数字信号处理 DSP 英文版课件4.0

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数字信号处理(第四版)第四章ppt

数字信号处理(第四版)第四章ppt

Digital Signal Processing
© 2013 Jimin Liang
Discrete-Time Systems Outline Discrete-time system examples Classification of DT systems Impulse and step responses Time-domain characteristics of LTI Simple interconnection schemes
Process a given sequence, called the input system, to generate another sequence, called the output sequence, with more desirable properties or to extract certain information about the input signal. DT system is usually also called the digital filter
12
Digital Signal Processing
© 2013 Jimin Liang
Discrete-Time Systems 4.2 Classification of DT systems Stable system
A system is stable if and only if for every bounded input, the output is also bounded, called BIBO stable.
Discrete-Time Systems 4.1 Discrete-time system examples (4) Linear Interpolator Linear factor-2 interpolator

最新版《数字信号处理(英)》精品课件Chap 4 Digital Processing of CT Signals

最新版《数字信号处理(英)》精品课件Chap 4   Digital Processing of CT Signals
4
Normalized digital angular frequency
Example :
CT signal:
xa (t ) A cos(2 f0t ) A cos(0t )
The sampled DT signal:
x[n] A cos(0nT ) 2 A cos(0 n ) A cos(0 n ) T Normalized digital angular frequency 0:
2
• DT signal processing algorithms are being used increasingly;
Simplified Block diagram of a CT signal processed by DT system
xa (t )
x[n] C/D Converter
Chap 4 Digital Processing of CT Signals
Discrete-Time Signal Processing of CTS; Sampling of CT Signals;
Analog Lowpass Filter Design;
1
4.1 Introduction
3
Other additional circuits
• To prevent aliasing, an analog anti-aliasing filter is employed before the S/H circuit; • To smooth the output signal of the D/A converter, which is a staircase-like waveform, an analog reconstruction filter is used.

数字信号处理(英文版)1-连续时间信号系统

数字信号处理(英文版)1-连续时间信号系统

Unit impulse function δ(t)
With a gate signal pτ(t), short the duration τ and keep the unit area
4/τ 2/τ 1/τ
1/τ
-τ/2
τ/2
-τ/4
τ/4
-τ/8 τ/8
When τ0, the amplitude tends to , which means it is impossible to define δ(t) by a regular function.
Typical signals and their representation
Gate signal
p (t ) 0
1
|t |

2
1 -τ/2 τ/2
|t |

2
The gate signal can be represented by unit step signals:

(t )dt (t )dt (t )dt u (t )
0
0

Properties of δ(t)
δ(t) is a even function, that is
δ(t) = δ(-t) We got δ(t) from a gate signal, and gate signal is an even function. It is also easy to give the math show of the even property.
Typical signals and their representation
Sinusoidal Asin(ωt+υ)

《数字信号处理原理》PPT课件

《数字信号处理原理》PPT课件

•Digital signal and image filtering
•Cochlear implants
•Seismic analysis
•Antilock brakes
•Text recognition
•Signal and image compression
•Speech recognition
•Encryption
•Satellite image analysis
•Motor control
•Digital mapping
•Remote medical monitoring
•Cellular telephones
•Smart appliances
•Digital cameras
•Home security
Upper Saddle River, New Jersey 07458
All rights reserved.
FIGURE 1-4 Four frames from high-speed video sequence. “ Vision Research, Inc., Wayne, NJ., USA.
Joyce Van de Vegte Fundamentals of Digital Signal Processing
ppt课件
11
Copyright ©2002 by Pearson Education, Inc.
Upper Saddle River, New Jersey 07458
All rights reserved.
Joyce Van de Vegte Fundamentals of Digital Signal Processing

