Analog Versus Digital Comparison Chart From

Total Page：16

File Type：pdf, Size：1020Kb

Analog versus Digital comparison chart From http://www.diffen.com/difference/Analog_vs_Digital Analog Digital Analog signal is a continuous signal which represents Digital signals are discrete time signals generated by Signal physical measurements. digital modulation. Waves Denoted by sine waves Denoted by square waves Uses continuous range of values to represent Uses discrete or discontinuous values to represent Representation information information Computers, CDs, DVDs, and other digital electronic Example Human voice in air, analog electronic devices. devices. Samples analog waveforms into a limited set of numbers Technology Analog technology records waveforms as they are. and records them. Subjected to deterioration by noise during Can be noise-immune without deterioration during Data transmissions transmission and write/read cycle. transmission and write/read cycle. Response to Noise More likely to get affected reducing accuracy Less affected since noise response are analog in nature Flexibility Analog hardware is not flexible. Digital hardware is flexible in implementation. Can be used in analog devices only. Best suited for Uses Best suited for Computing and digital electronics. audio and video transmission. Applications Thermometer PCs, PDAs There is no guarantee that digital signal processing can Analog signal processing can be done in real time and Bandwidth be done in real time and consumes more bandwidth to consumes less bandwidth. carry out the same information. Memory Stored in the form of wave signal Stored in the form of binary bit Power Analog instrument draws large power Digital instrument draws only negligible power Analog instruments usually have a scale which is Digital instruments are free from observational errors Errors cramped at lower end and give considerable like parallax and approximation errors. observational errors. .

Copy Link

Recommended publications

Radar Transmitter/Receiver
Introduction to Radar Systems Radar Transmitter/Receiver Radar_TxRxCourse MIT Lincoln Laboratory PPhu 061902 -1 Disclaimer of Endorsement and Liability • The video courseware and accompanying viewgraphs presented on this server were prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor the Massachusetts Institute of Technology and its Lincoln Laboratory, nor any of their contractors, subcontractors, or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, products, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof, or any of their contractors or subcontractors or the Massachusetts Institute of Technology and its Lincoln Laboratory. • The views and opinions expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or any of their contractors or subcontractors Radar_TxRxCourse MIT Lincoln Laboratory PPhu 061802 -2 Outline • Introduction • Radar Transmitter • Radar Waveform Generator and Receiver • Radar Transmitter/Receiver Architecture
[Show full text]
Digital Television Systems
This page intentionally left blank Digital Television Systems Digital television is a multibillion-dollar industry with commercial systems now being deployed worldwide. In this concise yet detailed guide, you will learn about the standards that apply to fixed-line and mobile digital television, as well as the underlying principles involved, such as signal analysis, modulation techniques, and source and channel coding. The digital television standards, including the MPEG family, ATSC, DVB, ISDTV, DTMB, and ISDB, are presented toaid understanding ofnew systems in the market and reveal the variations between different systems used throughout the world. Discussions of source and channel coding then provide the essential knowledge needed for designing reliable new systems.Throughout the book the theory is supported by over 200 figures and tables, whilst an extensive glossary defines practical terminology.Additional background features, including Fourier analysis, probability and stochastic processes, tables of Fourier and Hilbert transforms, and radiofrequency tables, are presented in the book’s useful appendices. This is an ideal reference for practitioners in the field of digital television. It will alsoappeal tograduate students and researchers in electrical engineering and computer science, and can be used as a textbook for graduate courses on digital television systems. Marcelo S. Alencar is Chair Professor in the Department of Electrical Engineering, Federal University of Campina Grande, Brazil. With over 29 years of teaching and research experience, he has published eight technical books and more than 200 scientific papers. He is Founder and President of the Institute for Advanced Studies in Communications (Iecom) and has consulted for several companies and R&D agencies.
