This document provides an overview of signals and systems topics for the Graduate Aptitude Test in Engineering (GATE). It begins with an outline of the syllabus, covering continuous and discrete time signals, linear time-invariant systems, and digital filter design techniques. The document then discusses various signal functions like unit impulse, unit step, and sinusoidal signals. It also covers different types of systems including causal/non-causal, linear/nonlinear, time-invariant/time-variant, stable/unstable systems. Examples are provided to illustrate key concepts related to classification of signals and systems.
This document discusses computational simulations of chaotic systems and the challenges of sensitivity analysis and optimization for such systems. It introduces the concept of Least Squares Shadowing as a solution, which formulates the problem as a least squares problem without an initial condition to avoid the divergence of solutions seen in traditional sensitivity analysis of chaotic systems. Algorithms for solving the Least Squares Shadowing problem are also presented.
The document describes methodology for estimating the channel impulse response from acoustic signals transmitted during the SAVEX15 experiment. Stationary source experiments involved transmitting chirp and m-sequence signals from a fixed source location and receiving the signals on a vertical receiver array up to 5 km away. Matched filtering of the received signals with the transmitted source signals was used to estimate the time-varying channel impulse response, which characterizes how the underwater acoustic channel responds to any input signal.
This document outlines the content of a lecture on signals and systems. The key points are:
- Signals represent patterns of variation over time and can be continuous or discrete. Systems process input signals to produce output signals.
- The course will cover time and frequency domain analysis, Laplace transforms, Fourier transforms, sampling theory and z-transforms.
- Students will be assessed via exams, assignments and quizzes. Recommended reading materials are listed.
- The specific lecture will introduce signals, systems, their mathematical representations in continuous and discrete time, and properties like causality, linearity and time-invariance. Exercises are to read the first chapter of a referenced text.
This document provides an overview of an advanced digital signal processing lecture. It discusses pre-requisites for the course including basic signals and communications knowledge and MATLAB proficiency. It outlines the course structure, including chapters covered, textbook references, and assessment breakdown. Key concepts from the first lecture are summarized such as characterizing signals as continuous or discrete, common signal representations including exponentials and sinusoids, and introducing linear time-invariant systems.
This document provides an overview of signals and systems. It begins with an introduction to signals, including definitions of key signal properties such as periodicity, causality, boundedness. It also distinguishes between continuous-time and discrete-time signals. The document then covers fundamental signal types including sinusoidal, exponential, unit step, and impulse signals. It concludes with discussions of signal processing concepts like the Fourier transform and basics of communication systems.
This document discusses using data mining techniques to build process models from full-scale plant data to optimize water and wastewater treatment processes. It provides several case studies where neural networks were used to model relationships between key process variables and contaminant levels. For example, one case study showed turbidity, color, and temperature accounted for 74% of the variability in chloroform levels. The document recommends using process models to predict contaminant levels, optimize chemical dosing, and evaluate "what if" scenarios to reduce operating costs while meeting regulations.
Digital Signal Processing by Dr. R. Prakash Rao Prakash Rao
1. The document discusses digital signal processing and provides an overview of key concepts including signal classification, typical signal processing operations, and Fourier transforms.
2. Signal types are classified based on characteristics like determinism, periodicity, stationarity. Common operations include scaling, delay, addition in time domain and filtering.
3. Fourier analysis decomposes signals into sinusoids using techniques like the discrete Fourier transform and fast Fourier transform. It is useful for analyzing how systems process different frequency components.
Monte Carlo simulations are used in measurement dosimetry to determine correction factors for detectors. The kQ factors in TG-51 are calculated using Monte Carlo techniques. Monte Carlo codes must use accurate cross sections and geometry descriptions to accurately calculate detector responses. Variance reduction techniques are needed to efficiently simulate rare events and reduce uncertainties when using Monte Carlo for dosimetry calculations.
This document discusses computational simulations of chaotic systems and the challenges of sensitivity analysis and optimization for such systems. It introduces the concept of Least Squares Shadowing as a solution, which formulates the problem as a least squares problem without an initial condition to avoid the divergence of solutions seen in traditional sensitivity analysis of chaotic systems. Algorithms for solving the Least Squares Shadowing problem are also presented.
The document describes methodology for estimating the channel impulse response from acoustic signals transmitted during the SAVEX15 experiment. Stationary source experiments involved transmitting chirp and m-sequence signals from a fixed source location and receiving the signals on a vertical receiver array up to 5 km away. Matched filtering of the received signals with the transmitted source signals was used to estimate the time-varying channel impulse response, which characterizes how the underwater acoustic channel responds to any input signal.
This document outlines the content of a lecture on signals and systems. The key points are:
- Signals represent patterns of variation over time and can be continuous or discrete. Systems process input signals to produce output signals.
- The course will cover time and frequency domain analysis, Laplace transforms, Fourier transforms, sampling theory and z-transforms.
- Students will be assessed via exams, assignments and quizzes. Recommended reading materials are listed.
- The specific lecture will introduce signals, systems, their mathematical representations in continuous and discrete time, and properties like causality, linearity and time-invariance. Exercises are to read the first chapter of a referenced text.
This document provides an overview of an advanced digital signal processing lecture. It discusses pre-requisites for the course including basic signals and communications knowledge and MATLAB proficiency. It outlines the course structure, including chapters covered, textbook references, and assessment breakdown. Key concepts from the first lecture are summarized such as characterizing signals as continuous or discrete, common signal representations including exponentials and sinusoids, and introducing linear time-invariant systems.
This document provides an overview of signals and systems. It begins with an introduction to signals, including definitions of key signal properties such as periodicity, causality, boundedness. It also distinguishes between continuous-time and discrete-time signals. The document then covers fundamental signal types including sinusoidal, exponential, unit step, and impulse signals. It concludes with discussions of signal processing concepts like the Fourier transform and basics of communication systems.
