REVISTA INGENIO
Implementation of a Weather Station using FPGA with Real Time Data Access and
Analysis
Implementación de una Estación Meteorológica Mediante FPGA con Acceso y Análisis de Datos
en Tiempo Real
Holger Santillán | Universidad Politécnica Salesiana, UPS -Ecuador
José Ochoa | Universidad Politécnica Salesiana, UPS -Ecuador
José Ordoñez | Universidad Politécnica Salesiana, UPS -Ecuador
Peregrina Wong | Universidad Politécnica Salesiana, UPS -Ecuador
https://doi.org/10.29166/ingenio.v8i2.7449 pISSN 2588-0829
2025 Universidad Central del Ecuador eISSN 2697-3243
CC BY-NC 4.0 —Licencia Creative Commons Reconocimiento-NoComercial 4.0 Internacional ng.revista.ingenio@uce.edu.ec
, (), - , . -
El presente estudio desarrolla una estación meteorológica con la tecnología Fiel-Programmable Gate
Array (FPGA) para obtener datos meteorológicos en entornos educativos, pero que puede adaptarse a
cualquier escenario real, utilizando sensores ADM1001 de temperatura y humedad, cantidad de lluvia,
dirección y velocidad del viento. El diseño propuesto pretende mejorar la precisión y rapidez con la que
se obtienen los datos meteorológicos en tiempo real. Para ello, se ha utilizado la plataforma Elvis II+
junto con la FPGA Xilinx Spartan 3E para las conexiones de los diferentes tipos de sensores. Además,
se utilizó el entorno de programación LabVIEW para el diseño y control del sistema asegurando una
interfaz fácil de usar para los usuarios. Para presentar los datos recogidos, se desarrolló una solución
Node-Network que permite visualizar los datos de forma eciente, los algoritmos empleados alcanzaron
una precisión del 85% en condiciones normales, el error cuadrático medio (RMSE) fue de 0.2 °C para la
temperatura y de 1.5% para la humedad. Esta visualización se consigue a través de una Raspberry Pi ya
que facilita el acceso y la gestión de la información meteorológica recogida en tiempo real, lo cual avala
la calidad del prototipo diseñado en el presente trabajo.
Recibido: 6/11/2024
Recibido tras revisión: 14/4/2025
Aceptado: 26/5/2025
Publicado: 10/07/2025
FPGA, Weather Station, Raspberry Pi 4,
LabVIEW, Sensors.
FPGA, Estación meteorológica, Raspbe-
rry Pi 4, LabVIEW, Sensores.
1. Introduction
e country is currently experiencing more extreme cli-
mate changes, and this makes it increasingly necessary
to monitor and predict these phenomena more accurate-
ly. According to, in Ecuador, several natural phenomena
signicantly aect the climate. e factors are measured
thanks to current technology, and the data obtained in
real time allows us to understand and react more eecti-
vely to changes [1].
Field-Programmable Gate Arrays, or FPGAs, are integra-
ted circuits that can be programmed aer manufacture.
ey are ideal for implementing complex handling algo-
rithms and maintenance activities on electronic devices.
e parallel processing and eciency of these devices
make FPGAs more ecient than microcontrollers and
digital signal processors, delivering higher performance
for real-time applications [2], [3].
e present study develops a weather station with the Fiel-Programmable Gate Array (FPGA) technology
to obtain meteorological data in educational environments, but that can be adapted to any real scenario,
using ADM1001 sensors for temperature and humidity, amount of rain, wind direction and wind speed.
e proposed design aims to improve the accuracy and speed with which weather data is obtained in real
time. For this purpose, the Elvis II+ platform together with the Xilinx Spartan 3E FPGA was used for the
connections of the dierent types of sensors. In addition, the LabVIEW programming environment was
used for the design and control of the system ensuring a user-friendly interface for the users. To present the
collected data, a Node-Network solution was developed to eciently visualize the data, the algorithms used
achieved an accuracy of 85% under normal conditions, the root mean square error (RMSE) was 0.2 °C for
temperature and 1.5% for humidity. is visualization is achieved through a Raspberry Pi since it facilitates
the access and management of the meteorological information collected in real time, which endorses the
quality of the prototype designed in the present work.
54
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
A real-time weather station has sensors and electronics,
such as sensors for basic weather variables, such as tem-
perature, humidity, uva ultraviolet radiation, and wind
direction. Learning Objects form another part of the su-
pport for the educational process which facilitates their
understanding [4], [5].
In this project, an experimental methodology is pro-
posed to allow the use of the weather station, which in-
volves an analysis of the functionality. e purpose is to
evaluate signicant parameters, such as humidity, tempe-
rature, the amount of rainfall, wind speed and direction.
Real-time analytics is made possible by FPGAs’ ability to
collect and process data instantly [6].
e system uses LabVIEW as a simulation tool for de-
velopment. LabVIEW is an acronym for Laboratory Vir-
tual Instrument Engineering Workbench. It allows the
development of a user interface that interacts with the
simulation equipment in an intuitive way by presenting
data in real time using boxes, graphs and specic mar-
kers for the reading of data from each sensor of the wea-
ther station. erefore, the system is educational for any
user who wishes to understand [7], [8].
Traditional weather stations oen contend with slow
data collection and limited processing. FPGA-based sys-
tems, in contrast, provide real-time data handling and hi-
gh-speed parallel processing capabilities. While IoT-based
weather stations oer remote connectivity and cloud in-
tegration, their common reliance on microcontrollers or
single-board computers may not match the speed or hard-
ware-level customization FPGAs deliver. Consequently,
FPGA weather stations eectively combine local data pro-
cessing with system recongurability, presenting a robust
solution for responsive and accurate climate monitoring.
To augment system functionality, technologies such as
Node-RED and CERN-ROOT are incorporated for data
ow control and scientic data analysis, respectively. No-
de-RED supplies a user-friendly visual programming in-
terface for managing sensor data, whereas CERN-ROOT
facilitates complex statistical processing and visualization.
e inclusion of these tools equips the FPGA-based sys-
tem with potent soware capabilities that complement its
hardware strengths, supporting both immediate respon-
ses and detailed data insights.
From an educational standpoint, FPGA-based wea-
ther stations function as valuable learning platforms.
ey expose students and researchers to interdiscipli-
nary content covering electronics, programming, signal
processing, and environmental science. Engaging with re-
al-time data and recongurable hardware allows learners
to achieve a deeper understanding of embedded systems
and their practical applications in contemporary environ-
mental monitoring.
Weather stations play a vital role in collecting clima-
te data that aects many sectors, from agriculture to di-
saster management. ey help farmers make decisions by
implementing accurate weather conditions, and to pre-
dict extreme events, they help reduce damage. Data is
also necessary for inclusion in the climate change agen-
da, allowing environmental policy to be evaluated and
adjusted [9].
