Matching Items (633)
Filtering by
- All Subjects: Clarinet and piano music
Description
Despite the wealth of folk music traditions in Portugal and the importance of the clarinet in the music of bandas filarmonicas, it is uncommon to find works featuring the clarinet using Portuguese folk music elements. In the interest of expanding this type of repertoire, three new works were commissioned from three different composers. The resulting works are Seres Imaginarios 3 by Luis Cardoso; Delirio Barroco by Tiago Derrica; and Memória by Pedro Faria Gomes. In an effort to submit these new works for inclusion into mainstream performance literature, the author has recorded these works on compact disc. This document includes interview transcripts with each composer, providing first-person discussion of each composition, as well as detailed biographical information on each composer. To provide context, the author has included a brief discussion on Portuguese folk music, and in particular, the role that the clarinet plays in Portuguese folk music culture.
ContributorsFerreira, Wesley (Contributor) / Spring, Robert S (Thesis advisor) / Bailey, Wayne (Committee member) / Gardner, Joshua (Committee member) / Hill, Gary (Committee member) / Schuring, Martin (Committee member) / Solis, Theodore (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsBurton, Charlotte (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-08
ContributorsDruesedow, Elizabeth (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
Description
The prospects of commercially available autonomous vehicles are surely tantalizing, however the implementation of these vehicles and their strain on the social dynamics between motorists and pedestrians remains unknown. Questions concerning how autonomous vehicles will communicate safety and intent to pedestrians remain largely unanswered. This study examines the efficacy of various proposed technologies for bridging the communication gap between self-driving cars and pedestrians. Displays utilizing words like “safe” and “danger” seem to be effective in communicating with pedestrians and other road users. Future research should attempt to study different external notification interfaces in real-life settings to more accurately gauge pedestrian responses.
ContributorsMuqolli, Endrit (Author) / Cooke, Nancy J. (Thesis advisor) / Chiou, Erin (Committee member) / Gray, Rob (Committee member) / Arizona State University (Publisher)
Created2019
Description
Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving.
One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario.
The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.
One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario.
The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.
ContributorsTuncali, Cumhur Erkan (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Kapinski, James (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2019
Description
Preventive maintenance is a practice that has become popular in recent years, largely due to the increased dependency on electronics and other mechanical systems in modern technologies. The main idea of preventive maintenance is to take care of maintenance-type issues before they fully appear or cause disruption of processes and daily operations. One of the most important parts is being able to predict and foreshadow failures in the system, in order to make sure that those are fixed before they turn into large issues. One specific area where preventive maintenance is a very big part of daily activity is the automotive industry. Automobile owners are encouraged to take their cars in for maintenance on a routine schedule (based on mileage or time), or when their car signals that there is an issue (low oil levels for example). Although this level of maintenance is enough when people are in charge of cars, the rise of autonomous vehicles, specifically self-driving cars, changes that. Now instead of a human being able to look at a car and diagnose any issues, the car needs to be able to do this itself. The objective of this project was to create such a system. The Electronics Preventive Maintenance System is an internal system that is designed to meet all these criteria and more. The EPMS system is comprised of a central computer which monitors all major electronic components in an autonomous vehicle through the use of standard off-the-shelf sensors. The central computer compiles the sensor data, and is able to sort and analyze the readings. The filtered data is run through several mathematical models, each of which diagnoses issues in different parts of the vehicle. The data for each component in the vehicle is compared to pre-set operating conditions. These operating conditions are set in order to encompass all normal ranges of output. If the sensor data is outside the margins, the warning and deviation are recorded and a severity level is calculated. In addition to the individual focus, there's also a vehicle-wide model, which predicts how necessary maintenance is for the vehicle. All of these results are analyzed by a simple heuristic algorithm and a decision is made for the vehicle's health status, which is sent out to the Fleet Management System. This system allows for accurate, effortless monitoring of all parts of an autonomous vehicle as well as predictive modeling that allows the system to determine maintenance needs. With this system, human inspectors are no longer necessary for a fleet of autonomous vehicles. Instead, the Fleet Management System is able to oversee inspections, and the system operator is able to set parameters to decide when to send cars for maintenance. All the models used for the sensor and component analysis are tailored specifically to the vehicle. The models and operating margins are created using empirical data collected during normal testing operations. The system is modular and can be used in a variety of different vehicle platforms, including underwater autonomous vehicles and aerial vehicles.
