This document provides an overview of signals and systems. It defines key terms like signals, systems, continuous and discrete time signals, analog and digital signals, deterministic and probabilistic signals, even and odd signals, energy and power signals, periodic and aperiodic signals. It also classifies systems as linear/non-linear, time-invariant/variant, causal/non-causal, and with or without memory. Singularity functions like unit step, unit ramp and unit impulse are introduced. Properties of signals like magnitude scaling, time reflection, time scaling and time shifting are discussed. Energy and power of signals are defined.
This document provides an overview of signals and systems. It defines key terms like signal, system, continuous and discrete time signals, analog and digital signals, periodic and aperiodic signals. It also discusses different types of signals like deterministic and probabilistic signals, energy and power signals. The document then classifies systems as linear/nonlinear, time-invariant/variant, causal/non-causal, and with/without memory. It provides examples of different signals and properties of signals like magnitude scaling, time shifting, reflection and scaling. Overall, the document introduces fundamental concepts in signals and systems.
Signal and System, CT Signal DT Signal, Signal Processing(amplitude and time ...Waqas Afzal
Signal and System(definitions)
Continuous-Time Signal
Discrete-Time Signal
Signal Processing
Basic Elements of Signal Processing
Classification of Signals
Basic Signal Operations(amplitude and time scaling)
This document contains lecture notes on signals and systems for a course at Chadalawada Ramanamma Engineering College. It includes:
1. An introduction to signals, systems, and some common elementary signals like the unit step, unit impulse, ramp, sinusoid, and exponential signals.
2. A classification of signals as continuous/discrete, deterministic/non-deterministic, even/odd, periodic/aperiodic, energy/power, and real/imaginary.
3. A discussion of basic operations on signals like amplitude scaling, addition, and subtraction.
The document discusses Fourier analysis techniques. It covers topics like line spectra and Fourier series, including periodic signals and average power. Key aspects covered include phasor representation of sinusoids, convergence conditions of Fourier series, and Parseval's power theorem relating signal power to Fourier coefficients.
DSP_2018_FOEHU - Lec 08 - The Discrete Fourier TransformAmr E. Mohamed
The document provides an overview of the Discrete Fourier Transform (DFT). It begins by discussing limitations of the discrete-time Fourier transform (DTFT) and z-transform in that they are defined for infinite sequences and continuous variables. The DFT avoids these issues by being a numerically computable transform for finite discrete-time signals. It works by taking a finite signal, making it periodic, and computing its discrete Fourier transform which is a discrete frequency spectrum. This makes the DFT highly suitable for digital signal processing. The document then provides details on computation of the DFT and its relationship to the DTFT and z-transform.
This document provides an overview of signals and systems. It defines a signal as a function of time that conveys information and a system as a device that operates on signals to produce responses. Signals are classified as continuous or discrete time, deterministic or non-deterministic, even or odd, periodic or aperiodic, energy or power, and real or imaginary. Systems are classified as linear or nonlinear, time variant or invariant, static or dynamic, causal or non-causal, invertible or non-invertible, and stable or unstable. Examples are provided to illustrate the different signal and system classifications.
The document discusses convolution, which is a mathematical operation used in signal and image processing. Convolution provides a way to multiply two arrays of numbers to produce a third array. It defines convolution as an integral that calculates the output of a linear time-invariant system by integrating the product of the input and impulse response functions. The key properties of convolution are that it is commutative, distributive, and associative. Examples are provided to demonstrate calculating the convolution of different signals.
The document summarizes several key properties of systems:
- Memory refers to whether a system's output depends on current and/or past input values. Causal systems are memoryless while anticausal systems require memory.
- Linearity means a system's output is a linear combination of its inputs. A linear system satisfies superposition and homogeneity.
- Time-invariance means a system's properties do not change over time such that a time shift of the input results in the same time shift of the output.
- Invertibility refers to whether the input of a system can be recovered from its output. An invertible system has a corresponding inverse system.
This document provides an overview of signals and systems. It defines key terms like signal, system, continuous and discrete time signals, analog and digital signals, periodic and aperiodic signals. It also discusses different types of signals like deterministic and probabilistic signals, energy and power signals. The document then classifies systems as linear/nonlinear, time-invariant/variant, causal/non-causal, and with/without memory. It provides examples of different signals and properties of signals like magnitude scaling, time shifting, reflection and scaling. Overall, the document introduces fundamental concepts in signals and systems.
Signal and System, CT Signal DT Signal, Signal Processing(amplitude and time ...Waqas Afzal
Signal and System(definitions)
Continuous-Time Signal
Discrete-Time Signal
Signal Processing
Basic Elements of Signal Processing
Classification of Signals
Basic Signal Operations(amplitude and time scaling)
This document contains lecture notes on signals and systems for a course at Chadalawada Ramanamma Engineering College. It includes:
1. An introduction to signals, systems, and some common elementary signals like the unit step, unit impulse, ramp, sinusoid, and exponential signals.
2. A classification of signals as continuous/discrete, deterministic/non-deterministic, even/odd, periodic/aperiodic, energy/power, and real/imaginary.
3. A discussion of basic operations on signals like amplitude scaling, addition, and subtraction.
The document discusses Fourier analysis techniques. It covers topics like line spectra and Fourier series, including periodic signals and average power. Key aspects covered include phasor representation of sinusoids, convergence conditions of Fourier series, and Parseval's power theorem relating signal power to Fourier coefficients.
DSP_2018_FOEHU - Lec 08 - The Discrete Fourier TransformAmr E. Mohamed
The document provides an overview of the Discrete Fourier Transform (DFT). It begins by discussing limitations of the discrete-time Fourier transform (DTFT) and z-transform in that they are defined for infinite sequences and continuous variables. The DFT avoids these issues by being a numerically computable transform for finite discrete-time signals. It works by taking a finite signal, making it periodic, and computing its discrete Fourier transform which is a discrete frequency spectrum. This makes the DFT highly suitable for digital signal processing. The document then provides details on computation of the DFT and its relationship to the DTFT and z-transform.