In many respects, traditional weather stations face se-
veral notable challenges. e high cost associated with
installation and maintenance can make the stations pro-
hibitive for many. Similarly, data handling accuracy and
speed are generally low, seemingly unable to respond
quickly to events that occur around the weather. In addi-
tion, real-time data analysis is absent in many traditional
systems, ultimately limiting its eectiveness in extreme
events [10].
e application of FPGAs in weather stations provides
several notable advantages. Due to the increase in com-
puting resources, a rapid reaction to changing weather
conditions is achieved. e system is improved in terms
of accuracy by customizing the hardware for the particu-
lar sensors used. In addition, additional upgrades and up
-
grades are constant due to the adaptability of the FPGA,
ensuring that the system can be modied or upgraded to
newer specications as needed [11].
e main purpose of the FPGA-based weather station
is to work with various sensors and collect environmen-
tal parameters including temperature, humidity, direc-
tion, and wind speed. One of the most important features
of this functionality is the ability to calculate and analyze
such data accurately. In addition, there are also educatio-
nal materials such as simulations and teaching tools that
are available for students to understand the concept and
application of the weather station.
is research expands the capabilities of real-time
meteorology by taking the implementation of soware
built specically around FPGA technology to the next
level. It equips weather stations with computerized sys-
tems that improve the eciency of their use as provided
by general systems using normal approaches. is sys-
tem can really help us control the weather better and is
a great start to creating even more interesting things in
the future.
Using FPGAs in a weather station greatly aects both
study and real-world use. is platform is a high-tech tool
that helps scientists better study weather and climate. is
method changes real-time weather tracking and can be
used in many situations, providing a exible way to mo-
nitor dierent things.
Within a detailed review of the technologies, current
methods of weather stations and real-time data proces-
sing. e analysis situates the project in the eld of me-
teorological technologies, demonstrating how current
innovations, such as the use of FPGAs, overcome the limi-
tations of traditional systems. By understanding the exis-
ting environment, one can see how the proposed project
55
Santillán H. et al.
will improve the accuracy and speed of collecting and
analyzing weather data.
e various sensors of the weather station stand out,
being the fundamental AMT1001 to collect accurate mea-
surements of critical hydroclimatic variables, such as tem-
perature and relative humidity. Integrated into the IoT
weather station layer, the AMT1001 oers level readings
to enable monitoring of environmental conditions. e
article used 65 samples, presented a correlation of 100%
in the data and a transmission eciency of 95.3%, vali-
dating the usefulness of AMT1001 in planning agricultu-
re and resource management systems [12].
e PRS-1 sensor, a compact rain gauge designed to
measure the amount of rainfall accurately by location.
is sensor is important for collecting the amount of ra-
infall that covers rainfall, something that is necessary for
Bangladeshi agriculture, where the lack of timely infor-
mation has resulted in crop losses, as PRS-1 not only gives
accurate measurements, but also stores and allows data
to be shared online, facilitating the estimation of rainfall
and agriculture based on historical and current data [13].
Ultrasonic cup anemometers are employed for wind
measurement, as wind speed is a key component in eva-
luating a generation site before installing a wind farm.
e low-cost anemometer was created using 3D addition
technology and manufacturing instructions with an en-
coder and phototransistor to convert rotation into wind
speed data using the Arduino Uno. e test results and
the 0.968 showed that its level of accuracy is high and it
may be a viable and economical option for future wind
projects [14].
e FPGA-based weather station uses the Xilinx Ar-
tix7 array and the Verilog HDL language to accurately
measure weather parameters. It uses a FT2232H USB-
UART interface to record atmospheric pressure, tempe-
rature, and humidity, and allows the interconnection of
various sensors. Data acquired in Nilgiri, Tamil Nadu,
India, is visualized using the CERN-ROOT data analysis
framework, providing a detailed and ecient graphical
representation of weather conditions [15], [4].
e transceiver schemes MPSK (Mary-Phase Shi
Keying) and MQAM (Mary-Quadrature Amplitude Mo-
dulation) are implemented in LabVIEW, considering the
eect of noise. ese schematics are reproduced with the
SDR NI-USRP 2920 kit, and the strength of the recei-
ved signal is measured with the R&S (Rohde & Schwarz)
sensor. e results show good agreement with the simu-
lations, and the detailed procedure serves as reference
material for prototyping and hands-on teaching of com-
munication systems [16].
e NI ELVIS II system facilitates the study of the-
se characteristics, allowing the analysis of tunable lters
in both the time and frequency domains. is educatio-
nal equipment is essential to understand the behavior of
lters of various orders, providing practical and detailed
examples that enrich learning in electrical engineering.
Its use in the curriculum is essential for a complete trai-
ning in lter analysis and design [17].
e topology for FPGA based on a systolic structure,
suitable for forward neural networks such as the multi-
layer perceptron (MLP). Implemented on the Zynq-7000
board with the MNEST dataset, the proposed architec-
ture improves accuracy and performance using specic
activation functions. is solution optimizes hardware
eciency in neural network applications, outperforming
traditional structures [18].
e proposed design uses the Spartan-3E FPGA to
generate and control a PWM signal with variable duty
cycle using a rotary encoder. e Spartan-3E, with its in-
ternal clock of 50 MHz, allows you to adjust the PWM
signal duty cycle from 0 to 100% by turning the encoder
and changing the signal frequency. is approach oers
high speed, customization and low cost, being suitable for
applications in power control and electronics. e gene-
rated PWM signal is analyzed with an oscilloscope to en-
sure its accuracy at various frequencies [19].
e Raspberry Pi 4 has been employed to develop a
low-cost disk imaging device useful in digital forensics
for copying evidence before direct analysis. Using Python,
the device allows digital evidence to be handled accurate-
ly and eciently, minimizing the risk of tampering. Tests
show that the Raspberry Pi 4 delivers solid performan-
ce in speed and accuracy, providing an economical alter-
native to more expensive equipment on the market [20].
Implementing an I2C controller on an FPGA to con-
nect the BH1750FVI light sensor and transmit data to Si-
mulink using an I2C bridge to UART. Using VHDL and a
nite state machine (FSM), the I2C controller and UART
have been developed. e verication is performed with
a hardware I2C analyzer, ensuring accurate acquisition
of sensor data. e UART controller then sends this data
serially to the COM port for analysis and processing in
Matlab-Simulink [21].
is protocol is ideal for systems with many distribu-
ted sensors, although the topology can become complex
in large networks. Deploying switched networks, which
divide the network into electronically managed segments,
helps handle this complexity without increasing weight
or radius. In this study, a long-line 1-Wire network with
a data switch integrated into a master microcontroller is
proposed, providing a reliable and economical solution
for temperature sensor monitoring [22].
e Modbus protocol facilitates communication in in-
dustrial systems by connecting control devices through a
simple interface. is article discusses how Node-Red, a
ow-based visual programming tool, integrates with Mo
-
dbus to control robots, such as Epson’s 6-axis and SCARA
robots. e combination of Modbus with Node-Red ena-
bles ecient and visual management of robotic systems,
demonstrating that visual programming can simplify the
56
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
development and implementation of control soware in
industrial environments [23].
