10/11: Design Day Booklet Team Project Page Artwork Feedback

(1)

From Students…

…to Professionals

The Capstone Experience

10/11: Design Day Booklet Team Project Page Artwork Feedback

Dr. Wayne Dyksen

Department of Computer Science and Engineering

Michigan State University

Fall 2021

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• An updated version of your Design Day booklet team page zipped folder with the artwork layout modifications is posted on our Downloads page. Get it. Unzip it.

• A PDF of this slide deck is posted on our Downloads page. Get it.

• Use the Design Day booklet team page Word template in this folder.

• Read the comments below about your team’s artwork.

• Leave the artwork layout basically as is.

• Redo your artwork if and as requested.

• If necessary…

▪…provide new high-resolution png originals appropriately numbered and named

▪…change pictures in your template to reflect any new artwork. Do so by right-clicking on the artwork and selecting “Change Picture > From a File…” to preserve the size and location.

• If necessary, place your new artwork in your project page team and in your team assets folder.

• If necessary, update your Word template and artwork files.

• Zip up your template assets folder using Windows zip.

• Upload your zipped assets folder to dd-booklet-zip-files-artwork-updates-by-teams-2 in our Microsoft Teams General Channel file space and your team’s private file space by 11:59 p.m., Monday, October 11.

The Capstone Experience Design Day Booklet Artwork Feedback 2

(3)

Team Ally Artwork Feedback (James)

Original Artwork Feedback

• Your artwork-1 blends into the white background. You were supposed to have added a border. I added it for you. It’ll cost you

• Your artwork and layout are basically good.

• I may have resized and moved things slightly.

• Leave the artwork layout as it is in the updated zipped folder.

• You don’t need to do anything with respect to artwork for this round. Just submit back to me the new zip file that I upload for your team. Make sure that you

download the NEW zip file and submit the NEW zip file.

The Capstone Experience Design Day Booklet Artwork Feedback 3

The Capstone Experience

Ally Financial

Digital Avatar Assistant

Michigan State University

Team Members (left to right) Akhil Arora

Ann Arbor, Michigan Nate Wood Novi, Michigan Xunran Zhou Wuhan, Hubei, China Zach Arnold Farmington Hills, Michigan

Ally

Project Sponsors Jared Allmond Detroit, Michigan Dzmitry Dubarav Detroit, Michigan Dan Lemont Detroit, Michigan Harish Naik Detroit, Michigan Arvy Rajasekaran Detroit, Michigan

P A G E N + 2

Ally Financial is a financial services company based in Detroit, operating as one of the largest car finance companies in the United States. Ally has amassed an immense customer base, financing cars for over 4 million people and having 2 million depositors. Ally also offers online banking and online trading, bolstering the services they provide for their customers.

As artificial intelligence has advanced, more and more people have thought about how an AI could be used as a service for them and could handle small tasks so they can focus on bigger problems.

One easy task that many students and young adults struggle with is budgeting and keeping track of how they are spending their money.

Our Digital Avatar Assistant helps Ally bank account holders keep track of a set budget for different merchant categories and analyze how they are spending their money.

Using natural language processing AI and machine learning, our chatbot has the ability to communicate with users using voices, texts, and buttons. Users can set a budget limit with our chatbot, our bot will then keep track of where they are spending most of their money and give reminders when the limit is exceeded.

When the user interacts with it, our chatbot will also be able to react with animated movement and facial expressions according to the user’s tone and intent.

The Digital Avatar Assistant is developed using Rasa Open

Source as a framework to understand natural language, hold

conversations, and connect to APIs. Our application uses Amazon

EC2 for machine learning and chatbot computations, Amazon S3

for model storage, DynamoDB for conversation storage and a

combination of Amazon Transcribe and Amazon Polly for

conversational functionality. The user interface is built with React.

(4)

Team Ally Artwork Feedback (James)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 4

The Capstone Experience

Ally Financial

Digital Avatar Assistant

Michigan State University

Ally

P A G E N + 2

Ally Financial is a financial services company based in Detroit, operating as one of the largest car finance companies in the United States. Ally has amassed an immense customer base, financing cars for over 4 million people and having 2 million depositors. Ally also offers online banking and online trading, bolstering the services they provide for their customers.

As artificial intelligence has advanced, more and more people have thought about how an AI could be used as a service for them and could handle small tasks so they can focus on bigger problems.

One easy task that many students and young adults struggle with is budgeting and keeping track of how they are spending their money.

Our Digital Avatar Assistant helps Ally bank account holders keep track of a set budget for different merchant categories and analyze how they are spending their money.

Using natural language processing AI and machine learning, our chatbot has the ability to communicate with users using voices, texts, and buttons. Users can set a budget limit with our chatbot, our bot will then keep track of where they are spending most of their money and give reminders when the limit is exceeded.

When the user interacts with it, our chatbot will also be able to react with animated movement and facial expressions according to the user’s tone and intent.

The Digital Avatar Assistant is developed using Rasa Open Source as a framework to understand natural language, hold conversations, and connect to APIs. Our application uses Amazon EC2 for machine learning and chatbot computations, Amazon S3 for model storage, DynamoDB for conversation storage and a combination of Amazon Transcribe and Amazon Polly for conversational functionality. The user interface is built with React.

The Capstone Experience

Ally Financial

Digital Avatar Assistant

Michigan State University

Ally

P A G E N + 2

Ally Financial is a financial services company based in Detroit, operating as one of the largest car finance companies in the United States. Ally has amassed an immense customer base, financing cars for over 4 million people and having 2 million depositors. Ally also offers online banking and online trading, bolstering the services they provide for their customers.

As artificial intelligence has advanced, more and more people have thought about how an AI could be used as a service for them and could handle small tasks so they can focus on bigger problems.

