Pulse Coded Modulation

Similar documents
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Switched Quasi-Logarithmic Quantizer with Golomb Rice Coding

Uncertainty in measurements of power and energy on power networks

Chapter 7 Channel Capacity and Coding

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH

Lecture 3: Shannon s Theorem

Error Probability for M Signals

EGR 544 Communication Theory

Entropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or

Negative Binomial Regression

(b) i(t) for t 0. (c) υ 1 (t) and υ 2 (t) for t 0. Solution: υ 2 (0 ) = I 0 R 1 = = 10 V. υ 1 (0 ) = 0. (Given).

VQ widely used in coding speech, image, and video

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION

#64. ΔS for Isothermal Mixing of Ideal Gases

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

Lossless Compression Performance of a Simple Counter- Based Entropy Coder

Asymptotic Quantization: A Method for Determining Zador s Constant

Research Article Green s Theorem for Sign Data

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

EEE 241: Linear Systems

Chapter 7 Channel Capacity and Coding

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder.

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Chapter 8 SCALAR QUANTIZATION

AGC Introduction

/ n ) are compared. The logic is: if the two

A Robust Method for Calculating the Correlation Coefficient

Formulas for the Determinant

Performing Modulation Scheme of Chaos Shift Keying with Hyperchaotic Chen System

The Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION

Lecture 10: May 6, 2013

On balancing multiple video streams with distributed QoS control in mobile communications

DPCM Compression for Real-Time Logging While Drilling Data

Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming 1

COMPLEX NUMBERS AND QUADRATIC EQUATIONS

Color Rendering Uncertainty

1. Estimation, Approximation and Errors Percentages Polynomials and Formulas Identities and Factorization 52

The Feynman path integral

Chapter 13: Multiple Regression

Why Monte Carlo Integration? Introduction to Monte Carlo Method. Continuous Probability. Continuous Probability

An Application of Fuzzy Hypotheses Testing in Radar Detection

NUMERICAL DIFFERENTIATION

CHARACTERISTICS OF COMPLEX SEPARATION SCHEMES AND AN ERROR OF SEPARATION PRODUCTS OUTPUT DETERMINATION

x = , so that calculated

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

Chapter 3 Describing Data Using Numerical Measures

CSE4210 Architecture and Hardware for DSP

Transform Coding. Transform Coding Principle

SIMPLE REACTION TIME AS A FUNCTION OF TIME UNCERTAINTY 1

Cokriging Partial Grades - Application to Block Modeling of Copper Deposits

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,

An Improved multiple fractal algorithm

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

System in Weibull Distribution

A NEW DISCRETE WAVELET TRANSFORM

On the correction of the h-index for career length

A New Design of Multiplier using Modified Booth Algorithm and Reversible Gate Logic

Introduction to Information Theory, Data Compression,

Tutorial 2. COMP4134 Biometrics Authentication. February 9, Jun Xu, Teaching Asistant

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm

Appendix B: Resampling Algorithms

Department of Electrical and Computer Engineering FEEDBACK AMPLIFIERS

Improvement of Histogram Equalization for Minimum Mean Brightness Error

Scroll Generation with Inductorless Chua s Circuit and Wien Bridge Oscillator

A Particle Filter Algorithm based on Mixing of Prior probability density and UKF as Generate Importance Function

Salmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2

Temperature. Chapter Heat Engine

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Analytical Chemistry Calibration Curve Handout

One-sided finite-difference approximations suitable for use with Richardson extrapolation

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD

Bézier curves. Michael S. Floater. September 10, These notes provide an introduction to Bézier curves. i=0

SUPPLEMENTARY INFORMATION

Generalized Linear Methods

CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER. In real environmental conditions the speech signal may be

Markov Chain Monte Carlo Lecture 6

Composite Hypotheses testing

Uncertainty and auto-correlation in. Measurement

Study on Non-Linear Dynamic Characteristic of Vehicle. Suspension Rubber Component

APPLICATION OF EDDY CURRENT PRINCIPLES FOR MEASUREMENT OF TUBE CENTERLINE

Edge Isoperimetric Inequalities

Chapter 1. Probability

ECE 534: Elements of Information Theory. Solutions to Midterm Exam (Spring 2006)

International Power, Electronics and Materials Engineering Conference (IPEMEC 2015)

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis

Linear Approximation with Regularization and Moving Least Squares

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

What would be a reasonable choice of the quantization step Δ?

Scalar and Vector Quantization

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

Analysis of Queuing Delay in Multimedia Gateway Call Routing

The Geometry of Logit and Probit

8.592J: Solutions for Assignment 7 Spring 2005

Transcription:

Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal converson of the voce sgnal s the flterng of the analog sgnal, meanng the lmtaton of the frequency band to [300 Hz, 3400 Hz]. The next step s the samplng, usng a frequency whch fulflls the samplng theorem, f s > *f m, and havng the value f s 8 khz. Have to be mentoned that the flterng has the purpose of avodng the alasng phenomenon. After the samplng process the next step s the compresson of the sgnal, process whch realzes the non-unform quantzaton. Compandng Compandng conssts n compresson of the sgnal to be transmtted at transmsson sde and expanson at recepton (compandng compressng + expandng). Ths operaton s performed accordng to the µ (1) and A () compresson laws. µ law A law ln(1 + µ x ) f ( x) sgn( x),0 x < 1 (1) ln(1 + µ ) - employed n USA and n Japan; - µ55; A x 1,0 x < 1 + ln ( A) A f ( x) () 1+ ln( A x ) 1 sgn( x), x < 1 1+ ln( A) A - employed n Europe; - A 87.6, value whch provdes contnuty of the functon; Problem: deduce the mathematcal expressons of the expanson laws correspondng to the presented compresson laws (varable x functon of varable y). In Fgure 1 t s presented the processng nvolved by the compresson and the expanson process: - the samples of the voce sgnal are quantzed on 16 bts usng unform quantzaton; - t s employed one of the compresson laws (1) and () - the least sgnfcant 8 bts are suppressed (see Fgure ) Fgure 1 Block schematc of the transmsson process usng compandng 1

