The document discusses estimation theory and its application in fields like communication and radar. It describes two major impairments in communication systems as inter-symbol interference and noise. It discusses adaptive filters and their ability to operate satisfactorily in unknown environments and track input statistics, making them useful for signal processing. Applications of adaptive filters include system identification, inverse modeling, prediction, and interference cancellation. Kalman filters are also discussed as being widely used in speech enhancement.
Lecture: Digital Signal Processing Batch 2009ubaidis
Here are the solutions to the questions:
(a) y[nT] = 3(−0.2)n u[n − 3], T = 2 ms
Energy: E = 9(0.2)3/(1−0.2) = 9
Power: P = 0 (since the signal decays to zero as n increases)
This is an energy signal.
(b) z[nT] = 4(1.1)n u[n + 1], T = 0.02 s
Energy: E = ∞ (since (1.1)n increases without bound)
Power: P = 4.4
This is a power signal.
(c
Digital Signal Processing-Digital FiltersNelson Anand
This document discusses digital signal processing using digital filters in MATLAB. It begins by introducing signals and their analog and digital processing. It then covers key digital signal processing tasks like filtering, transforms, and convolution. It describes different filter types including FIR and IIR, and filter design methods. MATLAB sessions are included to demonstrate filtering and filter design. The overall document provides a conceptual overview of digital filters and digital signal processing.
1. The document discusses digital signal processing (DSP) and provides an overview of key concepts such as analog and digital signals, sampling, quantization, coding, and the sampling theorem.
2. It also covers DSP applications in various fields like medical, military, industrial and more. Common signal types like deterministic, random, even, odd and sinusoidal signals are defined.
3. Real-time DSP considerations are discussed, noting the need to ensure processing time meets operational requirements for applications that must operate in real-time.
This document outlines the course contents for a Digital Signal Processing course taught by Dr. Somchai Jitapunkul. The course covers topics including discrete-time signals and systems, sampling, the z-transform, Fourier analysis, filter design techniques, and applications of digital signal processing. Exams include a final and design project. Recommended textbooks and references are provided. Application areas like communications, audio, image processing, and biomedical are briefly described.
In this presentation we described about Signal Filtering. If you have any query regarding signal filtering or this presentation then feel free to contact us at:
http://www.siliconmentor.com/
This document discusses discrete-time signal processing and audio signal processing. It covers topics like discrete-time signals, the z-transform, discrete Fourier transform (DFT) and fast Fourier transform (FFT). The key points are:
- Audio signals are typically sampled at 44.1 kHz and quantized to 16 bits per sample.
- The z-transform and discrete Fourier transform (DTFT) are used to analyze discrete-time signals in the transform domain, similar to the Laplace transform and continuous-time Fourier transform for analog signals.
- The discrete Fourier transform (DFT) provides a computational tool to calculate Fourier transforms by sampling the frequency domain at discrete points, resulting in periodicity in the time and
The document discusses estimation theory and its application in fields like communication and radar. It describes two major impairments in communication systems as inter-symbol interference and noise. It discusses adaptive filters and their ability to operate satisfactorily in unknown environments and track input statistics, making them useful for signal processing. Applications of adaptive filters include system identification, inverse modeling, prediction, and interference cancellation. Kalman filters are also discussed as being widely used in speech enhancement.
Lecture: Digital Signal Processing Batch 2009ubaidis
Here are the solutions to the questions:
(a) y[nT] = 3(−0.2)n u[n − 3], T = 2 ms
Energy: E = 9(0.2)3/(1−0.2) = 9
Power: P = 0 (since the signal decays to zero as n increases)
This is an energy signal.
(b) z[nT] = 4(1.1)n u[n + 1], T = 0.02 s
Energy: E = ∞ (since (1.1)n increases without bound)
Power: P = 4.4
This is a power signal.
(c
Digital Signal Processing-Digital FiltersNelson Anand
This document discusses digital signal processing using digital filters in MATLAB. It begins by introducing signals and their analog and digital processing. It then covers key digital signal processing tasks like filtering, transforms, and convolution. It describes different filter types including FIR and IIR, and filter design methods. MATLAB sessions are included to demonstrate filtering and filter design. The overall document provides a conceptual overview of digital filters and digital signal processing.
1. The document discusses digital signal processing (DSP) and provides an overview of key concepts such as analog and digital signals, sampling, quantization, coding, and the sampling theorem.
2. It also covers DSP applications in various fields like medical, military, industrial and more. Common signal types like deterministic, random, even, odd and sinusoidal signals are defined.
3. Real-time DSP considerations are discussed, noting the need to ensure processing time meets operational requirements for applications that must operate in real-time.
This document outlines the course contents for a Digital Signal Processing course taught by Dr. Somchai Jitapunkul. The course covers topics including discrete-time signals and systems, sampling, the z-transform, Fourier analysis, filter design techniques, and applications of digital signal processing. Exams include a final and design project. Recommended textbooks and references are provided. Application areas like communications, audio, image processing, and biomedical are briefly described.
