Digital Communications : Fundamentals and Applications. Digital Communications : Fundamentals and Applications

Similar documents
AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION

Human Emotion Recognition From Speech

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method

Phonetic- and Speaker-Discriminant Features for Speaker Recognition. Research Project

Segregation of Unvoiced Speech from Nonspeech Interference

OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS

Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier

Probabilistic Latent Semantic Analysis

WHEN THERE IS A mismatch between the acoustic

Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction

Speaker Identification by Comparison of Smart Methods. Abstract

A Neural Network GUI Tested on Text-To-Phoneme Mapping

Noise-Adaptive Perceptual Weighting in the AMR-WB Encoder for Increased Speech Loudness in Adverse Far-End Noise Conditions

THE RECOGNITION OF SPEECH BY MACHINE

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Python Machine Learning

University of Groningen. Systemen, planning, netwerken Bosman, Aart

English Language and Applied Linguistics. Module Descriptions 2017/18

Speaker Recognition. Speaker Diarization and Identification

Generative models and adversarial training

Learning Methods in Multilingual Speech Recognition

Lesson 1 Taking chances with the Sun

Degree Qualification Profiles Intellectual Skills

16.1 Lesson: Putting it into practice - isikhnas

Knowledge Transfer in Deep Convolutional Neural Nets

Sound and Meaning in Auditory Data Display

International Journal of Computational Intelligence and Informatics, Vol. 1 : No. 4, January - March 2012

Author's personal copy

Speech Synthesis in Noisy Environment by Enhancing Strength of Excitation and Formant Prominence

DOMAIN MISMATCH COMPENSATION FOR SPEAKER RECOGNITION USING A LIBRARY OF WHITENERS. Elliot Singer and Douglas Reynolds

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

Semi-Supervised GMM and DNN Acoustic Model Training with Multi-system Combination and Confidence Re-calibration

Speech Segmentation Using Probabilistic Phonetic Feature Hierarchy and Support Vector Machines

Mathematics. Mathematics

SARDNET: A Self-Organizing Feature Map for Sequences

Statewide Framework Document for:

On the Combined Behavior of Autonomous Resource Management Agents

Automatic Pronunciation Checker

Design Of An Automatic Speaker Recognition System Using MFCC, Vector Quantization And LBG Algorithm

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Speaker recognition using universal background model on YOHO database

National Survey of Student Engagement (NSSE) Temple University 2016 Results

Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification

Speech Emotion Recognition Using Support Vector Machine

JONATHAN H. WRIGHT Department of Economics, Johns Hopkins University, 3400 N. Charles St., Baltimore MD (410)

STA 225: Introductory Statistics (CT)

Radius STEM Readiness TM

UTD-CRSS Systems for 2012 NIST Speaker Recognition Evaluation

Course Law Enforcement II. Unit I Careers in Law Enforcement

This Performance Standards include four major components. They are

Speech Recognition at ICSI: Broadcast News and beyond

Genevieve L. Hartman, Ph.D.

Evaluation of Various Methods to Calculate the EGG Contact Quotient

*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN


DISTANCE LEARNING, SIMULATION AND COMMUNICATION 2011

Detection and Classification of Mu Rhythm using Phase Synchronization for a Brain Computer Interface

The dilemma of Saussurean communication

ADVANCES IN DEEP NEURAL NETWORK APPROACHES TO SPEAKER RECOGNITION

Protocol for using the Classroom Walkthrough Observation Instrument

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE

WASHINGTON Does your school know where you are? In class? On the bus? Paying for lunch in the cafeteria?

While you are waiting... socrative.com, room number SIMLANG2016

Learning Distributed Linguistic Classes

Switchboard Language Model Improvement with Conversational Data from Gigaword

Progress Monitoring for Behavior: Data Collection Methods & Procedures

A comparison of spectral smoothing methods for segment concatenation based speech synthesis

Using EEG to Improve Massive Open Online Courses Feedback Interaction

Integrating simulation into the engineering curriculum: a case study

Comparison of EM and Two-Step Cluster Method for Mixed Data: An Application

BODY LANGUAGE ANIMATION SYNTHESIS FROM PROSODY AN HONORS THESIS SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE OF STANFORD UNIVERSITY

Likelihood-Maximizing Beamforming for Robust Hands-Free Speech Recognition

HOLMER GREEN SENIOR SCHOOL CURRICULUM INFORMATION

ABSTRACT. A major goal of human genetics is the discovery and validation of genetic polymorphisms

Introduction to the Practice of Statistics

On-Line Data Analytics

First Grade Standards

Matrices, Compression, Learning Curves: formulation, and the GROUPNTEACH algorithms

USER ADAPTATION IN E-LEARNING ENVIRONMENTS

Writing up qualitative data in SAP: Some observations

The Efficacy of PCI s Reading Program - Level One: A Report of a Randomized Experiment in Brevard Public Schools and Miami-Dade County Public Schools

EQuIP Review Feedback

Operational Knowledge Management: a way to manage competence

1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature

Junior Fractions. With reference to the work of Peter Hughes, the late Richard Skemp, Van de Walle and other researchers.