This document provides an overview of signals and systems. It defines a signal as a function of time that conveys information and a system as a device that operates on signals to produce responses. Signals are classified as continuous or discrete time, deterministic or non-deterministic, even or odd, periodic or aperiodic, energy or power, and real or imaginary. Systems are classified as linear or nonlinear, time variant or invariant, static or dynamic, causal or non-causal, invertible or non-invertible, and stable or unstable. Examples are provided to illustrate the different signal and system classifications.
The document discusses convolution, which is a mathematical operation used in signal and image processing. Convolution provides a way to multiply two arrays of numbers to produce a third array. It defines convolution as an integral that calculates the output of a linear time-invariant system by integrating the product of the input and impulse response functions. The key properties of convolution are that it is commutative, distributive, and associative. Examples are provided to demonstrate calculating the convolution of different signals.
The document summarizes several key properties of systems:
- Memory refers to whether a system's output depends on current and/or past input values. Causal systems are memoryless while anticausal systems require memory.
- Linearity means a system's output is a linear combination of its inputs. A linear system satisfies superposition and homogeneity.
- Time-invariance means a system's properties do not change over time such that a time shift of the input results in the same time shift of the output.
- Invertibility refers to whether the input of a system can be recovered from its output. An invertible system has a corresponding inverse system.
This document provides an introduction to signals and systems. It defines signals as functions that represent information over time and gives examples such as sound waves and stock prices. Systems are defined as generators or transformers of signals. Signal processing involves manipulating signals to extract useful information, often by converting them to electrical forms. The document then classifies different types of signals such as continuous-time vs discrete-time, analog vs digital, deterministic vs random, and energy vs power signals. It also introduces some basic continuous-time signals like the unit step function, unit impulse function, and complex exponential signals.
DSP_2018_FOEHU - Lec 06 - FIR Filter DesignAmr E. Mohamed
This lecture discusses the design of finite impulse response (FIR) filters. It introduces the window method for FIR filter design, which involves truncating the ideal impulse response with a window function to obtain a causal FIR filter. Common window functions are presented such as rectangular, triangular, Hanning, Hamming, and Blackman windows. These windows trade off main lobe width and side lobe levels. The document provides an example design of a low-pass FIR filter using the Hamming window to meet given passband and stopband specifications.
EC8352- Signals and Systems - Unit 2 - Fourier transformNimithaSoman
This document discusses Fourier transforms and their applications. It begins by introducing Fourier transforms and noting that they are used widely in optics, image processing, speech processing, and medical signal processing. It then covers key topics such as:
- When periodic and aperiodic signals can be represented by Fourier series versus Fourier transforms
- Properties of continuous-time and discrete-time Fourier transforms
- Applications of Fourier transforms in filtering ECG signals, modeling diffractive gratings in optics, speech processing, and image processing
- Limitations of Fourier transforms in representing non-stable systems
The document provides an overview of Fourier transforms and their significance in decomposing signals into constituent frequencies, as well as examples of where they are applied in
1) Convolution represents a discrete-time (DT) or continuous-time (CT) linear time-invariant (LTI) system as the summation or integral of the input signal multiplied by the impulse response.
2) The impulse response completely characterizes an LTI system.
3) Convolution exploits the properties of time-invariance and linearity of LTI systems to represent the output of the system in terms of a convolution between the input and impulse response.
state space representation,State Space Model Controllability and Observabilit...Waqas Afzal
State Variables of a Dynamical System
State Variable Equation
Why State space approach
Block Diagram Representation Of State Space Model
Controllability and Observability
Derive Transfer Function from State Space Equation
Time Response and State Transition Matrix
Eigen Value
This document provides an overview of time-domain analysis of linear time-invariant (LTI) systems. It discusses impulse response and unit step response, which are used to characterize the memory and stability of systems. Transient responses like rise time and settling time are also examined. Convolution is introduced as a way to calculate the output of LTI systems using the impulse response. Difference equations are presented as a method to model discrete-time linear shift-invariant (LSI) systems.
This document provides an overview of discrete-time signals and systems in digital signal processing (DSP). It discusses key concepts such as:
1) Discrete-time signals which are represented by sequences of numbers and how common signals like impulses and steps are represented.
2) Discrete-time systems which take a discrete-time signal as input and produce an output signal through a mathematical algorithm, with the impulse response characterizing the system.
3) Important properties of linear time-invariant (LTI) systems including superposition, time-shifting of inputs and outputs, and representation using convolution sums or difference equations.
This document provides an introduction and syllabus for a signals and systems course taught by Prof. Satheesh Monikandan.B at the Indian Naval Academy. The syllabus covers topics such as signal classification, system properties, sampling, and transforms. It defines key concepts like signals, systems, continuous and discrete time signals, and linear and nonlinear systems. Elementary signals like sinusoidal, exponential, unit step, and impulse are also introduced.
Time response of discrete systems 4th lecturekhalaf Gaeid
1. The document discusses the time response of discrete-time systems, including their transient and steady-state response. It describes parameters for characterizing transient response such as rise time, delay time, peak time, and settling time.
2. Steady-state errors are also examined for different system types (Type-0, 1, 2 systems) and inputs (step, ramp, parabolic). Examples are provided to calculate steady-state errors.
3. The response of discrete-time systems is derived using impulse response sequences and convolution sums. The time response is broken into zero-input and zero-state responses.
The document contains details about sampling a bandpass signal with varying center frequency fo from 5 kHz to 50 kHz at a sampling rate of 25 kHz.
It analyzes the ranges of fo for which the sampling rate is adequate by calculating the variation in bandwidth (k) as fo changes. It concludes that the sampling rate of 25 kHz is adequate when fo is between 5-7.5 kHz, 15-20 kHz, 25-32.5 kHz, and 35-50 kHz.
This document defines key concepts in signal processing including signals, systems, and digital signal processing. It provides examples of signals that vary with time or other variables and carry information. Characteristics of signals like amplitude, frequency, and phase are described. Systems are defined as physical devices that operate on signals, with examples of filters. Signal processing involves passing signals through systems to perform operations like filtering. A block diagram shows the basic components of a digital signal processing system including analog to digital conversion, processing, and digital to analog conversion. Finally, advantages of digital over analog signal processing are listed such as programmability, accuracy, storage, and lower cost.