Both conventional manual and automatic weather sta-
tions rely on physical models and satellite data to make
their predictions. However, in extremes with complex mi-
croclimates such as Quito, these methods are oen inac-
curate, in part due to local factors such as the Intertropical
Convergence Zone that these structures are not armed to
deal with. Given the inability to adapt to these specici-
ties, their prognoses are oen wrong [24].
e study by Asanza proposes a weather monitoring
station with FPGA-based embedded systems. is sys-
tem acquires high-resolution data and predicts real-time
samples accurately. Additionally, a web application with
user-friendly interface presents the collected data [11].
ese stations are also inecient because they can-
not process information in real time and are expensive to
expand in number to improve accuracy. erefore, the-
se shortcomings argue the need for economical and no-
vel approaches, such as low-cost computers and advanced
machine learning techniques, that can guarantee the ac-
curacy of weather forecasts in extremes with complex mi-
croclimates [25].
e AASIP (Adaptive Arithmetic Signicance In-
ference Prediction) algorithm, which optimizes the ac-
curacy of the correction factor in the Sparse Implicit
Projection (SIP) by correcting based on the arithmetic
mean. AASIP improves accuracy in RC network list re-
duction by overcoming the limitations of traditional SIP
and adjusting high-order moments. e eectiveness of
the algorithm is validated through practical examples, de-
monstrating its ability to oer a more accurate approxi-
mation compared to previous methods [26].
Equation (1) of the arithmetic mean is:
(1)
Where
: arithmetic mean
an: number of data
∑: sumatoria
n: Total number of data
e article highlights the need to modernize weather
monitoring in Ecuador due to the lack of advanced te-
chnology. e National Institute of Meteorology and
Hydrology (INAMHI) is implementing a program to
replace traditional stations with more advanced techno-
logies, such as FPGAs, that would improve accuracy and
eciency in the collection of environmental data, such
as temperature, humidity and atmospheric pressure [11].
Similarly, the study recommends considering alterna-
tives such as static stations and wireless sensor networks
to automate and monitor data. It also suggests comparing
dierent data acquisition technologies, such as SRAM
FPGA memory, to reduce response times and take into
account environmental factors to improve the accuracy
of the monitoring system.
FPGAs have proven to be very eective in improving
accuracy and reducing response time in the acquisition
of meteorological data. Compared to DDR3 memory, FP-
GAs can accelerate data reading by up to ve times. In ad-
dition, systems using FPGAs with neural networks and
the VHDL language have shown high accuracy in mo-
nitoring variables such as humidity and light intensity.
e article states that an Arduino Uno was initia-
lly used to connect the sensors and transmit data, which
was replaced by Raspberry Pi due to its higher processing
power. FPGAs are used in our thesis due to their high per-
formance and energy eciency, which makes them suita-
ble for managing sensor data at high speed [11].
erefore, this article is about the technological out-
datedness of Ecuador’s weather stations and an innova-
tive solution based on FPGA systems. FPGA technology
has demonstrated its impressive number of abilities to
improve accuracy and eciency in the acquisition of en-
vironmental data, including temperature, humidity, rain-
fall, and light intensity. erefore, FPGA implementation
can modernize existing systems and make data acquisi-
tion extremely ecient and high-quality.
e article describes the weather station that the Xi-
linx Artix 7 FPGA used. is station included the fo-
llowing temperature, humidity and pressure sensors and
was programmed with VHDL. en, the data that emer-
ges from the station is processed and analyzed and visua-
lized with CERN-ROOT from tests carried out in Nilgiri,
Tamil Nadu, India [11].
When comparing the Xilinx Artix 7 and the Xilinx
Spartan 3E, it is observed that the Xilinx Artix 7 demands
more logical capabilities and memory, but its high pri-
ce can be a challenge. On the other hand, the Spartan 3E
costs relatively less and is accessible, it does not need a
high additional technical demand compared to the other.
Both are great for doing a project, but the most suitable
solution depends on the particular case and budget.
e article introduces a system called Weather Sta-
tion Monitoring, which uses IoT to provide weather data
anywhere. is system, built with ESP32 microcontroller
and MQTT broker, transmits data to the cloud and is dis-
played using NODE-RED in real time. It has temperatu-
re, humidity and wind speed sensors, with hydrological
sensors monitoring water level and ow, allowing users
to predict ooding [27].
This study uses NODE-RED for real-time vi-
sualization and recording of weather data, including
57
Santillán H. et al.
temperature, humidity, rainfall level, wind speed and di-
rection. Although this system lacks hydrological data, the
use of NODE-RED shows that there is still room for fu-
ture expansions. NODE-RED and LabVIEW ensure ac-
curate handling of the ow of weather data, providing
an environment monitoring solution that is eective and
easy to adapt.
e article describes a weather monitoring system
that uses IoT and LabVIEW to collect and analyze data
on temperature, humidity, vent velocity, pressure, and li-
ght intensity. e recording of this microcontroller data
is stored in an Excel macro le and displayed on an LCD
screen. In addition, the system uses a 4G module, the data
is sent to the IoT cloud, allowing data to be accessed from
remote locations via the mobile network [28].
In comparison, the system is simpler than the FP-
GA-based studio system, which oers greater accuracy,
but is more complex to program. Although the system
has some limitations in processing, it stands out for its
powerful ability to transfer data to the cloud through 4G,
which is still a signicant advantage.
Tools such as LabVIEW and CERN-ROOT are eec-
tive for accurately processing and visualizing scientic
data. LabVIEW facilitates real-time acquisition and mo-
nitoring; ROOT enables complex analysis of large volu-
mes of data and integrates well with architectures such
as FPGA. us, these tools improve the interpretation of
environmental phenomena and optimize system perfor-
mance [29].
Countries with complicated geographies such as Peru,
Bolivia or Colombia have challenges similar to ours here
in Ecuador with the coverage and accuracy of their wea-
ther stations. Recent studies show that using FPGA plat-
forms for environmental monitoring in dicult to access
areas is more ecient, as they take less time to respond
and are more reliable [30].
Modernizing meteorological stations with advanced
technology is key to the technological and scientic sove-
reignty of developing countries. For Ecuador, this means
promoting technical and engineering education, training
specialists and designing local solutions. is work, then,
aims to help build a more resilient scientic infrastructu-
re of our own [31].
2. Method
e development of the weather station is done by using
FPGA technology to capture, process and analyze wea-
ther data in real time. FPGAs are chosen because of their
ability to provide high accuracy and speed in data pro-
cessing based on need. is is required for applications
that need to constantly monitor various data points and
be up-to-date with the latest information in an instant.
erefore, FPGA technology is necessary to enable e-
cient and fast processing, given its need in advanced me-
teorological applications.
e methodology of this project was mainly expe-
rimental. It means that a series of tests were performed
to adjust the respective sensors and verify whether the
FPGA could handle the real-time data eectively. e-
refore, an adjustment of the system’s parameters and an
evaluation of its performance under various environmen-
tal conditions were performed. is ensured that the data
collected was considered accurate and reliable in practi-
cal situations.
On the other hand, the implementation of the weather
station with FPGA technology for monitoring environ-
mental conditions such as temperature, humidity, wind
direction and speed in real time, allowed a highly accu-
rate and fast data processing, which was the right choice
for advanced applications. LabVIEW was used to pro-
gram the FPGA, which provides a welcoming graphical
interface for designing and managing the data acquisition
system, making it very easy to install and read the data.