[Show full text]
Lecture 11 : Discrete Cosine Transform Moving Into the Frequency Domain
Lecture 11 : Discrete Cosine Transform Moving into the Frequency Domain Frequency domains can be obtained through the transformation from one (time or spatial) domain to the other (frequency) via Fourier Transform (FT) (see Lecture 3) — MPEG Audio. Discrete Cosine Transform (DCT) (new ) — Heart of JPEG and MPEG Video, MPEG Audio. Note : We mention some image (and video) examples in this section with DCT (in particular) but also the FT is commonly applied to filter multimedia data. External Link: MIT OCW 8.03 Lecture 11 Fourier Analysis Video Recap: Fourier Transform The tool which converts a spatial (real space) description of audio/image data into one in terms of its frequency components is called the Fourier transform. The new version is usually referred to as the Fourier space description of the data. We then essentially process the data: E.g . for filtering basically this means attenuating or setting certain frequencies to zero We then need to convert data back to real audio/imagery to use in our applications. The corresponding inverse transformation which turns a Fourier space description back into a real space one is called the inverse Fourier transform. What do Frequencies Mean in an Image? Large values at high frequency components mean the data is changing rapidly on a short distance scale. E.g .: a page of small font text, brick wall, vegetation. Large low frequency components then the large scale features of the picture are more important. E.g . a single fairly simple object which occupies most of the image. The Road to Compression How do we achieve compression? Low pass filter — ignore high frequency noise components Only store lower frequency components High pass filter — spot gradual changes If changes are too low/slow — eye does not respond so ignore? Low Pass Image Compression Example MATLAB demo, dctdemo.m, (uses DCT) to Load an image Low pass filter in frequency (DCT) space Tune compression via a single slider value n to select coefficients Inverse DCT, subtract input and filtered image to see compression artefacts.
[Show full text]
High Frequency Integrated MOS Filters C
N94-71118 2nd NASA SERC Symposium on VLSI Design 1990 5.4.1 High Frequency Integrated MOS Filters C. Peterson International Microelectronic Products San Jose, CA 95134 Abstract- Several techniques exist for implementing integrated MOS filters. These techniques fit into the general categories of sampled and tuned continuous- time niters. Advantages and limitations of each approach are discussed. This paper focuses primarily on the high frequency capabilities of MOS integrated filters. 1 Introduction The use of MOS in the design of analog integrated circuits continues to grow. Competitive pressures drive system designers towards system-level integration. System-level integration typically requires interface to the analog world. Often, this type of integration consists predominantly of digital circuitry, thus the efficiency of the digital in cost and power is key. MOS remains the most efficient integrated circuit technology for mixed-signal integration. The scaling of MOS process feature sizes combined with innovation in circuit design techniques have also opened new high bandwidth opportunities for MOS mixed- signal circuits. There is a trend to replace complex analog signal processing with digital signal pro- cessing (DSP). The use of over-sampled converters minimizes the requirements for pre- and post filtering. In spite of this trend, there are many applications where DSP is im- practical and where signal conditioning in the analog domain is necessary, particularly at high frequencies. Applications for high frequency filters are in data communications and the read channel of tape, disk and optical storage devices where filters bandlimit the data signal and perform amplitude and phase equalization. Other examples include filtering of radio and video signals.
