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This document provides an introduction to the ECE 366 Signals and Systems course taught in fall 2009. It outlines the course details such as lectures, textbook, assignments, exams. It also covers an overview of key concepts in signals and systems including definitions of signals, systems, their properties and classifications. Examples of applications are also discussed.

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week1.ppt12345667777777777777777777777777

This document provides information about a telecommunication systems course, including:
- The course code, title, credit hours, semester, instructor, and reference book.
- An outline of topics covered, including signals, modulation, linear systems, amplitude modulation, angle modulation, and transmitter/receiver block diagrams.
- A high-level overview of key concepts in signals, systems, and wireless communication systems.

Signal & systems

A signal is a pattern of variation that carry information.
Signals are represented mathematically as a function of one or more independent variable
basic concept of signals
types of signals
system concepts

Chapter-I Classification of Signals and Systems.pptx

This document provides an overview of the signals and systems course being taught by Joel Livin. The course covers topics like classification of signals and systems, convolution, Fourier transforms, modulation, and sampling. Assessment includes two tests, assignments, and a final exam. The document explains that signals and systems form the basis of fields like control systems and signal processing by allowing real-world systems to be modeled mathematically. It provides definitions and examples of continuous and discrete-time signals and systems, and describes properties and operations of signals like periodicity, even/odd functions, time shifting, scaling, and more. Tutorial problems are also included.

S&s lec1

This document summarizes key concepts in signals and systems. It discusses different types of signals including continuous-time and discrete-time signals. It covers signal classification such as even/odd signals and periodic/non-periodic signals. It also discusses energy and power signals. The document then explains systems and provides examples. It introduces important concepts in linear time-invariant systems including convolution and the Fourier transform. Finally, it discusses applications of signals and systems in areas like communication systems.

Ch1

This document provides an introduction to signals and systems. It defines a signal as a function that carries information about a physical phenomenon, and a system as an entity that processes signals to produce new outputs. Signals can be classified as continuous or discrete, deterministic or random, periodic or aperiodic, even or odd, energy-based or power-based, and causal or noncausal. The document discusses examples and properties of different signal types and how systems manipulate inputs to generate outputs. It covers key concepts like energy, power, periodicity, causality, and system modeling that are important foundations for signals and systems analysis.

Lecture 2.pptx

A signal is a function that conveys information and can vary over time or space. A system processes input signals to produce output signals. Signals can be classified based on properties like being continuous or discrete, analog or digital, periodic or aperiodic, and deterministic or random. Key signals include the unit impulse, which models an infinitely narrow pulse, and the unit step, which models a signal turning on at a certain time. Linear, time-invariant systems are completely characterized by their impulse response or transfer function. Common operations on signals include time shifting, scaling, and reversal.

Signals basics

A signal can be defined as a pattern of variation that carries information over time. Signals can be continuous analog signals defined by mathematical functions, or discrete digital signals represented by discrete samples. Analog signals are more accurate but digital signals are easier to store and analyze. Conversion between analog and digital signals involves sampling the analog signal at discrete time intervals and quantizing the amplitude into discrete levels. Signals can be analyzed in the time domain, looking at amplitude variation over time, or the frequency domain, looking at how many times different events occur over the total observation period.

Communication Systems_B.P. Lathi and Zhi Ding (Lecture No 1-3)

This document provides an overview of a communication systems course. It introduces the instructor, textbook, learning outcomes, and assessment criteria. The contents will cover communication systems fundamentals including analog and digital messages, modulation and detection techniques, source and error coding, and a brief history of telecommunications. Students will learn about signals, channels, modulation schemes like AM and FM, and analyze different transmission methods.

week1.ppt12345667777777777777777777777777

This document provides information about a telecommunication systems course, including:
- The course code, title, credit hours, semester, instructor, and reference book.
- An outline of topics covered, including signals, modulation, linear systems, amplitude modulation, angle modulation, and transmitter/receiver block diagrams.
- A high-level overview of key concepts in signals, systems, and wireless communication systems.

