Data Acquisition Tools Research Articles

MATLAB-Based Real-Time Data Acquisition Tool for Multimodal Biofeedback and Arduino-Based Instruments

AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.

Analysis of Concentration in English Education Learning Based on CNN Model

Obtaining the learning behavior of terminal learners solves the problem of inability to understand students’ concentration in class. When learners learn English, they can obtain learning concentration to understand learners’ concentration, in order to analyze the influence coefficient of various learning behavior data on learning concentration. This paper will design and implement a terminal data acquisition tool to collect the device perception information in the learning environment of learners, and capture the learner’s touch screen operation data based on the virtual simulation experiment, and then use the improved neural network to process the collected terminal sensor data for learning behavior. We identify and obtain the learner’s learning activity state, and finally fit the learner’s behavioral data weight through a linear regression equation, monitor the learner’s learning state, and explore the influencing factors of learning concentration.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography−mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Revolutionizing Rocket Development With Testing

The latest intstrumentation and data acquisition tools combined with an agile approach are being applied to develop the next generation of space launch systems

MULTI-MODAL TRIANGULATION FOR IN-SITU BORESIGHT CALIBRATION OF FRAME AND LINE CAMERAS ONBOARD GNSS/INS-ASSISTED UAVS

Abstract. Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with RGB and hyperspectral (HS) cameras have become popular data acquisition tool for several applications. To derive accurately georeferenced products from such systems, the spatial and rotational offset between the onboard sensors and the GNSS/INS unit must be determined. While the spatial relationship can be measured manually, establishing the angular offset – i.e., boresight angles – is more challenging and requires a calibration strategy. Given that the majority of RGB cameras are based on frame imaging mechanism, boresight calibration of these sensors is usually conducted through automated triangulation of overlapping images. On the other hand, most of current HS cameras are based on push-broom technology – also known as line cameras – which capture 1D images at a time. Consequently, automated triangulation of these non-overlapping images is not possible and thus, current boresight calibration strategies for HS cameras are based on calibration missions where special targets are deployed in the study site and are manually measured in the imagery. Although boresight calibration missions can lead to accurate system calibration parameters, they are expensive and labor intensive activities. To address these limitations and motivated by the new trend of UAVs equipped with both RGB and HS cameras, this study proposes a multi-modal triangulation approach to conduct an in-situ boresight calibration for the two cameras, simultaneously. Experimental results over an agricultural field and an urban area show that the proposed approach results in orthophotos with high visual quality and geolocation accuracy.

Flash Flood Risk Assessment and Mitigation in Digital-Era Governance Using Unmanned Aerial Vehicle and GIS Spatial Analyses Case Study: Small River Basins

Watercourses act like a magnet for human communities and were always a deciding factor when choosing settlements. The reverse of these services is a potential hazard in the form of flash flooding, for which human society has various management strategies. These strategies prove to be increasingly necessary in the context of increased anthropic pressure on the floodable areas. One of these strategies, Strategic Flood Management (SFM), a continuous cycle of planning, acting, monitoring, reviewing and adapting, seems to have better chances to succeed than other previous strategies, in the context of the Digital-Era Governance (DEG). These derive, among others, from the technological and methodological advantages of DEG. Geographic Information Systems (GIS) and Unmanned Aerial Vehicles (UAV) stand out among the most revolutionary tools for data acquisition and processing of data in the last decade, both in qualitative and quantitative terms. In this context, this study presents a hybrid risk assessment methodology for buildings in case of floods. The methodology is based on detailed information on the terrestrial surface—digital surface model (DSM) and measurements of the last historical flash flood level (occurred on 20 June 2012)—that enabled post-flood peak discharge estimation. Based on this methodology, two other parameters were calculated together with water height (depth): shear stress and velocity. These calculations enabled the modelling of the hazard and risk map, taking into account the objective value of buildings. The two components were integrated in a portal available for the authorities and inhabitants. Both the methodology and the portal are perfectible, but the value of this material consists of the detailing and replicability potential of the data that can be made available to administration and local community. Conceptually, the following are relevant (a) the framing of the SFM concept in the DEG framework and (b) the possibility to highlight the involvement and contribution of the citizens in mapping the risks and their adaptation to climate changes. The subsequent version of the portal is thus improved by further contributions and the participatory approach of the citizens.

Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture.

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science.

Water Flow Measurement-Based Data Acquisition Using Arduino Microcontroller and PLX-DAQ Software

The data acquisition for monitoring the water flows in real-time, which is available at any time, is needed for water management purposes. This paper aims to build a prototype of a water flow measurement system in an open channel of a rectangular weir box design using the American Standard Testing and Material (ASTM). This research contribution is a development of a water flow measurement, which can be used as a simulator for studies on measuring water discharge in real cases in the field. More, this instrument is based on the data logger using Arduino, which is designed at a low cost and is easy to use. This water flow equipment can be measured in real-time, so that data information can be directly obtained for analysis. The design of a data acquisition system can display water discharge data in real-time from time to time and allows data storage (data logger) as historical data that can be displayed whenever needed. The Arduino UNO ATMEGA 328 microcontroller was programmed to read the HC-SR04 water level sensor on a distance-based weir box displayed on the LCD. Monitoring and recording of data were displayed on the Parallax Data Acquisition tool (PLX-DAQ) software add-on for Microsoft Excel in real-time. The prototype was able to provide a real simulation of the water flow calculation process until the maximum design capacity of 784,384.87 liters per day. Tests on the overall performance of the water flow measurement system were carried out using flowing water media at 3 different flow conditions based on time. From the average log data of the tests carried out, the deviation of the measurement data against the ASTM calculation theory on average of 0.8 liters/minute. These results were quite good because of the 16,502 liters of water measured. The difference in the calculation results was only 1.003 liters.

