Make Code Lucid

What is Cyclic Redundancy Check? Cyclic Redundancy Check CRC is an error detection algorithm used in communication networks to check if the transmitted data contains any error. How CRC works? CRC generator at sender's end: 1. CRC used a n bit generator polynomial which works as divisor. Generator = 10101 then n=5 2. Append n-1 number of zeros to the data word. Data word = 110010101 Appended data word = 110010101 + 0000 = 1100101010000 3. Divide the appended data word by the generator by using binary division. 4. The remainder obtained after division is a n-1 bit CRC code. Remainder = n-1 bit CRC code =1011 5. Replace the n-1 zeros in data word with the n-1 bit CRC code. Final data word = 110010101 + 1011 = 1100101011011 6. Transmit the CRC appended data word. CRC checker at receiver's end: 1. Divide the received data word by the same generator. 2. If the remainder is zero than data is not erroneous else it

Hamming Code Implementation in C++

What is Hamming Code ? Hamming code is an error detection and correction technique used in computer networks to detect and correct the error which are introduced during the transmission of data over a communication channel. Hamming code algorithm can detect maximum two errors and can correct only one error per word. Hamming Code Generation with example Hamming code is a linear block of code which consist of parity bits inserted in between the data bits. The parity bits are inserted at each 2 n bit position where n=0,1,2,3……. For n=0, P0 parity bit will be inserted at 2 0 = 1 , i.e at first bit position For n=1, P1 parity bit will be inserted at 2 1 = 2, i.e at second position For n=2, P2 parity bit will be inserted at 2 2 = 4, i.e at forth position so on, The number of parity bits r to be inserted for a given m number of data bits is given by the following equation 2 r ≥ m+r+1 , where m = number of bits in data & r = number of pa

Cifar-10 Image Classification using CNN in Keras

In this article, you will learn how to build a Convolutional Neural Network (CNN) using Keras for image classification on Cifar-10 dataset from scratch. Load the Cifar-10 dataset Cifar-10 dataset is a subset of Cifar-100 dataset developed by Canadian Institute for Advanced research. Cifar-10 dataset consist of small photo images of 10 different categories such as dog, frog, horse, ship, truck etc. and is intentionally designed for computer vision and image classification problems. Although, Cifar-10 dataset is well understood and effectively solved it is useful for newbies for learning and exploring the working of Convolutional Neural Networks for image classification problems. Example below will load the cifar-10 dataset and print the shapes of the train and test data. We can infer from the shape that there are 50000 images in testing dataset and 10000 unseen images in testing dataset each with width and height of 32 pixels. The depth of the image is 3 that mean it is RG

MNIST digit classification using CNN in Keras

In this article, you will learn how to build a Convolutional Neural Networks (CNNs) using Keras for image classification on MNIST dataset from scratch. Load MNIST Dataset Modified National Institute of Standards and Technology dataset (MNIST), is a standard dataset commonly used for handwritten digit classification problem. Although, the MNIST dataset is well understood and effectively solved it is useful for newbies for learning and exploring the working of Convolutional Neural Network for image classification problems. It is composed of grayscale images of 28 *28 pixel in size each of handwritten single digit number ranging from 0 to 9, containing 60,000 images in training dataset and 10,000 separate images in test dataset. Let’s load the MNIST dataset using Keras API and print the shapes of training and testing data as illustrated below: Running the above code will load the training and testing dataset as well as their respective labels and images in different var

Labels

Adam
C
C code
C program
C++
Cifar-10
CNN
computer networks
Convolutional Neural Network
cpp

CRC
crc algorithm
Cyclic Redundancy Check
error correction
error detection
hamming code
handwritten digit recognition
image classification
image recognition
Keras
MNIST
neural network
Tensorflow

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