This document discusses using data mining techniques to build process models from full-scale plant data to optimize water and wastewater treatment processes. It provides several case studies where neural networks were used to model relationships between key process variables and contaminant levels. For example, one case study showed turbidity, color, and temperature accounted for 74% of the variability in chloroform levels. The document recommends using process models to predict contaminant levels, optimize chemical dosing, and evaluate "what if" scenarios to reduce operating costs while meeting regulations.
Digital Signal Processing by Dr. R. Prakash Rao Prakash Rao
1. The document discusses digital signal processing and provides an overview of key concepts including signal classification, typical signal processing operations, and Fourier transforms.
2. Signal types are classified based on characteristics like determinism, periodicity, stationarity. Common operations include scaling, delay, addition in time domain and filtering.
3. Fourier analysis decomposes signals into sinusoids using techniques like the discrete Fourier transform and fast Fourier transform. It is useful for analyzing how systems process different frequency components.
Monte Carlo simulations are used in measurement dosimetry to determine correction factors for detectors. The kQ factors in TG-51 are calculated using Monte Carlo techniques. Monte Carlo codes must use accurate cross sections and geometry descriptions to accurately calculate detector responses. Variance reduction techniques are needed to efficiently simulate rare events and reduce uncertainties when using Monte Carlo for dosimetry calculations.
Intoduction to Electronics & electronics devicesArslan chohan
This document provides an overview of an Analog Electronics course. It includes:
1. A brief introduction to analog electronics, which involves converting real-world signals into electrical signals and processing those analog signals.
2. An outline of the typical blocks in an analog electronic system, including transducers, filters, amplifiers, and converters between analog and digital domains.
3. Details about the course, including prerequisites, aims to provide a deeper understanding of analog circuit design. The course will cover topics like amplifiers, filters, oscillators, and power supplies through simulations and measurements.
SS - Unit 1- Introduction of signals and standard signalsNimithaSoman
This document provides an introduction to signals and systems. It discusses the classification of signals as continuous-time or discrete-time, periodic or aperiodic, deterministic or random, energy or power signals. It also discusses the classification of systems as continuous-time or discrete-time, linear or nonlinear, time-variant or time-invariant, causal or non-causal, stable or unstable. It then introduces some basic standard signals including step, ramp, impulse, sinusoidal, and exponential signals. It describes the properties and applications of these signals.
Radar 2009 a 11 waveforms and pulse compressionForward2025
The document describes a lecture on radar waveforms and pulse compression. It introduces matched filters and how they are implemented by convolving a reflected echo with a time-reversed transmit pulse. This maximizes the signal-to-noise ratio. Pulse compression techniques like linear frequency modulation and phase coding are then discussed, which allow the use of longer pulses that increase energy while maintaining high range resolution. The goal is to reduce the high peak power needs of short pulses for applications like airborne radar.
This document discusses dynamics of structures with uncertainties. It begins with an introduction to stochastic single degree of freedom systems and how natural frequency variability can be modeled using probability distributions. It then discusses how to extend this approach to stochastic multi degree of freedom systems using stochastic finite element formulations and modal projections. Key challenges with statistical overlap of eigenvalues are noted. The document provides mathematical models of equivalent damping in stochastic systems and examples of stochastic frequency response functions.
The document contains sample tasks and answers related to engineering concepts. Task 1 covers SI base units, derived units, and unit conversions. Task 2 discusses qualitative and quantitative research methods. Task 3 provides examples of qualitative and quantitative case studies. Subsequent tasks cover topics such as forces, moments, shear force diagrams, bending moment diagrams, buoyancy, material properties, heat transfer, electrical circuits, and more. The document serves as a reference for various engineering calculations and concepts.
Ch1 EE412 Introduction to DSP and .pptssuser3312b5
DSP stands for Digital Signal Processing. It's a branch of engineering that deals with the manipulation of digital signals using algorithms and mathematical techniques. DSP is used in a wide range of applications such as audio and speech processing, image and video processing, communications, radar, sonar, medical imaging, and many more. It's a fundamental technology underlying many modern electronic devices and syst
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.
This document outlines the course content for a signals and systems course focusing on analysis and design of filters. It discusses key topics like ideal and practical filter characteristics, linearity, causality, time-invariance, frequency response, and the Butterworth approximation for designing realistic low-pass, high-pass, band-pass and band-stop filters. Grading policies are also provided, distinguishing between 1st and 2nd year plans based on this topic and random signals.
ders 3.3 Unit root testing section 3 .pptxErgin Akalpler
The document discusses various unit root tests used to determine if a time series is stationary or non-stationary. It describes the Dickey-Fuller test and Augmented Dickey-Fuller test, which test for a unit root in a time series. The Augmented Dickey-Fuller test extends the Dickey-Fuller test by including lagged difference terms to account for autocorrelation. The tests are used to distinguish between trend-stationary and difference-stationary processes, which have different implications for forecasting and detecting spurious relationships between variables.
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.
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 experiment measured the firing rate of neurons in the ventral nerve cord of an anesthetized cockroach when exposed to different sound frequencies, including 200Hz, 400Hz, 600Hz, and an ultrasonic pest repellent of 25,000Hz. Eight trials were conducted for each frequency, and the average firing rate was calculated and statistically analyzed to determine the effect of different sound frequencies on neural activity. Electrodes were used to record extracellular action potentials from neurons and an oscilloscope and statistical tests analyzed the results.
Sampling and Reconstruction (Online Learning).pptxHamzaJaved306957
1. Sampling and reconstruction of signals was analyzed using the impulse sampling math model.
2. The analysis showed that a bandlimited signal can be perfectly reconstructed from its samples as long as the sampling rate is at least twice the bandwidth of the signal.
3. If the sampling rate is lower than the minimum required rate, aliasing error occurs where frequency components fold back into the baseband.
