The research were incorporated an extended cohort monitoring campaign, validation of an existing exposure model and development of a predictive model for COPD exacerbations evaluated against historical electronic health records.A miniature personal sensor unit were manufactured for the study from a prototype developed at the University of Cambridge. The units monitored GPS position, temperature, humidity, CO, NO, NO2, O3, PM10 and PM2.5.Three 6-month cohort monitoring campaigns were carried out, each including of 65 COPD patients.

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Dataset used for IEEE Sensors Article: Calibration of CO, NO2 and O3 Using Airify: a Low-Cost Sensor Cluster for Air Quality Monitoring

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This dataset is in support of my 2 Research Papers.  This dataset is in support of my research paper     " initially submitted to IEEE 

Paper Abstract

Instructions: 

Read Me

(1) This is an open access ,so everything  can be downloaded after login (free signup). You have to click on 'Title'.

(2) The folder 'Experimental' has recordings of Magnetic fields measured using  Magnetic Sensor,  mobile app(software) and mobile phone

  • Physical Magnetic Sensor(hardware)

                  Resolution of the sensor is 0.0976 uT

                  Maximum Range of the sensor : 3000.0044 uT

  • Physical orientation and angular velocity  Sensor  (hardware)

                        Resolution of the sensor is 0.0012216975 rad/s

                        Maximum Range of the sensor : 34.90549 rad/s

  •  Physical Proximity Sensor (hardware)

                          Resolution of the sensor : 1.0 cm 

                          Maximum Range of the sensor : 5.0 cm

  •  Physical Gravity Sensor (hardware)

                        Resolution of the sensor :  0.01 m/s2  & Maximum Range of the sensor :156.98999 m/s2

5gproof.zip has screenshots from wifi detection

Experimental Result   -    Lowest Recorded Reading : 11 uT

Highest Recorded Reading : 479 uT

 

Around 300 uT can be measured anywhere, if nearby has 5G equipment ( fluctuates to 50 uT then 111, then 200 , 286,  ...)    .   

Reading of 479 was measured, as few people were feeling unwell and when I checked, it was 420 uT, stationary and fluctuating to it around but that is not recorded.   So after some time, this was recorded.

 

 

(3) Zip do not contain simulation project folder.

(4)  The model used is given in  https://dx.doi.org/10.21227/9ab4-tv57.  But for this study, extra circuits has been added, details are given in the Paper.These are very elemenetary & easy for any degree holder,so not given.The parameters of 4g and 5g is in the paper ' '   . Pls contact university professor if any doubt.

(5) For  details like model block diagram, parameters, analysis, interpretation, mathematical formulae used to obtain these results etc. please refer "Transaction Paper".

(4) The folder Simulation_  is the data   of and has 8 subfolders

 

(3) The folder 'Simulation_4G' has folder

  • Heart
  • Ear
  • Tissue
  • Lungs

(4) The folder 'Simulation_5G' has 3  sub-folders

  • Low
  • Mid
  • High

(5) Each of the subfolder of 5G has folders

Scripts

Script is uploaded on only IEEE Codeocean, pls. follow instructions in the capsule given in ReadMe.txt.  Capsule of IEEE Codeocean (Matlab, MIT License) is:

  • Code:

 

Paper Citing : If want to cite this in paper etc. ,please refer DoI and/or this url.

Funding: There are no funders for this submission. The  author has himself fully self-financed.

Acknowlegement :  The author as such thankful to none and does not have any special name to be acknowledged.

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This work contains data gathered by a series of sensors (PM 10, PM 2.5, temperature, relative humidity, and pressure) in the city of Turin in the north part of Italy (more precisely, at coordinates 45.041903N, 7.625850E). The data has been collected for a period of 5 months, from October 2018 to February 2019. The scope of the study was to address the calibration of low-cost particulate matter sensors and compare the readings against official measures provided by the Italian environmental agency (ARPA Piemonte).

Instructions: 

A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN

Documentation for the air pollution monitoring station developed at Politecnico di Torino by:
Edoardo Giusto, Mohammad Ghazi Vakili under the supervision of Prof. Bartolomeo Montrucchio.

System Overview

This section includes a description of our architecture from several points of view, going from the hardware and software architecture, to the communication protocols.

Hardware Architecture

We target the following key characteristics of our system:

  1. The rapid and easy prototyping capabilities,
  2. Flexibility in connection scenarios, and
  3. Cheapness but also dependability of components.

