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This document provides recommended problems related to signals and systems. Problem P2.1 involves determining the frequency and period of cosine signals with different phase shifts. Problem P2.2 is similar but with discrete-time cosine signals. Problem P2.3 involves sketching shifted and scaled versions of a sample discrete-time signal. The remaining problems involve determining whether signals are even, odd, or neither; evaluating sums; exploring periodicity of signal sums; properties of even and odd signals; and sampling a continuous-time signal.

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PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx

This document provides an overview of chapter 1 on signals from a textbook on signals and systems. It defines a signal as a function that varies over time or another independent variable. It classifies signals as continuous-time or discrete-time, even or odd, periodic or aperiodic, and energy or power signals. It also discusses transformations of signals including time shifting, time scaling, and time reversal. Exponential and sinusoidal signals are examined for both continuous-time and discrete-time cases. Finally, it introduces the unit impulse and unit step functions.

Chapter1 - Signal and System

1. The document discusses signals and systems, including continuous-time and discrete-time signals. It covers topics like transformations of signals, exponential and sinusoidal signals, and basic properties of systems.
2. Continuous-time signals are represented as functions of time t, while discrete-time signals are represented as sequences indexed by integer n. Exponential and sinusoidal signals can be represented using complex exponential functions.
3. The document provides examples and formulas for calculating energy, power, and other properties of signals. It also describes how signals can be transformed through operations like time shifting, scaling, reversal, and periodicity.

Matlab Assignment Help

I am Green J. I am a Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Ph.D. in Matlab, University of Oxford, UK. I have been helping students with their assignments for the past 10 years. I solve assignments related to Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Signal Processing Assignments.

Ec8352 signals and systems 2 marks with answers

This document contains a question bank with two mark questions and answers related to signals and systems. Some key topics covered include:
- Definitions of continuous and discrete time signals like unit step, unit impulse, ramp functions.
- Classifications of signals as periodic, aperiodic, even, odd, energy and power.
- Properties of Fourier series and transforms including Dirichlet conditions, time shifting property, Parseval's theorem.
- Definitions of causal, non-causal, static and dynamic systems.
- Calculations of Fourier and Laplace transforms of basic signals like impulse, step functions.
So in summary, this document provides a review of fundamental concepts in signals and systems along with practice

Mid term solution

This document contains the mid-term exam for the Communication Networks course EE 333. The exam consists of 3 questions covering topics related to communication networks including:
1) Analytic expressions for the time taken for transmission of N packet blocks in an ARQ scheme with propagation delay. Expressions are given for N=1,2,3.
2) Code words, circular shifts, and error detection for CRC codes. An example error pattern is shown to be undetectable.
3) A Markov chain model for a communication channel and derivation of the maximum success probability.

Low rank tensor approximation of probability density and characteristic funct...

Low rank tensor approximation of probability density and characteristic funct...Alexander Litvinenko

This document summarizes a presentation on computing divergences and distances between high-dimensional probability density functions (pdfs) represented using tensor formats. It discusses:
1) Motivating the problem using examples from stochastic PDEs and functional representations of uncertainties.
2) Computing Kullback-Leibler divergence and other divergences when pdfs are not directly available.
3) Representing probability characteristic functions and approximating pdfs using tensor decompositions like CP and TT formats.
4) Numerical examples computing Kullback-Leibler divergence and Hellinger distance between Gaussian and alpha-stable distributions using these tensor approximations.Fourier Specturm via MATLAB

This document describes computing Fourier series and power spectra with MATLAB. It discusses:
1) Representing signals in the frequency domain using Fourier analysis instead of the time domain. Fourier analysis allows isolating certain frequency ranges.
2) Computing Fourier series coefficients involves representing a signal as a sum of sines and cosines with different frequencies, and using integral properties to solve for coefficients.
3) Examples are provided to demonstrate Fourier series reconstruction of simple signals like a sine wave and square wave. The square wave example is used to derive its Fourier series coefficients analytically.
4) Computing Fourier transforms of discrete data uses a discrete approximation to integrals via the trapezoidal rule. A Fast Fourier Transform algorithm improves efficiency

signal and system Lecture 1

This document provides an introduction to signals and systems. It defines signals as functions that represent information over time and gives examples such as sound waves and stock prices. Systems are defined as generators or transformers of signals. Signal processing involves manipulating signals to extract useful information, often by converting them to electrical forms. The document then classifies different types of signals such as continuous-time vs discrete-time, analog vs digital, deterministic vs random, and energy vs power signals. It also introduces some basic continuous-time signals like the unit step function, unit impulse function, and complex exponential signals.

