Lecture 9
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This document discusses various data representation techniques including excess-3 code, Gray code, binary coded decimal, ASCII, EBCDIC, Unicode, and logic gates. Excess-3 code represents each decimal number as a binary number in excess of three. Gray code ensures each code differs from its neighbor by one bit. ASCII and EBCDIC are character encoding standards that allow representation of text. Unicode supports encoding for many languages. The document also describes logic gates like AND, OR, NOT, XOR, and how logic expressions can be represented using gates.
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Dlc{binary to gray code conversion} ppt
BINARY TO GRAY CODE CONVERSION
1- WHAT IS A BINARY CODE ?
-> A binary code represents text or computer processor instructions using the binary number system's two binary digits, 0 and 1. The binary code assigns a bit string to each symbol or instruction.
2- WHAT IS A GRAY CODE ?
-> The reflected binary code(RBC), also known as Gray code after Frank Gray, is a binary numeral system where two successive values differ in only one bit. This code was originally designed to prevent spurious output from electromechanical switches.
THE GRAY CODE{Image in Ppt}
3- Binary-to-Gray code conversion
->
The MSB in the Gray code is the same as corresponding MSB in the binary number.
Going from left to right, add each adjacent pair of binary code bits to get the next Gray code bit. Discard carries.
ex: convert 101102 to Gray code
1 + 0 + 1 + 1 + 0 binary
1 1 1 0 1 Gray
CONVERTING CIRCUIT{Image in Ppt}
LOGIC DIAGRAM OF 4 BIT BINARY TO GRAY CODE CONVERTER{Image in Ppt}
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Dlc{binary to gray code conversion} ppt
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THE GRAY CODE{Image in Ppt}
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->
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The document summarizes various data representation techniques including Gray code, Excess-3 code, self-complementary code, weighted code, EBCDIC, and error detection codes. Gray code ensures that two successive numbers differ in only one bit. Excess-3 code represents each codeword as its decimal value plus 3 in binary. Self-complementary code allows a number to be obtained by complementing another number that adds up to 15. Weighted codes assign specific weights to each digit position. EBCDIC is an 8-bit character encoding used on IBM mainframe computers. Error detection codes add redundancy bits to facilitate error detection during data transmission.
Implementation of error correcting methods to the asynchronous Delay Insensit...
Abstract: This Paper provides an approach for reducing delay and area in asynchronous communication. A new class of error correcting Delay Insensitive (ie., unordered) codes is introduced for global asynchronous communication.It simultaneously provides timing-robustness and fault tolerance for the codes.A systematic and weighted code is targeted. The proposed error correcting unordered (ECU) code, called zero-sum can provide 1-bit correction.The extensions to the zero-sum code are given.The zero_sum⁺ code provides 3-bit error detection,or it can provide 2-bit detection and 1-bit correction.The zero_sum* code support 2-bit correction,while still guaranteeing 2-bit detection under different strategies of weight assignments. Zero_sum* code provides 2-bit correction coverage (50 % to 70%) of all 2-bit errors. The proposed method reduces delay occurred, due to the transfer of corrupted bits in a packet on the channel by the removal of timer and also reduces the area with the proposed Completion Detector (CD). Keywords : Asynchronous communication, Four phase protocol , error-correcting codes, delay insensitive and unordered.
Design and implementation of single bit error correction linear block code sy...
Linear block code (LBC) is an error detection and correction code that is widely used in
communication systems. In this paper a special type of LBC called Hamming code was implemented and
debugged using FPGA kit with integrated software environments ISE for simulation and tests the results of
the hardware system. The implemented system has the ability to correct single bit error and detect two bits
error. The data segments length was considered to give high reliability to the system and make an
aggregation between the speed of processing and the hardware ability to be implemented. An adaptive
length of input data has been consider, up to 248 bits of information can be handled using Spartan 3E500
with 43% as a maximum slices utilization. Input/output data buses in FPGA have been customized to meet
the requirements where 34% of input/output resources have been used as maximum ratio. The overall
hardware design can be considerable to give an optimum hardware size for the suitable information rate.
Analysis and Implementation of Hard-Decision Viterbi Decoding In Wireless Com...
Convolutional codes are also known as Turbo codes because of their error correction capability. These codes are
also awarded as Super product codes, because these codes have replaced the backward error correction codes.