It was decided to include a Raspberry Pi 4 in the sys-
tem design to improve data transfer speed and connectivi-
ty. is device serves as a bridge to send information from
the FPGA to other systems over a network, making it easy
to access and analyze the data using programs such as Mat-
lab-Simulink. In this way, an ecient solution for remote
communication and information processing is achieved.
e project goes beyond simple climate monitoring. It
also has an educational component designed for students,
who can recreate the prototype and experiment with the
weather station. is oers them a hands-on opportuni-
ty to learn about FPGA technology and meteorological
principles. By combining LabVIEW and Raspberry Pi 4
in the design, an enriching educational experience is pro-
vided. Figure 1 shows the diagram of the weather station,
which makes it easier to understand.
e following are the components for the project:
1. Humidity and temperature sensor AMD1001.
2. Directional anemometer.
3. Ultrasonic cup anemometers.
4. Tilting bucket rain gauge.
5. NI Elvis II+.
6. NI Digital Electronics FPGA Board.
7. Raspberry pi 4.
8. Screen.
9. Laptop.
10. Database.
58
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
Fig 1.
Weather station schematic with FPGA.
e development of the weather station using FPGA te-
chnology will be carried out in ve phases to ensure the
correct process. Information gathering and initial plan-
ning is a key section to understanding objectives clearly.
en, in the phase of study and selection of components,
the parts such as sensors and other devices are selected.
It ensures that everything is in order for the system to
run smoothly.
e next phase, development and implementation
of the project, where the FPGA is programmed and all
the components are connected. Once everything is as-
sembled, the calibration phase and adjustment of sensors
and components is carried out where the respective ad
-
justments and modications are made, nally, in the data
generation and analysis phase, the data obtained are co-
llected and examined to evaluate the performance of the
system and make nal adjustments, gure 2 shows the
phases for the implementation of the weather station.
Fig 2.
Phases for the implementation of the weather station
In the information gathering and initial planning pha-
se, you do thorough research to understand the project
requirements and set clear goals. Dierent types of wea-
ther sensors and FPGA technology are explored, as well
as programming and connectivity needs. is research
process allows the project scope, the necessary resources,
and the timeline to be dened, creating a solid founda-
tion for the next stages of development.
During the study and selection of components, the
variety of available sensors, such as temperature, humi-
dity and wind speed, is analyzed in detail to choose the
ones that best suit the needs of the project. e techni-
cal specications, FPGA compatibility, and cost of each
component are reviewed. is phase is key to ensuring
that the selected elements are suitable, ecient and relia-
ble for the system.
e weather station employs high-precision sensors to
ensure eective environmental monitoring. Temperature
and humidity sensors AMD1001 noted for their accuracy
and stability, providing reliable real-time data and quic-
kly adapting to variations in the environment. is capa-
bility is crucial for obtaining accurate information about
atmospheric conditions.
Directional and ultrasonic anemometers are used to
measure the wind. e former records the wind direc-
tion accurately, while ultrasonic anemometers, with no
moving parts, provide fast and accurate data, even in ad-
verse conditions. is combination allows for a complete
and accurate evaluation of wind behavior.
e tilting bucket rain gauge measures precipitation
with high accuracy and durability, while maintaining a
low maintenance requirement. Its robust design ensures
accurate measurements of rainfall accumulation in real
time, which is critical for water management and predic-
tion of extreme weather events.
e integration of the sensors with the FPGA is done
using standard communication protocols such as I2C
and SPI, ensuring accurate and ecient data transmis-
sion. Sensors employing I2C connect to the FPGA’s data
pins (SDA) and clock (SCL), allowing for easy connection
of multiple devices on a single bus, which is ideal for tem-
perature and humidity sensors.
Sensors that use the SPI protocol connect to the clock
pins (SCK), input data (MOSI), output data (MISO), and
chip selection (CS). is protocol is especially suitable
for sensors that require high transmission rate, such as
certain anemometers and rain gauges, providing ecient
performance in communicating fast and accurate data.
e FPGA receives signals from the sensors through
these protocols, processing and converting the data when
necessary, and preparing the information for real-time
analysis. is initial processing ensures that the data is
accurate and ready for immediate interpretation and use.
e NI Digital Electronics FPGA Board model was
chosen for this project in combination with the NI EL-
VIS II+, due to its robust processing power and compa-
tibility with the sensors used. is FPGA oers powerful
processing power essential to eciently handle real-time
data acquisition and analysis.
When choosing the FPGA, we decided on the Spar-
tan 3E because of its low cost, because it is readily availa-
ble in academic labs, and because it is compatible with the
NI ELVIS II+ platform. However, we also considered the
Artix 7, which has signicant technical advantages: many
more DSP blocks (740 versus the Spartan 3E’s 20), more
LUTs (52,160 compared to 9,312), uses less power (thanks
to its 28 nm technology versus the Spartan’s 90 nm), and
oers higher communications bandwidth. While the Ar-
tix 7 is better for processor-intensive tasks, Spartan 3E
met our needs for the project, oering a good balance of
performance, simplicity and cost for an educational envi-
ronment ended reliably to environmental changes.
59
Santillán H. et al.
e NI Digital Electronics FPGA Board stands out
for its wide availability of resources, such as Look-Up Ta-
bles (LUTs), registers, and digital signal processing (DSP)
blocks. LUTs allow for the implementation of combina-
tional logic and custom functions, while logs facilitate
ecient data management. DSP blocks are key to perfor-
ming complex calculations and advanced analysis, cru-
cial for interpreting data accurately.
e compatibility of this FPGA with project sensors,
which include devices for measuring temperature, humi-
dity, wind, and rainfall, ensures seamless integration. is
choice ensures that the FPGA can communicate and pro-
cess data smoothly and eectively, meeting the speed and
accuracy requirements needed for the project.
In the development and implementation phase of
the project, the FPGA is programmed using tools such
as LabVIEW as shown in Figure 3, to build the data ac-
quisition system. e selected sensors are integrated and
congured to work together. is stage includes both
building the hardware and programming the soware,
ensuring that all components collaborate eectively to
capture and process real-time weather data.
Fig 3.
Diagram of blocks made in LabVIEW.
e FPGA architecture is designed with specialized
modules that eciently manage the ow of data from
acquisition to transmission. e data acquisition modu-
le receives signals from the sensors and converts them
to digital format. is module, programmed with Lab-
VIEW, ensures accurate and fast capture of information,
which is critical for proper processing.
e signal processing module, also developed with
LabVIEW, uses DSP blocks to perform complex calcu-
lations and lter digital signals. is module transforms
raw data into actionable information, enabling accura-
te, real-time interpretation. Programming in LabVIEW
makes it easy to implement advanced algorithms and -
ne-tune analysis processes.
e processed data is temporarily stored in a storage
module, which preserves the intermediate information for
later analysis. For external communication, a Raspberry Pi
4 is used, which receives the data from the FPGA and sends
it to analysis platforms. e integration of LabVIEW into
FPGA programming ensures a smooth and ecient setup,
allowing for eective and continuous data transfer.
e collected data is managed by a combined system
of local storage on the FPGA and transmission to a ser-
ver for further analysis. e FPGA temporarily stores the
data, and a Raspberry Pi 4 acts as an intermediary to send
the information to an external database. In addition, the
data is exported to Excel les, providing an additional op-
tion for review and analysis.