Ss 2013 midterm

The document is a mid-semester exam for a signals, systems and controls course. It contains 6 multi-part questions testing various concepts related to continuous-time and discrete-time linear time-invariant systems including impulse responses, step responses, Fourier series, Fourier transforms, and frequency responses. Students are instructed to show all work and can use an open book with the signals and systems textbook.

Ss 2013 midterm

The document is a mid-semester exam for a signals, systems and controls course. It contains 6 multi-part questions testing various concepts related to continuous-time and discrete-time linear time-invariant systems including impulse responses, step responses, Fourier series, Fourier transforms, and frequency responses. Students are instructed to show all work and write clearly without marking on the exam paper.

03 Cap 2 - fourier-analysis-2015.pdf

This document discusses Fourier analysis and periodic functions. It defines periodic functions and introduces Fourier series representation of periodic functions as the sum of sinusoidal components. The key points are:
1) A periodic function can be represented by an infinite series of sinusoids of harmonically related frequencies, known as Fourier series representation.
2) The coefficients of the Fourier series (a0, an, bn) can be calculated from the integrals of the function over one period.
3) Periodic functions may exhibit odd symmetry, even symmetry, or half-wave symmetry, which determines which coefficients (an or bn) are zero.
4) Examples demonstrate the Fourier analysis of specific periodic waveforms, such as a

Find the compact trigonometric Fourier series for the periodic signal.pdf

Find the compact trigonometric Fourier series for the periodic signal x(t) and sketch the
amplitude and phase spectrum for first 4 frequency components. By inspection of the spectra,
sketch the exponential Fourier spectra. By inspection of spectra in part b), write the exponential
Fourier series for x(t)
Solution
ECE 3640 Lecture 4 – Fourier series: expansions of periodic functions. Objective: To build upon
the ideas from the previous lecture to learn about Fourier series, which are series representations
of periodic functions. Periodic signals and representations From the last lecture we learned how
functions can be represented as a series of other functions: f(t) = Xn k=1 ckik(t). We discussed
how certain classes of things can be built using certain kinds of basis functions. In this lecture we
will consider specifically functions that are periodic, and basic functions which are
trigonometric. Then the series is said to be a Fourier series. A signal f(t) is said to be periodic
with period T0 if f(t) = f(t + T0) for all t. Diagram on board. Note that this must be an everlasting
signal. Also note that, if we know one period of the signal we can find the rest of it by periodic
extension. The integral over a single period of the function is denoted by Z T0 f(t)dt. When
integrating over one period of a periodic function, it does not matter when we start. Usually it is
convenient to start at the beginning of a period. The building block functions that can be used to
build up periodic functions are themselves periodic: we will use the set of sinusoids. If the period
of f(t) is T0, let 0 = 2/T0. The frequency 0 is said to be the fundamental frequency; the
fundamental frequency is related to the period of the function. Furthermore, let F0 = 1/T0. We
will represent the function f(t) using the set of sinusoids i0(t) = cos(0t) = 1 i1(t) = cos(0t + 1)
i2(t) = cos(20t + 2) . . . Then, f(t) = C0 + X n=1 Cn cos(n0t + n) The frequency n0 is said to be
the nth harmonic of 0. Note that for each basis function associated with f(t) there are actually two
parameters: the amplitude Cn and the phase n. It will often turn out to be more useful to
represent the function using both sines and cosines. Note that we can write Cn cos(n0t + n) = Cn
cos(n) cos(n0t) Cn sin(n)sin(n0t). ECE 3640: Lecture 4 – Fourier series: expansions of periodic
functions. 2 Now let an = Cn cos n bn = Cn sin n Then Cn cos(n0t + n) = an cos(n0t) + bn
sin(n0t) Then the series representation can be f(t) = C0 + X n=1 Cn cos(n0t + n) = a0 + X n=1 an
cos(n0t) + bn sin(n0t) The first of these is the compact trigonometric Fourier series. The second
is the trigonometric Fourier series.. To go from one to the other use C0 = a0 Cn = p a 2 n + b 2 n
n = tan1 (bn/an). To complete the representation we must be able to compute the coefficients.
But this is the same sort of thing we did before. If we can show that the set of functions
{cos(n0t),sin(n0t)} is in fact an orthogonal set, then we can use the same.

