On the Hamming distance properties of group codes

1992 ◽  
Vol 38 (6) ◽  
pp. 1797-1801 ◽  
Author(s):  
G.D. Forney
Keyword(s):  
Hamming Distance ◽  
Group Codes ◽  
Distance Properties

CLASS OF BOOLEAN FUNCTIONS CONSTRUCTED USING SIGNIFICANT BITS OF LINEAR RECURRENCES OVER THE RING ℤ2n

2019 ◽  
Vol 6 (2) ◽  
pp. 90-94
Author(s):  
Hernandez Piloto Daniel Humberto
Keyword(s):  
Linear Function ◽  
Boolean Functions ◽  
Hamming Distance ◽  
Linear Recurrences ◽  
Maximum Period ◽  
Non Linear ◽  
The Given ◽  
Class Of Functions

In this work a class of functions is studied, which are built with the help of significant bits sequences on the ring ℤ2n. This class is built with use of a function ψ: ℤ2n → ℤ2. In public literature there are works in which ψ is a linear function. Here we will use a non-linear ψ function for this set. It is known that the period of a polynomial F in the ring ℤ2n is equal to T(mod 2)2α, where α∈ , n01- . The polynomials for which it is true that T(F) = T(F mod 2), in other words α = 0, are called marked polynomials. For our class we are going to use a polynomial with a maximum period as the characteristic polyomial. In the present work we show the bounds of the given class: non-linearity, the weight of the functions, the Hamming distance between functions. The Hamming distance between these functions and functions of other known classes is also given.

SISTEM PENGENALAN MANUSIA MELALUI KEUNIKAN FISIOLOGIS SELAPUT PELANGI MATA DENGAN MENGGUNAKAN FILTER LOG GABOR

KONVERGENSI ◽  
2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Bima Agung Pratama ◽  
Fajar Astuti Hermawati
Keyword(s):  
Iris Recognition ◽  
Hamming Distance ◽  
Rubber Sheet ◽  
Log Gabor

Penelitian ini mengajukan sebuah sistem pengenalan manusia melalui karakteristik pola fisiologis selaput pelangi (iris) matanya. Pengenalan selaput pelangi mata (iris recognition) merupakan suatu teknologi pengolahan citra yang digunakan untuk mendeteksi dan menampilkan selaput pelangi (iris) pada alat indera mata manusia saat kelopak mata terbuka. Terdapat beberapa tahap dalam proses pengenalan menggunakan pola iris mata manusia. Langkah pertama adalah melakukan proses segmentasi untuk mendapatkan daerah selaput pelangi (iris) mata yang berbentuk melingkat dengan menggunakan metode operator integro-diferensial. Selanjutnya dilakukan proses normalisasi hasil segmentasi menjadi bentuk polar dengan menerapkan metode metode Daughman’s rubber sheet model. Setelah itu diterapkan proses ekstraksi fitur atau pola dari citra ternormalisasi menggunakan filter Log-Gabor. Pencocokan untuk mengukur kesamaan antara pola iris mata manusia dengan pola-pola dalam basisdata sistem dilakukan menggunakan Hamming distance. Dalam percobaan pengenalan individu menggunakan basisdata iris mata MMU diperoleh akurasi sebesar 98%. Kata Kunci: Pengenalan selaput pelangi, Pengenalan iris mata, Filter log-Gabor, Segmentasi citra, Sistem biometrik

Implementasi Algoritma Hamming Distance dan Brute Force dalam Mendeteksi Kemiripan Source Code Bahasa Pemrograman C

2017 ◽  
Vol 8 (2) ◽  
Author(s):  
Andreas Budiman ◽  
Dennis Gunawan ◽  
Seng Hansun
Keyword(s):  
College Presidents ◽  
Hamming Distance ◽  
Source Code ◽  
Structural Type ◽  
Brute Force ◽  
Important Thing ◽  
The Past ◽  
Index Terms ◽  
Better Than

Plagiarism is a behavior that causes violence of copyrights. Survey shows 55% of college presidents say that plagiarism in students’ papers has increased over the past 10 years. Therefore, an application for detecting plagiarism is needed, especially for teachers. This plagiarism checker application is made by using Visual C# 2010. The plagiarism checker uses hamming distance algorithm for matching line code of the source code. This algorithm works by matching the same length string of the code programs. Thus, it needs brute will be matched with hamming distance. Another important thing for detecting plagiarism is the preprocessing, which is used to help the algorithm for detecting plagiarized source code. This paper shows that the application works good in detecting plagiarism, the hamming distance algorithm and brute force algorithm works better than levenstein distance algorithm for detecting structural type of plagiarism and this thesis also shows that the preprocessing could help the application to increase its percentage and its accuracy. Index Terms—Brute Force, Hamming Distance, Plagiarisme, Preprocessing.

