Data Analysis for Embedded Software Performance and Energy Consumption Estimation

Ruberg, Priit; Liiv, Elvar; Lass, Keijo; Ellervee, Peeter

doi:10.1109/ukrcon.2019.8879787

Cited by 3 publications

(1 citation statement)

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“…It has been observed that the current drawn by 8- to 32-bit MCUs deviates very slightly during active operation [ 40 ]. It is, therefore, sufficient to measure the time length of an operation to estimate relative energy consumption, as seen in Equation ( 6 ).…”

Section: Preliminariesmentioning

confidence: 99%

An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

Skosana

Abu‐Mahfouz

2023

Sensors

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A measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Multimedia Sensor Networks (WMSNs) is demanding because there is a sensitive weighing of energy efficiency against image quality that must be performed. Many measurement matrices have been proposed to deliver low computational complexity or high image quality, but only some have achieved both, and even fewer have been proven beyond doubt. A Deterministic Partial Canonical Identity (DPCI) matrix is proposed that has the lowest sensing complexity of the leading energy-efficient sensing matrices while offering better image quality than the Gaussian measurement matrix. The simplest sensing matrix is the basis of the proposed matrix, where random numbers were replaced with a chaotic sequence, and the random permutation was replaced with random sample positions. The novel construction significantly reduces the computational complexity as well time complexity of the sensing matrix. The DPCI has lower recovery accuracy than other deterministic measurement matrices such as the Binary Permuted Block Diagonal (BPBD) and Deterministic Binary Block Diagonal (DBBD) but offers a lower construction cost than the BPBD and lower sensing cost than the DBBD. This matrix offers the best balance between energy efficiency and image quality for energy-sensitive applications.

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Section: Preliminariesmentioning

confidence: 99%