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Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems
Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems is called ERT-CORE. It defines specific input parameters, i.e., system's workload, average request processing time and availability. Defined parameters reflect system's state and react on its changes. Based on these parameters system reliability evaluation is performed.
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We introduce a novel dataset of bee piping audio signals which was built by collecting 44 different recordings which were published by various beekeepers on the YouTube platform.Each recording has a duration varying from 2 to 13 seconds and is annotated according to the beekeeper comment respectively as Tooting or Quacking.We extracted the audio using ``YouTube soundtrack extraction'' from 14 distinct videos from which the signal is stored without a loss of quality into a WAVE file with a sampling frequency of F_s=22.05 kHz and a sample precision of 16 bits
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The dataset consists of training and test data and label matrices for single-pixel compressive DoA estimation for mmWave metasurface. The dataset will be uploaded soon. Currently, a small part of the dataset can be accessed through this repository. For detailed information, please visit Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation through https://arxiv.org/abs/2109.05466
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Several experimental measurement campaigns have been carried out to characterize Power Line Communication (PLC) noise and channel transfer functions (CTFs). This dataset contains a subset of the PLC CTFs, impedances, and noise traces measured in an in-building scenario.
The MIMO 2x2 CTFs matrices are acquired in the frequency domain, with a resolution of 74.769kHz, in the frequency range 1 - 100MHz. Noise traces, in the time domain with a duration of about 16 ms, have been acquired concurrently from the two multi-conductor ports.
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The data set deals with a typicla charge-discharge cycle of a supercapacitor (here a NICHICON UM series, 2.7V, 1F):
- Very fast charge of the EDLC
- Discharge at more or less constant power
Data have been sampled at 10 Hz with the biologic VMP3 potentiostat.
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Following the success of the previous editions at WCCI 2018 (Rio de Janeiro, Brazil) and CEC/GECCO 2019 (New Zealand and Prague, Czechia) we are launching a more challenging algorithm competition at major international conferences in the field of computational intelligence. This WCCI & GECCO 2020 competition proposes two testbeds in the energy domain:
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Radar devices can be used to monitor vital signs, such as respiratory and cardiac rates. For this purpose, the phase of an echo signal received from the chest or back of a human is usually used; subsequently, respiratory rate detection (RRD) and cardiac rate detection (CRD) can be achieved by estimating two fundamental frequencies corresponding to the respiratory and cardiac rates, respectively.
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