scholarly journals High order singular value decomposition for plant diversity estimation

2021 ◽  
Author(s):  
Alessandra Bernardi ◽  
Martina Iannacito ◽  
Duccio Rocchini
Keyword(s):  
Big Data ◽  
Singular Value Decomposition ◽  
Plant Diversity ◽  
Singular Value ◽  
High Order ◽  
Memory Storage ◽  
New Method ◽  
Ecological Framework ◽  
Value Decomposition ◽  
Diversity Estimates

AbstractWe propose a new method to estimate plant diversity with Rényi and Rao indexes through the so called High Order Singular Value Decomposition (HOSVD) of tensors. Starting from NASA multi-spectral images we evaluate diversity and we compare original diversity estimates with those realized via the HOSVD compression methods for big data. Our strategy turns out to be extremely powerful in terms of memory storage and precision of the outcome. The obtained results are so promising that we can support the efficiency of our method in the ecological framework.

Big Data Compression in Smart Grids via Optimal Singular Value Decomposition

2020 ◽  
Author(s):  
Seyed Naser Hashemipour ◽  
Jamshid Aghaei ◽  
Abdullah Kavousi-fard ◽  
Taher Niknam ◽  
Ladan Salimi ◽  
...  
Keyword(s):  
Big Data ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Smart Grids ◽  
Singular Value ◽  
Value Decomposition

scholarly journals A modified truncation singular value decomposition method for solving ill-posed problems

2018 ◽  
Vol 13 ◽  
pp. 174830181881360 ◽  
Author(s):  
Zhenyu Zhao ◽  
Riguang Lin ◽  
Zehong Meng ◽  
Guoqiang He ◽  
Lei You ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Decomposition Method ◽  
Singular Value ◽  
Numerical Results ◽  
New Method ◽  
Truncated Singular Value Decomposition ◽  
Singular Value Decomposition Method ◽  
Ill Posed ◽  
Value Decomposition

A modified truncated singular value decomposition method for solving ill-posed problems is presented in this paper, in which the solution has a slightly different form. Both theoretical and numerical results show that the limitations of the classical TSVD method have been overcome by the new method and very few additive computations are needed.

AUDIO WATERMARKING WITH ANGLE QUANTIZATION BASED ON DISCRETE WAVELET TRANSFORM AND SINGULAR VALUE DECOMPOSITION

2018 ◽  
Vol 6 (2) ◽  
pp. 41-52
Author(s):  
Divya Chadar ◽  
Shailja Shukla
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Auditory System ◽  
Audio Signal ◽  
Singular Value ◽  
New Method ◽  
Discrete Wavelet ◽  
Audio Watermark ◽  
Value Decomposition

An audio watermark is a unique electronic identifier embedded in an audio signal, typically used to identify ownership of copyright. Proposed work is a new method of audio watermark hiding inside another bigger cover standard audio cover. The method includes ‘harr’ wavelet based Discrete Wavelet Transform decomposition of frequencies hence the audio samples of watermark gets hidden only those parts of cover audio where human ears are less sensible according to Human Auditory System. Proposed method also includes the Singular Value Decomposition, which is required for making our method robust against the various communication of processing attacks like compression, filtering, fading or noise addition. Proposed work is also using the concept of angular modulation which initially modifies the audio watermark in to provide extra security and also extra robustness in communication. The design is been develop on MATLAB 2013b version and verification of design o the same. 

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