Ss important questions

1. The document provides a list of 2 mark questions and answers related to the Signals and Systems subject for the 3rd semester IT students.
2. It includes definitions of key terms like signal, system, different types of signals and their classifications. Properties of Fourier series and Fourier transforms are also covered.
3. The questions address topics ranging from periodic/aperiodic signals, even/odd signals, unit step and impulse functions, Fourier series, Fourier transforms, Laplace transforms, linear and time invariant systems.

7076 chapter5 slides

This document summarizes key aspects of designing an optimum receiver for binary data transmission presented in Chapter 5. It begins by representing signals using orthonormal basis functions to reduce the problem from waveforms to random variables. It then describes representing noise using a complete orthonormal set and how the noise coefficients are statistically independent Gaussian variables. Finally, it outlines how the optimum receiver works by projecting the received signal onto the basis functions, resulting in random variables that can be used for binary decision making to minimize the bit error probability.

Computing f-Divergences and Distances of\\ High-Dimensional Probability Densi...

Computing f-Divergences and Distances of\\ High-Dimensional Probability Densi...Alexander Litvinenko

Talk presented on SIAM IS 2022 conference.
Very often, in the course of uncertainty quantification tasks or
data analysis, one has to deal with high-dimensional random variables (RVs)
(with values in $\Rd$). Just like any other RV,
a high-dimensional RV can be described by its probability density (\pdf) and/or
by the corresponding probability characteristic functions (\pcf),
or a more general representation as
a function of other, known, random variables.
Here the interest is mainly to compute characterisations like the entropy, the Kullback-Leibler, or more general
$f$-divergences. These are all computed from the \pdf, which is often not available directly,
and it is a computational challenge to even represent it in a numerically
feasible fashion in case the dimension $d$ is even moderately large. It
is an even stronger numerical challenge to then actually compute said characterisations
in the high-dimensional case.
In this regard, in order to achieve a computationally feasible task, we propose
to approximate density by a low-rank tensor.signal and system Lecture 2

The document provides an overview of signals and systems topics to be covered in an EE 207 class, including detailed analysis of sinusoidal signals, phasor representation, frequency domain spectra, and practice problems. It defines a sinusoidal signal using amplitude, frequency, phase, and discusses representing the signal using a phasor or complex exponential. It also describes representing signals as the sum of complex conjugate signals, and plotting single-sided and double-sided frequency spectra. Practice problems cover determining if signals are periodic, calculating energy and power, representing signals using phasors, and sketching signals.

Signals and Systems.pptx

1) Signals can be classified as continuous-time or discrete-time based on how they are defined over time. Continuous-time signals are defined for every instant in time while discrete-time signals are defined at discrete time instances.
2) A system is defined as a set of elements or devices that produce an output response to an input signal. The relationship between input and output signals is represented by a system operator.
3) Signals and systems can be further classified based on their properties, such as being deterministic or random, periodic or aperiodic, causal or non-causal, and more. Basic operations on signals include time scaling, time reversal, and time shifting.

Signals and Systems.pptx

1) Signals can be classified as continuous-time or discrete-time based on their definition over time. Continuous-time signals are defined for every instant in time while discrete-time signals are defined at discrete time instances.
2) A system is defined as a set of elements or devices that produce an output in response to an input signal. The relationship between input and output signals is represented by a system operator.
3) Signals and systems can be further classified based on their properties, such as being deterministic or random, periodic or aperiodic, causal or non-causal, and more. Basic operations on signals include time scaling, time reversal, and time shifting.

unit 4,5 (1).docx

1. The figure shows an electrical circuit driven by a heartbeat generator. Its output is associated with a recorder for later examination. The document discusses Fourier analysis of periodic and aperiodic signals from the circuit.
2. The document discusses Fourier analysis properties such as linearity, time shifting, differentiation, and integration that are applied to analyze signals from various systems like the stock market or a microphone.
3. The document discusses using Fourier analysis to transform voltage level signals from a microphone into sound waves for recording and communication. It also discusses properties of the continuous-time Fourier series such as linearity and time shifting that are applied to analyze the signals.

PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx

PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx

Chapter1 - Signal and System

Chapter1 - Signal and System

Matlab Assignment Help

Matlab Assignment Help

Ec8352 signals and systems 2 marks with answers

Ec8352 signals and systems 2 marks with answers

Mid term solution

Mid term solution

Low rank tensor approximation of probability density and characteristic funct...

Low rank tensor approximation of probability density and characteristic funct...

Fourier Specturm via MATLAB

Fourier Specturm via MATLAB

signal and system Lecture 1

signal and system Lecture 1

Ss 2013 midterm

Ss 2013 midterm

Ss 2013 midterm

Ss 2013 midterm

03 Cap 2 - fourier-analysis-2015.pdf

03 Cap 2 - fourier-analysis-2015.pdf

Find the compact trigonometric Fourier series for the periodic signal.pdf

Find the compact trigonometric Fourier series for the periodic signal.pdf

Ss important questions

Ss important questions

7076 chapter5 slides

7076 chapter5 slides

Computing f-Divergences and Distances of\\ High-Dimensional Probability Densi...

Computing f-Divergences and Distances of\\ High-Dimensional Probability Densi...

signal and system Lecture 2

signal and system Lecture 2

Signals and Systems.pptx

Signals and Systems.pptx

Signals and Systems.pptx

Signals and Systems.pptx

unit 4,5 (1).docx

unit 4,5 (1).docx

signals and systems_isooperations.pptx

signals and systems_isooperations.pptx

519transmissionlinetheory-130315033930-phpapp02.pdf

Transmission line theory describes how power is transmitted through wires or other physical structures at microwave frequencies. Power propagates as electric and magnetic fields rather than being delivered through the wires. Transmission lines can be modeled as networks of inductors and capacitors. At microwave frequencies, circuit elements cannot be treated as lumped and must be divided into sections where elements can be considered lumped. Reflections occur when lines are not terminated in their characteristic impedance. Smith charts provide a graphical method to analyze complex transmission line problems. Key microwave components include connectors, splitters, phase shifters and detectors.

mwr-ppt-priyanka-160217084541.pdf

This document discusses waveguides, which guide electromagnetic waves through structures like pipes. It describes the different modes waves can propagate in, including the dominant TE10 mode. There is a cutoff frequency below which modes will not propagate. The usable frequency range is between the TE10 cutoff and twice that value. It also discusses group velocity, phase velocity, characteristic impedance, wavelength in a waveguide, impedance matching, and common passive components like couplers and bends.

microstriptl1st3-170309071044.pdf

This document discusses microstrip transmission lines. Microstrip lines consist of a conducting strip separated from a ground plane by a dielectric substrate. They support a quasi-TEM wave and can be easily fabricated using PCB technology. Characteristics such as the strip width, thickness, distance to the ground plane, and dielectric constant determine the lines' characteristic impedance. Coupled microstrip lines can also be used to transfer power between lines.

Lec2WirelessChannelandRadioPropagation.pdf

This document provides an overview of wireless channel and radio propagation concepts. It discusses topics like electromagnetic spectrum, frequency and wavelength, decibels, gain and attenuation, wireless communication systems, signal-to-noise ratio, bandwidth, Shannon capacity, Nyquist bandwidth, radio propagation models including path loss, shadowing, multipath fading, and specific models like two-ray ground model, Okumura-Hata model, and COST-231 model. Examples are provided to illustrate key concepts and formulas around Shannon capacity, Nyquist bandwidth, and radio propagation models.

Roger Freeman - Telecommunication System Engineering.pdf

This document is the copyright page and table of contents for the book "Telecommunication System Engineering" by Roger L. Freeman. It describes that the book is in its fourth edition and covers topics related to telecommunication network design, including basic telephony, local networks, analog switching, signaling, transmission, long-distance networks, link design, digital transmission systems, and digital switching and networks. The copyright protects the content of the book and limits how it can be reproduced or used without permission.