Data visualization and access is facilitated by a gra-
phical interface implemented with LabVIEW. is tool
allows you to create an intuitive user interface that pro-
vides real-time visualizations as shown in Figure 4. Users
can consult and manage weather information clearly
and eciently, optimizing data interpretation and deci-
sion-making.
Fig 4.
Weather station LabVIEW interface.
Calibrating and adjusting sensors and components in-
volves making precise adjustments to improve measure-
ment accuracy. Various tests are carried out to verify that
the system works correctly under dierent conditions.
is calibration process is essential to ensure that the
data collected is accurate and that the system is optimi-
60
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
zed for the environment in which it was used, Figure 5
shows the weather station with each sensor.
To calibrate the sensors, we used certied reference
equipment and data from ocial INAMHI weather sta-
tions, which allowed us to validate the accuracy of the
measurements. We conducted tests for a week in dierent
environments and at dierent times, comparing real-time
readings with standard values. With the AMD1001 sen-
sor, for example, we created a calibration curve using li
-
near regression in LabVIEW. is allowed us to adjust its
digital output according to the actual humidity and tem-
perature, achieving a margin of error of less than ±2%.
is ensured that the system responded reliably to envi-
ronmental changes.
Fig 5.
Connection of each sensor to the weather station.
Finally, in the data generation and analysis phase, the
operational data of the system is collected and analyzed
to evaluate the performance of the weather station. e
measurements are reviewed to ensure that the system is
working as expected, making nal adjustments, if neces-
sary, in gure 6 the project is shown from the scheme
proposed in gure 1.
Fig 6.
Weather station.
e methodology for developing the weather station with
FPGA technology is divided into ve essential stages:
data collection and initial planning, selection and study
of components, development and implementation of the
system, calibration and adjustment of the sensors, and
nally, generation and analysis of the data. is approach
ensures that the project progresses in an organized and
eective manner, achieving the stated objectives in an
orderly and detailed manner.
3. Results and discussion
3.1. RESULTS
e results are presented in a table of data collected
by the FPGA-based weather station. is table shows
the measurements obtained for various environmental
parameters, such as temperature, humidity, wind di-
rection and speed, at dierent time intervals. e data
have been recorded in real time, reecting the environ-
mental conditions throughout the observation period.
is information is crucial for evaluating system per-
formance and for performing detailed analyses on ob-
served weather variations.
At the weather station using an FPGA, the data obtai-
ned is sent to a Raspberry Pi for management. Aer being
processed by the FPGA, the Raspberry Pi stores this data
in an Excel le, which facilitates its analysis and manipu-
lation, using a common tool for the treatment of meteo-
rological information.
In addition, the Raspberry Pi allows you to store and
send the data to a server or database for more detailed
analysis. Using Excel as a storage format simplies the
organization of information and the creation of charts,
making it easier to identify trends and weather patter-
ns eectively.
To assess the accuracy of the system, the data obtai-
ned was compared with those reported by Google Wea-
ther for the same region. e comparison showed a high
correlation, with a Pearson correlation coecient of 0.98
for temperature and 0.95 for humidity. e mean squa-
re error (RMSE) was 0.2°C for temperature and 1.5% for
humidity, conrming the accuracy of the system relative
to the data provided by Google Weather.
Table 1 provides data obtained by the weather station,
where it details the temperature, humidity, wind direc-
tion, and amount of rain in the telecommunications club,
each entry in the table corresponds to a set of each sensor
facilitating the review and analysis of the environmental
information collected. is data will serve as a basis for
adjusting and optimizing the system, as well as for con-
ducting studies on recorded weather trends and patterns.
61
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
e rain and wind measurements showed little varia-
tion because we tested in an enclosed location where the
weather remained stable at all times. Since it did not rain
while we were taking the data, the rain sensor always read
the same. Similarly, the wind barely changed direction or
speed, which is normal for a site with little air movement
like the one we used for testing. is does not mean that
the system failed, but that it accurately measured the ac-
tual ambient conditions during the experiment.
e tests sought to validate the system under stable
conditions, to ensure the correct capture, transmission
and storage of data in real time. is allowed testing the
accuracy and consistency of each sensor before confron-
ting them with more demanding conditions. Comparison
with Google Weather showed high correlation, reinfor-
cing the validity of the measurements despite the low va-
riability. Future tests will be conducted in more dynamic
environments to evaluate the system in the face of abrupt
weather changes.
Real-time analysis made it possible to identify trends
and predict short-term weather conditions with remarka-
ble accuracy. e algorithms used reached an accuracy of
85% under normal conditions, which reects a high eec-
tiveness in anticipating typical climatic changes. Under
extreme conditions, the accuracy was 75%, indicating a
slight decrease in predictive capacity under severe events.
ese results show that, despite the variations, the system
is eective in weather prediction in dierent scenarios.
Fig 7.
Graph of each sensor of the weather station.
Figure 7 shows the measurements of each of the sensors
in 4 graphs, in the graph for the temperature it shows
that it remained constant, with slight variations around
23.7°C. Likewise, humidity remained at an average of
46.4%. ese data indicate that the climate was fairly sta-
ble during the time it was measured. In the case of wind
Table 1.
Data obtained from the weather station.
WEATHER STATION
Temperature Humidity Dir. Wind Cant. Of rain
123,710937 46,386719 4,8 1,397
223,59375 46,386719 4,8 1,397
323,59375 46,386719 4,8 1,397
423,59375 46,386719 4,8 1,397
523,710937 46,386719 4,8 1,397
623,710937 46,386719 4,8 1,397
723,710937 46,386719 4,8 1,397
823,710937 46,386719 4,8 1,397
923,710937 46,386719 2,4 1,397
10 23,710937 46,386719 2,4 1,397
11 23,710937 46,386719 2,4 1,397
12 23,59375 46,386719 2,4 1,397
13 23,710937 46,386719 2,4 1,397
14 23,710937 46,386719 2,4 1,397
15 23,710937 46,386719 2,4 1,397
16 23,710937 46,386719 4,8 1,397
17 23,710937 46,386719 4,8 1,397
18 23,710937 46,386719 4,8 1,397
19 23,710937 46,386719 4,8 1,397
20 23,710937 46,386719 4,8 1,397
62
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
speed, two variations are observed; Most measurements
show a speed of 4.8 km/h, but there is a period when it
decreases to 2.4 km/h. is variation suggests that the
wind was variable.
As for rainfall, a graph shows that it remained at a
constant level of 1,397 in all measurements. is indicates
that the rain, although present, was uniform and constant,
without major changes in its intensity. Figure 8 shows all
the measurements taken.
Fig 8.
Measurements from the weather station.
Figure 1 shows all the measurements combined, which
shows how dierent meteorological variables can behave
independently or show little relationship with each other
during the same period of time. is suggests that the
weather was fairly stable in the data collected and varia-
tions in one parameter, such as wind direction, did not
necessarily translate into changes in other parameters,
such as temperature or humidity.
For the evaluation and prediction of the system, data
were collected for ve continuous days (20 records/day,
100 in total). Seventy percent was used to train the pre-
diction algorithm and 30% for testing and verication,
which facilitated a reliable assessment of its accuracy un-
der controlled conditions.