Signal & systems

A signal is a pattern of variation that carry information.
Signals are represented mathematically as a function of one or more independent variable
basic concept of signals
types of signals
system concepts

Chapter-I Classification of Signals and Systems.pptx

This document provides an overview of the signals and systems course being taught by Joel Livin. The course covers topics like classification of signals and systems, convolution, Fourier transforms, modulation, and sampling. Assessment includes two tests, assignments, and a final exam. The document explains that signals and systems form the basis of fields like control systems and signal processing by allowing real-world systems to be modeled mathematically. It provides definitions and examples of continuous and discrete-time signals and systems, and describes properties and operations of signals like periodicity, even/odd functions, time shifting, scaling, and more. Tutorial problems are also included.

S&s lec1

This document summarizes key concepts in signals and systems. It discusses different types of signals including continuous-time and discrete-time signals. It covers signal classification such as even/odd signals and periodic/non-periodic signals. It also discusses energy and power signals. The document then explains systems and provides examples. It introduces important concepts in linear time-invariant systems including convolution and the Fourier transform. Finally, it discusses applications of signals and systems in areas like communication systems.

Ch1

This document provides an introduction to signals and systems. It defines a signal as a function that carries information about a physical phenomenon, and a system as an entity that processes signals to produce new outputs. Signals can be classified as continuous or discrete, deterministic or random, periodic or aperiodic, even or odd, energy-based or power-based, and causal or noncausal. The document discusses examples and properties of different signal types and how systems manipulate inputs to generate outputs. It covers key concepts like energy, power, periodicity, causality, and system modeling that are important foundations for signals and systems analysis.

Lecture 2.pptx

A signal is a function that conveys information and can vary over time or space. A system processes input signals to produce output signals. Signals can be classified based on properties like being continuous or discrete, analog or digital, periodic or aperiodic, and deterministic or random. Key signals include the unit impulse, which models an infinitely narrow pulse, and the unit step, which models a signal turning on at a certain time. Linear, time-invariant systems are completely characterized by their impulse response or transfer function. Common operations on signals include time shifting, scaling, and reversal.

Signals basics

A signal can be defined as a pattern of variation that carries information over time. Signals can be continuous analog signals defined by mathematical functions, or discrete digital signals represented by discrete samples. Analog signals are more accurate but digital signals are easier to store and analyze. Conversion between analog and digital signals involves sampling the analog signal at discrete time intervals and quantizing the amplitude into discrete levels. Signals can be analyzed in the time domain, looking at amplitude variation over time, or the frequency domain, looking at how many times different events occur over the total observation period.

Communication Systems_B.P. Lathi and Zhi Ding (Lecture No 1-3)

This document provides an overview of a communication systems course. It introduces the instructor, textbook, learning outcomes, and assessment criteria. The contents will cover communication systems fundamentals including analog and digital messages, modulation and detection techniques, source and error coding, and a brief history of telecommunications. Students will learn about signals, channels, modulation schemes like AM and FM, and analyze different transmission methods.

1.Basics of Signals

This document provides an overview of signals and systems. It begins with an introduction to signals, including definitions of key signal properties such as periodicity, causality, boundedness. It also distinguishes between continuous-time and discrete-time signals. The document then covers fundamental signal types including sinusoidal, exponential, unit step, and impulse signals. It concludes with discussions of signal processing concepts like the Fourier transform and basics of communication systems.

SP_BEE2143_C1.pptx

This document provides an introduction to signals and systems. It discusses various signal classifications including continuous-time vs discrete-time, and memory vs memoryless systems. Elementary signals such as unit step, impulse, and sinusoid functions are defined. Common signal operations including time reversal, time scaling, amplitude scaling and shifting are described. The relationships between the time and frequency domains are introduced. The document is intended to help students understand signal characteristics and operations in both the time and frequency domains.

Communication Systems_B.P. Lathi and Zhi Ding (Lecture No 4-9)

Lecture No 4: https://youtu.be/E3QT55J9uWs
Lecture No 5: https://youtu.be/pb7GdbcLnI0
Lecture No 6: https://youtu.be/aFXr1ufTF7Q
Lecture No 7: https://youtu.be/1Yt6ZCKhcYg
Lecture No 8: https://youtu.be/I8UWw3DC19Y
Lecture No 9: https://youtu.be/zRKFi3dotEc

10800120085.pdf

This document provides an introduction to signals and systems. It defines key concepts such as:
- Signals contain information about some phenomenon and can be represented as functions of variables like time or frequency.
- Systems respond to input signals by producing output signals. Examples of systems include electrical circuits, cameras, robots, and computer programs.
- Signals can be continuous-time functions of a real-valued variable like time, or discrete-time functions of an integer variable.
- Periodic, even, odd, exponential, and sinusoidal signals are introduced.
- Major application areas of signals and systems concepts include communications, audio/speech processing, and circuit design.

