Robust approach of video steganography using combined keypoints detection algorithm against geometrical and signal processing attacks

2020 ◽  
Vol 29 (04) ◽  
pp. 1
Author(s):  
Suganthi Kumar ◽  
Rajkumar Soundrapandiyan
Keyword(s):  
Signal Processing ◽  
Detection Algorithm ◽  
Robust Approach ◽  
Video Steganography ◽  
Keypoints Detection

Track Health Monitoring Using Wavelets

2010 ◽  
Author(s):  
Brad M. Hopkins ◽  
Saied Taheri
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Defect Detection ◽  
Health Monitoring ◽  
Finite Energy ◽  
Detection Algorithm ◽  
Transient Signal ◽  
Vertical Acceleration ◽  
Typical Day ◽  
The Fourier Transform

This paper presents a defect detection algorithm for rail health monitoring that could potentially be used with limited bogie. Current wheel and track monitoring requires expensive track instrumentation and/or time consuming operation of railway monitoring vehicles. The proposed health monitoring algorithm can potentially be used with a portable data acquisition system that can be relocated from train to train to monitor and diagnose the conditions of the track as a train is driven during typical day-to-day operation. The algorithm processes the data using wavelets and is able to locate defects and provide information that may help to distinguish between various types of rail defects. In recent years, wavelets have been used extensively in signal processing because of their ability to analyze a signal simultaneously in the time and frequency domains. The Fourier transform has been used traditionally in signal processing to locate dominant frequencies in a signal, but it is unable to provide time localization of those frequencies. Unlike the Fourier transform, the wavelet transform uses a set of basis functions with finite energy, which is advantageous for detecting the irregular events that may show up in a transient signal. The wavelets used in the proposed signal processing routine were chosen for optimal signal decomposition through consideration of the signals that are likely to be generated from common rail and wheel defects, including rail cracks, squats, corrugation, and, wheel out-of-rounds. A sample accelerometer signal was generated from information found in existing literature and was then processed using the proposed defect detection algorithm. Results show the potential of this algorithm to locate and diagnose defects from limited bogie vertical acceleration data. This study is intended to present a proof-of-concept for the proposed defect detection algorithm, providing a basis for which a more comprehensive defect detection and diagnosis algorithm can be developed.

Introduction to the use of wavelet multiresolution analysis for intelligent structural health monitoring

2004 ◽  
Vol 31 (5) ◽  
pp. 719-731 ◽  
Author(s):  
M.M Reda Taha ◽  
A Noureldin ◽  
A Osman ◽  
N El-Sheimy
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Multiresolution Analysis ◽  
Prestressed Concrete ◽  
Digital Signal ◽  
Detection Algorithm ◽  
Structural Health ◽  
Wavelet Multiresolution Analysis

This paper suggests the use of wavelet multiresolution analysis (WMRA) as a reliable tool for digital signal processing in structural health monitoring (SHM) systems. A damage occurrence detection algorithm using WMRA augmented with artificial neural networks (ANN) is described. The suggested algorithm allows intelligent monitoring of structures in real time. The probability of damage occurrence is determined by evaluating the wavelet norm index (WNI) representing the energy of a signal describing the change in the system dynamics due to damage. An example application of the proposed algorithm is presented using a finite element simulated structural dynamics of a prestressed concrete bridge. The new algorithm showed very promising results.Key words: structural health monitoring, neural networks, wavelet analysis, signal processing, damage detection.

scholarly journals Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2594 ◽  
Author(s):  
Delaram Jarchi ◽  
Javier Andreu-Perez ◽  
Mehrin Kiani ◽  
Oldrich Vysata ◽  
Jiri Kuchynka  ◽  
...  
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Sleep Disorders ◽  
Detection Algorithm ◽  
Learning Framework ◽  
Peak Detection Algorithm ◽  
Obstructive Sleep ◽  
Patient Groups ◽  
Synchrosqueezed Wavelet Transform ◽  
Pulse Peak

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.

scholarly journals SAR Target Detection Method Based on Empirical Mode Decomposition

2012 ◽  
Vol 6-7 ◽  
pp. 496-500
Author(s):  
Shi Qi Huang ◽  
Bei He Wang ◽  
Yi Hong Li ◽  
Bei Ge
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Target Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Radar Signal ◽  
Sar Images ◽  
Processing Theory ◽  
Mode Decomposition ◽  
Non Stationary Signal

Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective.

scholarly journals Lateral Shift Error due to Graduation Anomalies and Line-Detection Algorithm in Line Scale Measurement

2012 ◽  
Vol 6 (1) ◽  
pp. 84-91
Author(s):  
Akira Takahashi ◽  
◽  
Yuji Kokumai ◽  
Yuichi Takigawa ◽  
Keyword(s):  
Signal Processing ◽  
Detection Algorithm ◽  
Lateral Shift ◽  
Signal Processing Algorithm ◽  
Filter Width ◽  
Line Scale ◽  
Scale Calibration ◽  
On Line ◽  
Ordinary Line ◽  
Lateral Error

The measurement error resulting from graduation anomalies and the signal processing algorithm used for determining the positions of graduations on line scales was investigated by simulation and experiment. Optical image-forming simulations were carried out on models of 6-µm-wide graduations with three sizes of defects (0.5, 1.0 and 1.5 µm) at one edge. A digital filter was used in signal processing to obtain the first differential to determine the positions of the graduations. The minimum values of the lateral shift of the determined graduation positions were observed for the three defect sizes when using a 9-µm-wide differential filter. An experiment was also carried out on an ordinary line scale with 6-µm-wide graduations using a high-precision laser-interferometric line scale calibration system by measuring seven positions on the scale in the direction perpendicular to the measurement axis. The root mean square of the standard deviations from the linear fitting lines constructed using the measured positions over a 300-mm-long line scale was 2.8 nmwhen the differential filter width was 9 µm. It was demonstrated that a differential filter was effective in reducing the lateral error due to graduation anomalies.

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