Estimates for the Lower Bounds on the Inverse Elements of Strictly Diagonally Dominant Tridiagonal Matrices in Signal Processing

2010 ◽  
Vol 121-122 ◽  
pp. 929-933
Author(s):  
Chuan Dai Dong
Keyword(s):  
Signal Processing ◽  
Lower Bounds ◽  
Practical Applications ◽  
Tridiagonal Matrices ◽  
Diagonally Dominant

In the theory and practical applications, tridiagonal matrices play a very important role. In this paper, Motivated by the references, especially [2], we give the estimates for the lower bounds on the inverse elements of strictly diagonally dominant tridiagonal matrices.

Fast Algorithm for the Inverse Matrices of Adding Element Tridiagonal Periodic Matrices in Signal Processing

2010 ◽  
Vol 121-122 ◽  
pp. 682-686
Author(s):  
Yan Lei Zhao ◽  
Xue Ting Liu
Keyword(s):  
Signal Processing ◽  
Finite Difference ◽  
Boundary Value Problems ◽  
Difference Equations ◽  
Fast Algorithm ◽  
Boundary Value ◽  
Finite Difference Methods ◽  
Practical Applications ◽  
Tridiagonal Matrices ◽  
Difference Methods

Adding element tridiagonal matrices play a very important role in the theory and practical applications, such as the boundary value problems by finite difference methods, interpolation by cubic splines, three-term difference equations and so on. In this paper, we give a fast algorithm for the Inverse Matrices of periodic adding element tridiagonal matrices.

A Fast Algorithm for the Inverse of a Tridiagonal Period Matrices in Signal Processing

2010 ◽  
Vol 159 ◽  
pp. 469-476
Author(s):  
Xi Lian Fu
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
System Design ◽  
Lower Bounds ◽  
Nonlinear Kinetics ◽  
Computational Mathematics ◽  
Diagonally Dominant ◽  
Missile System ◽  
Economics And Biology ◽  
Bearing System

The theory and method of matrix computation, as an important tool, have much important applications such as in computational mathematics, physics, image processing and recognition, missile system design, rotor bearing system, nonlinear kinetics, economics and biology etc. In this paper, Motivated by the references, especially [2], we give the estimates for the lower bounds on the inverse elements of strictly diagonally dominant tridiagonal period matrices.

Estimates for the Lower Bounds on the Inverse Elements of Strictly Diagonally Dominant Tridiagonal Period Matrices in Signal Processing

2010 ◽  
Vol 159 ◽  
pp. 459-463
Author(s):  
Hong Ling Fan
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
System Design ◽  
Lower Bounds ◽  
Nonlinear Kinetics ◽  
Computational Mathematics ◽  
Diagonally Dominant ◽  
Missile System ◽  
Economics And Biology ◽  
Bearing System

The theory and method of matrix computation, as an important tool, have much important applications such as in computational mathematics, physics, image processing and recognition, missile system design, rotor bearing system, nonlinear kinetics, economics and biology etc. In this paper, Motivated by the references, especially [2], we give the estimates for the lower bounds on the inverse elements of strictly diagonally dominant tridiagonal period matrices.

Morphological Filtering Principles

2011 ◽  
pp. 1102-1111
Author(s):  
Jose Crespo
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Mathematical Morphology ◽  
Digital Form ◽  
Practical Applications ◽  
Theoretical Investigations ◽  
Morphological Filtering ◽  
The 1960S

In the last fifty years, approximately, advances in computers and the availability of images in digital form have made it possible to process and to analyze them in automatic (or semi-automatic) ways. Alongside with general signal processing, the discipline of image processing has acquired a great importance for practical applications as well as for theoretical investigations. Some general image processing references are (Castleman, 1979) (Rosenfeld & Kak, 1982) (Jain, 1989) (Pratt, 1991) (Haralick & Shapiro, 1992) (Russ, 2002) (Gonzalez & Woods, 2006). Mathematical Morphology, which was founded by Serra and Matheron in the 1960s, has distinguished itself from other types of image processing in the sense that, among other aspects, has focused on the importance of shapes. The principles of Mathematical Morphology can be found in numerous references such as (Serra, 1982) (Serra, 1988) (Giardina & Dougherty, 1988) (Schmitt & Mattioli, 1993) (Maragos & Schafer, 1990) (Heijmans, 1994) (Soille, 2003) (Dougherty & Lotufo, 2003) (Ronse, 2005).

Hands-On DSP Education through Experiments

2012 ◽  
Vol 49 (3) ◽  
pp. 302-309 ◽  
Author(s):  
Rifat Benveniste ◽  
Cem Ünsalan
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Engineering Education ◽  
Real Time ◽  
Digital Signal ◽  
Practical Applications ◽  
Hands On ◽  
New Graduate ◽  
Electrical Engineering Education ◽  
Secure Place

A new graduate from electrical engineering education must know about digital signal processing (DSP) to find a secure place in the competitive jobs market. Although this topic can be taught theoretically, its importance comes from practical applications. Therefore, students must be equipped with appropriate tools. Fortunately, DSP platforms serve this purpose. At Yeditepe University, we established a laboratory to guide students in real-time digital signal processing applications. We selected a Texas Instruments TMS320C6713 DSK platform for this purpose. In this study, we provide several laboratory applications on this platform. We also provide more advanced projects developed by our students which emerged from these applications. We observed that this laboratory improved the understanding of theoretical DSP concepts.

EDUCATIONAL PLATFORM FOR REAL-TIME DIGITAL SIGNAL PROCESSING: PRACTICAL APPLICATIONS OF DIGITAL FILTERS

2017 ◽  
Vol 36 (1) ◽  
Author(s):  
Wesley Becari ◽  
Rodrigo B. dos Santos ◽  
André B. Carlos ◽  
Rafael A. Biliatto ◽  
Henrique E. M. Peres
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Real Time ◽  
Digital Filters ◽  
Digital Signal ◽  
Practical Applications

scholarly journals On some common compressive sensing recovery algorithms and applications

2017 ◽  
Vol 30 (4) ◽  
pp. 477-510 ◽  
Author(s):  
Andjela Draganic ◽  
Irena Orovic ◽  
Srdjan Stankovic
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Practical Applications ◽  
Sensing Applications ◽  
Different Types ◽  
Acquisition Strategy ◽  
Basic Ideas ◽  
Significant Attention ◽  
Intensive Growth

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well.

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