The following dataset consists of utterances, recorded using 24 volunteers raised in the Province of Manitoba, Canada. To provide a repeatable set of test words that would cover all of the phonemes, the Edinburg Machine Readable Phonetic Alphabet (MRPA) [KiGr08], consisting of 44 words is used. Each recording consists of one word uttered by the volunteer and recorded in one continuous session.

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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/

The DataPort Repository contains the data used primarily for generating Figure 1.

Instructions: 

** Please note that this is under construction, and all data and code is still being uploaded whilst this notice is present. Thank-you. Tom **

All code is hosted as a GIT repository (below), as well as instructions, which can be found by clicking on the link/file called README.md in that repository.

https://github.com/thomasmhall-newcastle/IEEE-TNSRE-2016-lfLFPs

You are free to clone/pull this repository and use it under MIT license, on the understanding that any use of this code will be acknowledged by citing the original paper, DOI: 10.1109/TNSRE.2016.2612001, which is Open Access and can be found here: http://ieeexplore.ieee.org/document/7742994/

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The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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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.

Instructions: 

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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The data set is collected using Neurosky MindWave 2.0 Headset. It uses a single dry electrode placed at FP-1 position for the acquisition of EEG signals. The data is collected from Healthy Individuals and Epileptic Patients performing different Activities of Daily Living (ADLs) in an unconstraint environment. 

Instructions: 

The data files are stored in a comma-separated value (.csv) format.

60 sample files of activities performed by healthy individuals and 30 sample files of activities performed by epileptic patients are present in two separate folders in the .zip file.

The sampling frequency of the headset is 512Hz and each activity is performed for a duration of 20 seconds. Every data file contains raw EEG data in a single column.  

Disclaimer: This data was collected ethically with the consent of relevant local research committees. The anonymity of subjects and confidentiality of their mental health conditions was ensured.

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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.

Instructions: 

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.

https://osf.io/dsnke/.

They should be used in MATLAB R2009a.

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The Temperature and Speed Control Lab (TSC-Lab) is an application of feedback control with an ESP32, an LED, two heaters, two temperature sensors, one direct current motor and an optical encoder as a revolution per minute (rpm) meter. The heater power output is adjusted to maintain the desired temperature setpoint. Thermal energy from the heater is transferred by conduction, convection, and radiation to the temperature sensor.

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 This  dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling

 

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This code demonstrate the example use of FOPDT (First-Order-Plus-Dead-Time) model identification. The Algorithm used in "FOPDT_fun" is available in the reference:

S. Sharma and P. K. Padhy, "A Novel Iterative System Identification and Modeling Scheme with Simultaneous Time-Delay and Rational Parameter Estimation," in IEEE Access, 

vol. 8, pp. 64918-64931, 2020, doi: 10.1109/ACCESS.2020.2985132.

Instructions: 

The FOPDT_fun uses the input, output and sample time for identification.

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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.

Instructions: 

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.

https://osf.io/3sk7a/.

They should be used in Selfis01b.

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