In DSP to solve a convolution of a long duration sequence there are two popular methods. Overlap Add, Overlap Save. In this presentation i've discussed about both.
- Gourab Ghosh
The document summarizes key properties of the discrete Fourier transform (DFT). It describes linearity, periodicity, time/frequency shifts, conjugation, multiplication, convolution, correlation, and Parseval's theorem. Linearity means the DFT of a linear combination of signals is the linear combination of the DFTs. Periodicity means an N-point DFT is periodic with N samples. Shifts change the time or frequency domain representation. Multiplication in the time domain is convolution in the frequency domain. Correlation relates the time and frequency domain representations. Parseval's theorem relates the energy in the time and frequency domains.
Windowing techniques of fir filter designRohan Nagpal
Windowing techniques are used in FIR filter design to convert an infinite impulse response to a finite impulse response. The process involves choosing a desired frequency response, taking the inverse Fourier transform to get the impulse response, multiplying the impulse response by a window function, and realizing the filter. Common window functions include rectangular, Hanning, Hamming, and Blackman windows, which are selected based on the required stopband attenuation. The windowing technique allows designing FIR filters with a simple process but lacks flexibility compared to other design methods.
IIR filter realization using direct form I & IISarang Joshi
The document discusses IIR filter realization using Direct Form I and Direct Form II structures. It presents the difference equation and transfer function for an IIR filter. It also provides examples of implementing IIR filters using Direct Form I and Direct Form II structures based on a given difference equation or transfer function.
This document summarizes key aspects of the discrete Fourier transform (DFT). It defines the DFT, provides the formula for calculating it, and explains that the DFT transforms a discrete-time signal from the time domain to the frequency domain. It also outlines several important properties of the DFT, including linearity, shift property, duality, symmetry, and circular convolution. Examples are provided to illustrate duality and symmetry. References for further information on the discrete Fourier transform are also included.
The document discusses the z-transform, which is a mathematical tool used to analyze discrete-time control systems. The z-transform plays a similar role for discrete-time systems as the Laplace transform does for continuous-time systems. Some key properties of the z-transform discussed include the region of convergence, properties and theorems like the shifting theorem and initial/final value theorems, and applications to feedback control systems.
The document discusses the Radix-2 discrete Fourier transform (DFT) algorithm. It explains that the Radix-2 DFT divides an N-point sequence into two N/2-point sequences, computes the DFT of each subsequence, and then combines the results to compute the N-point DFT. It involves decimating the sequence, computing smaller DFTs, and combining results over multiple stages. The Radix-2 algorithm reduces the computation from O(N^2) for the direct DFT to O(NlogN) operations.
This document discusses memory interfacing with the 8085 microprocessor. It begins by describing the different types of computer memory, including primary/volatile memory (RAM and ROM) and secondary/non-volatile memory (magnetic tapes, disks, optical disks). It then discusses how the 8085 microprocessor interfaces with memory chips through an interface circuit. The interface circuit matches the memory chip signals to the microprocessor address and control signals. Memory interfacing involves selecting the appropriate memory chip, identifying the correct register using address lines, and enabling read/write buffers using control signals.
The document summarizes key properties of the discrete Fourier transform (DFT). It describes linearity, periodicity, time/frequency shifts, conjugation, multiplication, convolution, correlation, and Parseval's theorem. Linearity means the DFT of a linear combination of signals is the linear combination of the DFTs. Periodicity means an N-point DFT is periodic with N samples. Shifts change the time or frequency domain representation. Multiplication in the time domain is convolution in the frequency domain. Correlation relates the time and frequency domain representations. Parseval's theorem relates the energy in the time and frequency domains.
Windowing techniques of fir filter designRohan Nagpal
Windowing techniques are used in FIR filter design to convert an infinite impulse response to a finite impulse response. The process involves choosing a desired frequency response, taking the inverse Fourier transform to get the impulse response, multiplying the impulse response by a window function, and realizing the filter. Common window functions include rectangular, Hanning, Hamming, and Blackman windows, which are selected based on the required stopband attenuation. The windowing technique allows designing FIR filters with a simple process but lacks flexibility compared to other design methods.
IIR filter realization using direct form I & IISarang Joshi
The document discusses IIR filter realization using Direct Form I and Direct Form II structures. It presents the difference equation and transfer function for an IIR filter. It also provides examples of implementing IIR filters using Direct Form I and Direct Form II structures based on a given difference equation or transfer function.
