scholarly journals STUDY ON AUDIO AND VIDEO WATERMARKING

2013 ◽  
pp. 34-38
Author(s):  
HARLEEN KAUR
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Copyright Protection ◽  
Video Data ◽  
Video Watermarking ◽  
Video Signals ◽  
Common Group ◽  
The Common ◽  
Cropping Rotation ◽  
Pitch Shift

This paper gives the overview of audio and video watermarking. This paper introduces the basic requirements that affect the algorithms for audio and video watermarking which are perceptibility, robustness and security. The attacks which cause manipulations of the audio and video signals are also discussed. The common group of attacks on audio and video data is dynamics, filtering, conversion, compression, noise, modulation, time stretch and pitch shift, multiple watermark, cropping, rotation etc. The applications of audio and video watermarking are Fingerprinting, copyright protection, authentication, copy control etc. The audio watermarking techniques can be classified into Time-domain and Frequencydomain methods and video watermarking techniques are classified into spatial domain, frequency domain and formatspecific domain.

A REVIEW ON ATRIAL FIBRILLATION AND ITS RELATED ANALYSIS

2016 ◽  
Vol 78 (7-4) ◽  
Author(s):  
Priscilla Sim Chee Mei ◽  
Anita Ahmad
Keyword(s):  
Atrial Fibrillation ◽  
Signal Processing ◽  
Frequency Domain ◽  
Time Domain ◽  
Heart Rhythm ◽  
Basic Information ◽  
Heart Problem ◽  
Underlying Theory ◽  
The Common ◽  
Processing Steps

Atrial fibrillation (AF) has been widely stated as the most common arrhythmias (irregularities of heart rhythm) which could lead to severe heart problem such as stroke. Many studies have been conducted to understand and explain its mechanism by analyzing its signal, in either time domain or frequency domain. This paper aims to provide basic information on the AF by reviewing relevant papers. Overall, this paper will provide review on the underlying theory of AF, AF mechanism as well as the common relevant signal processing steps and analysis.

scholarly journals Emotion Recognition from Multiband EEG Signals Using CapsNet

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2212 ◽  
Author(s):  
Hao Chao ◽  
Liang Dong ◽  
Yongli Liu ◽  
Baoyun Lu
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Frequency Band ◽  
Experimental Results ◽  
Eeg Signals ◽  
Spatial Characteristics ◽  
Video Signals ◽  
Learning Framework ◽  
The Common

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced to recognize emotion states according to the input MFM. Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models. The experimental results demonstrate that the three characteristics contained in the MFM were complementary and the capsule network was more suitable for mining and utilizing the three correlation characteristics.

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

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