As each board has to include a limited number of modules, to facilitate our prototype development, we select the Raspberry Pi single-board computer as a monitoring board.
Due to our constraints in terms of cost, size and power consumption we select its Zero Wireless version based on the ARM11 microprocessor.

The basic operating principle of the system is the following. The data gathered from the sensors are stored in the MicroSD card of the RPi. At certain time intervals the RPi tries to connect to a Wi-Fi network and, if such a connection is established, it uploads the newly acquired data to a remote server.
The creation of the Wi-Fi network is achieved using a mobile phone set to operate as personal hot-spot, while on the remote server resides the database storing all the performed measurements.

Software Architecture

Wi-Fi connectivity was one of the requirements for the system, but at the same time, the system itself should have not to produce unnecessary electromagnetic noise, possibly impacting the operating ability of the host's appliances.
To reduce the time in which the Wi-Fi connection was active, the Linux OS was set to activate the specific interface at predefined time instants in order to connect to the portable hot-spot.
Once connected to the network, the system performed the following tasks:

  1. synchronization of the system and RTC clock with a remote Network Time Protocol (NTP) server,
  2. synchronization of the local samples directory with the remote directory residing on the server.
    The latter task is performed using the UNIX rsync utility, which has to be installed on both the machines.

To gather data from the sensors, a Python program has been implemented, which runs continuously with a separate process reading from each physical sensor plugged to the board and writing on the MicroSD card.
It has to be noted that for what concerns the PM sensors, since the UART communication had to take place using GPIOs, a Pigpiod deamon has been leveraged, to create digital serial ports over the Pi's pins.

The directories on the remote server are a simple copy of the MicroSD cards mounted on the boards.
Data in these directories have been inserted in a MySQL database.

Mechanical Design and Hardware Components

In order to easily stack more than one device together, a 3D printed modular case has been designed.
Several enclosing frames can be tied together using nuts and bolts, with the use of a single cap on top.
Figure shows the 3D board design, together with the final sensor and board configurations.

Each platform is equipped with 4 PM sensors (a good trade-off between size and redundancy), 1 Temperature (T) and Relative Humidity (HT) sensor and 1 Pressure (P) sensor.
As our target was to capture significant data sampling for the particulate matter we adopt the following sensors:

  1. The Honeywell HPMA115S0-XXX as PM sensor.
    As one of our targets was to evaluate these sensors' suitability for air pollution monitoring applications, we insert 4 instances of this sensor in every single platform.
    This sort of redundancy allows us to detect strange phenomena and to avoid several kind of malfunctions, making more stable the overall system.

  2. The DHT22 as temperature and relative humidity sensor.
    This is very widespread in prototyping applications, with several open-source implementation of its library, publicly available on the internet.

  3. The Bosch BME280 as a pressure sensor.
    This is a cheap but precise barometric pressure and temperature sensor which comes pre-soldered on a small PCB for easy prototyping.

The system also includes a Real Time Clock (RTC) module for the operating system to retrieve the correct time after a sudden power loss. The chosen device is the DS3231.
The DS3231 communicates via I2C interface and has native support in the Linux kernel.

As a last comment, notice that a Printed Circuit Board (PCB) was designed to facilitate connections and soldering of the various sensors and other components.

Database

Create database

The database structure can be created using the scripts located in the mysql_insertion folder of the Dataset/SQL_Table repository.

mysql -u <user> [-h <host>] [-p] < create_db.sql

Load SQL data (SQL Format)

Data formated in SQL can be loaded using the mysql command mysql -u username -p WEATHER_STATION < db_whole_data.sql, and the db_whole_data.sql is available in the SQL_data/ folder of the Dataset directory.

Load RAW data (CSV)

Data can be loaded using the python script sql_ins.py available in the mysql_insertion folder of the Dataset/SQL_Table repository.

python sql_ins.py <data_folder>

The script assumes the following folder structure:

* data_folder
|-- 01-board_table
|-- 02-unit_of_measure_table
|-- 03-param_type_table
|-- 04-board_config_table
|-- 05-physical_sensor_table
|-- 06-logical_sensor_table
|-- 07-board_sensor_connection_table
|-- 08-measure_table
|-- arpa
|-- mobility
|-- stations

Each folder contains a set of csv files. The script automatically loads data into the appropriate table and using the correct fields, which are specified as a list of parameters in the script. It is possible to edit the script to load only a subset of the folders.

