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- All Subjects: engineering
- Creators: Engineering Programs
- Creators: Sugar, Thomas
Description
In the search for chemical biosensors designed for patient-based physiological applications, non-invasive diagnostic approaches continue to have value. The work described in this thesis builds upon previous breath analysis studies. In particular, it seeks to assess the adsorptive mechanisms active in both acetone and ethanol biosensors designed for breath analysis. The thermoelectric biosensors under investigation were constructed using a thermopile for transduction and four different materials for biorecognition. The analytes, acetone and ethanol, were evaluated under dry-air and humidified-air conditions. The biosensor response to acetone concentration was found to be both repeatable and linear, while the sensor response to ethanol presence was also found to be repeatable. The different biorecognition materials produced discernible thermoelectric responses that were characteristic for each analyte. The sensor output data is presented in this report. Additionally, the results were evaluated against a mathematical model for further analysis. Ultimately, a thermoelectric biosensor based upon adsorption chemistry was developed and characterized. Additional work is needed to characterize the physicochemical action mechanism.
ContributorsWilson, Kimberly (Author) / Guilbeau, Eric (Thesis advisor) / Pizziconi, Vincent (Thesis advisor) / LaBelle, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2011
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
The field of robotics is rapidly expanding, and with it, the methods of teaching and introducing students must also advance alongside new technologies. There is a challenge in robotics education, especially at high school levels, to expose them to more modern and practical robots. One way to bridge this gap is human-robot interaction for a more hands-on and impactful experience that will leave students more interested in pursuing the field. Our project is a Robotic Head Kit that can be used in an educational setting to teach about its electrical, mechanical, programming, and psychological concepts. We took an existing robot head prototype and further advanced it so it can be easily assembled while still maintaining human complexity. Our research for this project dove into the electronics, mechanics, software, and even psychological barriers present in order to advance the already existing head design. The kit we have developed combines the field of robotics with psychology to create and add more life-like features and functionality to the robot, nicknamed "James Junior." The goal of our Honors Thesis was to initially fix electrical, mechanical, and software problems present. We were then tasked to run tests with high school students to validate our assembly instructions while gathering their observations and feedback about the robot's programmed reactions and emotions. The electrical problems were solved with custom PCBs designed to power and program the existing servo motors on the head. A new set of assembly instructions were written and modifications to the 3D printed parts were made for the kit. In software, existing code was improved to implement a user interface via keypad and joystick to give students control of the robot head they construct themselves. The results of our tests showed that we were not only successful in creating an intuitive robot head kit that could be easily assembled by high school students, but we were also successful in programming human-like expressions that could be emotionally perceived by the students.
ContributorsRathke, Benjamin (Co-author) / Rivera, Gerardo (Co-author) / Sodemann, Angela (Thesis director) / Itagi, Manjunath (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Volume depletion can lead to migraines, dizziness, and significant decreases in a subject's ability to physically perform. A major cause of volume depletion is dehydration, or loss in fluids due to an imbalance in fluid intake to fluid excretion. Because proper levels of hydration are necessary in order to maintain both short and long term health, the ability to monitor hydration levels is growing in clinical demand. Although devices capable of monitoring hydration level exist, these devices are expensive, invasive, or inaccurate and do not offer a continuous mode of measurement. The ideal hydration monitor for consumer use needs to be characterized by its portability, affordability, and accuracy. Also, this device would need to be noninvasive and offer continuous hydration monitoring in order to accurately assess fluctuations in hydration data throughout a specified time period. One particular method for hydration monitoring that fits the majority of these criteria is known as bioelectric impedance analysis (BIA). Although current devices using BIA do not provide acceptable levels of accuracy, portability, or continuity in data collection, BIA could potentially be modified to fit many, if not all, desired customer specifications. The analysis presented here assesses the viability of using BIA as a new standard in hydration level measurement. The analysis uses data collected from 22 subjects using an existing device that employs BIA. A regression derived for estimating TBW based on the parameters of age, weight, height, sex, and impedance is presented. Using impedance data collected for each subject, a regression was also derived for estimating impedance based on the factors of age, weight, height, and sex. The derived regression was then used to calculate a new impedance value for each subject, and these new impedance values were used to estimate TBW. Through a paired-t test between the TBW values derived by using the direct measurements versus the calculated measurements of impedance, the two samples were found to be comparable. Considerations for BIA as a noninvasive measurement of hydration are discussed.
ContributorsTenorio, Jorge Antonio (Author) / LaBelle, Jeffrey (Thesis director) / Pizziconi, Vincent (Committee member) / Spano, Mark (Committee member) / Barrett, The Honors College (Contributor) / W. P. Carey School of Business (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05
Description
The field of soft robotics is a very quickly growing field that has yet to be fully explored or implemented in all of the possible applications. Soft robotics shows the greatest degree of possibility for mimicking biological systems effectively and accurately. This study seeks to set the groundwork for the development of a biomimetic nautilus using soft robotic methods. The study shows background research and discusses the methods used to develop a nautilus themed sub aquatic robot that uses a double bladder system and a pump to generate thrust for movement. The study shows how the unit would be fabricated and constructed. The study also explores why the second stage of the design failed and how it could potentially be fixed in future iterations.
