Comments URL: https://news.ycombinator.com/item?id=48818570

Points: 2

# Comments: 0

I wonder what would be the result if they used a matrix that is orthogonal and closest to the source matrix. Usually one uses the Frobenius norm (root of the sum of all squared matrix entries). Maybe, one could even try another norm that gives a sparser matrix.

Is signature checking (much) cheaper than finding an opaque session ID in a database?

The CRB shows that for estimators (algorithms) that on average estimate a the correct value (unbiased estimators), there is a lower bound on the accuracy. Accuracy refers here to the variance (or equivalently to the mean squared error).

So, whatever fancy unbiased algorithm you will come up with, its average variance cannot be lower than the CRB.

So, the CRB tells you something about your measurement setup at hand. It can even show possibilities to improve estimation of certain parameters without being specific about the used algorithm.

The multi-parameter CRB gives a kind of "minimal" covariance matrix whose uncertainty ellipsoid is always inside the uncertainty ellipsoid of any unbiased multi-parameter estimator.

Comments URL: https://news.ycombinator.com/item?id=48293712

Points: 1

# Comments: 1

There are also some other PlugIns like TeXsword. But I have not tested them. And newer MS Office versions seem to support Latex macros directly. [2]

[1] https://github.com/Jonathan-LeRoux/IguanaTex

[2] https://support.microsoft.com/en-us/office/linear-format-equ...

I am using type hints in Python as much as possible for my hand-coding. And it catches a lot of bugs (especially during code refactoring) that I would not have noticed so easily.

To measure discrete events, I would prefer as unit "events per second" instead of Hz.

There is a problem in general: Units do not refer to what they count. For instance, when you have dashed road markings and you want to quantify the length of the dashes per road length you get as unit "meters per meters". And then people go and say this is unitless since the units "cancel". But for me, they do not: It stays "dash meter per road meter".

An alternative would be to construct a new function (or matrix) that is not only periodic in azimuth, but also in elevation (i.e., extend elevation to a full circle -pi to +pi). Then, one can simply compute two independent Fourie r transforms: along azimuth and along elevation. [1] The same idea works on matrices using the Discrete Fourier transform (DFT/FFT). However, you then have to accept things like that your data points are all equal at the poles.

[1] https://en.wikipedia.org/wiki/Double_Fourier_sphere_method

I liked my job at the university - independent of the final PhD. I enjoyed what I was doing. Most of the time I also enjoyed writing my dissertation, since I was given the opportunity to write about my stuff. And mostly I could write it in a way how I felt things are supposed to be explained.

However, I switched to Python during the last years.

Remote: yes

Willing to relocate: no

Technologies:

Digital signal processing, linear algebra, array signal processing, radar, C++, C#, Python, JupyterLab, Octave/Matlab, Git, Jenkins etc.

Résumé/CV:

1) PhD in direction of arrival estimation using antenna arrays

2) 8+ years as senior and/or lead developer in different environments (e.g. Music recognition, embedded devices)

3) Currently researcher in the field of Integrated Communications & Sensing (ICAS) bringing together wireless communications and radar functionalities

Email: quaternion {at} web {dot} de

Remote: Yes

Willing to relocate: No

Technologies: - Digital signal processing: linear algebra, estimation theory, statistical/Bayesian signal processing, compressed sensing and others - Programming: C++, C, C#, ARM assembly, AVX/SSE, Python, Bash, PowerShell 5/7.x, GNU Octave, Matlab - Used libraries: Boost (C++), ffmpeg, gSOAP, gRPC, libcurl, NumPy, SciPy, Pandas, PyTorch and others - Target platforms: x64, ARM Cortex M0/M3 - Tools: Git, Mercurial, SVN, CMake, GNU Make, Jenkins, Gitlab, rpmbuild - OS: GNU Linux (eg., Arch), Windows

Résumé/CV: - PhD and diploma in Electrical Engineering and Information Technology. - PhD study about digital signal processing applied to antenna arrays.

I am a software engineer and researcher with a strong background in digital signal processing. I worked in the field of music recognition (C++ & Python), in embedded systems (esp. ARM Cortex) and in the communication between control centers (ITCS systems) and public transportation vehicles. Currently I am into Integrated Communications and Sensing (ICAS) which brings together Radar and mobile communications.

I like to solve challenging problems - via software engineering, via signal processing, or via a combination of both. Linux and algorithms are topics I have a strong interest in. While I educate myself regarding other technologies like transformers and deep neural networks (e.g. applied to OFDM), and cyber security ("playing" OWASP Juice Shop).

In past jobs, I worked as a Senior Software Engineer and as a Lead Software Developer, respectively. Currently, I am a post-doc researcher at a German university.

Email: quaternion [at] web [dot] de

The problem is that your code has to work within this abstraction and can only solve problems covered by the inventors of the abstraction.