The dataset contains medical signs of the sign language including different modalities of color frames, depth frames, infrared frames, body index frames, mapped color body on depth scale, and 2D/3D skeleton information in color and depth scales and camera space. The language level of the signs is mostly Word and 55 signs are performed by 16 persons two times (55x16x2=1760 performance in total).
The signs are collected at Shahid Beheshti University, Tehran, and show local gestures. The SignCol software (code: https://github.com/mohaEs/SignCol , paper: https://doi.org/10.1109/ICSESS.2018.8663952 ) is used for defining the signs and also connecting to Microsoft Kinect v2 for collecting the multimodal data, including frames and skeletons. Two demonstration videos of the signs are available at youtube: vomit: https://youtu.be/yl6Tq7J9CH4 , asthma spray: https://youtu.be/PQf8p_YNYfo . Demonstration videos of the SignCol are also available at https://youtu.be/_dgcK-HPAak and https://youtu.be/yMjQ1VYWbII .
The dataset contains 13 zip files totally: One zipfile contains readme, sample codes and data (Sample_AND_Codes.zip), the next zip file contains sample videos (Sample_Videos.zip) and other 11 zip files contain 5 signs in each (e.g. Signs(11-15).zip). For quick start, consider the Sample_AND_Codes.zip.
Each performed gesture is located in a directory named in Sign_X_Performer_Y_Z format which shows the Xth sign performed by the Yth person at the Znd iteration (X=[1,...,55], Y=[1,...,16], Z=[1,2]). The actual names of the signs are listed in the file: table_signs.csv.
Each directory includes 7 subdirectories:
1. Times: time information of frames saved in CSV file.
2. Color Frames: RGB frames saved in 8 bits *.jpg format with the size of 1920x1080.
3. Infrared Frames: Infrared frames saved in 8 bits *.jpg format with the size of 512x424.
4. Depth Frames: Depth frames saved in 8 bits *.jpg format with the size of 512x424.
5. Body Index Frames: Body Index frames scaled in depth saved in 8 bits *.jpg format with the size of 512x424.
6. Body Skels data: For each frame, there is a CSV file containing 25 rows according to 25 joints of body and columns for specifying the joint type, locations and space environments. Each joint location is saved in three spaces, 3D camera space, 2D depth space (image) and 2D color space (image). The 21 joints are visible in this dataset.
7. Color Body Frames: frames of RGB Body scaled in depth frame saved in 8 bits *.jpg format with the size of 512x424.
Frames are saved as a set of numbered images and the MATLAB script PrReadFrames_AND_CreateVideo.m shows how to read frames and also how to create videos, if is required.
The 21 visible joints are Spine Base, Spine Mid, Neck, Head, Shoulder Left, Elbow Left, Wrist Left, Hand Left, Shoulder Right, Elbow Right, Wrist Right, Hand Right, Hip Left, Knee Left, Hip Right, Knee Right, Spine Shoulder, Hand TipLeft, Thumb Left, Hand Tip Right, Thumb Right. The MATLAB script PrReadSkels_AND_CreateVideo.m shows an example of reading joint’s informtaion, fliping them and drawing the skeleton on depth and color scale.
The updated information about the dataset and corresponding paper are available at GitHub repository MedSLset.
Terms and conditions for the use of dataset:
1- This dataset is released for academic research purposes only.
2- Please cite both the paper and dataset if you found this data useful for your research. You can find the references and bibtex at MedSLset.
3- You must not distribute the dataset or any parts of it to others.
4- The dataset just inclues image, text and video files and is scanned via malware protection softwares. You accept full responsibility for your use of the dataset. This data comes with no warranty or guarantee of any kind, and you accept full liability.
5- You will treat people appearing in this data with respect and dignity.
6- You will not try to identify and recognize the persons in the dataset.
Coventry-2018 is a human activity recognition dataset captured by three Panasonic® Grid-EYE (AMG8833) infrared sensors in March 2018. The Grid-EYE sensors represent a 60 field of view scene by an 8 × 8 array named frame. The data streams are synchronized to 10 frames per second and saved as *.csv recordings using the LabVIEW® software. Two layouts are considered in this dataset with different geometry sizes: 1) small layout; and 2) large layout.
Database set information
This article describes the possible design of the electron-ion trap combined density sensor and the composition of the upper atmosphere and simulation of the processes occurring in it. The simulation of the electric field between the electrodes of the trap and the motion of charged particles in it is carried out. The calculation of the maximum speed and energy of the particles below which the trap holds all charged particles, even in the case of the most unfavorable direction of their speed – along the gap between the electrodes.
A Indústria enfrenta desafios graves e fracassa sem competitividade. Atacando esta problemática, conferiu-se o oferecimento de maior eficiência a processos industriais para promover a produtividade, elevar a qualidade e impulsionar mudanças. A solução desenvolvida incluiu dispositivos com sensores não invasivos, simples de instalar, que contabilizam os itens sendo transportados em linhas de produção.
Os dados foram coletados utilizando o dispositivo IoT da EnergyNow Tecnologias denominado Prodbox™, o qual opera como um equipamento empregado para intensificar a produtividade e apontar maneiras estratégicas de modificar variáveis que interferem na visão de gestão sobre a produção.