In this presentation we described about Signal Filtering. If you have any query regarding signal filtering or this presentation then feel free to contact us at:
http://www.siliconmentor.com/
This document discusses discrete-time signal processing and audio signal processing. It covers topics like discrete-time signals, the z-transform, discrete Fourier transform (DFT) and fast Fourier transform (FFT). The key points are:
- Audio signals are typically sampled at 44.1 kHz and quantized to 16 bits per sample.
- The z-transform and discrete Fourier transform (DTFT) are used to analyze discrete-time signals in the transform domain, similar to the Laplace transform and continuous-time Fourier transform for analog signals.
- The discrete Fourier transform (DFT) provides a computational tool to calculate Fourier transforms by sampling the frequency domain at discrete points, resulting in periodicity in the time and
This document discusses digital signal processing and multirate digital signal processing. It covers topics like sampling rate conversion using interpolation and decimation filters, polyphase filters, and applications of multirate DSP systems. It also describes digital signal processors, focusing on architectures like Von Neumann, Harvard, and SHARC that are optimized for digital signal processing tasks through features like separate data/program memories, pipelining, and multiplier-accumulator units.
This document discusses digital signal processing (DSP). It begins with an introduction to DSP and defines different types of signals like analog, discrete, causal and random signals. It then explains the basic concepts of DSP systems including filters. The document discusses analog and digital filters in detail. It describes the two main types of digital filters - FIR and IIR filters. Finally, it provides examples of using DSP techniques for applications like audio effects generation and image processing.
Introductory Lecture to Audio Signal ProcessingAngelo Salatino
The document provides an introduction to audio signal processing and related topics. It discusses analog and digital audio signals, the waveform audio file format (WAV) specification including its header structure, and tools for audio processing like FFmpeg and MATLAB. Example code is given to read header metadata and audio samples from a WAV file in C++. While useful for understanding audio formats and processing, the solution contains an error and FFmpeg is noted as a better library for audio tasks.
This document discusses the sampling theorem and its applications. The sampling theorem states that a continuous-time signal that is bandlimited can be perfectly reconstructed from its samples if it is sampled at or above the Nyquist rate. The document covers key aspects of the sampling theorem including signal reconstruction using sinc functions, aliasing, and applications such as downsampling, upsampling, and oversampling.
Digital signal processing is a specialized microprocessor with its architecture optimized for operational needs of digital signal processing
Application's of DSP like STFT and Wavelet transform has been explained in detail with images.
This document discusses multirate digital signal processing. It explains that multirate systems use multiple sampling rates to process digital signals. Common operations in multirate systems are decimation, which decreases the sampling rate, and interpolation, which increases it. Decimation and interpolation can be realized through filtering and downsampling/upsampling. The document also provides examples of multirate applications like digital audio conversion and discusses tools like polyphase filters used in multirate signal processing.
1) The document discusses the design and implementation of digital filters using microprocessors. It examines how microprocessor characteristics like technology, architecture, and instruction sets influence digital filter performance.
2) The objectives are to investigate digital filter design suitable for identification and demonstrate the effectiveness of the proposed design procedure using examples.
3) Key goals are to find a methodology to study implementing digital filters on microprocessors and analyze the relationship between digital filter performance and microprocessor characteristics.
This document summarizes a presentation on multirate digital signal processing. Multirate systems involve processing signals at different sampling rates, using operations like decimation to lower the sampling rate and interpolation to increase it. Decimation involves downsampling by discarding samples, while interpolation involves upsampling by inserting zeros. These operations are used for applications like sampling rate conversion, audio/video encoding, and communications systems. Key aspects of multirate signal processing discussed include anti-alias filtering, sampling rate conversion using cascaded decimation and interpolation, and choosing optimal filter designs.
This document provides an overview of digital signal processing (DSP). It begins by defining an analog signal and a digital signal. It then describes the basic components of a DSP system, which includes an analog-to-digital converter (ADC) to convert the analog input signal to digital, a digital signal processor to process the digital signal, and a digital-to-analog converter (DAC) to reconstruct the analog output signal. Finally, it discusses some advantages and limitations of DSP systems compared to analog systems and provides examples of DSP applications.
This document outlines the course details for a digital signal processing course. The main goal of the course is to design digital linear time-invariant filters that are widely used in applications such as audio, communications, radar, and biomedical engineering. Topics that will be covered include sampling of continuous-time signals, discrete-time signals and systems, the z-transform, filter design techniques, discrete Fourier transforms, and applications of digital signal processing. Students will be evaluated based on midterm and final exams, quizzes, assignments, and a project.