Leveraging audio hardware for underwater acoustics

Both active and passive underwater acoustic systems frequently use purpose built hardware that is designed for a particular application. Transducers, electronics, and data acquisition systems for field experiments, therefore, are often expensive, tailored to limited frequency bands, or packaged for integration with a specific platform. Reliance on custom hardware, however, can make initial experimental investigations of new sensing paradigms cost prohibitive. The expense also creates an entry barrier to experimental work for students and researchers outside the underwater acoustics community. Audio hardware, however, is widely available, easily integrated using commercial data acquisition tools, and is often of relatively low cost. This presentation will describe two efforts to use audio hardware for inexpensive experimental investigations of underwater acoustics topics. The first, AirSAS, uses audio transducers and electronics to investigate synthetic aperture sonar problems in air that are analogous to those underwater. The second, the Citizen Scientist Hydrophone, integrates underwater transducers with consumer audio electronics to make an inexpensive, multipurpose passive sensing device. Challenges and limitations of using commercial audio hardware for underwater acoustics, especially as they relate to accuracy and data quality, will be discussed.

Global Navigation Satellite System (GNSS) and other geospatial tools for various applications

Nowadays the Global Satellite Navigation Systems (GNSS) play a fundamental role in many areas as civil aviation, maritime and land navigation and geomatics, owing to the ability to provide worldwide, tridimensional, all-weather position, and velocity and time synchronization. The final product of the Global Navigation Satellite System exercise is the Three - Dimensional coordinates (3D) of the receiving station. These coordinates have been found reliable for most geospatial applications. However, some applications in data management required other information apart from geodetic coordinates. Hence; GNSS has been integrated with other methods of data acquisition to improve the quality of data for various applications. These have helped in solving a lot of problems where individual methods failed. This paper examines some satellite-based systems and reports the integration of GNSS with other data acquisition tools such as geodetic levelling, Remote Sensing, Geographic Information System (GIS), Inertial Navigation System (INS) and many others. In some cases, the synergy had resulted in other satellite or payload programs such as Gravity Recovery and Climate Experiment (GRACE), while it has improved the GNSS applications in so many areas. GNSS integration is therefore recommended for any application that requires the use of the location.

Optimal PMU placements using sea lion optimization for adaptable distribution system

The technological advancements and policy reformations make the electric power distribution system an active network. The modern power system operational regulations encourage coordinated network operations among various entities and customer participation. Unlike traditional operational environments, real-time monitoring has become a mandate for secure and reliable operations. Phasor Measurement Units (PMU) is the most reliable data acquisition tool, and their application for State Estimation (SE) has already been proven. This work extends the application of PMUs for the distribution system. A multi-objective optimization framework is proposed considering the optimal placement of PMU, network reconfiguration, and topology expansion. A novel optimization tool, the sea lion optimizer has been chosen to address the developed optimization problem. The proposed operational model is employed in IEEE RBTS-2 bus system, IEEE 33 node system and 69 node radial distribution network. Numerical results provide new insights into PMUs role in enhancing distribution network operations. The statistical indices confirm that the intended optimizer performs well in the chosen highly constrained optimization environment.

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System.

Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

Robust imaging using electrical impedance tomography: review of current tools.

Electrical impedance tomography (EIT) is a medical imaging technique with many advantages and great potential for development in the coming years. Currently, some limitations of EIT are related to the ill-posed nature of the problem. These limitations are translated on a practical level by a lack of genericity of the developed tools. In this paper, the main robust data acquisition and processing tools for EIT proposed in the scientific literature are presented. Their relevance and potential to improve the robustness of EIT are analysed, in order to conclude on the feasibility of a robust EIT tool capable of providing resistivity or difference of resistivity mapping in a wide range of applications. In particular, it is shown that certain measurement acquisition tools and algorithms, such as faulty electrode detection algorithm or particular electrode designs, can ensure the quality of the acquisition in many circumstances. Many algorithms, aiming at processing acquired data, are also described and allow to overcome certain difficulties such as an error in the knowledge of the position of the boundaries or the poor conditioning of the inverse problem. They have a strong potential to faithfully reconstruct a quality image in the presence of disturbances such as noise or boundary modelling error.

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing.

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions.

Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.

