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Data Structure Articles
Page 163 of 164
EPC Gen 2 Architecture
EPC or Electronic Product Code is a universal identifier that are encoded on RFID (Radio Frequency Identification) tags to check identities of objects like inventory, assets and people, and track them. The second generation of this technology, as laid down by EPCglobal Tag Data Standard, is called EPC Gen 2.The architecture of EPC Gen 2, RFID network has two main components −Tags or labels − They are affixed on objects so that they can be identified or tracked.Readers or interrogators − They are the intelligent part of the system that tracks the tags.The architecture is shown as below −RFID tags ...
Read MoreElectronic Product Code (EPC)
Electronic Product Code (EPC) is a universal identifier that aims to render a unique identity to every possible physical object of the world. EPCs are mostly encoded on RFID (Radio Frequency Identification) tags that are used to check identities of objects like inventory, assets and people, and track them.EPC is the 96–bit number associated with an RFID tag for identifying a particular tag amongst other tags. It distinguishes two identical products, and also provide the product’s manufacture date, origin or batch number.The EPC structure is laid down by EPCglobal Tag Data Standard, an open standard freely defined by EPCglobal Inc.EPC ...
Read MoreHuffman Trees in Data Structure
DefinitionHuffman coding provides codes to characters such that the length of the code depends on the relative frequency or weight of the corresponding character. Huffman codes are of variable-length, and without any prefix (that means no code is a prefix of any other). Any prefix-free binary code can be displayed or visualized as a binary tree with the encoded characters stored at the leaves.Huffman tree or Huffman coding tree defines as a full binary tree in which each leaf of the tree corresponds to a letter in the given alphabet.The Huffman tree is treated as the binary tree associated with ...
Read MoreDictionary Operations in Data Structure
A dictionary is defined as a general-purpose data structure for storing a group of objects. A dictionary is associated with a set of keys and each key has a single associated value. When presented with a key, the dictionary will simply return the associated value.For example, the results of a classroom test could be represented as a dictionary with student's names as keys and their scores as the values:results = {'Anik' : 75, 'Aftab' :80, 'James' : 85, 'Manisha': 77, 'Suhana' :87, 'Margaret': 82}Main operations of dictionariesDictionaries typically support so many operations −retrieve a value (based on language, attempting to ...
Read MoreBayes' Rule in Data Structure
A way to update our beliefs depended on the arrival of new, relevant pieces of evidence are provided by Bayes rule. For example, if we were trying to provide the probability that a given person has cancer, we would initially just conclude it is whatever percent of the population has cancer. However, given extra evidence such as the fact that the person is a smoker, we can update our probability, since the probability of having cancer is greater given that the person is a smoker. This allows us to utilize prior knowledge to improve our probability estimations.The rule is explained ...
Read MoreBoole's Inequality in Data Structure
In probability theory, according to Boole's inequality, also denoted as the union bound, for any finite or countable set of events, the probability that at least one of the events happens is no higher than the sum of the probabilities of the individual events.In mathematics, the probability theory is denoted as an important branch that studies about the probabilities of the random event. The probability is denoted as the measurement of chances of happening an event which is an outcome of an experiment.For Example − tossing a coin is denoted as an experiment and getting head or tail is denoted ...
Read MoreHuffman Coding
Huffman coding is lossless data compression algorithm. In this algorithm a variable-length code is assigned to input different characters. The code length is related with how frequently characters are used. Most frequent characters have smallest codes, and longer codes for least frequent characters.There are mainly two parts. First one to create Huffman tree, and another one to traverse the tree to find codes.For an example, consider some strings “YYYZXXYYX”, the frequency of character Y is larger than X and the character Z has least frequency. So the length of code for Y is smaller than X, and code for X ...
Read MoreAmortized Complexity
Amortize AnalysisThis analysis is used when the occasional operation is very slow, but most of the operations which are executing very frequently are faster. In Data structures we need amortized analysis for Hash Tables, Disjoint Sets etc.In the Hash-table, the most of the time the searching time complexity is O(1), but sometimes it executes O(n) operations. When we want to search or insert an element in a hash table for most of the cases it is constant time taking the task, but when a collision occurs, it needs O(n) times operations for collision resolution.Aggregate MethodThe aggregate method is used to ...
Read MoreAsymptotic Notation - O(), o(), Ω(), ω(), and θ()
Asymptotic NotationsAsymptotic notations are used to represent the complexities of algorithms for asymptotic analysis. These notations are mathematical tools to represent the complexities. There are three notations that are commonly used.Big Oh NotationBig-Oh (O) notation gives an upper bound for a function f(n) to within a constant factor.Little o NotationsThere are some other notations present except the Big-Oh, Big-Omega and Big-Theta notations. The little o notation is one of them.Little o notation is used to describe an upper bound that cannot be tight. In other words, loose upper bound of f(n).Big Omega NotationBig-Omega (Ω) notation gives a lower bound for ...
Read MoreAsymptotic Complexity
Asymptotic AnalysisUsing asymptotic analysis, we can get an idea about the performance of the algorithm based on the input size. We should not calculate the exact running time, but we should find the relation between the running time and the input size. We should follow the running time when the size of input is increased.For the space complexity, our goal is to get the relation or function that how much space in the main memory is occupied to complete the algorithm.Asymptotic BehaviorFor a function f(n) the asymptotic behavior is the growth of f(n) as n gets large. Small input values ...
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