If you are looking for quality education that will help you specialize in one of the fields with the most professional prospects, this is your best option”

Advances in telecommunications are happening all the time, as this is one of the fastest evolving areas. It is therefore necessary to have IT experts who can adapt to these changes and have first-hand knowledge of the new tools and techniques that are emerging in this field.

This Postgraduate diploma in Signals and Communication addresses the complete range of topics involved in this field. Its study has a clear advantage over other programs that focus on specific blocks, which prevents students from knowing the interrelation with other areas included in the multidisciplinary field of telecommunications. In addition, the teaching team of this educational program has made a careful selection of each of the topics of this program in order to offer students the most complete study opportunity possible and always linked to current events.

This program is aimed at those interested in attaining a higher level of knowledge of Signals and Communication. The main objective of this Postgraduate diploma is for students to specialize their knowledge in simulated work environments and conditions in a rigorous and realistic manner so that they can later apply it in the real world.

As it is a 100% online Postgraduate diploma program, students are not constrained by fixed timetables or the need to move to another physical location, but can access the contents at any time of the day, balancing their professional or personal life with their academic life.

Do not miss the opportunity to study this Postgraduate diploma in Signals and Communication at TECH. It's the perfect opportunity to advance your career"

This Postgraduate diploma in Signals and Communication contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of case studies presented by computer security experts
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies in Signals and Communication
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

This Postgraduate diploma is the best investment you can make when choosing a refresher program to update your existing knowledge of Signals and Communication”

The teaching staff includes professionals from the field of design, who bring their experience to this specialization program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year. To do so, professionals will be assisted by an innovative interactive video system created by renowned Signals and Communication experts.

This program comes with the best educational material, providing you with a contextual approach that will facilitate your learning"

This 100% online Postgraduate diploma will allow you to combine your studies with your professional work. You choose where and when to study"

The structure of the contents has been designed by the best professionals in the sector the telecommunication, with extensive experience and recognized prestige in the profession.

We have the most complete and up-to-date educational program on the market. We strive for excellence and for you to achieve it too"

Module 1. Random Signals and Lineal Systems

1.1. Probability Theory

1.1.1. Concept of Probability. Probability Space
1.1.2. Conditional Probability and Independent Events
1.1.3. Total Probability Theorem. Bayes’ Theorem
1.1.4. Compound Experiments. Bernoulli Tests

1.2. Random Variables

1.2.1. Random Variable Definition
1.2.2. Probability Distributions
1.2.3. Main Distributions
1.2.4. Functions of Random Variables
1.2.5. Moments of Random Variable
1.2.6. Generator Functions 

1.3. Random Vectors

1.3.1. Random Vector Definition
1.3.2. Joint Distribution
1.3.3. Marginal Distributions
1.3.4. Conditional Distributions
1.3.5. Linear Correlation Between Two Variables
1.3.6. Normal Multivariant Distribution

1.4. Random Processes

1.4.1. Definition and Description of Random Processes
1.4.2. Random Processes in Discrete Time
1.4.3. Random Processes in Continuous Time
1.4.4. Stationary Processes
1.4.5. Gaussian Processes
1.4.6. Markovian Processes

1.5. Queuing Theory in Telecommunications

1.5.1. Introduction
1.5.2. Basic Concepts
1.5.2. Model Description
1.5.2. Example of the Application of Queuing Theory in Telecommunications

1.6. Random Processes. Temporal Characteristics

1.6.1. Concept of Random Process
1.6.2. Processes Qualification
1.6.3. Main Statistics
1.6.4. Stationarity and Independence
1.6.5. Temporary Averages
1.6.6. Ergodicity

1.7. Random Processes. Spectral Characteristic

1.7.1. Introduction
1.7.2 Power Density Spectrum
1.7.3. Properties of the Power Spectral Density 
1.7.4. Relationship between the Power Spectrum and Autocorrelation

1.8. Signals and Systems. Properties

1.8.1. Introduction to Signals
1.8.2. Introduction to Systems
1.8.3. Basic Properties of Systems:

1.8.3.1. Linearity
1.8.3.2. Time Invariance
1.8.3.3. Causality
1.8.3.4. Stability
1.8.3.5. Memory
1.8.3.6. Invertibility 

1.9. Lineal Systems with Random Inputs

1.9.1. Fundamentals of Linear Systems
1.9.2. Response to Linear Systems and Random Signals
1.9.3. Systems with Random Noise
1.9.4. Spectral Characteristics of the System Response
1.9.5. Equivalent Noise Bandwidth and Temperature
1.9.6. Noise Source Model 