This document summarizes key aspects of the discrete Fourier transform (DFT). It defines the DFT, provides the formula for calculating it, and explains that the DFT transforms a discrete-time signal from the time domain to the frequency domain. It also outlines several important properties of the DFT, including linearity, shift property, duality, symmetry, and circular convolution. Examples are provided to illustrate duality and symmetry. References for further information on the discrete Fourier transform are also included.
The document discusses the z-transform, which is a mathematical tool used to analyze discrete-time control systems. The z-transform plays a similar role for discrete-time systems as the Laplace transform does for continuous-time systems. Some key properties of the z-transform discussed include the region of convergence, properties and theorems like the shifting theorem and initial/final value theorems, and applications to feedback control systems.
The document discusses the Radix-2 discrete Fourier transform (DFT) algorithm. It explains that the Radix-2 DFT divides an N-point sequence into two N/2-point sequences, computes the DFT of each subsequence, and then combines the results to compute the N-point DFT. It involves decimating the sequence, computing smaller DFTs, and combining results over multiple stages. The Radix-2 algorithm reduces the computation from O(N^2) for the direct DFT to O(NlogN) operations.
This document discusses memory interfacing with the 8085 microprocessor. It begins by describing the different types of computer memory, including primary/volatile memory (RAM and ROM) and secondary/non-volatile memory (magnetic tapes, disks, optical disks). It then discusses how the 8085 microprocessor interfaces with memory chips through an interface circuit. The interface circuit matches the memory chip signals to the microprocessor address and control signals. Memory interfacing involves selecting the appropriate memory chip, identifying the correct register using address lines, and enabling read/write buffers using control signals.
Coherent and Non-coherent detection of ASK, FSK AND QASKnaimish12
This document discusses different digital communication techniques including coherent and non-coherent detection methods for amplitude shift keying (ASK), frequency shift keying (FSK) and quadrature amplitude shift keying (QASK). Coherent detection requires a reference carrier wave and exploits phase information, while non-coherent detection does not require a reference wave. It then describes the receiver designs for coherent and non-coherent detection of ASK and FSK. For QASK, it outlines raising the input signal to the fourth power before bandpass filtering and frequency division to recover the transmitted bit sequence.
This presentation discusses the Serial Communication features in 8051, the support for UART. It also discusses serial vs parallel communication, simplex, duplex and full-duplex modes, MAX232, RS232 standards
The document discusses linear time-invariant (LTI) systems. It explains that:
1) The response of an LTI system to any input can be found by convolving the system's impulse response with the input. This is done using a convolution sum in discrete time and a convolution integral in continuous time.
2) Discrete-time signals and continuous-time signals can both be represented as weighted sums or integrals of shifted impulse functions.
3) For LTI systems, the impulse responses are simply time-shifted versions of the same underlying function, allowing the system to be fully characterized by its impulse response.
The document discusses decimation in time (DIT) and decimation in frequency (DIF) fast Fourier transform (FFT) algorithms. DIT breaks down an N-point sequence into smaller DFTs of even and odd indexed samples, recursively computing smaller and smaller DFTs until individual points remain. DIF similarly decomposes the computation but by breaking the frequency domain spectrum into smaller DFTs. Both algorithms reduce the computational complexity of computing the discrete Fourier transform from O(N^2) to O(NlogN) operations.
This document discusses linear time-invariant (LTI) systems and convolution. Convolution is a fundamental concept in signal processing that is used to determine the output of an LTI system given its impulse response and an input signal. The convolution of two signals is obtained by decomposing the input signal into scaled and shifted impulses, taking the scaled and shifted impulse response for each impulse, and summing them to find the overall output. Convolution amplifies or attenuates different frequency components of the input independently. It plays an important role in applications like image processing and edge detection. Examples are provided to demonstrate evaluating convolution of periodic sequences.
Nyquist criterion for distortion less baseband binary channelPriyangaKR1
binary transmission system
From design point of view – frequency response of the channel and transmitted pulse shape are specified; the frequency response of the transmit and receive filters has to be determined so as to reconstruct [bk]
The document discusses the Fast Fourier Transform (FFT) algorithm. It begins by explaining how the Discrete Fourier Transform (DFT) and its inverse can be computed on a digital computer, but require O(N2) operations for an N-point sequence. The FFT was discovered to reduce this complexity to O(NlogN) operations by exploiting redundancy in the DFT calculation. It achieves this through a recursive decomposition of the DFT into smaller DFT problems. The FFT provides a significant speedup and enables practical spectral analysis of long signals.
The document summarizes different types of shifters used in microprocessor design including logical, arithmetic, barrel, and funnel shifters. It describes the function of each shifter type and provides examples. It then focuses on funnel shifters, explaining they can perform all shift operations, and describes two types of funnel shifter designs - array and multilevel funnel shifters. The array design uses an array of multiplexers while the multilevel design uses multiple levels of smaller multiplexers.