A signal is a pattern of variation that carry information.
Signals are represented mathematically as a function of one or more independent variable
basic concept of signals
types of signals
system concepts
An Application of Uncertainty Quantification to MPMwallstedt
The document discusses uncertainty quantification (UQ) methods like Latin hypercube sampling that can be applied to material point method (MPM) simulations to characterize outputs given uncertain inputs. It provides examples of using UQ on an MPM cantilever beam model, finding new correlations between inputs like beam thickness and outputs like vibration frequency. Code mistakes were discovered and correcting them led to additional insights from re-running the UQ analysis.
This document describes research on modeling and simulating the sound generated by airflow around a circular cylinder. It begins with the inspiration from an IYPT problem and reviews relevant literature. Background theory is provided on computational aeroacoustics methods like Lighthill's acoustic analogy. The project approach uses 2D and 3D large eddy simulation CFD to model the unsteady flow field, then applies the Ffowcs Williams-Hawkins equation to propagate sound from the source to receivers. Results show good agreement with experiments in drag coefficient, lift coefficient, Strouhal number, and acoustic spectra. Future work is proposed on flexible bodies, rotating cylinders, and propulsion.
Mechanical shock testing is conducted to determine a product's fragility and ruggedness. Shock testing involves subjecting products to short duration pulses of high acceleration to simulate impacts experienced during shipping and handling. There are two main types of shock response: velocity shock and acceleration shock. Shock testing provides information to improve product designs and determine appropriate packaging. Common test procedures include using half sine pulses to evaluate velocity shock and trapezoidal pulses for acceleration shock. Shock testing data is used to establish a product's damage boundary and ensure it can withstand typical distribution environments.
1) The document discusses the seismic behavior of frame structures equipped with passive dampers. It covers damping reduction factors, complex damping theory applied to adjacent buildings connected by dampers, peak interstory velocity profiles, nonlinear viscous damping ratios, and behavior factors for damped structures.
2) Complex damping theory models a damped structure using a generalized single-degree-of-freedom system and complex frequencies and modes. This allows modeling of energy dissipation through damping.
3) Nonlinear viscous dampers can be modeled using an equivalent linear damper. The equivalent damping ratio depends on the maximum displacement and approaches the design damping ratio as the displacement decreases toward the design level.
- The document summarizes a presentation on NCRP Report No. 147 which provides guidance on shielding calculations for diagnostic x-ray equipment.
- It discusses the history and development of the report including previous publications that informed it. It also outlines the key concepts in the report such as controlled/uncontrolled areas, occupancy factors, transmission data, and workload measurements.
- Examples are given of recommended occupancy factors and how transmission curves and workload distributions are used in shielding calculations.
simulated aneeleaning in artificial intelligence .pptxneelamsanjeevkumar
1) The document discusses neural networks and backpropagation. It introduces feedforward neural networks and describes how error backpropagation works by calculating gradients through the network to optimize weights.
2) Error backpropagation involves calculating the gradients of the loss function with respect to the weights in each layer and using these gradients to update weights through gradient descent.
3) Problems with neural networks include difficulty interpreting hidden layers and overfitting, which Bayesian neural networks and regularization can help address.
Intoduction to Electronics & electronics devicesArslan chohan
This document provides an overview of an Analog Electronics course. It includes:
1. A brief introduction to analog electronics, which involves converting real-world signals into electrical signals and processing those analog signals.
2. An outline of the typical blocks in an analog electronic system, including transducers, filters, amplifiers, and converters between analog and digital domains.
3. Details about the course, including prerequisites, aims to provide a deeper understanding of analog circuit design. The course will cover topics like amplifiers, filters, oscillators, and power supplies through simulations and measurements.
SS - Unit 1- Introduction of signals and standard signalsNimithaSoman
This document provides an introduction to signals and systems. It discusses the classification of signals as continuous-time or discrete-time, periodic or aperiodic, deterministic or random, energy or power signals. It also discusses the classification of systems as continuous-time or discrete-time, linear or nonlinear, time-variant or time-invariant, causal or non-causal, stable or unstable. It then introduces some basic standard signals including step, ramp, impulse, sinusoidal, and exponential signals. It describes the properties and applications of these signals.
Radar 2009 a 11 waveforms and pulse compressionForward2025
The document describes a lecture on radar waveforms and pulse compression. It introduces matched filters and how they are implemented by convolving a reflected echo with a time-reversed transmit pulse. This maximizes the signal-to-noise ratio. Pulse compression techniques like linear frequency modulation and phase coding are then discussed, which allow the use of longer pulses that increase energy while maintaining high range resolution. The goal is to reduce the high peak power needs of short pulses for applications like airborne radar.
This document discusses dynamics of structures with uncertainties. It begins with an introduction to stochastic single degree of freedom systems and how natural frequency variability can be modeled using probability distributions. It then discusses how to extend this approach to stochastic multi degree of freedom systems using stochastic finite element formulations and modal projections. Key challenges with statistical overlap of eigenvalues are noted. The document provides mathematical models of equivalent damping in stochastic systems and examples of stochastic frequency response functions.
The document contains sample tasks and answers related to engineering concepts. Task 1 covers SI base units, derived units, and unit conversions. Task 2 discusses qualitative and quantitative research methods. Task 3 provides examples of qualitative and quantitative case studies. Subsequent tasks cover topics such as forces, moments, shear force diagrams, bending moment diagrams, buoyancy, material properties, heat transfer, electrical circuits, and more. The document serves as a reference for various engineering calculations and concepts.
Ch1 EE412 Introduction to DSP and .pptssuser3312b5
DSP stands for Digital Signal Processing. It's a branch of engineering that deals with the manipulation of digital signals using algorithms and mathematical techniques. DSP is used in a wide range of applications such as audio and speech processing, image and video processing, communications, radar, sonar, medical imaging, and many more. It's a fundamental technology underlying many modern electronic devices and syst
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.
