Edge Detction Using Block Processing and Lookup Table

Author(s):  
M. Sushma Sri ◽  
B. Rajendra Naik ◽  
K. Jayasankar
2010 ◽  
Vol 19 (07) ◽  
pp. 1449-1464 ◽  
Author(s):  
BYUNGHEE CHOI ◽  
YOUNGSOO SHIN

A reduced supply voltage must be accompanied by a reduced threshold voltage, which makes this approach to power saving susceptible to process variation in transistor parameters, as well as resulting in increased subthreshold leakage. While adaptive body biasing is efficient for both compensating process variation and suppressing leakage current, it suffers from a large overhead of control circuit. Most body biasing circuits target an entire chip, which causes excessive leakage of some blocks and misses the chance of fine grain control. We propose a new adaptive body biasing scheme, based on a lookup table for independent control of multiple functional blocks on a chip, which controls leakage and also compensates for process variation at the block level. An adaptive body bias is applied to blocks in active mode and a large reverse body bias is applied to blocks in standby mode. This is achieved by a central body bias controller, which has a low overhead in terms of area, delay, and power consumption. The problem of optimizing the required set of bias voltages is formulated and solved. A design methodology for semicustom design using standard-cell elements is developed and verified with benchmark circuits.


2008 ◽  
Vol 13 (5) ◽  
pp. 050501 ◽  
Author(s):  
Narasimhan Rajaram ◽  
Tri H. Nguyen ◽  
James W. Tunnell

2015 ◽  
Vol 7 (7) ◽  
pp. 168781401559253 ◽  
Author(s):  
Qingqi Zhuo ◽  
Yanling Qian ◽  
Yue Li ◽  
Nantian Wang
Keyword(s):  

2021 ◽  
Vol 13 (6) ◽  
pp. 3235
Author(s):  
J. Enrique Sierra-García ◽  
Matilde Santos

Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.


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