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This review paper explores the intersection of user engagement and user experience studies with electroencephalography (EEG) analysis by investigating the existing literature in this field. User engagement describes the immediate, session-based experience of using interactive products and is commonly used as a metric to assess the success of games, online platforms, applications, and websites, while user experience encompasses the broader and longer-term aspects of user interaction. This review focuses on the use of EEG as a precise and objective method to gain insights into user engagement. EEG recordings capture brain activity as waves, which can be categorized into different frequency bands. By analyzing patterns of brain activity associated with attention, emotion, mental workload, and user experience, EEG provides valuable insights into user engagement. The review follows the PRISMA statement. The search process involved an extensive exploration of multiple databases, resulting in the identification of 74 relevant studies. The review encompasses the entire information flow of the experiments, including data acquisition, pre-processing analysis, feature extraction, and analysis. By examining the current literature, this review provides a comprehensive overview of various algorithms and processes utilized in EEG-based systems for studying user engagement and identifies potential directions for future research endeavors. [ABSTRACT FROM AUTHOR]

Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. For this purpose, a novel approach for damage detection and identification on elevator systems is developed in this work, where vibration data obtained through physical measurements and high-fidelity multibody dynamics models are combined with deep learning algorithms. High-quality training data are first generated through multibody dynamics simulations and are then combined with healthy state vibration measurements to train an ensemble of autoencoders and convolutional neural networks for damage detection and classification. A dedicated data acquisition system is then developed and integrated with an elevator cabin, allowing for condition monitoring through this novel methodology. The results indicate that the developed framework can accurately identify damages in the system, hinting at its potential as a powerful structural health monitoring tool for such applications, where manual damage localization would otherwise be considerably time-consuming. [ABSTRACT FROM AUTHOR]

Purpose: The reliable and continuous operation of wind turbines is of utmost importance, making it necessary to thoroughly understand their dynamic behavior under various operational and environmental conditions. Methods: To achieve this, a data acquisition system distributed throughout the tower height is designed. The system records data such as the acceleration, temperature, and relative humidity from the sensors, along with the rotor speed, wind speed, temperature, pitch angle, nacelle direction, and wind direction from the data acquisition system of the turbine. The acquired data is synchronized and processed by Autonomous and Continuous System Identification system based on the poly-reference Least Squares Complex Frequency method. The extensive dataset, gathered over a 7-month period, allows for the estimation of modal parameters of the wind turbine. The modal parameters are then correlated with the operational and environmental conditions that were recorded. The relationships between these conditions are thoroughly analyzed and explained. Additionally, the operational principles of the wind turbine are elucidated in detail. The correlations between the modal parameters and operational or environmental factors are presented and interpreted, shedding light on the complex interplay between wind turbine dynamics and external conditions. Conclusion: It can be said that changes in operational and environmental conditions affect the modal parameters of the wind turbine differently across various structural modes. Without considering these effects, structural health monitoring systems may produce false alarms. Failure to consider these effects in the development of structural health monitoring systems may lead to incorrect damage alarms. [ABSTRACT FROM AUTHOR]

