I am learning pbr theory by the document of filament.
In the Energy gain in diffuse reflectance section, it add a [TODO: talk about the issue with fr+fd].
I can't find any resources about this.
Can anyone tell me about this? Or give me some resources to learn.
Thanks.
Related
The mediapipe library has been working well for our project so far, however, the only problem we have is the hip joint points are too low and we need them higher. There isn't a way of getting them higher right? I'm assuming that's just the way it is.
For a better explanation, please read here:
https://github.com/swisstackle/fp/issues/1
If there isn't a way of fixing this with mediapipe, do you guys know any other libraries where the hip points are higher up?
Dear members and seniors,
Hope you all are well. Does anyone know whether we could arrange base R plot and ggplot in R and could save high resolution (300 dpi) for publication. I tried to do it, but not work. So asking in case anyone knows and could share example.
Kind Regards,
synat
I have a task: to determine the sound source location.
I had some experience working with tensorflow, creating predictions on some simple features and datasets. I assume that for this task, there would be necessary to analyze the sound frequences and probably other related data on training and then prediction steps. The sound goes from the headset, so human ear is able to detect the direction.
1) Did somebody already perform that? (unfortunately couldn't find any similar project)
2) What kind of caveats could I meet while trying to achieve that?
3) Am I able to do that using this technology approach? Are there any other sound processing frameworks / technologies / open source projects that could help me ?
I am asking that here, since my research on google, github, stackoverflow didn't show me any relevant results on that specific topic, so any help is highly appreciated!
This is typically done with more traditional DSP with multiple sensors. You might want to look into time difference of arrival(TDOA) and direction of arrival(DOA). Algorithms such as GCC-PHAT and MUSIC will be helpful.
Issues that you might encounter are: DOA accuracy is function of the direct to reverberant ratio of the source, i.e. the more reverberant the environment the harder it is to determine the source location.
Also you might want to consider the number of location dimensions you want to resolve. A point in 3D space is much more difficult than a direction relative to the sensors
Using ML as an approach to this is not entirely without merit but you will have to consider what it is you would be learning, i.e. you probably don't want to learn the test rooms reverberant properties but instead the sensors spatial properties.
I am a university student and trying to write a program to solve 1-D Schrodinger's equation with some kinda of potential equations.
I am not a CS major so really have no clue to start.
I did research online but didn't find thing that is suitable for entry level :( The only thing I understand now is that I probably need to use some algorithm solving differential equations
Could anyone give me some suggestions or references on how I could start? For example, how to transform the physical problem into computer science program and what kinda algorithm I should look for?
I am interested in computational so trying on this HARD problem ;)
Thank you all!
go to www.google.com
search for :how can i solve Schrodinger in matlab
go to here
Return stackoverflow
This is not hundred percent programming related question, but I was not able to find answer on the net.
Is there some kind of detector to record frequency/intensity of light radiation source? something like spectroscopy detector, but instead of actual machine, just the module which can be integrated in project. I have tried searching on Google but I do not even know what such device is called
if you know the more appropriate place to ask, can you let me know please.
Thank you
As far as I know, no sensor exists to directly measure the frequency of a visible light source.
The final detector of automated spectroscopes is generally a (typically linear) CCD.
The other parts of the spectroscope disperse the light into a rainbow-like spectrum, so reddish photons hit pixels towards one end of the CCD, and bluish photons hit pixels towards the other end of the CCD.
If you only want to discriminate a few frequency bands (rather than a high-resolution spectrum of hundreds of frequency bands), then things are much simpler -- you can either use a few color filters, or you can use a few LED of different colors.
"Think Small Revisited: Handheld Spectroscopy" by John Coates 2007
"Handheld Spectroscopy" from Ocean Optics
Andrey, did you consider asking in the group of W.S. Jenks at the ISU? They might have a portable OceanOptics spectrometer or know somebody who has.
To get on-topic again, these nice thingies can be controlled though a Java-based framework.