[Show full text]
(MSEE) COMMUNICATION, NETWORKING, and SIGNAL PROCESSING TRACK*OPTIONS Curriculum Program of Study Advisor: Dr
MASTER OF SCIENCE IN ELECTRICAL ENGINEERING (MSEE) COMMUNICATION, NETWORKING, AND SIGNAL PROCESSING TRACK*OPTIONS Curriculum Program of Study Advisor: Dr. R. Sankar Name USF ID # Term/Year Address Phone Email Advisor Areas of focus: Communications (Systems/Wireless Communications), Networking, and Digital Signal Processing (Speech/Biomedical/Image/Video and Multimedia) Course Title Number Credits Semester Grade 1. Mathematics: 6 hours Random Process in Electrical Engineering EEE 6545 3 Select one other from the following Linear and Matrix Algebra EEL 6935 3 Optimization Methods EEL 6935 3 Statistical Inference EEL 6936 3 Engineering Apps for Vector Analysis** EEL 6027 3 Engineering Apps for Complex Analysis** EEL 6022 3 2. Focus Area Core Courses: 15 hours (Select a major core with 2 sets of sequences from groups A or B and a minor core (one course from the other group) A. Communications and Networking Digital Communication Systems EEL 6534 3 Mobile and Personal Communication EEL 6593 3 Broadband Communication Networks EEL 6506 3 Wireless Network Architectures and Protocols EEL 6597 3 Wireless Communications Lab EEL 6592 3 B. Signal Processing Digital Signal Processing I EEE 6502 3 Digital Signal Processing II EEL 6752 3 Speech Signal Processing EEL 6586 3 Deep Learning EEL 6935 3 Real-Time DSP Systems Lab (DSP/FPGA Lab) EEL 6722C 3 3. Electives: 3 hours A. Communications, Networking, and Signal Processing Selected Topics in Communications EEL 7931 3 Network Science EEL 6935 3 Wireless Sensor Networks EEL 6935 3 Digital Signal Processing III EEL 6753 3 Biomedical Image Processing EEE 6514 3 Data Analytics for Electrical and System Engineers EEL 6935 3 Biomedical Systems and Pattern Recognition EEE 6282 3 Advanced Data Analytics EEL 6935 3 B.
[Show full text]
The Equivalence of Digital and Analog Signal Processing *
Reprinted from I ~ FORMATIO:-; AXDCOXTROL, Volume 8, No. 5, October 1965 Copyright @ by AcademicPress Inc. Printed in U.S.A. INFORMATION A~D CO:";TROL 8, 455- 4G7 (19G5) The Equivalence of Digital and Analog Signal Processing * K . STEIGLITZ Departmentof Electrical Engineering, Princeton University, Princeton, l\rew J er.~e!J A specific isomorphism is constructed via the trallsform domaiIls between the analog signal spaceL2 (- 00, 00) and the digital signal space [2. It is then shown that the class of linear time-invariaIlt realizable filters is illvariallt under this isomorphism, thus demoIl- stratillg that the theories of processingsignals with such filters are identical in the digital and analog cases. This means that optimi - zatioll problems illvolving linear time-illvariant realizable filters and quadratic cost functions are equivalent in the discrete-time and the continuous-time cases, for both deterministic and random signals. FilIally , applicatiolls to the approximation problem for digital filters are discussed. LIST OF SY~IBOLS f (t ), g(t) continuous -time signals F (jCAJ), G(jCAJ) Fourier transforms of continuous -time signals A continuous -time filters , bounded linear transformations of I.J2(- 00, 00) {in}, {gn} discrete -time signals F (z), G (z) z-transforms of discrete -time signals A discrete -time filters , bounded linear transfornlations of ~ JL isomorphic mapping from L 2 ( - 00, 00) to l2 ~L 2 ( - 00, 00) space of Fourier transforms of functions in L 2( - 00,00) 512 space of z-transforms of sequences in l2 An(t ) nth Laguerre function * This work is part of a thesis submitted in partial fulfillment of requirements for the degree of Doctor of Engineering Scienceat New York University , and was supported partly by the National ScienceFoundation and partly by the Air Force Officeof Scientific Researchunder Contract No.
[Show full text]
Analog Output Signal the Signal Saturation Can Be Controlled by Setting the Respective ▪ Introduction AO-LL and the AO-UL
FieldGuide Enhance Operations Analog Output Signal The Signal Saturation can be controlled by setting the respective ▪ Introduction AO-LL and the AO-UL. The AO-LL and the AO-UL are programmable Yokogawa’s pressure transmitters with BRAIN or HART within the parameter limits of the transmitter via the FieldMate. communication have a 4 to 20 mA analog signal corresponding to the Primary Variable (PV). This output signal is generated from the digital signal supplied by the DPHarp sensor using a 15BitD/A signal converter with 0.004% resolution. The transmitters are designed to drive output slightly greater than the 4 to 20 mA “Base” signal. The intention is to set analog alarm thresholds recognizably beyond the normal operating 4 to 20 mA range, to indicate measurement our of range, and to set further alarm thresholds to indicate a fault condition. ▪ Applicable Models > EJA-E Series: All models with either BRAIN or HART communication > EJX-A Series: All models with either BRAIN or HART communication ▪ Process Measurement Out-of-Range Standard Analog Output Signal Yokogawa’s standard analog output transmitters are factory set to an Analog Output– Lower Limit (AO-LL) and Analog Output-Upper Limit The AO-LL and the AO-UL can be set to any value between 3.6 mA to (AO-UL) of 3.6 mA and 21.6 mA respectively. This allows for a small 21.6 mA. amount of linear over-range process readings. This over-range signal is referred to as Signal Saturation. During operation, if the AO-LL or Although FieldMate is highlighted here, any Hart Communicator has AO-UL limits are reached, the analog signal locks to the respective access to these functions.