Measurement intervals of 15 minutes were dened
for the timely capture of diurnal environmental changes.
is frequency was selected to balance detailed monito-
ring with storage and processing eciency, especially in
stable weather.
e low variability of the data reects the controlled
testing environment. is initial phase focused on ensuring
consistent sensor performance and system stability before
transitioning to dynamic outdoor conditions, which will be
explored in future phases for further validation.
3.2. DISCUSSION
e article describes a weather station built with a Xilinx
Artix7, whose code is written in Verilog and used with
Xilinx ISE soware. is system performs read, write
and control operations through the FT2232H module
and a computer, thus facilitating the development and
compilation of designs. e integration of sensors such
as TMP36, HS1101 LF, and BMP180 demonstrates the
versatility of the design, although working with Xilinx
ISE requires advanced knowledge in digital design and
VHDL programming [4].
On the other hand, the work carried out uses Lab-
VIEW soware for its design and programming since it
oers a better platform to work ideal for data processing
in addition to an intuitive graphical interface which fa-
cilitates the development of applications since it is faster
and more accessible, especially for systems that integra
-
te several types of sensors and require humidity, tempe-
rature, atmospheric pressure and UV light in addition to
the extensive library and tools it oers and facilitated the
integration of these sensors.
Although Xilinx ISE stands out for its accuracy and
speed in data acquisition, it has been discontinued since
2013. However, its eectiveness in weather applications is
still remarkable, according to the paper. Despite this, the
transition to LabVIEW is recommended for its intuitive
interface and ability to communicate with various proto-
cols such as USB, serial, and Ethernet, which improves
eciency compared to Xilinx ISE [4].
e article addresses the technological challenges in
meteorology for developing countries such as Ecuador,
where many weather stations are inoperative due to the
obsolescence of their equipment. Solutions such as sta-
tic stations or wireless sensor networks (WSNs) are men-
tioned. However, a combination of FPGA technology for
data processing and Arduino UNO for sensor connection
is proposed, oering a reasonable cost and better perfor-
mance in data monitoring [11].
Compared to the project carried out which focuses
on the educational environment of the Salesian Polyte
-
chnic University, where a weather station will be develo-
ped using FPGA for data processing and Raspberry Pi for
its visualization. In addition, manuals will be created de-
tailing design and programming in LabVIEW. e goal
is to not only build a weather station, but also to provide
valuable educational resources for students interested in
FPGAs, LabVIEW, and sensor integration.
e article presents an IoT-based weather station that
uses the NodeMCU (12E) Wi-Fi ESP8266 microcontro-
ller with DHT11 sensors for temperature and humidity
and MQ135 for air quality. An app called VIT Weather
Station was developed compatible with Android and iOS
for real-time visualization of weather data. is project
demonstrates how IoT can connect devices to collect and
63
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
disseminate environmental data, further addressing air
pollution issues and providing essential information for
public health [29].
Unlike the IoT-based solution, which uses a ESP8266
microcontroller and sensors to transmit data in real-time
via a mobile app, the project focuses on FPGA technolo-
gy. is choice allows for greater processing power and
accuracy in the collection and analysis of weather data.
e FPGA oers superior exibility and eciency, ideal
for applications that require detailed and optimized hand-
ling of environmental data.
In the article a weather station is presented that provi-
des real-time data which uses Raspberry Pi since this pla
-
tform the use of this technology is due to the great cost of
doing it with other devices, this system is based on Iot te
-
chnology for the measurement of several environmental
parameters such as temperature, Humidity, pressure and
precipitation is data is collected and stored in Google
Cloud SQL so that every person with internet availabili-
ty can see it [30].
In contrast to the project, FPGAs are used for the
collection of meteorological data, an option 80% more
expensive than Raspberry Pi but which oers greater be-
nets in processing and accuracy. Although FPGA requi-
res a higher investment, its ability to handle and optimize
data with high accuracy is worth the cost. Unlike the Ras-
pberry Pi, which has limitations in data processing and
support, FPGA provides greater exibility and perfor-
mance, making it a better choice for applications that
need high reliability.
e article presents a weather monitoring system ba-
sed on a network of wireless sensors. It uses a Wi-Fi-ca
-
pable ESP8266 microcontroller and transmits the data to
a Raspberry Pi 3, which acts as a web server with a LAMP
system (Linux, Apache, MySQL and PHP) for storing and
viewing the information. e data can be extracted in Ex-
cel format for analysis [31].
In contrast, the project uses Node-Red for data vi-
sualization and storage, providing a graphical interface
to design data ows without the need for extensive pro-
gramming. In addition, Excel les created in LabVIEW
are used to back up the data, oering a simple and eecti-
ve solution for information management without relying
on complex platforms.
Data obtained from the weather station indicate a
constant temperature of 23.7°C and a humidity of 46.4%.
Wind direction measurements vary between 2.4 km/h
and 4.8 k/h, while the amount of rain remains xed at
1,397. is uniformity in the data suggests that the sys-
tem provides stable and reliable readings for environmen-
tal monitoring during the period evaluated.
e results meet expectations for consistency and ac-
curacy. However, the lack of variability in wind direction
and in the amount of rainfall could indicate limitations
in sensor sensitivity or system conguration. It is advisa-
ble to investigate these aspects to improve the responsi-
veness and accuracy of the system.
When comparing the project of the system with the
one described in the article, which uses a Xilinx Artix7
with advanced sensors and programming in Verilog, it
was found that both systems present stability in data co-
llection. However, the system in the aforementioned ar-
ticle oers greater exibility and integration capacity,
highlighting how advanced technologies can optimize
accuracy and functionality in weather stations [4].
4. Conclusions
e information collected showed a stability of tempe-
rature, humidity and the amount of rainfall during the
observation period. e temperature was approximate-
ly 23.71 °C and the humidity was around 46.39%. e
wind direction varied between 2.4 km/h and 4.8 km/h,
with a predominance of 4.8 km/h, which means modera-
te winds. e amount of rain has always been the same,
1,397 throughout the period.
ese observations suggest that the measurement
environment has been stable and that the FPGA-based
data collection system. Slight variations in wind direction
could reect minor weather changes or sensor uctua-
tions. Taken together, the data collected demonstrates the
system’s eectiveness in real-time monitoring, providing
a solid foundation for future analysis and applications.
e system has the capacity to generate a new mea-
surement every 5 seconds, an attribute derived from its
FPGA-based architecture and its integration with Lab-
VIEW, which enables high-resolution environmental mo-
nitoring. However, for the initial validation and training
of the prediction model, 20 records were acquired daily
for ve days. is approach was adopted in order to en-
sure the stability of the environment and eciency in the
subsequent data analysis.
Comparing the temperature and humidity data taken
with the FPGA and the Arduino, it is observed that the
FPGA oers greater accuracy compared to the Arduino,
which can be attributed to its superior processing capacity
and the quality of the sensors used. In addition, one nota-
ble change is the sampling rate: while the Arduino recom-
mends that readings should not be taken more frequently
than once every two seconds, the FPGA is capable of ta-
king data every 0.5 seconds. is allows for faster chan-
ges in environmental conditions, providing more detailed
and real-time monitoring.
