SIGNAL PROCESSING
Beyond the amplitude and time pairs, signals can be categorized in several other ways, often used in advanced Signal Processing and System Analysis: Deterministic vs. Random, Periodic vs. Aperiodic, and Energy vs. Power.
DOMAINS
Time Domain vs. Frequency Domain Information: In practical signal processing, we categorize information based on how it is "hidden" in the signal: Time Domain Information describes when an event happens and its amplitude. Example: A sensor recording a sudden vibration. The exact millisecond the vibration starts and how "big" it is provides the information. Relevant Tool: Step response. Frequency Domain Information: This describes the periodic patterns or "rhythm" within a signal. Example: In audio processing, information is often found in the pitch (frequency) of a sound rather than a single moment in time. Relevant Tool: Fast Fourier Transform (FFT).
In signal processing, information is the specific content or meaning that is extracted from a signal. While a signal is the physical "carrier" (like an electrical pulse or a light wave), information is the non-random pattern within that signal that reduces uncertainty for the receiver.The field generally defines information using three key frameworks: Information as Uncertainty Reduction (The Shannon View)
THE SHANNON VIEW
In the world of Digital Signal Processing (DSP), we use Claude Shannon’s definition: Information is that which reduces uncertainty. 1) The Logic: If you already know exactly what a signal is going to do, it contains zero information. Information only exists when there is a "surprise" or a set of possible outcomes that the signal resolves. 2) The Unit: Measured in bits. One bit of information is the amount of data needed to choose between two equally likely possibilities (like a 0 or 1). 3)Redundancy: If a signal repeats the same value over and over, signal processing sees this as "redundancy" and uses compression to remove the extra parts while keeping the core information.
SIGNAL PROCESSING
Beyond the amplitude and time pairs, signals can be categorized in several other ways, often used in advanced Signal Processing and System Analysis: Deterministic vs. Random, Periodic vs. Aperiodic, and Energy vs. Power.I n signal processing, information is the specific content or meaning that is extracted from a signal. While a signal is any detectable variation in the physical medium like an electrical pulse or light wave the information is the non-random pattern within that signal that reduces uncertainty for the receiver. It is easy to use these words interchangeably, but they are technically different. Information is the reduction of uncertainty, The Code or The Sequence (0101) or the instruction. Signal is the physical manifestation of that The Code. In both physics and biology, a signal is the physical "messenger" that carries information. If information is the content of a message, the signal is the vehicle or the actual physical change that travels from point A to point B. INFORMATION: To convey information is the purpose of the variation in a physical medium. A simple constant voltage doesn't convey information. It's the change from one state to another (e.g. turning a switch on and off, or modulating a carrier wave) that represents the data, sound, or image being sent.
SIGNALS AND TIME
Engineers and Scientists categorize signals in several ways, primarily on how they behave over time. Time Domain vs. Frequency Domain Information: In practical signal processing, we categorize information based on how it is "hidden" in the signal: Time Domain Information describes when an event happens and its amplitude. Example: A sensor recording a sudden vibration. The exact millisecond the vibration starts and how "big" it is provides the information. Relevant Tool: Step response. Frequency Domain Information: This describes the periodic patterns or "rhythm" within a signal. Example: In audio processing, information is often found in the pitch (frequency) of a sound rather than a single moment in time. Relevant Tool: Fast Fourier Transform (FFT). In signal processing, information is the specific content or meaning that is extracted from a signal. While a signal is the physical "carrier" (like an electrical pulse or a light wave), information is the non-random pattern within that signal that reduces uncertainty for the receiver.The field generally defines information using three key frameworks: Information as Uncertainty Reduction (The Shannon View). In the world of Digital Signal Processing (DSP), we use Claude Shannon’s definition: Information is that which reduces uncertainty. 1) The Logic: If you already know exactly what a signal is going to do, it contains zero information. Information only exists when there is a "surprise" or a set of possible outcomes that the signal resolves. 2) The Unit: Measured in bits. One bit of information is the amount of data needed to choose between two equally likely possibilities (like a 0 or 1). 3)Redundancy: If a signal repeats the same value over and over, signal processing sees this as "redundancy" and uses compression to remove the extra parts while keeping the core information.
Esther Bryce
Founder / Interior designer
Lianne Wilson
Broker
Jaden Smith
Architect
Jessica Kim
Photographer
Our team
Our strength lies in our individuality. Set up by Esther Bryce, the team strives to bring in the best talent in various fields, from architecture to interior design and sales.
Esther Bryce
Founder / Interior designer
Lianne Wilson
Broker
Jaden Smith
Architect
Jessica Kim
Photographer
Our team
Our strength lies in our individuality. Set up by Esther Bryce, the team strives to bring in the best talent in various fields, from architecture to interior design and sales.
Esther Bryce
Founder / Interior designer
Lianne Wilson
Broker
Jaden Smith
Architect
Jessica Kim
Photographer