A deep sea video quality dataset with their subjective quality scores.

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The dataset contains the path loss measurements obtained with a LoRa (868 MHz) transmitting radio and five receivers. The receivers move in a search area covering both outdoor and indoor areas wherein a double-slope path loss is experienced. The data can be used to test range-based localization algorithm through the received signal strength.

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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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Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.

Instructions: 

Please download and unzip the following files corresponding to the three experiments described in our paper https://doi.org/10.3390/app11198860:

  1. Experiment-1.zip
  2. Experiment-2.zip
  3. Experiment-3.zip

and follow the instructions in the ReadMe.pdfs.

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The given Dataset is record of different group people either healthy subjects or subclinical cardiovascular disease(CVD) with history coronary heart disease or hypertension for superficial body features, original photoplethysmography imaging(iPPG) signal and characteristics.

The main purpose of the dataset is to understand the relationship between CVD and high-dimensional ippg characteristics.

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The SoftCast scheme has been proposed as a promising alternative to traditional video broadcasting systems in wireless environments. In its current form, SoftCast performs image decoding at the receiver side by using a Linear Least Square Error (LLSE) estimator. Such approach maximizes the reconstructed quality in terms of Peak Signal-to-Noise Ratio (PSNR). However, we show that the LLSE induces an annoying blur effect at low Channel Signal-to-Noise Ratio (CSNR) quality. To cancel this artifact, we propose to replace the LLSE estimator by the Zero-Forcing (ZF) one.

Instructions: 

For more information, please refer to the following paper:

Anthony Trioux, Giuseppe Valenzise, Marco Cagnazzo, Michel Kieffer, François-Xavier Coudoux, et al., A Perceptual Study of the Decoding Process of the SoftCast Wireless Video Broadcast Scheme. 2021 IEEE Workshop on Multimedia Signal Processing (MMSP), Oct. 2021, Tampere, Finland.

The SoftCast Database consists of 8 RAW HD reference videos and 156 cropped videos transmitted and received through the SoftCast linear video coding and transmission scheme considering either the LLSE or the ZF estimator. Each video has a duration of 5 seconds. Note that only the luminance is considered in this database. Furthermore, the number of frames depends on the framerate of the video (125 frames for 25fps and 150frames for 30fps).

The GoP-size was set to 32 frames, 2 compression ratio (CR) were considered: CR=1 (no compression applied) and CR=0.25 (75% of the DCT coefficients are discarded before transmission). The Channel Signal-to-Noise Ratio (CSNR) considered in this test vary from 0 to 27dB by 3dB step. This database was evaluated by 30 participants (9 women and 21 men). They were asked to select which one of the two displayed version of the reconstructed videos they prefered based on a Forced-choice PairWise Comparison test. A training session was organized prior to the test for each observer in order to familiarize them with the procedure. 

Video files are named using the following structure:

Video_filename_y_only_GoP_32_CR_X_Y_ZdB_crop.yuv where X equals either 1 or 0.25 Y refers to the estimator used (ZF or LLSE) and Z is either equal to 0,3,6,9,12,15,18,21,24 or 27dB.

The original video files are denoted: Video_filename_y_only_crop.yuv.

Each video file is in *.yuv format (4:2:0) where the chrominance plans are all set to 128. (This process allows to perform the VMAF computation as VMAF requires either a yuv420p, yuv422p, yuv444p, yuv420p10le, yuv422p10le or yuv444p10le video format).

The preference scores for each of the stimuli are available in the PWC_scores.xls file.

The objective scores (frame by frame) for each videos are available in the objective_scores_ZF_LLSE.zip file.

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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

 

 

The corresponding paper is published here (https://doi.org/10.1109/jsac.2022.3157397).

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

Instructions: 

 

The dataset_[SNR]_[Scenario]_[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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