Signals and systems( chapter 1)

This document provides an overview of signals and systems. It defines key terms like signal, system, continuous and discrete time signals, analog and digital signals, periodic and aperiodic signals. It also discusses different types of signals like deterministic and probabilistic signals, energy and power signals. The document then classifies systems as linear/nonlinear, time-invariant/variant, causal/non-causal, and with/without memory. It provides examples of different signals and properties of signals like magnitude scaling, time shifting, reflection and scaling. Overall, the document introduces fundamental concepts in signals and systems.

Combined Lecture 01-20.pdf

This document provides an overview of a course on principles of communication systems. It discusses the course objectives to introduce analog and digital communication systems and methods used to modulate and demodulate signals. It also covers topics like communication system components, bandwidth, signal-to-noise ratio, channel capacity, modulation, and classification of signals as deterministic/random, periodic/non-periodic, continuous/discrete, and energy/power signals. Key concepts like the unit impulse and unit step functions are also introduced.

Introduction to communication system.pdf

- Communication systems allow the exchange of information between two points, through various applications like telephone, television, radio, and computer networks.
- A basic communication system consists of a source encoder, transmitter, channel, receiver, and source decoder. The source encoder converts a message signal into bits, the transmitter converts these bits into a format suitable for transmission over the channel, the receiver decodes the received signal back into bits, and the source decoder converts these bits back into the original message.
- Communication systems can operate in simplex, half-duplex, or full-duplex mode. Key modulation techniques include amplitude modulation, frequency modulation, and phase modulation. Communication channels can be wired like optical fiber or wireless like

EE8591 Digital Signal Processing Unit -1

EE8591 UNIT I

1. signal and systems basics

This document provides an overview of signals and systems. It defines a signal as a pattern of variation that carries information over time. Signals can be continuous or discrete. Systems process input signals to produce output signals. A system can be represented as the ratio between its output and input signals. Examples of systems include electrical circuits and communication systems. Properties like linearity, time-invariance, and causality define important classes of systems. Key signal types include periodic, exponential, step, and pulse signals. Continuous systems are often modeled with differential equations while discrete systems use difference equations.

Intro signals and_systems

This document introduces signals and systems. It defines a signal as a function of time that conveys information and a system as a collection of items that transforms an input to an output. There are two types of signals: continuous-time signals that have a value for all points in time and discrete-time signals that have a value for only specific points in time, typically formed by sampling a continuous signal. Similarly, there are continuous-time and discrete-time systems. Most real systems are hybrid systems that use both continuous-time and discrete-time components. The purpose of system design is to create predictable input-output relationships, which are easiest to achieve if the system is linear and time-invariant.

Classification of Signal.pdf

1. A signal is defined as a function that represents variation in a physical quantity over time or space. Signals can be classified as continuous or discrete, deterministic or random, periodic or non-periodic, and more.
2. Analog signals are continuous over time while digital signals have discrete levels. Analog to digital conversion involves sampling, quantization, and encoding to represent analog signals digitally.
3. Sampling converts a continuous signal to a discrete signal by taking values at regular time intervals. Quantization maps infinite amplitude values to a finite set of values. Encoding represents each quantized value with a binary code.

unit 11.ppt

The document discusses discrete Fourier transforms (DFT). It begins with an overview of the syllabus which includes discrete signals and systems, introduction to DFT, properties of DFT, and applications of DFT such as filtering and fast Fourier transform (FFT) algorithms. It then provides background on digital signal processing, discussing how analog signals are converted to digital signals using sampling and then processed digitally before being reconstructed back to analog signals. The benefits and limitations of digital versus analog signal processing are also summarized.

Lecture1 Intro To Signa

This document outlines the content of a lecture on signals and systems. The key points are:
- Signals represent patterns of variation over time and can be continuous or discrete. Systems process input signals to produce output signals.
- The course will cover time and frequency domain analysis, Laplace transforms, Fourier transforms, sampling theory and z-transforms.
- Students will be assessed via exams, assignments and quizzes. Recommended reading materials are listed.
- The specific lecture will introduce signals, systems, their mathematical representations in continuous and discrete time, and properties like causality, linearity and time-invariance. Exercises are to read the first chapter of a referenced text.

