Signal and data processing

In summary, signal processing involves manipulating and analyzing signals to extract useful information, while data processing involves converting raw data into meaningful information. These techniques have a wide range of applications, including image and audio processing, telecommunications, medical imaging, and financial analysis. Various techniques are used in signal and data processing, such as digital filtering and machine learning, depending on the type of data and desired outcome. These methods play a crucial role in scientific research by enabling the detection of patterns and trends that would be difficult to identify manually, leading to new discoveries and advancements in various fields of science.
  • #1
amro
1
0
>Hii everybody
Am having some problems and facing some dificulties concerning, the topic of operational amplifiers.
More precisly:
- feeding back with the inverting and non inverting terminlas, to get a ten times amplification for my input signal(DC coupled).
- feed back to get an integral over dt.
- feed back to get 10 times amplification.


i mean i did not find any good script that talk about these topics for the OP-amps.

May someone help me

i ll be so thankfull
 
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  • #2
"focus.ti.com/lit/an/sloa058/sloa058.pdf"[/URL]
 
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  • #3


Signal and data processing involves the manipulation of signals and data in order to extract useful information and make decisions. In the context of operational amplifiers, signal processing refers to the amplification and filtering of signals, while data processing involves the conversion and analysis of data.

In your case, it seems like you are specifically struggling with the concept of feedback in operational amplifiers. Feedback is a key concept in signal processing, as it allows for precise control and manipulation of signals. In the case of operational amplifiers, feedback can be used to achieve specific amplification or integration of signals.

To achieve a ten times amplification for your input signal, you can use a feedback loop with the inverting or non-inverting terminals. By adjusting the feedback resistor values, you can control the amplification factor. Similarly, for an integral over dt, you can use a feedback loop with a capacitor to integrate the input signal over time.

I understand that finding good resources on these specific topics can be challenging, but there are many resources available online such as textbooks, tutorials, and forums that can help you understand these concepts better. Additionally, reaching out to your peers or seeking help from a mentor or professor can also be beneficial.

Remember, signal and data processing can be complex, but with patience and persistence, you can overcome any difficulties you may face. I wish you the best of luck in your studies and research.
 

1. What is signal processing?

Signal processing is the manipulation and analysis of signals, which are representations of physical quantities that vary over time or space. This can involve techniques such as filtering, encoding, and decoding to extract useful information from the signals.

2. What is data processing?

Data processing is the conversion of raw data into meaningful and usable information. This can involve tasks such as cleaning, organizing, and analyzing data to reveal patterns, trends, and insights.

3. What are some common applications of signal and data processing?

Signal and data processing have a wide range of applications, including image and audio processing, telecommunications, medical imaging, control systems, and financial analysis. They are useful in any field where there is a need to extract useful information from signals or data.

4. What techniques are used in signal and data processing?

There are various techniques used in signal and data processing, including digital filtering, Fourier analysis, time-frequency analysis, statistical analysis, and machine learning. The specific techniques used depend on the type of signal or data being processed and the desired outcome.

5. How do signal and data processing contribute to scientific research?

Signal and data processing play a crucial role in scientific research by enabling scientists to extract valuable insights from complex data and signals. They allow for the detection of patterns and trends that would be difficult or impossible to identify with manual analysis. This can lead to new discoveries and advancements in various fields of science.

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