The design and implementation of an anthropomorphic robotic hand control system for the Bioengineering and Neuroimaging Laboratory LNB of the ESPOL were elaborated. The myoelectric signals were obtained using a bioelectric data acquisition board (CYTON BOARD) using six channels out of 8 available, which had an amplitude of 200 [uV] at a sampling frequency of 250 [Hz].

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<p>The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.

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In this appendix, the tested implementation in Matlab of our 2D-TDOA localization algorithm is given for the easier repetition of the obtained results and the future hardware implementation, due to the complexity of the formulas (25)-(31).

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This dataset was used to quantify the effects of environmental change on SSTDR measurements from solar panels. We collect illuminance (Lux), temperature (deg F), and humidity (%) alongside SSTDR waveforms on a fault free string. Data is collected once per minute in January 2020, and twice per minute in August-September 2020.

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This is a dataset is an example of a distribution of 20 correlated Bernoulli random variables.

Q_joint ... is 5 cells each consists of the joint distributions of 4,8,12,16,20 bits, respectively. The dimension of each cell is 2^n X 1, .e., a vertical column and n=4,8,12,16,20.

Q_conditional... is 5 cells each consists of the conditional distributions of 4 bits given 0, 4, 8,12,16 bits, respectively. In other words, 1:4 bits, 5:8 bits given 1:4 bits, 9:12 bits given 1:8 bits, 13:16 bits given 1:12 bits, 17:20 given 1:16 bits. The dimension of each cell is 2^4=16 X 2^n, i.e., a vertical column and n=4,8,12,16.

Q_ marginal... is 5 cells each consists of the marginal distributions of each 4 consecutive bits, i.e., 1:4 - 5:8 - 9:12 - 13:16 - 17:20, respectively. The dimension of each cell is 16 X 1, i.e., q vertical column.

Also, a MATLAB code is uploaded to extract conditional and marginal distributions from any given discrete distribution.

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Three raw (i.e., In-Phase and Quadrature data with a software radio, and observation files) GNSS dataset were recorded using a LabSat Version 3 inside of the West Virginia University greenhouse and two outside recordings were also made to provide a quality reference and comparison. The outdoor location had to be an ideal location for satellite signal reception and the indoor location was a greenhouse room where satellite visibility was limited, susceptible to attenuation, occlusion and multipath.

GNSS-SDR: These recordings were also played back into a software defined receiver(GNSS-SDR), using the L1_E1.conf file (Also attached) which allows Galileo signals in thesolution and obtained the following rinex files:Note: These rinex files are in a 3.02 format.

• GSDR302c05.20O

• GSDR315b49.20O

• GSDR307x52.20O

• GSDR309n41.20O

• GSDR310p27.20O

Other useful outputted files by GNSS-SDR are also included in the Run folder.RTKLIB: The rinex output files from NovAtel and GNSS-SDR were then post processed usingRTKLIB (rtkpost) ver.2.4.2. For RTKLIB the following settings were used.

• Position Mode/Solution Type: PPP Kinematic

• Combined filter type

• Iono-Free LC Correction/Broadcast

• Saastamoinen Tropospheric Correction

• Broadcast Satellite Ephemeris/Clocks (Used GPS and Galileo Broadcast ephemeris files and considered sp3 and clk from CDDIS/IGS)

The station and orbit files downloaded from CDDIS used to process the data areincluded. Rtkpost outputs its solutions in a .pos and .stat file format. For post processingof GNSS-SDR solutions, the output files are named GSDR#Month_#day and forNovAtel are NV#month_#day. In this folder, .mat files for each day for NovAtel andGNSS-SDR are also attached. These files contain the extracted data from the .pos and.stat files so they can be analyzed in MATLAB. Output figures from Rtkpost are alsoincluded.

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A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine onedimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

fGn series used for simulations in the article "Sobre la Generación de Tráfico Autosimilar con Dependencia de Largo Alcance Empleando Mapas Caóticos Unidimensionales Afines por Tramos (Versión Extendida)", "On the Generation of Self-similar with Long-range Dependent Traffic Using Piecewise Affine Chaotic One-dimensional Maps (Extended Version)". Available at:

https://arxiv.org/abs/2104.04135.

https://easychair.org/publications/preprint/Xwx3.

They should be used in MATLAB R2009a.

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This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.

fGn series used for simulations in the article "Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series". Available at:

https://arxiv.org/abs/2103.02091.

https://www.techrxiv.org/articles/preprint/Preliminaries_on_the_Accurate....

https://easychair.org/publications/preprint/RQsp.

They should be used in Selfis01b.

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Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)

"Object classification from randomized EEG trials"

If you use this code or data, please cite the above paper.

See the paper "Object classification from randomized EEG trials" on IEEE Xplore.

Code for analyzing the dataset is included in the online supplementary materials for the paper.

The code from the online supplementary materials is also included here.

If you use this code or data, please cite the above paper.

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