On generalized Blackman-Harris sampling operators

Graf, Olga; Tamberg, Gert

doi:10.1109/sampta.2017.8024377

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For

, there exists a choice of parameters, which allows us to have the order of approximation of the corresponding sampling operators estimated by

[ 19 ]. Another choice of the parameter vector

in Equation (21), where the parameter vector

has components

and

for

gives us by Equation (19) a family of rapidly decreasing kernels

(see [ 24 ] for corresponding operator norms and [ 25 , 26 , 27 ] for truncation errors).…”

Section: Proposed Dual-source (Solar and Wind) Linear Energy Predimentioning

confidence: 99%

Dual-Source Linear Energy Prediction (LINE-P) Model in the Context of WSNs

Ahmed

Tamberg

Moullec

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “LINE-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; LINE-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where LINE-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.

show abstract