The Fourier transform decomposes a signal into its constituent frequencies, representing it in the frequency domain rather than the spatial domain, which can make certain operations and analyses easier to perform; it has both magnitude and phase components that provide information about the frequency content and relative phases of the signal. The discrete Fourier transform (DFT) is a sampled version of the continuous Fourier transform that is useful for digital signal and image processing applications.
State space analysis shortcut rules, control systems, Prajakta Pardeshi
This document discusses different types of state space analysis including physical variable form, phase variable form using canonical forms I and II, parallel realization, converting between state models and transfer functions, state transition matrices, and observability and controllability. It provides examples of obtaining state space models from electrical circuits using different approaches like writing standard state equations, using canonical forms, and parallel realization from transfer functions. It also outlines how to check for observability and controllability of state space models.
The document provides an introduction to adaptive filters, which are computational devices that model the relationship between input and output signals in real time to minimize the error between the actual and desired response. It describes the basic elements of adaptive filters including input/output signals, filter structure, coefficients, and adaptive algorithm. It also summarizes common adaptive filter structures like FIR, IIR, and linear combiners and applications such as system identification, inverse modeling, signal prediction, and interference cancellation.
The document discusses various properties of signals including:
- Analog signals can have an infinite number of values while digital signals are limited to a set of values.
- Phase describes the position of a waveform relative to a reference point in time.
- Total energy and average power of continuous and discrete signals can be calculated through integrals and sums.
- Periodic, even, odd, exponential, and sinusoidal signals are described.
- Unit impulse and step signals are defined for both discrete and continuous time domains.
- A signal's frequency spectrum shows the collection of component frequencies and bandwidth is the range of these frequencies.
Chapter3 - Fourier Series Representation of Periodic SignalsAttaporn Ninsuwan
This document discusses Fourier series representation of periodic signals. It introduces continuous-time periodic signals and their representation as a linear combination of harmonically related complex exponentials. The coefficients in the Fourier series representation can be determined by multiplying both sides of the representation by complex exponentials and integrating over one period. The key steps are: 1) multiplying both sides by e-jω0t, 2) integrating both sides from 0 to T=2π/ω0, and 3) using the fact that the integral equals T when k=n and 0 otherwise to obtain an expression for the coefficients an. Examples are provided to illustrate these concepts.
State space analysis, eign values and eign vectorsShilpa Shukla
This document discusses state space analysis and the conversion of transfer functions to state space models. It covers:
1. The need to convert transfer functions to state space form in order to apply modern time domain techniques for system analysis and design.
2. Three possible representations for realizing a transfer function as a state space model: first companion form, second companion form, and Jordan canonical form.
3. The concepts of eigenvalues and eigenvectors, and how they relate to state space models.
4. Worked examples of converting transfer functions to state space models in first and second companion forms, as well as the Jordan canonical form for systems with repeated and non-repeated roots.
The document provides an overview
DSP_2018_FOEHU - Lec 02 - Sampling of Continuous Time SignalsAmr E. Mohamed
The document discusses sampling of continuous-time signals. It defines different types of signals and sampling methods. Ideal sampling involves multiplying the signal by a train of impulse functions to select sample values at regular intervals. For practical sampling, a train of rectangular pulses is used to approximate ideal sampling. Flat-top sampling is achieved by convolving the ideally sampled signal with a rectangular pulse, resulting in samples held at a constant height for the sample period. The Nyquist sampling theorem states that a signal must be sampled at least twice its maximum frequency to avoid aliasing when reconstructing the original signal from samples. An anti-aliasing filter can be used before sampling to prevent aliasing from high frequencies above half the sampling rate.
1. The document discusses continuous-time signals and systems. It defines signals and systems, and how they are classified based on properties like being continuous or discrete, and having one or more independent variables.
2. It describes various operations that can be performed on signals, including time shifting, time reversal, time compression/expansion, and amplitude scaling. These transformations change the signal while preserving the information content.
3. Systems are defined as entities that process input signals to produce output signals. Examples of signal processing systems include communication systems, control systems, and systems that interface between continuous and discrete domains.
This document provides a summary of key concepts in signals and systems. It defines a signal as a function that conveys information about a physical phenomenon, and a system as an entity that manipulates signals to produce new output signals. It classifies signals as continuous-time or discrete-time, even or odd, periodic or non-periodic, deterministic or random, and energy or power. It also covers basic operations on signals, elementary signal types, system properties like memory and invertibility, and ways to interconnect multiple systems in series, parallel or with feedback.
This document provides an introduction to signals and systems. It defines signals as functions that represent information over time and gives examples such as sound waves and stock prices. Systems are defined as generators or transformers of signals. Signal processing involves manipulating signals to extract useful information, often by converting them to electrical forms. The document then classifies different types of signals such as continuous-time vs discrete-time, analog vs digital, deterministic vs random, and energy vs power signals. It also introduces some basic continuous-time signals like the unit step function, unit impulse function, and complex exponential signals.
DSP_2018_FOEHU - Lec 06 - FIR Filter DesignAmr E. Mohamed
This lecture discusses the design of finite impulse response (FIR) filters. It introduces the window method for FIR filter design, which involves truncating the ideal impulse response with a window function to obtain a causal FIR filter. Common window functions are presented such as rectangular, triangular, Hanning, Hamming, and Blackman windows. These windows trade off main lobe width and side lobe levels. The document provides an example design of a low-pass FIR filter using the Hamming window to meet given passband and stopband specifications.
