Data Acquisition System Research Articles

FRF-based modal testing of the highest timber building in the world in operation using wireless accelerometers

Abstract This paper describes practical application of a novel and oven-controlled crystal oscillator (OCXO) high-precision synchronised wireless data acquisition system for a modal testing of the tallest all-timber building in the world – the 18-storey, 88.8m tall Mjøstårnet (the Mjøsa Tower) in Brumunddal, Norway. The modal testing was challenging as it was conducted in an occupied building in normal operation and was based on measuring both dynamic excitation and response yielding a set of frequency response functions (FRFs) across four different floors of the building. To the best knowledge of the authors, this was the first time that an FRF-based modal test was attempted on such a large building structure in operation. Moreover, although the structure was in operation, the testing was completed in only 48h, including commissioning and de-commissioning of all of the instrumentation, yielding a full set of point- and transfer accelerance FRFs. The paper presents the equipment used, key logistical challenges and quality assurance steps made to assure measurement of good quality FRF data in such short period of time demonstrating feasibility of the whole exercise. A total of seven modes of vibration, including higher order modes of vibration were estimated by curve-fitting the measured FRF data. This includes the 3rd bending and 2nd torsion modes difficult to measure via the standard ambient vibration testing of tall buildings. These modes could be used to investigate a number of uncertain modelling features in tall timber buildings, such as the stiffness of timber joints.

Development of silicon-on-insulator direct electron detector with analog memories in pixels for sub-microsecond imaging.

We have developed a high-speed recordable direct electron detector based on silicon-on-insulator technology. The detector has sixteen analog memories in each pixel to record sixteen images with sub-microsecond temporal resolution. A dedicated data acquisition system has also been developed to display and record the results on a personal computer. The performance of the direct electron detector as an image sensor is evaluated under electron irradiation with an energy of 30 keV in a low-voltage transmission electron microscope equipped with a photocathode electron gun. We demonstrate that the detector can record images at an exposure time of 100 ns and an interval of 900 ns.

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Characterization and Performance Evaluation of ESP32 for Real-time Synchronized Sensor Networks

In recent years, technological advancement characterized by heightened connectivity and digitization has revolutionized our interaction with the world. This evolution has been exemplified by the emergence of the Internet of Things, which has introduced a new era of digitization and automation, primarily propelled by devices based on the ESP32 microcontroller. This study seeks to comprehensively evaluate and characterize a fully functional sensor network built upon the ESP32 microcontroller. The evaluation covers three key aspects: network performance, the ESP32’s built-in ADC peripheral, and the data acquisition system. The ESP32 device has demonstrated its capability to consistently sample sensors at a high rate with remarkable stability. The ESP32’s built-in ADC exhibited undesirable behavior for analog devices, attributed to noise and non-linearity. As a result, in applications requiring precision with analog sensors, the utilization of a cost-effective external ADC, as employed in this study, is recommended. Finally, during the network analysis of the sensor network, no issues were identified. The data acquisition system exhibited no bottlenecks, and packet losses remained minimal, regardless of the sampling period.

MEASUREMENT OF BASIC HEMODYNAMICS DURING MULTIPOST TRANSCUTANEOUS EXTERNAL EAR STIMULATION: A TWO-CASE STUDY

The objectives of the investigation were to assess the short-term responses of the cardio-vascular system on the multipost transcutaneous stimulation (tANS) of the external ear (EE). The scope was to measure the forefinger photopleths (FPPG), toe photopleths (TPPG), aortic phonocardiogram (APCG), mitral phonocardiogram (MPCG) and brachial arterial blood pressure (BABP), assuming that they could be altered with the tANS. For the tANS, stimulator, two silicone ear plugs with four platinum electrodes each, and a large common electrode (CE), were used. Trials were carried out with two female volunteers, aged 25 years and 28 years. To capture the heart sounds, two customized electronic stethoscopes (transducers) were used. BABP was measured using a pressure transducer and blood-pressure appliance. To measure the FPPGs, a pulse oximeter and SpO2 finger clip were used. To measure the TPPGs, another pulse oximeter and customized SpO2 foot clip was used. Signals were gathered using a high-performance data-acquisition system. An offline analysis was made just before and just after the start of the tANS. To record the pulsations in the right brachial artery, the 2D ultrasound mode (B) of the ultrasound device was used before and during the tANS. Then, diagrams showing the vein cross-sectional area over time were constructed. The results show that vascular time intervals between heart sounds S1 and S2, captured as APCG and systolic peaks of the FPPG in the second volunteer, were slightly larger during the separate tANS of the left white (LW) and (RW) EE post than before the tANS. Furthermore, vascular time intervals between the heart sounds S1 and S2, captured as MPCG and systolic peaks of the TPPG in the second volunteer, were slightly smaller during the separate tANS of LW and RW EE post than before the tANS. Finally, tANS of the LW post elicited a slightly lower heart rate than the one measured when LW was not stimulated. In contrast, the tANS of the RW post elicited a slightly higher heart rate than the one measured when RW was not stimulated. It was also shown that the pattern of the vein cross-sectional area over time with tANS was different compared to both the FPPG and TPPG.