This document discusses multirate digital signal processing. It explains that multirate systems use multiple sampling rates to process digital signals. Common operations in multirate systems are decimation, which decreases the sampling rate, and interpolation, which increases it. Decimation and interpolation can be realized through filtering and downsampling/upsampling. The document also provides examples of multirate applications like digital audio conversion and discusses tools like polyphase filters used in multirate signal processing.
The document discusses sampling theory and analog-to-digital conversion. It begins by explaining that most real-world signals are analog but must be converted to digital for processing. There are three steps: sampling, quantization, and coding. Sampling converts a continuous-time signal to a discrete-time signal by taking samples at regular intervals. The sampling theorem states that the sampling frequency must be at least twice the highest frequency of the sampled signal to avoid aliasing. Finally, it provides an example showing how to calculate the minimum sampling rate, or Nyquist rate, given the highest frequency of a signal.
This document discusses linear time-invariant (LTI) systems in discrete time. It introduces the convolution sum representation of LTI systems, where the output of an LTI system with impulse response h[n] and input x[n] is given by y[n]=x[n]*h[n]=∑k x[k]h[n-k]. Several examples are worked through to demonstrate calculating the output of an LTI system given its impulse response and input. The document also discusses representing discrete time signals as the sum of shifted unit impulse functions and properties of LTI systems like time-invariance.
The document discusses the Fast Fourier Transform (FFT) algorithm. It explains that FFT reduces the number of computations needed to calculate the Discrete Fourier Transform (DFT) of a sequence by decomposing the DFT into successive DFTs of smaller sizes. Specifically, it breaks down the N point DFT into multiple N/2 point DFTs recursively until it reaches DFTs of size 1. This decomposition reduces the complexity from O(N^2) for DFT to O(NlogN) for FFT.
The document provides an introduction to microcontrollers, specifically focusing on the Intel 8051 microcontroller. It defines microcontrollers and distinguishes them from microprocessors by noting that microcontrollers contain peripherals like RAM, ROM, I/O ports and timers on a single chip, while microprocessors require external circuitry. It then describes the architecture and features of the Intel 8051 microcontroller, including its 4KB program memory, 128 bytes of data memory, 32 general purpose registers, two timers, interrupts and I/O ports. Development tools for microcontrollers like editors, assemblers, compilers and debuggers/simulators are also discussed.
1. The document discusses different types of waveguides including parallel plate, rectangular, and circular waveguides. It provides information on their modes of propagation, field components, cutoff frequencies, and other related parameters.
2. Formulas are presented for calculating propagation constants, cutoff frequencies, wavelengths, velocities, and impedances for TE and TM waves in various waveguide structures.
3. Examples are worked out demonstrating the application of the formulas to determine parameters for given waveguide geometries and operating frequencies.
5. convolution and correlation of discrete time signals MdFazleRabbi18
This document discusses convolution and correlation of discrete time signals. It defines convolution as a mathematical way of combining two signals to form a third signal, which is equivalent to finite impulse response filtering. Convolution relates the input, output, and impulse response of a linear time-invariant system. The document also provides examples of discrete linear convolution and periodic convolution. It then defines correlation as a measure of similarity between signals, discussing cross-correlation and auto-correlation, and providing examples of calculating each.
This presentation covers:
Some basic definitions & concepts of digital communication
What is Phase Shift Keying(PSK) ?
Binary Phase Shift Keying – BPSK
BPSK transmitter & receiver
Advantages & Disadvantages of BPSK
Pi/4 – QPSK
Pi/4 – QPSK transmitter & receiver
Advantages of Pi/4- QPSK
The overlap-add method is an efficient way to evaluate the discrete convolution of long signals using short blocks. It involves breaking the long signals into shorter blocks, zero-padding the blocks, convolving them, and adding the results. For example, an input signal of length N is broken into blocks of length M, with M-1 zeros added to each block. The blocks are convolved with the impulse response and added to produce the final output. This method was used to convolve an example input signal of length 10 with an impulse response of length 3.
This document summarizes key concepts about linear time-invariant (LTI) systems including:
1. LTI systems exhibit superposition and the output is the summation of individual impulse responses.
2. Inputs can be represented as a linear combination of shifted unit impulses. The output is the convolution sum of the input and impulse response.
3. Convolution represents the output of an LTI system and can be computed using a summation or integral. Common properties like commutativity and distributivity apply.
