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)


The downloadable files contain all data and associated scripts that generate results as seen in the article. The major component description and detailed setup and run instructions are also provided in the README file.


Dataset of rosbags collected during autonomous drone flight inside a warehouse of stockpiles. PCD files created using reconstruction method proposed by article.

Data still being move to IEEE-dataport. 


Bag files contais multiple topics. Proposed method uses mainly Velodyne lidar pointcloud information and DJI imu


CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.


Download and extract the ZIP file containing all files. There is python code available (under 'scripts') to easily load the data set. Other programming languages should also handle .jpg, .hdf and .csv files for easy access. For easy access with python, a pickle dump file has been added. This has no extra information compared to the .csv file.


A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.


An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function


This file would need to be unzipped for access