Most of us know that 32 degrees Fahrenheit is the temperature at which water freezes. We also know that this correlates to 0 degrees Celsius. But for many of us, our knowledge stops there and to convert between the two scales we must resort to either looking it up using some sort of app or calculating it by hand using a formula that in involves multiplying by either nine-fifths of five-ninths and adding or subtracting 32 and we often have to lookup the formula to remember which.

I was first exposed to Newton's Method in my undergraduate numerical methods course. I've used it a number of times since then in undergraduate and graduate courses, but I didn't begin to gain an appreciation for its power until my recent navigation systems course. To take a take a dive into Newton's Method and deepen my understanding of it, I spent a day tinkering with various Newton's Method implementations to solve different type of problems.

I wanted to put together a collection of engineering and technical resources. I'll make this post an ongoing project and update it as I find new things. For now, I'll post an initial list of resources I've used and I'll add more as I remember or discover them.

One of my homework problems for my modern physics course asked me to calculate the highest velocity of a particle for which the Newtonian approximation of kinetic energy $\frac{1}{2}mv^2$ is accurate to within an error of $0.5\%$ of the actual, relativistic, kinetic energy.

Abstract. As an undergraduate, I remember many occasions where I needed to become proficient in a topic as a step to achieving some further goal. It still happens to me today and I see it happening to my students. This post on Arduino is for people in that situation. You need to build the skill, but diving deep into it isn’t your priority and you just want to learn enough to get by for now. With that in mind, this post deliberately glosses over many of the details, but tries to provide links wherever possible to allow to dive deeper on the topics that either don’t quite sink in right away or that you want to learn more about.

Here, a derivation of the Tsiolkovsky rocket equation from Newton's Second Law is shown. As a disclaimer, this is not original work and is not intended to be. It is shown for reference purposes. In fact multiple derivations of the equation can be found here.

My absolute favorite feature of Python is lists. An elegantly dynamic data structure with a beautiful syntax. Everything from negative step indexing to their use as iterators makes Python lists, and therefore Python, the easiest solution to so many problems. One of the less obvious, but extremely powerful features of Python lists are list comprehensions.

This is my second post with a handful of pastable commands to improve your use of Bash. If you haven't seen the first post, you can check it out here. This one is all about GREP. the GNU Regular Expression Parser. A command line utility that has nearly infinite uses and applications.

Abstract. This article provides some simple "pastable" Bash examples to do some useful things.

My website is constantly undergoing changes. I'm constantly finding things I want to do better, cleaner, simpler. This post details my change from hosting my site on Amazon Web Services to GitHub Pages using Jekyll.