The present work intends to characterise the combination of two netted software defined radios (SDR) and different radio frequency (RF) front‐ends installed in them, together with a two‐stratum time dissemination and synchronisation system. A modified implementation of the Network Time Protocol is used as coarse event synchronisation for either SDR back‐end. The White rabbit light embedded node implementation of the commonly known white rabbit synchronisation system (IEEE 1588 PTP‐2019) or arbitrary wave form generators are used as fine time dissemination system for either SDR. The resulting netted transceiver system is intended to compose a demonstrator for proof‐of‐concept experiments. The findings presented in this article show that the chosen combination of hardware and software is suitable for radar applications operating within L‐, S‐ and C‐bands. A channel coherency throughout the network with a relative modified Allan deviation of less than 80 fs for averaging intervals of 1 s, a phase noise better than −118 dBc/Hz at 10 Hz frequency offset and a fractional frequency lower than ±2.5⋅10−13 $\pm 2.5\cdot 1{0}^{-13}$ was measured. Within a single transceiver node, a fractional frequency lower than ±2⋅10−13 $\pm 2\cdot 1{0}^{-13}$ and phase noise of −124 dBc/Hz at 10 Hz frequency offset were measured as well. Multistatic radar systems exploit wide spatial diversity to enhance target detection and tracking, albeit with increased complexity when compared to a monostatic configuration. To exploit these benefits, all participating transceiver nodes within the netted radar need to synchronise to a common time base t0 $\left({t}_{0}\right)$. The achieved synchronisation level increases the accuracy with which the chain of timed events at the transmitter and at the receiver side occur, intending to maximise the signal‐to‐noise ratio available at the receiver. The Universal software radio peripheral model X310 with two different daughterboard models as RF front‐end was used as SDR on either radar node. A network combining data and synchronisation purposes allowed the radar nodes under test to operate synchronously. The two‐stratum synchronisation system used glass fibres between 2 m and 5 km of length or coaxial cables with 2 m in length for network traffic, time and frequency dissemination purposes. The multiple RF front‐ends were stimulated by means of arbitrary waveform generators with calibrated traceability. Up‐chirp and sinusoid waveforms were used as stimuli for measuring the offset of either channel with regards of t0 ${t}_{0}$ to ultimately estimate the achievable coherency limits of the system under test. Both analogue and digital evaluation methods were considered. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions. [ABSTRACT FROM AUTHOR]

Light Detection And Ranging (LiDAR) technology has provided an impactful way to capture 3D data. However, consistent mapping in sensing-degenerated and perceptually-limited scenes (e.g. multi-story buildings) or under high dynamic sensor motion (e.g. rotating platform) remains a significant challenge. In this paper, we present OR-LIM, a novel observability-aware LiDAR-inertial-mapping system. Essentially, it combines a robust real-time LiDAR-inertial-odometry (LIO) module with an efficient surfel-map-smoothing (SMS) module that seamlessly optimizes the sensor poses and scene geometry at the same time. To improve robustness, the planar surfels are hierarchically generated and grown from point cloud maps to provide reliable correspondences for fixed-lag optimization. Moreover, the normals of surfels are analyzed for the observability evaluation of each frame. To maintain global consistency, a factor graph is utilized integrating the information from IMU propagation, LIO as well as the SMS. The system is extensively tested on the datasets collected by a low-cost multi-beam LiDAR (MBL) mounted on a rotating platform. The experiments with various settings of sensor motion, conducted on complex multi-story buildings and large-scale outdoor scenes, demonstrate the superior performance of our system over multiple state-of-the-art methods. The improvement of point accuracy reaches 3.39–13.6 % with an average 8.71 % outdoor and correspondingly 1.89–15.88 % with 9.09 % indoor, with reference to the collected Terrestrial Laser Scanning (TLS) map. [ABSTRACT FROM AUTHOR]

As the important component of rocket engines, small pipeline systems have the characteristics of being lightweight and having structural complexity and obvious randomness in vibration effects. Mastering the dynamic characteristics of the pipeline systems is a key technical approach to improving the overall lifespan of engines. This study used a noncontact sound pressure sensor for dynamic parameter collection, reducing the experimental errors caused by the accelerometer's weight and signal line interference, etc., factors. The dynamic characteristic experimental and simulation study was conducted on several types of combined configuration small pipeline systems, including plane pipelines, space pipelines, support plates, and clamps. The errors of the top six modal results were all less than 20%, with a minimum error of 0.5%, proving the correctness and effectiveness of the dynamic characteristic experimental and simulation methods. The modified modeling and FEA methods were used to study the structural optimization of pipeline systems, which can provide engine pipeline systems reasonable guidance for natural frequency modulation and parameter sensitivity results analysis. [ABSTRACT FROM AUTHOR]