For future work, an improvement in the resources
used is recommended where sensors for air quality and
solar radiation could be added to obtain more data. e
GUI (Graphical User Interface) could be optimized to
create a more intuitive and user-friendly visualization. In
64
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
addition, more advanced data transmission functions and
technologies could be automated for autonomous main-
tenance applications. ese improvements will increase
the eciency and versatility of the system.
In short, this project highlights the excellence of the
technology used in weather stations with high precision of
capture and processing of data in real time. Education, es
-
pecially thanks to the use of Raspberry Pi and Node-RED,
considers it a valuable experience with a wide range of
applications. In addition, the recommendations to im-
prove this system through the acquisition of new advan-
ced sensors are practical. e project takes environmental
work to an advanced level and promises to be a spring-
board for future innovations in the sector.
e FPGA is suitable for real-time weather and data
processing applications due to its high accuracy and e-
ciency. is paper outlines how environmental data co-
llection and analysis can benet from this technology. As
the need for real-time data grows, this component is the
key to future advanced weather systems. e works pro
-
vided here enable exponential growth in this eld.
Seeking to have articial intelligence present in an FP-
GA-based weather station has many benets. is inclu-
des real-time data processing, AI algorithm optimization,
and reducing energy consumption. In addition, with the
help of AI, abnormalities can be easily detected, weather
predictions can be improved, and automated maintenance
tasks can be enabled. Such integration improves the wea-
ther system, making it more accurate and adaptable; is
makes it reliable and useful for all users.
To extend the coverage and accuracy of weather
data, a network connecting weather stations with FPGAs
should be created. is integration would allow for more
detailed data collection to analyze climate patterns at the
regional and global levels in depth. With more complete
and reliable data, research and decision-making on clima-
te would be improved, facilitating a better understanding
and management of climate variability.
is would mean that future research should include
cutting-edge sensors in air quality measurement and ex-
plore networks of distributed sensors. Climate prediction
can be improved by developing machine learning algori-
thms. On the other hand, it should be studied how using
emerging communication technologies such as 5G and
its contribution to disaster management could be ecient
when it comes to addressing extreme events.
References
[1] R. Llugsi, “Climatic Challenges of Quito,” in 2023 IEEE
13th International Conference on Pattern Recognition
Systems, ICPRS 2023, Institute of Electrical and Elec-
tronics Engineers Inc., 2023. https://doi.org/10.1109/
ICPRS58416.2023.10179029
[2] Z. Zhang, Q. Yu, L. Njilla, and C. Kamhoua, “FP-
GA-oriented moving target defense against security
threats from malicious FPGA tools,” Proceedings of the
2018 IEEE International Symposium on Hardware Orien-
ted Security and Trust, HOST 2018, 2018, pp. 163–166,
https://doi.org/10.1109/HST.2018.8383907
[3] Y. Luo, M. A. Awal, W. Yu, and I. Husain, “FPGA im-
plementation for rapid prototyping of high performan-
ce voltage source inverters,” CPSS Transactions on Power
Electronics and Applications, vol. 6, no. 4, 2021, pp. 320–
331, https://doi.org/10.24295/CPSSTPEA.2021.00030
[4] K. Ramesh, “High-Precision and Low-Latency FP-
GA-Based Weather Station,” in 2022 International
Conference on Computer, Power and Communications
(ICCPC), IEEE, Feb. 2022, pp. 77–82. https://doi.
org/10.1109/ICCPC55978.2022.10072196
[5] A. B. Mourão and J. F. D. M. Netto, “Inclusive model
application using accessible learning objects to support
the teaching of mathematics,” Informatics in Educa-
tion, vol. 18, no. 1, pp. 213–226, 2019, https://doi.
org/10.15388/infedu.2019.10
[6] S. Stoyanov, Z. Kuzmanov, and T. Stoyanova, “Weather
Monitoring System Using IoT-based DIY Automatic
Weather Station,” in 2024 9th International Conferen-
ce on Energy Eciency and Agricultural Engineering,
EE and AE 2024 - Proceedings, Institute of Electri-
cal and Electronics Engineers Inc., 2024. https://doi.
org/10.1109/EEAE60309.2024.10600523
[7] H. Santillán, A. Mantilla, D. Cárdenas, and P. Wong,
“Design of a low-cost portable electrocardiograph for
telemedicine application,” Memoria Investigaciones en
Ingeniería, no. 26, pp. 244–264, Jul. 2024, https://doi.
org/10.36561/ing.26.15
[8] T. Barathkumar, S. Divya Dharshin, J. Richard, and T.
Sathiyabalan, “Battery Monitoring System using Lab-
view Interface for Arduino,” in Proceedings of 2024 Inter-
national Conference on Science, Technology, Engineering
and Management, ICSTEM 2024, Institute of Electri-
cal and Electronics Engineers Inc., 2024. https://doi.
org/10.1109/ICSTEM61137.2024.10561237
[9] Y. Adalberto and S. Casadiego, “Importance of meteoro-
logical stations for decision making in agriculture.”, ht-
tps://doi.org/10.13140/RG.2.2.33323.46886
[10] J. Mabrouki, M. Azrour, D. Dhiba, Y. Farhaoui, and S. El
Hajjaji, “IoT-based data logger for weather monitoring
using arduino-based wireless sensor networks with re-
mote graphical application and alerts,” Big Data Mining
and Analytics, vol. 4, no. 1, pp. 25–32, Feb. 2021, https://
doi.org/10.26599/BDMA.2020.9020018
[11] V. Asanza, R. E. Pico, D. Torres, S. Santillan, and J. Ca-
dena, “FPGA based meteorological monitoring sta-
tion,” in 2021 IEEE Sensors Applications Symposium, SAS
2021 - Proceedings, Institute of Electrical and Electro-
nics Engineers Inc., Aug. 2021. https://doi.org/10.1109/
65
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
SAS51076.2021.9530151
[12] L. Camargo A, B. Medina D, y J. Gómez R, “PROTO-
TIPO ELECTRÓNICO IoT PARA EL SEGUIMIENTO
DE VARIABLES AMBIENTALES,” REVISTA AMBIEN-
TAL AGUA, AIRE Y SUELO, vol. 13, no. 2, pp. 1–9, Nov.
2022, https://doi.org/10.24054/raaas.v13i2.2723
[13] M. J. Alam, S. A. Ra, A. A. Badhan, M. N. Islam, S. I.