DSP_2018_FOEHU - Lec 1 - Introduction to Digital Signal ProcessingAmr E. Mohamed
This lecture provides an introduction to digital signal processing. It defines what a signal is and discusses different types of signals including analog, discrete-time, and digital signals. It also covers signal classifications such as deterministic vs random, stationary vs non-stationary, and finite vs infinite length signals. The lecture then discusses analog signal processing systems and digital signal processing systems as well as transformations between time and frequency domains. It provides an overview of pros and cons of analog vs digital signal processing and examples of applications of digital signal processing.
This document discusses the process of sampling in signal processing. It defines key terms like analog and digital signals, sampling frequency, and samples. It explains how sampling works by taking regular measurements of a continuous signal's amplitude over time. This converts it into a discrete-time signal. It discusses applications of sampling like audio sampling, where signals are typically sampled above 20 kHz. It also discusses video sampling rates and speech sampling rates. The document contains examples and diagrams to illustrate these concepts.
Introduction to digital signal processing 2Hossam Hassan
The document discusses digital signal processing. It begins by listing the objectives, which include explaining how analog signals are converted to digital form through sampling and analog-to-digital conversion. It then covers digital signal processing basics, how analog signals are converted to digital via sampling and ADCs, different types of ADCs, digital signal processors and their applications, and digital-to-analog conversion.
Advanced Topics In Digital Signal ProcessingJim Jenkins
This four-day course from Applied Technology Institute examines advanced digital signal processing techniques used in modern fourth generation modems. The course will cover topics such as digital filters, channelizers, filter design techniques, digital baseband transmission, signal conditioning, sigma-delta converters, carrier centered modulation and demodulation, synchronization, and adaptive filters. Students will learn how to size and design efficient digital filters, understand multirate signal processing, and limitations of DSP-based solutions. The instructor, Dr. Fred Harris, is an expert in DSP and its applications in communication systems.
Subband coding decomposes a source signal into constituent frequency bands using digital filters like low-pass and high-pass filters. This separation into subbands allows each frequency component to be encoded and decoded separately, improving compression performance over techniques that treat the whole signal as one. The basic subband coding algorithm involves analysis using filtering and decimation to separate the signal, quantization and coding of the subband signals, and synthesis by decoding, upsampling and reconstruction filtering to reconstruct the original signal. Applications of subband coding include speech coding, audio coding and image compression, with MPEG audio standards using subband coding with 32 filters and bandwidths of f/64.
The document discusses implementing convolution on an FPGA. It begins by introducing convolution and its applications in image processing. It then discusses the scope and technical approach of implementing discrete linear convolution on FPGA kits in order to perform convolution on images in real-time. The document outlines the structure of FPGAs, including configurable logic blocks and wiring tracks. It also discusses software requirements and provides an organization plan for subsequent chapters on linear convolution, FPGA technology, and a literature survey.
Real-Time Signal Processing: Implementation and Applicationsathish sak
This document discusses real-time signal processing, including what it means, why it is used, and platforms for implementation. Real-time signal processing allows signals to be collected, analyzed, and modified in real-time as they occur. It is used to avoid time and money lost when collecting and processing data separately. Common platforms include software/PC, hardware like FPGAs, and firmware/hardware like DSPs, each with their own benefits and drawbacks relating to flexibility, speed, cost, and practicality. The document focuses on DSPs as a popular "middle ground" option and discusses code generation applications and the Embedded Target for TI's C6711 DSP.
Digital signal processing (DSP) algorithms rely on performing sums of products, which is more efficiently implemented in dedicated DSP processors compared to general purpose processors. DSP processors consume less power and cost less than general purpose processors like Pentium for implementing algorithms involving convolution, filtering, Fourier transforms, and other operations commonly used in DSP. Q-notation specifies the fractional bit representation for fixed-point numbers used in many DSP implementations.
This document contains the course syllabus for the Signals and Systems course at Karpagam Institute of Technology. It covers five units: (1) classification of signals and systems, (2) analysis of continuous time signals, (3) linear time invariant continuous time systems, (4) analysis of discrete time signals, and (5) linear time invariant discrete time systems. The first unit defines common signals like step, ramp, impulse, and sinusoidal signals and classifies signals and systems. It also introduces concepts of continuous and discrete time signals, periodic and aperiodic signals, and deterministic and random signals.
This document contains lecture notes on signals and systems for a course at Chadalawada Ramanamma Engineering College. It includes:
1. An introduction to signals, systems, and some common elementary signals like the unit step, unit impulse, ramp, sinusoid, and exponential signals.
2. A classification of signals as continuous/discrete, deterministic/non-deterministic, even/odd, periodic/aperiodic, energy/power, and real/imaginary.
3. A discussion of basic operations on signals like amplitude scaling, addition, and subtraction.
This document discusses digital signal processing and multirate digital signal processing. It covers topics like sampling rate conversion using interpolation and decimation filters, polyphase filters, and applications of multirate DSP systems. It also describes digital signal processors, focusing on architectures like Von Neumann, Harvard, and SHARC that are optimized for digital signal processing tasks through features like separate data/program memories, pipelining, and multiplier-accumulator units.