This document outlines the course content for a signals and systems course focusing on analysis and design of filters. It discusses key topics like ideal and practical filter characteristics, linearity, causality, time-invariance, frequency response, and the Butterworth approximation for designing realistic low-pass, high-pass, band-pass and band-stop filters. Grading policies are also provided, distinguishing between 1st and 2nd year plans based on this topic and random signals.
ders 3.3 Unit root testing section 3 .pptxErgin Akalpler
The document discusses various unit root tests used to determine if a time series is stationary or non-stationary. It describes the Dickey-Fuller test and Augmented Dickey-Fuller test, which test for a unit root in a time series. The Augmented Dickey-Fuller test extends the Dickey-Fuller test by including lagged difference terms to account for autocorrelation. The tests are used to distinguish between trend-stationary and difference-stationary processes, which have different implications for forecasting and detecting spurious relationships between variables.
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.
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 experiment measured the firing rate of neurons in the ventral nerve cord of an anesthetized cockroach when exposed to different sound frequencies, including 200Hz, 400Hz, 600Hz, and an ultrasonic pest repellent of 25,000Hz. Eight trials were conducted for each frequency, and the average firing rate was calculated and statistically analyzed to determine the effect of different sound frequencies on neural activity. Electrodes were used to record extracellular action potentials from neurons and an oscilloscope and statistical tests analyzed the results.
Sampling and Reconstruction (Online Learning).pptxHamzaJaved306957
1. Sampling and reconstruction of signals was analyzed using the impulse sampling math model.
2. The analysis showed that a bandlimited signal can be perfectly reconstructed from its samples as long as the sampling rate is at least twice the bandwidth of the signal.
3. If the sampling rate is lower than the minimum required rate, aliasing error occurs where frequency components fold back into the baseband.
A signal is a pattern of variation that carry information.
Signals are represented mathematically as a function of one or more independent variable
basic concept of signals
types of signals
system concepts
An Application of Uncertainty Quantification to MPMwallstedt
The document discusses uncertainty quantification (UQ) methods like Latin hypercube sampling that can be applied to material point method (MPM) simulations to characterize outputs given uncertain inputs. It provides examples of using UQ on an MPM cantilever beam model, finding new correlations between inputs like beam thickness and outputs like vibration frequency. Code mistakes were discovered and correcting them led to additional insights from re-running the UQ analysis.
This document describes research on modeling and simulating the sound generated by airflow around a circular cylinder. It begins with the inspiration from an IYPT problem and reviews relevant literature. Background theory is provided on computational aeroacoustics methods like Lighthill's acoustic analogy. The project approach uses 2D and 3D large eddy simulation CFD to model the unsteady flow field, then applies the Ffowcs Williams-Hawkins equation to propagate sound from the source to receivers. Results show good agreement with experiments in drag coefficient, lift coefficient, Strouhal number, and acoustic spectra. Future work is proposed on flexible bodies, rotating cylinders, and propulsion.
Mechanical shock testing is conducted to determine a product's fragility and ruggedness. Shock testing involves subjecting products to short duration pulses of high acceleration to simulate impacts experienced during shipping and handling. There are two main types of shock response: velocity shock and acceleration shock. Shock testing provides information to improve product designs and determine appropriate packaging. Common test procedures include using half sine pulses to evaluate velocity shock and trapezoidal pulses for acceleration shock. Shock testing data is used to establish a product's damage boundary and ensure it can withstand typical distribution environments.
1) The document discusses the seismic behavior of frame structures equipped with passive dampers. It covers damping reduction factors, complex damping theory applied to adjacent buildings connected by dampers, peak interstory velocity profiles, nonlinear viscous damping ratios, and behavior factors for damped structures.
2) Complex damping theory models a damped structure using a generalized single-degree-of-freedom system and complex frequencies and modes. This allows modeling of energy dissipation through damping.
3) Nonlinear viscous dampers can be modeled using an equivalent linear damper. The equivalent damping ratio depends on the maximum displacement and approaches the design damping ratio as the displacement decreases toward the design level.
- The document summarizes a presentation on NCRP Report No. 147 which provides guidance on shielding calculations for diagnostic x-ray equipment.
- It discusses the history and development of the report including previous publications that informed it. It also outlines the key concepts in the report such as controlled/uncontrolled areas, occupancy factors, transmission data, and workload measurements.
- Examples are given of recommended occupancy factors and how transmission curves and workload distributions are used in shielding calculations.
simulated aneeleaning in artificial intelligence .pptxneelamsanjeevkumar
1) The document discusses neural networks and backpropagation. It introduces feedforward neural networks and describes how error backpropagation works by calculating gradients through the network to optimize weights.
2) Error backpropagation involves calculating the gradients of the loss function with respect to the weights in each layer and using these gradients to update weights through gradient descent.
3) Problems with neural networks include difficulty interpreting hidden layers and overfitting, which Bayesian neural networks and regularization can help address.
The document provides an introduction to the back-propagation algorithm, which is commonly used to train artificial neural networks. It discusses how back-propagation calculates the gradient of a loss function with respect to the network's weights in order to minimize the loss through methods like gradient descent. The document outlines the history of neural networks and perceptrons, describes the limitations of single-layer networks, and explains how back-propagation allows multi-layer networks to learn complex patterns through error propagation during training.
This document discusses the design methodology for Internet of Things (IoT) systems. It outlines the 10 steps in the IoT design process: 1) defining the purpose and requirements, 2) specifying the processes, 3) specifying the domain model, 4) specifying the information model, 5) specifying the services, 6) specifying the IoT level, 7) specifying the functional view, 8) specifying the operational view, 9) integrating devices and components, and 10) developing the IoT application. Embedded computing logic and common hardware platforms like Arduino and Raspberry Pi are also discussed.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution such as inheritance, mutation, selection, and crossover. They are commonly used to find optimal or near-optimal solutions to difficult problems by mimicking natural selection. A genetic algorithm initializes a population of random solutions and uses selection, crossover, and mutation to generate new solutions. The fittest solutions survive to be selected for the next generation. This process is repeated until a termination condition is reached. Genetic algorithms are inspired by biological evolution and can be applied to optimization and search problems.