One easy task that many students and young adults struggle with is budgeting and keeping track of how they are spending their money.

Our Digital Avatar Assistant helps Ally bank account holders keep track of a set budget for different merchant categories and analyze how they are spending their money.

Using natural language processing AI and machine learning, our chatbot has the ability to communicate with users using voices, texts, and buttons. Users can set a budget limit with our chatbot, our bot will then keep track of where they are spending most of their money and give reminders when the limit is exceeded.

When the user interacts with it, our chatbot will also be able to react with animated movement and facial expressions according to the user’s tone and intent.

The Digital Avatar Assistant is developed using Rasa Open

Source as a framework to understand natural language, hold

conversations, and connect to APIs. Our application uses Amazon

EC2 for machine learning and chatbot computations, Amazon S3

for model storage, DynamoDB for conversation storage and a

combination of Amazon Transcribe and Amazon Polly for

conversational functionality. The user interface is built with React.

(5)

Team Amazon Artwork Feedback (James)

Original Artwork Feedback

• Your artwork and layout are basically good.

• I may have resized and moved things slightly.

• Leave the artwork layout as it is in the updated zipped folder that I posted yesterday.

• You don’t need to do anything with respect to artwork for this round. Just submit back to me the new zip file that I upload for your team. Make sure that you

download the NEW zip file and submit the NEW zip file.

• Nice work!

The Capstone Experience Design Day Booklet Artwork Feedback 5

Computer Science and Engineering CSE498

Amazon

Amazon Web Services: AWSome Availability Zones

Michigan State University

Team Members (left to right) Wynton Huang

Ann Arbor, Michigan Jamison Heiner Plymouth, Michigan Iris Kim

Shanghai, Shanghai, China Jung Chak

Taipei, Taiwan, Taiwan Jake Hood DeWitt, Michigan

Amazon

Project Sponsors Jennifer Beer Detroit, Michigan Jeremy Fry Detroit, Michigan Garret Gaw Detroit, Michigan Derek Gebhard Detroit, Michigan Erik Kamman Detroit, Michigan Tyler Rozwadowski Detroit, Michigan William Tanner Detroit, Michigan

P A G E N + 3

Founded in Bellevue, Washington in 1994, Amazon is a Fortune 500 company that provides a variety of services to customers as the world’s largest online retailer and cloud services provider.

Customers using Amazon’s cloud platform, Amazon Web Services, can choose to break their application up into many parts, each hosted in a different location (called an Availability Zone).

Doing so helps prevent natural disasters from causing service outages.

Distributed applications are spread across multiple servers, which need to communicate with each other for the application to function. This communication can take a meaningful amount of time. Choosing which Availability Zones to use to minimize this delay is important; this traditionally requires extensive manual testing.

Our AWSome Availability Zones web application continuously and automatically measures the delay between Availability Zones, allowing Amazon Web Services customers to easily choose the fastest Availability Zones for their application, saving them time and money.

AWSome Availability Zones provides customers with an easy- to-understand visualization of the delay between Availability Zones using an interactive map with a familiar look and feel.

Experienced Amazon Web Services customers can opt to use our AWSome Availability Zones system to explore more detailed views of the network latency data, allowing them to answer specific questions they have, quickly and seamlessly.

Our software’s front end is built using Angular, and its back

end uses Amazon Web Services EC2 (Elastic Cloud Compute)

instances to measure network latency between Availability Zones,

which it stores in DynamoDB.

(6)

Team Amazon Artwork Feedback (James)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 6

Computer Science and Engineering CSE498

Amazon

Amazon Web Services: AWSome Availability Zones

Michigan State University

Amazon

P A G E N + 3

Founded in Bellevue, Washington in 1994, Amazon is a Fortune 500 company that provides a variety of services to customers as the world’s largest online retailer and cloud services provider.

Customers using Amazon’s cloud platform, Amazon Web Services, can choose to break their application up into many parts, each hosted in a different location (called an Availability Zone).

Doing so helps prevent natural disasters from causing service outages.

Distributed applications are spread across multiple servers, which need to communicate with each other for the application to function. This communication can take a meaningful amount of time. Choosing which Availability Zones to use to minimize this delay is important; this traditionally requires extensive manual testing.

Our AWSome Availability Zones web application continuously and automatically measures the delay between Availability Zones, allowing Amazon Web Services customers to easily choose the fastest Availability Zones for their application, saving them time and money.

AWSome Availability Zones provides customers with an easy- to-understand visualization of the delay between Availability Zones using an interactive map with a familiar look and feel.

Experienced Amazon Web Services customers can opt to use our AWSome Availability Zones system to explore more detailed views of the network latency data, allowing them to answer specific questions they have, quickly and seamlessly.

Our software’s front end is built using Angular, and its back end uses Amazon Web Services EC2 (Elastic Cloud Compute) instances to measure network latency between Availability Zones, which it stores in DynamoDB.

Computer Science and Engineering CSE498

Amazon

Amazon Web Services: AWSome Availability Zones

Michigan State University

Amazon

P A G E N + 3

Founded in Bellevue, Washington in 1994, Amazon is a Fortune 500 company that provides a variety of services to customers as the world’s largest online retailer and cloud services provider.

Customers using Amazon’s cloud platform, Amazon Web Services, can choose to break their application up into many parts, each hosted in a different location (called an Availability Zone).

Doing so helps prevent natural disasters from causing service outages.

Distributed applications are spread across multiple servers, which need to communicate with each other for the application to function. This communication can take a meaningful amount of time. Choosing which Availability Zones to use to minimize this delay is important; this traditionally requires extensive manual testing.

Our AWSome Availability Zones web application continuously and automatically measures the delay between Availability Zones, allowing Amazon Web Services customers to easily choose the fastest Availability Zones for their application, saving them time and money.

