Sunday 2 July 2023

8051 Flag Bits and PSW Register

What is Flag bits and the PSW register?

The 8051 has a flag register to indicate or specify arithmetic conditions or order such as the carry bit. The flag record or register in the 8051 is called the program status word (PSW) register. In this section we discuss or talk about various bits of this register or record and provide some examples of how it is altered.

PSW register, Program Status Word, 8051 pins
Program Status word Image-1

PSW(Program Status Word) register

It is also mention or referred to as the flag register. Although the 8 bits of PSW register or record wide, used by the 8051 of only 6 bits. The two unused bits are exact flags or user-definable. Four of the flags are called depending or conditional flags, meaning that they indicate or specify some depending or conditions that result after an instruction or command are executed. These four are

1.       CY (carry),

2.       P (parity),

3.       AC (auxiliary carry),

4.        and OV (overflow).

CY, the carry flag

This flag is set whenever or anytime there is a carry out from the D7 bit. This flag bit is affected or high-flown after an 8-bit addition or subtraction. It can also be set to 1 or 0 straight or directly by an instruction or command such as “CLR C” and where “SETB C” it is “SETB C” means for “set bit carry” and “CLR C” means for “clear carry”. More about these and other bit-addressable.

AC, the auxiliary carry flag

If there is a move or carry from D3 to D4 during an ADD or SUB working or operation, this bit is set; otherwise, it is remove or cleared. This flag is used by command that perform BCD (binary coded decimal) arithmetic.

P, the parity flag

The parity flag reflects or give back the number of 1s in the A (accumulator) register only. If the A register contains or carry an odd number of 1s, then P=1. Where as P=0 if A has an even number of 1s.

OV, the overflow flag

In general or wide, the carry flag is used to detect or find errors in unsigned arithmetic operations or working. The overflow flag is only used to detect or find errors in signed arithmetic operations.


Computer stuff kit tricks of Topics 71.




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Wednesday 28 June 2023

Types of Trees in C language

Type of Trees in C

A subtree of a tree T is a tree consisting or include of a node in T and its entire issue or descendant in T. (This is different from the set or formal definition of subtree used in graph hypothesis or theory.) The subtree corresponding or keep in touch to the root intersection or node is the entire tree; the subtree corresponding or keep in touch to any other intersection or node is called a proper subtree (in likeness or analogy to the term true or proper subset).

Trees in C language, C Language, C programming
Types of Trees in C Image-1

A balanced tree is one where all of the flag or leaves of the tree are within one sheet or layer of one of each other.

A complete tree is a type of steady or balanced tree, except that it has one more extra or additional constraint or limitation. In a balanced tree, all flag or leaves are of depth n or n+1. In a absolute or complete tree, all of the flag or leaves of depth n+1 are further to the left than the flag or leaves of depth n. Moreover or furthermore, in a complete tree, all branch intersection or nodes (except those at depth n) must have the maximum or largest number of children.

A perfect tree is even more particular or specific. It requires that all of the flag or leaves be of the same depth or drop and that every divide or branching node has the maximum or largest number of children.

A binary tree is a tree data shape or structure in which each intersection or node has at most two children. Typically the first intersection or node is known as the parent and the child intersection or nodes are called left and right. In type theory, a binary tree with intersection or nodes of type A is defined express or inductively as TA=ua. 1+Axaxa. Binary trees are often or commonly used to implement or device binary search trees and binary heaps.

1.1   Traversal

Many problems or trouble require we visit* the intersection or nodes of a tree in a planned or systematic way: tasks or function such as counting or add up how many intersection or nodes exist or finding the maximum or largest element. Three different system or methods are workable or possible for binary trees: preorder, postorder, and in-order, which all do the same three things: recursively or looping traverse both the left and rights subtrees and visit the current intersection or node. The difference is when the task or algorithm visits the current intersection or node:

Visit means execute or performing some running or operation involving or require the current intersection or node of a tree, like incrementing or raise a counter or checking if the value of the current intersection or node is greater than any other recorded.


Computer stuff kit tricks of Topics 70.



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Tuesday 27 June 2023

Trees in C Language

What is Trees in C?

1.1   Introduction

Mathematically, it is not a tree, but arborescence or resembling a tree: an acyclic connected graph where each intersection or node has zero or more children nodes or intersection and at most one parent node. Furthermore, the children of each node or intersection have a specific order.

 A node or intersection is a structure which may carry or contain a value, a condition, or represent a separate information or data structure (which could be a tree of its own). up as they do in creation or nature). A node that has a child is called the child’s parent intersection or node (or superior node, or ancestor). A node or intersection has at most one parent.

Trees in C, C Language, C basic language
Trees in C language image-1

Nodes or intersections that do not have any children are called leaf nodes. They are also referred to as last or terminal nodes.

 The depth of a node or intersection is the length of the path to its tuber or root (i.e., its root path). This is commonly needed in the manipulation or trick of the various self balancing trees, AVL Trees in particular.

 The topmost node or intersection in a tree is called the tuber or root node. Being the topmost intersection or node, the tuber or root node will not have parents. It is the intersection or node at which performance or operations on the tree commonly begin (although some algorithms begin with the leaf intersection or nodes and raise or work up ending at the root). All other intersection or nodes can be reached from it by following edges or links. (in the formal definition, each such path is also unique or single).