Coherent and Non-coherent detection of ASK, FSK AND QASKnaimish12
This document discusses different digital communication techniques including coherent and non-coherent detection methods for amplitude shift keying (ASK), frequency shift keying (FSK) and quadrature amplitude shift keying (QASK). Coherent detection requires a reference carrier wave and exploits phase information, while non-coherent detection does not require a reference wave. It then describes the receiver designs for coherent and non-coherent detection of ASK and FSK. For QASK, it outlines raising the input signal to the fourth power before bandpass filtering and frequency division to recover the transmitted bit sequence.
This presentation discusses the Serial Communication features in 8051, the support for UART. It also discusses serial vs parallel communication, simplex, duplex and full-duplex modes, MAX232, RS232 standards
The document discusses linear time-invariant (LTI) systems. It explains that:
1) The response of an LTI system to any input can be found by convolving the system's impulse response with the input. This is done using a convolution sum in discrete time and a convolution integral in continuous time.
2) Discrete-time signals and continuous-time signals can both be represented as weighted sums or integrals of shifted impulse functions.
3) For LTI systems, the impulse responses are simply time-shifted versions of the same underlying function, allowing the system to be fully characterized by its impulse response.
The document discusses decimation in time (DIT) and decimation in frequency (DIF) fast Fourier transform (FFT) algorithms. DIT breaks down an N-point sequence into smaller DFTs of even and odd indexed samples, recursively computing smaller and smaller DFTs until individual points remain. DIF similarly decomposes the computation but by breaking the frequency domain spectrum into smaller DFTs. Both algorithms reduce the computational complexity of computing the discrete Fourier transform from O(N^2) to O(NlogN) operations.
This document discusses linear time-invariant (LTI) systems and convolution. Convolution is a fundamental concept in signal processing that is used to determine the output of an LTI system given its impulse response and an input signal. The convolution of two signals is obtained by decomposing the input signal into scaled and shifted impulses, taking the scaled and shifted impulse response for each impulse, and summing them to find the overall output. Convolution amplifies or attenuates different frequency components of the input independently. It plays an important role in applications like image processing and edge detection. Examples are provided to demonstrate evaluating convolution of periodic sequences.
Nyquist criterion for distortion less baseband binary channelPriyangaKR1
binary transmission system
From design point of view – frequency response of the channel and transmitted pulse shape are specified; the frequency response of the transmit and receive filters has to be determined so as to reconstruct [bk]
The document discusses the Fast Fourier Transform (FFT) algorithm. It begins by explaining how the Discrete Fourier Transform (DFT) and its inverse can be computed on a digital computer, but require O(N2) operations for an N-point sequence. The FFT was discovered to reduce this complexity to O(NlogN) operations by exploiting redundancy in the DFT calculation. It achieves this through a recursive decomposition of the DFT into smaller DFT problems. The FFT provides a significant speedup and enables practical spectral analysis of long signals.
The document summarizes different types of shifters used in microprocessor design including logical, arithmetic, barrel, and funnel shifters. It describes the function of each shifter type and provides examples. It then focuses on funnel shifters, explaining they can perform all shift operations, and describes two types of funnel shifter designs - array and multilevel funnel shifters. The array design uses an array of multiplexers while the multilevel design uses multiple levels of smaller multiplexers.
This document discusses multirate digital signal processing. It explains that multirate systems use multiple sampling rates to process digital signals. Common operations in multirate systems are decimation, which decreases the sampling rate, and interpolation, which increases it. Decimation and interpolation can be realized through filtering and downsampling/upsampling. The document also provides examples of multirate applications like digital audio conversion and discusses tools like polyphase filters used in multirate signal processing.
The document discusses sampling theory and analog-to-digital conversion. It begins by explaining that most real-world signals are analog but must be converted to digital for processing. There are three steps: sampling, quantization, and coding. Sampling converts a continuous-time signal to a discrete-time signal by taking samples at regular intervals. The sampling theorem states that the sampling frequency must be at least twice the highest frequency of the sampled signal to avoid aliasing. Finally, it provides an example showing how to calculate the minimum sampling rate, or Nyquist rate, given the highest frequency of a signal.
This document discusses linear time-invariant (LTI) systems in discrete time. It introduces the convolution sum representation of LTI systems, where the output of an LTI system with impulse response h[n] and input x[n] is given by y[n]=x[n]*h[n]=∑k x[k]h[n-k]. Several examples are worked through to demonstrate calculating the output of an LTI system given its impulse response and input. The document also discusses representing discrete time signals as the sum of shifted unit impulse functions and properties of LTI systems like time-invariance.