EC8352- Signals and Systems - Unit 2 - Fourier transformNimithaSoman
This document discusses Fourier transforms and their applications. It begins by introducing Fourier transforms and noting that they are used widely in optics, image processing, speech processing, and medical signal processing. It then covers key topics such as:
- When periodic and aperiodic signals can be represented by Fourier series versus Fourier transforms
- Properties of continuous-time and discrete-time Fourier transforms
- Applications of Fourier transforms in filtering ECG signals, modeling diffractive gratings in optics, speech processing, and image processing
- Limitations of Fourier transforms in representing non-stable systems
The document provides an overview of Fourier transforms and their significance in decomposing signals into constituent frequencies, as well as examples of where they are applied in
1) Convolution represents a discrete-time (DT) or continuous-time (CT) linear time-invariant (LTI) system as the summation or integral of the input signal multiplied by the impulse response.
2) The impulse response completely characterizes an LTI system.
3) Convolution exploits the properties of time-invariance and linearity of LTI systems to represent the output of the system in terms of a convolution between the input and impulse response.
state space representation,State Space Model Controllability and Observabilit...Waqas Afzal
State Variables of a Dynamical System
State Variable Equation
Why State space approach
Block Diagram Representation Of State Space Model
Controllability and Observability
Derive Transfer Function from State Space Equation
Time Response and State Transition Matrix
Eigen Value
This document provides an overview of time-domain analysis of linear time-invariant (LTI) systems. It discusses impulse response and unit step response, which are used to characterize the memory and stability of systems. Transient responses like rise time and settling time are also examined. Convolution is introduced as a way to calculate the output of LTI systems using the impulse response. Difference equations are presented as a method to model discrete-time linear shift-invariant (LSI) systems.
This document provides an overview of discrete-time signals and systems in digital signal processing (DSP). It discusses key concepts such as:
1) Discrete-time signals which are represented by sequences of numbers and how common signals like impulses and steps are represented.
2) Discrete-time systems which take a discrete-time signal as input and produce an output signal through a mathematical algorithm, with the impulse response characterizing the system.
3) Important properties of linear time-invariant (LTI) systems including superposition, time-shifting of inputs and outputs, and representation using convolution sums or difference equations.
This document provides an introduction and syllabus for a signals and systems course taught by Prof. Satheesh Monikandan.B at the Indian Naval Academy. The syllabus covers topics such as signal classification, system properties, sampling, and transforms. It defines key concepts like signals, systems, continuous and discrete time signals, and linear and nonlinear systems. Elementary signals like sinusoidal, exponential, unit step, and impulse are also introduced.
Time response of discrete systems 4th lecturekhalaf Gaeid
1. The document discusses the time response of discrete-time systems, including their transient and steady-state response. It describes parameters for characterizing transient response such as rise time, delay time, peak time, and settling time.
2. Steady-state errors are also examined for different system types (Type-0, 1, 2 systems) and inputs (step, ramp, parabolic). Examples are provided to calculate steady-state errors.
3. The response of discrete-time systems is derived using impulse response sequences and convolution sums. The time response is broken into zero-input and zero-state responses.
The document contains details about sampling a bandpass signal with varying center frequency fo from 5 kHz to 50 kHz at a sampling rate of 25 kHz.
It analyzes the ranges of fo for which the sampling rate is adequate by calculating the variation in bandwidth (k) as fo changes. It concludes that the sampling rate of 25 kHz is adequate when fo is between 5-7.5 kHz, 15-20 kHz, 25-32.5 kHz, and 35-50 kHz.
This document defines key concepts in signal processing including signals, systems, and digital signal processing. It provides examples of signals that vary with time or other variables and carry information. Characteristics of signals like amplitude, frequency, and phase are described. Systems are defined as physical devices that operate on signals, with examples of filters. Signal processing involves passing signals through systems to perform operations like filtering. A block diagram shows the basic components of a digital signal processing system including analog to digital conversion, processing, and digital to analog conversion. Finally, advantages of digital over analog signal processing are listed such as programmability, accuracy, storage, and lower cost.
The Fourier transform decomposes a signal into its constituent frequencies, representing it in the frequency domain rather than the spatial domain, which can make certain operations and analyses easier to perform; it has both magnitude and phase components that provide information about the frequency content and relative phases of the signal. The discrete Fourier transform (DFT) is a sampled version of the continuous Fourier transform that is useful for digital signal and image processing applications.
State space analysis shortcut rules, control systems, Prajakta Pardeshi
This document discusses different types of state space analysis including physical variable form, phase variable form using canonical forms I and II, parallel realization, converting between state models and transfer functions, state transition matrices, and observability and controllability. It provides examples of obtaining state space models from electrical circuits using different approaches like writing standard state equations, using canonical forms, and parallel realization from transfer functions. It also outlines how to check for observability and controllability of state space models.
The document provides an introduction to adaptive filters, which are computational devices that model the relationship between input and output signals in real time to minimize the error between the actual and desired response. It describes the basic elements of adaptive filters including input/output signals, filter structure, coefficients, and adaptive algorithm. It also summarizes common adaptive filter structures like FIR, IIR, and linear combiners and applications such as system identification, inverse modeling, signal prediction, and interference cancellation.
The document discusses various properties of signals including:
- Analog signals can have an infinite number of values while digital signals are limited to a set of values.
- Phase describes the position of a waveform relative to a reference point in time.
- Total energy and average power of continuous and discrete signals can be calculated through integrals and sums.
- Periodic, even, odd, exponential, and sinusoidal signals are described.
- Unit impulse and step signals are defined for both discrete and continuous time domains.
- A signal's frequency spectrum shows the collection of component frequencies and bandwidth is the range of these frequencies.
Chapter3 - Fourier Series Representation of Periodic SignalsAttaporn Ninsuwan
This document discusses Fourier series representation of periodic signals. It introduces continuous-time periodic signals and their representation as a linear combination of harmonically related complex exponentials. The coefficients in the Fourier series representation can be determined by multiplying both sides of the representation by complex exponentials and integrating over one period. The key steps are: 1) multiplying both sides by e-jω0t, 2) integrating both sides from 0 to T=2π/ω0, and 3) using the fact that the integral equals T when k=n and 0 otherwise to obtain an expression for the coefficients an. Examples are provided to illustrate these concepts.
