This document provides an overview of binary and hexadecimal number systems. It discusses converting between binary, decimal, and hexadecimal numbers, as well as mathematical operations in binary. It also covers using an IPO (input, processing, output) approach to problem-solving. Finally, it discusses setting up Ruby programming language on a flash drive to prepare for an introduction to Ruby programming in the next class. The homework assignment is to complete a math review due at the start of the next class.
The document summarizes different searching and sorting algorithms. It discusses linear search and binary search for searching algorithms. It explains that linear search has O(n) time complexity while binary search has O(log n) time complexity. For sorting algorithms, it describes bubble sort, selection sort, and insertion sort. It provides pseudocode to illustrate how each algorithm works to sort a list or array.
Unicode is a standard for representing characters that supports all languages. It maps each character to a unique code point and can represent characters using UTF-8, UTF-16, or UTF-32 encodings. UTF-8 uses a variable number of bytes (1-4) to encode each code point, allowing for compact representation and support of a wide range of languages.
This document provides information about Plessey and MSI symbology barcodes. It explains the formats and encoding rules for Plessey barcodes, including how bits and digits are represented. It also describes MSI Plessey barcodes, including characteristics like character sets, coding rules, checksums, and usage fields. The document contains details on Plessey variations like MSI and provides examples and references.
The linear search algorithm involves checking all elements of an array or data structure sequentially until the target element is found. In the worst case, all elements must be checked, resulting in O(n) time complexity where n is the number of elements. However, if the target is the first element, it requires only constant O(1) time. The algorithm is simple to implement but does not scale well to large data sets as the search time grows linearly with the number of elements.
This document provides an overview of binary and hexadecimal number systems. It discusses converting between binary, decimal, and hexadecimal numbers, as well as mathematical operations in binary. It also covers using an IPO (input, processing, output) approach to problem-solving. Finally, it discusses setting up Ruby programming language on a flash drive to prepare for an introduction to Ruby programming in the next class. The homework assignment is to complete a math review due at the start of the next class.
The document summarizes different searching and sorting algorithms. It discusses linear search and binary search for searching algorithms. It explains that linear search has O(n) time complexity while binary search has O(log n) time complexity. For sorting algorithms, it describes bubble sort, selection sort, and insertion sort. It provides pseudocode to illustrate how each algorithm works to sort a list or array.
Unicode is a standard for representing characters that supports all languages. It maps each character to a unique code point and can represent characters using UTF-8, UTF-16, or UTF-32 encodings. UTF-8 uses a variable number of bytes (1-4) to encode each code point, allowing for compact representation and support of a wide range of languages.
This document provides information about Plessey and MSI symbology barcodes. It explains the formats and encoding rules for Plessey barcodes, including how bits and digits are represented. It also describes MSI Plessey barcodes, including characteristics like character sets, coding rules, checksums, and usage fields. The document contains details on Plessey variations like MSI and provides examples and references.
The linear search algorithm involves checking all elements of an array or data structure sequentially until the target element is found. In the worst case, all elements must be checked, resulting in O(n) time complexity where n is the number of elements. However, if the target is the first element, it requires only constant O(1) time. The algorithm is simple to implement but does not scale well to large data sets as the search time grows linearly with the number of elements.