This document discusses digital signal processing (DSP). It begins with an introduction to DSP and defines different types of signals like analog, discrete, causal and random signals. It then explains the basic concepts of DSP systems including filters. The document discusses analog and digital filters in detail. It describes the two main types of digital filters - FIR and IIR filters. Finally, it provides examples of using DSP techniques for applications like audio effects generation and image processing.
Introductory Lecture to Audio Signal ProcessingAngelo Salatino
The document provides an introduction to audio signal processing and related topics. It discusses analog and digital audio signals, the waveform audio file format (WAV) specification including its header structure, and tools for audio processing like FFmpeg and MATLAB. Example code is given to read header metadata and audio samples from a WAV file in C++. While useful for understanding audio formats and processing, the solution contains an error and FFmpeg is noted as a better library for audio tasks.
This document discusses the sampling theorem and its applications. The sampling theorem states that a continuous-time signal that is bandlimited can be perfectly reconstructed from its samples if it is sampled at or above the Nyquist rate. The document covers key aspects of the sampling theorem including signal reconstruction using sinc functions, aliasing, and applications such as downsampling, upsampling, and oversampling.
Digital signal processing is a specialized microprocessor with its architecture optimized for operational needs of digital signal processing
Application's of DSP like STFT and Wavelet transform has been explained in detail with images.
This document discusses multirate digital signal processing. It explains that multirate systems use multiple sampling rates to process digital signals. Common operations in multirate systems are decimation, which decreases the sampling rate, and interpolation, which increases it. Decimation and interpolation can be realized through filtering and downsampling/upsampling. The document also provides examples of multirate applications like digital audio conversion and discusses tools like polyphase filters used in multirate signal processing.
1) The document discusses the design and implementation of digital filters using microprocessors. It examines how microprocessor characteristics like technology, architecture, and instruction sets influence digital filter performance.
2) The objectives are to investigate digital filter design suitable for identification and demonstrate the effectiveness of the proposed design procedure using examples.
3) Key goals are to find a methodology to study implementing digital filters on microprocessors and analyze the relationship between digital filter performance and microprocessor characteristics.
This document summarizes a presentation on multirate digital signal processing. Multirate systems involve processing signals at different sampling rates, using operations like decimation to lower the sampling rate and interpolation to increase it. Decimation involves downsampling by discarding samples, while interpolation involves upsampling by inserting zeros. These operations are used for applications like sampling rate conversion, audio/video encoding, and communications systems. Key aspects of multirate signal processing discussed include anti-alias filtering, sampling rate conversion using cascaded decimation and interpolation, and choosing optimal filter designs.
This document provides an overview of digital signal processing (DSP). It begins by defining an analog signal and a digital signal. It then describes the basic components of a DSP system, which includes an analog-to-digital converter (ADC) to convert the analog input signal to digital, a digital signal processor to process the digital signal, and a digital-to-analog converter (DAC) to reconstruct the analog output signal. Finally, it discusses some advantages and limitations of DSP systems compared to analog systems and provides examples of DSP applications.
This document outlines the course details for a digital signal processing course. The main goal of the course is to design digital linear time-invariant filters that are widely used in applications such as audio, communications, radar, and biomedical engineering. Topics that will be covered include sampling of continuous-time signals, discrete-time signals and systems, the z-transform, filter design techniques, discrete Fourier transforms, and applications of digital signal processing. Students will be evaluated based on midterm and final exams, quizzes, assignments, and a project.
DSP_2018_FOEHU - Lec 1 - Introduction to Digital Signal ProcessingAmr E. Mohamed
This lecture provides an introduction to digital signal processing. It defines what a signal is and discusses different types of signals including analog, discrete-time, and digital signals. It also covers signal classifications such as deterministic vs random, stationary vs non-stationary, and finite vs infinite length signals. The lecture then discusses analog signal processing systems and digital signal processing systems as well as transformations between time and frequency domains. It provides an overview of pros and cons of analog vs digital signal processing and examples of applications of digital signal processing.
This document discusses the process of sampling in signal processing. It defines key terms like analog and digital signals, sampling frequency, and samples. It explains how sampling works by taking regular measurements of a continuous signal's amplitude over time. This converts it into a discrete-time signal. It discusses applications of sampling like audio sampling, where signals are typically sampled above 20 kHz. It also discusses video sampling rates and speech sampling rates. The document contains examples and diagrams to illustrate these concepts.
Introduction to digital signal processing 2Hossam Hassan
The document discusses digital signal processing. It begins by listing the objectives, which include explaining how analog signals are converted to digital form through sampling and analog-to-digital conversion. It then covers digital signal processing basics, how analog signals are converted to digital via sampling and ADCs, different types of ADCs, digital signal processors and their applications, and digital-to-analog conversion.