Turbo codes are much more efficient than previous backward error correction codes because these are Forward
error correction (FEC) codes and there is no need for a feedback link to request the transmitter for
retransmission of data, when bits are corrupted in the information channel. A Viterbi decoder decodes stream of
digital data bits that has been encoded by Convolutional encoder. In this paper we introduce a RSC (Recursive
Systematic Convolutional) encoder with constraint length of 2 code rate of 1/3. The RSC encoder and Viterbi
decoder both are implemented on paper, as well as in MATLAB. Simulation results are also presented by using
MATLAB.
Reed solomon code
Reed Solomon codes are a type of linear block code that can detect and correct errors in transmitted data. Reed Solomon codes operate by dividing a message into blocks, calculating redundant parity data for each block based on the message contents, and transmitting the blocks with parity. At the receiver, the redundant data enables detection and correction of errors that may have occurred during transmission. Popular examples include RS(255,223) codes that can correct up to 16 byte errors per codeword. Reed Solomon codes are widely used in storage devices and wireless communications due to their high error correction capability.
Introduction to computer_lec_02_fall_2018
This document discusses data representation in computers. It covers numbering systems like binary and hexadecimal, binary logic and logical operations, and character representation using ASCII and Unicode encoding. The binary numbering system represents numbers using bits that can be either 0 or 1. Hexadecimal is more compact than binary and easy to convert between. Binary logic uses truth tables to understand logical operators like AND, OR and NOT. Characters are assigned unique binary codes using ASCII with 8 bits or Unicode with 16 bits.
DLD-W3-L1.pptx
This document summarizes a lecture on number systems and binary codes for an electrical engineering course. It discusses hexadecimal, octal, and binary number systems as well as binary-decimal conversions. It also covers signed, unsigned, and 2's complement numbers, floating point format, BCD code, Gray code, and error detection codes like parity bits. Examples are provided for binary-octal/hexadecimal conversions, 2's complement, BCD addition, and using parity bits. The lecture aims to explain alternate number representations and error detection methods for digital logic design.
Chapter 5: Cominational Logic with MSI and LSI
This document discusses combinational logic circuits using MSI (Medium Scale Integration) and LSI (Large Scale Integration) components. It covers various MSI components like adders, decoders, encoders, multiplexers that are used as basic building blocks. Specific circuits discussed include 4-bit parallel adder, BCD adder, magnitude comparator, priority encoder, octal to binary encoder, decoder and their applications in implementing Boolean functions using multiplexers.
Computer-codes.pptx
What are computer codes, Types of computer codes, BCD, EBCDIC, ASCII, Gray code, Excess-3 code, Unicode. Conversion of a given decimal digit in to its equivalent BCD code, ASCII Conversions, Gray code conversions, Types of Unicode, EBCDIC code.
12.Representation of signed binary numbers. Binary codes - BCD code, Gray co...
This document discusses representation of binary numbers and binary codes. It covers:
- Unsigned and signed binary number representation including sign-magnitude and 1's/2's complement forms.
- Binary coded decimal (BCD) which assigns a 4-bit code to each decimal digit from 0-9. BCD is useful for digital displays that show decimal numbers.
- Excess-3 code which adds 3 to each decimal digit before converting to binary.
- Gray code which increments only one bit at a time to avoid errors during counting. Conversion between binary and gray code is covered.
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International Journal of Computational Engineering Research(IJCER)
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Lecture 7
This document discusses backpropagation algorithms for neural networks. It explains that backpropagation aims to optimize neural network weights so the network can learn to map inputs to correct outputs. As an example, it provides a single training set with inputs of 0.05 and 0.10 mapped to outputs of 0.01 and 0.99 respectively. It also references error functions, the forward pass through the network, and the logistic function.
Lecture 6
A multilayer neural network contains an input layer, two or more hidden layers, and an output layer. Information flows from the input nodes to the first hidden layer, then between subsequent hidden layers, and finally to the output layer. Multilayer networks can represent complex decision boundaries and functions through their multiple layers of nodes. They are trained using backpropagation, which calculates error signals starting from the output layer and propagates these errors back through the network to update weights between layers. This process of forward activation and backward error propagation is repeated over many iterations to gradually adjust the weights and minimize errors.