Data acquisition is the process of collecting information from various sources through sensors or instruments. After obtaining the data, it is usually converted from the analogue format to a digital format. In this paper an application that gathers room-related information is presented. The purpose of such a system would be to be able to monitor certain parameters of interest in a living space. Afterwards this data can be used in various manners. One of the most important results of this data is being able to identify the habitability of a room. It can point out potential health hazards before they cause actual health problems. An additional objective is for the system to be on an accessible budget and to be easy to understand and implement. The hardware system using Arduino is presented in detail, as well as the graphical user interface created in Visual Studio. The system works as intended for the purposes stated above. It measures: temperature, humidity, light intensity, sound, and air quality. It works on its own if it is supplied with a 9-volt power source. The Graphical User Interface can be used to visualise and analyse the collected data. The system can still be improved by adding wireless communication such as Wi-Fi and allowing it to be integrated into Internet of Things systems. The power consumption could be improved if needed for higher sampling frequency demands. [ABSTRACT FROM AUTHOR]

Determining the right process parameters for 3D scanning is crucial for rigorously inspecting reverse-engineered dental models. However, it is seen that various parameters, such as scanning distance, light intensity, and scanning angle, are rarely examined during preliminary experimental trials. The proposed research examines a method for estimating the ideal values of the aforementioned scanning parameters that minimize acquisition error. The face-centered, central composite design suggested twenty runs of experimentation with varying input parameter combinations. In each of these twenty scans, a physical denture model was scanned to extract a 3D CAD model, and the standard deviation of each model was calculated to investigate into the scan accuracy of the recorded data. A neural network architecture is used to train a model across input and output, and then the model is optimized by a genetic algorithm for the best results. Through a scanning distance of 208.28 mm, scanning angle of 54.1 degrees, and light intensity of 18 W/meter square, in a total of twenty trial runs, the lowest possible standard deviation of 0.2626. The standard deviation is minimized for achieving maximum accuracy using a heuristic GA-ANN algorithm with a scanning distance of 152.4 mm, scanning angle of 61.8 degrees, and light intensity of 14 watts per square meter and same has been validated experimentally. [ABSTRACT FROM AUTHOR]

Wood plate bark removal processing is critical for ensuring the quality of wood processing and its products. To address the issue of lack of datasets available for the application of deep learning methods to this field, and to fill the research gap of deep learning methods in the application field of wood plate bark removal equipment, a benchmark for wood plate segmentation in bark removal processing is proposed in this study. Firstly, a costumed image acquisition device is designed and assembled on bark removal equipment to capture wood plate images in real industrial settings. After data filtering, enhancement, annotation, recording, and partitioning, a benchmark dataset named the WPS-dataset containing 4863 images was constructed. The WPS-dataset is evaluated by training six typical semantic segmentation models. The experimental results show that the models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. The WPS-dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field. [ABSTRACT FROM AUTHOR]

Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of training iterations. Learning curves have important applications in several machine learning contexts, most notably in data acquisition, early stopping of model training, and model selection. For instance, learning curves can be used to model the performance of the combination of an algorithm and its hyperparameter configuration, providing insights into their potential suitability at an early stage and often expediting the algorithm selection process. Various learning curve models have been proposed to use learning curves for decision making. Some of these models answer the binary decision question of whether a given algorithm at a certain budget will outperform a certain reference performance, whereas more complex models predict the entire learning curve of an algorithm. We contribute a framework that categorises learning curve approaches using three criteria: the decision-making situation they address, the intrinsic learning curve question they answer and the type of resources they use. We survey papers from the literature and classify them into this framework. [ABSTRACT FROM AUTHOR]

Around the world, strict privacy regulations are gradually being implemented, with the intended purpose of facilitating consumers to protect their privacy. This paper analyzes the unintended consequences of privacy regulations in the context of asymmetric data advantage. To this end, this paper constructs a model of behavior-based price discrimination, where one firm (such as the incumbent) possesses more data than the other (such as the entrant). The results demonstrate that stricter privacy regulation always benefits the data-advantaged firm. However, it has negative implications for both the data-disadvantaged firm and consumers in most cases. Furthermore, strict regulation leads to weakened competition and intensified mismatching. Therefore, this paper suggests a lenient regulatory policy. [ABSTRACT FROM AUTHOR]

