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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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. 

Instructions: 

The dataset is available in the MATLAB format *.mat. The instructions and basic examples to display data are available in "script_load_dataset.m".

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Due to the multi-path propagation and extreme sensitivity to minor changes in the propagation medium, the coda waves open new fascinating possibilities in non-destructive evaluation and acoustic imaging. However, their noise-like structure and high spurious sensitivity for ambient conditions (temperature, humidity, and others) make it challenging to perform localized inspection in the overall coda wave evolution.

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This is a CSI dataset towards 5G NR high-precision positioning,

which is fine-grainedgeneral-purpose and 3GPP R16 standards-complied.

 

5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).

Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G  NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.

Instructions: 

 

The dataset_[SNR]_[date]_[time].mat contains: 

1) a 4-D matrix, features, representing the feature data, and

2) a structure array, labels, labeling the ground truth of UE positions.

[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.

The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).

The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.

The value of Ng corresponds to the number of UEs in labels.

 

 Colsed beta test is running.

In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.

 

In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.

 

Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.

To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.

 

 Release note 

2021-07-23 :

1) Recruit participants for colsed beta test.

2021-07-22 :

1)Expend our dataset with more CSI data with low SNR levels noise.

2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.

Closed beta test will start after suggestion collection.

2021-07-18 :

1)Expend our dataset with more CSI data with different SNR levels noise.

2)Publish map files for Scenario 1 indoor office.

 

 

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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.

Instructions: 

The files must be downloaded and placed in the same folder so that they can be compiled in MATLAB, remember that these data were obtained in open loop for their respective identification of this system.

Temperature plant data:

  • In any data "dato.csv" the first column represents the temperature acquired by sensor 1, the second column the temperature acquired by sensor 2, third and fourth column represent the state of activation (1) or deactivation (0) of transistor 1 and 2, respectively.

DC motor speed plant data:

  • The motor speed control plant allows the engine RPM to be measured with the help of an optical echoder. The speed and direction of the motor is controlled through a motodriver.

Source code:

 

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Many different signals are gained from the human body, they are called Biomedical signals or biosignals, they can be at cell level, organ level, or molecular level. electroencephalogram (EEG), which is electrical activity from the brain, electrical activity from the heart called electrocardiogram (ECG), electrical activity from the muscle sound signals known as electromyogram (EMG), the electroretinogram from the eye, and so on. Studying these signals can be so helpful for doctors, it can help them examine and predict and cure many diseases.

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This is a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open-source standalone graphical user interface (GUI) based application. This open-source digitization tool can be used to digitize paper ECG records thereby enabling new prediction

algorithms.

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Despite the growing knowledge on the significance of submarine groundwater discharge (SGD), mapping its occurrence is a continuing challenge.  This study explores the capability and applicability of low-cost, off-the-shelf, recreational-grade echosounders (RGES) to image different types and locate point sources of bubbly coastal SGD. Standard and systematic methodologies for efficient imaging and processing were established.

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