MHB Synthesizing Functions using K-maps

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SUMMARY

The discussion focuses on synthesizing the function $f$(x1,x2,x3) using Karnaugh maps (K-maps) for minimization. The truth table provided indicates that $f$ can be expressed as $$\sum$$m(0,3,5,6), leading to the initial sum of products: x1!x2!x3! + x1!x2x3 + x1x2!x3 + x1x2x3!. The consensus among participants is that the function is already in its simplest sum of products form, and no further simplification is possible through K-map grouping. Diagonal clumping is explicitly noted as not permissible in K-map methodology.

PREREQUISITES
  • Understanding of truth tables and their construction
  • Familiarity with Karnaugh maps (K-maps) for function minimization
  • Knowledge of Boolean algebra and sum of products (SoP) form
  • Basic skills in digital logic design
NEXT STEPS
  • Study the principles of Karnaugh map grouping techniques
  • Learn about Boolean algebra simplification methods
  • Explore advanced digital logic design concepts
  • Practice synthesizing functions from various truth tables
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Students and professionals in digital logic design, electrical engineering, and computer science who are looking to enhance their understanding of function synthesis and minimization techniques using K-maps.

shamieh
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For a timing diagram - synthesize the function $f$(x1,x2,x3) in the simplest sum of products form.

So I have a picture of this timing diagram, which I can't really show on here unless i physically took a picture and uploaded it, but it's really irrelevant because I know I have the correct truth table, so hopefully we can work with that.

So my Truth Table reads:

  1. x1 x2 x3 | f
  2. 0 0 0 | 1
  3. 0 0 1 | 0
  4. 0 1 0 | 0
  5. 0 1 1 | 1
  6. 1 0 0 | 0
  7. 1 0 1 | 1
  8. 1 1 0 | 1
  9. 1 1 1 | 0

So now I know I have $f$(x1,x2,x3) = $$\sum$$m(0,3,5,6)

Which means I have:

x!x2!x3! + x1!x2x3 + x1x2!x3 + x1x2x3!

So I need to put this function in the simplest sum of products form.. So I'm assuming i need to minimize the function that I just got above? If I am on the right track- then I now need to use a K-Map to find the minimization.

So here it goes.. (This is my K-Map)

... x2 x3
.. 00 01 11 10
x1 0[1) 0 1 0]
.. 1[0 1 0 (1]

So my question Is what now? How should I group all these 1s? Just group each of them by themselves? And if so, How do I read off what is going on here?
Would I read it like this ? x1!x2!x3! + x1x2!x3 + x1!x2x3 + x1x2x3! ?
Thanks for your time.

If this is something you can't explain or think I should just read more up on, please let me know, because I can take constructive criticism. I just want to make sure I know how to do these.
 
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Yeah, diagonal clumping isn't allowed on K-maps. There's no simplification possible for that function, and you already have the simplest SoP. That's what I say.
 
Your help is greatly appreciated. If only you guys knew how much you really help me. I seriously live on this forum - thanks to teachers who machine gun through chapters and T.A.s who can barely speak English. You all are very helpful. Appreciate everything you have supplied and helped me with.

Sham
 
shamieh said:
Your help is greatly appreciated. If only you guys knew how much you really help me. I seriously live on this forum - thanks to teachers who machine gun through chapters and T.A.s who can barely speak English. You all are very helpful. Appreciate everything you have supplied and helped me with.

Sham

Thanks very much for those kind words! I can assure you, it works both ways. When we get courteous users who ask interesting questions, that makes it all worth-while!
 
We have many threads on AI, which are mostly AI/LLM, e.g,. ChatGPT, Claude, etc. It is important to draw a distinction between AI/LLM and AI/ML/DL, where ML - Machine Learning and DL = Deep Learning. AI is a broad technology; the AI/ML/DL is being developed to handle large data sets, and even seemingly disparate datasets to rapidly evaluated the data and determine the quantitative relationships in order to understand what those relationships (about the variaboles) mean. At the Harvard &...

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