scholarly journals Adaptive vector greedy splitting algorithm

2021 ◽  
Author(s):  
Evgeny Klavir
Keyword(s):  
Signal Processing ◽  
Main Idea ◽  
Digital Signal ◽  
Haar Wavelet ◽  
Computer Language ◽  
Computer Engineering ◽  
Splitting Algorithm ◽  
Haar Wavelet Transform ◽  
Orthonormal Bases ◽  
Input Signals

We introduce a new transform through a construction that we have called the Adaptive Vector Greedy Splitting algorithm. The main idea behind this algorithm is an optimization step based on the simple Bathtub Principle. We use the Vector Greedy Splitting algorithm to build orthonormal bases for a given vector of random variables (also called signals). A particular basis constructed in this way may be used for signal coompression, audio pattern recognition and other applications of signal processing. We compare performance of the Vector Greedy Splitting algorithm with the Haar wavelet transform applied to the same vector of input signals. The implementation of the algorithms and statistics accumulation are made using the ANSI C computer language and Matlab. The work uses advanced methods of Computer Engineering and Digital Signal Processing.

scholarly journals Adaptive vector greedy splitting algorithm

2021 ◽  
Author(s):  
Evgeny Klavir
Keyword(s):  
Signal Processing ◽  
Main Idea ◽  
Digital Signal ◽  
Haar Wavelet ◽  
Computer Language ◽  
Computer Engineering ◽  
Splitting Algorithm ◽  
Haar Wavelet Transform ◽  
Orthonormal Bases ◽  
Input Signals

We introduce a new transform through a construction that we have called the Adaptive Vector Greedy Splitting algorithm. The main idea behind this algorithm is an optimization step based on the simple Bathtub Principle. We use the Vector Greedy Splitting algorithm to build orthonormal bases for a given vector of random variables (also called signals). A particular basis constructed in this way may be used for signal coompression, audio pattern recognition and other applications of signal processing. We compare performance of the Vector Greedy Splitting algorithm with the Haar wavelet transform applied to the same vector of input signals. The implementation of the algorithms and statistics accumulation are made using the ANSI C computer language and Matlab. The work uses advanced methods of Computer Engineering and Digital Signal Processing.

Multidisciplinary in Cryptology

2014 ◽  
pp. 1-28
Author(s):  
Sattar B. Sadkhan Al Maliky ◽  
Nidaa A. Abbas
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Complexity Theory ◽  
Finite Groups ◽  
General Structure ◽  
Digital Signal ◽  
Computer Engineering ◽  
Control Engineering ◽  
Wide Range ◽  
Subject Areas

To reach the high depths of knowledge and expertise that are required nowadays, scientists focus their attention on minute areas of study. However, the most complex problems faced by scientists still need the application of different disciplines to tackle them, which creates a necessity for multi-disciplinary collaboration. Cryptology is naturally a multidisciplinary field, drawing techniques from a wide range of disciplines and connections to many different subject areas. In recent years, the connection between algebra and cryptography has tightened, and established computational problems and techniques have been supplemented by interesting new approaches and ideas. Cryptographic engineering is a complicated, multidisciplinary field. It encompasses mathematics (algebra, finite groups, rings, and fields), probability and statistics, computer engineering (hardware design, ASIC, embedded systems, FPGAs), and computer science (algorithms, complexity theory, software design), control engineering, digital signal processing, physics, chemistry, and others. This chapter provides an introduction to the disciplinary, multidisciplinary, and their general structure (interdisciplinary, trans-disciplinary, and cross-disciplinary). And it also gives an introduction to the applications of the multidisciplinary approaches to some of the cryptology fields. In addition, the chapter provides some facts about the importance of the suitability and of the multidisciplinary approaches in different scientific, academic, and technical applications.

Macromedia Flash-Based Animations for Teaching of the Digital Signal Processing Principles

2011 ◽  
Vol 219-220 ◽  
pp. 1518-1522
Author(s):  
Kurban Ubul ◽  
Guljamal Ubul ◽  
Alim Aysa
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Learning Experience ◽  
Digital Signal ◽  
Subject Area ◽  
Engineering Science ◽  
Computer Engineering ◽  
Job Market ◽  
Approach To Teaching ◽  
The Subject

Digital Signal Processing (DSP) is an important and growing subject area in Electrical/Computer Engineering (ECE), Computer Science and other Engineering/Science disciplines. Since 1997, the authors have taught an undergraduate DSP courses at Xinjiang University (XJU). While the subject of DSP has become very popular with ECE students and with the growing DSP job market, the subject matter is still considered to be a difficult and complex one for students. This paper presents an approach to teaching DSP basic concepts using a platform which developed by the tool, Macromedia Flash. The authors of XJU had enhanced the learning experience for their students by adding the platform to their class offering to reduce the difficulty of understanding the theoretical DSP.

An Energy-Efficient Haar Wavelet Transform Architecture for Respiratory Signal Processing

2020 ◽  
pp. 1-1
Author(s):  
Morgana M. da Rosa ◽  
Henrique B. Seidel ◽  
Guilherme Paim ◽  
Eduardo A. da Costa ◽  
Sergio Almeida ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Energy Efficient ◽  
Haar Wavelet ◽  
Haar Wavelet Transform ◽  
Respiratory Signal

A Review on the Relationship between Computer Engineering, Discrete-Math and Graph Theory

2020 ◽  
Vol 27 (3) ◽  
pp. 38-44
Author(s):  
Arjuwan Al-Jawadi ◽  
Ayhan Al-Shumam
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Graph Theory ◽  
Main Idea ◽  
Digital Signal ◽  
Discrete Math ◽  
Computer Engineering ◽  
Main Concept ◽  
And Mathematics ◽  
The Relationship

This review is based on understanding the main concept between computer engineering and mathematics based on two of their most important fields, the discrete-math and graph theory. and answering the question that was asked by many students over the years of working in the university, about the necessity of studying mathematics while majoring computer engineering. Most of the students face the same problem over years for not having the vision to connect between studying materials of their specialization and general ones, in particular between studying discrete-math engineering as in Engineering analysis, and discrete-math as in the Digital signal Processing (DSP), and between algebraic mathematics. Moreover, they do not understand the main idea of the transition between different time or frequency domains, by converting the work in real-time domain systems to work in discrete–time or frequency domain systems. And they do ignore the importance of studying graph theory, in which recent researches have proved the powerful of using graphs in learning tasks, developing an important field of computer engineering, the machine learning, where the standard neural networks (SNNs) have been developed to graph neural networks (GNNs). A figure was concluded at the end of the review to brief the importance of discrete-math developing the relationship between computer engineering in general and graph theory’s role in developing machine learning in particular.

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