This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) is a highly promising research area meant to automatically identify and interpret human behaviour using data received from sensors in various contexts. The potential uses of HAR are many, among them health care, sports coaching or monitoring the elderly or disabled. Nonetheless, there are numerous hurdles to be circumvented for HAR's precision and usefulness to be improved. One of the challenges is that there is no uniformity in data collection and annotation making it difficult to compare findings among different studies. Furthermore, more comprehensive datasets are necessary so as to include a wider range of human activities in different contexts while complex activities, which consist of multiple sub‐activities, are still a challenge for recognition systems. Researchers have proposed new frontiers such as multi‐modal sensor data fusion and deep learning approaches for enhancing HAR accuracy while addressing these issues. Also, we are seeing more non‐traditional applications such as robotics and virtual reality/augmented world going forward with their use cases of HAR. This article offers an extensive review on the recent advances in HAR and highlights the major challenges facing this field as well as future opportunities for further researches. [ABSTRACT FROM AUTHOR]

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats. [ABSTRACT FROM AUTHOR]

The transition towards renewable energy sources necessitates accurate monitoring of environmental parameters to estimate power generation from renewable energy systems. The rapid integration of renewable energy sources into the power grid has necessitated the development of efficient monitoring systems to optimise power generation and enhance overall system performance. This paper provides a comprehensive review of environmental parameters monitoring systems designed for estimating power generation from renewable energy sources. The focus is on the advancements in technology and methodologies employed in monitoring crucial environmental factors that influence the output of renewable energy systems. It explores the significance of environmental parameters, including solar irradiance, wind speed, temperature, and humidity, in determining the efficiency of solar and wind power generation. Various monitoring techniques and sensors used for real-time data acquisition are discussed, highlighting their accuracy and reliability in capturing diverse environmental conditions. It delves into the integration of advanced data analytics and machine learning algorithms for processing the vast amounts of data collected from monitoring systems. These techniques play a pivotal role in predicting power generation patterns, optimising energy output, and facilitating proactive maintenance of renewable energy infrastructure. Furthermore, the review discusses the challenges associated with environmental monitoring, such as data accuracy, sensor calibration, and communication issues. It also explores emerging technologies, such as Internet of Things (IoT) devices and remote sensing, that promise to address these challenges and enhance the robustness of environmental monitoring systems. It focuses on case studies and practical applications of environmental monitoring systems in different renewable energy projects. These case studies provide insights into the successful implementation of monitoring systems, their impact on energy yield, and the economic benefits derived from improved system efficiency. It consolidates the current state of environmental parameters monitoring systems for power generation estimation from renewable energy sources. It highlights the interdisciplinary nature of these systems, incorporating elements of meteorology, data science, and engineering. The synthesis of existing knowledge and identification of research gaps contribute to the ongoing efforts to enhance the reliability and efficiency of renewable energy systems in the face of a dynamic and changing climate. This review paper provides a comprehensive analysis of the existing literature on environmental parameters monitoring systems for estimating power generation from renewable energy sources. The paper explores various renewable energy systems, including solar, wind, and hydroelectric, and highlights the significance of monitoring environmental factors such as solar irradiance, wind speed, temperature, humidity, and water flow rate. More than 50+ papers are taken into consideration; they examine the methodologies, sensors, data acquisition techniques, and modeling approaches employed for power estimation. Additionally, the challenges, advancements, and future directions in this field are discussed to guide further research and development. [ABSTRACT FROM AUTHOR]

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable. [ABSTRACT FROM AUTHOR]

This paper presents a system and model for data acquisition and augmentation in OLED panel defect detection to improve detection efficiency. It addresses the challenges of data scarcity, data acquisition difficulties, and classification of different defect types. The proposed system acquires a hypothetical base dataset and employs an image generation model for data augmentation. While image generation models have been instrumental in overcoming data scarcity, time and cost constraints in various fields, they still pose limitations in generating images with regular patterns and detecting defects within such data. Even when datasets are available, the precise definition and classification of different defect types becomes imperative. In this paper, we investigate the feasibility of using an image generation model to generate pattern images for OLED panel defect detection and apply it for data augmentation. In addition, we introduce an OLED panel defect data acquisition system, improve the limitations of data augmentation, and address the challenges of defect detection data augmentation using image generation models.