数字信号处理英文影印版课件4-1

数字信号处理英文影印版课件4-1

数字信号处理Digital Signal Processing 电子信息工程系韩建峰KeywordsSections⏹Sampling sinusoids⏹Sampling theorem⏹Discrete-to-Continuous Conversion SummaryLECTURE 1Reading assignments This lecture •Chapter 4•Section 4-1KeywordsPart AContinuous-to-Discrete Conversion Sampling & ReconstructionAliasing & FoldingLECTURE OBJECTIVES•SAMPLING can cause ALIASING •Spectrum for digital signals,x [n]•Normalized Frequencyππωω22ˆ+==ss f f T ALIASINGReviewSignalsSampling Reconstruction•Continuous-time Signal•But the key point is that any computer represent ation is discrete.•So, do sampling!•And, how?()cos()x t A tωϕ=+•Sample a continuous-time signal at equally spaced time instants.Take a “snapshot” every Ts.Speech, audio andso on.•Or, compute the values of a discrete-time signal directly from a formula.2=-+[]53x n n nSAMPLING x(t)•SAMPLING PROCESS•Convert x(t) to numbers x[n]•“n” is an integer; x[n] is a sequence ofvalues•Think of “n” as the storage address inmemory•UNIFORM SAMPLING at t = nTs•IDEAL: x[n] = x(nT)sSAMPLING RATE, f s •SAMPLING RATE (f)s–f=1/T ss•NUMBER of SAMPLES PER SECOND –T= 125 microsec f s= 8000 samples/secs–UNITS ARE HERTZ: 8000 Hz •UNIFORM SAMPLING at t = nT= n/f ss–IDEAL: x[n] = x(nT)=x(n/f s)s•Examples of continuous-time signals exist in the “real-world” outside the computer.•Simple mathematical formula.•More general continuous-time signals can be represented as sum of sinusoids.•So, we will use sinusoidal signal as the basis for our study of sampling.sf s T n A n x ωωωϕω==+=ˆ)ˆcos(][)cos()(][)cos()(ϕωϕω+==+=s s nT A nT x n x t A t x •Change x(t) into x[n] DERIV ATION))cos((][ϕω+=n T A n x s DEFINE DIGITAL FREQUENCYDigital Frequency ωˆ•V ARIES from 0to 2π, as f varies from 0 to the sampling frequency•UNITS are radians, not rad/sec–DIGITAL FREQUENCY is NORMALIZEDss f f T πωω2ˆ==Sample RateHow to select theT sSample TheoremA interesting phenomenon•Exercise 4.1•Is this the only possible answer?Hz 1000at sampled )2400cos()(2==s f t t x π21000[]cos(2400)cos(2.4)cos(0.42)cos(0.4)nx n n n n n πππππ===+=()cos(400)x t t π⇒=Aliasing[]cos(0.4)x n n π=Illustration of aliasingDifferent frequency, but same values at n=0,1,2,3…•2.4πis an alias of 0.4π•Exercise 4.2Aliasing•How does aliasing arise in a mathematical treat ment of discrete-time signal?•The last example:12[]cos(0.4)[]cos(2.4)x n n x n n ππ==2[]cos(0.42)cos(0.4)x n n n n πππ=+=Periodic function with period 2πAliasing Derivation-1and we substitute: t ←n f sIf x (t )=A cos(2π(f + f s )t +ϕ)then: x [n ]=A cos(2π(f + f s )n f s +ϕ)or, x [n ]=A cos(2πf f s n +2π n +ϕ)Aliasing Derivation-22ˆs sfT f πωω==+2π 2()22ˆthen: s s s s sf f f f f f f πππω+==+ˆand we want: []cos()x n A n ωϕ=+If x (t )=A cos(2π(f + f s )t +ϕ)t ←nf sFolded Aliasx (t )=A cos(2π(-f + f s )t -ϕ)SAME DIGITAL SIGNALˆ[]cos()x n A n ωϕ=+x [n ]=A cos((2πf T s )n -2π n +ϕ) x [n ]=A cos((-2πfT s )n +(2π f s T s )n -ϕ)x [n ]=x (nT s )=A cos(2π(-f + f s )nT s -ϕ)Aliasing2ˆs sfT f πωω==+2π 2ˆ2s sfT f πωωπ==-+Folded AliasAlisingPrincipal Aliasingˆˆˆ, 2, 2 integer l l l ωωππω+-=General FormulaSpectrum of a Discrete-Time Signal•PLOT versus NORMALIZED FREQUENCY •INCLUDE ALL SPECTRUM LINES –ALIASES•ADD MULTIPLES of 2π•SUBTRACT MULTIPLES of 2π–FOLDED ALIASES•ALIASES of NEGATIVE FREQS12X*–0.5π12X–1.5π12X0.5π2.5π–2.5πˆω12X12X*12X*1.5π))80/)(100(2cos(][ϕπ+=n A n x 80s f Hz=sf fπω2ˆ=ˆ2sff ωπ=f s =125Hz12X*0.4π12X–0.4π1.6π–1.6πˆω12X12X*))125/)(100(2cos(][ϕπ+=n A n x•DEMO: Strobe Movies 12•What is the meaning of this DEMO?•Can you give us more examples in the real world?f Camera: 30 Frames/s Human Eyessf'fSummary2ˆs sfT f πωω==+2π 2ˆ2s sfT f πωωπ==-+Folded AliasAlisingPrincipal Aliasingˆˆˆ, 2, 2 integer l l l ωωππω+-=General FormulaHomeworkP-4.1Review: Chapter 4, Section 4-1 Preview:Chapter 4, Section 4-2,4-4。

