RESUMO
Although the number of Internet of Things devices increases every year, efforts to decrease hardware energy demands and to improve efficiencies of the energy-harvesting stages have reached an ultra-low power level. However, no current standard of wireless communication protocol (WCP) can fully address those scenarios. Our focus in this paper is to introduce treNch, a novel WCP implementing the cross-layer principle to use the power input for adapting its operation in a dynamic manner that goes from pure best-effort to nearly real time. Together with the energy-management algorithm, it operates with asynchronous transmissions, synchronous and optional receptions, short frame sizes and a light architecture that gives control to the nodes. These features make treNch an optimal option for wireless sensor networks with ultra-low power demands and severe energy fluctuations. We demonstrate through a comparison with different modes of Bluetooth Low Energy (BLE) a decrease of the power consumption in 1 to 2 orders of magnitude for different scenarios at equal quality of service. Moreover, we propose some security optimizations, such as shorter over-the-air counters, to reduce the packet overhead without decreasing the security level. Finally, we discuss other features aside of the energy needs, such as latency, reliability or topology, brought again against BLE.
RESUMO
Non-invasive fetal electrocardiography (ECG) is based on the acquisition of signals from abdominal surface electrodes. The composite abdominal signal consists of the maternal electrocardiogram along with the fetal electrocardiogram and other electrical interferences. These recordings allow for the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper introduces a procedure for fetal heart rate extraction from a single-channel abdominal ECG signal. The procedure is composed of three main stages: a method based on wavelet for signal denoising, a new clustering-based methodology for detecting fetal QRS complexes, and a final stage to correct false positives and false negatives. The novelty of the procedure thus relies on using clustering techniques to classify singularities from the abdominal ECG into three types: maternal QRS complexes, fetal QRS complexes, and noise. The amplitude and time distance of all the local maxima followed by a local minimum were selected as features for the clustering classification. A wide set of real abdominal ECG recordings from two different databases, providing a large range of different characteristics, was used to illustrate the efficiency of the proposed method. The accuracy achieved shows that the proposed technique exhibits a competitve performance when compared to other recent works in the literature and a better performance over threshold-based techniques.
