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Archived Theses
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Archived Theses
A theoretical study and a micro-scale sensing platform for the detection of germination in Bacillus stearothermophilus spores
Electrical sensing techniques offer reliable and inexpensive solutions for detecting various activities of microorganisms. However, this technology has not yet been extensively applied in health-care industry to verify the success of sterilization processes. One of such processes includes high temperatures, often combined with elevated pressures, where Bacillus Stearothermophilus bacteria spore germination is monitored for sterilization verification through optical means. This project aims to investigate the change in medium conductance during the germination of Bacillus Stearothermophilus spores, and to design a micro-scale device for detecting this biological event. Germination is a process of spore transformation from endospore to vegetative cell in favorable environment. During germination, spores release most of its ions content including DPA2-, Ca2+, Mn2+, Mg2+, K+ and Na+ into the medium and absorb water for core hydration and expansion. The released ions cause a change in the medium conductance. Bacillus Stearothermophilus spores contain a considerable amount of DPA2-compared to other ions, hence it is the major electrically conductive element. The DPA2- electrical conductivity was calculated by using a theoretical model based on two equations, the Stokes-Einstein equation for particle diffusion and the molar conductivity equation. For diffusion calculation, single DPA2- molecule was approximated as a spherical particle, whose radius was calculated based on its crystal data. Using the dimensions of the spore, maximum coverage for 1 cm2 area was calculated to be 45.45 × 106, yet, due to expected experimental artifacts, only 10% of the total spore coverage was taken into account. In addition, it is been reported that, due to the lack of suitable conditions for a specific number of spores' activation, only a partial number of them would germinate. Therefore, only 10% of the spore coverage germination was assumed, thus 1% of the calculated total spore coverage was considered for the conductance calculations. The conductance change value at 1% of spore germination was found to be 7.7 mS/m. The same theoretical model was again applied for 5%, 10%, 25%, 50% and 100% of spores DPA2 yield to investigate the sensitivity of the micro-chip design. Three micro-chip designs with 1 cm2 substrate area and different electrode geometries were developed each loaded with 0.05 mL food solution. These designs were simulated in COMSOL Multiphysics® software to observe the conductance change during germination. It has been shown that, 1% spore germination can change the solution resistance from 124.01 kΩ to 66.03 Ω. This work demonstrates that solution resistance can be monitored for the early detection of Bacillus Stearothermophilus spore germination and can be used in industrial sterilization verification systems.
Some machine learning techniques for medical diagnosis
There is a huge increase in the amount of biological data obtained with the great breakthroughs in technology, and the amount of this data increases exponentially every day. The use of data mining algorithms and machine learning methods has become widespread in the field of health, because analysis and interpretation of biological data is very difficult with traditional methods. In this study, biochemistry and hormone values are used to make some prediction on medical diagnosis with some machine learning methods such as linear regression, generalized linear regression, deep learning, random forests, gradient boosted trees and support vector machines. With the analysis of the laboratory results according to the reference ranges, it is tried to reveal some new relations of biochemistry and hormone parameters. Finally, the results are presented and the performance of the model is evaluated.
A non-resonant kinetic energy harvester for bio-implantableapplications
Bio-implantable devices are attracting much attention, as they provide effective solutions for monitoring and treatment of diverse health problems. Examples for already-developed devices include pressure sensors, pacemakers, muscle stimulators, and glucose sensors that can control and monitor diseases ranging from heart-related disorders to diabetes. Batteries that are currently used in bio-implants have limited energy storage capacities, requiring frequent battery replacements and thus limiting the functionality of such medical devices. In this respect, energy harvesting from human body movements offers a promising alternative as on-board power for implantable medical devices. The aim of this project is to develop an implantable power generator capable of converting the kinetic energy existing in body movements into electricity at any part of the body. The generator is designed to be bio-compatible with a volume less than 1 cm3 for implantability. Energy is generated from daily activities such as walking, running, and twisting through the utilization of electromagnetic induction principles. An important aspect of the design is the non-resonant device nature. Accordingly, it is possible to generate energy from a variety of body activities at different frequencies. Simulations were performed on COMSOL software for design optimization. The device was manufactured using a combination of micro-fabrication and CNC technologies. Initial testing on a linear shaker platform resulted in an open circuit voltage and output power of 6.25 mV and 0.33 µW, respectively. The device was integrated on a wristband to test real-life performance. The voltage and power during normal walking were measured to be 4.4 mV and 0.14 µW. These values increased to 6.85 mV and 0.2 µW when running. Several improvements in the design and testing scheme were proposed to take these values at even higher levels. The findings and results reported here are important steps forward to develop electronic self sufficient bio-implants that do not need battery replacements and can generate their own energy autonomously in the body.
Integrative clustering approaches for cancer subtype discovery
Cancer is a heterogeneous disease and identification of cancer subtypes is critical for personalized treatment and drug development. Recently, cancer genome projects have produced multiple types of high-throughput data for thousands of cancer patients. Exploiting the complementary information between different data types can improve finding subtypes. In the first part of this thesis, we apply multi-view kernel k-means to integrate multiple genomic datasets (i.e.,gene expression, DNA methylation and miRNA expression) on two cancer datasets. We show that combining kernels (i.e., that correspond to different views) with learned weights give better clusters compared to combining the kernels uniformly or using each data set independently. We also demonstrate an improved performance compared to existing models that integrate the same data types. In terms of biological significance, Kaplan-Meier analysis shows that our discovered clusters have distinct survival profiles with statistically significant log-rank test p-values. In the second part, we target the high dimensionality problem of genomic datasets by extending a single-view sparse k-means framework to multi-view setting. This extension allows us to perform integrative clustering and feature selection simultaneously by learning both feature weights and view weights. We confirm that performing feature selection improves the clusters for the majority of the datasets. Altogether, our results indicate that integration of multiple genomic characterizations and the application of feature selection enable the discovery of subtypes that improve over current patient stratifications.
MIMO for 5th generation heterogeneous communication networks: Modeling and performance analysis
The day-to-day increasing demand of high data rate and connectivity across all of the devices has lead us to the verge of remanipulating the current wireless deployment for the future generation networks. Current 4G deployment either uses small heterogeneous cellular networks(HetNets) or multi-antenna approach to fulfill the demand. Amalgamate of these two features can lead to a significant improvement. This piece of work is a contribution to the analysis of this combined network paradigm. This dissertation proposes a tractable analysis for use of MIMO in HetNets. A downlink analysis by deploying K-tier BSs, with different transmit power and geographical density across all k-tiers is studied. Along with it, localization of different class of BSs is carried out through well behaved independent Point Process, namely Poisson Point Process(PPP). All of this baseline model setup allow the derivation of analytical expression for coverage probability, average rate, signal to interference and noise ratio(SINR) and area spectral efficiency. Insightful results obtained shows that increasing the BSs does not necessarily increase the coverage probability, but it increases by adding less loaded small BSs. For the MIMO based HetNets with a fair spatial model, a tractable framework shows that spreading the many small SISO systems is preferable over fewer MIMO BSs. But with large number of independent SISO systems, it is more preferable to use MIMO BSs with same number of antennas.
A comparative study of kalman filter and its variants in acoustic echo cancellation
In telecommunication, when a far-end speaker's voice is reflected in the near-end, this reflection causes communication problems and it is known as acoustic echo. Despite its wide coverage in the literature, acoustic echo cancellation (AEC) has been mostly examined for linear systems. The number of approaches to mitigate echo in non-linear systems is not sufficient. In this thesis, Kalman filter and its variants such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) have been compared for this purpose.