Sns slide 1 2011

The document provides information about a signals and systems course taught by Mr. Koay Fong Thai. It includes announcements about course policies, assessments, and schedule. Students are advised to ask questions, work hard, and submit assignments on time. The use of phones and laptops in class is strictly prohibited. The course aims to introduce signals and systems analysis using various transforms. Topics include signals in the time domain, Fourier transforms, Laplace transforms, and z-transforms. Reference books and a lecture schedule are also provided.

lecture5.ppt

This document summarizes a lecture on equalization techniques for digital communications.
1) The optimal receiver structure for transmission over a channel consists of a whitened matched filter frontend and a maximum likelihood sequence estimator (MLSE) such as the Viterbi algorithm. However, the MLSE has high complexity.
2) Equalization filters combined with a memoryless decision device can provide a lower complexity alternative to the MLSE. Linear equalizers like zero-forcing and minimum mean squared error (MMSE) are discussed, as well as decision feedback equalizers.
3) The lecture reviews transmission models and optimal receivers developed in previous lectures, and establishes an input-output model of the transmission system to serve as the basis

Signal

This document provides an overview of signals and systems. It defines a signal as a physical quantity that varies with time and contains information. Signals are classified as deterministic or non-deterministic, periodic or aperiodic, even or odd, energy-based or power-based, and continuous-time or discrete-time. Systems are combinations of elements that process input signals to produce output signals. Key properties of systems include causality, linearity, time-invariance, stability, and invertibility. Applications of signals and systems are found in control systems, communications, signal processing, and more.

Presentation3.ppt

This document discusses measurement system behavior and characterization. It aims to understand terminology like amplitude, frequency, and phase response. It also aims to understand how instruments modify signals and characterize systems by order. First order systems have a time constant that describes their response. Harmonic excitation of first order systems produces amplitude and phase responses that depend on the ratio of excitation frequency to time constant. The behavior of complete measuring systems and their components is determined by mechanical and electrical elements.

Signals and system

This document provides an overview of signals and systems. It defines key terms like signals, systems, continuous and discrete time signals, analog and digital signals, deterministic and probabilistic signals, even and odd signals, energy and power signals, periodic and aperiodic signals. It also classifies systems as linear/non-linear, time-invariant/variant, causal/non-causal, and with or without memory. Singularity functions like unit step, unit ramp and unit impulse are introduced. Properties of signals like magnitude scaling, time reflection, time scaling and time shifting are discussed. Energy and power of signals are defined.

Chapter 1.pptx

This document provides an introduction to digital logic design concepts. It defines analog and digital quantities, explaining that digital systems represent information using discrete binary values of 1s and 0s. The advantages of digital systems are ease of design, accuracy, programmability and reliability. Common digital components like logic gates, flip-flops, and integrated circuits are described. Fundamental logic functions such as arithmetic, comparison, encoding/decoding are also introduced.

AI assisted telemedicine KIOSK for Rural India.pptx

It gives the overall description of SIH problem statement " AI assisted telemedicine KIOSK for Rural India".

Welding Metallurgy Ferrous Materials.pdf

Welding Metallurgy Explained

1.Basics of Signals

This document provides an overview of signals and systems. It begins with an introduction to signals, including definitions of key signal properties such as periodicity, causality, boundedness. It also distinguishes between continuous-time and discrete-time signals. The document then covers fundamental signal types including sinusoidal, exponential, unit step, and impulse signals. It concludes with discussions of signal processing concepts like the Fourier transform and basics of communication systems.

SP_BEE2143_C1.pptx

This document provides an introduction to signals and systems. It discusses various signal classifications including continuous-time vs discrete-time, and memory vs memoryless systems. Elementary signals such as unit step, impulse, and sinusoid functions are defined. Common signal operations including time reversal, time scaling, amplitude scaling and shifting are described. The relationships between the time and frequency domains are introduced. The document is intended to help students understand signal characteristics and operations in both the time and frequency domains.