1.10. LTI Systems

1.10.1. Introduction
1.10.2. Discrete-Time LTI Systems
1.10.3. Continuous-Time LTI Systems
1.10.4. Properties of LTI Systems
1.10.5. Systems Described by Differential Equations

Module 2. Communication Theory

2.1. Introduction: Telecommunication Systems and Transmission Systems

2.1.1. Introduction
2.1.2. Basic Concepts and History
2.1.3. Telecommunication Systems
2.1.4. Transmission Systems

2.2. Signal Characterization

2.2.1. Deterministic vs. Random Signals
2.2.2. Periodic and Non-Periodic Signal
2.2.3. Energy and Power Signal
2.2.4. Baseband and Bandpass Signal
2.2.5. Basic Parameters of a Signal

2.2.5.1. Average Value 
2.2.5.2. Average Energy and Power
2.2.5.3. Maximum Value and Effective Value
2.2.5.4. Energy and Power Spectral Density
2.2.5.5. Power Calculation in Logarithmic Units

2.3. Disturbances in Transmission Systems

2.3.1. Ideal Channel Transmission
2.3.2. Classification of Disturbances
2.3.3. Lineal Distortion
2.3.4. Non-Lineal Distortion
2.3.5. Crosstalk and Interference
2.3.6. Noise

2.3.6.1. Types of Noise 
2.3.6.2. Characterization

2.3.7. Narrow Band Pass Signals 

2.4. Analog Communications. Concepts Concepts

2.4.1. Introduction
2.4.2. General Concepts
2.4.3. Baseband Transmission 

2.4.3.1. Modulation and Demodulation 
2.4.3.2. Characterization
2.4.3.3. Multiplexing

2.4.4. Mixers 
2.4.5. Characterization
2.4.6. Types of Mixers 

2.5. Analog Communications. Lineal Modulations

2.5.1. Basic Concepts
2.5.2. Amplitude Modulation (AM)

2.5.2.1. Characterization
2.5.2.2. Parameters
2.5.2.3. Modulation/Demodulation

2.5.3. Double Side Band (DSB) Modulation 

2.5.3.1. Characterization
2.5.3.2. Parameters
2.5.3.3. Modulation/ Demodulation

2.5.4. Single Side Band (SSB) Modulation

2.5.4.1. Characterization
2.5.4.2. Parameters
2.5.4.3. Modulation/Demodulation

2.5.5. Vestigial Sideband Modulation (VSB) 

2.5.5.1. Characterization
2.5.5.2. Parameters
2.5.5.3. Modulation/Demodulation

2.5.6. Quadrature Amplitude Modulation (QAM) 

2.5.6.1. Characterization
2.5.6.2. Parameters
2.5.6.3. Modulation/Demodulation

2.5.7. Noise in Analog Modulations 

2.5.7.1. Approach
2.5.7.2. Noise in DBL 
2.5.7.3. Noise in BLU
2.5.7.4. Noise in AM

2.6. Analog Communications. Angular Modulations

2.6.1. Phase and Frequency Modulation 
2.6.2. Narrow Band Angular Modulation
2.6.3. Spectrum Calculation
2.6.4. Generation and Demodulation
2.6.5. Angular Demodulation with Noise 
2.6.6. Noise in PM
2.6.7. Noise in FM
2.6.8. Comparison between Analog Modulations

2.7. Digital Communication Introduction. Transmission Models 

2.7.1. Introduction
2.7.2. Fundamental Parameters 
2.7.3. Advantages of Digital Systems
2.7.4. Limitations of Digital Systems
2.7.5. PCM Systems
2.7.6. Modulations in Digital Systems
2.7.7. Demodulations in Digital Systems