The document discusses the Fast Fourier Transform (FFT) algorithm. It explains that FFT reduces the number of computations needed to calculate the Discrete Fourier Transform (DFT) of a sequence by decomposing the DFT into successive DFTs of smaller sizes. Specifically, it breaks down the N point DFT into multiple N/2 point DFTs recursively until it reaches DFTs of size 1. This decomposition reduces the complexity from O(N^2) for DFT to O(NlogN) for FFT.
The document provides an introduction to microcontrollers, specifically focusing on the Intel 8051 microcontroller. It defines microcontrollers and distinguishes them from microprocessors by noting that microcontrollers contain peripherals like RAM, ROM, I/O ports and timers on a single chip, while microprocessors require external circuitry. It then describes the architecture and features of the Intel 8051 microcontroller, including its 4KB program memory, 128 bytes of data memory, 32 general purpose registers, two timers, interrupts and I/O ports. Development tools for microcontrollers like editors, assemblers, compilers and debuggers/simulators are also discussed.
1. The document discusses different types of waveguides including parallel plate, rectangular, and circular waveguides. It provides information on their modes of propagation, field components, cutoff frequencies, and other related parameters.
2. Formulas are presented for calculating propagation constants, cutoff frequencies, wavelengths, velocities, and impedances for TE and TM waves in various waveguide structures.
3. Examples are worked out demonstrating the application of the formulas to determine parameters for given waveguide geometries and operating frequencies.
5. convolution and correlation of discrete time signals MdFazleRabbi18
This document discusses convolution and correlation of discrete time signals. It defines convolution as a mathematical way of combining two signals to form a third signal, which is equivalent to finite impulse response filtering. Convolution relates the input, output, and impulse response of a linear time-invariant system. The document also provides examples of discrete linear convolution and periodic convolution. It then defines correlation as a measure of similarity between signals, discussing cross-correlation and auto-correlation, and providing examples of calculating each.
This presentation covers:
Some basic definitions & concepts of digital communication
What is Phase Shift Keying(PSK) ?
Binary Phase Shift Keying – BPSK
BPSK transmitter & receiver
Advantages & Disadvantages of BPSK
Pi/4 – QPSK
Pi/4 – QPSK transmitter & receiver
Advantages of Pi/4- QPSK
The overlap-add method is an efficient way to evaluate the discrete convolution of long signals using short blocks. It involves breaking the long signals into shorter blocks, zero-padding the blocks, convolving them, and adding the results. For example, an input signal of length N is broken into blocks of length M, with M-1 zeros added to each block. The blocks are convolved with the impulse response and added to produce the final output. This method was used to convolve an example input signal of length 10 with an impulse response of length 3.
This document summarizes key concepts about linear time-invariant (LTI) systems including:
1. LTI systems exhibit superposition and the output is the summation of individual impulse responses.
2. Inputs can be represented as a linear combination of shifted unit impulses. The output is the convolution sum of the input and impulse response.
3. Convolution represents the output of an LTI system and can be computed using a summation or integral. Common properties like commutativity and distributivity apply.
The document discusses efficient methods for computing the discrete Fourier transform (DFT) of real sequences by exploiting symmetry properties. It describes how the DFTs of two real sequences can be computed using a single DFT, and how the DFT of a real sequence can be obtained from the DFT of an extended complex sequence. The document also covers using the DFT to compute linear convolution, including breaking long convolutions into overlapping shorter convolutions that can each use the efficient DFT method.
The document summarizes various greedy algorithms and optimization problems that can be solved using greedy approaches. It discusses the greedy method, giving the definition that locally optimal decisions should lead to a globally optimal solution. Examples covered include picking numbers for largest sum, shortest paths, minimum spanning trees (using Kruskal's and Prim's algorithms), single-source shortest paths (using Dijkstra's algorithm), activity-on-edge networks, the knapsack problem, Huffman codes, and 2-way merging. Limitations of the greedy method are noted, such as how it does not always find the optimal solution for problems like shortest paths on a multi-stage graph.
This document discusses time complexity analysis of algorithms. It explains that time complexity is defined as the number of basic operations performed by an algorithm as the input size increases. The common basic operations are assignments, comparisons, and arithmetic operations. The document provides examples of analyzing time complexity and determining the dominant operations and order of growth for different algorithms. It introduces the O(g(n)) notation to describe the asymptotic upper bound of an algorithm's time complexity.
Algorithm And analysis Lecture 03& 04-time complexity.Tariq Khan
This document discusses algorithm efficiency and complexity analysis. It defines key terms like algorithms, asymptotic complexity, Big O notation, and different complexity classes. It provides examples of analyzing time complexity for different algorithms like loops, nested loops, and recursive functions. The document explains that Big O notation allows analyzing algorithms independent of machine or input by focusing on the highest order term as the problem size increases. Overall, the document introduces methods for measuring an algorithm's efficiency and analyzing its time and space complexity asymptotically.