Modern Telecommunications_ Basic Principles and Practices ( PDFDrive ).pdf

This document provides an introduction to modern telecommunications. It discusses the historical development of electromagnetic theory and how this led to radio wave transmission and advances in telecommunications technology. It describes the reasons why electromagnetic communication systems are used, including the ability to transmit signals over long distances without limitations of power. It also introduces some of the fundamental concepts in telecommunications, such as modulation, multiplexing, tuned circuits and receiver design.

microstrip transmission lines explained.pdf

This document is a lecture on microstrip lines and multilayer microstrip lines. It discusses the design and characteristics of microstrip lines, including formulas for characteristic impedance and attenuation. It also covers striplines and compares them with microstrip lines. Finally, it discusses the advantages of multilayer microstrip lines for reducing losses at high frequencies and controlling expansion coefficients. Formulas for the effective dielectric permittivity and characteristic impedance of multilayer microstrip lines are also provided.

microstriptl1st3-170309071044 (1).pdf

This document discusses different types of microstrip transmission lines. Microstrip lines consist of a conducting strip separated from a ground plane by a dielectric substrate and support a quasi-TEM wave. They can be easily fabricated using PCB technology and are widely used in microwave integrated circuits. The characteristic impedance of a microstrip line depends on the strip width, thickness, distance to the ground plane, and dielectric constant. Coupled microstrip lines can transfer power between the lines and are used in devices like directional couplers. Coplanar strip lines similarly consist of conducting strips on the same substrate surface.

lecture3_2.pdf

1) The lecture discusses the time domain analysis of continuous time linear and time-invariant systems. It covers topics such as impulse response, convolution, and how the output of an LTI system can be determined from its impulse response and the input signal.
2) An example of analyzing the voltage response of an RC circuit to an arbitrary input is presented. The output is the sum of the zero-input response, due to initial conditions, and zero-state response, which is a convolution of the impulse response and input signal.
3) Detectors of high energy photons can be modeled as having an exponential decay impulse response. Examples of characterizing real detectors through measurements of energy resolution, timing resolution, and coincidence point spread

L8. LTI systems described via difference equations.pdf

The document discusses linear constant-coefficient difference equations which can be used to describe discrete linear time-invariant (LTI) systems. It provides 4 ways to describe discrete LTI systems: 1) via the impulse response, 2) via difference equations, 3) via the frequency response, and 4) via the transfer function. It explains that a difference equation describes the relationship between the input and output of a system but does not uniquely specify the system's output. Additional auxiliary conditions are needed to fully characterize the system. Common auxiliary conditions include initial rest, where the output is 0 before any input is applied.

Lec 06 (2017).pdf

This document discusses diodes and applications of diodes in rectifier circuits. It provides examples of calculating output voltages, currents, and power dissipation in half-wave, full-wave, and bridge rectifier circuits using ideal and practical diode models. Key points covered include:
- Calculating DC output voltage, peak inverse voltage, and load current in half-wave and full-wave rectifiers.
- Using Kirchhoff's laws and diode voltage drops to solve circuits containing multiple diodes.
- Rectifier circuits double the output frequency compared to the input AC supply frequency.
- Practical diodes have forward voltage drops and resistance that must be accounted for in calculations.

solutions to recommended problems

The document provides solutions to recommended problems involving complex exponentials, Euler's formula, and properties of complex numbers. It includes step-by-step workings for problems involving manipulating complex expressions, plotting complex numbers on the complex plane, performing integrals of exponential functions, and describing linear transformations of signals. Diagrams are used to illustrate geometric interpretations on the complex plane.

Assignment properties of linear time-invariant systems

The document provides solutions to problems related to linear time-invariant systems. It includes:
1. Verifying the impulse response of an integrator and differentiator system.
2. Showing a system is not memoryless, causal, and stable based on its impulse response.
3. Verifying the impulse response of a second-order system.
4. Demonstrating properties like commutativity and distributivity of linear time-invariant systems using convolutions.

Introduction problems

This document provides a series of practice problems related to complex numbers, Euler's formula, and signals and systems:
(1) It asks the learner to evaluate expressions involving a complex number z = 2eiπ/3, such as its real and imaginary parts.
(2) Another problem involves showing relationships between the real and imaginary parts of an arbitrary complex number z.
(3) Using Euler's formula, it has the learner derive expressions for the cosine and sine of an angle in terms of e.
(4) Additional problems involve expressing complex functions in polar form and plotting them, integrating signals, and sketching shifted and scaled versions of a signal x(t).