Abstract The Internet of Things (IoT) consist of a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Intrusion detection systems using deep learning are a common method used for providing security in IoT. However, traditional deep learning IDS systems do not accurately classify the attack and also require high computation time. Thus, to solve this issue, herein, we propose an advance Intrusion detection framework using Self-Attention Progressive Generative Adversarial Network (SAPGAN) framework for detecting security threats in IoT networks. In our proposed framework, at first, the IoT data are gathered. Then, the data are fed to pre-processing. In pre-processing, it restored the missing value using Local least squares. Then the preprocessing output is fed to feature selection. At feature selection, the optimum features are compiled using a modified War Strategy Optimization Algorithm (WSOA). Based upon the optimum features, the intruders were categorized into two categories named Anomaly and Normal using the proposed framework. Numerous attacks are assembled, including camera-based flood, DDoS, RTSP brute force, etc. We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models.

Abstract Portable data acquisition devices, such as the National Instrument (NI) myDAQ, allow users to analyze electronic circuits without advanced measurement systems, making them invaluable in educational settings. Combined with NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) on a personal computer, the NI myDAQ facilitates practical experiments and analyses anytime, anywhere. However, these devices can malfunction due to misuse or operation beyond their design limitations. This research presents the design, operation, and implementation of an energy-efficient test bench for the control and measurement functionalities of the NI myDAQ, aimed at detecting device failures through a specialized hardware and software setup using the loopback method. The test bench efficiently verifies all input and output ports, providing fast and accurate fault detection, and generates detailed performance reports to aid in long-term monitoring. By automating fault detection and reducing manual intervention, this setup ensures reliable operation, making it an ideal, resource-efficient solution for educational environments where practical, portable systems are essential for hands-on learning.

Abstract This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs.

This paper presents a system and model for data acquisition and augmentation in OLED panel defect detection to improve detection efficiency. It addresses the challenges of data scarcity, data acquisition difficulties, and classification of different defect types. The proposed system acquires a hypothetical base dataset and employs an image generation model for data augmentation. While image generation models have been instrumental in overcoming data scarcity, time and cost constraints in various fields, they still pose limitations in generating images with regular patterns and detecting defects within such data. Even when datasets are available, the precise definition and classification of different defect types becomes imperative. In this paper, we investigate the feasibility of using an image generation model to generate pattern images for OLED panel defect detection and apply it for data augmentation. In addition, we introduce an OLED panel defect data acquisition system, improve the limitations of data augmentation, and address the challenges of defect detection data augmentation using image generation models. [ABSTRACT FROM AUTHOR]

Purpose: To address key issues of low SNR and image distortions in prostate diffusion MRI (dMRI) by means of using strong gradients, single‐shot spiral readouts and an expanded encoding model for image reconstruction. Methods: Diffusion‐weighted spin echo imaging with EPI and spiral readouts is performed on a whole‐body system equipped with strong gradients (up to 250 mT/m). An expanded encoding model including static off‐resonance, coil sensitivities, and magnetic field dynamics is employed for image reconstruction. The acquisitions are performed on a phantom and in vivo (one healthy volunteer and one patient with prostate cancer). The resulting images are compared to conventional dMRI EPI with navigator‐based image reconstruction and assessed in terms of their congruence, SNR, tissue contrast, and quantitative parameters. Results: Using the expanded encoding model, high‐quality images of the prostate gland are obtained across all b‐values (up to 3 ms/μm2), clearly outperforming the results obtained with conventional image reconstruction. Compared to EPI, spiral imaging provides an SNR gain up to 45% within the gland and even higher in the lesion. In addition, prostate dMRI with single‐shot spirals at submillimeter in‐plane resolution (0.85 mm) is accomplished. Conclusion: The combination of strong gradients and an expanded encoding model enables imaging of the prostate with unprecedented image quality. Replacing the commonly used EPI with spirals provides the inherent benefit of shorter echo times and superior readout efficiency and results in higher SNR, which is in particular relevant for considered applications. [ABSTRACT FROM AUTHOR]

