Raspberry Pi Projects

This is course 4 of this specialization (although it can be taken out of order) and focuses on applying experience and knowledge gained in the first three courses to build physical electronics hardware. Specifically, this course focuses on four areas: circuit simulation, schematic entry, PCB layout, and 3D CAD modeling. There are many excellent commercial applications available in these areas, however to give everyone access we'll be using all free and open-source software. By the end of this course you should feel comfortable using free and open-source software to design your own printed circuit board and any bracketry or case to hold it, customized for your application. Module 1 covers circuit simulation using several open-source projects and simulation methods for simulating transient response of circuits as well as frequency-domain response of filters. Additionally, we'll use open-source filter synthesis tools to help you quickly design and simulation filters. Module 2 is all about creating professional looking electrical schematics. This is both an art and a skill and we'll cover the technical elements of using schematic entry software as well as broad concepts that are portable to any commercial application. Module 3 takes our schematic and turns it into a physical PCB design. Understanding this process of how the schematic and the PCB layout work together is critical. We'll be demonstrating this with open-source software, but again, the concepts apply to any commercial software you may have access to. Module 4 demonstrates the powerful idea of co-designing your electrical and mechanical systems together. We'll create a 3D model of our electrical PCB and bring it into 3D CAD software to design mechanical parts around it. Tying together these two applications opens another dimension in customizing your projects.

In this course you will use a Raspberry Pi 4 to build a complete network-connected project with sensors and motors and access it from your smartphone. We'll explore all the parts which make this work, so you can use this experience as a foundation for your own projects. We'll use the Raspberry Pi as an "embedded system" (as opposed to a desktop computer) so you're ready to build a Raspberry Pi into your projects as the brains that make it all work. Want to build your own Internet of Things (IoT) device? Home automation? Robotics? This is the class to learn how it all works, to get you building on your own. No experience in embedded systems, programming, or electronics is assumed, and optional bonus sections are provided for those who want a fast start in Python programming, Linux essentials, and basic electronics. The course is divided into four modules to explore each focus area with demontrations and extras along the way: 1) installing and configuring a Raspberry Pi, 2) accessing the Raspberry Pi over the network, 3) programmatically controlling external sensors and motors, and 4) accessing the embedded device through a web interface. After these four modules you'll get started building your own projects right away, and the three follow-on courses in this Coursera specialization dive into each area to really boost your skills and the complexity of your projects. I hope you enjoy all the courses and I hope you take your builds to the next level.

This course on integrating sensors with your Raspberry Pi is course 3 of a Coursera Specialization and can be taken separately or as part of the specialization. Although some material and explanations from the prior two courses are used, this course largely assumes no prior experience with sensors or data processing other than ideas about your own projects and an interest in building projects with sensors. This course focuses on core concepts and techniques in designing and integrating any sensor, rather than overly specific examples to copy. This method allows you to use these concepts in your projects to build highly customized sensors for your applications. Some of the ideas covered include calibrating sensors and the trade-offs between different mathematical methods of storing and applying calibration curves to your sensors. We also discuss accuracy, precision, and how to understand uncertainty in your measurements. We study methods of interfacing analog sensors with your Raspberry Pi (or other platform) with amplifiers and the theory and technique involved in reducing noise with spectral filters. Lastly, we borrow from the fields of data science, statistics, and digital signal processing, to post-process our data in Python.