Advanced Topics In Digital Signal ProcessingJim Jenkins
This four-day course from Applied Technology Institute examines advanced digital signal processing techniques used in modern fourth generation modems. The course will cover topics such as digital filters, channelizers, filter design techniques, digital baseband transmission, signal conditioning, sigma-delta converters, carrier centered modulation and demodulation, synchronization, and adaptive filters. Students will learn how to size and design efficient digital filters, understand multirate signal processing, and limitations of DSP-based solutions. The instructor, Dr. Fred Harris, is an expert in DSP and its applications in communication systems.
Subband coding decomposes a source signal into constituent frequency bands using digital filters like low-pass and high-pass filters. This separation into subbands allows each frequency component to be encoded and decoded separately, improving compression performance over techniques that treat the whole signal as one. The basic subband coding algorithm involves analysis using filtering and decimation to separate the signal, quantization and coding of the subband signals, and synthesis by decoding, upsampling and reconstruction filtering to reconstruct the original signal. Applications of subband coding include speech coding, audio coding and image compression, with MPEG audio standards using subband coding with 32 filters and bandwidths of f/64.
The document discusses implementing convolution on an FPGA. It begins by introducing convolution and its applications in image processing. It then discusses the scope and technical approach of implementing discrete linear convolution on FPGA kits in order to perform convolution on images in real-time. The document outlines the structure of FPGAs, including configurable logic blocks and wiring tracks. It also discusses software requirements and provides an organization plan for subsequent chapters on linear convolution, FPGA technology, and a literature survey.
Real-Time Signal Processing: Implementation and Applicationsathish sak
This document discusses real-time signal processing, including what it means, why it is used, and platforms for implementation. Real-time signal processing allows signals to be collected, analyzed, and modified in real-time as they occur. It is used to avoid time and money lost when collecting and processing data separately. Common platforms include software/PC, hardware like FPGAs, and firmware/hardware like DSPs, each with their own benefits and drawbacks relating to flexibility, speed, cost, and practicality. The document focuses on DSPs as a popular "middle ground" option and discusses code generation applications and the Embedded Target for TI's C6711 DSP.
Digital signal processing (DSP) algorithms rely on performing sums of products, which is more efficiently implemented in dedicated DSP processors compared to general purpose processors. DSP processors consume less power and cost less than general purpose processors like Pentium for implementing algorithms involving convolution, filtering, Fourier transforms, and other operations commonly used in DSP. Q-notation specifies the fractional bit representation for fixed-point numbers used in many DSP implementations.
This document contains the course syllabus for the Signals and Systems course at Karpagam Institute of Technology. It covers five units: (1) classification of signals and systems, (2) analysis of continuous time signals, (3) linear time invariant continuous time systems, (4) analysis of discrete time signals, and (5) linear time invariant discrete time systems. The first unit defines common signals like step, ramp, impulse, and sinusoidal signals and classifies signals and systems. It also introduces concepts of continuous and discrete time signals, periodic and aperiodic signals, and deterministic and random signals.
This document contains lecture notes on signals and systems for a course at Chadalawada Ramanamma Engineering College. It includes:
1. An introduction to signals, systems, and some common elementary signals like the unit step, unit impulse, ramp, sinusoid, and exponential signals.
2. A classification of signals as continuous/discrete, deterministic/non-deterministic, even/odd, periodic/aperiodic, energy/power, and real/imaginary.
3. A discussion of basic operations on signals like amplitude scaling, addition, and subtraction.
This document provides an overview of signals and systems classification. It discusses:
1) Signals can be continuous-time or discrete-time, periodic or non-periodic, deterministic or random, even or odd.
2) Systems can be causal or non-causal, linear or nonlinear, time-invariant or time-variant, stable or unstable.
3) Key system properties include memory/memoryless, and examples of discrete-time systems are presented.
Classification of signals and systems as well as their properties are given in the PPT .Examples related to types of signals and systems are also given .
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.
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.
The document provides an overview of discrete time signal processing concepts including:
1) Signals can be classified as continuous, discrete, deterministic, random, periodic, and non-periodic. Systems can be linear, time-invariant, causal, stable/unstable, and recursive/non-recursive.
2) Digital signal processing has advantages over analog such as precision, stability and easy implementation of operations. It also has drawbacks like needing ADCs/DACs and being limited by sampling frequency.
3) Discrete time signals are only defined at discrete time instances while continuous time signals are defined for all time. Both can be represented graphically, functionally, through tables or sequences.
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.
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.
This document provides an overview of an advanced digital signal processing lecture. It discusses pre-requisites for the course including basic signals and communications knowledge and MATLAB proficiency. It outlines the course structure, including chapters covered, textbook references, and assessment breakdown. Key concepts from the first lecture are summarized such as characterizing signals as continuous or discrete, common signal representations including exponentials and sinusoids, and introducing linear time-invariant systems.