Algoritma Fisher-Yates Shuffle dan Flood Fill sebagai Maze Generator pada Game Labirin

2019 ◽  
Vol 10 (2) ◽  
pp. 59-64
Author(s):  
D.J. Owen Hoetama ◽  
Farica Perdana Putri ◽  
P.M. Winarno
Keyword(s):  
Hamming Distance ◽  
Problem Solution ◽  
Average Percentage ◽  
Rate Difference ◽  
Level Problem ◽  
Index Terms ◽  
Flood Fill ◽  
Static Level ◽  
Wall Position

Maze game is an interesting game and used to spend time. However, in the maze game, the level used forthis game still uses static levels. Static levels make the maze shape stay the same if we play the same level. Thus, players will quickly feel bored because it finds the same complexity. Maze generator is a static level problem solution on the maze game. This research uses Fisher-Yates Shuffle algorithm and Flood Fill algorithm to make maze generator. Fisher-Yates Shuffle algorithm is used for wall position randomization and Flood Fill algorithm to keep the maze results to remain resolved. The results of the application implementation yielded 30 mazes and were tested using the Hamming Distance algorithm, yielding that the result of the maze formed is always different. The average percentage rate difference produced 48% each time the maze was formed. The results of the maze that was formed performed perfect maze checking with the result of 83.33% percentage. Index Terms— Fisher-Yates Shuffle, Flood Fill, MazeGenerator, Hamming Distance

An Information-theoretical Model for Breast Cancer Detection

2008 ◽  
Vol 47 (04) ◽  
pp. 322-327 ◽  
Author(s):  
D. Blokh ◽  
N. Zurgil ◽  
I. Stambler ◽  
E. Afrimzon ◽  
Y. Shafran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Hamming Distance ◽  
Formal Model ◽  
Methodological Approach ◽  
Breast Cancer Detection ◽  
Receptor Expression ◽  
Diagnostic Model ◽  
Model Parameters ◽  
Two Stages

Summary Objectives: Formal diagnostic modeling is an important line of modern biological and medical research. The construction of a formal diagnostic model consists of two stages: first, the estimation of correlation between model parameters and the disease under consideration; and second, the construction of a diagnostic decision rule using these correlation estimates. A serious drawback of current diagnostic models is the absence of a unified mathematical methodological approach to implementing these two stages. The absence of aunified approach makesthe theoretical/biomedical substantiation of diagnostic rules difficult and reduces the efficacyofactual diagnostic model application. Methods: The present study constructs a formal model for breast cancer detection. The diagnostic model is based on information theory. Normalized mutual information is chosen as the measure of relevance between parameters and the patterns studied. The “nearest neighbor” rule is utilized for diagnosis, while the distance between elements is the weighted Hamming distance. The model concomitantly employs cellular fluorescence polarization as the quantitative input parameter and cell receptor expression as qualitative parameters. Results: Twenty-four healthy individuals and 34 patients (not including the subjects analyzed for the model construction) were tested by the model. Twenty-three healthy subjects and 34 patients were correctly diagnosed. Conclusions: The proposed diagnostic model is an open one,i.e.it can accommodate new additional parameters, which may increase its effectiveness.

scholarly journals Deep Neural Networks Classification via Binary Error-Detecting Output Codes

2021 ◽  
Vol 11 (8) ◽  
pp. 3563
Author(s):  
Martin Klimo ◽  
Peter Lukáč ◽  
Peter Tarábek
Keyword(s):  
Neural Networks ◽  
Categorical Data ◽  
Coding Theory ◽  
Hamming Distance ◽  
Activation Function ◽  
Block Codes ◽  
Ease Of Use ◽  
Linear Block Codes ◽  
Binary Coding ◽  
The One

One-hot encoding is the prevalent method used in neural networks to represent multi-class categorical data. Its success stems from its ease of use and interpretability as a probability distribution when accompanied by a softmax activation function. However, one-hot encoding leads to very high dimensional vector representations when the categorical data’s cardinality is high. The Hamming distance in one-hot encoding is equal to two from the coding theory perspective, which does not allow detection or error-correcting capabilities. Binary coding provides more possibilities for encoding categorical data into the output codes, which mitigates the limitations of the one-hot encoding mentioned above. We propose a novel method based on Zadeh fuzzy logic to train binary output codes holistically. We study linear block codes for their possibility of separating class information from the checksum part of the codeword, showing their ability not only to detect recognition errors by calculating non-zero syndrome, but also to evaluate the truth-value of the decision. Experimental results show that the proposed approach achieves similar results as one-hot encoding with a softmax function in terms of accuracy, reliability, and out-of-distribution performance. It suggests a good foundation for future applications, mainly classification tasks with a high number of classes.

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