Communication Systems_B.P. Lathi and Zhi Ding (Lecture No 4-9)

Lecture No 4: https://youtu.be/E3QT55J9uWs
Lecture No 5: https://youtu.be/pb7GdbcLnI0
Lecture No 6: https://youtu.be/aFXr1ufTF7Q
Lecture No 7: https://youtu.be/1Yt6ZCKhcYg
Lecture No 8: https://youtu.be/I8UWw3DC19Y
Lecture No 9: https://youtu.be/zRKFi3dotEc

10800120085.pdf

This document provides an introduction to signals and systems. It defines key concepts such as:
- Signals contain information about some phenomenon and can be represented as functions of variables like time or frequency.
- Systems respond to input signals by producing output signals. Examples of systems include electrical circuits, cameras, robots, and computer programs.
- Signals can be continuous-time functions of a real-valued variable like time, or discrete-time functions of an integer variable.
- Periodic, even, odd, exponential, and sinusoidal signals are introduced.
- Major application areas of signals and systems concepts include communications, audio/speech processing, and circuit design.

Signals and systems( chapter 1)

This document provides an overview of signals and systems. It defines key terms like signal, system, continuous and discrete time signals, analog and digital signals, periodic and aperiodic signals. It also discusses different types of signals like deterministic and probabilistic signals, energy and power signals. The document then classifies systems as linear/nonlinear, time-invariant/variant, causal/non-causal, and with/without memory. It provides examples of different signals and properties of signals like magnitude scaling, time shifting, reflection and scaling. Overall, the document introduces fundamental concepts in signals and systems.

Combined Lecture 01-20.pdf

This document provides an overview of a course on principles of communication systems. It discusses the course objectives to introduce analog and digital communication systems and methods used to modulate and demodulate signals. It also covers topics like communication system components, bandwidth, signal-to-noise ratio, channel capacity, modulation, and classification of signals as deterministic/random, periodic/non-periodic, continuous/discrete, and energy/power signals. Key concepts like the unit impulse and unit step functions are also introduced.

Introduction to communication system.pdf

- Communication systems allow the exchange of information between two points, through various applications like telephone, television, radio, and computer networks.
- A basic communication system consists of a source encoder, transmitter, channel, receiver, and source decoder. The source encoder converts a message signal into bits, the transmitter converts these bits into a format suitable for transmission over the channel, the receiver decodes the received signal back into bits, and the source decoder converts these bits back into the original message.
- Communication systems can operate in simplex, half-duplex, or full-duplex mode. Key modulation techniques include amplitude modulation, frequency modulation, and phase modulation. Communication channels can be wired like optical fiber or wireless like

EE8591 Digital Signal Processing Unit -1

EE8591 UNIT I

1. signal and systems basics

This document provides an overview of signals and systems. It defines a signal as a pattern of variation that carries information over time. Signals can be continuous or discrete. Systems process input signals to produce output signals. A system can be represented as the ratio between its output and input signals. Examples of systems include electrical circuits and communication systems. Properties like linearity, time-invariance, and causality define important classes of systems. Key signal types include periodic, exponential, step, and pulse signals. Continuous systems are often modeled with differential equations while discrete systems use difference equations.

Intro signals and_systems

This document introduces signals and systems. It defines a signal as a function of time that conveys information and a system as a collection of items that transforms an input to an output. There are two types of signals: continuous-time signals that have a value for all points in time and discrete-time signals that have a value for only specific points in time, typically formed by sampling a continuous signal. Similarly, there are continuous-time and discrete-time systems. Most real systems are hybrid systems that use both continuous-time and discrete-time components. The purpose of system design is to create predictable input-output relationships, which are easiest to achieve if the system is linear and time-invariant.

Classification of Signal.pdf

1. A signal is defined as a function that represents variation in a physical quantity over time or space. Signals can be classified as continuous or discrete, deterministic or random, periodic or non-periodic, and more.
2. Analog signals are continuous over time while digital signals have discrete levels. Analog to digital conversion involves sampling, quantization, and encoding to represent analog signals digitally.
3. Sampling converts a continuous signal to a discrete signal by taking values at regular time intervals. Quantization maps infinite amplitude values to a finite set of values. Encoding represents each quantized value with a binary code.

unit 11.ppt

The document discusses discrete Fourier transforms (DFT). It begins with an overview of the syllabus which includes discrete signals and systems, introduction to DFT, properties of DFT, and applications of DFT such as filtering and fast Fourier transform (FFT) algorithms. It then provides background on digital signal processing, discussing how analog signals are converted to digital signals using sampling and then processed digitally before being reconstructed back to analog signals. The benefits and limitations of digital versus analog signal processing are also summarized.

