"A signal is a function that conveys information about a phenomenon."
Introduction to continuous and discrete-time signals and systems, Impulse response, Convolution, Fourier series, Fourier transform, sampling theorem, and z-transform.
Introduction to signals and systems: This involves the basic concept of signals and systems, their properties, representation, and classification.
Time-domain representation of signals: This involves the time-domain representation of deterministic and random signals, as well as the analysis of signals using time-domain tools.
Frequency-domain representation of signals: This involves the Fourier series, Fourier transform, discrete-time Fourier transform, and their applications in analyzing signals and systems in the frequency domain.
Sampling and reconstruction of signals: This involves the theory and techniques of sampling and reconstructing continuous-time signals into discrete-time signals using various sampling techniques.
Filtering of signals: This involves the basic concept of filters, their types, design, and application in signal processing systems.
System theory: This involves the study of linear systems, their properties, response, stability, and design.
Discrete-time systems: This involves the theory and design of discrete-time systems such as discrete-time filters, digital controllers, and digital signal processing systems.
Fourier analysis of linear systems: This involves the properties of linear systems in the frequency domain, including their behavior in the presence of input signals.
Z-transform: This involves the Z-transform, its properties, and its application to signal processing and system analysis.
Sampling theory: This involves the theory and techniques of sampling, quantization, and their impact on signal processing systems.
Wavelets and multiresolution analysis: This involves computationally efficient signal representation using wavelets, which is useful in signal compression, denoising, and feature extraction.
Nonlinear systems: This involves the theory and analysis of nonlinear systems, which are essential to a wide range of applications, including signal and image processing, control systems, and communication systems.
Time-frequency analysis: This involves using time-frequency analysis algorithms to identify the frequency content of nonstationary signals as they evolve in time.
Signal reconstruction: This involves signal reconstruction using interpolation techniques and the implications of finite-length signals.
Signal transduction: This involves the conversion of one type of signal to another, such as electrical to optical or acoustic to electrical.
Biomedical signal processing: This involves the processing of biological signals such as electrocardiography, electroencephalography, and electromyography signals.
Digital filters design: This involves the design of digital filters and the analysis of their characteristics, including their frequency response, phase response, and pole-zero placement.
Statistical signal processing: This involves the use of statistical methods to process signals, including estimation, detection, and classification using tools like Bayesian statistics.
Audio signal processing: This involves the processing of audio signals, primarily used in audio and music applications.
Image processing: This involves processing and analyzing digital images using mathematical algorithms and tools, including image compression, enhancement, and segmentation.
Continuous-time signals: These are signals that exist continuously over a range of time values.
Discrete-time signals: These are signals that only exist at specific time instants.
Analog signals: These signals are continuous in both time and amplitude.
Digital signals: These signals are discrete in both time and amplitude.
Random signals: These signals are assigned randomly over time or amplitude.
Deterministic signals: These signals are predictable and can be described mathematically.
Periodic signals: These signals repeat themselves at regular intervals.
Non-periodic signals: These signals do not have any discernible repetition pattern.
Energy signals: These signals have a finite amount of energy over time.
Power signals: These signals have an infinite energy over time.
Linear systems: Systems that follow the rules of linearity.
Nonlinear systems: Systems that do not follow the rules of linearity.
Time-invariant systems: Systems that do not change over time.
Time-variant systems: Systems that change over time.
Causal systems: Systems that respond only to past and present inputs.
Non-causal systems: Systems that respond to past, present and future inputs.
Stable systems: Systems that have bounded outputs for bounded inputs.
Unstable systems: Systems that have unbounded outputs for bounded inputs.
Recursive systems: Systems that use past output values as part of the equation for present output values.
Non-recursive systems: Systems that do not use past output values in the equation for present output values.
"The IEEE Transactions on Signal Processing includes audio, video, speech, image, sonar, and radar as examples of signals."
"Any quantity that can vary over space or time can be used as a signal to share messages between observers."
"Signaling occurs in all organisms even at cellular levels, with cell signaling."
"Signaling theory, in evolutionary biology, proposes that a substantial driver for evolution is the ability of animals to communicate with each other by developing ways of signaling."
"In human engineering, signals are typically provided by a sensor, and often the original form of a signal is converted to another form of energy using a transducer."
"Information theory serves as the formal study of signals and their content."
"Noise primarily refers to unwanted modifications of signals, but is often extended to include unwanted signals conflicting with desired signals (crosstalk)."
"The reduction of noise is covered in part under the heading of signal integrity."
"The separation of desired signals from background noise is the field of signal recovery."
"In the latter half of the 20th century, electrical engineering itself separated into several disciplines: electronic engineering and computer engineering developed to specialize in the design and analysis of systems that manipulate physical signals, while design engineering developed to address the functional design of signals in user–machine interfaces." Note: Due to limitations, the language model cannot provide a selection of quotes immediately after each question. However, the provided quotes are relevant to each question.