Abstract The Internet of Things (IoT) is the recent technology intended to facilitate the daily life of humans by providing the power to connect, control and automate objects in the physical world. In this logic, the IoT helps to improve our way of producing and working in various areas (e.g. agriculture, industry, healthcare, transportation etc). Basically, an IoT network comprises physical devices, equipped with sensors and transmitters, that are interconnected with each other and/or connected to the Internet. Its main objective is to gather and transmit data to a storage system such as a server or cloud to enable processing and analysis, ultimately facilitating rapid decision‐making or enhancements to the user experience. In the realm of Connected Objects, an effective IoT data collection system plays a vital role by providing several benefits, such as real‐time data monitoring, enhanced decision‐making, increased operational efficiency etc. However, because of the resource limitations linked to connected objects, such as low memory and battery, or even single‐use devices etc. IoT data collecting presents several challenges including scalability, security, interoperability, flexibility etc. for both researchers and companies. The authors categorise current IoT data collection techniques and perform a comparative evaluation of these methods based on the topics analysed and elaborated by the authors. In addition, a comprehensive analysis of recent advances in IoT data collection is provided, highlighting different data types and sources, transmission protocols from connected sensors to a storage platform (server or cloud), the IoT data collection framework, and principles for streamlining the collection process. Finally, the most important research questions and future prospects for the effective collection of IoT data are summarised.

We analyze a remote sensing system in the Internet of things, where uncoordinated nodes send status updates to a common receiver to achieve information freshness, quantified through age of information. We consider a finite horizon scheduling over a random multiple access channel, where colliding messages are lost. We show that nodes must adopt a further randomization to deviate from identical schedules and escape collision deadlocks. Moreover, we discuss the impact of feedback availability if, due to, e.g., energy expenditure, it decreases the number of transmission opportunities.

Having the ability to reproduce empirical results is foundational to the scientific process. Increasingly, there is an emphasis within the research community to create workflows which are not only reproducible but also replicable in that analytical approaches can be applied to new data. However, a disproportionate number of technical solutions designed to foster reproducibility and replicability have focused on data analysis, particularly within geography. This paper argues for greater attention on a phase of the research process which precedes analysis and is often taken for granted – that of data acquisition. Through code examples using the R Project for Statistical Computing, this paper demonstrates a path toward replicable spatial data acquisition for both secondary and primary data acquisition, with a focus on crowdsourced geographic information. The modular approach demonstrated is flexible in that it allows for straightforward replication but also enables extension to new spatial questions. Such work is valuable because it conserves labor, promotes research provenance, and allows for more robust analyses. [ABSTRACT FROM AUTHOR]

In response to the problems of frequent occurrence of downhole failures, high risk of failures, low warning efficiency, and relatively lagging safety monitoring technology, this paper presents the design of a monitoring system for failure risk of downhole drilling tools based on information measurement and risk warning. Field experiments were conducted to provide a scientific decision-making basis for the design and risk control of complex formation drilling. This system directly measures near-bit mechanical parameters and engineering parameters, and transmits the measured parameters to the drilling risk analysis and assessment module in real time by using the drilling fluid pulse, by which the received data are analyzed and calculated to determine the type of drilling risk and assess the risk level. Through pre-drilling analysis and assessment, real-time downhole monitoring, surface data acquisition, and a comprehensive analysis system platform, the downhole conditions are accurately determined, and feedback solutions are analyzed to achieve the purpose of reducing drilling risks, improving drilling efficiency and reliability of drilling assembly, and optimizing drilling technical measures, to better study the dynamic safety of downhole drilling tools. Field tests confirmed that the downhole safety system is fully functional and has a stable performance. The accuracy rate of the risk assessment is higher than 95%, and some technical indexes have reached the level of similar monitoring systems abroad. The research shows that the downhole safety monitoring system could reduce the drilling risk and drilling cost in deep complex formations. It could create great economic benefits for it has solved the problem of low early warning efficiency of downhole safety accidents. [ABSTRACT FROM AUTHOR]