Signals and systems: Part ii

This document provides recommended problems related to signals and systems. The problems cover topics such as sketching signals, expressing signals in different forms, analyzing systems using block diagrams, and properties of linear time-invariant systems. Example signals and systems are given to analyze concepts like causality, invertibility, stability, and how connections between systems can affect their properties. The problems provide opportunities to apply knowledge of signals and systems.

Signals and Systems part 2 solutions

- The document discusses signals and systems problems and solutions.
- It provides examples of signals, systems, and their properties including linearity, time-invariance, causality, stability, and invertibility.
- It analyzes signals including impulse functions, step functions, and periodic functions. It also analyzes systems including integrators, differentiators, and cascaded systems.

convulution

This document provides solutions to problems involving convolution. Specifically:
- It works through examples of convolving different signals graphically and using the convolution formula.
- It evaluates convolutions of impulse trains with various impulse responses.
- It discusses properties of linear, time-invariant systems and their inverses under convolution.
The document contains detailed step-by-step working of multiple convolution problems to demonstrate the techniques involved in evaluating convolutions both graphically and analytically. It also explores properties like time-invariance and inverses of systems.

properties of LTI

This document contains recommended problems related to properties of linear time-invariant systems. The problems cover topics like verifying impulse responses, determining if systems are memoryless, causal or stable, analyzing cascaded systems, and properties of convolution. Example systems include integrators, first-order difference and differential equations. The problems are meant to test understanding of concepts like stability, causality, impulse responses, and properties of LTI systems.

signal space analysis.ppt

This document discusses signal-space analysis and representation of bandpass signals. It can be summarized as follows:
1) A bandpass real signal x(t) can be represented using its complex envelope x(t) and carrier frequency fc. This results in an in-phase (I) and quadrature-phase (Q) representation of the signal.
2) Signals can be viewed as vectors in a vector space. Basic algebra concepts like groups, fields, and vector spaces are introduced.
3) Key concepts discussed include orthonormal bases, projection theorems, Gram-Schmidt orthonormalization, and representing signals in inner product spaces which allows defining notions of length and angle between signals.

519transmissionlinetheory-130315033930-phpapp02.pdf

519transmissionlinetheory-130315033930-phpapp02.pdf

mwr-ppt-priyanka-160217084541.pdf

mwr-ppt-priyanka-160217084541.pdf

microstriptl1st3-170309071044.pdf

microstriptl1st3-170309071044.pdf

Lec2WirelessChannelandRadioPropagation.pdf

Lec2WirelessChannelandRadioPropagation.pdf

Roger Freeman - Telecommunication System Engineering.pdf

Roger Freeman - Telecommunication System Engineering.pdf

Modern Telecommunications_ Basic Principles and Practices ( PDFDrive ).pdf

Modern Telecommunications_ Basic Principles and Practices ( PDFDrive ).pdf

microstrip transmission lines explained.pdf

microstrip transmission lines explained.pdf

microstriptl1st3-170309071044 (1).pdf

microstriptl1st3-170309071044 (1).pdf

lecture3_2.pdf

lecture3_2.pdf

L8. LTI systems described via difference equations.pdf

L8. LTI systems described via difference equations.pdf

Lec 06 (2017).pdf

Lec 06 (2017).pdf

solutions to recommended problems

solutions to recommended problems

Assignment properties of linear time-invariant systems

Assignment properties of linear time-invariant systems

Introduction problems

Introduction problems

Signals and systems: Part ii

Signals and systems: Part ii

Signals and Systems part 2 solutions

Signals and Systems part 2 solutions

convulution

convulution

properties of LTI

properties of LTI

signal space analysis.ppt

signal space analysis.ppt

IS Code SP 23: Handbook on concrete mixes

SP-23: Hand Bank on Concrete Mixes required at the time designing

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Notes of Construction management and entrepreneurship

readers writers Problem in operating system

Readers Writers Problem

21EC63_Module1B.pptx VLSI design 21ec63 MOS TRANSISTOR THEORY

VLSI design 21ec63
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Trends in CAD CAM