数字信号处理基于计算机的方法英文改编版第四版课程设计

数字信号处理基于计算机的方法英文改编版第四版课程设计

Digital Signal Processing Using Computer-Based Methods -Course Design for the 4th EditionIntroductionDigital Signal Processing (DSP) is an area of study that has witnessed significant growth and advancement in recent times. Technological advancements have made it possible to work with signals and signals processing methods more effectively and efficiently. The use of computers has also contributed significantly to the development of DSP methods. In this course design, we will provide an overview of the Digital Signal Processing course designed for the 4th edition of the book titled Digital Signal Processing Using Computer-Based Methods.Overview of the CourseThis course is designed to provide students with a fundamental understanding of digital signal processing concepts, their applications, and techniques for analyzing signals. The course is divided into eight modules, covering the following topics:1.Introduction to Digital Signal Processing2.Discrete-Time Signals and Systems3.Discrete Fourier Transform4.Z-Transform and Analysis of LTI Systems5.FIR Filter Design6.IIR Filter Design7.Multirate Signal Processing8.DSP Applications in Speech and Image ProcessingThe course will cover both theoretical and practical aspects of DSP, including hands-on experience with MATLAB software. The course involves lectures, discussions, and assignments, which will enable students to develop an in-depth understanding of DSP concepts and their applications.Course ObjectivesThe primary objectives of this course are to: - Develop an in-depth understanding of digital signal processing concepts and techniques - Familiarize students with the use of MATLAB for signal analysis and processing - Develop skills for designing digital filters and analyzing signals using the Fourier and Z-transforms - Provide practical experience with signal processing applications in speech and image processingCourse OutlineModule 1: Introduction to Digital Signal Processing •Basic concepts of digital signal processing•Analog-to-digital conversion•Sampling theorem•Signal quantizationModule 2: Discrete-Time Signals and Systems•Discrete-time signals and their characteristics•Discrete-time systems and their properties•Convolution and correlation of discrete-time signalsModule 3: Discrete Fourier Transform•Fourier series and Fourier Transform•Discrete Fourier Transform (DFT) and its properties •Fast Fourier Transform (FFT) algorithmsModule 4: Z-Transform and Analysis of LTI Systems •Z-Transform and its properties•Transfer function and Frequency Response of LTI systems•Analysis of LTI systems using Z-TransformModule 5: FIR Filter Design•Design of Finite Impulse Response (FIR) filters•Windowing techniques and their effects•Filter design using Fourier SeriesModule 6: IIR Filter Design•Design of Infinite Impulse Response (IIR) filters•Pole-zero locations and their effects•Butterworth and Chebyshev filter designs Module 7: Multirate Signal Processing•Sampling rate conversion using decimation and interpolation•Polyphase decomposition and filter banks•Multistage decimation and interpolation Module 8: DSP Applications in Speech and Image Processing •Speech analysis and synthesis•Speech coding and compression•Image enhancement and restoration•Image compressionEvaluationThe grading for this course will be based on your performance in the following components: - Regularassignments and quizzes: 20% - Mid-term examination: 30% - Final examination: 50%ConclusionThis course in Digital Signal Processing will provide students with a comprehensive understanding of digital signal processing concepts and their applications. The course will focus on fundamental principles, practical applications, and hands-on experience with digital signal processing using MATLAB. Upon successful completion of this course, students will have the skills and knowledge to analyze and design digital signal processing systems.。

