Fingerprint-based Bluetooth Localization (Fingerprint-based Bluetooth Localization) is a commonly used indoor positioning algorithm. Its basic principle is to establish a fingerprint database by recording the Bluetooth signal strength values at different locations, and then according to the Bluetooth signal strength values received by the device, Match the most similar fingerprints to determine where the device is located.
A Heap is a special Tree-based data structure in which the tree is a complete binary tree. Generally, there are two types of Heap: Max-Heap (root node is greater than its child nodes) and Min-Heap (root node is smaller than its child nodes).
We can simply iterate over all items from the given string and compare the adjacent values each time with the help of stack before pushing the element in.
We can’t use the similar solution we did for Design a Queue with Stack. It is because unlike Stack, moving elements from one Queue to another one won’t change the sequence of elements. We have to pop out all previous elements added into the queue when adding a new element, in this way we can simulate a Stack.
Since Queue is FIFO but Stack is FILO. If we need to use Stack to implement a Queue, we need to use at least two Stacks. So we use one stack which only handle Push operations, and another Stack which only handle Pop/Peek operations. And we move elements from the Pop only Stack to the other one when Pop/Peek get called. It will reverse the FILO stack elements sequence after that. So we get a FIFO sequence.
Search if a given string pattern (needle) is part of a target string (haystack) is a common problem. The naive approach is to use two nested loops with O(n * m) time complexity. KMP is a better way which has a better performance.
Let’s take a look at a easy problem on Leetcode 27. Remove Element. We will demonstrate how to remove an element from an array without allocating extra space for another array.
Usually when you get a problem about searching the common items between multiple strings, the brute-force solution’s time complexity is usually too high. We can use hashmap to lower the time complexity.