This document provides a summary of key concepts in signals and systems. It defines a signal as a function that conveys information about a physical phenomenon, and a system as an entity that manipulates signals to produce new output signals. It classifies signals as continuous-time or discrete-time, even or odd, periodic or non-periodic, deterministic or random, and energy or power. It also covers basic operations on signals, elementary signal types, system properties like memory and invertibility, and ways to interconnect multiple systems in series, parallel or with feedback.
This document provides an overview of discrete time systems and their representations. It discusses key concepts such as:
- The difference between continuous and discrete time systems
- Representing discrete time systems using difference equations and block diagrams
- Classifying systems as static/dynamic, time-variant/invariant, linear/nonlinear, causal/non-causal, and stable/unstable
- Examples are provided to illustrate different system types.
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.
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.
SS - Unit 1- Introduction of signals and standard signalsNimithaSoman
This document provides an introduction to signals and systems. It discusses the classification of signals as continuous-time or discrete-time, periodic or aperiodic, deterministic or random, energy or power signals. It also discusses the classification of systems as continuous-time or discrete-time, linear or nonlinear, time-variant or time-invariant, causal or non-causal, stable or unstable. It then introduces some basic standard signals including step, ramp, impulse, sinusoidal, and exponential signals. It describes the properties and applications of these signals.
This document provides an overview of signals and systems. It begins with definitions of key terms like signal, continuous time signal, discrete time signal, analog signal, and digital signal. It then covers classifications of signals such as periodic vs aperiodic, even vs odd, energy vs power, and deterministic vs random. Common elementary signals are also defined, including the step signal, ramp signal, and parabolic signal. The relationships between these elementary signals are described. The overall purpose is to introduce fundamental concepts regarding signals and systems.
The document discusses signals and systems. It defines different types of signals including standard signals like step, ramp, pulse and sinusoidal signals. Signals are classified as continuous-time or discrete-time, periodic or aperiodic, deterministic or random, and energy or power signals. Systems are classified as linear or nonlinear, time-variant or time-invariant, causal or non-causal, and stable or unstable. The document also discusses one-dimensional, two-dimensional, and three-dimensional signals and different signal properties including analog versus digital signals and continuous versus discrete time signals. Various standard signals and their applications are described such as the Heaviside, ramp, delta, and sinc functions.
Radar 2009 a 3 review of signals, systems, and dspVi Binh Q. Le
This document provides an overview of signals, systems, and digital signal processing as they relate to radar systems engineering. It begins with introductions to continuous and discrete-time signals and systems. It then covers topics like sampling theory, the discrete Fourier transform, and finite impulse response filters. The goal is to give non-electrical engineering majors a brief introduction to relevant concepts from signals and systems courses to enhance their understanding of radar systems. Various signal processing techniques are applied to received radar signals to enable optimum target detection.
Radar 2009 a 3 review of signals systems and dspForward2025
This document contains lecture notes from a course on radar systems engineering. It reviews key concepts from signals, systems, and digital signal processing that are important for understanding radar systems. These include continuous and discrete-time signals, sampling theory, the discrete Fourier transform, finite impulse response filters, and analog-to-digital conversion. The notes provide an overview of these topics and their application in radar signal processing, with the goal of giving non-electrical engineering students a basic understanding to enhance their learning in the radar systems course.
Radar 2009 a 3 review of signals, systems, and dspsubha5
This document provides an overview of signals, systems, and digital signal processing as they relate to radar systems engineering. It begins with introductions to continuous and discrete-time signals and systems. It then covers topics like sampling theory, the discrete Fourier transform, and finite impulse response filters. The goal is to give non-electrical engineering majors a brief introduction to relevant concepts from signals and systems courses to enhance their understanding of radar systems. Various signal processing techniques are applied to received radar signals to enable optimum target detection.
Similar to Signals&Systems: Quick pointers to Fundamentals (20)
This document discusses probabilistic models for inference using Hidden Markov Models (HMM) and Bayesian networks. It provides references on HMM, Bayesian probability, and temporal models. It explains that probabilistic models are needed to handle uncertain knowledge and probabilistic reasoning, unlike logic-based models. The document outlines contents on learning and inference in HMM and Bayesian networks. It discusses uncertainty, Bayesian probability, generative models, inferences in Bayesian networks, and using temporal models like HMM. Mathematical representations of inference in HMM are also presented.
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Performance appraisal/ assessment in higher educational institutes (HEI)Minakshi Atre
This document outlines assessment criteria and methodology for evaluating the performance of university/college teachers based on their teaching, research, and other academic activities. It provides details on calculating research scores for various publications and academic activities. Key points include:
- Assessment is based on evidence like publications, project sanctions, and student awards.
