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Since endurance of the glider is very important for the long-term marine observation, a LEC (least energy consumption) algorithm is designed to minimize the energy cost in the glider motion.
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Modern systems are unstructured and therefore algorithms like self-organized power consumption approximation algorithm (SOPCA), which are used to monitor power consumption of the different devices.
To achieve better equivalent fuel consumption, genetic algorithm is implemented to fine-tune the membership functions.
The performance in term of time consumption of algorithm using parallel processing (using GPU) is then compared to serial processing (using CPU).
Energy consumption management algorithm is triggered.
In this section, we describe the procedure of energy consumption management algorithm in detail.
In this section, we use computer simulation to evaluate the performance of energy consumption management algorithm.
Then, we present the processing of energy consumption management algorithm with M-N sleeping policy in details.
The Least Energy Consumption (LEC) algorithm is proposed to minimize the motion energy cost with consideration of ocean flow existence.
In other words, instead of deactivating as many cells as possible to minimize the static energy consumption, the algorithm deactivates the cells to find the best possible balance between the static and load-dependent energy consumption.
Compared to classical sensors performing motion detection downstream the image acquisition, the offered processing capabilities are somehow limited, but the chosen analog architecture, on which they are implemented, offers a better compromise between power consumption and algorithm performance.
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