This document discusses algorithm analysis and complexity. It defines key terms like algorithm, asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like summing array elements. The running time is expressed as a function of input size n. Common complexities like constant, linear, quadratic, and exponential time are introduced. Nested loops and sequences of statements are analyzed. The goal of analysis is to classify algorithms into complexity classes to understand how input size affects runtime.
how to calclute time complexity of algortihmSajid Marwat
This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.
Testing of Matrices Multiplication Methods on Different ProcessorsEditor IJMTER
There are many algorithms we found for matrices multiplication. Until now it has been
found that complexity of matrix multiplication is O(n3). Though Further research found that this
complexity can be decreased. This paper focus on the algorithm and its complexity of matrices
multiplication methods.
Introduction to data structures and complexity.pptxPJS KUMAR
The document discusses data structures and algorithms. It defines data structures as the logical organization of data and describes common linear and nonlinear structures like arrays and trees. It explains that the choice of data structure depends on accurately representing real-world relationships while allowing effective processing. Key data structure operations are also outlined like traversing, searching, inserting, deleting, sorting, and merging. The document then defines algorithms as step-by-step instructions to solve problems and analyzes the complexity of algorithms in terms of time and space. Sub-algorithms and their use are also covered.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
Department of MathematicsMTL107 Numerical Methods and Com.docxsalmonpybus
Department of Mathematics
MTL107: Numerical Methods and Computations
Exercise Set 8: Approximation-Linear Least Squares Polynomial approximation, Chebyshev
Polynomial approximation.
1. Compute the linear least square polynomial for the data:
i xi yi
1 0 1.0000
2 0.25 1.2840
3 0.50 1.6487
4 0.75 2.1170
5 1.00 2.7183
2. Find the least square polynomials of degrees 1,2 and 3 for the data in the following talbe.
Compute the error E in each case. Graph the data and the polynomials.
:
xi 1.0 1.1 1.3 1.5 1.9 2.1
yi 1.84 1.96 2.21 2.45 2.94 3.18
3. Given the data:
xi 4.0 4.2 4.5 4.7 5.1 5.5 5.9 6.3 6.8 7.1
yi 113.18 113.18 130.11 142.05 167.53 195.14 224.87 256.73 299.50 326.72
a. Construct the least squared polynomial of degree 1, and compute the error.
b. Construct the least squared polynomial of degree 2, and compute the error.
c. Construct the least squared polynomial of degree 3, and compute the error.
d. Construct the least squares approximation of the form beax, and compute the error.
e. Construct the least squares approximation of the form bxa, and compute the error.
4. The following table lists the college grade-point averages of 20 mathematics and computer
science majors, together with the scores that these students received on the mathematics
portion of the ACT (Americal College Testing Program) test while in high school. Plot
these data, and find the equation of the least squares line for this data:
:
ACT Grade-point ACT Grade-point
score average score average
28 3.84 29 3.75
25 3.21 28 3.65
28 3.23 27 3.87
27 3.63 29 3.75
28 3.75 21 1.66
33 3.20 28 3.12
28 3.41 28 2.96
29 3.38 26 2.92
23 3.53 30 3.10
27 2.03 24 2.81
5. Find the linear least squares polynomial approximation to f(x) on the indicated interval
if
a. f(x) = x2 + 3x+ 2, [0, 1]; b. f(x) = x3, [0, 2];
c. f(x) = 1
x
, [1, 3]; d. f(x) = ex, [0, 2];
e. f(x) = 1
2
cosx+ 1
3
sin 2x, [0, 1]; f. f(x) = x lnx, [1, 3];
6. Find the least square polynomial approximation of degrees 2 to the functions and intervals
in Exercise 5.
7. Compute the error E for the approximations in Exercise 6.
8. Use the Gram-Schmidt process to construct φ0(x), φ1(x), φ2(x) and φ3(x) for the following
intervals.
a. [0,1] b. [0,2] c. [1,3]
9. Obtain the least square approximation polynomial of degree 3 for the functions in Exercise
5 using the results of Exercise 8.
10. Use the Gram-Schmidt procedure to calculate L1, L2, L3 where {L0(x), L1(x), L2(x), L3(x)}
is an orthogonal set of polynomials on (0,∞) with respect to the weight functions w(x) =
e−x and L0(x) = 1. The polynomials obtained from this procedure are called the La-
guerre polynomials.
