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- All Subjects: engineering
- All Subjects: energy
- Creators: Chemical Engineering Program
- Creators: Sugar, Thomas
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Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Engineering is a multidisciplinary field with a variety of applications. However, since there are so many disciplines of engineering, it is often challenging to find the discipline that best suits an individual interested in engineering. Not knowing which area of engineering most aligns to one’s interests is challenging when deciding on a major and a career. With the development of the Engineering Interest Quiz (EIQ), the goal was to help individuals find the field of engineering that is most similar to their interests. Initially, an Engineering Faculty Survey (EFS) was created to gather information from engineering faculty at Arizona State University (ASU) and to determine keywords that describe each field of engineering. With this list of keywords, the EIQ was developed. Data from the EIQ compared the engineering students’ top three results for the best engineering discipline for them with their current engineering major of study. The data analysis showed that 70% of the respondents had their major listed as one of the top three results they were given and 30% of the respondents did not have their major listed. Of that 70%, 64% had their current major listed as the highest or tied for the highest percentage and 36% had their major listed as the second or third highest percentage. Furthermore, the EIQ data was compared between genders. Only 33% of the male students had their current major listed as their highest percentage, but 55% had their major as one of their top three results. Women had higher percentages with 63% listing their current major as their highest percentage and 81% listing it in the top three of their final results.
ContributorsWagner, Avery Rose (Co-author) / Lucca, Claudia (Co-author) / Taylor, David (Thesis director) / Miller, Cindy (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Hydrocephalus is a chronic medical condition characterized by the excessive accumulation of cerebrospinal fluid in the brain. It is estimated that 1-2 of every 1000 babies in the United States is born with congenital hydrocephalus, with many individuals acquiring hydrocephalus later in life through brain injury. Despite these alarming statistics, current shunts for the treatment of hydrocephalus display operational failure rates as high as 40-50% within two years following implantation. Failure of current shunts is attributed to complexity of design, external implantation, and the requirement of multiple catheters. The presented hydrogel wafer check valve avoids all the debilitating features of current shunts, relying only on the swelling of hydrogel for operation, and is designed to directly replace failed arachnoid granulations- the brain’s natural cerebrospinal fluid drainage valves. The valve was validated via bench-top (1) hydrodynamic pressure-flow response characterizations, (2) transient response analysis, and (3) overtime performance response in brain-analogous conditions. In-vitro measurements display operation in range of natural CSF draining (cracking pressure, PT ~ 1–110 mmH2O and outflow hydraulic resistance, Rh ~ 24 – 152 mmH2O/mL/min), negligible reverse flow leakages (flow, QO > -10 µL/min), and demonstrate the valve’s operational reproducibility of this new valve as an implantable treatment.
ContributorsAmjad, Usamma Muhammad (Author) / Chae, Junseok (Thesis director) / Appel, Jennie (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Solid oxide fuel cells have become a promising candidate in the development of high-density clean energy sources for the rapidly increasing demands in energy and global sustainability. In order to understand more about solid oxide fuel cells, the important step is to understand how to model heterogeneous materials. Heterogeneous materials are abundant in nature and also created in various processes. The diverse properties exhibited by these materials result from their complex microstructures, which also make it hard to model the material. Microstructure modeling and reconstruction on a meso-scale level is needed in order to produce heterogeneous models without having to shave and image every slice of the physical material, which is a destructive and irreversible process. Yeong and Torquato [1] introduced a stochastic optimization technique that enables the generation of a model of the material with the use of correlation functions. Spatial correlation functions of each of the various phases within the heterogeneous structure are collected from a two-dimensional micrograph representing a slice of a solid oxide fuel cell through computational means. The assumption is that two-dimensional images contain key structural information representative of the associated full three-dimensional microstructure. The collected spatial correlation functions, a combination of one-point and two-point correlation functions are then outputted and are representative of the material. In the reconstruction process, the characteristic two-point correlation functions is then inputted through a series of computational modeling codes and software to generate a three-dimensional visual model that is statistically similar to that of the original two-dimensional micrograph. Furthermore, parameters of temperature cooling stages and number of pixel exchanges per temperature stage are utilized and altered accordingly to observe which parameters has a higher impact on the reconstruction results. Stochastic optimization techniques to produce three-dimensional visual models from two-dimensional micrographs are therefore a statistically reliable method to understanding heterogeneous materials.