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Energy market

OSHA LOTO training, LOTO, lock out tag out

LOTO _ OSHA

Time-State Analytics: MinneAnalytics 2024 Talk

Slides from my talk at MinneAnalytics 2024 - June 7, 2024
https://datatech2024.sched.com/event/1eO0m/time-state-analytics-a-new-paradigm
Across many domains, we see a growing need for complex analytics to track precise metrics at Internet scale to detect issues, identify mitigations, and analyze patterns. Think about delays in airlines (Logistics), food delivery tracking (Apps), detect fraudulent transactions (Fintech), flagging computers for intrusion (Cybersecurity), device health (IoT), and many more.
For instance, at Conviva, our customers want to analyze the buffering that users on some types of devices suffer, when using a specific CDN.
We refer to such problems as Multidimensional Time-State Analytics. Time-State here refers to the stateful context-sensitive analysis over event streams needed to capture metrics of interest, in contrast to simple aggregations. Multidimensional refers to the need to run ad hoc queries to drill down into subpopulations of interest. Furthermore, we need both real-time streaming and offline retrospective analysis capabilities.
In this talk, we will share our experiences to explain why state-of-art systems offer poor abstractions to tackle such workloads and why they suffer from poor cost-performance tradeoffs and significant complexity.
We will also describe Conviva’s architectural and algorithmic efforts to tackle these challenges. We present early evidence on how raising the level of abstraction can reduce developer effort, bugs, and cloud costs by (up to) an order of magnitude, and offer a unified framework to support both streaming and retrospective analysis. We will also discuss how our ideas can be plugged into existing pipelines and how our new ``visual'' abstraction can democratize analytics across many domains and to non-programmers.

Vernier Caliper and How to use Vernier Caliper.ppsx

A vernier caliper is a precision instrument used to measure dimensions with high accuracy. It can measure internal and external dimensions, as well as depths.
Here is a detailed description of its parts and how to use it.

DBMS Commands DDL DML DCL ENTITY RELATIONSHIP.pptx

DBMS commands

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Press Tool and It's Primary Components.pdf

Press Tool and It's Primary Components

CONFINED SPACE ENTRY TRAINING FOR OIL INDUSTRY ppt

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SCADAmetrics Instrumentation for Sensus Water Meters - Core and Main Training 2024 July 09