IoT-Based Monitoring, Communication, and Control of Small Wind Turbines Using IoT Cloud Service

Addressing global climate change requires a shift to sustainable and renewable energy sources. Utilizing wind power via wind turbines presents a viable remedy. However, because wind patterns are unpredictable and these turbines are located in remote areas, effectively operating wind farms continues to be a difficulty. In this work, we present an integrated system that uses IOT Cloud Service to monitor, connect with, and manage small wind turbines utilizing IoT (Internet of Things) technology. We solve the issues with data collection and control those older turbines without supervisory control and data acquisition (SCADA) systems confront by fusing cloud-enabled IoT hubs with on-site sensing units. Our method guarantees effective control of wind energy devices, two-way communication, and real-time monitoring. With an emphasis on utilizing IoT cloud services, this paper offers a thorough examination of the application of Internet of Things (IoT) technology for the monitoring, communication, and control of tiny wind turbines. In decentralized and off-grid energy applications, small wind turbines are vital, and maximizing energy output and guaranteeing long-term sustainability requires optimizing their performance and maintenance. The scalability, flexibility, and real-time capabilities required to efficiently operate small wind energy projects are sometimes absent from traditional monitoring and control systems.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.

Power System State Estimation Based on Fusion of PMU and SCADA Data

This paper introduces a novel hybrid filtering algorithm that leverages the advantages of Phasor Measurement Units (PMU) to address state estimation challenges in power systems. The primary objective is to integrate the benefits of PMU measurements into the design of traditional power system dynamic estimators. It is noteworthy that PMUs and Supervisory Control and Data Acquisition (SCADA) systems typically operate at different sampling rates in power system estimation, necessitating synchronization during the filtering process. To address this issue, the paper employs a predictive interpolation method for SCADA measurements within the framework of the Extended Kalman Filter (EKF) algorithm. This approach achieves more accurate estimates, closer to real observation data, by averaging the KL distribution. The algorithm is particularly well-suited for state estimation tasks in power systems that combine traditional and PMU measurements. Extensive simulations were conducted on the IEEE-14 and IEEE-30 test systems, and the results demonstrate that the fused estimator outperforms individual estimators in terms of estimation accuracy.

Miniaturized preamplifier integration in ultrasound transducer design for enhanced photoacoustic imaging.

Photoacoustic imaging (PAI) utilizes the photoacoustic effect to record both vascular and functional characteristics of a biological tissue. Photoacoustic signals have typically low amplitude that cannot be read efficiently by data acquisition systems. This necessitates the use of one or more amplifiers. These amplifiers are somewhat bulky (e.g., the ZFL-500LN+, Mini-Circuits, USA, or 351A-3-50-NI, Analog Modules Inc., USA). Here, we describe the fabrication and development process of a transducer with a built-in low-noise preamplifier that is encased within the transducer housing. This new, to the best of our knowledge, design could be advantageous for applications where a compact transducer + preamplifier is required. We demonstrate the performance of this compact detection unit in a laser scanning photoacoustic microscopy system by imaging a rat ear ex vivo and a rat brain vasculature in vivo.

Design and implementation of a beam-synchronized data acquisition system for CSNS accelerator

Design and implementation of a beam-synchronized data acquisition system for CSNS accelerator

Assessment of Maximal Oxygen Uptake (VO2 Max) in Athletes and Nonathletes Assessed in Sports Physiology Laboratory.