2.8. Digital Communication Digital Base Band Transmission

2.8.1. PAM Binary Systems 

2.8.1.1. Characterization
2.8.1.2. Signal Parameters
2.8.1.3. Spectral Model 

2.8.2. Binary Receptor per Basic Sample 

2.8.2.1. Bipolar NRZ 
2.8.2.2. Bipolar RZ
2.8.2.3. Error Rate

2.8.3. Optimal Binary Receptor

2.8.3.1. Context
2.8.3.2. Error Rate Calculation 
2.8.3.3. Optimal Receptor Filter Design
2.8.3.4. SNR Calculation 
2.8.3.5. Loans
2.8.3.6. Characterization

2.8.4. M-PAM Systems 

2.8.4.1. Parameters
2.8.4.2. Constellations
2.8.4.3. Optimal Receptor 
2.8.4.4. Bit Error Ratio (BER) 

2.8.5. Signal Vectorial Space
2.8.6. Constellation of a Digital Modulation
2.8.7. M-Signal Receptors 

2.9. Digital Communication Digital Bandpass Transmission. Digital Modulations

2.9.1. Introduction
2.9.2. ASK Modulation

2.9.2.1. Characterization
2.9.2.2. Parameters
2.9.2.3. Modulation/Demodulation

2.9.3. QAM Modulation

2.9.3.1. Characterization
2.9.3.2. Parameters
2.9.3.3. Modulation/Demodulation

2.9.4. PSK Modulation

2.9.4.1. Characterization
2.9.4.2. Parameters
2.9.4.3. Modulation/Demodulation

2.9.5. FSK Modulation

2.9.5.1. Characterization
2.9.5.2. Parameters
2.9.5.3. Modulation/Demodulation

2.9.6. Other Digital Modulations 
2.9.7. Comparison between Digital Modulations

2.10. Digital Communication Comparison, IS, Diagram and

2.10.1. Comparison between Digital Modulations

2.10.1.1. Modulation Energy and Power 
2.10.1.2. Enveloping 
2.10.1.3. Protection Against Noise
2.10.1.4. Spectral Model
2.10.1.5. Channel Codification Techniques 
2.10.1.6. Synchronization Signals
2.10.1.7. SER Symbol Error Rate

2.10.2. Limited Bandwidth Channels 
2.10.3. Interference between Symbols (IS) 

2.10.3.1. Characterization
2.10.3.2. Limitations

2.10.4. Optimal Receptor in PAM without IS 
2.10.5. Eye Diagrams

Module 3: Information Theory

3.1. Introduction to Information Theory

3.1.1. Communications System Reference Model
3.1.2. Information Sources
3.1.3. Communication Channels
3.1.4. Source Coding Concept
3.1.5. Channel Codification Concept

3.2. Shannon Entropy

3.2.1. Introduction
3.2.2. Definition
3.2.3. Entropy Function Choice
3.2.4. Properties

3.3. Source Coding

3.3.1. Block Codes
3.3.2. Shannon’s First Theorem: Optimal Codes
3.3.3. Huffman’s Algorithm
3.3.4. Entropy of a Stochastic Process and of Markov Chain

3.4. Channel Capacity

3.4.1. Mutual Information
3.4.2. Information Processing Theorem
3.4.3. Channel Capacity
3.4.4. Capacity Calculation

3.5. Noise Channel

3.5.1. Reliable Transmission on an Unreliable Medium
3.5.2. Shannon’s Second Theorem
3.5.3. Capacity Limit of a Noise Channel
3.5.4. Optimal Decoding

3.6. Error Control with Linear Codes

3.6.1. Introduction
3.6.2. Linear Codes
3.6.3. Generator and Parity Check Matrix
3.6.4. Syndrome Decoding
3.6.5. Typical Matrix
3.6.6. Error Detection and Correction
3.6.7. Error Probability
3.6.8. Hamming Codes
3.6.9. MacWilliams’ Identity
3.6.10. Distance Dimensions

3.7. Error Control with Cyclic Codes

3.7.1. Matrix Definition and Description
3.7.2. Systematic Cyclic Codes
3.7.3. Encoding Circuits
3.7.4. Error Detection
3.7.5. Cyclic Code Decoding
3.7.6. Cyclic Structure of Hamming Codes
3.7.7. Shortened and Irreducible Cyclic Codes
3.7.8. Cyclic, Ring and Ideal Codes