Detecting Wikipedia Vandalism using Machine Learning Notebook for PAN at CLEF 2011

Why Did My Detector Do That?!

Body-Conducted Speech Recognition and its Application to Speech Support System

Soft Computing based Learning for Cognitive Radio

2010 National Survey of Student Engagement University Report

arxiv: v1 [cs.cv] 10 May 2017

Linking Task: Identifying authors and book titles in verbose queries

Web as Corpus. Corpus Linguistics. Web as Corpus 1 / 1. Corpus Linguistics. Web as Corpus. web.pl 3 / 1. Sketch Engine. Corpus Linguistics

Assignment 1: Predicting Amazon Review Ratings

Learning Disability Functional Capacity Evaluation. Dear Doctor,

stateorvalue to each variable in a given set. We use p(x = xjy = y) (or p(xjy) as a shorthand) to denote the probability that X = x given Y = y. We al

Conversation Starters: Using Spatial Context to Initiate Dialogue in First Person Perspective Games

Ph.D. in Behavior Analysis Ph.d. i atferdsanalyse

Master s Programme in Computer, Communication and Information Sciences, Study guide , ELEC Majors

Create A City: An Urban Planning Exercise Students learn the process of planning a community, while reinforcing their writing and speaking skills.

Transcription:

4-1 Figure 4.1 Basic digital communication transformations 4-2 Figure 4.2 Phasor representation of a sinusoid. 4-3 Figure 4.3 Amplitude modulation. 4-4 Figure 4.4 Narrowband frequency modulation. Figure 4.5 Digital modulations. (a) PSK. (b) FSK. (c) ASK. (d) ASK/PSK (APK). 4-5 4-6 Figure 4.6 Two-dimensional signal space, with arbitrary equal-amplitude vectors s 1 and s 2. 1

Figure 4.7 (a) Correlator receiver with reference signals {s i (t)}. (b) Correlator receiver with reference signals {Ψ(t)}. 4-7 Figure 4.8 Binary correlator receiver. (a) Using a single correlator. (b) Using two correlators. 4-8 Figure 4.9 Conditional probability density functions; p(z/s 1 ), p(z/s 2 ). 4-9 Figure 4.10 (a) Sampled matched filter. (b) Sampled matched filter detection example, neglecting noise. 4-10 Figure 4.11 Signal space and decision regions for a QPSK system. 4-11 4-12 Figure 4.12 Demodulator for MPSK signals. 2

Figure 4.13 In-phase and quadrature components of the received signal vector r. 4-13 Figure 4.14 Partitioning the signal space for a 3-ary FSK signal. 4-14 4-15 Figure 4.15 Mobile radio link. 4-16 Figure 4.16 Signal space for DPSK. Figure 4.17 Differential PSK (DPSK). (a) Differential encoding. (b) Differentially coherent detection. (c) Optimum differentially coherent detection. 4-17 4-18 Figure 4.18 Quadrature receiver. 3

Figure 4.19 Noncoherent detection of FSK using envelope detectors. 4-19 Figure 4.20 Minimum tone spacing for noncoherently detected orthogonal FSK signaling. 4-20 4-21 Figure 4.21 Quadrature type modulator. 4-22 Figure 4.22 Lead/Lag relationships of sinusoids. Figure 4.23 Quadrature implementation of a D8PSK modulator. 4-23 4-24 Figure 4.24 Modulator/demodulator example. 4

Figure 4.25 Bit error probability for several types of binary systems. 4-25 Figure 4.26 DPSK detection. (a) four-channel differentially coherent detection of binary DPSK. (b) Equivalent two-channel detector for binary DPSK. 4-26 4-27 Figure 4.27 Ideal P B versus E b /N o curve. Figure 4.28 Bit error probability for coherently detected M-ary orthogonal signaling. 4-28 Figure 4.29 Bit error probability for coherently detected multiple phase signaling. 4-29 4-30 Figure 4.30 MPSK signal sets for M = 2, 4, 8, 16. 5

Figure 4.31 In-phase and quadrature BPSK components of QPSK signaling. 4-31 4-32 Figure 4.32 MFSK signal sets for M = 2, 3. Figure 4.33 Symbol error probability versus SNR for coherent FSK signaling. 4-33 Figure 4.34 Mapping P E versus SNR into P E versus E b /N o for orthogonal signaling. (a) Unnormalized. (b) Normalized. 4-34 Figure 4.35 Symbol error probability for coherently detected multiple phase 4-35 signaling. Figure 4.36 Symbol error probability for coherently detected M-ary orthogonal signaling. 4-36 6

Figure 4.37 Symbol error probability for noncoherently detected M-ary orthogonal signaling. 4-37 4-38 Figure 4.38 Example of P B versus P E. Figure P4.1 Figure 4.39 Binary-coded versus Gray-coded decision regions in an MPSK signal space. (a) Binary coded. (b) Gray coded. 4-39 4-40 Figure P4.2 7