Genetic algorithms are a type of evolutionary algorithm developed in the 1970s. They are inspired by Darwinian evolution and use techniques like mutation, crossover and selection. The original genetic algorithm, called the simple genetic algorithm, represents solutions as binary strings and uses one-point crossover and bit-flip mutation. It has been improved upon with different representations, operators, and selection mechanisms, but provides a useful benchmark for testing new genetic algorithms.
Genetic algorithms are a type of evolutionary algorithm that use techniques inspired by Darwinian evolution, such as inheritance, mutation, selection, and crossover. They are commonly used to generate useful solutions to optimization and search problems by evolving candidate solutions over generations. Genetic algorithms work on a population of candidate solutions represented by chromosomes. They evolve toward better solutions through techniques like selection of the fittest solutions, crossover of parent solutions to create new solutions, and random mutation of new solutions. The algorithm terminates when either a maximum number of generations has been produced or a satisfactory fitness level has been reached in the population.
This document discusses methods for linear model selection and regularization, including subset selection methods like best subset selection and stepwise selection, and shrinkage methods like ridge regression and the lasso. Subset selection methods aim to identify a subset of predictive variables, while shrinkage methods shrink coefficient estimates towards zero to reduce variance. Ridge regression minimizes a loss function with an L2 penalty on coefficients, tending to shrink large coefficients. The lasso uses an L1 penalty instead, allowing some coefficients to be shrunk exactly to zero for variable selection. Cross-validation is used to select the optimal tuning parameter value.
The document discusses Internet of Things (IoT) including its definition, characteristics, architecture layers, technologies, protocols, devices, gateways, clouds, issues and applications. It specifically describes LoRa technology, its architecture and how it enables long range wireless connectivity for IoT applications. Commercial IoT cloud platforms and popular hardware platforms for IoT development are also mentioned.
LoRa is a wireless technology that allows low-powered devices to transmit small amounts of data over long distances using radio frequency. A LoRa end node consists of a radio module and microprocessor that collects and transmits sensor data wirelessly. A LoRa gateway receives data from end nodes and forwards it over the internet to a network server and application server where the data can be processed. LoRaWAN is an open standard protocol defined by the LoRa Alliance that specifies how end nodes and gateways communicate in a star topology over both uplinks and downlinks.
First-order logic extends propositional logic by introducing elements like variables, predicates, quantifiers, and functions that allow representing relationships between objects and the scope of statements. Predicates represent properties or relations that can be true or false for different instances, containing variables that can be substituted. Atomic formulas are the basic building blocks, consisting of a predicate applied to arguments like variables or constants. Complex formulas can then be built by combining atomic formulas using logical connectives like conjunction and negation.
Alpha-beta pruning is an optimization technique used in game tree search to reduce the number of nodes evaluated by the minimax algorithm. It works by pruning branches that cannot possibly change the outcome. This reduces calculation time and allows problems to be solved faster by cutting off parts of the game tree that do not need to be analyzed. Alpha represents the best score the maximizing player can guarantee, and beta represents the best score the minimizing player can guarantee.
The document discusses the history and specifications of the Raspberry Pi, a series of small single-board computers developed in the UK to promote teaching computer science. Key points include:
- The Raspberry Pi was created by the Raspberry Pi Foundation in the UK and first released in 2012. It was inspired by the 1980s BBC Micro computer.
- It is a credit-card sized computer that plugs into a monitor and keyboard. Various models range in price from $5 to $35.
- Models include the Raspberry Pi 1 Model B/B+, Raspberry Pi 2 Model B, and Raspberry Pi 3 Model B. The Pi 3 added WiFi and Bluetooth connectivity.
- The
This document discusses multiple linear regression. It begins by explaining linear regression and its applications. It then discusses multiple linear regression, where there is more than one independent variable. As an example, it describes using multiple linear regression to estimate company profits based on various independent variables. The document provides resources for learning more about linear regression in Python.
Adaline and Madaline are adaptive linear neuron models. Adaline is a single linear neuron that can be trained with the least mean square algorithm or stochastic gradient descent. Madaline is a network of multiple Adalines that can be trained with Madaline Rule II to perform non-linear functions like XOR. Madaline has applications in tasks like echo cancellation, signal prediction, adaptive beamforming antennas, and translation-invariant pattern recognition. Conjugate gradient descent converges faster than gradient descent for minimizing quadratic functions.
Neural networks are parallel computing devices.docx.pdfneelamsanjeevkumar
Neural networks are parallel computing systems modeled after the human brain that can perform tasks like pattern recognition and data analysis. Artificial neural networks (ANNs) are composed of interconnected nodes that operate similarly to biological neurons. ANNs learn by adjusting the weights between nodes from examples to detect patterns in data. The history of ANNs began in the 1940s with early models of neural networks and research into biological neurons. Significant developments continued through the 1960s-1980s with multilayer perceptrons and backpropagation, leading to today's applications of ANNs to complex problems.