At the recever the 8 bt PCM word s converted back nto a 16 bt word by appendng 8 LSBs havng the value 10000000 (18 10 ) see Fg., reducng n ths way to half the quantzaton error. In the next step the expanson functon s appled to the obtaned 16bt PCM word. The µ compresson law Fgure The re-quantzaton process In Fgure 3 t s presented the normalzed µ law compresson characterstc for dfferent values of the µ parameter, and n Fgure 4 t s presented the approxmated characterstc of the µ compresson law. Fgure 3 Normalzed µ law compresson characterstc

Fgure 4 Approxmated/segmented µ law compresson characterstc postve values only In practce the characterstc presented n Fgure 3 (µ55) s approxmated n 16 segments, 8 for the postve values and 8 for the negatve values (Fgure 4), and each segment s splt n 16 sub segments (Fgure 5). Insde each segment on the X axs t s performed a unform quantzaton on 4 bts. Fgure 5 - Segment 1 (splttng n sub-segments) Encodng of the nput sgnal s performed n the followng way (Fgure 6): 3

- the most sgnfcant bt, b 7, ndcates the sgnal polarty; - the next 3 bts, b 6 b 5 b 4 ndcate the segment; - the last 4 bts, b 3 b b 1 b 0, ndcate the sub segment; Fgure 6 PCM encodng wth compresson Questons: 1) It s gven a compresson characterstc approxmated n 4 segments, and each segment s splt nto 8 sub-segments. How many bts are requred for PCM encodng? ) A compresson characterstc s descrbed by the sequence of coordnates (0,0) (0.1,0.5) - (0.3,0.5) - (0.6,0.75) (1,1). Gve the sequence of coordnates for the segmented expanson characterstc. Calculaton of some of the compresson characterstcs parameters: x - Compresson rato for segment : Rc, where x represents the y length of segment at the nput of the characterstc (X axs), and y s the length of segment at the output of the characterstc (Y axs) (see Fgure 4); the length of any segment at the output s 0.15. When the compresson rato s smaller than 1 means that we have expanson, and when the compresson raton s larger than 1 means that we have compresson. - Input quantzaton step on segment : represents the length of the subsegments of the nput segment and can be computed as: x x q. No. sub segments 16 - Output quantzaton step: represents the length of the sub-segments of the output segments and can be computed as: y 0.15 q o 0.007815. 16 16 - Elementary quantzaton step: represents the quantzaton step used for unform quantzaton provdng the same (or smlar) performances as the non-unform quantzaton (14 bts n the case of µ law): q 0.000107 14 e. b - Number of elementary quantzaton steps of the nput quantzaton q step: n q e q - The power of the quantzaton nose on segment : Pnq. 1 4

- Total (average) power of the quantzaton nose: 8, 8 0 Pnq p Pnq where p represents the probablty that the ampltude of the sample s located n segment. If we have the same occurrence probablty of each sample (unform ampltude dstrbuton) then the probablty that a sample s located n segment s equal wth the length of ths segment, f the characterstc s normalzed, meanng that p x. - Total (average) power of the quantzaton nose (consderng only postve segments): 8 ' 1 Pnq p Pnq, p has the same defnton. Segment () x Rc q n Pnq 1 0.00391 0.031368 0.00045063.00755 5.00464E-09 0.007839 0.0671 0.000489938 4.013568.0003E-08 3 0.01564 0.151 0.0009775 8.00768 7.9655E-08 4 0.0314 0.51 0.001965 16.0768 3.0951E-07 5 0.06 0.4976 0.0038875 31.8464 1.5939E-06 6 0.119 0.95 0.0074375 60.98 4.6097E-06 7 0.581.0648 0.0161315 13.147.16848E-05 8 0.5019 4.015 0.03136875 56.978 8.19999E-05 Table 1 Computed parameters for the µ compresson characterstc If we compute the total power of the quantzaton nose, when non-unform quantzaton s employed, we obtan Pnq 47.39*10-6 W and f we compute the total power of the unform quantzaton, on the same number of bts, we get Pnq unform 5*10-6 W. Even f the nose power of the unform quantzaton s smaller than the nose power of the non-unform quantzaton, we can notce n Table 1 that for the frst segments, meanng for low sgnal values where the hearng s more senstve, the nose power s much hgher n the case of unform quantzaton than n the case of non-unform quantzaton. The quantzaton error at hgh sgnal levels s less detectable by the human hearng (havng less mportance n ths case) but nfluences sgnfcantly the total quantzaton nose power, whch s more mportant for data transmssons. Supplementary nformaton at: - http://www.educypeda.be/electroncs/telephonetopcs.htm - http://telecom.tb.net Questons 1) Specfy the advantages and dsadvantages of the non-unform quantzaton. ) Calculate the parameters gven n Table 1 for the A compresson law. 3) A compresson characterstc s approxmated n 4 segments: (0,0) (1/8,0.5) (1/4,0.5) (1/,0.75) (1,1), and each segment s dvded nto 4 subsegments. Whch s the code generated f the nput sample s ampltude s 0.755? (The coordnates of the characterstc are specfed as (x,y)) 5