AWSome Availability Zones provides customers with an easy- to-understand visualization of the delay between Availability Zones using an interactive map with a familiar look and feel.

Experienced Amazon Web Services customers can opt to use our AWSome Availability Zones system to explore more detailed views of the network latency data, allowing them to answer specific questions they have, quickly and seamlessly.

Our software’s front end is built using Angular, and its back

end uses Amazon Web Services EC2 (Elastic Cloud Compute)

instances to measure network latency between Availability Zones,

which it stores in DynamoDB.

(7)

Team Anthropocene Institute 1 Artwork Feedback (James)

Original Artwork Feedback

• Your artwork blends into the white background. You were supposed to have added a border. I added it for you. If you update this artwork, add a border as per my instructions.

• Other than the border, your artwork and layout are basically good.

• Leave the artwork layout as it is in the updated zipped folder that I posted yesterday.

• You don’t need to do anything with respect to artwork for this round. Just submit back to me the new zip file that I upload for your team. Make sure that you

download the NEW zip file and submit the NEW zip file.

• Nice work!

The Capstone Experience Design Day Booklet Artwork Feedback 7

The Capstone Experience

Anthropocene Institute

Air Pollution Health Outcomes Forecasting Tool

Michigan State University

Team Members (left to right) Lukas Richters

Farmington Hills, Michigan Tate Bond

Grand Rapids, Michigan Lindsey Boivin Novi, Michigan Hannah Francisco Buffalo, New York Zhendong Liu Hefei, Anhui, China

Anthropocene Institute 1

Project Sponsors Micha Brown Palo Alto, California Richard Chan Palo Alto, California Jason Gwo Palo Alto, California Michiya Hibino Palo Alto, California Richard Lee Palo Alto, California Frank Ling Palo Alto, California Carl Page Palo Alto, California

P A G E N + 4

The Anthropocene Institute is an organization that partners with researchers, governments, experts and investors to address one of humanity’s most pressing concerns – climate change. The organization provides support to projects related to clean energy, anti-pollution efforts and climate innovation and brings down any political or financial barriers they may experience.

The Anthropocene Institute has turned its attention towards air pollution in hopes of researching the effect that air quality has on premature deaths and health complications. Some of these potential health complications include increased asthma, infant mortality and lung cancer. Fine particulate matter (PM2.5) is our designated metric for air pollution, as it is one of the deadliest pollutants. It has been estimated that PM2.5 is responsible for more deaths than automobile accidents and murders combined.

Currently, there are very few publicly accessible tools which explore the health effects of air pollution. The Air Pollution Health Outcomes Forecasting Tool provides the public with a detailed analysis of the air quality in their area, along with the resulting health effects. Users can select their county, or explore other areas, to see the predicted health effects of living in that area, based on the air quality. While this software can be used by anyone, it is most useful for policymakers and investors to determine areas in need of assistance or those with opportunities for development.

The front end consists of an interactive heat map and

responsive tools developed with HTML, CSS and JavaScript. Our

software is served via a Python Flask back end which

communicates data from our machine learning models. Our data is

retrieved from Purple Air sensors as well as the EPA and CDC

databases. The machine learning models utilize this data and the

scikit-learn library to generate predictions of health effects and

pollution levels throughout the United States.

(8)

Team Anthropocene Institute 1 Artwork Feedback (James)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 8

The Capstone Experience

Anthropocene Institute

Air Pollution Health Outcomes Forecasting Tool

Michigan State University

Anthropocene Institute 1

P A G E N + 4

The Anthropocene Institute is an organization that partners with researchers, governments, experts and investors to address one of humanity’s most pressing concerns – climate change. The organization provides support to projects related to clean energy, anti-pollution efforts and climate innovation and brings down any political or financial barriers they may experience.

The Anthropocene Institute has turned its attention towards air pollution in hopes of researching the effect that air quality has on premature deaths and health complications. Some of these potential health complications include increased asthma, infant mortality and lung cancer. Fine particulate matter (PM2.5) is our designated metric for air pollution, as it is one of the deadliest pollutants. It has been estimated that PM2.5 is responsible for more deaths than automobile accidents and murders combined.

Currently, there are very few publicly accessible tools which explore the health effects of air pollution. The Air Pollution Health Outcomes Forecasting Tool provides the public with a detailed analysis of the air quality in their area, along with the resulting health effects. Users can select their county, or explore other areas, to see the predicted health effects of living in that area, based on the air quality. While this software can be used by anyone, it is most useful for policymakers and investors to determine areas in need of assistance or those with opportunities for development.

The front end consists of an interactive heat map and responsive tools developed with HTML, CSS and JavaScript. Our software is served via a Python Flask back end which communicates data from our machine learning models. Our data is retrieved from Purple Air sensors as well as the EPA and CDC databases. The machine learning models utilize this data and the scikit-learn library to generate predictions of health effects and pollution levels throughout the United States.

The Capstone Experience

Anthropocene Institute

Air Pollution Health Outcomes Forecasting Tool

Michigan State University

Anthropocene Institute 1

P A G E N + 4

The Anthropocene Institute is an organization that partners with researchers, governments, experts and investors to address one of humanity’s most pressing concerns – climate change. The organization provides support to projects related to clean energy, anti-pollution efforts and climate innovation and brings down any political or financial barriers they may experience.

The Anthropocene Institute has turned its attention towards air pollution in hopes of researching the effect that air quality has on premature deaths and health complications. Some of these potential health complications include increased asthma, infant mortality and lung cancer. Fine particulate matter (PM2.5) is our designated metric for air pollution, as it is one of the deadliest pollutants. It has been estimated that PM2.5 is responsible for more deaths than automobile accidents and murders combined.