 In some trees, such as mass or heaps, the root intersection or node has special properties. Every intersection or node in a tree can be seen as the tuber or root node of the subtree rooted at that node.

An inner node or internal node is any node of a tree that has child intersection or nodes and is thus not a leaf node.


Computer stuff kit tricks of Topics 69.







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Monday 26 June 2023

Searching Algorithms in C

What is searching Algorithms?

Searching is an operation or performance which finds the site or location of a given section or element in a list. The search is said to be victorious or unsuccessful or pointless depending on whether the section or element that is to be searched is begin or found or not. The following are the dissimilar types of searches

1.       Linear Search

2.       Binary Search

 

Algorithms in C, C language, C programming
Searching Algorithms Image-1

1.1   Linear Search

It is also known as successive or sequential search, which is fit or suitable for searching a set of data for a certain or particular value.
It is the easy or simplest procedure or method of searching, and it can be put in or applied to a sorted or an unsorted list.
It work or operates by checking every section or element of a list one at a time in order or sequence until a match is found. Linear search runs in O(N). If the information or data are distributed or give out randomly, on average N/2 balancing or comparisons will be needed.
The best case or instance is that the value is equal to the firs section or element tested, in which case only1 balancing or comparison is needed. The poorest or worst case is that the worth or value is not in the list (or is the last thing or item in the list), in which case N balancing or comparisons are needed.
Suppose or think an array A having section or elements 1 to N is to be searched for a worth or value x: if not found, the result is zero.

1.2   Binary Search

The most effective or fruitful technique that can be applied to sorted data or records is the binary search technique. This technique or ability is faster than the other searching techniques.

1.       The given sorted data or records are divided into two halves.

2.       The key is first compared or measure with the key area or field of the middle record.

3.       If the match or tie is found, the key index or sign is returned.

4.       If it does not match or tie, then required key must be either in the lower or upper half.

5.       If the key is less than the key field or area of the middle data or record, the key is searched in the lower half otherwise it is checked or inspect in the upper half.


Computer stuff kit tricks of Topics 68.




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Sunday 25 June 2023

Sorting Algorithms in C

What is sorting Algorithms?

Sorting means set out or arranging a set of data in some order. There are unlike or different methods that are used to sort the data in arise or ascending or descending or go down order. Few of them are listed and talk about or discussed in detail below.

sorting Algorithms, bubble sort, selection sort, quick sort
Sorting Algorithms in C Image-1


1.       Bubble sort

2.       Selection sort

3.       Insertion sort

4.       Heap sort

5.       Merge sort

6.       Quick sort

1.1   Bubble Sort

Bubble sort is a simple sorting task or algorithm. The task or algorithm gets its name from the way smaller part or elements “bubble” to the top of the list. Because it only uses balancing or comparisons to work or operate on elements, it is a balancing or comparison sort.

1.2   Selection sort

Selection sort is a sorting task or algorithm, particular or specifically an in-place balancing or comparison sort.

1.3   Insertion sort

Insertion sort is a simple sorting task or algorithm, a balancing or comparison sort in which the sorted array (or list) is built one entry at a time. It is much less logical on large lists than more up to date algorithms or task such as quick sort, heap sort, or merge sort. However, insertion sort give several advantages:

1.       Simple implementation or execution

2.       Efficient or adequate for (quite) small data sets

3.       Adaptive or flexible, i.e. efficient for data or record sets that are already substantially or greatly sorted: the time difficulty or complexity is O(n +d), where d is the number of inversions or reversal.

4.       More logical or efficient in practice than most other simple foursquare or quadratic (i.e. O(n square)) algorithms or task such as selection sort or bubble sort: the average running time is n square/4, and the management or running time is linear in the best case

5.       Stable, i.e. does not change the respective or relative order of part or elements with equal keys

6.       In-place, i.e. only requires a continuous or constant amount O(1) of additional memory space

7.       Online, i.e. can sort a list as it collect or receives it.

1.4   Heap sort

The heap sort works as its name submit or suggests. It begins by place or building a heap out of the data set, and then take out or removing the largest item and set or placing it at the end of the sorted array. After removing the largest item, it rebuild or reconstructs the heap, removes the largest last or remaining item, and places it in the next open place or position from the end of the sorted array. This is frequent or repeated until there are no items left in the heap and the sorted array is full. Elementary or easy implementations require two arrays – one to hold the mass or heap and the other to hold the sorted part or elements.

1.5   Merge sort

Merge sort is an O(n log n) balancing or comparison-based sorting task or algorithm. In most implementations or execution it is stable, meaning that it protect or preserves the input order of equal part or elements in the sorted output. It is an example of the divide and conquer algorithmic or task paradigm. It was create or invented by john von Neumann in 1945.

1.6   Quick sort

The quick sort algorithm was grow or developed by C. A. R. Hoare while in the Soviet Union, as a stay with or visiting student at Moscow State University. At that time, Hoare worked in a project a tool or machine translation for the National Physical Laboratory.


Computer stuff kit tricks of Topics 67.






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