ContributorsCarlson, Caleb Elijah (Author) / Polygerinos, Panagiotis (Thesis director) / Parsey, John (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The goal of our research was to develop and validate a method for predicting the mechanical behavior of Additively Manufactured multi-material honeycomb structures. Multiple approaches already exist in the field for modeling the behavior of cellular materials, including the bulk property assumption, homogenization and strut level characterization [1]. With the bulk property approach, the structure is assumed to behave according to what is known about the material in its bulk formulation, without regard to its geometry or scale. With the homogenization technique, the specimen that is being tested is treated as a solid material within the simulation environment even if the physical specimen is not. Then, reduced mechanical properties are assigned to the specimen to account for any voids that exist within the physical specimen. This approach to mechanical behavior prediction in cellular materials is shape dependent. In other words, the same model cannot be used from one specimen to the next if the cell shapes of those lattices differ in any way. When using the strut level characterization approach, a single strut (the connecting member between nodes constituting a cellular material) is isolated and tested. With this approach, there tends to be a significant deviation in the experimental data due to the small size of the isolated struts. Yet it has the advantage of not being shape sensitive, at least in principle. The method that we developed, and chose to test lies within the latter category, and is what we have coined as the Representative Lattice Element (RLE) Method. This method is modeled after the well-established Representative Volume Element (RVE) method [2]. We define the RLE as the smallest unit over which mechanical tests can be conducted that will provide results which are representative of the larger lattice structure. In other words, the theory is that a single member (or beam in this case) of a honeycomb structure can be taken, tests can be conducted on this member to determine the mechanical properties of the representative lattice element and the results will be representative of the mechanical behavior whole structure. To investigate this theory, we designed specimens, conducted various tensile and compression tests, analyzed the recorded data, conducted a micromechanics study, and performed structural simulation work using commercial Finite Element Analysis software.
ContributorsSalti, Ziyad Zuheir (Co-author) / Eppley, Trevor (Co-author) / Bhate, Dhruv (Thesis director) / Song, Kenan (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
A control method based on the phase angle is used to control oscillating systems. The phase oscillator uses the sine and cosine of the phase angle to change key properties of a mass-spring-damper system, including amplitude, frequency, and equilibrium. An inverted pendulum is used to show a further application of the phase oscillator. Two methods of control based on the phase oscillator are used for swing-up and balancing of the pendulum. The first control method involves two separate stages. The scenarios where this control works are discussed. The second control method uses variable coefficients to result in a smooth transition between swing-up and balancing.
ContributorsBates, Andrew (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2015
Description
The American Diabetes Association reports that diabetes costs $322 billion annually and affects 29.1 million Americans. The high out-of-pocket cost of managing diabetes can lead to noncompliance causing serious and expensive complications. There is a large market potential for a more cost-effective alternative to the current market standard of screen-printed self-monitoring blood glucose (SMBG) strips. Additive manufacturing, specifically 3D printing, is a developing field that is growing in popularity and functionality. 3D printers are now being used in a variety of applications from consumer goods to medical devices. Healthcare delivery will change as the availability of 3D printers expands into patient homes, which will create alternative and more cost-effective methods of monitoring and managing diseases, such as diabetes. 3D printing technology could transform this expensive industry. A 3D printed sensor was designed to have similar dimensions and features to the SMBG strips to comply with current manufacturing standards. To make the sensor electrically active, various conductive filaments were tested and the conductive graphene filament was determined to be the best material for the sensor. Experiments were conducted to determine the optimal print settings for printing this filament onto a mylar substrate, the industry standard. The reagents used include a mixture of a ferricyanide redox mediator and flavin adenine dinucleotide dependent glucose dehydrogenase. With these materials, each sensor only costs $0.40 to print and use. Before testing the 3D printed sensor, a suitable design, voltage range, and redox probe concentration were determined. Experiments demonstrated that this novel 3D printed sensor can accurately correlate current output to glucose concentration. It was verified that the sensor can accurately detect glucose levels from 25 mg/dL to 400 mg/dL, with an R2 correlation value as high as 0.97, which was critical as it covered hypoglycemic to hyperglycemic levels. This demonstrated that a 3D-printed sensor was created that had characteristics that are suitable for clinical use. This will allow diabetics to print their own test strips at home at a much lower cost compared to SMBG strips, which will reduce noncompliance due to the high cost of testing. In the future, this technology could be applied to additional biomarkers to measure and monitor other diseases.
ContributorsAdams, Anngela (Author) / LaBelle, Jeffrey (Thesis advisor) / Pizziconi, Vincent (Committee member) / Abbas, James (Committee member) / Arizona State University (Publisher)
Created2017