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結果:


Martin Ryba
Martin Ryba
最後のアクティビティ: 2023 年 12 月 12 日

Seeing a colleague make this mistake (one I've had to fix multiple times in other's work too) makes me want to ask the community: would you like the awgn() function/blocks to give the option for creating a SNR at the bandwidth of the signal? Your typical flow is something like this:
  • Create a signal, usually at some nominal upsampling factor (e.g., 4) such that it's now nicely over sampled, especially if you're using a RRC or similar pulse shaping filter.
  • Potentially add a frequency offset (which might make the sample frequency even higher)
  • Add AWGN channel model for a desired SNR
  • Put this into your detector/receiver model
The problem is, when someone says, "I'm detecting XYZ at foo SNR," it should not magically improve as a function of the oversample. The problem isn't that awgn() generates white noise, that's what it's supposed to do and the typical receiver has noise across the entire band. The problem is that SNR is most properly defined as the signal power over the noise power spectral density times the signal's noise equivalent bandwidth. Now I looked and there's no handy function for computing NEBW for an input signal (there's just a function for assessing analysis windows). In practice it can get a bit tricky. The occupied bandwidth or HPBW are often close enough to the NEBW, we're usually not haggling over hundredths of a dB. So, in my not so humble opinion, the "measured" flag for awgn() should give an option for bandwidth matching or at least document the behavior better in the help page. All too often I'm seeing 3-6 (or worse) dB errors because people aren't taking the signal's bandwidth into account.
David
David
最後のアクティビティ: 2023 年 12 月 1 日

Unlike last year's contest, there are some new technologies this year that might offer some advantages. Namely generative AI's like ChatGPT, Bard, etc. Not to be excluded, MathWorks just launched the AI Chat Playground :)
Chen Lin
Chen Lin
最後のアクティビティ: 2023 年 11 月 10 日

The 2023 community contest - MATLAB Flipbook Mini Hack - starts today on Nov. 6th!
Participants across all skill levels are welcome to join! You can participate by creating a new animation or remixing an existing one with up to 2,000 characters of MATLAB code.
Contest Tips:
  1. Before you start, we highly recommend you check out the two examples - Bouncing and Spinning - to understand how the contest works.
  2. Share your thoughts, ask questions, or connect with others in our contest discussion channel.
Note that the first week (Nov. 6th, 2023, ~ Nov. 12th, 2023) is for creating entries only. Voting does not begin until the second week.
We look forward to seeing your creative work. Let the contest begin!
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 31 日

Just in time for Halloween.
Chen Lin
Chen Lin
最後のアクティビティ: 2024 年 3 月 12 日

Share your fun photos in the comments!
Julian
Julian
最後のアクティビティ: 2024 年 2 月 5 日

I know the latest version of MATLAB R2023b has this feature already, put it should be added to R2023a as well because of its simplicity and convenience.
Basically, I want to make a bar graph that lets me name each column in a basic bar graph:
y=[100 99 100 200 200 300 500 800 1000];
x=["0-4" "5-17" "18-29" "30-39" "40-49" "50-64" "65-74" "75-84" "85+"];
bar(x,y)
However, in R2023a, this isn't a feature. I think it should be added because it helps to present data and ideas more clearly and professionally, which is the purpose of a graph to begin with.
Matt J
Matt J
最後のアクティビティ: 2024 年 3 月 1 日

Would it be a good thing to have implicit expansion enabled for cat(), horzcat(), vertcat()? There are often situations where I would like to be able to do things like this:
x=[10;20;30;40];
y=[11;12;13;14];
z=cat(3, 0,1,2);
C=[x,y,z]
with the result,
C(:,:,1) =
10 11 0
20 12 0
30 13 0
40 14 0
C(:,:,2) =
10 11 1
20 12 1
30 13 1
40 14 1
C(:,:,3) =
10 11 2
20 12 2
30 13 2
40 14 2
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 11 月 10 日

Wait for Walter, the rest of us are mere users.
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

MATLAB Training
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

MATLAB Training
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

Mathworks tech support
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

Stand Back. I'm going to try MATLAB.
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

Embarassed by Walter Roberson
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

I use MATLAB.
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

MATLAB Reloaded
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日

MATLAB rule!
Image Analyst
Image Analyst
最後のアクティビティ: 2023 年 10 月 16 日