This document outlines a course on pattern recognition and neural networks. The course objectives are to introduce students to fundamentals of pattern recognition and its applications, implement supervised and unsupervised algorithms for pattern classification, analyze computational methods like linear discriminant functions and nearest neighbor rule, apply concepts in pattern recognition, image processing and computer vision, and use pattern and neural classifiers for classification applications. On successful completion, students will be able to implement fundamentals of pattern recognition and neural networks and design/apply different pattern recognition techniques to applications of interest. The course contains 5 units covering topics like introduction to pattern recognition and supervised learning, unsupervised learning and clustering analysis, introduction to simple neural networks, backpropagation and associative memory, and neural networks based on
This document outlines a course on pattern recognition and neural networks. The course objectives are to introduce students to fundamentals of pattern recognition and its applications, implement supervised and unsupervised algorithms for pattern classification, analyze computational methods like linear discriminant functions and nearest neighbor rule, apply concepts in pattern recognition, image processing and computer vision, and use pattern and neural classifiers for classification applications. On successful completion, students will be able to implement fundamentals of pattern recognition and neural networks and design/apply different pattern recognition techniques to applications of interest. The course contains 5 units covering topics like introduction to pattern recognition and supervised learning, unsupervised learning and clustering analysis, introduction to simple neural networks, backpropagation and associative memory, and neural networks based on
This document discusses back-propagation networks and learning rules. It explains that back-propagation networks can have hidden layers that allow them to represent more complex relationships than perceptrons. The learning rule for back-propagation networks involves calculating error terms that are used to adjust the weights in the network to reduce error. An example of using a back-propagation network to classify jets and sharks is also presented.
This document discusses backpropagation, an algorithm used to train feedforward neural networks. It begins by explaining gradient descent and how it is used to minimize error in the network by adjusting weights. It then describes how backpropagation specifically works to calculate the gradient of the error with respect to the weights in each layer by propagating error backwards from the output layer through the hidden layers. The general backpropagation rule is provided to update weights based on this error gradient calculation.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
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1. 1
SIGNALS AND SYSTEMS
For
Graduate Aptitude Test in Engineering
SIGNALS AND SYSTEMS
For
Graduate Aptitude Test in Engineering
By
Dr. N. Balaji
Professor of ECE
JNTUK
2. 2
Session: 2
Topic : Classification of Signals and Systems
Date : 12.05.2020
Session: 2
Topic : Classification of Signals and Systems
Date : 12.05.2020
By
Dr. N. Balaji
Professor of ECE
3. 3
Syllabus
Syllabus
• Continuous-time signals: Fourier series and Fourier transform representations,
sampling theorem and applications; Discrete-time signals: discrete-time Fourier
transform (DTFT), DFT, FFT, Z-transform, interpolation of discrete-time signals;
• LTI systems: definition and properties, causality, stability, impulse response,
convolution, poles and zeros, parallel and cascade structure, frequency response,
group delay, phase delay, digital filter design techniques.
4. 4
Contents
Contents
Unit Impulse, Unit Step, Unit Ramp functions and their Properties
Example Problem on properties of the functions.
Classification of Systems
Causal and Non-causal Systems
Linear and Non Linear Systems
Time Variant and Time-invariant Systems
Stable and Unstable Systems
Static and Dynamic Systems
Invertible and non-invertible Systems
Solved Problems of previous GATE Exam
5. 5
Unit Impulse Function
Unit Impulse Function
One of the more useful functions in the study of linear systems is an Unit Impulse Function.
An ideal impulse function is a function that is zero everywhere but at the origin, where it is
infinitely high. However, the area of the impulse is finite.
• The unit impulse function,
= undefined for t=0 and has the following special property
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
6. 6
Unit Impulse Function
Unit Impulse Function
• A consequence of the delta function is that it can be approximated by a narrow pulse as
the width of the pulse approaches zero while the area under the curve =1.
→
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
7. 7
Unit Impulse Function
Unit Impulse Function
;
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
8. 8
Representation of Impulse Function
Representation of Impulse Function
• The area under an impulse is called its strength or weight. It is represented graphically
by a vertical arrow. An impulse with a strength of one is called a unit impulse.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
9. 9
Unit Impulse Train
Unit Impulse Train
• The unit impulse train is a sum of infinitely uniformly- spaced impulses and is given by
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
10. 10
• is an even signal
• It is a neither energy nor power signal.
• Weight/strength of impulse
• Area of weighted impulse
= weight of impulse
Properties of an Impulse Function
Properties of an Impulse Function
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
11. 11
• Eg:-
• Scaling property of impulse:-
= )
Scaling Property of an Impulse Function
Scaling Property of an Impulse Function
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
12. 12
Multiplication property of an Impulse Function
Multiplication property of an Impulse Function
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
13. 13
Multiplication property of an Impulse Function
Multiplication property of an Impulse Function
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
14. 14
Sampling Property of an Impulse Function
Sampling Property of an Impulse Function
• The Sampling Property
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
15. 15
Example Problem based on Sampling property
Example Problem based on Sampling property
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
16. 16
Proof of Sampling Property
Proof of Sampling Property
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
17. 17
Example problem on Sampling Property
Example problem on Sampling Property
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
19. 19
Example problem on Sampling Property
Example problem on Sampling Property
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
20. 20
Derivatives of impulse function
Derivatives of impulse function
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
21. 21
Example problem for Derivatives of an Impulse function
Example problem for Derivatives of an Impulse function
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
22. 22
Unit Step Function
Unit Step Function
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
23. 23
Unit Ramp Function
Unit Ramp Function
, 0
ram p u u
0 , 0
t
t t
t d t t
t
•The unit ramp function is the integral of the unit step function.
•It is called the unit ramp function because for positive t, its slope is one amplitude
unit per time.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
24. 24
Relation among Ramp, Step and Impulse Signals
Relation among Ramp, Step and Impulse Signals
Acknowledgement : Lecture slides from http://DrSatvir.in
25. 25
Sinusoidal and Exponential Signals
Sinusoidal and Exponential Signals
Sinusoids and exponentials are important in signal and system analysis because
they arise naturally in the solutions of the differential equations.
Sinusoidal Signals can expressed in either of two ways :
cyclic frequency form- A sin (2Пfot) = A sin(2П/To)t
radian frequency form- A sin (ωot)
ωo = 2Пfo = 2П/To
To = Time Period of the Sinusoidal Wave
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
26. 26
Sinusoidal and Exponential Signals Contd.
