Model-based signal processing
Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and...
সংরক্ষণ করুন:
| প্রধান লেখক: | |
|---|---|
| বিন্যাস: | গ্রন্থ |
| ভাষা: | Undetermined |
| প্রকাশিত: |
Hoboken, N.J.
IEEE Press
2006
|
| বিষয়গুলি: | |
| ট্যাগগুলো: |
ট্যাগ যুক্ত করুন
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
|
| Thư viện lưu trữ: | Trung tâm Học liệu Trường Đại học Cần Thơ |
|---|
| সংক্ষিপ্ত: | Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool. |
|---|