11. Use the zeros of T̃3, to construct an interpolating polynomial of degree 2 for the following
functions on the interval [-1,1]:
a. f(x) = ex, b. f(x) = sinx, c. f(x) = ln(x+ 2), d. f(x) = x4.
12. Find a bound for the maximum error of the approximation in Exercise 1 on the interval
[-1,1].
13. Use the zer.
The document summarizes the Agrawal-Kayal-Saxena (AKS) primality test, which can determine if a number n is prime in polynomial time. It discusses different categories of primality tests, including tests where n being prime implies the test holds, tests where the test holding implies n is prime, and the AKS test where the test holding if and only if n is prime. The AKS algorithm is described along with proof of correctness and runtime analysis showing it runs in polynomial time. An example application of the AKS test to the number 1993 is provided to demonstrate it.
The document provides examples and explanations for performing arithmetic operations with rational numbers including integers, fractions, and decimals. It demonstrates how to add, subtract, multiply, and divide rational numbers, as well as how to evaluate expressions involving rational numbers. The examples are aligned with California state math standards for performing operations with rational numbers.
1) The document describes the divide-and-conquer algorithm design paradigm. It can be applied to problems where the input can be divided into smaller subproblems, the subproblems can be solved independently, and the solutions combined to solve the original problem.
2) Binary search is provided as an example divide-and-conquer algorithm. It works by recursively dividing the search space in half and only searching the subspace containing the target value.
3) Finding the maximum and minimum elements in an array is also solved using divide-and-conquer. The array is divided into two halves, the max/min found for each subarray, and the overall max/min determined by comparing the subsolutions.
1) The document describes the divide-and-conquer algorithm design paradigm. It splits problems into smaller subproblems, solves the subproblems recursively, and then combines the solutions to solve the original problem.
2) Binary search is provided as an example algorithm that uses divide-and-conquer. It divides the search space in half at each step to quickly determine if an element is present.
3) Finding the maximum and minimum elements in an array is another problem solved using divide-and-conquer. It recursively finds the max and min of halves of the array and combines the results.
This document discusses discrete-time signals and their representation in the time domain. It defines discrete-time signals as sequences of numbers called samples, denoted as x[n], where n is an integer index. Samples can be real or complex values. Discrete-time signals can be finite or infinite in length. Basic operations on discrete-time signals include addition, multiplication, time-shifting, and more. Sampling rate alteration processes like interpolation and decimation can generate new sequences from a given signal with a different sampling rate.
This document discusses methods for finding the period of a periodic function using discrete Fourier transforms (DFT). It presents two algorithms:
1. Algorithm I handles the special case where the period s divides the number of sample points N. It uses DFT to obtain frequencies that reveal the period.
2. Algorithm II handles the general case where s does not necessarily divide N. It uses continued fractions to approximate measured frequencies as rational numbers, whose denominators likely equal the period s.
The document also discusses applications to integer factorization by finding the period of functions over finite fields, and limitations of the classical approach that motivate the use of quantum computing.
This document discusses polynomial functions in MATLAB. It covers:
- Defining polynomials as coefficient vectors and finding roots.
- Adding, subtracting, multiplying and dividing polynomials using functions like conv and deconv.
- Evaluating and differentiating polynomials with polyval and polyder.
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2. CONTENTS:
1. Why overlap save & overlap add method ?
2. Steps to perform overlap save method.
3. Example of overlap save method.
4. Steps to perform overlap add method.
5. Example of overlap add method.
6. Difference between overlap save & add method.
7. Reference
8. Q & A
2
3. FILTERING USING DFT:
1. In practical application we often come across linear filtering of long
data sequences.
2. DFT involves operation on block of data.
3. The block size of data should minimum because digital processors
have limited memory.
4. Two methods of filtering:
3
4. FILTERING USING DFT:
1. In practical application we often come across linear filtering of long
data sequences.
2. DFT involves operation on block of data.
3. The block size of data should minimum because digital processors
have limited memory.
4. Two methods of filtering:
OVERLAP SAVE METHOD
OVERLAP ADD METHOD
4
6. OVERLAP SAVE METHOD
STEP-1:
Determine length ‘M’, which is the length of the impulse
response data sequences i.e. h[n] & determine ‘M-1’.