Lecture1 Intro To Signa

This document outlines the content of a lecture on signals and systems. The key points are:
- Signals represent patterns of variation over time and can be continuous or discrete. Systems process input signals to produce output signals.
- The course will cover time and frequency domain analysis, Laplace transforms, Fourier transforms, sampling theory and z-transforms.
- Students will be assessed via exams, assignments and quizzes. Recommended reading materials are listed.
- The specific lecture will introduce signals, systems, their mathematical representations in continuous and discrete time, and properties like causality, linearity and time-invariance. Exercises are to read the first chapter of a referenced text.

Sns slide 1 2011

The document provides information about a signals and systems course taught by Mr. Koay Fong Thai. It includes announcements about course policies, assessments, and schedule. Students are advised to ask questions, work hard, and submit assignments on time. The use of phones and laptops in class is strictly prohibited. The course aims to introduce signals and systems analysis using various transforms. Topics include signals in the time domain, Fourier transforms, Laplace transforms, and z-transforms. Reference books and a lecture schedule are also provided.

lecture5.ppt

This document summarizes a lecture on equalization techniques for digital communications.
1) The optimal receiver structure for transmission over a channel consists of a whitened matched filter frontend and a maximum likelihood sequence estimator (MLSE) such as the Viterbi algorithm. However, the MLSE has high complexity.
2) Equalization filters combined with a memoryless decision device can provide a lower complexity alternative to the MLSE. Linear equalizers like zero-forcing and minimum mean squared error (MMSE) are discussed, as well as decision feedback equalizers.
3) The lecture reviews transmission models and optimal receivers developed in previous lectures, and establishes an input-output model of the transmission system to serve as the basis

Signal

This document provides an overview of signals and systems. It defines a signal as a physical quantity that varies with time and contains information. Signals are classified as deterministic or non-deterministic, periodic or aperiodic, even or odd, energy-based or power-based, and continuous-time or discrete-time. Systems are combinations of elements that process input signals to produce output signals. Key properties of systems include causality, linearity, time-invariance, stability, and invertibility. Applications of signals and systems are found in control systems, communications, signal processing, and more.

Presentation3.ppt

This document discusses measurement system behavior and characterization. It aims to understand terminology like amplitude, frequency, and phase response. It also aims to understand how instruments modify signals and characterize systems by order. First order systems have a time constant that describes their response. Harmonic excitation of first order systems produces amplitude and phase responses that depend on the ratio of excitation frequency to time constant. The behavior of complete measuring systems and their components is determined by mechanical and electrical elements.

Signals and system

This document provides an overview of signals and systems. It defines key terms like signals, systems, continuous and discrete time signals, analog and digital signals, deterministic and probabilistic signals, even and odd signals, energy and power signals, periodic and aperiodic signals. It also classifies systems as linear/non-linear, time-invariant/variant, causal/non-causal, and with or without memory. Singularity functions like unit step, unit ramp and unit impulse are introduced. Properties of signals like magnitude scaling, time reflection, time scaling and time shifting are discussed. Energy and power of signals are defined.

Chapter 1.pptx

This document provides an introduction to digital logic design concepts. It defines analog and digital quantities, explaining that digital systems represent information using discrete binary values of 1s and 0s. The advantages of digital systems are ease of design, accuracy, programmability and reliability. Common digital components like logic gates, flip-flops, and integrated circuits are described. Fundamental logic functions such as arithmetic, comparison, encoding/decoding are also introduced.

1.Basics of Signals

1.Basics of Signals

SP_BEE2143_C1.pptx

SP_BEE2143_C1.pptx

Communication Systems_B.P. Lathi and Zhi Ding (Lecture No 4-9)

Communication Systems_B.P. Lathi and Zhi Ding (Lecture No 4-9)

10800120085.pdf

10800120085.pdf

Signals and systems( chapter 1)

Signals and systems( chapter 1)

Combined Lecture 01-20.pdf

Combined Lecture 01-20.pdf

Introduction to communication system.pdf

Introduction to communication system.pdf

EE8591 Digital Signal Processing Unit -1

EE8591 Digital Signal Processing Unit -1

1. signal and systems basics

1. signal and systems basics

Intro signals and_systems

Intro signals and_systems

Classification of Signal.pdf

Classification of Signal.pdf

unit 11.ppt

unit 11.ppt

Lecture1 Intro To Signa

Lecture1 Intro To Signa

Sns slide 1 2011

Sns slide 1 2011

lecture5.ppt

lecture5.ppt

Signal

Signal

Presentation3.ppt

Presentation3.ppt

Signals and system

Signals and system

Chapter 1.pptx

Chapter 1.pptx

Amplitude Modulation and Demodulation Techniques

Amplitude Modulation and Demodulation Techniques

AI assisted telemedicine KIOSK for Rural India.pptx

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IEEE Aerospace and Electronic Systems Society as a Graduate Student Member

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The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.

