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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
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
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
Recurring incidents between pedestrians, bicycles, and vehicles at the intersection of Rural Road and Spence Avenue led to a team of students conducting their own investigation into the current conditions and analyzing a handful of alternatives. An extension of an industry-standard technique was used to build a control case which alternatives would be compared to. Four alternatives were identified, and the two that could be modeled in simulation software were both found to be technically feasible in the preliminary analysis.
ContributorsFellows, Christopher Lee (Author) / Lou, Yingyan (Thesis director) / Zhou, Xuesong (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Women have evolved in the engineering profession over the decades. However, there is still a lot more room for female presence in the industry as women currently make up about 12-15% of working engineers. Based on many studies and surveys, it is clear that female confidence in their own performance and a feeling of belonging in the industry has evolved for the better. The studies and surveys also show that women still lack a certain confidence to get their engineering degree and then to pursue a career in engineering once they receive their degree. Research shows that the main cause for this is due to the stereotype that engineering is a masculine profession. Men and women both have this mindset because it has become a societal norm that most people go along with and do not even realize it. Unfortunately, it is very hard to overcome and change a societal norm, therefore, something needs to be done in order to fix this mindset. (Crawford). Based on studies and research, there are many ways the stereotype is being combatted. Social media has become a huge component in advocating for female engineers. Men and women are helping to fight the status quo by supporting female engineers and lobbying against people who think women do not belong in the industry. Industry professionals are teaming up with schools to figure out ways to make STEM programs more exciting for all young kids, but especially girls. They are also working to provide more mentors and role models for young girls in order to cheer them on and make them more confident in their abilities when learning and applying the STEM curriculum, as studies have proven that providing young girls with mentors can really help foster more female engineers in the long run. (Crawford). With all of the positive support and promotions of female engineers in the past few years, it is evident that women can certainly progress at a much faster pace than in previous decades.
ContributorsAcosta, Jazlyn (Co-author) / Venne, Hunter (Co-author) / Ward, Kristen (Thesis director) / Lou, Yingyan (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Automated vehicles are becoming more prevalent in the modern world. Using platoons of automated vehicles can have numerous benefits including increasing the safety of drivers as well as streamlining roadway operations. How individual automated vehicles within a platoon react to each other is essential to creating an efficient method of travel. This paper looks at two individual vehicles forming a platoon and tracks the time headway between the two. Several speed profiles are explored for the following vehicle including a triangular and trapezoidal speed profile. It is discovered that a safety violation occurs during platoon formation where the desired time headway between the vehicles is violated. The aim of this research is to explore if this violation can be eliminated or reduced through utilization of different speed profiles.
ContributorsLarson, Kurt Gregory (Author) / Lou, Yingyan (Thesis director) / Chen, Yan (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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