Currently, there are very few publicly accessible tools which explore the health effects of air pollution. The Air Pollution Health Outcomes Forecasting Tool provides the public with a detailed analysis of the air quality in their area, along with the resulting health effects. Users can select their county, or explore other areas, to see the predicted health effects of living in that area, based on the air quality. While this software can be used by anyone, it is most useful for policymakers and investors to determine areas in need of assistance or those with opportunities for development.

The front end consists of an interactive heat map and

responsive tools developed with HTML, CSS and JavaScript. Our

software is served via a Python Flask back end which

communicates data from our machine learning models. Our data is

retrieved from Purple Air sensors as well as the EPA and CDC

databases. The machine learning models utilize this data and the

scikit-learn library to generate predictions of health effects and

pollution levels throughout the United States.

(9)

Team Anthropocene Institute 2 Artwork Feedback (James)

Original Artwork Feedback

• Your artwork and layout are good.

• I may have resized and moved things slightly.

• Leave the artwork layout as it is in the updated zipped folder that I posted yesterday.

• You don’t need to do anything with respect to artwork for this round. Just submit back to me the new zip file that I upload for your team. Make sure that you

download the NEW zip file and submit the NEW zip file.

• Nice work!

The Capstone Experience Design Day Booklet Artwork Feedback 9

Computer Science and Engineering CSE498

Anthropocene Institute

Electricity Grid Planning Tool

Michigan State University

Team Members (left to right) Tyler Smith

Charlotte, Michigan Amanuel Engeda East Lansing, Michigan Nafisa Lenseni Canton, Michigan Nic Weller Jackson, Michigan Hunter Paul Rochester, Michigan

Anthropocene Institute 2

Project Sponsors Richard Chan Palo Alto, California Frank Ling Palo Alto, California Carl Page Palo Alto, California

P A G E N + 5

The Anthropocene Institute is a non-governmental organization with the mission to utilize science and technology to address the planet’s needs. It drives and facilitates innovation in clean energy to address the urgency of climate change. The Institute also supports start-ups and universities to develop emerging and disruptive energy technologies that are clean, safe and reliable.

The Anthropocene Institute is looking at Small Modular Reactors (SMR) as a viable option for new sources of power generation in comparison to coal and gas plants because of the minimum effect SMRs have on the atmosphere, namely emissions.

Proper SMR placement considers the area’s power generation, power outage occurrence rates, and power consumption from the nearby population. Taking all of these factors into account would be a time-consuming process, let alone comparing placements.

Our Electricity Grid Planning Tool Project solves this issue by using machine learning to predict generation and consumption of electricity with or without SMR placement on the grid. Our models both manipulate and interpret vast demand and generation data to define values for the problem of where the “best”

substation for placing an SMR is located.

Our web application provides an easy-to-use interface designed for electricity grid planners seeking to better understand the cost and benefits for deploying SMRs. Users simply view specific substations, and our application provides them with statistics and recommendations relating to the cost benefits analysis of deploying a SMR in that area.

The machine learning models were developed in Python

with scikit-learn. The user interface is built on JavaScript, CSS and

HTML with an Apache web server that utilizes the Google Maps

API.

(10)

Team Anthropocene Institute 2 Artwork Feedback (James)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 10

Computer Science and Engineering CSE498

Anthropocene Institute

Electricity Grid Planning Tool

Michigan State University

Anthropocene Institute 2

P A G E N + 5

The Anthropocene Institute is a non-governmental organization with the mission to utilize science and technology to address the planet’s needs. It drives and facilitates innovation in clean energy to address the urgency of climate change. The Institute also supports start-ups and universities to develop emerging and disruptive energy technologies that are clean, safe and reliable.

The Anthropocene Institute is looking at Small Modular Reactors (SMR) as a viable option for new sources of power generation in comparison to coal and gas plants because of the minimum effect SMRs have on the atmosphere, namely emissions.

Proper SMR placement considers the area’s power generation, power outage occurrence rates, and power consumption from the nearby population. Taking all of these factors into account would be a time-consuming process, let alone comparing placements.

Our Electricity Grid Planning Tool Project solves this issue by using machine learning to predict generation and consumption of electricity with or without SMR placement on the grid. Our models both manipulate and interpret vast demand and generation data to define values for the problem of where the “best”

substation for placing an SMR is located.

Our web application provides an easy-to-use interface designed for electricity grid planners seeking to better understand the cost and benefits for deploying SMRs. Users simply view specific substations, and our application provides them with statistics and recommendations relating to the cost benefits analysis of deploying a SMR in that area.

The machine learning models were developed in Python with scikit-learn. The user interface is built on JavaScript, CSS and HTML with an Apache web server that utilizes the Google Maps API.

Computer Science and Engineering CSE498

Anthropocene Institute

Electricity Grid Planning Tool

Michigan State University

Anthropocene Institute 2

P A G E N + 5

The Anthropocene Institute is a non-governmental organization with the mission to utilize science and technology to address the planet’s needs. It drives and facilitates innovation in clean energy to address the urgency of climate change. The Institute also supports start-ups and universities to develop emerging and disruptive energy technologies that are clean, safe and reliable.

The Anthropocene Institute is looking at Small Modular Reactors (SMR) as a viable option for new sources of power generation in comparison to coal and gas plants because of the minimum effect SMRs have on the atmosphere, namely emissions.

Proper SMR placement considers the area’s power generation, power outage occurrence rates, and power consumption from the nearby population. Taking all of these factors into account would be a time-consuming process, let alone comparing placements.

Our Electricity Grid Planning Tool Project solves this issue by using machine learning to predict generation and consumption of electricity with or without SMR placement on the grid. Our models both manipulate and interpret vast demand and generation data to define values for the problem of where the “best” substation for placing an SMR is located.

