Signature Replication Machine; An Exploration in Attempts at Developing Best Practices and Standards in the Internet of Things

When trusting technology with data that is crucial to identification, there are many
questions that arise. As we trust the machines with more personalized information such as
banking and social security numbers, security arises as a prevalent issue. Online banking has
become normalized and financial transactions with applications like Venmo, and CashApp; each
bank has its own mobile or web application for users to manage their balance and pay bills
online. While security has been very well established in large databases, a new frontier of
devices called the Internet of Things has opened a new door for both accessibility and cost.
When the trust in technology shifts over from large databases for financial information to these
easily accessible devices in the Internet of Things, the amount of emphasis on security needs to
also transition as well.

The STS research paper has explored the rise of security on these Internet of Things
devices and explored through a case study a scenario when the security was lacking. The
parallel was drawn between standard cybersecurity with large databases and security specifically
for Internet of Things devices. The case study was used to parallel another researcher’s attempts
to establish Actor-Network Theory for cybersecurity. Through the research, a similar version of
Actor-Network Theory was formed for Internet of Things security and portrays the effect of
largescale malware on all stakeholders regarding the Internet of Things devices.

The technical capstone project aimed to create a device that would allow signature or
handwriting replication from virtual digital drawings or pictures to be drawn on paper. A server
takes a handwriting sample as an input and processes it to send wirelessly to the machine that
replicated the handwriting sample. The machine then simulates the strokes on the handwriting
sample and replicates the image by simulating the original user’s writing.
To conclude the executive summary, all the technical work has been achieved and the
machine was able to successfully replicate not only signatures but also drawings as well. This
technical project helped my development in a professional team setting and we were all delighted
by the result.

I would like to acknowledge Professor Harry Powell and Todd Delong for major
assistance with the technical capstone project. I would then like to acknowledge Professor Sean
Ferguson for major assistance with the STS research project.

School of Engineering and Applied Science
Bachelor of Science in Electrical Engineering
Technical Advisor: Harry Powell
STS Advisor: Sean Ferguson
Technical Team Members: Zichao Hu, Yu-Jiyun Tao