[Show full text]
Analog Signal Processing Circuits in Organic Transistor Technology a Dissertation Submitted to the Department of Electrical Engi
ANALOG SIGNAL PROCESSING CIRCUITS IN ORGANIC TRANSISTOR TECHNOLOGY A DISSERTATION SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Wei Xiong October 2010 © 2011 by Wei Xiong. All Rights Reserved. Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/ This dissertation is online at: http://purl.stanford.edu/fk938sr9136 ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Boris Murmann, Primary Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Zhenan Bao I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Robert Dutton Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives. iii Abstract Low-voltage organic thin-film transistors offer potential for many novel applications. Because organic transistors can be fabricated near room temperature, they allow integrated circuits to be made on flexible plastic substrates.
[Show full text]
The Big Picture: HDTV and High-Resolution Systems (Part 6 Of
Chapter 4 TV and HRS Technologies INTRODUCTION picture on the TV screen by scanning electron beams (one for each primary color) across the picture tube As an entertainment medium, HDTV is not and varying their intensity in exact synchronism revolutionary. It is simply another step in the with the original picture signal. ongoing evolution of television that began with black and white (B&W) TV in the 1940s and will The 1950s technologies used today to bring color continue into the future with as yet undreamed of TV pictures to the home have a variety of shortcom- technologies. Each successive generation of TV ings and imperfections that modern systems can technology attempts to provide a more realistic correct. TV production formats, established in the picture and sound within the constraints of low-cost, 1930s and 40s, were originally based on 35-mm easy-to-use consumer technology. motion picture film. This gave today’s TV picture its Here we describe conventional NTSC1 television, nearly square shape (or aspect ratio) of 4 units wide Advanced Television (ATV) systems, and some of by 3 high (4:3). Research has found, however, a their underlying technologies (box 4-l). The conver- strong viewer preference for screens 5 to 6 units gence of ATVS, computer and telecommunications wide by 3 units high—as seen in today’s theatres— equipment toward High Resolution Systems (HRS) that correspond to the human field of vision.4 is discussed later. The original motion picture standard was 16 CONVENTIONAL TELEVISION: pictures per second—manually cranked cameras could go no faster.5 At that rate the viewer saw a PRODUCTION, TRANSMISSION, significant ‘flicker’ in the picture displayed (hence AND RECEPTION the term, the ‘flicks’ ‘).6 In developing TV transmis- Television systems involve three distinct activi- sion standards, engineers sought a system that sent ties: l)production, 2) transmission, and 3) display of pictures often enough that viewers did not see them the TV program.
