Brian Mulyadi
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Brian Mulyadi

Touchless Chess Clock β™ŸπŸ•°

If you are not familiar with the chess clock, it consists of two countdown timers to track the amount of time left for each player to make a move. Every time a player makes a move, they will press the button to pause their time and resume the opponent's time. Once your time is up, you lose the game!

The requirements for this product:

  • It has to show two countdown timers

  • The user can set the time before starting the timer

  • Player A's timer starts when the program is run

  • When Player A's button is pressed, the countdown timer for A stops and the countdown timer for B starts at the same time. The color of the boxes also has to be swapped.

  • When one player's timer reaches 0, show "Game Over" message

Storyboard and setup

To extend the functionalities of the chess clock, I plan to incorporate some of the sensors available and connect them to the Raspberry Pi.

I use the Proximity Sensor and the SparkFun Qwiic Button for this project.

Here is how it is in action:

Shout out to Panda Xu and Joshua Schmidt for the inspiration at Designing Interactive Devices class.

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Designing Data Products

As a part of our final project, we aim to analyze the likelihood of a Congress Bill to be passed given a set of variables.

Understanding the increasingly important process of forming regulations and laws in the country, we decided to explore the possibility in determining the likelihood of a proposed bill being passed by the House. This exercise can be useful for lobbyists, corporations in formulating political strategies, or even the general public during voting periods.

Problem Statement:

To discover relevant factors that can help to predict the likelihood of a Bill being passed in the House

Dataset used in this exercise comes from

Member - Details of Congresspeople in the House in each session with supporting information on leanings, gender, time served on the House

Bills - Details on the bills originating in the House in each session with supporting information on policy areas, sponsor, cosponsors, date of introduction, outcome of bill etc.

Feature Vectors

  • Policy area

  • Is the bill introduced by a Congressperson from the majority house?

  • Number of cosponsors

  • % of cosponsors from the majority house

  • Is the sponsor in a leadership role?

  • Political leaning of the sponsor Seniority of the sponsor

  • % of votes against the party (sponsor)

Using the different models and optimization methods, we produced the following result:

Main Findings

By observing the best model, we can draw several key insights as follows:

  • Higher polarization leads to a higher success/fail rate of passing a bill

  • Number of cosponsors in majority help in passing a bill

  • Certain policy areas have higher likelihood of being passed (i.e. National Security, Emergency Management, etc.)

Use cases

Law Firms and Clients - Consider a world where law firms are able to reach out to their clients and alert them that a bill is more likely to pass and explain how it would impact their business.

Corporations & Lobbyists - Our model has the ability to recreate how companies interact with Washington, helping them make decisions about investing efforts in the political process.

Societal Impact - Citizens will have the ability to stay informed about important bills impacting their personal lives like health and employment related bills. Political outcomes can be volatile and impact financial markets. Using our model, the prediction can promote stability.

Read more about the project

Designing Data Products
2 min read