Lecture 5
The document discusses several key concepts in machine learning including reinforcement learning, evolutionary learning, features, training/test/validation sets, overfitting, underfitting, and clustering. Reinforcement learning involves training an agent through rewards/punishments without being directly told what to do. Evolutionary learning follows biological evolution principles of inheritance, variation, and selection. Features represent attributes of an object encoded in a vector. Training/test/validation sets are used to develop and evaluate models. Overfitting and underfitting refer to models fitting the training data too closely or not closely enough, respectively. Clustering groups similar objects together.
Lecture 4
This document provides an overview of machine learning. It defines machine learning as a computer program that improves its performance at tasks through experience. The document discusses different types of machine learning including supervised learning, unsupervised learning, reinforcement learning, and evolutionary learning. It also defines important machine learning concepts such as regression, classification, and clustering. Examples are provided to illustrate these machine learning techniques.
Lecture 3
The document discusses the limitations of perceptrons and how multilayer perceptrons can solve more complex problems. It explains that while a single perceptron can only learn linearly separable problems, multilayer perceptrons using techniques like backpropagation can learn nonlinear and more complex relationships. The document also provides information on decision boundaries and feature vectors, and how multilayer perceptrons are able to learn problems with nonlinear decision boundaries. Machine learning concepts like learning, training from data, and the growth of data are also briefly covered.
Lecture 2
This document discusses neural networks and how they can be trained. It covers several key points:
- Neural networks are assemblies of simple processing units (McCulloch-Pitts neurons) that can perform any computation when connected together and having their weights solved for.
- Training neural networks involves adapting the weights to minimize error between the network's actual and desired outputs on a training set, using a learning rule.
- A simple perceptron network has weights that are adjusted based on the difference between the desired and actual outputs, proportional to a learning rate.
- Bias inputs and weights allow the network to function when all other inputs are zero, replacing the need for an adjustable threshold parameter.
Lecture 1
This document discusses neural networks and the biological structures they are inspired by. It contains the following key points:
- Neurons are the fundamental units of the brain and nervous system, receiving sensory input and sending motor commands. A neuron has dendrites that receive input, an axon that sends output signals, and a cell body with DNA.
- McCulloch and Pitts designed one of the first neural networks in 1943, with simplified neuron models that sum weighted inputs and fire if the threshold is reached.
- Hebb's rule describes how the connection between neurons is strengthened when one neuron repeatedly helps cause the other to fire.
- Neural networks are biologically inspired but also have limitations compared to real
Amplifiers and biopotential amplifiers new
What is Amplifiers?
Classification of Amplifiers
Based on number of stages
Single-stage Amplifier
Based on its output
ased on the input signals
Based on the frequency range
Based on the Coupling method
Based on the Transistor
Configuration
CE amplifier
CC amplifier
Comparaison between CB, CE
and CC Amplifies
Noise in Amplifier
Signal to Noise Ratio (SNR)
Biopotential Amplifiers
Typical bio-amplifier requirements
Voltage and Frequency
Range for Biopotentials
Electrocardiograph Amplifiers
Interference Reduction Techniques
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The document discusses neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS). fNIRS uses near-infrared light to monitor changes in oxygenated and deoxygenated hemoglobin in the brain in response to neural activity. It provides a portable, low-cost way to monitor brain activity and oxygenation levels during cognitive tasks. The document discusses how fNIRS works, its applications in detecting brain activity and lie detection by measuring hemodynamic responses in the prefrontal cortex. It also compares fNIRS to other neuroimaging techniques such as fMRI and discusses its potential for use in brain-computer interfaces.
Lecture 10
The document discusses modern CPUs and their specifications. It provides details about AMD and Intel CPUs including their core counts, clock speeds, thermal designs and cache sizes. It also covers CPU architecture types like RISC and CISC. Additional topics covered include microprocessors, hardware components, operating systems like DOS and GUI, computer buses and USB standards.
Lecture 8
A bit is the smallest unit of data that can represent either a 1 or 0. Eight bits form a byte, which can represent one character. Common units for data storage are bytes, kilobytes, megabytes and gigabytes. There are different number systems used to represent numeric values in computing, including binary, octal and hexadecimal. Binary numbers can be added, subtracted and multiplied using logic gates. Decimal numbers can be converted to their binary or octal equivalent through repeated division and obtaining remainders.
