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- All Subjects: Technology
- Creators: Computer Science and Engineering Program
- Status: Published
To gain insight into the state of the industry and current position of independent bookstores, I will first examine the past fifty years of the brick-and-mortar bookstore, followed by a Porter’s Five Forces analysis of the industry threats and a SWOT analysis to compare the strengths and weaknesses of independent bookstores. Next, the patrons of independent bookstores will be discussed with a focus on the two largest consumer groups of Millennials and Baby Boomers, their characteristics, and the opportunities they provide to bookstores. After this there will be an exploration of the competitors to brick-and-mortar bookstores, focusing on Amazon and then touching on some of the other rivals to bookstores’ consumer base. The next section will be an in-depth analysis of a variety of bookstores across the United States, with attention to their successful practices, goals, concerns, and failures. First, there will be a comparison of industry success and failure through case studies of Borders and Powell’s bookstores. Next, there will be a comparison of five beloved independent bookstores across the country to share their varied competitive advantages that are the secret to their success. Finally, there are primary source interviews with the employees of three major Phoenix bookstores, which provide insight into the goals, current projects, attitudes, and inner strengths of these businesses. Finally, the thesis will conclude with a section offering solutions and suggestions for independent bookstores to pursue based on the primary and secondary research discussed above. These recommendations are focused on five key areas:
• Community
• Consumers
• Store Design
• Technology
• Diversification
Ultimately, the information provided by this research and these interviews indicates that while vital business changes are being pursued by independent and chain bookstores across the United States, the independent bookstore shows no signs of disappearing in favor of online vendors or e-readers.
Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.