Sinusoidal and Exponential Signals Contd.
x(t) = A sin (2Пfot+ θ)
= A sin (ωot+ θ)
x(t) = Aeat Real Exponential
= Aejω̥t = A[cos (ωot) +j sin (ωot)] Complex Exponential
θ = Phase of sinusoidal wave
A = amplitude of a sinusoidal or exponential signal
fo = fundamental cyclic frequency of sinusoidal signal
ωo = radian frequency
Sinusoidal signal
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
27. 27
x(t) = e-at x(t) = eαt
Real Exponential Signals and damped Sinusoidal
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
28. 28
Signum Function
Signum Function
1 , 0
sg n 0 , 0 2 u 1
1 , 0
t
t t t
t
Precise Graph Commonly-Used Graph
The Signum function, is closely related to the unit-step
function.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
29. 29
Rectangular Pulse or Gate Function
Rectangular Pulse or Gate Function
Rectangular pulse,
1 / , / 2
0 , / 2
a
a t a
t
t a
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
30. 30
The Unit Triangle Function
The Unit Triangle Function
A triangular pulse whose height and area are both one but its base width is not one, is called unit triangle function. The
unit triangle is related to the unit rectangle through an operation called convolution.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
31. 31
Sinc Function
Sinc Function
The unit Sinc function is
related to the unit Rectangle
function through the Fourier
Transform.
It is used for noise removal in
signals
sin
sinc
t
t
t
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
35. 35
Introduction to System
Introduction to System
• Systems process input signals to produce output signals
• A system is a combination of elements that processes one or
more signals to accomplish a function and produces output.
system
output signal
input signal
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
36. 36
Types of Systems
Types of Systems
• Causal and non-causal
• Linear and Non Linear
• Time Variant and Time-invariant
• Stable and Unstable
• Static and Dynamic
• Invertible and non-invertible Systems
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
37. 37
Causal, Anti Causal and Non-Causal Signals
Causal, Anti Causal and Non-Causal Signals
• Causal signals are signals that are zero
for all negative time(or spatial
positions).
• Anticausal are signals that are zero for
all positive time.
• Non-causal signals are signals that
have nonzero values in both positive
and negative time.
Causal signal
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
38. 38
Causal and Non-causal Systems
Causal and Non-causal Systems
• Causal system : A system is said to be causal if the present
value of the output signal depends only on the present
and/or past values of the input signal.
• Examples: 1. y[n]=x[n]+1/2x[n-1]
2. y(t) = x(t)
3. y(t) = x(t-1)
4. y(t) = x(t) + x(t-1)
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
39. 39
Causal and Non-causal Systems
• Non-causal system : A system is said to be Non-causal if the present
value of the output signal depends also on the future values of the
input signal.
• Example: 1. y[n]=x[n+1]+1/2x[n-1]
2. y(t) = x(t+1)
3. y(t) = x(t) + x(t+1)
4. y(t) = x(t-1) + x(t+1)
5. y(t) = x(t-1) + x(t) + x(t+1)
40. 40
Exercise on Causal and Non-causal Systems
Exercise on Causal and Non-causal Systems
Q) Check whether the following are casual or non-casual
system.
1. y(t) = x(2t) 7. y(t) =
2. y(t) = x(-t) 8. y(t) =
3. y(t)= x(sin t) 9.y(t) =
4. y
5. y(t) = odd [x(t)]
6. y(t) = sin (t+2) x(t-1)
41. 41
Solution to the Problems
1. y(t) = x(2t)
Substitute t=1 in the above then y(1) = x(2)
Hence the given System is Non-Casual
2. y(t) = x(-t)
Substitute t=1 in the above then y(-1) = x(1)
Hence the given System is System is non-casual
3. y(t) = x(sin t)
Substitute t= - in the above y(-Π) = x(0) (- Π = -3.14)
System is non-casual
42. 42
Solution to the Problems
Solution to the Problems
4. y
substitute t= -1 y(-1) = x(-2), which is past value of input.
0, substitute t = 1 y(1) = x(0), which is past.
System is Casual.
5. y(t) = odd x(t)
y(t) =
x(t) – x(−t)
substitute t=-1 then y(-1) =
x(−1) – x(1)
, which
is dependent on future value. Hence the given System is non-casual
43. 43
Solution to the Problems on Causal and non-causal System
Solution to the Problems on Causal and non-causal System
6. y(t) = sin(t+2) x(t-1)
put t = 1
y(1) = sin(3) x(0)
Constant coefficient Past value
System is casual
7. y(t) =
y(t) =
Present output depends on present
and past values
Hence, the system is casual
8. y(t) =
y(t) =
Present output depends on future values
also
Hence, the system is non-casual
9. y(t) =
y(t) =
Present output depends on future values
also. Hence, the system is non-casual
44. 44
Linear and Non Linear Systems
Linear and Non Linear Systems
• A system is said to be linear if it satisfies the principle of superposition or if it satisfies the
properties of Homogeneity and Additivity.
• Consider a system where an input of x1[t] produces an output of y1[t]. Further suppose
that a different input, x2[t], produces another output, y2[t]. The system is said to
be additive, if an input of x1[t] + x2[t] results in an output of y1[t] + y2[t], for all possible
input signals.
• Homogeneity means that a change in the input signal's amplitude results in a
corresponding change in the output signal's amplitude. In mathematical terms, if an input
signal of x[t] results in an output signal of y[t], an input of cx[t] results in an output
of cy[t], for any input signal and c is a constant.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
46. 46
For linearity:
1.Output should be zero for zero input.
2.There should not be any nonlinear operation
Example : The functions like Sin, Cos, tan, Cot, Sec,
cosec, Log, Exponential, Modulus, Square, Cube, Root,
Sampling function(), sinc(), Sgn() etc.…. have nonlinear
operations.