Athletes' physical prowess plays a crucial role in their ability to succeed in any sporting endeavor. Each athlete on the field must have an exceptional aerobic capacity to withstand fierce competition and stringent regulatory guidelines.Maximal oxygen uptake (VO2max) is a quantitative measure of aerobic capacity and is regardedas one of themost reliable indicators ofcardiorespiratory and overall physical fitness of an individual by sports physiologists. The study aims to evaluate the VO2 max of athletes in comparison with nonathletes during treadmill and lower limb cycle ergometry exercises as assessed in the Sports Physiology Laboratory of a rural medical college.Treadmill exercise and bicycle ergometer exercise are the most common to perform as indoor aerobic exercises to assess one's physical fitness. Both these tests are equally useful in eliciting cardiac and vascular responses, so both these modalities were used to assess aerobic fitness. This cross-sectional study, which examined participants aged 17-25, included 30 athletes (cases) and 120 age- and sex-matched controls. The VO2 max was evaluated using the Metabolic Module of Lab Chart Software, which was investigated through the PowerLab data acquisition system, AD Instruments (Bella Vista,NSW, Australia). The mean age of male athletes was 20.51 ± 2.69 years and of female athletes was 20.53 ± 1.62 years. The mean and standard deviation of VO2 max on the treadmill for male cases was 52.37 ± 8.78 mL/kg/min and for female cases was 40.96 ± 4.06 mL/kg/min, and on a cycle ergometer for male cases was 45.21 ± 9.43 mL/kg/min and for female cases was 34.32 ± 5.12 mL/kg/min. For the control group, the mean age of control males was 21.2 ± 2.62 years and of control females was 20.36 ± 1.5 years. The mean and standard deviationof VO2 max on the treadmill for control males was 33.35 ± 3.77 mL/kg/min and for control females was 25.09 ± 7.07 mL/kg/min, and on the cycle ergometer for control males was 34.17 ± 2.75 mL/kg/min and for control females was 24.15 ± 5.35 mL/kg/min. This study showed significantly (p < 0.001) higher VO2 max levels in athletes of both genders compared to their age- and sex-matched controls upon exercise on the treadmill and cycle ergometer. This study underscores the significance of better cardiorespiratory fitness in athletes than nonathletes, giving pertinent insights about their aerobic capacity, which is precisely measured and expressed in terms of VO2.

Design and Development of Arduino-Based Four-Channel Data Acquisition System with Digital Temperature Control for Chemiresistive Sensors

We report the design and development of simple and economical Arduino-based four-channel data acquisition system with digital temperature control that is suitable for sensor parameter optimization involved in the search of appropriate chemiresistive gas sensing material for a particular gas. The developed system is capable of simultaneously capturing the data as well as controlling the desired operating temperature (up to 300 °C) of four sensing elements. This is achieved by employing sixteen analog and digital channels of the Arduino nano microcontroller. The sensing circuit is capable of recording the resistance changes from 20 kΩ to 5 MΩ at a rate of 15 kHz. The developed system has been tested rigorously, validated and demonstrated for its effectiveness towards investigating the gas sensing properties of chemiresistive NO2 sensors realized using ZnO nanowires. The present simple and low-cost alternative finds its application for developing the target specific chemiresistive sensors in particular for investigating the gas sensing properties of metal oxide semiconductors.

Design and Experiments of the Data Acquisition System for Bale Rolling Characteristic Parameters on a Large-Scale Round Bale Machine

The parameters of the roll characteristics of a large-scale round bale machine were collected in real time to investigate the bale rolling mechanism. This investigation develops a set of adaptable and highly integrated data acquisition systems for the bale rolling performance parameters of large-type round bale machines. A rolling experiment is conducted using sunflower straw as the material, and the power consumption and radial tension of the roller-round bale machine during the bale rolling process are studied. In the grass core formation stage, the round bale machine’s torque need was minimal, the radial tension of the bale remained nearly constant, and the bale chamber was primarily filled with loose sunflower straw. The motor torque and the straw bale’s radial tension both showed a tendency of gradual increase when the round bale machine was in the grass-filling stage. The motor torque and bale radial tension displayed a roughly linear trend of rapid rise as the sunflower straw continued to enter the rolling bale chamber; this was when the round bale machine was in the compressed bale rolling stage. When the power consumption of the round bale machine was measured using the data acquisition system during the test bench empty run and core-creation stage, the energy consumption comparison analysis produced a relative error of 5.8%. During the stage of bale rolling and compression, the data acquisition system monitored the power consumption of the round bale machine. The relative error was 9.5%. The data acquisition system of the round bale machine test bed has an accuracy of 90.5%–94.2% when measuring the machine’s power consumption, indicating that it is a stable and efficient system. This study provides a foundation for further research on intelligent the roller-round bale machine.

High-Speed Surface Property Recognition with a 140 GHz Frequency

In the field of integrated sensing and communication, there is a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous systems’ decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for such conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was prepared for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method’s effectiveness in high-speed conditions, suggesting terahertz frequencies’ potential in future sensing and communication applications.