3.8. Data Forwarding Strategies

3.8.1. Introduction
3.8.2. ARQ Strategies
3.8.3. Types of ARQ Strategies

3.8.3.1. Stop and Wait
3.8.3.2. Continuous Sending with Simple Rejection
3.8.3.3. Continuous Sending with Selective Rejection

3.8.4. Efficient Cadence Analysis

3.9. Source Compression: Audio, Image and Video

3.9.1. Introduction
3.9.2. Audio

3.9.2.1. Audio Formats
3.9.2.2. Audio Compression Standards (MP3)

3.9.3. Image

3.9.3.1. Image Formats
3.9.3.2. Image Compression Standards (JPEG)

3.9.4. Video

3.9.4.1. Video Formats
3.9.4.2. Video Compression Standards (MPEG)
3.9.4.3. MPEG Compression Techniques
3.9.4.4. Coding Based on Transforms and DCT
3.9.4.5. Entropy Coding (Huffman Coding)
3.9.4.6. Other Compression Standards

3.10. Introduction to Reed Solomon and Convolutional Codes

3.10.1. Introduction to Reed Solomon Codes
3.10.2. Ratio and Reed Solomon Codes’ Correction Capability
3.10.3. RS Encoding and Decoding with Matlab
3.10.4. Introduction to Convolutional Codes
3.10.5. Choice of Convolutional Codes

Module 4: Digital Signal Processing

4.1. Introduction

4.1.1. Meaning of “Digital Signal Processing” 
4.1.2. Comparison between DSP and ASP
4.1.3. The History of DSP
4.1.4. Applications of DSP

4.2. Discrete Time Signals

4.2.1. Introduction
4.2.2. Sequence Classification 

4.2.2.1. Unidimensional and Multidimensional Sequences 
4.2.2.2. Odd and Even Sequences 
4.2.2.3. Periodic and Aperiodic Sequences 
4.2.2.4. Deterministic and Random Sequences 
4.2.2.5. Energy and Power Sequences
4.2.2.6. Real and Complex Systems 

4.2.3. Real Exponential Sequences 
4.2.4. Sinusoidal Sequences 
4.2.5. Impulse Sequence 
4.2.6. Step Sequence 
4.2.7. Random Sequence

4.3. Discrete Time Systems

4.3.1. Introduction
4.3.2. System Classification 

4.3.2.1. Linearity
4.3.2.2. Invariance
4.3.2.3. Stability
4.3.2.4. Causality

4.3.3. Difference Equations
4.3.4. Discrete Convolution 

4.3.4.1. Introduction
4.3.4.2. Deduction of the Discrete Convolution Formula 
4.3.4.3. Properties
4.3.4.4. Graphical Method for Calculating Convolution 
4.3.4.5. Justification of Convolution

4.4. Sequences and Systems in the Frequency Domain

4.4.1. Introduction
4.4.2. Discrete-Time Fourier Transform (DTFT) 

4.4.2.1. Definition and Justification 
4.4.2.2. Observations
4.4.2.3. Inverse Transform (IDTFT) 
4.4.2.4. Properties of DTFT
4.4.2.5. Examples
4.4.2.6. DTFT Calculation in a Computer 

4.4.3. Frequency Response of a LI System in Discrete Time 

4.4.3.1. Introduction
4.4.3.2. Frequency Response According to Impulse Response 
4.4.3.3. Frequency Response According to the Difference Equation 

4.4.4. Bandwidth Relationship- Response Time 

4.4.4.1. Duration Relationship - Signal Bandwidth
4.4.4.2. Implication in Filters 
4.4.4.3. Implications in Spectral Analysis 

4.5. Analog Signal Sample

4.5.1. Introduction
4.5.2. Sampling and Aliasing

4.5.2.1. Introduction
4.5.2.2. Aliasing Visualization in the Time Domain
4.5.2.3. Aliasing Visualization in the Frequency Domain
4.5.2.4. Example of Aliasing

4.5.3. Relationship between Analog and Digital Frequency 
4.5.4. Antialiasing Filter 
4.5.5. Simplification of the Antialiasing Filter 