- Teachers are evaluated on teaching, administrative duties, student guidance, organizing seminars, and research output.
- Research publications are scored based on journal impact factor, with higher impact journals earning more points.
- Guidelines address scoring for joint publications and research supervision.
- The maximum points allowed from categories like invited lectures and policy papers is 30% of the total research score.
This document provides an overview of digital signal processing (DSP) preliminaries. It discusses key topics like sampling, discrete-time signals, the sampling theorem, analog to digital conversion, aliasing, and the relationship between analog and digital frequencies. It also introduces important figures in the development of DSP like Harry Nyquist, Claude Shannon, and Alan Turing. Examples are provided to illustrate the sampling theorem and cases where it guarantees the original analog signal can or cannot be recovered from samples. An overview of a typical DSP block diagram and advantages of DSP over analog signal processing are also presented.
This document discusses several digital signal processing applications:
1) A two-band digital crossover system that splits an audio signal into low and high frequencies to be played through different speakers.
2) An ECG system that uses notch filters to remove 60Hz interference from power lines and allow detection of heart rate.
3) Speech noise reduction and coding systems that compress speech signals for transmission.
4) The compact disc recording and playback system which uses anti-aliasing filters, sampling, quantization, encoding, and laser etching to store digital audio that is then reconstructed through decoding, interpolation, DAC, and filtering.
This includes discussion of DSP applications such as two band digital crossover system,woofers, sqawkers, tweeters, interference cancellation in ECG, speech noise reduction, speech coding and compression, CD recording system
Waltz algorithm in artificial intelligenceMinakshi Atre
The document discusses the Waltz algorithm for constraint satisfaction problems. It presents the algorithm in three parts. Part 1 discusses constraints in search and knowledge representation, and how constraint propagation allows reaching a global solution using local search. It provides an example of line labeling in computer vision. Part 2 discusses how constraints can reduce complexity in perceptual tasks like line drawings. It explains Waltz's labeling scheme and valid junction configurations. Part 3 works through an example of applying Waltz labeling to a pyramid drawing, showing how constraints successively eliminate possible labelings until a unique solution is reached.
The document discusses perception in artificial intelligence. It defines perception as acquiring, interpreting, and organizing sensory information. Perception involves both sensation, where sensors convert signals into data, and higher-level processes that make sense of the data. The document then discusses challenges in perception like abstraction and uncertainty in relations. It also notes perception is influenced by both internal and external factors beyond just signals.
This document discusses search algorithms and problem solving through searching. It begins by defining search problems and representing them using graphs with states as nodes and actions as edges. It then covers uninformed search strategies like breadth-first and depth-first search. Informed search strategies use heuristics to guide the search toward more promising areas of the problem space. Examples of single agent pathfinding problems are given like the traveling salesman problem and Rubik's cube.
The document discusses the composite video signal (CVS) which consists of picture information, blanking pulses, and synchronizing pulses. It provides details on:
1) The CVS contains horizontal and vertical sync pulses to synchronize the transmitter and receiver scanning and blank retrace lines.
2) Blanking pulses are added during the horizontal and vertical retrace intervals to make the retraces invisible.
3) The sync pulses occupy the upper 25% of the signal amplitude while the picture information varies between 10-75% to encode brightness levels.
4) Horizontal sync pulses are added at the end of each line and vertical sync pulses are added after each field is scanned.
The document discusses MPEG-2 video compression. It explains that MPEG-2 builds on MPEG-1 by providing backward compatibility and exploiting both intraframe and interframe redundancies to achieve high compression ratios. It describes how video frames are organized into Groups of Pictures (GOPs) containing I, P, and B frames. The compression steps of discrete cosine transform, weighting, re-quantization, entropy coding, and run length coding are explained. It also discusses how motion compensation of P and B frames further reduces file sizes by only encoding differences between frames.
This document provides an overview of digital television (DTV) standards and technologies. It discusses:
1. The DVB standard architecture and key components like MPEG transport streams.
2. Video and audio coding standards used in DTV like MPEG-1, MPEG-2, MPEG-4, and H.264.
3. The ATSC digital television standard developed in the United States, including its use of 8-VSB modulation, forward error correction techniques, and the "cliff effect" in reception.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
2. Introduction to Signals & Systems
Objectives & Outcomes:
Signals
Systems
Transforms
RV and
Probability
Objectives
Outcomes
Mathematical
representatio
n
Classification
System
classification
Identification,
classification &
basic signal
operations
3. Fundamentals
Signals: Introduction, Graphical, Functional, Tabular and Sequence representation of
Continuous and Discrete time signals. Basics of Elementary signals: Unit step, Unit
ramp, Unit parabolic, Impulse, Sinusoidal, Real exponential, Complex exponential,
Rectangular pulse, Triangular, Signum, Sinc and Gaussian function.
Operations on signals: time shifting, time reversal, time scaling, amplitude scaling,
signal addition, subtraction, signal multiplication. Communication, control system
and Signal processing examples.