Our web application provides an easy-to-use interface designed for electricity grid planners seeking to better understand the cost and benefits for deploying SMRs. Users simply view specific substations, and our application provides them with statistics and recommendations relating to the cost benefits analysis of deploying a SMR in that area.

The machine learning models were developed in Python with

scikit-learn. The user interface is built on JavaScript, CSS and

HTML with an Apache web server that utilizes the Google Maps

API.

(11)

Team Atomic Object Artwork Feedback (Luke)

Original Artwork Feedback

• The layout of your artwork is ok, but the content is awful. It’s nothing but empty space. My instructions explicitly say NOT to include artwork like this.

• Fill in everything with meaningful information so it illustrates how your software may be use.

• Redo all of your artwork.

• As an asided, did Atomic Object pick or approve this color scheme? IMHO, it’s very bad.

• I resized and moved things slightly.

• Read the instructions carefully, over and over.

The Capstone Experience Design Day Booklet Artwork Feedback 11

The Capstone Experience

Atomic Object

Stroodle: Learning Management System

Michigan State University

Team Members (left to right) Jake Bosio

West Grove, Pennsylvania Shachi Joshi Rochester Hills, Michigan Sean Ohare Farmington Hills, Michigan Gabrie Italia Shelby Township, Michigan

Atomic Object

Project Sponsors Micah Alles Grand Rapids, Michigan Jonah Bailey Ann Arbor, Michigan Dylan Goings Ann Arbor, Michigan

P A G E N + 6

Operating for over 20 years, Atomic Object is a software design and development consultancy based out of the Midwest cities of Ann Arbor, Grand Rapids and Chicago. Atomic Object has worked with over 175 clients and created over 250 applications across different industries, from tech startups to Fortune 500 companies.

Learning management systems are utilized by many educational institutions to administer, track and deliver course materials and student work.

Popular learning management systems offer many features to manage and engage in course activities but fail to deliver them in a simple and intuitive application. Our Stroodle: Learning Management System provides tools for students and instructors alike to participate in an online course, while streamlining the user experience.

Students are provided a dashboard with a summary of important and pertinent information for all their enrolled courses.

Students can access individual course pages to interact with course material prepared by the instructor.

Instructors manage their course by uploading documents, such as reading materials or assignments, for their students to view. They can also organize upcoming events and deadlines for their students on the course calendar. An instructor can create a quiz to assess their students. The results of these quizzes are available in a student’s gradebook along with scores of other graded assignments.

Announcements are sent out by instructors and students can keep track of important events by receiving course notifications on their mobile device.

The Stroodle front end is built using ReactJS and React Native.

The back end is built using Node.js and Express.js, which

communicates with a PostgreSQL database.

(12)

Team Atomic Object Artwork Feedback (Luke)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 12

The Capstone Experience

Atomic Object

Stroodle: Learning Management System

Michigan State University

Atomic Object

P A G E N + 6

Operating for over 20 years, Atomic Object is a software design and development consultancy based out of the Midwest cities of Ann Arbor, Grand Rapids and Chicago. Atomic Object has worked with over 175 clients and created over 250 applications across different industries, from tech startups to Fortune 500 companies.

Learning management systems are utilized by many educational institutions to administer, track and deliver course materials and student work.

Popular learning management systems offer many features to manage and engage in course activities but fail to deliver them in a simple and intuitive application. Our Stroodle: Learning Management System provides tools for students and instructors alike to participate in an online course, while streamlining the user experience.

Students are provided a dashboard with a summary of important and pertinent information for all their enrolled courses.

Students can access individual course pages to interact with course material prepared by the instructor.

Instructors manage their course by uploading documents, such as reading materials or assignments, for their students to view. They can also organize upcoming events and deadlines for their students on the course calendar. An instructor can create a quiz to assess their students. The results of these quizzes are available in a student’s gradebook along with scores of other graded assignments.

Announcements are sent out by instructors and students can keep track of important events by receiving course notifications on their mobile device.

The Stroodle front end is built using ReactJS and React Native.

The back end is built using Node.js and Express.js, which communicates with a PostgreSQL database.

The Capstone Experience

Atomic Object

Stroodle: Learning Management System

Michigan State University

Atomic Object

P A G E N + 6

Operating for over 20 years, Atomic Object is a software design and development consultancy based out of the Midwest cities of Ann Arbor, Grand Rapids and Chicago. Atomic Object has worked with over 175 clients and created over 250 applications across different industries, from tech startups to Fortune 500 companies.

Learning management systems are utilized by many educational institutions to administer, track and deliver course materials and student work.

Popular learning management systems offer many features to manage and engage in course activities but fail to deliver them in a simple and intuitive application. Our Stroodle: Learning Management System provides tools for students and instructors alike to participate in an online course, while streamlining the user experience.

Students are provided a dashboard with a summary of important and pertinent information for all their enrolled courses.

Students can access individual course pages to interact with course material prepared by the instructor.

Instructors manage their course by uploading documents, such as reading materials or assignments, for their students to view. They can also organize upcoming events and deadlines for their students on the course calendar. An instructor can create a quiz to assess their students. The results of these quizzes are available in a student’s gradebook along with scores of other graded assignments.

Announcements are sent out by instructors and students can keep track of important events by receiving course notifications on their mobile device.

The Stroodle front end is built using ReactJS and React Native.

The back end is built using Node.js and Express.js, which

communicates with a PostgreSQL database.

(13)

Team Auto-Owners Artwork Feedback (Luke)

Original Artwork Feedback

• Your artwork looks great, but the table of simulation data is, IMHO, rather compared to the fantastic images of your game, so…

• I enlarged your game images and moved them around and I replaced the simulation data artwork with a photo of someone using an Oculus Rift. I “stole” this from an Auto-Owners project from a few semester back. You’ll want to replace this with a similar image of one of you using the Oculus.

