Adaptive CU partition and early skip mode detection for H.266/VVC

Author(s):  
Qiuwen Zhang ◽  
Yihan Wang ◽  
Bin Jiang ◽  
Xiao Wang ◽  
Rijian Su
2010 ◽  
Vol 17 (10) ◽  
pp. 887-890 ◽  
Author(s):  
Liquan Shen ◽  
Yiwen Sun ◽  
Zhi Liu ◽  
Zhaoyang Zhang

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 355
Author(s):  
Yeu-Torng Yau ◽  
Chao-Wei Wang ◽  
Kuo-Ing Hwu

In this paper, two light-load efficiency improvement methods are presented and applied to the ultrahigh step-down converter. The two methods are both based on skip mode control. Skip Mode 1 only needs one half-bridge driver integrated circuit (IC) to drive three switches, so it has the advantages of easy signal control and lower cost, whereas Skip Mode 2 requires one half-bridge driver integrated circuit IC, one common ground driver IC, and three independent timing pulse-width-modulated (PWM) signals to control three switches, so the cost is higher and the control signals are more complicated, but Skip Mode 2 can obtain slightly higher light-load efficiency than Skip Mode 1. Although the switching frequency used in these methods are reduced, the transferred energy is unchanged, but the output voltage ripple is influenced to some extent.


2021 ◽  
pp. 113305
Author(s):  
Wenting Wei ◽  
Han Lin ◽  
Tingting Hao ◽  
Sui Wang ◽  
Yufang Hu ◽  
...  

Author(s):  
Md Rejwanul Haque ◽  
Masudul H Imtiaz ◽  
Xiangrong Shen ◽  
Edward Sazonov

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sven Lißner ◽  
Stefan Huber

Abstract Background GPS-based cycling data are increasingly available for traffic planning these days. However, the recorded data often contain more information than simply bicycle trips. GPS tracks resulting from tracking while using other modes of transport than bike or long periods at working locations while people are still tracking are only some examples. Thus, collected bicycle GPS data need to be processed adequately to use them for transportation planning. Results The article presents a multi-level approach towards bicycle-specific data processing. The data processing model contains different steps of processing (data filtering, smoothing, trip segmentation, transport mode recognition, driving mode detection) to finally obtain a correct data set that contains bicycle trips, only. The validation reveals a sound accuracy of the model at its’ current state (82–88%).


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