State space analysis, eign values and eign vectorsShilpa Shukla
This document discusses state space analysis and the conversion of transfer functions to state space models. It covers:
1. The need to convert transfer functions to state space form in order to apply modern time domain techniques for system analysis and design.
2. Three possible representations for realizing a transfer function as a state space model: first companion form, second companion form, and Jordan canonical form.
3. The concepts of eigenvalues and eigenvectors, and how they relate to state space models.
4. Worked examples of converting transfer functions to state space models in first and second companion forms, as well as the Jordan canonical form for systems with repeated and non-repeated roots.
The document provides an overview
DSP_2018_FOEHU - Lec 02 - Sampling of Continuous Time SignalsAmr E. Mohamed
The document discusses sampling of continuous-time signals. It defines different types of signals and sampling methods. Ideal sampling involves multiplying the signal by a train of impulse functions to select sample values at regular intervals. For practical sampling, a train of rectangular pulses is used to approximate ideal sampling. Flat-top sampling is achieved by convolving the ideally sampled signal with a rectangular pulse, resulting in samples held at a constant height for the sample period. The Nyquist sampling theorem states that a signal must be sampled at least twice its maximum frequency to avoid aliasing when reconstructing the original signal from samples. An anti-aliasing filter can be used before sampling to prevent aliasing from high frequencies above half the sampling rate.
1. The document discusses continuous-time signals and systems. It defines signals and systems, and how they are classified based on properties like being continuous or discrete, and having one or more independent variables.
2. It describes various operations that can be performed on signals, including time shifting, time reversal, time compression/expansion, and amplitude scaling. These transformations change the signal while preserving the information content.
3. Systems are defined as entities that process input signals to produce output signals. Examples of signal processing systems include communication systems, control systems, and systems that interface between continuous and discrete domains.
This document provides a summary of key concepts in signals and systems. It defines a signal as a function that conveys information about a physical phenomenon, and a system as an entity that manipulates signals to produce new output signals. It classifies signals as continuous-time or discrete-time, even or odd, periodic or non-periodic, deterministic or random, and energy or power. It also covers basic operations on signals, elementary signal types, system properties like memory and invertibility, and ways to interconnect multiple systems in series, parallel or with feedback.
Signals and Systems is an introduction to analog and digital signal processing, a topic that forms an integral part of engineering systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, defense electronics, consumer electronics, and consumer products.
This document contains a question bank with two mark questions and answers related to signals and systems. Some key topics covered include:
- Definitions of continuous and discrete time signals like unit step, unit impulse, ramp functions.
- Classifications of signals as periodic, aperiodic, even, odd, energy and power.
- Properties of Fourier series and transforms including Dirichlet conditions, time shifting property, Parseval's theorem.
- Definitions of causal, non-causal, static and dynamic systems.
- Calculations of Fourier and Laplace transforms of basic signals like impulse, step functions.
So in summary, this document provides a review of fundamental concepts in signals and systems along with practice
This document discusses signals and their classification. It defines signals, analog and digital signals, periodic and aperiodic signals. It also discusses representing signals in Matlab and Simulink. Key signal types covered include exponential, sinusoidal, unit impulse and step functions. Matlab is presented as a tool for programming and analyzing discrete signals while Simulink can be used to model and simulate continuous systems.
This document contains the course syllabus for the Signals and Systems course at Karpagam Institute of Technology. It covers five units: (1) classification of signals and systems, (2) analysis of continuous time signals, (3) linear time invariant continuous time systems, (4) analysis of discrete time signals, and (5) linear time invariant discrete time systems. The first unit defines common signals like step, ramp, impulse, and sinusoidal signals and classifies signals and systems. It also introduces concepts of continuous and discrete time signals, periodic and aperiodic signals, and deterministic and random signals.
This document provides an overview of signals and systems. It defines a signal as a physical quantity that varies with time and contains information. Signals are classified as deterministic or non-deterministic, periodic or aperiodic, even or odd, energy-based or power-based, and continuous-time or discrete-time. Systems are combinations of elements that process input signals to produce output signals. Key properties of systems include causality, linearity, time-invariance, stability, and invertibility. Applications of signals and systems are found in control systems, communications, signal processing, and more.
This document provides an introduction to basic system analysis concepts related to continuous time signals and systems. It defines key signal types such as continuous/discrete time signals, periodic/non-periodic signals, even/odd signals, deterministic/random signals, and energy/power signals. It also discusses important system concepts like linear/non-linear systems, causal/non-causal systems, time-invariant/time-variant systems, stable/unstable systems, and static/dynamic systems. Finally, it introduces common signal types like unit step, unit ramp, and delta/impulse functions as well as concepts like time shifting, scaling, and inversion of systems.
This document discusses signals and systems. It begins with an introduction that signals arise in many areas like communications, circuit design, etc. and a signal contains information about some phenomenon. A system processes input signals to produce output signals.
It then discusses different types of signals like continuous-time and discrete-time signals. Deterministic signals can be written mathematically while stochastic signals cannot. Periodic signals repeat and aperiodic signals do not. Even and odd signals have specific properties related to their symmetry.
Operations on signals are also covered, including addition, multiplication by a constant, multiplication of two signals, time shifting which delays or advances a signal, and time scaling which compresses or expands a signal. Common signal models
This document provides an introduction to signals and systems. It discusses various signal classifications including continuous-time vs discrete-time, and memory vs memoryless systems. Elementary signals such as unit step, impulse, and sinusoid functions are defined. Common signal operations including time reversal, time scaling, amplitude scaling and shifting are described. The relationships between the time and frequency domains are introduced. The document is intended to help students understand signal characteristics and operations in both the time and frequency domains.
This document provides an overview of signals and systems. It defines a signal as a function that conveys information about a physical phenomenon over time or space, and a system as an entity that manipulates signals to produce new output signals. The document then covers topics such as classification of signals as continuous-time vs discrete-time, even vs odd, periodic vs non-periodic, deterministic vs random. It also discusses basic operations on signals like time shifting and scaling. Elementary signals like exponential, sinusoidal, step, and impulse functions are introduced. Properties of systems like linearity, time-invariance, causality, memory, invertibility, and different types of interconnections between systems are outlined.
