Online video traffic modelling with wavelet transform

2000 ◽  
Vol 36 (16) ◽  
pp. 1368 ◽  
Author(s):  
D. Girma ◽  
O. Lazaro ◽  
J. Dunlop
Keyword(s):  
Wavelet Transform ◽  
Online Video ◽  
Video Traffic ◽  
Traffic Modelling

Unified method for video traffic modelling on IP networks

2002 ◽  
Vol 38 (10) ◽  
pp. 492 ◽  
Author(s):  
C.M. Arthur ◽  
D. Girma
Keyword(s):  
Ip Networks ◽  
Video Traffic ◽  
Traffic Modelling ◽  
Unified Method

MPEG-encoded variable bit-rate video traffic modelling

2005 ◽  
Vol 152 (5) ◽  
pp. 749 ◽  
Author(s):  
C.H. Liew ◽  
C.K. Kodikara ◽  
A.M. Kondoz
Keyword(s):  
Variable Bit Rate ◽  
Bit Rate ◽  
Video Traffic ◽  
Traffic Modelling ◽  
Variable Bit Rate Video

Nicolas Schulwitz: Alles auf eine Karte

2016 ◽  
Vol 21 (06) ◽  
pp. 70-73
Author(s):  
Christian Schünemann
Keyword(s):  
Online Video ◽  
Start Up

Bei einer Hochzeit hörte Nicolas Schulwitz (32) von einer zündenden Idee: Wie wäre es, mit einer Online-Video-Sprechstunde das Verhältnis von Arzt und Patient zu revolutionieren? Gesagt, getan. Schulwitz kündigte einen sicheren Job und gründete das Start-up-Unternehmen Patientus.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

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