[amp_mcq option1=”Euclidean distance between code vectors” option2=”Hamming distance of error correcting codes” option3=”Both A and B” option4=”None of the above” correct=”option2″]<\/p>\n
The correct answer is: B. Hamming distance of error correcting codes<\/strong><\/p>\n Hamming distance is a measure of the number of bits that are different between two code words. A code with a large Hamming distance is more resistant to errors, because any single error will only change a small number of bits in the received code word, and the decoder will be able to correct the error.<\/p>\n Euclidean distance is a measure of the distance between two points in a Euclidean space. It is defined as the square root of the sum of the squares of the differences between the coordinates of the two points. Euclidean distance is not a good measure of the distance between two code words, because it does not take into account the fact that code words are sequences of bits, and that errors can change any bit in the code word.<\/p>\n Therefore, the emphasis must be on maximizing the Hamming distance of error correcting codes, not the Euclidean distance between code vectors.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”Euclidean distance between code vectors” option2=”Hamming distance of error correcting codes” option3=”Both A and B” option4=”None of the above” correct=”option2″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[685],"tags":[],"class_list":["post-19527","post","type-post","status-publish","format-standard","hentry","category-information-theory-and-coding","no-featured-image-padding"],"yoast_head":"\n