4.5.5.1. Sampling Admitting Aliasing
4.5.5.2. Oversampling 

4.5.6. Simplification of the Reconstruction Filter 
4.5.7. Quantization Noise

4.6. Discrete Fourier Transform

4.6.1. Definition and Foundations
4.6.2. Inverse Transformer 
4.6.3. Examples of DFT Application and Programming 
4.6.4. Periodicity of the Sequence and its Spectrum 
4.6.5. Convolution by Means of DFT 

4.6.5.1. Introduction
4.6.5.2. Circular Displacement 
4.6.5.3. Circular Convolution 
4.6.5.4. Frequency Domain Equivalent 
4.6.5.5. Convolution through the Frequency Domain
4.6.5.6. Lineal Convolution through Circular Convolution 
4.6.5.7. Summary and Example of Time Calculations 

4.7. Rapid Fourier Transform 

4.7.1. Introduction
4.7.2. Redundancy in DFT 
4.7.3. Algorithm by Decomposition in Time 

4.7.3.1. Algorithm Basis
4.7.3.2. Algorithm Development 
4.7.3.3. Number of Complex Multiplications Required 
4.7.3.4. Observations
4.7.3.5. Calculation Time 

4.7.4. Variants and Adaptations of the Above Algorithm 

4.8. Spectral Analysis

4.8.1. Introduction
4.8.2. Periodic Signals Coincident with the Sampling Window 
4.8.3. Non-Coincident Periodic Signals with the Sampling Window 

4.8.3.1. Spurious Content in the Spectrum and Use of Windows
4.8.3.2. Error Caused by the Continuous Component
4.8.3.3. Error in the Magnitude of the Non-Coincident Components
4.8.3.4. Spectral Analysis Bandwidth and Resolution
4.8.3.5. Increasing the Length of the Sequence by Adding Zeros
4.8.3.6. Application in a Real Signal 

4.8.4. Stationary Random Signals

4.8.4.1. Introduction
4.8.4.2. Power Spectral Density
4.8.4.3. Periodogram
4.8.4.4. Independence of Samples
4.8.4.5. Feasibility of Averaging
4.8.4.6. Scaling Factor of the Periodogram Formula
4.8.4.7. Modified Periodogram 
4.8.4.8. Averaging with Overlap 
4.8.4.9. Welch Method
4.8.4.10. Segment Size
4.8.4.11. Implementation in MATLAB

4.8.5. Non-Stationary Random Signals 

4.8.5.1. STFT 
4.8.5.2. Graphic Representation of the STFT 
4.8.5.3. Implementation in MATLAB
4.8.5.4. Spectral and Temporal Resolution
4.8.5.5. Other Methods

4.9. Design of FIR Filters

4.9.1. Introduction
4.9.2. Mobile Average
4.9.3. Lineal Relationship between Phase and Frequency 
4.9.4. Lineal Phase Requirement 
4.9.5. Window Method 
4.9.6. Frequency Sample Method 
4.9.7. Optimal Method 
4.9.8. Comparison between the Previous Design Methods 

4.10. Design of IIR Filters

4.10.1. Introduction
4.10.2. Design of First Order IIR Filters 

4.10.2.1. Low-Pass Filters
4.10.2.2. High-Pass Filters

4.10.3. The Z Transform 

4.10.3.1. Definition
4.10.3.2. Existence
4.10.3.3. Rational Functions of Z, Zeros and Poles 
4.10.3.4. Displacements of a Sequence
4.10.3.5. Transfer Functions
4.10.3.6. Start of TZ Operation 

4.10.4. Bilinear Transformation 

4.10.4.1. Introduction
4.10.4.2. Deduction and Validation of the Bilinear Transformation 

4.10.5. Design of Butterworth-Type Analog Filters
4.10.6. Butterworth-Type IIR Low-Pass Filter Design Example 

4.10.6.1. Specifications of Digital Filters 
4.10.6.2. Transition to Analog Filter Specifications 
4.10.6.3. Design of Analog Filters 
4.10.6.4. Transformation of Ha(s) to H(z) Using TB 
4.10.6.5. Verification of Compliance with Specifications 
4.10.6.6. Digital Filter Difference Equation 

4.10.7. Automated Design of IIR Filters 
4.10.8. Comparison between FIR Filters and IIR Filters

4.10.8.1. Efficiency
4.10.8.2. Stability
4.10.8.3. Sensitivity to Coefficient Quantification 
4.10.8.4. Distortion of Wave F

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