This document provides an overview of signals and systems classification. It discusses:
1) Signals can be continuous-time or discrete-time, periodic or non-periodic, deterministic or random, even or odd.
2) Systems can be causal or non-causal, linear or nonlinear, time-invariant or time-variant, stable or unstable.
3) Key system properties include memory/memoryless, and examples of discrete-time systems are presented.
Classification of signals and systems as well as their properties are given in the PPT .Examples related to types of signals and systems are also given .
The document outlines the goals and material to be covered in three upcoming classes on signals and systems. The classes will: (1) define different types of signals and explore the concept of a system, (2) examine linear, time-invariant systems and their representation in the time and frequency domains, and (3) review Fourier series/transforms and their practical applications including sampling, aliasing, and signal conversion.
This document provides an overview of chapter 1 on signals from a textbook on signals and systems. It defines a signal as a function that varies over time or another independent variable. It classifies signals as continuous-time or discrete-time, even or odd, periodic or aperiodic, and energy or power signals. It also discusses transformations of signals including time shifting, time scaling, and time reversal. Exponential and sinusoidal signals are examined for both continuous-time and discrete-time cases. Finally, it introduces the unit impulse and unit step functions.
1) Signals can be classified as continuous-time or discrete-time based on how they are defined over time. Continuous-time signals are defined for every instant in time while discrete-time signals are defined at discrete time instances.
2) A system is defined as a set of elements or devices that produce an output response to an input signal. The relationship between input and output signals is represented by a system operator.
3) Signals and systems can be further classified based on their properties, such as being deterministic or random, periodic or aperiodic, causal or non-causal, and more. Basic operations on signals include time scaling, time reversal, and time shifting.
1) Signals can be classified as continuous-time or discrete-time based on their definition over time. Continuous-time signals are defined for every instant in time while discrete-time signals are defined at discrete time instances.
2) A system is defined as a set of elements or devices that produce an output in response to an input signal. The relationship between input and output signals is represented by a system operator.
3) Signals and systems can be further classified based on their properties, such as being deterministic or random, periodic or aperiodic, causal or non-causal, and more. Basic operations on signals include time scaling, time reversal, and time shifting.
The document provides information about a signals and systems course taught by Mr. Koay Fong Thai. It includes announcements about course policies, assessments, and schedule. Students are advised to ask questions, work hard, and submit assignments on time. The use of phones and laptops in class is strictly prohibited. The course aims to introduce signals and systems analysis using various transforms. Topics include signals in the time domain, Fourier transforms, Laplace transforms, and z-transforms. Reference books and a lecture schedule are also provided.
Unit 1 -Introduction to signals and standard signalsDr.SHANTHI K.G
1) The document introduces various types of signals including continuous time signals, discrete time signals, standard signals like step signals, ramp signals, impulse signals, sinusoidal signals, and exponential signals.
2) Continuous time signals are defined for every instant in time while discrete time signals are defined for discrete instants in time. Common standard signals include unit step, ramp, parabolic, pulse, sinusoidal, and exponential signals.
3) Examples of applications of the standard signals are mentioned such as step signals being used for switching devices on and off, and sinusoidal signals being used to represent any sound signal.
1. The document provides a list of 2 mark questions and answers related to the Signals and Systems subject for the 3rd semester IT students.
2. It includes definitions of key terms like signal, system, different types of signals and their classifications. Properties of Fourier series and Fourier transforms are also covered.
3. The questions address topics ranging from periodic/aperiodic signals, even/odd signals, unit step and impulse functions, Fourier series, Fourier transforms, Laplace transforms, linear and time invariant systems.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
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the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
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Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
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represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
2. Terms
• Signal
• voltage over time
• State
• the variables of a differential equation
• System
• linear time invariant transfer function
3. Chapter 1 : Signals And Systems
1.0 Introduction
Signals and Systems subject is focusing on a signal involving dependent
variable (i.e : time even though it can be others such as a distance , position ,
temperature , pressure and others)
Signals & Systems
• Signal
– physical form of a waveform
– e.g. sound, electrical current, radio wave
• System
– a channel that changes a signal that passes through it
– e.g. a telephone connection, a room, a vocal tract
Input Signal System Output Signal
systemInput
signal
Output
signal
4. Chapter 1 : Signals And Systems
1.1 Signals and Systems
Definition
a) Signal
• A function of one/more variable which convey information on the natural of a
physical phenomenon.
• Examples : human speech, sound, light, temperature, current etc
b) Systems
• An entity that processes of manipulates one or more signals to accomplish a
function, thereby yielding new signal.
• Example: telephone connection
5. Chapter 1 : Signals And Systems
e) Energy and power signals
1.2 Classification of Signals
There are several classes of signals
a) Continuous time and discrete time signals
c) Real and Complex signals
b) Analog and digital signals
f) Periodic and aperiodic signals
d) Even and Odd Signals
6. Chapter 1 : Signals And Systems
a) Continuous time and discrete time signals
• Continuous signals : signal that is specified for a continuum (ALL) values
time t
: can be described mathematically by continuous
function of time as :
x(t) = A sin (ω0 t + ɸ)
where A : Amplitude
ω : Radian freq in rad / sec
ɸ : phase angle in rad / degree
• Discrete time signals : signal that is specified only at discrete values of t
7. Chapter 1 : Signals And Systems
b) Analog and digital signals
• Analog signals : signal whose amplitude can take on any value in a
continuous range
• Digital signals : signal whose amplitude can take only a finite number
of values
(signal which associated with computer since involve
binary 1 / 0 )
8. Chapter 1 : Signals And Systems
Examples of signals
11. Chapter 1 : Signals And Systems
c) Deterministic and probabilistic signals
• Deterministic signals : a signal whose physical description is known
completely either in a mathematical form or a
graphical form and its future value can be determined.
• Probabilistic signals : a signal whose values cannot be predicted precisely
but are known only in terms of probabilistic value such
as mean value / mean-squared value and therefore
the signal cannot be expressed in mathematical form.