Smartphone-Based Photogrammetry Assessment in Comparison with a Compact Camera for Construction Management Applications

Close-range photogrammetry (CRP) has proven to be a remarkable and affordable technique for data modeling and measurements extraction in construction management applications. Nevertheless, it is important to aim for making CRP more accessible by using smartphones on-site directly without a pre-calibration procedure. This study evaluated the potential of smartphones as data acquisition tools in comparison with compact cameras based on the quality and accuracy of their photogrammetric results in extracting geometrical measurements (i.e., surface area and volume). Two concrete specimens of regular shapes (i.e., beam and cylinder) along with an irregular-shaped sand pile were used to conduct this study. The datasets of both cameras were analyzed and compared based on lens distortions, image residuals, and projections multiplicity. Furthermore, the photogrammetric models were compared according to various quality criteria, processing time, and memory utilization. Though both cameras were not pre-calibrated, they both provided highly accurate geometrical estimations. The volumetric estimation error ranged from 0.37% to 2.33% for the compact camera and 0.67% to 3.19% for the smartphone. For surface area estimations, the error ranged from 0.44% to 0.91% for the compact camera and 0.50% to 1.89% for the smartphone. Additionally, the smartphone data required less processing time and memory usage with higher applicability compared with the compact camera. The implication of these findings is that they provide professionals in construction management with an assessment of a more direct and cost-effective 3D data acquisition tool with a good understanding of its reliability. Moreover, the assessment methodology and comparison criteria presented in this study can assist future research in conducting similar studies for different capturing devices in construction management applications. The findings of this study are limited to small quantification applications. Therefore, it is recommended to conduct further research that assesses smartphones as a photogrammetric data acquisition tool for larger construction elements or tracking ongoing construction activities that involve measurements estimation.

Intra-Source Provenance Study on Monte Arci (Sardinia) Obsidian by pXRF: Role of the Data Acquisition and Analysis Tools

Intra-Source Provenance Study on Monte Arci (Sardinia) Obsidian by pXRF: Role of the Data Acquisition and Analysis Tools

Linear Feature-Based Triangulation for Large-Scale Orthophoto Generation Over Mechanized Agricultural Fields

Unmanned aerial vehicles (UAVs) equipped with imaging and ranging sensors have become an effective remote sensing data acquisition tool for digital agriculture. Among potential products derived from UAVs, high-resolution orthophotos play an important role in several phenotyping activities, such as canopy cover estimation and flowering date identification. Current structure from motion (SfM) tools for image-based 3-D reconstruction and orthophoto generation cannot perform well when working with large-scale imagery over mechanized agricultural fields. This failure is mainly due to their inability to identify enough conjugate points among overlapping images captured at low altitudes. This study addresses such limitation through a new strategy that uses plant row segments as linear features in the triangulation process. The linear features are derived in two steps. First, an automated approach is implemented to extract plant row segments from the LiDAR data which are then back-projected to the imagery using available trajectory and system calibration parameters. In the second step, a machine-assisted strategy is used to adjust the line segments in image space for deriving accurate linear features. In the proposed framework, the triangulation process is conducted by investigating two mathematical models—referred to as object-space and image-space coplanarity constraints—for incorporating linear features in the bundle adjustment (BA). The orthophoto is generated using the refined trajectory and system calibration parameters derived from the BA process. Several experimental results over an agricultural filed show that the proposed framework outperforms commonly used SfM tools, e.g., Pix4D Mapper Pro and Agisoft Metashape in terms of generating orthophotos with high visual quality and geolocation accuracy. Also, results indicate that the object-space coplanarity constraint is more robust against potential noise in line measurements when compared to the image-space coplanarity model. However, both models lead to high absolute accuracy in the range of ±2–4 cm when the noise level in the image measurements of points along the line is reasonable, i.e., ~5–10 pixels.

Object Recognition and Virtual Retail Algorithms, Metaverse and Immersive Technologies, and Simulation Modeling and Spatial Data Acquisition Tools in Extended Reality Environments

Object Recognition and Virtual Retail Algorithms, Metaverse and Immersive Technologies, and Simulation Modeling and Spatial Data Acquisition Tools in Extended Reality Environments

Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution

A technological gap to monitor fruit quality evolution in the food supply chain is causing a huge waste of fruits. A digital twin is a promising tool to minimize fruit waste by monitoring and predicting the status of fresh produce throughout its life. In post-harvest engineering, the digital twin could be defined as a virtual representation of real produce. The objective of this work is to present a new approach to create a machine learning-based digital twin of banana fruit to monitor its quality changes throughout storage. The thermal camera has been used as a data acquisition tool due to its capability to detect the surface and physiological changes of fruits throughout the storage. In this study, after constructing the dataset of thermal data belonging to four classes, the training of the model has been performed using intelligent technologies from SAP. The solution has applied a deep convolutional neural network to monitor the fruit status based on the thermal information, and the training process has shown higher accuracy. Thus, 99% of prediction accuracy has been achieved which is proved to be a promising technique for the development of fruit digital twins. The application of thermal imaging techniques can be used as a data source to create a machine learning-based digital twin of fruit that can minimize waste in the food supply chain.