Classification of signals: Deterministic, Random, periodic , Non periodic, Energy ,
Power, Causal , Non- Causal, Even and odd signal.
Systems: Introduction, Classification of Systems: Lumped Parameter and Distributed
Parameter System, static and dynamic systems, causal and non-causal systems,
Linear and Non- linear systems, time variant and time invariant systems, stable and
unstable systems, invertible and non- invertible systems.
4. Flow of the Presentation
Part 1:
Discussion of the
fundamentals of
signals and
systems
Part 2:
Sample code
implementation
using MATLAB
7. Signals
Unit step
Unit ramp
Unit Impulse
Sinusoidal
Real exponential and Complex exponential
Rectangular pulse
Triangular
Signum
Sinc
Gaussian function
Unit parabolic
8. Signals
Why Signals?
Definition
Mathematical expression
Tabular form
CT and DT form
Graphical form
Properties
Signals are Patterns!
They help us build the
mathematical models for the
nature of the real system
responses!
9. Example of Signal : Impulse/ Delta function
Definition
Mathematical expression
Tabular form
CT and DT form
Graphical representation
Properties
Definition: Area under the curve is 1
Mathematical expression:
Graphical representation
Properties:
Equivalence property
Sampling property &
Scaling property
11. We are discrete signals and follow Nyquist
Courtesy: researchgate.net
12. Operation Real life examples
Amplitude scaling Audio Amplifier
Amplitude/ signal
addition/ subtraction
Audio Mixer
Amplitude/ signal
Multiplication
Modulation
Time reflection Radar (coming back to
station)
Time scaling Sound of siren
Time shifting Radar
Signal
Operations
Time
Shifting
Scaling
Reflection/
reversal
Amplitude
Scaling
Addition
Multiplication
13. Step1 : mathematical
expression is given for that
operation
Step 2: Prepare the table for
amplitude and time index
Step 3: Develop the
graphical representation of
the final signal
Flow of carrying out the signal operation
14. Classification of Signals
Deterministic, Random
Periodic , Non periodic
Energy , Power
Causal , Non- Causal
Even and odd signal
class condition examples
Periodic
x(t) =
x(T+t)
Sine/
cosine
Energy Rect
signal
Causal
Response
occurs only
when input is
applied
All real time
signals,
music signal
Even/
odd
x( t ) = -
x(-t)
Cosine is
odd
Random and deterministic signals :
Noise and Music Signal
15. Systems
Definition
Introduction
Classification of Systems:
Lumped Parameter and Distributed Parameter System,
Static and dynamic systems,
Causal and non-causal systems,
Linear and Non- linear systems,
Time variant and time invariant systems,
Stable and unstable systems,
Invertible and non- invertible systems
16. System Classification
Class/ Type Definition/ Description Condition Mathematical
Examples
Real life examples
Lumped Parameter
& Distributed
parameters
A lumped system:
function of time alone
A distributed system : all
dependent variables are
functions of
time and one or more
spatial variables
Represented by
ordinary differential
equations (ODEs)
Represented by
partial differential
equations (PDEs)
Ex. Transmission
lines are distributed
systems
Ex. RLC filters/
systems are
lumped parameter
systems
Static & dynamic
systems
Depends only on present
input for an output
{ Static: Memoryless }
{ Dynamic: with memory
}
y(n) = x(n)
y(n) = x(n) + x(n -1)
Multiplexers
Flip-flops
17. System Classification
Class/ Type Definition/
Description
Condition Mathematical
Examples
Real life
examples
Causal & non-
causal systems
Output occurs
only if input is
applied
Non-causal
systems are
hypothetical
x(t) = 0 for t<0 y(t) = x(t) + x(t - 1)
y(t) = x(t+3) + x(2t)
Speech signals
---
Linear & Non-
linear systems
Superposition
theorem
(homogeneity
and additivity)
F[a1x1(t) + a2x2(t)]
= a1y1(t) + a2y2(t)
y(t) = t.x(t)
Y(t) = x(t). X(t-1)
Typical RLC
circuit
18. System Classification
Class/ Type Definition/
Description
Condition Mathematical
Examples
Real life examples
Time variant &
time invariant
systems
Input shifted,
output is also
shifted by the
same amount
x(t-to) = x(t, to) y(t) = x(2.t) RC circuits: if C value
changes with time,
then time-varying
system,
Else if R,C are
constant then time
varying system
Stable & unstable
systems
BIBO condition Absolute
summability
y(t) = a.x(t) Mass-damper system
is stable but
integrator ckt is
unstable
Invertible & non-
invertible systems
Y(t) = T{x(t)} and
when we take z(t)
= T^-1{y(t), we get
z(t) = y(t) then it’s
y(t) = 10 +
x(t)
y(t) = x^2(t)
V = I.R