Pengolahan Sinyal Digital: 04 Sistem dan Fungsi Transfer

Summary

This video covers the fundamentals of digital signal processing, focusing on system transfer functions. It explores various types of systems, including linear, time-invariant, and causal systems, and explains their characteristics through mathematical models. Key concepts such as superposition, homogeneity, and system stability are discussed in detail. The importance of understanding the function transfer for input-output relationships is emphasized. The video also highlights practical challenges in signal processing, including system identification, filtering, and equalization, making complex theoretical concepts more accessible for viewers.

Takeaways

• 😀 A system is a device or setup that operates on signals to produce a response, where input is also called excitation and output is the system's response.
• 😀 Systems can be analog or digital, and both types can be used in various applications, producing either single or multiple inputs and outputs.
• 😀 Systems are classified into four types based on input and output: Single input single output (SISO), Single input multiple output (SIMO), Multiple input single output (MISO), and Multiple input multiple output (MIMO).
• 😀 Systems can be categorized by properties like linear or nonlinear, time-variant or time-invariant, static or dynamic, and causal or non-causal.
• 😀 A linear system satisfies the principles of superposition and homogeneity. This means that the output of the system can be predicted by adding inputs or scaling them.
• 😀 Time-invariant systems maintain the same behavior over time, while time-variant systems exhibit different responses depending on when the input is applied.
• 😀 Linear time-invariant (LTI) systems are easier to predict and analyze compared to time-variant systems, and they form the foundation of signal processing.
• 😀 A dynamic system depends on previous inputs or events, while static systems do not rely on past information.
• 😀 Causal systems only depend on the present and past inputs, not future inputs. Non-causal systems, on the other hand, require future input values.
• 😀 Stability in systems is defined by whether bounded inputs result in bounded outputs. A stable system will produce predictable responses to controlled inputs.
• 😀 Signal processing aims to design transfer functions that will produce desired system outputs based on specific inputs, with tasks such as system identification, filtering, and equalization being key components of the process.

Q & A

What is a system in the context of digital signal processing?

A system in digital signal processing is a device or apparatus that operates on a signal to produce an output response. The input is often referred to as 'excitation,' and the output is called the 'response.'

What are the four classifications of systems based on the number of inputs and outputs?

The four classifications of systems based on the number of inputs and outputs are: Single Input, Single Output (SISO); Single Input, Multiple Output (SIMO); Multiple Input, Single Output (MISO); and Multiple Input, Multiple Output (MIMO).

What defines a system as linear?

A system is considered linear if it satisfies the principles of superposition and homogeneity. Superposition means that the response to a sum of inputs is the sum of the responses, while homogeneity means that if the input is scaled, the output is scaled by the same factor.

What is the difference between a time-variant and a time-invariant system?

A time-variant system is one where the input-output relationship changes over time, meaning the response is different at different times. In contrast, a time-invariant system has a constant input-output relationship, and shifting the input in time results in a correspondingly shifted output.

What does a dynamic system depend on?

A dynamic system depends on past events or inputs. It has memory and changes over time based on previous inputs or states, unlike a static system, which does not depend on past information.

What is the significance of causal and non-causal systems?

A causal system's output depends only on the current and past inputs, not future ones, making it physically realizable. A non-causal system, on the other hand, requires future input information to determine the output, which is not physically feasible.

What is the difference between an invertible and non-invertible system?

An invertible system is one where the output can be reversed back to the original input. A non-invertible system cannot regenerate the input from the output, meaning the relationship between input and output is not fully reversible.

What does stability in a system mean?

A system is stable if a bounded input results in a bounded output. In other words, if the input signal does not grow unbounded, the output signal should also remain within a predictable, finite range.

What is the role of a transfer function in a system?

A transfer function is a mathematical model that describes the relationship between the input and output of a system. It can be represented in terms of time, frequency, or other domains, and helps to understand and predict the system's behavior.

What are the main problems addressed in signal processing?

The main problems in signal processing are identifying systems based on input-output data, designing filters to produce desired outputs, and equalization, where the goal is to determine the input needed to achieve a specific output.