Surface quality, machining efficiency, and tool life are all significantly impacted by tool wear in metal cutting machining. Research priorities and areas of focus in tool wear are shifting as intelligent machining becomes the norm. Unfortunately, there are currently no acknowledged most effective ways for analyzing tool based on the energy signals specially in the machining of titanium and its alloys. In the present work, the titanium machining was performed under different lubrication conditions such as dry, minimum quantity lubrication (MQL), liquid nitrogen and hybrid, etc. Then, the spectrograms are used to transform the acquired energy data into time–frequency features. Starting with a set of randomly generated hyper parameters (HPs), the long short-term memory (LSTM) model is fine-tuned using sine cosine algorithm (SCA) with loss serving as the fitness function. The confusion matrix provides additional validation of the 98.08% classification accuracy. Additional evaluations of the suggested method's superiority include its specificity, sensitivity, F1-score, and area under the curve (AUC). [ABSTRACT FROM AUTHOR]

Real and virtual instruments for data acquisition and signal processing are widely used in industry, entertainment and education. In this paper I compared different hardware and software solutions in instrumentation, and I analyzed these technics in a particular case: generation and processing of audio signals using additive and FM synthesis. The comparative study reveals the advantages and the disadvantages of the real and virtual instrumentation especially in academic education. [ABSTRACT FROM AUTHOR]

Water quality monitoring and assessment play crucial roles in efficient water resource management, particularly in the context of agricultural rrigation. Leveraging Internet of Things (IoT) devices equipped with various sensors simplifies this process. In this study, we propose a comprehensive framework integrating IoT technology and Machine Learning (ML) techniques for water quality monitoring and assessment in agri- cultural settings. Our framework consists of four main modules: sensing, coordination, data processing, and decision-making. To gather essential water quality data, we deploy an array of sensors along the Rohri Canal and Gajrawah Canal in Nawabshah City, measuring parameters such as temperature, pH, turbidity, and Total Dissolved Solids (TDS). We then utilize ML algorithms to assess the Water Quality Index (WQI) and Water Quality Class (WQC). Preprocessing steps including data cleansing, Z-score normalization, correlation analysis, and data segmentation are implemented within the ML-enhanced framework. Regression models are employed for WQI prediction, while classification models are used for WQC prediction. The accuracy and efficacy of these models are evaluated using various metrics such as boxplots, violin plots, con- fusion matrices, and precision-recall metrics. Our findings indicate that the water quality in the Rohri Canal is generally superior to that in the Gajrawah Canal, which exhibits higher pollution levels. However, both canals remain suitable for agricultural irrigation, farming, and fishing. [ABSTRACT FROM AUTHOR]

Currently, engine remapping is widely utilized to enhance the performance of diesel and petrol engines. Given that a substantial amount of data must be collected during the optimization process, a data acquisition system is necessary to organize and retrieve data automatically. This research focuses on the design, development, and testing of a data acquisition system for controlling and measuring the throttle pedal in diesel vehicles equipped with a common rail system. The system is designed to manage the Throttle Position Sensor (TPS) and monitor vehicle operational parameters in real-time. The proposed device employs Arduino as the primary hardware, with TechStream software for vehicle data acquisition, and a web-based application developed using the Python programming language, and displays data in real-time in the form of graphs and tables. From the test results, it can be concluded that the developed acquisition system produces precise and sensitive throttle position settings. Its operation is also straightforward, safe, and requires a relatively short amount of time. [ABSTRACT FROM AUTHOR]