O dispositivo utiliza sensores não obstrutivos para contabilizar o número de itens que atravessam a linha de detecção gerada entre o transmissor e o receptor instalados.
Notadamente, os dados coletados são enviados para a nuvem, onde podem, quando integrados a uma plataforma de análise, ser processados para apresentar indicadores de acompanhamento de produtividade. Um sistema inteligente pode processar os dados coletados e apresentar métricas que permitem ao gestor identificar formas de aumentar a produção, bem como etapas que estão prejudicando a produtividade. Além disso, alertas customizados podem ser configurados para prover informação sobre a parada ou inatividade detectada pelo dispositivo.
Os dados gerados através do dispositivo podem ser utilizados para entender melhor variáveis sobre o ritmo de produção e, a partir delas, fomentar projeções de produção, calculando-se a relação entre itens produzidos e período de tempo necessário (segundos, minutos, horas, dias, semanas, etc).
Algumas sugestões sobre abordagens a serem consideradas:
Verifique se políticas de aumento de produtividade estão sendo efetivas.
Distribuia melhor os funcionários em etapas diferentes de uma linha de produção.
Correlacione etapas de produção com variáveis que estejam interferindo na produtividade para resolver problemáticas internas.
We document a feedback controller design for a nonlinear electrostatic transducer that exhibits
a~strong unloaded resonance. Challenging features of this type of transducer include the presence
of multiple fixed points (some of which are unstable), nonlinear force-to-deflection transfer,
effective spring-constant softening due to electrostatic loading and associated resonance
frequency shift. Furthermore, due to the utilization of low-pass filters in the electronic readout
fscodt12_modified_ADRC_2FIR_um_scaling.zcosIvan Ryger, 5/6/2020Tested on Scilab 6.0.2/ XcosThe program is solely for non-commercial scientific/educational purposes. The author does not hold any responsibility for the misuse of the code.Short description of the programfscodt12_modified_ADRC_2FIR_um_scaling.zcos is a simulation model of an electrostatic transducer connected in a loop of state feedback controller. The conversion between measured capacitance and voltage is achieved by analytic expressions for a capacitive bridge, sinusoidally excited and connected to a synchronous demodulator. The output is fed through a polynomial approximation section, where the deflection information is inferred from known conversion between deflection -> capacitance -> bridge voltage. The signal is fed through a FIR filter simulating the lowpass filter inherent to the lock-in demodulator. Both feedback force and measured deflection signals are fed into ADRC block, to estimate the internal states of the controlled system (transducer). Based on this output estimation, the state feedback controller calculates the compensating force. This is fed to a force-> voltage conversion block, whose output is in units of voltage(electrostatic bias). Instructions: 1. open the file simulation file (xcos)2. go to "Simulation"- > "set context" and change the pointer to the directory with files ("cd" command)3. run the command " exec('filter_fs_80k_fp_1k.sce') " from the Scilab console. This will load the matrix B into memory, containing the FIR filter coefficients4. run the simulation5. run the command from the console "exec('nice_plots2.sce')"
The Here East Digital Twin was a six month trial of a real-time 3D data visualisation platform, designed for the purpose of supporting operational management in the built environment.
1. View the project video for context: https://vimeo.com/311089492
2. Review details of the Envirosensor implementation: https://github.com/virtualarchitectures/ICRI_Envirosensor
3. One option for analysing the data is using Spark and Python in a Jupyter Notebook as outlines in the following example GitHub repository: https://github.com/virtualarchitectures/ICRI_Envirosensor_Data_Analysis
Academic spaces are an environment that promotes student performance not only because of the quality of its equipment, but also because of its ambient comfort conditions, which can be controlled by means of actuators that receive data from sensors. Something similar can be said about other environments, such as home, business, or industry environment. However, sensor devices can cause faults or inaccurate readings in a timely manner, affecting control mechanisms. The mutual relationship between ambient variables can be a source of knowledge to predict a variable in case a sensor fails.
[17-APR-2020: WE ARE STILL UPLOADING THE DATASET, PLEASE WAIT UNTIL IT IS COMPLETED] -The dataset comprises a set of 11 different actions performed by 17 subjects that is created for multimodal fall detection. Five types of falls and six daily activities were considered in the experiment. Data collection comes from five wearable sensors, one brainwave helmet sensor, six infrared sensors around the room and two RGB-cameras. Three attempts per action were recorded. The dataset contains raw signals as well as three windowing-based feature sets.
We will upload the instructions in the following days.
Dataset of V2V (vehicle to vehicle communication), GPS, inertial and WiFi data collected during a road vehicle trip in the city of Porto, Portugal. Four cars were driven along the same route (approx. 12 km), facing everyday traffic conditions with regular driving behavior. No special environments or settings were chosen, other than keeping the vehicles in communication reach of each other for as long as possible while being safe and compliant with the road rules.
There is a folder with data collected by each of the vehicles.
ID1 - Seat Leon
ID2 - Audi A4
ID3 - Nissan Micra
ID4 - Fiat Punto
Equipment collecting data:
- NEC LinkBird MX
- GPS receiver (rooftop, connected to the LinkBird)
- Smartphone Nexus 4 running SensorReader (SR) fixed on the windshield
- Smartphone Nexus 4 or Nexus 5 running SenseMyCity (SMC) fixed on the dashboard (different positions in each vehicle)