System Usage

To replicate the experiments, the user should clone the raspberry pi image into a MicroSD (16-32 GB).
To do this, s/he can issue the command dd if=/path/to/image of=/path/of/microsd bs=4m on Linux.
The sampling scripts are run by a systemd unit automatically at system startup. The same systemd unit handles also the automatic respawn of the processes if some problems occur. The data are stored in the /home/alarm/ws/data directory, with filenames corresponding to the date of acquisition.

In order to upload these data to a database, it is possible to use the guide contained in the "database" directory.

In order to perform calibration and tests, it is recommended to take a look at the guide contained in the "analysis" directory. A Python class has been implemented to perform calibration of sensors against the ARPA reference ones. The resulting calibration can then be applied to a time window of choice.

3D Model

A 3D model of the case has been developed using SketchUp online software.
The resulting model is split in 5 different parts, each large enough to fit in our 3D printer (Makerbot Replicator 2X).
The model is stackable, meaning that several cases can be put on top of each other, with a single roof piece.

Printed Circuit Board

A PCB has been developed using KiCad software, so to create a hat for the RPi0 connecting all the sensors.

WS Analysis library documentation (v0.2)

The aim of this package is to provide fast and easy access and analysis to the Weather Station database. This package is located in the analysis directory, and it is compatible only with Python 3. Please follow the readme file for more information.

Directory Structure

project
├── 3D_Box
│   ├── Cap_v0_1stpart.skp
│   ├── Cap_v0_2dpart.skp
│   ├── ws_rpzero_noGPS_v1.skp
│   ├── ws_sensors_2d_half_v2.skp
│   └── ws_sensors_half_v2.skp
├── analysis
│   ├── arpa_station.json
│   ├── board.json
│   ├── example.py
│   ├── extract.py
│   ├── out.pdf
│   ├── requirements.txt
│   ├── ws_analysis
│   │   ├── __pycache__
│   │   │   └── ws_analysis.cpython-37.pyc
│   │   ├── rpt.txt
│   │   └── script_offset.py
│   ├── ws_analysis.md
│   ├── ws_analysis.pdf
│   ├── ws_analysis.py
│   └── ws_analysis.pyc
├── Dataset
│   ├── db_setup.html
│   ├── db_setup.md
│   ├── db_setup.pdf
│   ├── er_diagram.pdf
│   ├── mysql_insertion
│   │   ├── extract_to_file.py
│   │   ├── remove_duplicate.py
│   │   └── sql_ins.py
│   ├── SQL_Table
│   │   ├── create_db.sql
│   │   ├── create_measure_table.sql
│   │   └── load_data.sql
│   └── SQL_data
│      └── db_whole_data.sql.gz
├── PCB
│   └── WS_v2_output.tar.xz
├── readme.html
├── readme.md
├── readme.pdf
└── scripts
├── python
│   ├── csv
│   │   ├── arpa_retrieve.py
│   │   ├── filemerge.py
│   │   ├── gpx2geohash.py
│   │   ├── parse_csv.py
│   │   └── validation.py
│   └── mpu9250
│   └── gyro.py
└── README.md

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454 Views

This work presents a methodology of constructing three models respectively without blades, with straight blades and with curved blades, coupled for artificial simulated fog-haze environment with computational fluid dynamics (CFD), to predict the impact of the rotating blades on the flow velocities in the enclosed environment by simulation. Atmospheric flow characteristics and variation of flow velocities were analyzed, and the influences of different rotating blades on flow velocities were compared to get the related simulation results in three models. 

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100 Views

These videos show bottom and side views of the treatment by a He/O2 plasma jet of a solution containing ultra-pure water, potassium iodide and starch. As the plasma reaches the liquid, a purple filament characteristic of the triiodide ion/starch complex appears. This complex is formed by the reaction between iodide ion and a reactive oxygen and nitrogen species (RONS) such as ozone, hydrogen peroxide, hydroxide ion, nitric oxyde or nitrate. Therefore, the formation of these RONS in the liquid phase is temporally and spatially quantified.

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111 Views

It is possible to construct "aerosol cytometers" based on different types of Zhulanov's laser  aerosol counters | diffusion aerosol spectrometers (DAS) [1-8] and "hydrosol cytometers" based on hydrosol particle counters (adopted for ocean marine, ocean and hydrothermal conditions [9,10]).

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287 Views