Conformal, stretchable, breathable, wireless epidermal surface electromyography sensor system for hand gesture recognition and rehabilitation of stroke hand function

Surface electromyography (sEMG) plays a significant role in the everyday practice of clinic hand function rehabilitation. The materials and design of current typical clinic sEMG electrodes are rigid Ag/AgCl or flexible polyimide (PI) film, which cannot provide a stable interface between electrodes and skin for adequate long-term high-quality data. Thus, conformal, soft, breathable, wireless epidermal sEMG sensor systems have broad potential relevance to clinic rehabilitation settings. Herein, we demonstrate a stretchable epidermal sEMG sensor array system with optimized materials and structure strategies for hand gesture recognition and hand function rehabilitation. The optimized serpentine structures with marvelous stretchability and improved fill ratio, provide lower impedance and high-quality sEMG signals. Moreover, the easy-to-use airhole method further ensures stable and long-term contact with the skin for recording. In addition, integrated with a customized flexible wireless data acquisition system, the capability for real-time 8-channel sEMG monitoring is developed, and taking together with the CNN-based algorithm, the system can automatically and reliably realize the 7 kinds of hand gestures with an accuracy of 81.02%. Moreover, the low-cost yet high-performance epidermal sEMG sensor system demonstrated its conceptual feasibility in quantitatively evaluation of stroke patient’s hand and facilitating human-robot collaboration in hand rehabilitation by proof-of-the-concept clinical testing.

Comparative study for the performance of pure artificial intelligence software sensor and self-organizing map assisted software sensor in predicting 5-day biochemical oxygen demand for Kauma Sewage Treatment Plant effluent in Malawi

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater.Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents’ temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors.Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi.

The design of a scientific data management system based on DOMAS at CSNS-II (preliminary stage)

At the second stage of China Spallation Neutron Source (CSNS-II), it is predicted that 2 PB raw experimental data will be produced annually from twenty instruments. Scientific computing puts forward higher requirements for data sharing, utilization, retrieval, analysis efficiency, and security. However, the existing data management system (DMS) based on ICAT has several limitations including poor scalability of metadata database, imperfect data-management lifecycle and inflexible API. To ensure the accuracy, usability, scalability and efficiency of CSNS-II experimental data, a new scientific data management system is therefore designed based on the DOMAS framework developed by the Computing Center of IHEP. The data acquisition, transmission, storage and service systems are re-designed and tailored specifically for CSNS-II. Upon its completion, the new DMS will overcome the existing challenges and offer functions such as online display, search functionality and rapid download capabilities for metadata, raw data and analyzed data; flexible and user-friendly authorization; and data lifecycle management. Ultimately, the implementation of the new Data Management System (DMS) is expected to enhance the efficiency of experimental data analysis, propelling CSNS-II to achieve international advanced standards. Furthermore, it aims to reinforce self-reliance and technological strength in the field of science and technology at a high level in China. The development and deployment of the new DMS begin at the end of 2023.

The data acquisition methodology to build the source information base for personnel monitoring (technological approach)

The All-Russia monitoring of the staffing needs in librarianship is an efficient way to get valuable current and prospective data through using the latest technological solutions and efficient interaction between public libraries affiliated with regional/municipal agencies of culture of the Russian Federation constituents.The authors discuss the technology and results of data acquisition, as well as the obtained staff monitoring database. For the first time, the tested method of data acquisition enabled the Russian State Library to monitor the staff needs in librarianship in the multitasking mode and to apply the cloud technology with the professional application designed for online operation on the national scope. The authors explore the differences between national and foreign software providers in rendering specific services to online users. The professional application enabled to conduct monitoring in the shortest term, to optimize expenses and to save resources in complex environment. Among methodological solutions, the Center of Educational Programs Monitoring of the Research Educationa Activities Department of the Russian State Library, have designed the standardized data collection forms for every level of libraries.The effectiveness of this methodology enabled to launch the system of data acquisition for the All-Russian monitoring of staff needs in the library sphere, to systematize data by the RF constituents and as a data set, and to build the source database for staff monitoring in librarianship.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

Characterization and Calibration of a Photodiode Based Pyranometer for Solar Energy Applications

&lt;p&gt;In the domain of solar science, a pyranometer assumes a pivotal position, functioning as an indispensable tool for appraising solar irradiance across the abbreviated wavelengths of the solar spectrum, spanning from 300 to 3000 nanometers. Conventional pyranometers typically employ intricate and labour intensive thermopiles. These devices rely on arrays of thermocouples interconnected either serially or in parallel and necessitate rare earth minerals for efficient operation. In this investigation, we advocate for an innovative methodology employing photodetectors, specifically photodiodes and phototransistors, to surmount the limitations linked with traditional pyranometers. Photodiodes, commonly utilized for this purpose, proffer numerous benefits, including accelerated response times, customizable spectral range selection, and cost-effectiveness. Nonetheless, they cannot entirely supplant thermopiles owing to impediments such as the generation of dark current in the absence of illumination, temperature-dependent functionalities, and saturation at elevated irradiance levels. To counteract these constraints, we have devised a pioneering design of a photodiode-based pyranometer that incorporates a data acquisition system and correction mechanism. This innovative approach furnishes a feasible resolution for autonomous solar radiation measurement, furnishing enhanced precision and dependability.&lt;/p&gt;