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Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.

Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.

CEC 352 - SATELLITE COMMUNICATION UNIT 1

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官方认证美国密歇根州立大学毕业证学位证书原版一模一样

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- 1. September 2, 2009 ECE 366, Fall 2009 Introduction to ECE 366 Selin Aviyente Associate Professor
- 2. September 2, 2009 ECE 366, Fall 2009 Overview • Lectures: M,W,F 8:00-8:50 a.m., 1257 Anthony Hall • Web Page: http://www.egr.msu.edu/~aviyente/ece366_09 • Textbook: Linear Systems and Signals, Lathi , 2nd Edition, Oxford Press. • Office Hours: M,W 3:00-4:30 p.m., 2210 Engineering Building • Pre-requisites: ECE 202, 280
- 3. September 2, 2009 ECE 366, Fall 2009 Course Requirements • 2 Midterm Exams-40% – October 16th – November 20th • Weekly HW Assignments-10% – Assigned Friday due next Friday (except during exam weeks) – Will include MATLAB assignments. – Should be your own work. – No late HWs will be accepted. – Lowest HW grade is dropped. • Final Project-15% (MATLAB based project) • Final Exam-35%, December 15th
- 4. September 2, 2009 ECE 366, Fall 2009 Policies • Cheating in any form will not be tolerated. This includes copying HWs, cheating on exams. • You are allowed to discuss the HW questions with your friends, and me. • However, you have to write up the homework solutions on your own. • Lowest HW grade will be dropped.
- 5. September 2, 2009 ECE 366, Fall 2009 Course Outline • Part 1- Continuous Time Signals and Systems – Basic Signals and Systems Concepts – Time Domain Analysis of Linear Time Invariant (LTI) Systems – Frequency Domain Analysis of Signals and Systems • Fourier Series • Fourier Transform • Applications
- 6. September 2, 2009 ECE 366, Fall 2009 Course Outline • Part 2- Discrete Time Signals and Systems – Basic DT Signals and Systems Concepts – Time Domain Analysis of DT Systems – Frequency Domain Analysis of DT Signals and Systems • Z-transforms • DTFT
- 7. September 2, 2009 ECE 366, Fall 2009 Signals • A signal is a function of one or more variables that conveys information about a physical phenomenon. • Signals are functions of independent variables; time (t) or space (x,y) • A physical signal is modeled using mathematical functions. • Examples: – Electrical signals: Voltages/currents in a circuit v(t),i(t) – Temperature (may vary with time/space) – Acoustic signals: audio/speech signals (varies with time) – Video (varies with time and space) – Biological signals: Heartbeat, EEG
- 8. September 2, 2009 ECE 366, Fall 2009 Systems • A system is an entity that manipulates one or more signals that accomplish a function, thereby yielding new signals. • The input/output relationship of a system is modeled using mathematical equations. • We want to study the response of systems to signals. • A system may be made up of physical components (electrical, mechanical, hydraulic) or may be an algorithm that computes an output from an input signal. • Examples: – Circuits (Input: Voltage, Output: Current) • Simple resistor circuit: – Mass Spring System (Input: Force, Output: displacement) – Automatic Speaker Recognition (Input: Speech, Output: Identity) ) ( ) ( t Ri t v
- 9. September 2, 2009 ECE 366, Fall 2009 Applications of Signals and Systems • Acoustics: Restore speech in a noisy environment such as cockpit • Communications: Transmission in mobile phones, GPS, radar and sonar • Multimedia: Compress signals to store data such as CDs, DVDs • Biomedical: Extract information from biological signals: – Electrocardiogram (ECG) electrical signals generated by the heart – Electroencephalogram (EEG) electrical signals generated by the brain – Medical Imaging • Biometrics: Fingerprint identification, speaker recognition, iris recognition