Shuvo, and A. M. Saleque, “Design and implementation
of a low-cost IoT based tipping bucket rain gauge,” in
2021 International Conference on Emerging Smart Com-
puting and Informatics, ESCI 2021, Institute of Electrical
and Electronics Engineers Inc., Mar. 2021, pp. 279–283.
https://doi.org/10.1109/ESCI50559.2021.9396954
[14] J. S. Gutarra, J. A. Gastelo-Roque, y J. Sulluchuco, “A
cup anemometer using 3D additive manufacturing,”
in 2020 IEEE XXVII International Conference on Elec-
tronics, Electrical Engineering and Computing (INTER-
CON), IEEE, Sep. 2020, pp. 1–4. https://doi.org/10.1109/
INTERCON50315.2020.9220193
[15] A. Almasoud, M. Abbas, M. Aboelola, and A. Alghai-
hab, “FPGA Based DCO With Fine Control Correlation
Calibration Technique,” in Proceedings of the Interna-
tional Conference on Electrical Engineering and Infor-
matics, Institute of Electrical and Electronics Engineers
Inc., 2022, pp. 157–161. https://doi.org/10.1109/ICo-
nEEI55709.2022.9972266
[16] B. H. Raghunandan, A. Mahesh, and M. Babu M.p, “Wi-
reless Communication System Design Using Labview
and Soware Dened Radio,” in 7th IEEE Internatio-
nal Conference on Computational Systems and Infor-
mation Technology for Sustainable Solutions, CSITSS
2023 - Proceedings, Institute of Electrical and Elec-
tronics Engineers Inc., 2023. https://doi.org/10.1109/
CSITSS60515.2023.10334108
[17] Y. M. Inshakov y E. B. Solovyeva, “Simulation of Tuna-
ble Electrical Circuits Using NI ELVIS II Complex,” in
Proceedings of the 2020 IEEE International Conference
“Quality Management, Transport and Information Secu-
rity, Information Technologies”, IT and QM and IS 2020,
Institute of Electrical and Electronics Engineers Inc.,
Sep. 2020, pp. 477–480. https://doi.org/10.1109/ITQ-
MIS51053.2020.9322801
[18] V. A. Sumayyabeevi, J. J. Poovely, N. Aswathy, y S. Chin-
nu, “A New Hardware Architecture for FPGA Imple-
mentation of Feed Forward Neural Networks,” in
ACCESS 2021 - Proceedings of 2021 2nd International
Conference on Advances in Computing, Communication,
Embedded and Secure Systems, Institute of Electrical and
Electronics Engineers Inc., 2021, pp. 107–111. https://
doi.org/10.1109/ACCESS51619.2021.9563342
[19] M. Bilal, Razia, A. Hussain, M. Khan, A. Munaim, y
M. Alim, “PWM Waveform Generation and Control
Using Rotary Encoder on Spartan-3E Starter Board,”
in IMTIC 2023 - 7th International Multi-Topic ICT
Conference 2023: AI Convergence towards Sustainable
Communications, Institute of Electrical and Electro-
nics Engineers Inc., 2023. https://doi.org/10.1109/IM
-
TIC58887.2023.10178475
[20] C. A. Nugroho y A. Amiruddin, “A Low-cost Disk Ima-
ging Device Using Raspberry Pi 4 for Digital Forensics,”
in Proceedings - 2023 IEEE International Conference on
Cryptography, Informatics, and Cybersecurity: Cryptogra-
phy and Cybersecurity: Roles, Prospects, and Challenges,
ICoCICs 2023, Institute of Electrical and Electronics En-
gineers Inc., 2023, pp. 314–319. https://doi.org/10.1109/
ICoCICs58778.2023.10276963
[21] P. Bagdalkar y L. Ali, “Interfacing of light sensor with
FPGA using I2C bus,” in 2020 6th International Confe-
rence on Advanced Computing and Communication Sys-
tems (ICACCS), IEEE, Mar. 2020, pp. 843–846. https://
doi.org/10.1109/ICACCS48705.2020.9074372
[22] V. Nicolau, M. Andrei, y B. Olteanu, “Data Switch in
Long Line 1-Wire Networks for Monitoring System of
Large Sensor Arrays,” in IEEE International Symposium
for Design and Technology of Electronics Packages, SIIT-
ME - Conference Proceedings, Institute of Electrical and
Electronics Engineers Inc., 2023, pp. 265–268. https://
doi.org/10.1109/SIITME59799.2023.10430495
[23] A. Garbev, “Possibilities for Visual Programming in
Robotics via Node-RED,” in International Conferen-
ce Automatics and Informatics, ICAI 2022 - Procee-
dings, Institute of Electrical and Electronics Engineers
Inc., 2022, pp. 367–371. https://doi.org/10.1109/
ICAI55857.2022.9960113
[24] H. J. Santillán Carranza, P. Wong Wong, K. E. R. Alvara-
do, y M. J. Huayamave Cordero, “Implementación de un
Sistema Meteorológico Educativo Usando IoT y Raspe-
rry PI,” INGENIO, vol. 7, no. 2, pp. 4–17, Jun. 2024, ht-
tps://doi.org/10.29166/ingenio.v7i2.5265
[25] R. L. Canar, A. Fontaine, P. L. Morillo, y S. El Yacou-
bi, “Deep Learning to implement a Statistical Weather
Forecast for the Andean City of Quito,” in 2020 IEEE
ANDESCON, IEEE, Oct. 2020, pp. 1–6. https://doi.
org/10.1109/ANDESCON50619.2020.9272106
[26] Z. Peng et al., “Average-Approximation SIP Algorithms
Used to Improve Accuracy of RC Model Order Reduc-
tion,” in 2023 International Symposium of Electronics De-
sign Automation, ISEDA 2023, Institute of Electrical and
Electronics Engineers Inc., 2023, pp. 71–75. https://doi.
org/10.1109/ISEDA59274.2023.10218670
[28] H. Ma, Y. Qiao, J. Li, y X. Mao, “Design of an AI Sma-
ll-Scale Meteorological Monitoring System,” in IMCEC
2024 - IEEE 6th Advanced Information Management,
[27] N. U. Khuzairi, S. Saon, A. K. Mahamad, M. S. M. Zai-
nordin, S. Yamaguchi, y M. A. Bin Ahmadon, “Weather
Station Monitoring System with Node-RED,” in GCCE
2023 - 2023 IEEE 12th Global Conference on Consumer
Electronics, Institute of Electrical and Electronics Engi-
neers Inc., 2023, pp. 678–681. https://doi.org/10.1109/
GCCE59613.2023.10315514
66
Implementation of a Weather Station using FPGA with Real Time Data Access and Analysis
Communicates, Electronic and Automation Control
Conference, Institute of Electrical and Electronics Engi-
neers Inc., 2024, pp. 297–301. https://doi.org/10.1109/
IMCEC59810.2024.10575073
[29] N. Tandan et al., “Design and Implementation of
IoT Based Local Weather Station - An Experimen-
tal Setup,” in IBSSC 2022 - IEEE Bombay Section
Signature Conference, Institute of Electrical and Elec-
tronics Engineers Inc., 2022. https://doi.org/10.1109/
IBSSC56953.2022.10037309
[30] V. Mathur, Y. Saini, V. Giri, V. Choudhary, U. Bharad-
waj, and V. Kumar, “Weather Station Using Raspberry
Pi,” in Proceedings of the IEEE International Conference
Image Information Processing, Institute of Electrical and
Electronics Engineers Inc., 2021, pp. 279–283. https://
doi.org/10.1109/ICIIP53038.2021.9702687
[31] S. B. Merrah y A. Adane, “Meteorological sensor ne-
twork based on ESP8266,” in 2023 International
Conference on Advances in Electronics, Control and Com-
munication Systems, ICAECCS 2023, Institute of Elec-
trical and Electronics Engineers Inc., 2023. https://doi.
org/10.1109/ICAECCS56710.2023.10105060