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- 1. 2 Signals and Systems: Part I Recommended Problems P2.1 Let x(t) = cos(wx(t + rx) + Ox). (a) Determine the frequency in hertz and the period of x(t) for each of the follow ing three cases: (i) r/3 0 21r (ii) 3r/4 1/2 7r/4 (iii) 3/4 1/2 1/4 (b) With x(t) = cos(wx(t + rx) + Ox) and y(t) = sin(w,(t + -r,)+ 0,), determine for which of the following combinations x(t) and y(t) are identically equal for all t. WX T 0 x WY TY Oy (i) r/3 0 2r ir/3 1 - r/3 (ii) 3-r/4 1/2 7r/4 11r/4 1 37r/8 (iii) 3/4 1/2 1/4 3/4 1 3/8 P2.2 Let x[n] = cos(Qx(n + Px) + Ox). (a) Determine the period of x[n] for each of the following three cases: Ox PX Ox (i) r/3 0 27r (ii) 3-r/4 2 r/4 (iii) 3/4 1 1/4 (b) With x[n] = cos(Q,(n + PX) + Ox) and y[n] = cos(g,(n + Py) + 6,), determine for which of the following combinations x[n] and y[n] are identically equal for all n. QX PX Ox X, Q, PO (i) r/3 0 27 8r/3 0 0 (ii) 37r/4 2 w/4 3r/4 1 -ir (iii) 3/4 1 1/4 3/4 0 1 P2.3 (a) A discrete-time signal x[n] is shown in Figure P2.3. P2-1
- 2. Signals and Systems P2-2 x [n] 2 -3 -2 -1 0 1 2 3 4 5 6 Figure P2.3 Sketch and carefully label each of the following signals: (i) x[n - 2] (ii) x[4 - n] (iii) x[2n] (b) What difficulty arises when we try to define a signal as.x[n/2]? P2.4 For each of the following signals, determine whether it is even, odd, or neither. (a) (b) Figure P2.4-1 1 (c) x(t) (d) x [n ] Figure P2.4-3 t -2 F3 0 x~nn P2.44n 1 2 3 4 Figure P2.4-4
- 3. Signals and Systems: Part I / Problems P2-3 (e) (f) x [n] 2 x[n] -3 -2 -1 02 1 2 3 4 n -3 -2 -1 0 1 2 3 Figure P2.4-5 Figure P2.4-6 P2.5 Consider the signal y[n] in Figure P2.5. y[n] 2'. e n -3 -2-1 0 1 2 3 Figure P2.5 (a) Find the signal x[n] such that Ev{x[n]} = y[n] for n > 0, and Od(x[n]} = y[n] for n < 0. (b) Suppose that Ev{w[n]} = y[n] for all n. Also assume that w[n] = 0 for n < 0. Find w[n]. P2.6 (a) Sketch x[n] = a"for a typical a in the range -1 < a < 0. (b) Assume that a = -e-' and define y(t) as y(t) = eO'. Find a complex number # such that y(t), when evaluated at t equal to an integer n, is described by (-e- )". (c) For y(t) found in part (b), find an expression for Re{y(t)} and Im{y(t)}. Plot Re{y(t)} and Im{y(t)} for t equal to an integer. P2.7 Let x(t) = /2(1 + j)ej"1 4 e(-i+ 2 ,). Sketch and label the following: (a) Re{x(t)} (b) Im{x(t)} (c) x(t + 2) + x*(t + 2)
- 4. Signals and Systems P2-4 P2.8 Evaluate the following sums: 5 (a) T 2(3n n=0 (b) b b n=2 2n (c)Z -~3 n=o Hint:Convert each sum to the form N-1 C ( a' = SN or Cn n=o n =0 and use the formulas SN = C aN 1 C for lal < 1 1a 1-a P2.9 (a) Let x(t) and y(t) be periodic signals with fundamental periods Ti and T2, respec tively. Under what conditions is the sum x(t) + y(t) periodic, and what is the fundamental period of this signal if it is periodic? (b) Let x[n] and y[n] be periodic signals with fundamental periods Ni and N 2, respectively. Under what conditions is the sum x[n] + y[n] periodic, and what is the fundamental period of this signal if it is periodic? (c) Consider the signals x(t) = cos-t + 2 sin 3 ' 33 y(t) = sin irt Show that z(t) = x(t)y(t) is periodic, and write z(t) as a linear combination of harmonically related complex exponentials. That is, find a number T and com plex numbers Ck such that z(t) = jce(21'/T' k P2.10 In this problem we explore several of the properties of even and odd signals. (a) Show that if x[n] is an odd signal, then +o0 ( x[n] = 0 n=-00 (b) Show that if xi[n] is an odd signal and x2[n] is an even signal, then x,[n]x 2[n] is an odd signal.
- 5. Signals and Systems: Part I / Problems P2-5 (c) Let x[n] be an arbitrary signal with even and odd parts denoted by xe[n] = Ev{x[n]}, x[n] = Od{x[n]} Show that ~x[n] = [n] + ~3X'[n] (d) Although parts (a)-(c) have been stated in terms of discrete-time signals, the analogous properties are also valid in continuous time. To demonstrate this, show that Jx 2 (t )dt = 2x(t)dt + J 2~(t) dt, where xe(t) and x,(t) are, respectively, the even and odd parts of x(t). P2.11 Let x(t) be the continuous-time complex exponential signal x(t) = ei0O' with fun damental frequency wo and fundamental period To = 27r/wo. Consider the discrete- time signal obtained by taking equally spaced samples of x(t). That is, x[n] = x(nT) = eswonr (a) Show that x[n] is periodic if and only if T/TO is a rational number, that is, if and only if some multiple of the sampling interval exactly equals a multiple of the period x(t). (b) Suppose that x[n] is periodic, that is, that T p - , (P2.11-1) To q where p and q are integers. What are the fundamental period and fundamental frequency of x[n]? Express the fundamental frequency as a fraction of woT. (c) Again assuming that T/TO satisfies eq. (P2.11-1), determine precisely how many periods of x(t) are needed to obtain the samples that form a single period of x[n].
- 6. MIT OpenCourseWare http://ocw.mit.edu Resource: Signals and Systems Professor Alan V. Oppenheim The following may not correspond to a particular course on MIT OpenCourseWare, but has been provided by the author as an individual learning resource. For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.