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Power Consumption section explains the sources of power consumption.
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With respect to these values and the accompanied considerable improvements in power consumption (cf. Section 6.1), the presented small increase in on-chip area emphasizes the strength and relevance of our approach.
Before performing our tests related to the energy optimisation, it was necessary to validate the accuracy of the power consumption prediction models of Section Power consumption prediction models.
To calculate the energy expenditure, we have used the radio power consumption model described in Section 3 with settings and.
In this paper, we investigate an intermediate architecture, where smart circuit breakers provide measurements of aggregate power consumption at room (or section) level granularity.
The power-consumption of the Elastic Graph Matching (EGM) algorithm is estimated with the aid of our proposed power consumption model described in Section 2. The estimated power consumption equals 1.0498 W while the physically measured power consumption equals 1.061 W, resulting in an estimation error of only.
As discussed in detail in the following sections, power consumption can be reduced by applying Static Power Management (SPM), Dynamic Power Management (DPM), or even by applying both solutions to the system.
In this section, we study the processing power consumption of different error correcting algorithms, This section presents empirical analysis of energy consumption caused by transmitting redundant bits in the coded data packets.
In this section, the power consumption of the macrocell BS is determined as a function of the load V caused by voice calls.
In Section 'Relating power consumption and the number of voice and data calls', a model is given for the relation between traffic load and power consumption based on measurements.
The rest of the paper is structured as follows: Section 2 describes the problem scenario, Section 3 presents our power consumption model and performance measures.
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