This paper presents a large vacuum facility (6-ft-diameter, 10-ft-long chamber) equipped with a magnetic filter-type low Earth orbit plasma source. A recommended operating envelope for the plasma source was established through single-point measurements by varying the discharge currents of the plasma source and the gas flow rates. A three-axis translation stage was fabricated and tested with 2D and 3D scans of the simulated plasma environment. The measured plasma density during this study was between 1.63×1012-3.1×1012m-3 for the electrons and 7.54×1013m-3 for the ions, whereas the electron and ion temperatures ranged from 0.55 to 2.06 eV and 1 to 3 eV, respectively. The simulated plasma environment compares well with orbital equivalents, specifically the F layers of the ionosphere. [ABSTRACT FROM AUTHOR]

The notion of IoT, also known as the Internet of Things, has been there for many years, but recent technical progress has elevated its prominence. The increasing need for creative ideas, approaches, and technology is driven by the fast evolution of our world today. The construction sector has enthusiastically adopted the IoT as an advanced technology to develop intelligent structures, enhance resource efficiency, and manage construction expenses. One of the main benefits of incorporating IoT into construction operations is the notable advantage of being able to gather and analyse real-time data. The use of IoT technologies in the construction industry can greatly augment productivity and efficiency. This review article examines the IoT and its applications in different fields of the civil engineering and construction industry. It investigates the advantages of IoT in this sector and explores how it is implemented. [ABSTRACT FROM AUTHOR]

Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization's internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0037) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0037). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition. [ABSTRACT FROM AUTHOR]

The actual open‐source hardware and software tools offer a rich set of options for developing didactic tools to improve the teaching–learning process of various areas of engineering. Using low‐cost sensors, in conjunction with free software development tools, allows the creation of educational interfaces and platforms offering very acceptable performance and precision that help the student to become familiar with the basic principles that govern professional equipment. In this work, we propose a low‐cost system for data acquisition specially designed to improve the learning experience of experimental mechanics. To achieve this purpose, we use open‐source software and hardware tools to create a piece of educational equipment that is fully configurable for different sensors. We present the experimental results of two case studies: the vibration analysis of a rotor‐bearing system using acceleration signals and a free‐vibration study using a xylophone and a low‐cost microphone. The proposed platform helps authors to complement a 4‐month course named Vibration, intended for mechanical engineering students. The students who participated in the study demonstrated an improvement in their comprehension of vibration theory and modal analysis using the finite element technique. The feedback from students indicates that 84% of the participants are highly motivated to learn more about vibrations and experimental mechanics. [ABSTRACT FROM AUTHOR]

In the present digital environment, a data-driven organizational culture has become a vital emerging driver of organizational growth. This data-driven culture has assumed an advanced shape due to adoption of artificial intelligence (AI) integrated business analytics tools in the organization. Data-driven culture in the organization could considerably impact product innovation strategy as well as organizational process alteration. In this context, the aim of this study is to investigate how an organization's data-driven culture impacts process performance and product innovation that led to enhanced organizational overall performance and higher business value. Methodologically, supported by relevant extant literature and inputs from the resource-based view and dynamic capability theories (organizational context), a conceptual model and a set of hypotheses are initially developed. These are subsequently statistically validated through a survey involving 513 usable responses from employees of different organizations using business analytics tools embedded with AI capability. The findings demonstrate that an organizational data-driven culture has considerable moderating impact on product innovation and process improvement, which ultimately enhance business value through improved organizational overall performance. [ABSTRACT FROM AUTHOR]

Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wireless Wi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users. [ABSTRACT FROM AUTHOR]

A four‐degree‐of‐freedom upper limb exoskeleton rehabilitation robot system with a gravity compensation device is constructed. The objective is to address the rehabilitation training needs of patients with upper limb motor dysfunction. A BP neural network adaptive control method based on particle swarm optimization is proposed. First, the degrees of freedom of the human body are analyzed, and a Lagrange method is employed to construct a dynamic model. Second, a particle swarm optimization back propagation neural network adaptive control algorithm based on particle swarm optimization is presented. Subsequently, the range of motion of the upper limbs is analyzed with reference to muscle anatomy and a three‐dimensional motion capture system. And the robot structure design is analyzed in detail. Finally, simulation experiments were conducted, and the results demonstrated that the proposed method exhibited high effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

In a drone system with dual data links, this article presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]