12. Chapter 1 : Signals And Systems
d) Energy and power signals
• Energy signals : a signal with finite energy signal
• Power signals : a signal with finite and nonzero power
Finite energy signal Infinite energy signal
13. Chapter 1 : Signals And Systems
1.9 Energy and Power Signals
For an arbitrary signal x(t) , the total energy , E is defined as
The average power , P is defined as
14. Chapter 1 : Signals And Systems
1.9 Energy and Power Signals
Based on the definition , the following classes of signals are defined :
a) x(t) is energy signal if and only if 0 < E < ∞ so that P = 0.
b) x(t) is a power signal if and only if 0 < P < ∞ thus implying that E = ∞.
c) Signals that satisfy neither property are therefore neither energy nor power
signals.
15. Exercise
1) Calculate the total energy of the
rectangular pulse shown in figure.
2) Given a signal as listed below, determine
whether x(t) is energy, power or neither
signal. Justify the answer.
a. x(t) = cos t
b. x(t) = 3 e-4t u(t)
16. Chapter 1 : Signals And Systems
e) Periodic and aperiodic signal
• Periodic signals : signal that repeats itself within a specific time or in
other words, any function that satisfies :
where T is a constant and is called the fundamental period of the function.
• Aperiodic signals : signal that does not repeats itself and therefore does
not have the fundamental period.
( ) ( )f t f t T= +
0
2
ω
π
=T
17. Chapter 1 : Signals And Systems
Examples of signals
Periodic signal Aperiodic signal
19. Chapter 1 : Signals And Systems
e) Periodic and aperiodic signal (continue)
• Any continuous time signal x(t) is classified as periodic if the signal satisfies
the condition :
x(t) = x(t + nT) where n = 1 , 2 , 3 ....
• The sum of two or more signals is periodic if the ratio (evaluation of two
values) of their periods can be expressed as rational number. The new
fundamental period and frequency can be obtained from a periodic signal.
• The sum of two or more signals is aperiodic if the ratio (evaluation of two
values) of their periods is expressed as irrational number and no new
fundamental period can be obtained.
A rational number is a number that can be written as a simple fraction (i.e. as a ratio).
20. • Periodic and aperiodic signal
(continue)
x(t) = x(t + nT)
Tοοοο = 2ππππ / ωωωωοοοο
ΩοΩοΩοΩο = m
2ππππ N
21. Chapter 1 : Signals And Systems
Exercise 1
• Determine whether listed x(t) below is periodic or aperiodic signal. If a signal is
periodic, determine its fundamental period.
a) x(t) = sin 3t
b) x(t) = 2 cos 8πt
c) x(t) = 3 cos (5πt + π/2)
d) x(t) = cos t + sin √2 t
e) x(t) = sin2
t
f) x(t) = e
j[(π/2)t-1]
0
2
ω
π
=T
23. Chapter 1 : Signals And Systems
Exercise 2
• Determine whether the following signals are periodic or aperiodic. Find the
new fundamental period if necessary.
a) x3(t) = 6 x1(t) + 2 x2(t)
b) x5(t) = 6 x1(t) + 2 x2(t) + x4(t)
where : x1(t) = sin 13t
x2(t) = 5 sin (3000t + π/4)
x4(t) = 2 cos (600πt – π/3)
24. Chapter 1 : Signals And Systems
Exercise 3
• Given x1(t) = 2 sin (5t) , x2(t) = 5 sin (3t) and x3(t) = 5 sin (2t + 25o) .
• If x(t) = x1(t) – 3x2(t) + 2x3(t) , determine whether x(t) is periodic or aperiodic
signal .
• If it is periodic, determine it’s period and frequency
25. • Even and Odd
A signal x ( t ) or x[n] is referred to as an even signal if
x ( - t ) = x ( r )
x [ - n ] = x [ n ]
A signal x ( t ) or x[n] is referred to as an odd signal if
x ( - t ) = - x ( t )
x [ - n ] = - x [ n ]
Examples of even and odd signals are shown in Fig. 1-2.
Chapter 1 : Signals And Systems
26.
27. Chapter 1 : Signals And Systems
is an important and unique sub-class of aperiodic signals
they are either discontinuous or continuous derivatives
they are basic signals to represent other signals
0 ; t < 0
1 ; t ≥ 0
u(t) =
u(t)
1
t
a) Unit Step, u(t)
1.4) Singularity Functions
28. Chapter 1 : Signals And Systems
b) Unit Ramp , r(t)
0 ; t < 0
t ; t ≥ 0
r(t) =
r(t)
1
t1
29. Chapter 1 : Signals And Systems
c) Unit impulse, δ(t)
1 ; t = 0
0 ; t ≠ 0
δ(t) =
δ(t)
1
t
30.
31. 1.5) Representation of Signals
A deterministic signal can be represented in
terms of:
1. sum of singularity functions
2. sum of steps functions and
3. piece-wise continuous functions
32. Chapter 1 : Signals And Systems
Sum Of Singularity Function
Express signal in term of sum of singularity function
Note : the ramp function r(t) can be described by step function as :
r(t) = t u(t)
r(t±a) = (t±a) u(t±a)
Example
Express the following signal in term of sum of singularity function.
-1 1
-1
1
x(t)
t
Answer
x(t) = u(t+1) – r(t+1) + r(t-1) + u(t-1)
= u(t+1) – (t+1)u(t+1) + (t-1)u(t-1) + u(t-1)
33. Chapter 1 : Signals And Systems
Exercise
Express the following signals in term of sum of singularity function.
