"The Art of Insight in Science and Engineering: Mastering Complexity" by Sanjoy Mahajan.
It beautifully treats estimation and problem solving techniques, illustrated by examples from science and engineering. Instead of aiming for a complete, thorough and accurate treatment of problems, its goal is to teach shortcuts to sacrifice some accuracy for much reduced effort. This is a refreshing change to academia where rigor is often pursued at all cost. But in the real world rigor rarely matter, and simplifications are almost always worthwhile, especially initially since we can always refine models if required.
I first read it as an undergraduate and use the estimation and problem solving techniques from it almost daily. Though well hidden, the pdf is available for free on the website of the publisher.
Yes, it can easily correspond to a BPF, for example when trying to track a sinusoidal signal, say the AC line frequency. Then you can be reasonably certain that it will be near 50 Hz (or 60 Hz), and the resulting Kalman filter will be a sharp band-pass filter centered around that (when neglecting higher harmonics).
The 4th derivative is quite important for good motion control where it is usually called 'snap'. Specifically, it is relevant both for feedforward control design [1] and trajectory planning [2]. As shown in the latter, it is advantageous to design trajectories based on segments of constant snap. Consequently, also including 'snap' in the feedforward signals makes the achieved position profiles notably smoother.
For electronics, my vote goes to various spectacular application notes from the electronics industry that have stood the test of time. In comparison to the usual literature (textbooks, papers, etc.), these are often laser-focused on helping the user, often at a holistic level, including practical issues. This is a rare case where the incentives of the readers and producers are well aligned:
In order to sell their products, manufacturers need to teach their prospective customers enough to use their products adequately. If a product is good, but a customer makes (potentially silly) mistakes in using it, both the customer and the manufacturer lose -- which is exactly what application notes are intended to counteract.
# Example 1: Old HP application notes
Quote:
"In a real sense, Hewlett-Packard sold MEASUREMENTS as well as products. According to one marketing professional, when you go to a hardware store to buy a 5mm drill bit, what you really want is a 5mm hole. So, likewise, as HP developed their massive line of innovative measurement instruments, the customers often had to be educated in the newer processes of the new measurement techniques, permitted by the newest product."
I'm too young to have experienced the heyday of HP as a test & measurement company, but they produced spectacularly good material. Many of their application notes introducing the fundamentals of a field such as spectrum analysis, signal analysis, modal testing etc. remain excellent introductions even today, despite being decades old and thus predating my birth. I've thoroughly enjoyed the following ones (amongst others):
# Example 2: LTC application notes, especially by Jim Williams
A big chunk of my electronics knowledge comes from data sheets and application notes. The application notes by Jim Williams (RIP) stand out to me. Jim obviously was very gifted, but always sides with the (probably much less skilled) reader, making complicated material accessible. He always retains a holistic picture, and also addresses many practical aspects one can easily stumble upon. He does it all with a minimum of math, a maximum of intuition, and a great sense of humour.
While there are many dozens of application notes by him, I particularly like the following one:
It beautifully treats estimation and problem solving techniques, illustrated by examples from science and engineering. Instead of aiming for a complete, thorough and accurate treatment of problems, its goal is to teach shortcuts to sacrifice some accuracy for much reduced effort. This is a refreshing change to academia where rigor is often pursued at all cost. But in the real world rigor rarely matter, and simplifications are almost always worthwhile, especially initially since we can always refine models if required.
I first read it as an undergraduate and use the estimation and problem solving techniques from it almost daily. Though well hidden, the pdf is available for free on the website of the publisher.