Linearity Condition
Linearity Condition
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
47. 47
Problem based on Linearity and Non-Linearity
Problem based on Linearity and Non-Linearity
1. y(t) = x(t) + 2
If input is (t), then (t) is output
If input is (t) then (t) is output
If input is (t) + (t) then the output must be (t) + (t)
(t) (t) = (t) + 2
(t) (t) = (t) + 2
(t) + (t) (t) + (t)
(t) + 2 + (t) + 2
= (t) + (t) + 4
(t) + (t) ≠ (t) + (t) + 4
Hence the system is non-linear
48. 48
2.
Check whether the given system is linear or nonlinear
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
49. 49
Time Invariant and Time Variant Systems
Time Invariant and Time Variant Systems
• A system is said to be time invariant if a time delay or time advance of
the input signal leads to a identical time shift in the output signal.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
51. 51
Time Variant and Time in Variant System
Time Variant and Time in Variant System
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
55. 55
Stable and Unstable Systems
Stable and Unstable Systems
• A system is said to be bounded-input bounded- output stable
(BIBO stable) if every bounded input results in a bounded
output.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
56. 56
Stable and Unstable Systems Contd.
Stable and Unstable Systems Contd.
Example
y[n]=1/3(x[n]+x[n-1]+x[n-2])
3
y[n]
1
x[n] x[n 1] x[n 2]
1
(| x[n]| | x[n 1]| | x[n 2]|)
3
x x x x
1
(M M M ) M
3
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
57. 57
Stable and Unstable Systems Contd.
Stable and Unstable Systems Contd.
Example: The system represented by y(t) = A x(t) is
unstable ; A˃1
Reason: let us assume x(t) = u(t), then at every instant
u(t) will keep on multiplying with A and
hence it will not result in a bounded value and it may
tend to infinite value.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
58. 58
Stable and Unstable Systems
Stable and Unstable Systems
1. y(t) = x(t) +2
put x(t) = 10
y(t) = 10 + 2
= 12
As input is bounded value, output is also a bounded value.
Hence System is Stable
2. y(t) = t x(t)
put x(t) = 10
y(t) = 10t
As ‘t’ can be any value between -∞ to ∞,
y(t) is unbounded. Hence System is Unstable
59. 59
Problems on Stable and Unstable Systems
Problems on Stable and Unstable Systems
3. y(t) =
put x(t) = 2
y(2) =
When ‘t’ is 0 and Π, then sin(t) has values of sin 0 = 0 and
Sin(Π) = 0 respectively.
Therefore, y(t) = i.e., y(t) is unstable as output is not bounded
60. 60
Static Systems
Static Systems
• A static system is memoryless system
• It has no storage devices
• Its output signal depends on present values of the input
signal
• For example
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
61. 61
Dynamic Systems
Dynamic Systems
• A dynamic system possesses memory
• It has the storage devices
• A system is said to possess memory if its output signal
depends on past values and future values of the input signal
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
62. 62
Example: Static or Dynamic?
Example: Static or Dynamic?
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
63. 63
Example: Static or Dynamic?
Example: Static or Dynamic?
Answer:
• The system shown above is RC circuit
• R is memoryless
• C is memory device as it stores charge because of which
voltage across it can’t change immediately
• Hence given system is dynamic or memory system
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
65. 65
Exercise Problems
Exercise Problems
Check whether given system is Static or Dynamic
1. y(t) = x(t) + x(t-1)
2. y(t) = x(-t)
3. y(t) = x(sin t)
4. y(t) = x(t-1)
5. y(t) = Even [x(t)]
6. y(t) = Real [x(t)]
66. 66
Invertible & Non-invertible Systems
Invertible & Non-invertible Systems
• If a system is invertible if it has an Inverse System. Otherwise it is
non-invertible system
• Example: y(t)=2x(t)
– System is invertible must have inverse, that is:
– For any x(t) we get a distinct output y(t)
– Thus, the system must have an Inverse
• x(t)=1/2 y(t)=z(t)
y(t)
System
Inverse
System
x(t) x(t)
y(t)=2x(t)
System
(multiplier)
Inverse
System
(divider)
x(t) x(t)
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
67. 67
Check whether the following Systems are invertible
Check whether the following Systems are invertible
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
68. 68
Check whether the following Systems are invertible
Check whether the following Systems are invertible
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
69. 69
Check whether the following Systems are invertible
Check whether the following Systems are invertible
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
71. 71
Gate 2013 question
Gate 2013 question
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
72. 72
Gate 2013 solution
Gate 2013 solution
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
73. 73
Gate 2011 question
Gate 2011 question
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
74. 74
Gate 2011 solution
Gate 2011 solution
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
75. 75
Gate 2010 question
Gate 2010 question
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
76. 76
Gate 2010 solution
Gate 2010 solution
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
77. 77
Gate 2008 question
Gate 2008 question
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
78. 78
Gate 2008 solution
Gate 2008 solution
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
79. 79
Gate 2005 question
Gate 2005 question
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
80. 80
Gate 2005 solution
Gate 2005 solution
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
81. 81
Gate 2004 question
Gate 2004 question
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
82. 82
Gate 2004 solution
Gate 2004 solution
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
83. 83
Gate 2004 question
Gate 2004 question
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
84. 84
Gate 2004 solution
Gate 2004 solution
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff
85. 85
Sources and Reference Material
Sources and Reference Material
Sources:
i) Lecture slides of Michael D. Adams and
ii) Lecture slides of Prof. Paul Cuff
iii) Solved Problems from Standard Textbooks.
Disclaimer: The material presented in this presentation is taken
from various standard Textbooks and Internet Resources and the
presenter is acknowledging all the authors.
Acknowledgement : Lecture slides of Michael D. Adams and Prof. Paul Cuff