Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEncryption using a private vectors generate triangular matrices from the top (or bottom)
التشفير باستخدام متجهات تولد مصفوفات مثلثية ذات طبيعة خاصة
2439
5 186
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نقدم في هذا البحث نمطاً جديداً من التشفير باستخدام مفتاح التشفير عبارة عن مصفوفات عددية خاصة (مثلثية عليا أو سفلى) محققة لشرطي مصفوفة Hill, و هذه المصفوفات متولدة من متجهات مركباتها أعداد أولية نسبيا مع المقاس n = 256 و علاقة التشفير هي ناتج ضرب مصفوفة النص الأصلي بمفاتح التشفير.
This research gives a new type of encryption, using vectors give me a private encryption key, which generates a triangular matrices from the top (bottom), and check conditions matrix Hill. These matrices resulting from private vectors constitute a relatively preliminary numbers of size n = 256 The encryption process produces by multiplying the original matrix encryption keys.
References used
Christof Paar & Jan Pelzl, Understanding Cryptography, © Springer-Verlag Berlin Heidelberg 2010
(GARRETT, P.: Making, Breaking Codes. An Introduction to Cryptology. Prentice–Hall(2007
(GOLDREICH, O.: Foundations of Cryptography. Basic Applications.Cambridge University Press (2009
rate research
Read More
This paper presents a new type of encryption, using a matrix
asymmetric and symmetric matrix inverse matrix clear text, which
is an internal encryption.
As well as asymmetric encryption, where the ciphertext is inversely
symmetric matrix.
Decryp
tion matrix related to any asymmetric encryption keys
depends on public and private, and is applied to the coded messages
used in the current system ASCII our computers.
تقدم ورقة البحث نمطا جديداً من التشفير باستخدام علاقة فيثاغورث المولدة لثلاثية عددية
أولية , و استثمارها في التشفير و المطبق على الرسائل المرمزة بنظام ASCII المستخدم
في حواسيبنا الحالية. تم في هذا البحث بناء مفتاح عددي شبه خاص لفك التشفير
اعتماد
ا على دالة فيثاغورث طبق مع مفتاح عددي آخر للتشفير بحيث تم الوصول لدالة
فك التشفير بطريقة صحيحة تتعلق بدالة فيثاغورث (دالة التشفير).
Mammography is widely used technique for breast cancer screening. There are
various other techniques for breast cancer screening but mammography is the most reliable
and effective technique. The images obtained through mammography are of low contra
st
which causes problem for the radiologists to interpret. Hence, a high quality image is
mandatory for the processing of the image for extracting any kind of information. Many
contrast enhancement algorithms have been developed over the years. This work presents a
method to enhancement Microcalcifications in digitized mammograms. The method is
based Mainly on the combination of Image Processing. The top-Hat and bottom–hat
transforms are a techniques based on Mathematical morphology operations. This
algorithm has been tested on mini-Mias database which have three types of breast tissues .
For evaluation of performance of image enhancement algorithm, the Contrast
Improvement Index (CII) and Peak Signal to Noise Ratio (PSNR) have been used.
Experimental results suggest that algorithm can be improve significantly overall
detection of the Computer-Aided Diagnosis (CAD) system especially for dense breast.
R-Studio
الاوامر الخاصة في لغة البرمجة R
ايعازات عامة
الكائنات Objects –
الأشعة Vectors
المصفوفات Matrices
One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties. The remarkable success of word embeddings for this purpose suggests that high-quality representations can
be obtained by summarizing the sentence contexts of word mentions. In this paper, we propose a method for learning word representations that follows this basic strategy, but differs from standard word embeddings in two important ways. First, we take advantage of contextualized language models (CLMs) rather than bags of word vectors to encode contexts. Second, rather than learning a word vector directly, we use a topic model to partition the contexts in which words appear, and then learn different topic-specific vectors for each word. Finally, we use a task-specific supervision signal to make a soft selection of the resulting vectors. We show that this simple strategy leads to high-quality word vectors, which are more predictive of semantic properties than word embeddings and existing CLM-based strategies.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