[Show full text]
Acquiring an Analog Signal: Bandwidth, Nyquist Sampling Theorem, and Aliasing
Acquiring an Analog Signal: Bandwidth, Nyquist Sampling Theorem, and Aliasing Overview Learn about acquiring an analog signal, including topics such as bandwidth, amplitude error, rise time, sample rate, the Nyquist Sampling Theorem, aliasing, and resolution. This tutorial is part of the Instrument Fundamentals series. Contents wwWhat is a Digitizer? wwBandwidth a. Calculating Amplitude Error b. Calculating Rise Time wwSample Rate a. Nyquist Sampling Theorem b. Aliasing wwResolution wwSummary ni.com/instrument-fundamentals Next Acquiring an Analog Signal: Bandwidth, Nyquist Sampling Theorem, and Aliasing What Is a Digitizer? Scientists and engineers often use a digitizer to capture analog data in the real world and convert it into digital signals for analysis. A digitizer is any device used to convert analog signals into digital signals. One of the most common digitizers is a cell phone, which converts a voice, an analog signal, into a digital signal to send to another phone. However, in test and measurement applications, a digitizer most often refers to an oscilloscope or a digital multimeter (DMM). This article focuses on oscilloscopes, but most topics are also applicable to other digitizers. Regardless of the type, the digitizer is vital for the system to accurately reconstruct a waveform. To ensure you select the correct oscilloscope for your application, consider the bandwidth, sampling rate, and resolution of the oscilloscope. Bandwidth The front end of an oscilloscope consists of two components: an analog input path and an analog-to-digital converter (ADC). The analog input path attenuates, amplifies, filters, and/or couples the signal to optimize it in preparation for digitization by the ADC.
[Show full text]
Signal Shorts
Signal Shorts Bill Fawcett Director of Engineering The Problem WMRA has published a coverage map which shows where we expect our signal to reach, based on formulas devised by the FCC. Yet even within our established coverage area, there are locales in which the signal sounds "fuzzy". In most cases, the topography of the area is causing "multi-path" interference, that is, reflections of the signal off of nearby mountains adds to the direct signal, but delayed somewhat in time, and scrambles the received signal. This is the same phenomenon that causes "ghosts" on your TV (shadow- images on your screen, not the "X-Files"). Multipath is a significant problem along the I-81 corridor North of Harrisonburg to Strasburg. Also at Penn Laird along Route 33. Multipath problems are so site-specific that moving a radio a few feet may make a difference. The next time you get a fuzzy signal at a traffic light you may note that it clears up by advancing a few feet (watch out for the car in front of you, or you may have other, more serious, problems). Switching to mono, if possible, will also help. Other areas suffer from terrain obstructions. This may be noted at Massanutten Resort, and in the UVA area of Charlottesville. For those outside of the predicted coverage area, signal strength (the lack of) may be a problem, as may interference. North of Charlottesville this is a big problem, with interference from a high-power station in Washington D.C. causing problems. Lastly, the downtown Charlottesville area suffers from interference from WCYK's 105.1 translator.
[Show full text]
Adaptive Translinear Analog Signal Processing a Prospectus
ADAPTIVE TRANSLINEAR ANALOG SIGNAL PROCESSING A PROSPECTUS Eric J. McDonald, Koji Mensa Odame, and Bradley A. Minch School of Electrical and Computer Engineering Cornell University Ithaca, NY 14853-5401 [email protected] ABSTRACT are directly implementable as networks of MITES. We have devised a systematic method of transforming high- One common implementation of a MITE unit is shown level time-domain descriptions of linear and nonlinear adap- in Fig. 1. In this case, we operate a floating-gate PMOS tive signal-processing algorithms into compact, continuous- transistor in weak-inversion in addition to using a cascode time analog circuitry using basic units called multiple-input transistor to reduce the effects of the gate-to-drain capac- translinear elements (MITES). In this paper, we describe itance and channel-length modulation. The input voltages, the current state of the an and illustrate the method with an VI and V,, capacitively couple into the floating gate through unit-sized capacitors. The drain current of this device is example of an analog phase-locked loop (PLL). given by I = Isew(I’,+1’d/UT, 1. CIRCUIT SYNTHESIS METHODOLOGY where I, is a pre-exponential scaling current, w is the weight- The class of dyiamic translinear circuits [ 1-31 makes possi- ing cwfficient of the inputs (accounting for the body-effect 2.’ _.ble a promising approach to the structured design of analog and the capacitive voltage divider, Cunjt/Ctota,),and UT is ”. .‘ signal and information processing systems [4]. Such cir- the thermal voltage, kT/q. ’ cuits are capable of accurately realizing a wide range of lin- ear and nonlinear systems whose behavior can be described 2.
[Show full text]