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- All Subjects: engineering
- Creators: Sugar, Thomas
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
In this dissertation, three complex material systems including a novel class of hyperuniform composite materials, cellularized collagen gel and low melting point alloy (LMPA) composite are investigated, using statistical pattern characterization, stochastic microstructure reconstruction and micromechanical analysis. In Chapter 1, an introduction of this report is provided, in which a brief review is made about these three material systems. In Chapter 2, detailed discussion of the statistical morphological descriptors and a stochastic optimization approach for microstructure reconstruction is presented. In Chapter 3, the lattice particle method for micromechanical analysis of complex heterogeneous materials is introduced. In Chapter 4, a new class of hyperuniform heterogeneous material with superior mechanical properties is investigated. In Chapter 5, a bio-material system, i.e., cellularized collagen gel is modeled using correlation functions and stochastic reconstruction to study the collective dynamic behavior of the embed tumor cells. In chapter 6, LMPA soft robotic system is generated by generalizing the correlation functions and the rigidity tunability of this smart composite is discussed. In Chapter 7, a future work plan is presented.
ContributorsXu, Yaopengxiao (Author) / Jiao, Yang (Thesis advisor) / Liu, Yongming (Committee member) / Wang, Qing Hua (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018
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
Two-dimensional transition metal dichalcogenides (TMDCs) such as
molybdenum disulfide (MoS2), tungsten disulfide (WS2), molybdenum diselenide (MoSe2) and tungsten diselenide (WSe2) are attractive for use in biotechnology, optical and electronics devices due to their promising and tunable electrical, optical and chemical properties. To fulfill the variety of requirements for different applications, chemical treatment methods are developed to tune their properties. In this dissertation, plasma treatment, chemical doping and functionalization methods have been applied to tune the properties of TMDCs. First, plasma treatment of TMDCs results in doping and generation of defects, as well as the synthesis of transition metal oxides (TMOs) with rolled layers that have increased surface-to-volume ratio and are promising for electrochemical applications. Second, chemical functionalization is another powerful approach for tuning the properties of TMDCs for use in many applications. To covalently functionalize the basal planes of TMDCs, previous reports begin with harsh treatments like lithium intercalation that disrupt the structure and lead to a phase transformation from semiconducting to metallic. Instead, this work demonstrates the direct covalent functionalization of semiconducting MoS2 using aryl diazonium salts without lithium treatments. It preserves the structure and semiconducting nature of MoS2, results in covalent C-S bonds on basal planes and enables different functional groups to be tethered to the MoS2 surface via the diazonium salts. The attachment of fluorescent proteins has been used as a demonstration and it suggests future applications in biology and biosensing. The effects of the covalent functionalization on the electronic transport properties of MoS2 were then studied using field effect transistor (FET) devices.
molybdenum disulfide (MoS2), tungsten disulfide (WS2), molybdenum diselenide (MoSe2) and tungsten diselenide (WSe2) are attractive for use in biotechnology, optical and electronics devices due to their promising and tunable electrical, optical and chemical properties. To fulfill the variety of requirements for different applications, chemical treatment methods are developed to tune their properties. In this dissertation, plasma treatment, chemical doping and functionalization methods have been applied to tune the properties of TMDCs. First, plasma treatment of TMDCs results in doping and generation of defects, as well as the synthesis of transition metal oxides (TMOs) with rolled layers that have increased surface-to-volume ratio and are promising for electrochemical applications. Second, chemical functionalization is another powerful approach for tuning the properties of TMDCs for use in many applications. To covalently functionalize the basal planes of TMDCs, previous reports begin with harsh treatments like lithium intercalation that disrupt the structure and lead to a phase transformation from semiconducting to metallic. Instead, this work demonstrates the direct covalent functionalization of semiconducting MoS2 using aryl diazonium salts without lithium treatments. It preserves the structure and semiconducting nature of MoS2, results in covalent C-S bonds on basal planes and enables different functional groups to be tethered to the MoS2 surface via the diazonium salts. The attachment of fluorescent proteins has been used as a demonstration and it suggests future applications in biology and biosensing. The effects of the covalent functionalization on the electronic transport properties of MoS2 were then studied using field effect transistor (FET) devices.
ContributorsChu, Ximo (Author) / Wang, Qing Hua (Thesis advisor) / Sieradzki, Karl (Committee member) / Green, Alexander (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2018
Description
Flame retardants (FRs) are applied to variety of consumer products such as textiles and polymers for fire prevention and fire safety. Substantial research is ongoing to replace traditional FRs with alternative materials that are less toxic, present higher flame retardancy and result in lower overall exposure as there are potential health concerns in case of exposure to popular FRs. Carbonaceous nanomaterials (CNMs) such as carbon nanotubes (CNTs) and graphene oxide (GO) have been studied and applied to polymer composites and electronics extensively due to their remarkable properties. Hence CNMs are considered as potential alternative materials that present high flame retardancy. In this research, different kinds of CNMs coatings on polyester fabric are produced and evaluated for their use as flame retardants. To monitor the mass loading of CNMs coated on the fabric, a two-step analytical method for quantifying CNMs embedded in polymer composites was developed. This method consisted of polymer dissolution process using organic solvents followed by subsequent programmed thermal analysis (PTA). This quantification technique was applicable to CNTs with and without high metal impurities in a broad range of polymers. Various types of CNMs were coated on polyester fabric and the efficacy of coatings as flame retardant was evaluated. The oxygen content of CNMs emerged as a critical parameter impacting flame retardancy with higher oxygen content resulting in less FR efficacy. The most performant nanomaterials, multi-walled carbon nanotubes (MWCNTs) and amine functionalized multi-walled carbon nantoubes (NH2-MWCNT) showed similar FR properties to current flame retardants with low mass loading (0.18 g/m2) and hence are promising alternatives that warrant further investigation. Chemical/physical modification of MWCNTs was conducted to produce well-dispersed MWCNT solutions without involving oxygen for uniform FR coating. The MWCNTs coating was studied to evaluate the durability of the coating and the impact on the efficacy during use phase by conducting mechanical abrasion and washing test. Approximately 50% and 40% of MWCNTs were released from 1 set of mechanical abrasion and washing test respectively. The losses during simulated usage impacted the flame retardancy negatively.