1
-1
1
x(t)
t
2 3 4
-1
2
x(t)
t
1 2-2
1
Answer
x(t)= r(t) – 2r(t-1) + 2r(t+3) +r(t-4)
= r(t) – 2(t-1)u(t-1) +2(t+3)u(t+3) +(t-4)u(t-4)
Answer
x(t) = 2δ(t+2) - u(t+2) + r(t+1) – r(t-1) – u(t-1) + δ(t+2)
34. Chapter 1 : Signals And Systems
Exercise
Sketch the following signal if the sum of singularity function of the signal is given as :
a) x(t) = r(t) + r (t-1) - u (t-1)
b) y(t) = u(t+1)-r(t+1)+r(t-1)+u(t-1)
c) x(t) = r(t) + r(t+1) + 2u(t+1) – r(t+1) + 2r(t) – r(t-1) + u(t-2) – 2u(t-3)
35. Chapter 1 : Signals And Systems
Piece – wise continous function
Description of signal from a general form of y = mx + c
Example
Given the signal x(t) as shown below , express the signals x(t) in terms of piece
wise continuous function
x(t)
-1
t
1 2
1
0
Solution
- t - 1 ; -1 < t < 0
t ; 0 < t < 1
1 ; 1 < t < 2
0 ; elsewhere
x(t) =
36. Chapter 1 : Signals And Systems
Exercise
Example
Given the signal x(t) as shown below , express the signals x(t) in terms of piece
wise continuous function .
x(t)
-1
t
1 2
1
0
37. Chapter 1 : Signals And Systems
Exercise
Example
Given the signal x(t) as shown below , express the signals x(t) in terms of piece
wise continuous function .
x(t)
-1
t
1 2
1
0
Solution
t + 1 ; -1 < t < 0
-1 ; 1 < t < 2
0 ; elsewhere
x(t) =
38. Chapter 1 : Signals And Systems
1.6 Properties of Signals
There are 4 properties of signals
a) Magnitude scaling
b) Time reflection
c) Time scaling
d) Time shifting
39. Chapter 1 : Signals And Systems
a) Magnitude scaling : Any arbitrary real constant is multiplied to a signal and
the result is, for a unit step the amplitude changes, for a unit ramp, the slope
changes and for a unit impulse, the area changes
A
3
t
3u(t)
-A
-2
t
-2u(t)
A
2
t
2r(t)
1
A
-2
t
-2r(t)
A
3
t
3δ(t)
0
A
-0.5
t
-0.5δ(t)
0
slope = 2
slope = -2
40. Chapter 1 : Signals And Systems
b) Time reflection : The mirror image of the signal with respect to the y-axis
u(t)
1
t
u(-t)
r(t)
t
r(-t)
δ(t)
1
t
δ(-t)
00
slope = -1
41. Chapter 1 : Signals And Systems
c) Time scaling : The expansion or compression of the signal with
respect to time t axis
x(kt)
t
x(kt)
b
a
x(0.5t)
t
a
2b
x(2t)
t
x(2t)
0.5b
a
k > 1 compression
k < 1 expansion
42. Chapter 1 : Signals And Systems
f) Time shifting : The shifting of the signal with respect to the x - axis
u(t)
1
t
u(t-1) u(t+1)
r(t)
t
r(t-1)
1
r(t)
t
r(t+1)
δ(t)
3
t
3δ(t-2)
0
-0.5
t
-0.5δ(t+2)
1
u(t)
1
t
-1
slope = 1
slope = 1
2
-2
-1
43. Chapter 1 : Signals And Systems
Example 1
Given the signal x(t) as shown below , sketch y(t) = 3x (1- t/2) .
x(t)
-1
t
1 2
1
0
44. Chapter 1 : Signals And Systems
Example 2
Given the signal x(t) as shown below , sketch y(t) = 2x (-0.5t+1) using both
graphical and analytical method.
x(t)
-1 t
1 2
-1
1
45. Chapter 1 : Signals And Systems
Exercise
Given the signal x(t) as shown below , sketch y(t) = -2x (2-0.5t) + 1
using both graphical and analytical method.
x(t)
-1
t
1 2
-1
1
3-2
46. Chapter 1 : Signals And Systems
1.9 Energy and Power Signals
For an arbitrary signal x(t) , the total energy , E is defined as
The average power , P is defined as
47. Chapter 1 : Signals And Systems
1.9 Energy and Power Signals
Based on the definition , the following classes of signals are defined :
a) x(t) is energy signal if and only if 0 < E < ∞ so that P = 0.
b) x(t) is a power signal if and only if 0 < P < ∞ thus implying that E = ∞.
c) Signals that satisfy neither property are therefore neither energy nor power
signals.
48. Chapter 1 : Signals And Systems
2.0 Classification Of System
i) a) With memory(dynamic) : the present output depends on past
and/or future input.
b) Without memory(static) : the present output depends only on
present input.
ii) a) Causal : the output does not depends on future but can
depends on the past or the present input..
b) Non-causal : the output depends on future input
49. Chapter 1 : Signals And Systems
2.0 Classification Of System
iii) a) Time variant : y2 (t) ≠ y1 (t- to)
: same input produces different output at different time
b) Time invariant : y2 (t) = y1 (t- to)
: same input produces same output at different time
where :
y1 (t- to) is the output corresponding to the time shifting , (t- to) at y1 (t)
y2(t) is the output corresponding to the input x2(t) where x2(t) = x1 (t- to)
50. Chapter 1 : Signals And Systems
2.0 Classification Of System
4) a) Linear : y(t) = ay1(t) + by2(t) (superposition applied)
b) Non linear : y(t) ≠ ay1(t) + by2(t) (superposition not applied )
where :
If an excitation x1[t] causes a response y1[t] and an excitation x2 [t] causes a
response y2[n] , then an excitation :
x [t = ax1[t] + bx2[t] (to be presented as y(t) in solution)
y [t] = ay1[t] + by2[t]
will cause the response
51. Chapter 1 : Signals And Systems
Example
The following system is defined by the input – output relationship where x(t) is the
input and y(t) is the output.
a) y(t) = 10x2 (t+1) e) y’(t) + y(t) = x(t)
b) y(t) = 10x(t) + 5 f) y’(t) + 10y(t) + 5 = x(t)
c) y(t) = cos (t) x(t) + 5 g) y’(t) + 3y(t) = x(t) + 2x2(t)
Determine whether the system is :
i. Static or dynamic
ii. Causal or non-causal
iii. Time - variant or time invariant
iv. Linear or non-linear