• Leave the artwork layout as it is in the updated zipped folder that I posted.

The Capstone Experience Design Day Booklet Artwork Feedback 13

Computer Science and Engineering CSE498

Auto-Owners Insurance

Yard Wars: Weathering the Storm

Michigan State University

Team Members (left to right) Carolus Huang

Xiamen, Fujian, China Graham Cornish Charlotte, Michigan Brandon Byiringiro Okemos, Michigan John Reichenbach Shelby Township, Michigan

Auto-Owners

Project Sponsors Tony Dean Lansing, Michigan Ross Hacker Lansing, Michigan Scott Lake Lansing, Michigan

P A G E N + 7

Auto-Owners Insurance is a Fortune 500 company headquartered in Lansing, Michigan with over 48,000 licensed insurance agents. Auto-Owners provides automotive, home, life, and business insurance to nearly 3 million placeholders in 26 states.

As an insurance company, it is important for Auto-Owners agents to be able to gather and analyze data regarding causes for a claim. This helps them to better assist their clients who could be at risk of property damage and may need to submit a claim in the future.

Yard Wars: Weathering the Storm is a virtual reality application where Auto-Owners associates simulate storms on a virtual residence. Effects of the storm are viewed in real time and in first-person, and data is gathered, stored and displayed on an external website for analysis.

Users start by selecting the difficulty for the simulation, which changes the settings for the weather and number of trees that can be placed. The user also has the option to choose custom settings for the storm and trees. Then, they create trees of two different types with varying heights and diameters and place them around the virtual residence. Once they have finished placing trees, they begin the storm simulation.

As the storm progresses, trees can fall and possibly cause damage to the home. Fallen trees, any damage caused, and the simulation settings are recorded and sent to an external database and then to our website. The website is accessed via authentication, and then the data is available for viewing and analyzing.

Our virtual reality software is developed in Unity and written

in C#. The software is run using an Oculus Rift headset, Oculus

Touch controllers, and Oculus sensors. We use a MySQL database

to manage the data from the simulation, and it is communicated

using PHP to the website, which is hosted on the same server.

(14)

Team Auto-Owners Artwork Feedback (Luke)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 14

Computer Science and Engineering CSE498

Auto-Owners Insurance

Yard Wars: Weathering the Storm

Michigan State University

Auto-Owners

P A G E N + 7

Auto-Owners Insurance is a Fortune 500 company headquartered in Lansing, Michigan with over 48,000 licensed insurance agents. Auto-Owners provides automotive, home, life, and business insurance to nearly 3 million placeholders in 26 states.

As an insurance company, it is important for Auto-Owners agents to be able to gather and analyze data regarding causes for a claim. This helps them to better assist their clients who could be at risk of property damage and may need to submit a claim in the future.

Yard Wars: Weathering the Storm is a virtual reality application where Auto-Owners associates simulate storms on a virtual residence. Effects of the storm are viewed in real time and in first-person, and data is gathered, stored and displayed on an external website for analysis.

Users start by selecting the difficulty for the simulation, which changes the settings for the weather and number of trees that can be placed. The user also has the option to choose custom settings for the storm and trees. Then, they create trees of two different types with varying heights and diameters and place them around the virtual residence. Once they have finished placing trees, they begin the storm simulation.

As the storm progresses, trees can fall and possibly cause damage to the home. Fallen trees, any damage caused, and the simulation settings are recorded and sent to an external database and then to our website. The website is accessed via authentication, and then the data is available for viewing and analyzing.

Our virtual reality software is developed in Unity and written in C#. The software is run using an Oculus Rift headset, Oculus Touch controllers, and Oculus sensors. We use a MySQL database to manage the data from the simulation, and it is communicated using PHP to the website, which is hosted on the same server.

Computer Science and Engineering CSE498

Auto-Owners Insurance

Yard Wars: Weathering the Storm

Michigan State University

Auto-Owners

P A G E N + 7

Auto-Owners Insurance is a Fortune 500 company headquartered in Lansing, Michigan with over 48,000 licensed insurance agents. Auto-Owners provides automotive, home, life, and business insurance to nearly 3 million placeholders in 26 states.

As an insurance company, it is important for Auto-Owners agents to be able to gather and analyze data regarding causes for a claim. This helps them to better assist their clients who could be at risk of property damage and may need to submit a claim in the future.

Yard Wars: Weathering the Storm is a virtual reality application where Auto-Owners associates simulate storms on a virtual residence. Effects of the storm are viewed in real time and in first-person, and data is gathered, stored and displayed on an external website for analysis.

Users start by selecting the difficulty for the simulation, which changes the settings for the weather and number of trees that can be placed. The user also has the option to choose custom settings for the storm and trees. Then, they create trees of two different types with varying heights and diameters and place them around the virtual residence. Once they have finished placing trees, they begin the storm simulation.

As the storm progresses, trees can fall and possibly cause damage to the home. Fallen trees, any damage caused, and the simulation settings are recorded and sent to an external database and then to our website. The website is accessed via authentication, and then the data is available for viewing and analyzing.

Our virtual reality software is developed in Unity and written

in C#. The software is run using an Oculus Rift headset, Oculus

Touch controllers, and Oculus sensors. We use a MySQL database

to manage the data from the simulation, and it is communicated

using PHP to the website, which is hosted on the same server.

(15)

Team Bosch Artwork Feedback (Luke)

Original Artwork Feedback

• Your artwork and layout are basically good, but…

• Your artwork blends into the white background. You were supposed to have added a border. I added it for you. It’ll cost you. When you update this artwork, add a border as per my instructions.

• Your artwork is very blurry. Submit MUCH higher resolutions version of both.

