Wednesday, December 7, 2022

Wednesday Linkorama!

Round up of things I found interesting.

The photo above came from a Gizomodo article about NASA’s Latest Artemis 1 Moon Images. Highly recomed you take a look!

San Francisco narrowly avoids killer robots! Look like the city was thinking about deploying police robots that carried explosives.

How damn became darn. The explanation of how and why use substitute swears.

Sunday, December 4, 2022

The Periodogram or Something Else?

The periodogram is the simplest and most common spectral estimator and, in R is implemented as

findPer <- function(x){(1/length(x))*abs(fft(x))^2},

but there are may othere spectral estimators*.

So, which spectral estimator should you pick? The flow chart below presents a reasonable way to pick between spectral estimators. The first question you should ask is, do you want to assume a model for the data? If you don’t know anything about the data or don’t trust what you know, then move to the left side of the flow chart. If the Fourier methods have enough resolution, use the periodogram or the Blackman-Tukey spectral estimator (BTSE). If they don’t, you could try the minimum variance spectral estimator (MVSE). If the MVSE does not have enough resolution, try a high order autoregressive (AR) estimate. The AR method does assume a model for the data, but a high enough AR model can estimate any PSD.

If you want to assume a model, we move to the right side of the flow chart. If we know the data consists of sinusoids in noise, then the multiple signal clarifier (MUSIC) estimator is a good choice. If the important features are peaks, but the data is not made up of sinusoids in noise, the AR estimator is a good choice. If nulls are the important features, then use a moving average (MA) estimator. If the important features are nulls and peaks, use the autoregressive moving average (ARMA) estimator. Finally, if you have no idea about the PSD shape and want to use a model, you could try a high order AR.

* For more info on spectral estimation, see my new book! -> Spectral Density Estimation by Example

Wednesday, November 30, 2022

Wednesday Linkorama

Round up of things I found interesting.

Space elevators have been a part of SiFi for a long time, this article talks about possibly building them for real! Space elevators are cables that go from the ground into space that a vehicle can use to climb into space. This article describes the basic ideas about how they work and why you may want to use one instead of rockets.

Crypto is starting to get the attention of regulators.

Diplomacy is a classic social board game focused on trickery and negotiations between players. The first games computers got really good at (like chess) required a lot of calculation. This article talks about Cicero, an AI that (allegedly) has human level performance in the game Diplomacy. So, computers may be getting better at other types of games.

Wednesday, November 2, 2022

Wednesday Linkorama

Round up of things I found interesting.

A Reddit user bought an old Netflix server and reconfigured it for personal storage.

New compression claims 10x over MP3. MP3 is already compressed so ... If this is 10x and sounds as good then they really have something.

Normally autonomous trucks are 5 years out, but Aurora says they are coming in 2024. So, maybe they are on to something?

Wednesday, October 26, 2022

Wednesday Linkorama

Round up of things I found interesting.

gsignal is an R package that is an improvement over the signal package. The signal package is a group of signal processing functions like you might find in Matlab. It was helpful but was missing some common functions, and some functions did not play well with complex numbers. The gsingal packaged added some key functions and works with complex numbers. It has the poly function but seems to be missing the roots function.

Ventura, the new mac operating system, is available. I'm going for it. Wish me luck!

Copilot is an app that helps write code. It uses what you wrote to suggest code snippets as you write the code. It looks interesting; I'm going to give it a try.

Tuesday, October 25, 2022

My new book is published!

This book provides the tools to use, understand, and apply power spectral density (PSD) estimation. By the end of this book, you will be able to select an appropriate PSD estimation method based on your data, perform the estimation, and understand the results. To accomplish this, we cover the methods and just enough theory so you can use the estimation techniques correctly and interpret the results meaningfully.

Get it here -> Spectral Density Estimation by Example

November Fog