数字信号处理 DSP 英文版课件1.4

数字信号处理 DSP 英文版课件1.4

2( A E ) cos(10 t ) 2( B C ) cos(30 t ) 2D cos(20 t ) 2 Fcos(35 t )
(b)理想预滤波,不混迭。y(t)中仅包含 x(t)中 [-20kHz, 20kHz] 内的 频率分量,各自的幅度不变。 之后同例 1.4.4 法求 y a (t ) 。
其中 f A~ F =5, 15, 25, 30, 45, 62.5 kHz,audible frequencies: f A , f B , audible part:
x1 (t ) 2 Acos(10 t ) 2B cos(30 t)
(a) 无预滤波,必混迭。y(t)= x(t)。同例 1.4.4 法求 y a (t ) 。
T 2f / f s (radians/sample)
频闪间隔T内轮子转过的周数:
(1.4.10)
fT f / f s (cycles/sample)
f: physical frequency, , fT :digital frequency 数字频率是模拟角频率和模拟频率相对采样率 f s 归一 化(nomalize)的结果。

x(t )
Example 1.4.1:
A sinusoid: frequency f=10Hz, sampling rate f s =12Hz,
f a =?
Solution: Sampled signal has replicated frequencies: ….10-2*12, 10-12, 10, 10+12, 10+2*12,….. Nyquiste interval: [-6, 6] Reconstructed sinusoid has frequency:

最新版《数字信号处理(英)》精品课件ch8 Digital Filter Structures

最新版《数字信号处理(英)》精品课件ch8 Digital Filter Structures
Note That: In either case , the signal variables and the filter coefficients cannot be represented with infinite precision.
3
Introduction
So, a direct implementation of a digital filter based on either the difference equation or the finite convolution sum may not provide satisfactory performance due to the finite precision arithmetic
18
8.1.3 The Delay-free Loop ProbIem
Analysis of this structure yields
u[n] = w[n] + y[n] y[n] = B(v[n] + Au[n])
which when combined results in
llR system can’t be implemented using the convolution sum, because the impulse response is of infinite length
y[n] k h[k ]x[n k ]

2
Introduction
9
8.1.1 Basic Building Blocks
The computational algorithm of an LTI digital filter can be conveniently represented in block diagram form using the basic building blocks shown below
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w1(n), w2(n): contents of the two registers
At time n:
Example 4.1.5: An IIR filter h(n) 0.75 u (n) . Using convolution, derive
n
closed-form expressions(闭合形式表示) for the output signal y(n) when the input is: (a)A unit step(单位阶跃) x(n)=u(n) (b)An alternating step x(n) (1) u (n) (正负交替的阶跃)
Algorithmic form:
for each input sample x,do:
y:= w1
w1:=x
The delay register is typically initialized (初始化) to zero: w1(0)=0.
double delay:
I/O equation: y(n)=x(n-2)
State-space representation:
y(n)=w1(n) (output equation) w1 (n+1)=x (n) (state updating equation) w1(n): content of the register or internal state at time n
4.1.6 Flip-and-Slide Form
Flip-and-Slide Form of convolution:
h(n) is flipped(反转) around and slid (slide平移) over the input sequence x(n). y(n) is obtained by computing the dot product(点积) of flipped h with the input samples aligned(对准) below it.
ydc h(m)
m
(4.1.24)
Example 4.1.7:An integrator(积分器)-like FIR filter:
y(n) G[ x(n) x(n 1) ... x(n 14)] ,
G, or h(n) 0, 0 n 14 otherwise
Direct form
y ( n)
min( n , M )
m max( 0,n L 1)
h( m) x ( n m)
n
(4.1.16)
可分阶段写为: (p.136)