ContributorsNosaka, Takayuki (Author) / Herckes, Pierre (Thesis advisor) / Westerhoff, Paul (Committee member) / Wang, Qing Hua (Committee member) / Arizona State University (Publisher)
Created2018
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
Cornhole, traditionally seen as tailgate entertainment, has rapidly risen in popularity since the launching of the American Cornhole League in 2016. However, it lacks robust quality control over large tournaments, since many of the matches are scored and refereed by the players themselves. In the past, there have been issues where entire competition brackets have had to be scrapped and replayed because scores were not handled correctly. The sport is in need of a supplementary scoring solution that can provide quality control and accuracy over large matches where there aren’t enough referees present to score games. Drawing from the ACL regulations as well as personal experience and testimony from ACL Pro players, a list of requirements was generated for a potential automatic scoring system. Then, a market analysis of existing scoring solutions was done, and it found that there are no solutions on the market that can automatically score a cornhole game. Using the problem requirements and previous attempts to solve the scoring problem, a list of concepts was generated and evaluated against each other to determine which scoring system design should be developed. After determining that the chosen concept was the best way to approach the problem, the problem requirements and cornhole rules were further refined into a set of physical assumptions and constraints about the game itself. This informed the choice, structure, and implementation of the algorithms that score the bags. The prototype concept was tested on their own, and areas of improvement were found. Lastly, based on the results of the tests and what was learned from the engineering process, a roadmap was set out for the future development of the automatic scoring system into a full, market-ready product.
ContributorsGillespie, Reagan (Author) / Sugar, Thomas (Thesis director) / Li, Baoxin (Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2023-05
Description
As the world moves towards faster production times, quicker shipping, and overall, more demanding schedules, the humans caught in the loop are subject to physical duress causing them to physically break down and have muscular skeletal injuries. Surprisingly, with more automation in logistics houses, the remaining workers must be quicker and do more, again resulting in muscular-skeletal injuries. To help alleviate this strain, a class of robotics and wearables has arisen wherein the human is assisted by a worn mechanical device. These devices, traditionally called exoskeletons, fall into two general categories: passive and active. Passive exoskeletons employ no electronics to activate their assistance and instead typically rely on the spring-like qualities of many materials. These are generally lighter weight than their active counterparts, but also lack the assistive power and can even interfere in other routine operations. Active exoskeletons, on the other hand, aim to avoid as much interference as possible by using electronics and power to assist the wearer. Properly executed, this can deliver power at the most opportune time and disengage from interference when not needed. However, if the tuning is mismatched from the human, it can unintentionally increase loads and possibly lead to other future injuries or harm. This dissertation investigates exoskeleton technology from two vantage points: the designer and the consumer. In the first, the creation of the Aerial Porter Exoskeleton (APEx) for the US Air Force (USAF). Testing of this first of its kind exoskeleton revealed a peak metabolic savings of 8.13% as it delivers 30 N-m of torque about each hip. It was tested extensively in live field conditions over 8 weeks to great success. The second section is an exploration of different commercially available exoskeletons and the development of a common set of standards/testing protocols is described. The results show a starting point for a set of standards to be used in a rapidly growing sector.
ContributorsMartin, William Brandon (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Hollander, Kevin (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the extensive technological progress made in the areas of drives, sensors and processing, exoskeletons and other wearable devices have become more feasible. However, the stringent requirements in regards to size and weight continue to exert a strong influence on the system-wide design of these devices and present many obstacles to a successful solution. On the other hand, while the area of controls has seen a significant amount of progress, there also remains a large potential for improvements. This dissertation approaches the design and control of wearable devices from a systems perspective and provides a framework to successfully overcome the often-encountered obstacles with optimal solutions. The electronics, drive and control system design for the HeSA hip exoskeleton project and APEx hip exoskeleton project are presented as examples of how this framework is used to design wearable devices. In the area of control algorithms, a real-time implementation of the Fast Fourier Transform (FFT) is presented as an alternative approach to extracting amplitude and frequency information of a time varying signal. In comparison to the peak search method (PSM), the FFT allows extracting basic gait signal information at a faster rate because time windows can be chosen to be less than the fundamental gait frequency. The FFT is implemented on a 16-bit processor and the results show the real-time detection of amplitude and frequency coefficients at an update rate of 50Hz. Finally, a novel neural networks based approach to detecting human gait activities is presented. Existing neural networks often require vast amounts of data along with significant computer resources. Using Neural Ordinary Differential Equations (Neural ODEs) it is possible to distinguish between seven different daily activities using a significantly smaller data set, lower system resources and a time window of only 0.1 seconds.
ContributorsBoehler, Alexander (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Hollander, Kevin (Committee member) / Arizona State University (Publisher)
Created2021