• I resized and moved things slightly.

• Leave the artwork layout as it is in the updated zipped folder that I posted.

The Capstone Experience Design Day Booklet Artwork Feedback 15

The Capstone Experience

Bosch

Hardware in the Loop (HIL) Vehicle Simulator

Michigan State University

Team Members (left to right) Justin Armstrong

Burton, Michigan Luke Monroe Brighton, Michigan Aditya Raj Bokaro, Jharkhand, India Christian Zawisza Ann Arbor, Michigan Alan Wagner Westfield, New Jersey

Bosch

Project Sponsors Steve Koski Plymouth, Michigan Matt Lee Plymouth, Michigan Troy McCormick Plymouth, Michigan John Notorgiacomo Plymouth, Michigan

P A G E N + 8

Bosch is a global engineering and technology company with roughly 395,000 employees worldwide. Founded in Germany in 1886, Bosch is the world’s leading supplier of automotive components.

Currently, Bosch uses a Hardware in the Loop Vehicle Simulator to correct errors with their software and perform tests.

The system connects to specific hardware to simulate a vehicle on the road. The previous hardware, however, was quite costly and therefore only two systems were available to all Bosch engineers in North America. To resolve this, Bosch selected the PEAK PCAN USB Pro FD as a low-cost replacement for the previous hardware.

Our Hardware in the Loop system reimplements the core functionality of Bosch’s previous system on the PCAN hardware.

Our software allows the user to perform basic vehicle maneuvers and operations such as steering, braking, acceleration and more. The main functionality of our system is automatic cruise control (ACC). The user engages ACC by pressing the on button in the ACC square of the main dash view. The plus and minus buttons are used to set the speed of the vehicle.

The user interface is designed to allow anyone with driving experience to control the simulation with ease. The interface is broken up into squares that housing the controls for any given vehicle action to perform. A graph is displayable to show the signals being sent to and from the PEAK hardware with their corresponding values.

Above the controls for the vehicle is the dashboard. This displays the same basic information you would find in a real car including the current speed, rpm, fuel level and more.

The entire software system is written in Python 3. The front

end is built using the open-source toolkit wxPython while

communication with the hardware is done using PCAN Basic API.

(16)

Team Bosch Artwork Feedback (Luke)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 16

The Capstone Experience

Bosch

Hardware in the Loop (HIL) Vehicle Simulator

Michigan State University

Team Members (left to right) Justin Armstrong

Burton, Michigan Luke Monroe Brighton, Michigan Aditya Raj Bokaro, Jharkhand, India Christian Zawisza Ann Arbor, Michigan Alan Wagner Westfield, New Jersey

Bosch

P A G E N + 8

Bosch is a global engineering and technology company with roughly 395,000 employees worldwide. Founded in Germany in 1886, Bosch is the world’s leading supplier of automotive components.

Currently, Bosch uses a Hardware in the Loop Vehicle Simulator to correct errors with their software and perform tests.

The system connects to specific hardware to simulate a vehicle on the road. The previous hardware, however, was quite costly and therefore only two systems were available to all Bosch engineers in North America. To resolve this, Bosch selected the PEAK PCAN USB Pro FD as a low-cost replacement for the previous hardware.

Our Hardware in the Loop system reimplements the core functionality of Bosch’s previous system on the PCAN hardware.

Our software allows the user to perform basic vehicle maneuvers and operations such as steering, braking, acceleration and more. The main functionality of our system is automatic cruise control (ACC). The user engages ACC by pressing the on button in the ACC square of the main dash view. The plus and minus buttons are used to set the speed of the vehicle.

The user interface is designed to allow anyone with driving experience to control the simulation with ease. The interface is broken up into squares that housing the controls for any given vehicle action to perform. A graph is displayable to show the signals being sent to and from the PEAK hardware with their corresponding values.

Above the controls for the vehicle is the dashboard. This displays the same basic information you would find in a real car including the current speed, rpm, fuel level and more.

The entire software system is written in Python 3. The front end is built using the open-source toolkit wxPython while communication with the hardware is done using PCAN Basic API.

The Capstone Experience

Bosch

Hardware in the Loop (HIL) Vehicle Simulator

Michigan State University

Team Members (left to right) Justin Armstrong Burton, Michigan Luke Monroe Brighton, Michigan Aditya Raj Bokaro, Jharkhand, India Christian Zawisza Ann Arbor, Michigan Alan Wagner Westfield, New Jersey

Bosch

P A G E N + 8

Bosch is a global engineering and technology company with roughly 395,000 employees worldwide. Founded in Germany in 1886, Bosch is the world’s leading supplier of automotive components.

Currently, Bosch uses a Hardware in the Loop Vehicle Simulator to correct errors with their software and perform tests.

The system connects to specific hardware to simulate a vehicle on the road. The previous hardware, however, was quite costly and therefore only two systems were available to all Bosch engineers in North America. To resolve this, Bosch selected the PEAK PCAN USB Pro FD as a low-cost replacement for the previous hardware.

Our Hardware in the Loop system reimplements the core functionality of Bosch’s previous system on the PCAN hardware.

Our software allows the user to perform basic vehicle maneuvers and operations such as steering, braking, acceleration and more. The main functionality of our system is automatic cruise control (ACC). The user engages ACC by pressing the on button in the ACC square of the main dash view. The plus and minus buttons are used to set the speed of the vehicle.

The user interface is designed to allow anyone with driving experience to control the simulation with ease. The interface is broken up into squares that housing the controls for any given vehicle action to perform. A graph is displayable to show the signals being sent to and from the PEAK hardware with their corresponding values.

Above the controls for the vehicle is the dashboard. This displays the same basic information you would find in a real car including the current speed, rpm, fuel level and more.