STEP-2:
Given input sequence x[n] size of DFT is ‘N’.
let assume, N=5
6
9. OVERLAP SAVE METHOD
Input Data Sequence x[n]
9
x1[n]
x2[n]
x4[n]
x3[n]
M-1 zeros
M-1 data
M-1 data
M-1 data
L L LL
10. OVERLAP SAVE METHOD
STEP-4:
Perform Circular Convolution of h[n] & blocks of x[n]
i.e. y1[n]= x1[n] h[n]
y2[n]= x2[n] h[n]
y3[n]= x3[n] h[n]
y4[n]= x4[n] h[n]
10
N
N
N
N
13. OVERLAP SAVE EXAMPLE
Ques. Given x[n]={3,-1,0,1,3,2,0,1,2,1} & h[n]={1,1,1}
Let, N=5
Length of h[n], M= 3
Therefore, M-1= 2
We know,
N=(L+M-1)
5=L+3-1
L=3
∴Pad L-1=2 zeros with h[n] i.e. h[n]={1,1,1,0,0}
13
14. OVERLAP SAVE EXAMPLE
3 -1 0 1 3 2 0 1 2 1
14
0 0 3 -1 0
-1 0 1 3 2
3 2 0 1 2
1 2 1 0 0
x1[n]
x2[n]
x4[n]
x3[n]
M-1(=2) no. of previous data
L(=3) no. of new data
15. OVERLAP SAVE EXAMPLE
Performing yk[n]= xk[n] h[n], where k=1,2,3,4
1. y1[n]= {-1,0,3,2,2}
2. y2[n]= {4,1,0,4,6}
3. y3[n]= {6,7,5,3,3}
4. y4[n]= {1,3,4,3,1}
15
N
16. OVERLAP SAVE EXAMPLE
-2 -1 0 1 2 3 4 5 6 7 8 9 10 11
16
3 2 2
0 4 6
5 3 3
4 3 1
-1 0
4 1
6 7
1 3
X=> discard
X
X
X
X
n-
y1[n]
y1[n]
y1[n]
y1[n]
17. OVERLAP SAVE EXAMPLE
-2 -1 0 1 2 3 4 5 6 7 8 9 10 11
17
3 2 2
0 4 6
5 3 3
4 3 1
-1 0
4 1
6 7
1 3
X=> discard
X
X
X
X
n-
y1[n]
y1[n]
y1[n]
y1[n]
y[n] 3 2 2
18. OVERLAP SAVE EXAMPLE
-2 -1 0 1 2 3 4 5 6 7 8 9 10 11
18
3 2 2
0 4 6
5 3 3
4 3 1
-1 0
4 1
6 7
1 3
X=> discard
X
X
X
X
n-
y1[n]
y1[n]
y1[n]
y1[n]
y[n] 3 2 2 0 4 6
19. OVERLAP SAVE EXAMPLE
-2 -1 0 1 2 3 4 5 6 7 8 9 10 11
19
3 2 2
0 4 6
5 3 3
4 3 1
-1 0
4 1
6 7
1 3
X=> discard
X
X
X
X
n-
y1[n]
y1[n]
y1[n]
y1[n]
y[n] 3 2 2 0 4 6 5 3 3
22. OVERLAP ADD METHOD
STEP-1:
Determine length ‘M’, which is the length of the impulse
response data sequences i.e. h[n] & determine ‘M-1’.
STEP-2:
Given input sequence x[n] size of DFT is ‘N’.
let assume, N=5
22
23. OVERLAP ADD METHOD
STEP-3:
Determine the length of the new data, ‘L’
STEP-4:
Pad ‘M-1’ zeros to xk[n]
Pad ‘L-1’ zeros to h[n]
23
xk[n] ‘M-1’ zeros (padded)
h[n] ‘L-1’ zeros (padded)
24. OVERLAP ADD METHOD
Input Data Sequence x[n]
24
x1[n]
x2[n]
x4[n]
x3[n]
M-1 zeros
M-1 zeros
M-1 zeros
M-1 zeros
L L LL
25. OVERLAP ADD METHOD
STEP-4:
Perform Circular Convolution of h[n] & blocks of x[n]
i.e. y1[n]= x1[n] h[n]
y2[n]= x2[n] h[n]
y3[n]= x3[n] h[n]
y4[n]= x4[n] h[n]
25
N
N
N
N
36. OVERLAP SAVE vs ADD
METHOD
Overlap Save
Overlapped values has to be
discarded.
It does not require any addition.
It can be computed using linear
convolution
Overlap Add
Overlapped values has to be
added.
It will involve adding a number
of values in the output.
Linear convolution is not
applicable here.
36
37. CONCLUSION:
37
Overlap Add and Save methods are almost similar.
From the differences one can choose any of the
methods, which is suitable at that time.
38. REFERENCE:
Digital Signal Processing - P. Rameshbabu
Discrete Time Signal Processing - Oppenheim & Schafer
38