ContributorsPhan, Richard Dylan (Author) / Jiao, Yang (Thesis director) / Ren, Yi (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The recovery of biofuels permits renewable alternatives to present day fossil fuels that cause devastating effects on the planet. Pervaporation is a separation process that shows promise for the separation of ethanol from biologically fermentation broths. The performance of thin film composite membranes of polydimethylsiloxane (PDMS) and zeolite imidazolate frameworks (ZIF-71) dip coated onto a porous substrate are analyzed. Pervaporation performance factors of flux, separation factor and selectivity are measured for varying ZIF-71 loadings of pure PDMS, 5 wt%, 12.5 wt% and 25 wt% at 60 oC with a 2 wt% ethanol/water feed. The increase in ZIF-71 loadings increased the performance of PDMS to produce higher flux, higher separation factor and high selectivity than pure polymeric films.
ContributorsLau, Ching Yan (Author) / Lind, Mary Laura (Thesis director) / Durgun, Pinar Cay (Committee member) / Lively, Ryan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Chemical Engineering Program (Contributor)
Created2014-05
Description
Currently, approximately 40% of the world’s electricity is generated from coal and coal power plants are one of the major sources of greenhouse gases accounting for a third of all CO2 emissions. The Integrated Gasification Combined Cycle (IGCC) has been shown to provide an increase in plant efficiency compared to traditional coal-based power generation processes resulting in a reduction of greenhouse gas emissions. The goal of this project was to analyze the performance of a new SNDC ceramic-carbonate dual-phase membrane for CO2 separation. The chemical formula for the SNDC-carbonate membrane was Sm0.075Nd0.075Ce0.85O1.925. This project also focused on the use of this membrane for pre-combustion CO2 capture coupled with a water gas shift (WGS) reaction for a 1000 MW power plant. The addition of this membrane to the traditional IGCC process provides a purer H2 stream for combustion in the gas turbine and results in lower operating costs and increased efficiencies for the plant. At 900 °C the CO2 flux and permeance of the SNDC-carbonate membrane were 0.65 mL/cm2•min and 1.0×10-7 mol/m2•s•Pa, respectively. Detailed in this report are the following: background regarding CO2 separation membranes and IGCC power plants, SNDC tubular membrane preparation and characterization, IGCC with membrane reactor plant design, process heat and mass balance, and plant cost estimations.
ContributorsDunteman, Nicholas Powell (Author) / Lin, Jerry (Thesis director) / Dong, Xueliang (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor) / School of Sustainability (Contributor)
Created2014-05
Description
The two central goals of this project were 1) to develop a testing method utilizing coatings on ultra-thin stainless steel to measure the thermal conductivity (k) of battery electrode materials and composites, and 2) to measure and compare the thermal conductivities of lithium iron phosphate (LiFePO4, "LFP") in industry-standard graphite/LFP mixtures as well as graphene/LFP mixtures and a synthesized graphene/LFP nanocomposite. Graphene synthesis was attempted before purchasing graphene materials, and further exploration of graphene synthesis is recommended due to limitations in purchased product quality. While it was determined after extensive experimentation that the graphene/LFP nanocomposite could not be successfully synthesized according to current literature information, a mixed composite of graphene/LFP was successfully tested and found to have k = 0.23 W/m*K. This result provides a starting point for further thermal testing method development and k optimization in Li-ion battery electrode nanocomposites.
ContributorsStehlik, Daniel Wesley (Author) / Chan, Candace K. (Thesis director) / Dai, Lenore (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2014-05
Description
In microbial fuel cells (MFCs) the biocathode is developed as a potential alternative to chemical cathodic catalysts, which are deemed as expensive and unsustainable for applications. These cells utilize different types of microorganisms as catalysts to promote biodegradation of organic matter while simultaneously converting energy released in metabolic reactions into electrical energy. Most current research have focused more on the anodic microbes, including the current generating bacteria species, anodic microbial community composition, and the mechanisms of the extracellular electron transfer. Compared to the anode, research on the microbes of the biocathode of the MFCs are very limited and are heavily focused on the role of the bacteria in the system. Thus, further understand of the mechanism of the microbial community in the biocathode will create new engineering applications for sustainable energy. Previous research conducted by Strycharz-Glaven et al. presented an electrochemical analysis of a Marinobacter-dominated biocathode communitygrown on biocathodes in sediment/seawater-based MFCs. Chronoamperometry results indicated that current densities up to -0.04 A/m2 were produced for the biocathode. Cyclic voltammetry responses indicated a midpoint potential at 0.196 V ± 0.01 V. However, the reactor design for these experiments showed that no oxygen is supplied to the electrochemical system. By incorporating an air diffusion membrane to the cathode of the reactor, chronoamperometry results have produced current density in the system up to -0.15 A/m2. Cyclic voltammetry results have also displayed a midpoint potential of 0.25 V ± 0.01 V under scan rates of 0.2 mV/s. Thus, this electrochemical setup has increased the current output of the system.
ContributorsWang, Zixuan (Author) / Torres, Cesar (Thesis director) / Hart, Steven (Committee member) / Materials Science and Engineering Program (Contributor) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05