Lecture 7
The document discusses different ways of representing numeric data in computing systems. It covers external representation using symbols like letters and numbers, and internal representation using binary digits (0s and 1s). Numeric data is categorized as integer or real numbers, and further as signed or unsigned values. Different number systems like binary, octal, decimal, and hexadecimal are explained, along with how values are represented and converted between these systems using place value and arithmetic operations on the base of each system. Conversion examples are provided between decimal, binary, octal, and hexadecimal numbers.
Lecture 6
There are two types of LCD screens: passive-matrix and active-matrix. Passive matrix LCD screens arrange elements in rows and columns and activate them sequentially, which can cause ghosting. Active matrix LCD screens use transistors to independently control each element, allowing for higher quality images. Printers also come in impact and non-impact varieties. Impact printers like dot matrix printers work by striking pins against an inked ribbon. Non-impact printers like inkjet and laser printers work without physical contact by spraying or fusing tiny droplets of ink. Laser printers create electrostatic images on a drum using a laser beam.
Lecture 5
The document discusses different types of computer output devices. It describes monitors, printers, 3D printers, braille readers, GPS devices, plotters, headphones, projectors, sound cards, video cards, and different types of displays including CRT displays and LCD monitors. It provides details on what each device is and how it functions, as well as advantages and disadvantages of CRT and LCD displays.
Lecture 4
The document discusses various input devices used in computers. It describes keyboards, mice, joysticks, light pens, trackballs, scanners, graphics tablets, microphones, magnetic ink card readers, optical character readers, optical mark readers, bar code readers, and webcams. For each device, it provides details on what it is and how it functions to input data into a computer. The keyboard section contains the most detail, breaking down its different types of keys and their purposes.
Lecture 18
An information system is a group of components that collect, organize, store, and communicate information to help organizations operate, manage their resources, and compete in the marketplace. There are different types of information systems that support various organizational levels, including transaction processing systems, office automation systems, knowledge work systems, management information systems, decision support systems, and executive support systems. The systems development life cycle is the process used to plan, create, test, and deploy information systems and includes steps like system planning, analysis, design, implementation, testing, and maintenance.
Lecture 17
The document discusses various topics related to online work and e-commerce. It begins by describing different types of internet connections like dial-up, DSL, cable, satellite, and mobile. It then discusses the hardware needed like modems and routers. It provides definitions and examples of different types of e-commerce like B2B, B2C, C2C, C2B, B2A, and C2A. The advantages of e-commerce are described as reaching a global market, shortening distribution chains, increased productivity and competitiveness for businesses, and cost reduction. The disadvantages are strong dependence on technology, lack of adequate legislation, consumer reluctance, privacy and identity issues, and security concerns.
Lecture 16
Bitmap images store color information for each individual pixel arranged in a pattern, while vector images use mathematical formulas to draw lines and curves. Bitmaps can be edited by changing pixel colors, while vectors are edited by manipulating lines and curves. Vector images tend to be smaller files since they just store formulas rather than individual pixel data.
Graphics software allows users to manipulate images on a computer and can be categorized as raster or vector based. Common software includes paint programs for photo editing, draw programs, and CAD for design. Word processors, publishing software, illustration programs, and presentation software allow combining text and graphics. Clip art contains images that can be imported, while multimedia integrates various media types into an interactive experience.
Lecture 3
The document provides an overview of the internet and its key components. It discusses how the internet originated from the ARPANET project in 1968 and evolved to become a global system of interconnected computer networks using TCP/IP. The key elements that enable the internet are described, including protocols like TCP and IP, packets, routers, domain names, HTML, email and the World Wide Web. Common internet services like file transfer, communication tools, information retrieval and e-commerce are also summarized. Connection types, firewalls and other networking concepts are briefly covered as well.
Lecture 15
Productivity software includes programs that help users create documents, spreadsheets, presentations, and databases. Examples include word processors, spreadsheet applications like Excel, and database management systems. Word processors allow editing documents and changing formats. Spreadsheets organize and calculate data in rows and columns, and are useful for tasks like tracking finances, grades, lists, and stats. Database management systems help users create and manage databases to store and retrieve information. Productivity software can be obtained through commercial purchases, shareware, freeware, or as open source code.