ContributorsMian, Sami T. (Author) / Collofello, James (Thesis director) / Chen, Yinong (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of this thesis was to design a market entrance strategy for Company X to enter the microcontroller (MCU) market within the Internet of Things (IoT). The five IoT segments are automotive; medical; retail; industrial; and military, aerospace, and government. To reach a final decision, we will research the markets, analyze make versus buy scenarios, and deliver a financial analysis on the chosen strategy. Based on the potential financial benefits and compatibility with Company X's current business model, we recommend that Company X enter the automotive segment through mergers & acquisitions (M&A). After analyzing the supply chain structure of the automotive IoT, we advise Company X to acquire Freescale Semiconductor for $46.98 per share.
ContributorsBradley, Rachel (Co-author) / Fankhauser, Elisa (Co-author) / McCoach, Robert (Co-author) / Zheng, Weilin (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / School of Accountancy (Contributor) / School of International Letters and Cultures (Contributor) / WPC Graduate Programs (Contributor)
Created2015-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: 1. Understand if there is monetization potential in autonomous vehicle data 2. Create a financial model of what detailing the viability of AV data monetization 3. Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data.
ContributorsCarlton, Corrine (Co-author) / Clark, Rachael (Co-author) / Quintana, Alex (Co-author) / Shapiro, Brandon (Co-author) / Sigrist, Austin (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The focus of this study was to address the problem of prohibitively expensive LiDARs currently being used in autonomous vehicles by analyzing the capabilities and shortcomings of affordable LiDARs as replacements. This involved the characterization of affordable LiDARs that are currently available on the market. The characterization of the LiDARs involved testing refresh rates, field of view, distance the sensors could detect, reflectivity, and power of the emitters. The four LiDARs examined in this study were the Scanse, RPLIDAR A2, LeddarTech Vu8, and LeddarTech M16. Of these low cost LiDAR options we find the two best options for use in affordable autonomous vehicle sensors to be the RPLIDAR A2 and the LeddarTech M16.
ContributorsMurphy, Thomas Joseph (Co-author) / Gamal, Eltohamy (Co-author) / Yu, Hongbin (Thesis director) / Houghton, Todd (Committee member) / Electrical Engineering Program (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: Understand if there is monetization potential in autonomous vehicle data Create a financial model of what detailing the viability of AV data monetization Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data. First, in order to brainstorm how this data could be monetized, we generated potential use cases, defined probable customers of these use cases, and how the data could generate value to customers as a means to understand what the "price" of autonomous vehicle data might be. While we came up with an extensive list of potential data monetization use cases, we evaluated our list of use cases against six criteria to narrow our focus into the following five: Government, Insurance Companies, Mapping, Marketing purposes, and Freight. Based on our research, we decided to move forward with the insurance industry as a proof of concept for autonomous vehicle data monetization. Based on our modeling, we concluded there is a significant market for autonomous vehicle data monetization moving forward. Data accessibility is a key driver in how profitable a particular company and their competitors can be in this space. In order to effectively monetize this data, it would first be important to understand the method by which a company obtains access to the data in the first place. Ultimately, based on our analysis, Company X has positioned itself well to take advantage of the new trends in autonomous vehicle technology. With more strategic investments and innovation, Company X can be a key benefactor of this unprecedented space in the near future.
ContributorsShapiro, Brandon (Co-author) / Quintana, Alex (Co-author) / Sigrist, Austin (Co-author) / Clark, Rachael (Co-author) / Carlton, Corrine (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05