Impact of CSI latency on video quality in MIMO systems employing singular value decomposition

2007 ◽  
Vol 43 (18) ◽  
pp. 972 ◽  
Author(s):  
M. Tesanovic ◽  
D.R. Bull ◽  
A. Doufexi ◽  
V. Sgardoni ◽  
A.R. Nix
Keyword(s):  
Singular Value Decomposition ◽  
Mimo Systems ◽  
Video Quality ◽  
Singular Value ◽  
Value Decomposition

scholarly journals Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 34
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Lorenzo Manoni ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Singular Value Decomposition ◽  
Embedded System ◽  
Mimo Systems ◽  
Singular Value ◽  
Measurement Unit ◽  
Comprehensive Treatment ◽  
Mathematical Tool ◽  
Value Decomposition ◽  
Speed Accuracy

Singular value decomposition (SVD) is a central mathematical tool for several emerging applications in embedded systems, such as multiple-input multiple-output (MIMO) systems, data analytics, sparse representation of signals. Since SVD algorithms reduce to solve an eigenvalue problem, that is computationally expensive, both specific hardware solutions and parallel implementations have been proposed to overcome this bottleneck. However, as those solutions require additional hardware resources that are not in general available in embedded systems, optimized algorithms are demanded in this context. The aim of this paper is to present an efficient implementation of the SVD algorithm on ARM Cortex-M. To this end, we proceed to (i) present a comprehensive treatment of the most common algorithms for SVD, providing a fairly complete and deep overview of these algorithms, with a common notation, (ii) implement them on an ARM Cortex-M4F microcontroller, in order to develop a library suitable for embedded systems without an operating system, (iii) find, through a comparative study of the proposed SVD algorithms, the best implementation suitable for a low-resource bare-metal embedded system, (iv) show a practical application to Kalman filtering of an inertial measurement unit (IMU), as an example of how SVD can improve the accuracy of existing algorithms and of its usefulness on a such low-resources system. All these contributions can be used as guidelines for embedded system designers. Regarding the second point, the chosen algorithms have been implemented on ARM Cortex-M4F microcontrollers with very limited hardware resources with respect to more advanced CPUs. Several experiments have been conducted to select which algorithms guarantee the best performance in terms of speed, accuracy and energy consumption.

scholarly journals A Novel Blind and Robust Video Watermarking Technique in Fast Motion Frames Based on SVD and MR-SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Imen Nouioua ◽  
Nouredine Amardjia ◽  
Sarra Belilita
Keyword(s):  
Singular Value Decomposition ◽  
Video Quality ◽  
Singular Value ◽  
Video Watermarking ◽  
Digital Video Watermarking ◽  
Video Frames ◽  
Fast Motion ◽  
Good Transparency ◽  
Value Decomposition

In this work, a novel and efficient digital video watermarking technique based on the Singular Value Decomposition performed in the Multiresolution Singular Value Decomposition domain is proposed. While most of the existing watermarking schemes embed the watermark in all the video frames, which is time-consuming and also affects the perceptibly of the video quality, the proposed method chooses only the fast motion frames in each shot to host the watermark. In doing so, the number of frames to be processed is consequently reduced and a better quality of the watermarked video is also ensured since the human visual system cannot notice the variations in fast moving regions. The watermark information is embedded by Quantization Index Modulation which is a blind watermarking algorithm. The experimental results demonstrate that the proposed method can achieve a very good transparency, while being robust against various kinds of attacks such as filtering, noising, compression, and frame collusion. Compared with several methods found in the literature, the proposed method gives a better robustness.

scholarly journals Adaptive vector quantization in SVD MIMO system backward link with limited number of active sub channels

2004 ◽  
Vol 1 (3) ◽  
pp. 113-123
Author(s):  
Predrag Ivanis ◽  
Dusan Drajic
Keyword(s):  
Singular Value Decomposition ◽  
Vector Quantization ◽  
Bit Error Rate ◽  
Error Rate ◽  
Mimo Systems ◽  
Mimo System ◽  
Singular Value ◽  
Lbg Algorithm ◽  
Adaptive Vector Quantization ◽  
Value Decomposition

This paper presents combination of Channel Optimized Vector Quantization based on LBG algorithm and sub channel power allocation for MIMO systems with Singular Value Decomposition and limited number of active sub channels. Proposed algorithm is designed to enable maximal throughput with bit error rate bellow some tar- get level in case of backward channel capacity limitation. Presence of errors effect in backward channel is also considered.

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