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- 3. Excess-3 Code 22-Aug-17 3 It is a binary code in which each decimal number is expressed in excess of three, i.e., 3 It is said to be self-complementing code (a code is said to be self-complementing if the code word of the 9’s complement of N, i.e., (9 – N) can be obtained from the code word of N, interchanging all the 1’s and 0’s). The BCD code is not self-complementing.
- 4. Excess-3 Code 22-Aug-17 4 Say, N = 7 = (1010)E3 Now 9 – N = 9 – 7 = 2 And 2 = (0101)E3 1’s complement of (1010)E3 is (0101)E3 = 2 It is a non-weighted code. Decimal Digit Excess-3 0 0 0 1 1 1 0 1 0 0 2 0 1 0 1 3 0 1 1 0 4 0 1 1 1 5 1 0 0 0 6 1 0 0 1 7 1 0 1 0 8 1 0 1 1 9 1 1 0 0
- 5. Gray Code 22-Aug-17 5 The Gray code is an un-weighted code not suited to arithmetic operations. It is useful for I/O devices, analog to digital converters. Each gray code differs from its neighbor by a single bit. Codes which have such a property is known as cyclic code.
- 6. Binary to Gray Code 22-Aug-17 6 The MSD of gray code is the same as the MSD of binary code. Add each pair of adjacent bits (starting from MSD) of the binary code to get the next gray digit disregarding the carries.
- 7. Gray Code to Binary 22-Aug-17 7 MSD of the gray code is the MSD of binary. Starting from MSD, produce bi+1=bi+gi+1 disregarding the carries.
- 8. Error Detecting Code 22-Aug-17 8 Computers are very reliable, but errors may be occurred during data input, processing, output operation, or data communication. Errors may be caused by dust particles on storage media, by improper humidity levels or by electrical disturbances during data transmission between units. Method for detecting errors by adding an extra bit to each bit string of usually 7 bits. This additional bit is called parity bit.
- 9. Error Detecting Code 22-Aug-17 9 This checking feature is called a parity checking. There are two types of parity checking codes: (i) Odd parity code, and (ii) Even parity code. In Odd parity method, the value of parity bit is chosen so that the total number of 1’s in the code group (bit string plus parity bit) is an odd number. In Even parity method, the value of parity bit is chosen so that the total number of 1’s in the code group (bit string plus parity bit) is an even number.
- 10. Representation of Characters 22-Aug-17 10 Coding of characters has been standardized to facilitate exchange of recoded data between computers. Two popular standard codes: EBCDIC (Extended Binary Coded Decimal Interchange Code) ASCII (American Standard Code for Information Interchange)
- 11. EBCDIC 22-Aug-17 11 The EBCDIC is an 8 bit code primarily used by IBM and IBM compatible computer system. It allows a large variety of printable special characters and non-printable control characters. All 256 (28) bit combinations have not yet been assigned characters.
- 12. ASCII 22-Aug-17 12 These codes are of two types: ASCII-7 and ASCII-8. ASCII-7 is a 7 bit code that allows 128 (27) different characters. ASCII-8 is a 8 bit code that allows 256 (28) different character.
- 13. Unicode 22-Aug-17 13 It is 16 bit code to represent different 65000+ characters/ symbols of different languages. Unicode can represent many of the world’s languages.
- 14. Logic Gates 22-Aug-17 14 OR Gate AND Gate NOT Gate NOR Gate NAND Gate XOR Gate XNOR Gate
- 15. Logic Gates 22-Aug-17 15 OR Gate: Example: out= A+B
- 16. AND Gate 22-Aug-17 16 Example: Y= A.B
- 17. NOT Gate 22-Aug-17 17 Example: out = 𝑨
- 18. NOR Gate 22-Aug-17 18 Example: out = A+B
- 19. NAND Gate 22-Aug-17 19 Example: out = 𝑨. 𝑩
- 22. Logic Expression with Logic Gate 22-Aug-17 22 Y = AB + CD F = 𝑿 𝒀 Z + 𝑿 Y Z + X 𝒀 F = (A+B).(C+D) Y = (A+B).( 𝑨 + 𝑩) F =( 𝑿 𝒀 + 𝑿 Z + 𝑿𝒀𝒁)⨁( 𝑿 𝒀 Z + 𝑿 Y Z + X 𝒀)