Time-Scale Invariant Audio Watermarking Based on the Statistical Features in Time Domain

2007 ◽  
pp. 93-108 ◽  
Author(s):  
Shijun Xiang ◽  
Jiwu Huang ◽  
Rui Yang
Keyword(s):  
Time Domain ◽  
Time Scale ◽  
Audio Watermarking ◽  
Statistical Features ◽  
Scale Invariant

scholarly journals Robust and Reversible Audio Watermarking by Modifying Statistical Features in Time Domain

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Shijun Xiang ◽  
Le Yang ◽  
Yi Wang
Keyword(s):  
Time Domain ◽  
Gaussian Noise ◽  
Prediction Error ◽  
Medical Image ◽  
Reversible Watermarking ◽  
Audio Watermarking ◽  
Prediction Errors ◽  
Statistical Features ◽  
Three Samples ◽  
Common Signal

Robust and reversible watermarking is a potential technique in many sensitive applications, such as lossless audio or medical image systems. This paper presents a novel robust reversible audio watermarking method by modifying the statistic features in time domain in the way that the histogram of these statistical values is shifted for data hiding. Firstly, the original audio is divided into nonoverlapped equal-sized frames. In each frame, the use of three samples as a group generates a prediction error and a statistical feature value is calculated as the sum of all the prediction errors in the frame. The watermark bits are embedded into the frames by shifting the histogram of the statistical features. The watermark is reversible and robust to common signal processing operations. Experimental results have shown that the proposed method not only is reversible but also achieves satisfactory robustness to MP3 compression of 64 kbps and additive Gaussian noise of 35 dB.

Extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau

2021 ◽  
Author(s):  
Junyuan Fei ◽  
Jintao Liu
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Time Scale ◽  
Proper Time ◽  
Cumulative Distribution ◽  
The Tibetan Plateau ◽  
Statistical Features ◽  
Catchment Scale ◽  
Intermittent Rivers ◽  
Intermittent River

<p>Highly intermittent rivers are widespread on the Tibetan Plateau and deeply impact the ecological stability and social development downstream. Due to the highly intermittent rivers are small, seasonal variated and heavy cloud covered on the Tibetan Plateau, their distribution location is still unknown at catchment scale currently. To address these challenges, a new method is proposed for extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau. The proposed method first determines the proper time scale of extracting highly intermittent river, based on which the statistical features are calculated to amplify the difference between land covers. Subsequently, the synoptic cumulative distribution location is extracted through Random Forest model using the statistical features above as explanatory variables. And the precise result is generated by combining the synoptic result with critical flow accumulation area.  The highly intermittent river segments are derived and assessed in an alpine catchment of Lhasa River Basin. The results show that the the intra-annual time scale is sufficient for highly intermittent river extraction. And the proposed method can extract highly intermittent river cumulative distribution locations with total precision of 0.62, distance error median of 64.03 m, outperforming other existing river extraction method.</p>

Pipeline Leak Detection by Using Time-Domain Statistical Features

2017 ◽  
Vol 17 (19) ◽  
pp. 6431-6442 ◽  
Author(s):  
Fang Wang ◽  
Weiguo Lin ◽  
Zheng Liu ◽  
Shuochen Wu ◽  
Xiaobo Qiu
Keyword(s):  
Time Domain ◽  
Leak Detection ◽  
Statistical Features

scholarly journals Characteristic Variability Time Scales of Long Gamma-Ray Bursts

2003 ◽  
Vol 214 ◽  
pp. 339-340
Author(s):  
Rongfeng Shen ◽  
Liming Song
Keyword(s):  
Power Density ◽  
Time Scales ◽  
Time Domain ◽  
Time Scale ◽  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
Time Dilation ◽  
Dilation Effect ◽  
The Time Domain ◽  
Intrinsic Characteristic

We determine the characteristic variability time scales for 410 bright long GRBs by locating the maximums of their Power Density Spectra (PDSs) defined and calculated in the time domain. The averaged characteristic variability time scale decreases with peak fluxe. This is consistent with the time dilation effect expected by cosmological origin of GRBs. The occurrence distribution of the characteristic variability time scale shows bimodality, which might be interpreted as that the long GRB sample is composed of two sub-classes with different intrinsic characteristic variability time scales.

Fault Diagnosis of Motor Rotor Based on Fuzzy C-Means Clustering Analysis

2013 ◽  
Vol 273 ◽  
pp. 409-413 ◽  
Author(s):  
Yu Xiang Cao ◽  
Xue Jun Li ◽  
Ling Li Jiang
Keyword(s):  
Time Domain ◽  
Clustering Analysis ◽  
Statistical Features ◽  
Fuzzy C Means ◽  
Time Domain Signal ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Fault Diagnostic ◽  
Characteristic Features ◽  
The Time Domain

For the fuzziness of the fault symptoms in motor rotor, this paper proposes a fault diagnostic method which based on the time-domain statistical features and the fuzzy c-means clustering analysis (FCM). This method is to extract the characteristic features of time-domain signal via time-domain statistics and to import the extracted characteristic vector to classifier. And then the fuzzy c-means realizes the classification by confirming the distance among samples, which is based on the degree of membership between the sample and the clustering center. The fault diagnostic cases of motor rotor show that the method which bases on the time-domain statistical features-FCM can detect the rotor fault effectively and distinguish the different types of fault correctly. Therefore, it can be used as an important means of rotor fault identification.

Audio Watermarking Based on Hybrid Low and High Wavelet Frequencies

2016 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Aree Ali Mohammed
Keyword(s):  
Time Domain ◽  
Wavelet Transformation ◽  
Signal To Noise Ratio ◽  
Audio Watermarking ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Digital Audio ◽  
Test Results ◽  
Digital Audio Watermarking ◽  
Over Time

Transform-domain digital audio watermarking has a performance advantage over time-domain watermarking by virtue of the fact that frequency  transforms offer better exploitation of the human auditory system (HAS). In this research paper an adaptive audio watermarking is proposed based on the low and high wavelet frequencies band (LF, HF). The embedded watermark can be of any types of signal (text, audio and image). The insertion of the watermark data is performing in a frequency domain after applying discrete wavelet transformation on the cover audio segments. The normalize correlation and the signal to noise ratio metrics are used to test the performance of the proposed method in terms of the robustness and imperceptibility. Test results show that an improvement of the robustness against some type of attacks when the watermark is adaptively embedded in a different wavelet bands.

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