I’ve been trying to simulate a circuit I found online using the MultiSim circuit simulator, which I’ve come to appreciate and find pretty cool. One of the components in this circuit is a 78L10AC voltage regulator, which outputs 10V if the current drawn by the load circuit is up to 100mA. Unfortunately, MultiSim does not have any representative in the 78L10 family of voltage regulators. To be fair, it does have regulators for 5V, 8V, 9V, 12V, and many others, but not 10V. I spent some time searching online for a SPICE model for 78L10, but nothing promising came up. I also tried inferring the SPICE model from the 78Lxx datasheet from Texas Instruments, but unfortunately, the datasheet leaves most resistor values unspecified, most likely to account for the different models of the 78Lxx family, so, there were too many unknown variables to replicate the original design. Then, Anqi pointed me to the configurable LM317 voltage regulator and in particular to this fun tutorial by Sparkfun’s Pete Dokter on how to use it. It turns out that you can use LM317 and its low-power version, LM317L, both of which are supported in MultiSim, to output a wide range of voltages. So, here’s a 10V voltage regulator with output current up to 100mA:
Friday night in Montreal, with the streets being almost blocked by a snowstorm that left us half a meter of snow between our door and the outside world. It is also a night in which my next-door neighbours have decided to test the full extent of their speaker’s bass capabilities — currently playing a remix of Lana Del Rey’s Blue Jeans. It’s impressive to hear that they are accompanying the song during its high notes; perhaps it’s time to throw that neighbourhood karaoke party after all.
What’s also impressive is that this note has diverged so early on, from its first paragraph. This note is actually about a literature review of a set of pattern recognition and robotics papers, whose existence I think is important to know. I wrote this review as a partial requirement for my upcoming Ph.D. comprehensive exam at McGill. The purpose of the literature review is to summarize ~20 papers related to your research area, and the committee is free to ask whatever question they wish around these papers and their references, trying to see exactly what are the boundaries of the student’s knowledge and how this knowledge has been acquired: by memorization or by actual understanding. If the student is unclear about a method or an algorithm, this committee, which consists of some of the most experienced professors in the department, will most likely detect it and dig deep enough with questions to see exactly what the student does not understand and why.
My review document covers some methods in these broad topics:
- Bayesian filtering and estimation
- Sampling-based path planning
- Classification and ensemble methods
- Active vision
The summary is quite dense, and admittedly one could spend an entire document focusing on one of these areas. I though it was going to be more fun though to write a compact document. I should clarify that this document has not been reviewed yet by the committee, so I haven’t received much feedback on it, and I don’t know exactly how many errors it contains. Notwithstanding that, I thought it is useful enough to share.
If you are searching for a new activity this semester, and you think you want to give horseback riding a chance, then you can go to the McGill Athletics website and sign up for 1 of 12 available slots. The lessons take place at a school called Equitation Elysee, in a farm in St. Lazare, one-hour drive away from downtown Montreal.
Commuting there by public transportation is next to impossible, but if you don’t have a car, the instructor will put you in contact with other students who do, so you can share the ride. The students range from 5-year olds to 70-year olds, and a previous background is not assumed. The instructor, Jo Sweet, is very experienced, she will tell you when you’re doing things wrong and will help you individually to improve your technique. A typical lesson starts with the student grooming the horse in the stables before the ride, then guiding the horse to the arena for the exercises. I should mention that the purpose of the lessons is to control the gait and speed of the horse, and eventually perform jumps; not to simply go riding along trails. The arena is cool though, and there is classical music playing while you’re training. The school runs all year round, and you can contact Jo even if you’re not a McGill student.
One of the courses I was taking during my third year of undergraduate studies was a full-year Abstract Algebra course, covering among others ring theory and Galois theory in great detail. The course was a requirement for my program, and it was one of the few courses that I was not sure at all I’d enjoy. It turned out that many of my friends in Math enjoyed it, but I didn’t. I felt no, or very few connections with Computer Science (later I discovered through friends that if you study advanced complexity theory and theoretical Computer Science it will be very useful), and as a result I started feeling unmotivated.
At some point during the same year I found a book called “Mathematics and Technology” by Christiane Rousseau and Yvan Saint-Aubin. This book was different, extremely refreshing and motivating. Each chapter is a technological problem that has been solved via applied mathematics. For instance, it presents the math behind: the trilateration of the GPS, the motion of a robotic arm in 3D, error correcting codes, public-key cryptography, Google’s PageRank algorithm, why MP3‘s are sampled at 44.1Khz, the JPEG compression standard, the DNA computer (super-cool chapter!), and many other applications. The book is simply a collection of really interesting problems, accompanied by the mathematical background and principles that allow the reader to understand the basic solutions to those problems. It’s not a perfect or comprehensive book by any means, but it is a beautiful one, in the sense that every chapter is a nicely-told story and the provided theory is strongly tied to each application. I think the mathematical maturity it requires is at most that of a 3rd year undergraduate student in math, though I’m sure you could understand most of it with a basic Calculus and Linear Algebra background.
Richard Hamming’s “You and your research” is an essay that is often recommended reading for graduate students, as a means of advice from an accomplished scientist. I agree with most of the points he raised: work on the important problems in your field, get the courage to try to solve them, be prepared, be patient and positive etc. There is one point that he made though, which has really bothered me from the day I first read the transcription of his speech:
I had incipient ulcers most of the years that I was at Bell Labs. I have since gone off to the Naval Postgraduate School and laid back somewhat, and now my health is much better. But if you want to be a great scientist you’re going to have to put up with stress. You can lead a nice life; you can be a nice guy or you can be a great scientist.
This really doesn’t make much sense to me. Why does willingness to compromise your health make you a more dedicated scientist? Is a sick or dead scientist better than a healthy one? Is constant stress and fear a prerequisite for being productive and creative? I don’t think they are.
I think I prefer Emerson’s “The American Scholar“.