feedback from participants, highlighting its usability and straightforward functions. This study suggests that phone-based VR has the potential to be a valuable therapy tool for PD patients, pending further research and clinical validation. Phone-based VR may extend therapy availability and efficacy, promising improved care for PD patients.


Existing state of charge (SOC) estimation methods for wireless sensor network (WSN) nodes primarily use mathematical models with parameters that are not updated on the fly. This results in erroneous SOC values that degrade the performance of energy-aware optimizations. Furthermore, these SOC estimation methods are often calibrated on empirical datasets, which discourages rapid WSN deployment. As a solution, this thesis proposes "Collaborative Nodes and Model Parameter Recalibration" (CNMR) and its variants which exploit collaboration between different nodes of the WSN for battery profiling and model parameter identification. This approach has not been explored elsewhere, to the best of our knowledge. CNMR recalibrates both the Equivalent Circuit Model (ECM) and segmented Open Circuit Voltage (OCV) model without the need for extensive datasets. Moreover, modifications on CNMR are implemented to improve its performance in terms of SOC estimation accuracy and sensor network lifetime. Partial CNMR I recalibrates its segmented-OCV model but uses numerical algorithms for subspace state-space identification (N 4SID) and prediction error minimization (PEM) for its predetermined battery model parameters. Finally, Partial CNMR II recalibrates its battery model and uses piece-wise least squares regression for the predetermined segmented-OCV model. The methods are assessed through experimental setups with full-length and variable-length battery datasets based on the loading profiles of WSN applications. The evaluations and hypothesis tests through Bayes factors (BF) show that these methods outperform non-collaborative SOC estimation approaches in low-current applications with variable length battery datasets.


There is an increasing interest in the use of renewable energy (RE) to support the mining industry’s sustainability goals and this study has applied Multi-Criteria Decision Analysis for the selection process to identify the most appropriate energy mix integrating renewable energy for a specific mine site in Palawan, and the Hybrid Optimization of Multiple Energy Resources (HOMER) tool for the optimization and design of the electrical system. Solar energy was identified as the best resource for the site based on the established selection criteria. Technical and economic analysis show that diesel generator combined with solar photovoltaic (PV) and battery is the optimum plant configuration with a levelized cost of electricity (LCOE) of 9.17 PhP/kWh. This is also projected to reduce costs by 42.25% and greenhouse gas (GHG) emissions by 83.92%. The study has practical benefits for off-grid mining sites in the Philippines. This paper also aims to attract attention of mine owners and experts on renewable energy technologies in order to give alternatives in powering the mining industry. It is recommended that the design be installed and actual data be taken.


As the country's economy and population expand, energy and water consumption in the Philippines also rise. The current electrification and water supply methods are not only ineffective, unsustainable, and expensive, but it also fails to secure meeting future demands. Energy transition (Diesel-only to HRES) systems modeling studies show significant efforts in addressing energy issues by reducing electricity costs. Unlike in electrification, studies modeled only a few islands concerning water access. Because energy and water are interdependent, a separate analysis of the two issues may not yield the best possible solution. In this work, we extend the study of water access in the Philippines by conducting a techno-economic analysis of RO-coupled HRES on 634 off-grid islands with our in-house tool, ISLA. Each island houses a microgrid comprising solar PV, a wind turbine, lithium-ion batteries, and an additional or existing diesel generator to meet and sustain load and water demands. The levelized costs of 0.279 USD/kWh and 5.85 USD/m^3 as results show that the augmentation could save over 64% on electricity and 88% on water costs compared to current methods. However, for this to materialize, a total cost of 4.79 billion USD and an initial capital cost of 1.17 billion USD are required. These insights are utilized in formulating strategies to seek improved energy policies and private sector participation which addresses barriers to its deployment (such as cost). In addition, it is suggested that this study be extended by quantifying its profitability in addition to other RE and locations from other nations.


This study used spatial analysis to explore the relationship between sanitation-related diseases and academic performance among primary school children in the Philippines. The study is a two-part analysis: (a) spatial autocorrelation and cluster analysis, and (b) regression modeling. Global models (Ordinary Least Squares Estimation) were adequate to estimate academic performance at the province level, city level, and Negros-Cebu Area using water and sanitation, disease, health facility, and socioeconomic variables as predictors. However, the relationship between academic performance, poverty incidence, and water conditions in the Greater Manila Area was best modeled using Multiscale Geographically Weighted Regression. Cluster analysis also showed that the hotspots of provinces and cities with poor water supply also tend to have poor sanitation facilities and high disease incidence rates. Moreover, this study found that for every increase in the percentage of households with piped drinking water, there is an increase of 0.18 points to the provincial Math average. Similarly, a 0.36-point increase in the provincial Math average is observed for every unit increase in the percentage of households with sanitary toilets. This study also provided evidence that increasing rural health units could positively impact academic performance at the city level, as an increase of 1.27 points in city-aggregated Math average is observed for every unit increase in rural health units per 100,000 population.