The entire software system is written in Python 3. The front

end is built using the open-source toolkit wxPython while

communication with the hardware is done using PCAN Basic API.

(17)

Team Delta Dental Data Science Artwork Feedback (James)

Original Artwork Feedback

• Your artwork and layout are basically good.

• I may have resized and moved things slightly.

• Leave the artwork layout as it is in the updated zipped folder that I posted yesterday.

• You don’t need to do anything with respect to artwork for this round. Just submit back to me the new zip file that I upload for your team. Make sure that you

download the NEW zip file and submit the NEW zip file.

• Nice work!

The Capstone Experience Design Day Booklet Artwork Feedback 17

Computer Science and Engineering CSE498

Delta Dental of Michigan, Ohio and Indiana Smart Benefit Plan Recommender Engine

Michigan State University

Team Members (left to right) Nicholas Lenaghan Dearborn, Michigan Derek Nguyen Ann Arbor, Michigan Nicole Keller Sterling Heights, Michigan Arden Knoll Okemos, Michigan

Delta Dental Data Science

Project Sponsors Mukundan Agaram Okemos, Michigan Shikha Mohindra Okemos, Michigan Ayush Singh Okemos, Michigan

PAGE N + 9

Delta Dental is an insurance company that provides dental coverage to more than 80 million Americans, spanning across all 50 states, making it the largest dental care provider in the nation.

They take pride in tailoring benefit plans to their customers’

needs, whether they be a small business, a family or an individual.

Before recommending the ideal benefit plan to a customer, Delta Dental underwriters must apply their domain expertise as they sift through massive amounts of data.

Underwriters at Delta Dental gather information from potential clients, which is then used to generate a custom-tailored dental coverage plan for their customers. Creating personalized dental plans for each customer takes a significant amount of time, as there are many factors that need to be considered.

Our Smart Benefit Plan Recommender Engine aids underwriters by automatically matching new customers with benefit plans that are used by similar customers. Our model uses a combination of internal and external data to divide customers into groups that share many similarities. Each group is assigned an ideal benefit plan and when the system is given new data, it can easily provide a recommendation by mapping the new data to a group and its respective benefit plan.

Our system makes the insurance shopping experience less stressful for customers by allowing users to input their information through an easy-to-use interface and providing immediate benefit plan recommendations and links to sign up for them. Customers can access, and easily navigate, the web application using a browser on any device.

The front end of our system is written using Angular, while the

back end is written in Python. The data is stored in a Snowflake

database, and the clustering models were developed in Jupyter

Notebook using the pandas and sci-kit learn libraries.

(18)

Team Delta Dental Data Science Artwork Feedback (James)

Original Artwork Modified Artwork

The Capstone Experience Design Day Booklet Artwork Feedback 18

Computer Science and Engineering CSE498

Delta Dental of Michigan, Ohio and Indiana Smart Benefit Plan Recommender Engine

Michigan State University

Delta Dental Data Science

PAGE N + 9

Delta Dental is an insurance company that provides dental coverage to more than 80 million Americans, spanning across all 50 states, making it the largest dental care provider in the nation.

They take pride in tailoring benefit plans to their customers’

needs, whether they be a small business, a family or an individual.

Before recommending the ideal benefit plan to a customer, Delta Dental underwriters must apply their domain expertise as they sift through massive amounts of data.

Underwriters at Delta Dental gather information from potential clients, which is then used to generate a custom-tailored dental coverage plan for their customers. Creating personalized dental plans for each customer takes a significant amount of time, as there are many factors that need to be considered.

Our Smart Benefit Plan Recommender Engine aids underwriters by automatically matching new customers with benefit plans that are used by similar customers. Our model uses a combination of internal and external data to divide customers into groups that share many similarities. Each group is assigned an ideal benefit plan and when the system is given new data, it can easily provide a recommendation by mapping the new data to a group and its respective benefit plan.

Our system makes the insurance shopping experience less stressful for customers by allowing users to input their information through an easy-to-use interface and providing immediate benefit plan recommendations and links to sign up for them. Customers can access, and easily navigate, the web application using a browser on any device.

The front end of our system is written using Angular, while the back end is written in Python. The data is stored in a Snowflake database, and the clustering models were developed in Jupyter Notebook using the pandas and sci-kit learn libraries.

Computer Science and Engineering CSE498

Delta Dental of Michigan, Ohio and Indiana Smart Benefit Plan Recommender Engine

Michigan State University

Delta Dental Data Science

P AGE N + 9

Delta Dental is an insurance company that provides dental coverage to more than 80 million Americans, spanning across all 50 states, making it the largest dental care provider in the nation.

They take pride in tailoring benefit plans to their customers’

needs, whether they be a small business, a family or an individual.

Before recommending the ideal benefit plan to a customer, Delta Dental underwriters must apply their domain expertise as they sift through massive amounts of data.

Underwriters at Delta Dental gather information from potential clients, which is then used to generate a custom-tailored dental coverage plan for their customers. Creating personalized dental plans for each customer takes a significant amount of time, as there are many factors that need to be considered.

Our Smart Benefit Plan Recommender Engine aids underwriters by automatically matching new customers with benefit plans that are used by similar customers. Our model uses a combination of internal and external data to divide customers into groups that share many similarities. Each group is assigned an ideal benefit plan and when the system is given new data, it can easily provide a recommendation by mapping the new data to a group and its respective benefit plan.

Our system makes the insurance shopping experience less stressful for customers by allowing users to input their information through an easy-to-use interface and providing immediate benefit plan recommendations and links to sign up for them. Customers can access, and easily navigate, the web application using a browser on any device.

The front end of our system is written using Angular, while the

back end is written in Python. The data is stored in a Snowflake

database, and the clustering models were developed in Jupyter

Notebook using the pandas and sci-kit learn libraries.