- 10. September 2, 2009 ECE 366, Fall 2009 Classification of Signals • One-dimensional vs. Multi-dimensional: The signal can be a function of a single variable or multiple variables. – Examples: • Speech varies as a function of timeone- dimensional • Image intensity varies as a function of (x,y) coordinatesmulti-dimensional – In this course, we focus on one-dimensional signals.
- 11. September 2, 2009 ECE 366, Fall 2009 • Continuous-time vs. discrete-time: – A signal is continuous time if it is defined for all time, x(t). – A signal is discrete time if it is defined only at discrete instants of time, x[n]. – A discrete time signal is derived from a continuous time signal through sampling, i.e.: period sampling is T nT x n x s s ), ( ] [
- 12. September 2, 2009 ECE 366, Fall 2009 • Analog vs. Digital: – A signal whose amplitude can take on any value in a continuous range is an analog signal. – A digital signal is one whose amplitude can take on only a finite number of values. – Example: Binary signals are digital signals. – An analog signal can be converted into a digital signal through quantization.
- 13. September 2, 2009 ECE 366, Fall 2009 • Deterministic vs. Random: – A signal is deterministic if we can define its value at each time point as a mathematical function – A signal is random if it cannot be described by a mathematical function (can only define statistics) – Example: • Electrical noise generated in an amplifier of a radio/TV receiver.
- 14. September 2, 2009 ECE 366, Fall 2009 • Periodic vs. Aperiodic Signals: – A periodic signal x(t) is a function of time that satisfies – The smallest T, that satisfies this relationship is called the fundamental period. – is called the frequency of the signal (Hz). – Angular frequency, (radians/sec). – A signal is either periodic or aperiodic. – A periodic signal must continue forever. – Example: The voltage at an AC power source is periodic. ) ( ) ( T t x t x T f 1 0 0 0 ) ( ) ( ) ( T a a T b b T dt t x dt t x dt t x T f 2 2
- 15. September 2, 2009 ECE 366, Fall 2009 • Causal, Anticausal vs. Noncausal Signals: – A signal that does not start before t=0 is a causal signal. x(t)=0, t<0 – A signal that starts before t=0 is a noncausal signal. – A signal that is zero for t>0 is called an anticausal signal.
- 16. September 2, 2009 ECE 366, Fall 2009 • Even vs. Odd: – A signal is even if x(t)=x(-t). – A signal is odd if x(t)=-x(-t) – Examples: • Sin(t) is an odd signal. • Cos(t) is an even signal. – A signal can be even, odd or neither. – Any signal can be written as a combination of an even and odd signal. 2 ) ( ) ( ) ( 2 ) ( ) ( ) ( t x t x t x t x t x t x o e
- 17. September 2, 2009 ECE 366, Fall 2009 Properties of Even and Odd Functions • Even x Odd = Odd • Odd x Odd = Even • Even x Even = Even • Even + Even = Even • Even + Odd = Neither • Odd + Odd = Odd 0 ) ( ) ( 2 ) ( 0 dt t x dt t x dt t x a a o a e a a e
- 18. September 2, 2009 ECE 366, Fall 2009 • Finite vs. Infinite Length: – X(t) is a finite length signal if it is nonzero over a finite interval a<t<b – X(t) is infinite length signal if it is nonzero over all real numbers. – Periodic signals are infinite length.
- 19. September 2, 2009 ECE 366, Fall 2009 • Energy signals vs. power signals: – Consider a voltage v(t) developed across a resistor R, producing a current i(t). – The instantaneous power: p(t)=v2(t)/R=Ri2(t) – In signal analysis, the instantaneous power of a signal x(t) is equivalent to the instantaneous power over 1 resistor and is defined as x2(t). – Total Energy: – Average Power: 2 / 2 / 2 ) ( 1 lim T T T dt t x T 2 / 2 / 2 ) ( lim T T T dt t x
- 20. September 2, 2009 ECE 366, Fall 2009 • Energy vs. Power Signals: – A signal is an energy signal if its energy is finite, 0<E<∞. – A signal is a power signal if its power is finite, 0<P<∞. – An energy signal has zero power, and a power signal has infinite energy. – Periodic signals and random signals are usually power signals. – Signals that are both deterministic and aperiodic are usually energy signals. – Finite length and finite amplitude signals are energy signals.