A Modified Least Significant Bit Embedding with Error Correction

2013 ◽  
Vol 284-287 ◽  
pp. 3256-3259
Author(s):  
Hsin Ying Liang ◽  
Chia Hsin Cheng ◽  
Cheng Ying Yang ◽  
Kun Fu Zhang

This paper proposes a modified Least significant bit (LSB) embedding capable of both a high embedding payload and error correction. The method proposed in this paper combines the techniques of both LSB embedding and multilevel coding to produce stego images with error correction capability and high embedding payloads. The proposed method divides cover work into multiple blocks, and each LSB for all the pixels in each block is considered a layer. Reed-Muller codes are used to encode cipher and embed data into every layer. LSB embedding has no inherent capability to correct errors in cipher extraction, but the proposed method can correct some errors according to the error correction capability of multilevel coding. Compared with LSB embedding, simulation results show that the proposed method has a similar peak signal noise ratio (PSNR) and embedding payload. The peak signal noise ratio (PSNR) exceeds 40 dB by using our proposed method. Additionally, our proposed method offers significantly superior embedding payloads and error correction capabilities.

2013 ◽  
Vol 300-301 ◽  
pp. 746-749
Author(s):  
Wei Wei Hu ◽  
Chang Ming Wang ◽  
Ai Jun Zhang

In order to improve the decreasing resolution ability of Propagator Method (PM) algorithm under the environments like low signal noise ratio and small number of snapshots, a new weighted projection PM algorithm is proposed in this paper. This algorithm orthogonalizes noise subspace to get a new one, gains the signal subspace with the relationship between it and noise subspace, and weights the signal subspace and noise subspace with values gained by projecting integral value of steering vector in the field around the signals to each element of subspace. Simulation results show that the proposed method can keep computation simple, and also can decrease signal noise ratio threshold and snapshots threshold, so it has the better resolution ability and higher precision in snapshot deficient and low signal noise ratio scenario.


2013 ◽  
Vol 347-350 ◽  
pp. 2474-2478
Author(s):  
Wei Hong Fu ◽  
Cheng Wang ◽  
Nai An Liu ◽  
Qing Liang Kong ◽  
Wei Xin Tian

In this paper, a new precoding scheme is proposed based on the combination of Block Diagonalization (BD) and SLNR (Signal Leakage Noise Ratio) maximization. Then a new user selection algorithm is proposed based on the joint precoding scheme. BD precoding will cause performance loss in the single antenna terminals when the number of terminal antenna is inconsistent. The algorithm we proposed can overcome the drawback by using the maximum SLNR for single-antenna users and BD precoding for multi-antenna users respectively. Simulation results show that the proposed algorithm will enhance the system sum-rate performance significantly when SNR (Signal Noise Ratio) over 5dB. The performance improves by 30% when SNR reaches 20dB.


Author(s):  
R. F. Egerton

An important parameter governing the sensitivity and accuracy of elemental analysis by electron energy-loss spectroscopy (EELS) or by X-ray emission spectroscopy is the signal/noise ratio of the characteristic signal.


2012 ◽  
Vol 71 (5) ◽  
pp. 445-453
Author(s):  
M. D. Rasnikov ◽  
I. T. Rozhkov

Author(s):  
Ryan Xiao ◽  
William Wang ◽  
Ang Li ◽  
Shengqiu Xu ◽  
Binghai Liu

Abstract With the development of semiconductor technology and the increment quantity of metal layers in past few years, backside EFA (Electrical Failure Analysis) technology has become the dominant method. In this paper, abnormally high Signal Noise Ratio (SNR) signal captured by Electro-Optical Probing (EOP)/Laser Voltage Probing (LVP) from backside is shown and the cause of these phenomena are studied. Based on the real case collection, two kinds of failure mode are summarized, and simulated experiments are performed. The results indicate that when a current path from power to ground is formed, the high SNR signal can be captured at the transistor which was on this current path. It is helpful of this consequence for FA to identify the failure mode by high SNR signal.


2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Xiongpeng Tang ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Gebdang Biangbalbe Ruben ◽  
Zhenxin Bao ◽  
...  

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.


2013 ◽  
Vol 27 (12) ◽  
pp. 4014-4027 ◽  
Author(s):  
Hsin-Ying Liang ◽  
Hung-Chi Chu ◽  
Chuan-Bi Lin ◽  
Kuang-Hao Lin

Sign in / Sign up

Export Citation Format

Share Document