0 n M ( input-on) y (n)
m 0
h ( m) x ( n m )
M

n
(c)A square pulse of duration L=25, x(n)=u(n)-u(n-25) (持续时 长 25 点的方脉冲) In each case, determine the steady-state response of the filter.
Solution: (a) x(n) u (n)
M n L 1 (steady state)
y ( n ) h ( m) x ( n m )
m 0
(4.1.23)

L 1 n L 1 M (input-off) y (n)
m n L 1
h ( m) x ( n m )
M
(4.1.23) can be considered as the generic I/O equation for FIR filters. But, for programming purposes, one must work with (4.1.16) which does not let the indices exceed the array bounds. (p.137) 稳态阶段的 y (n) h(m) x(n m) (4.1.23)可作为通用式子,
y(n) 4
(b) x(n) (1) u (n)
n
做法与(a)类似 (c) x(n) u(n) u(n 25) 响应中只有 input-on 和 input-off 暂态阶段。 当 n=0~24, y (n)
m 0 n
x(n m)h(m) (0.75) m
steady-state (稳态):
yn h0 xn h1 xn 1 ... hM xn M
M+1 items
input-off transients:
The last M outputs after the input has been turned off
4.1.7 Transient and Steady State Behavior
Three subranges of y(n):
0 n M ( input-on transients)
M n L 1 (steady state) L 1 n L 1 M (input-off transients)
If Lx<Lh, the steady-state range does not exist- the input is too short to exhibit steady behavior .
Basic building blocks: adder, multiplier, delay
4.2.1 Pure Delays singlt each time instant n, two steps are carried out:
(a) the current content w1(n) = x(n-1) is clocked out to the output,y(n)=w1(n); (b) the current input x(n) gets stored in the register, where it will be held for one sampling instant and become the output at the next time (n+1): w1 (n+1)=x (n).
m 0 M
但编程时应用 y ( n)
min( n , M )
m max( 0 ,n L 1)
h( m) x ( n m)
(4.1.16)避免下标出错。
4.1.8 Convolution of Infinite Sequences x 和 h 中有一个或均为无限长时,无限长 y(n) (0 n ) 的 暂稳态情况: M= , L< , input-on transients and input-off transients (Lx<Lh, the input is too short to exhibit steady behavior) M< , L= , input-on transients and steady state M= , L= , input-on transients. The steady state response is the limit of y(n) for n―>
input-on transients(暂态)of y(n): The first M output samples when the input is turned on. Less than M+1 input samples are used for obtaining y(n), or the input sequence is assumed to be padding zeros (补零) to compute (M+1)-dimensional dot product.
G=0.1时,稳态输出值=DC gain
ydc h(m) G 1.5
m 0 m 0
14
14
Example 4.1.8:x(n) 同上例
ban a) h( n) 0
14
for
0 n 14
,
M=14
otherwise
ydc h(m) 1 a M 1 0.987
h(n) 0.75 n u (n)
x is flipped
y(n) h(m) x(n m) 0.75m u(m)u(n m)
m
n
m
1 0.75 n 1 0.75 m 4 3(0.75) n 1 0.75 m 0
(n 0)
因 h(n)和 x(n)无限长,y(n)的稳态阶段出现在 n ,即
m 0
b) differentiator (微分器) h=[0.2, -1, 2, -2, 1, -0.2], M=5
ydc
m0
h ( m) 0
5
Example 4.1.8 (a):
Example 4.1.8 (b):
4.1.10 Overlap-Add Block Convolution Method
m 0
n
n
当 n 25 y (n)
m n 24
(0.75) m
4.1.9 Programming Considerations
DC gain: The steady-state value of the output of a filter when the input remains constant for a long period of time. For a unity input, the DC gain is:
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