bluuebunny/crossref_metadata_2025 huggingface dataset:
Classification Strategies for P300-Based BCI-Spellers Adopting the Row Column Paradigm
Authors: Gannouni Sofien , Belwafi Kais , Alangari Nourah , AboAlsamh Hatim , Belghith Abdelfettah
Published: November 2022

Acknowledging the importance of the ability to communicate with other people, the researcher community has developed a series of BCI-spellers, with the goal of regaining communication and interaction capabilities with the environment for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, we believe that the development of efficient signal processing algorithms and strategies will go a long way towards achieving novel assistive technologies using new human–computer interfaces. In this paper, we present various classification strategies that would be adopted by P300 spellers adopting the row/column paradigm. The presented strategies have obtained high accuracy rates compared with existent similar research works.
Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning
Authors: Freitas Victor Carvalho Galvão De , Araujo Valbério Gonzaga De , Crisóstomo Daniel Carlos de Carvalho , Lima Gustavo Fernandes De , Neto Adrião Duarte Dória , Salazar Andrés Ortiz
Published: November 2022

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.
Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream
Authors: Olaizola Igor , Quartulli Marco , Unzueta Elias , Goicolea Juan , Flórez Julián
Published: November 2022

Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the “fourth industrial revolution” that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes.
Low-Cost Sensor for Continuous Measurement of Brix in Liquids
Authors: Jaywant Swapna A. , Singh Harshpreet , Arif Khalid Mahmood
Published: November 2022

This paper presents a Brix sensor based on the differential pressure measurement principle. Two piezoresistive silicon pressure sensors were applied to measure the specific gravity of the liquid, which was used to calculate the Brix level. The pressure sensors were mounted inside custom-built water-tight housings connected together by fixed length metallic tubes containing the power and signal cables. Two designs of the sensor were prepared; one for the basic laboratory testing and validation of the proposed system and the other for a fermentation experiment. For lab tests, a sugar solution with different Brix levels was used and readings from the proposed sensor were compared with a commercially available hydrometer called Tilt. During the fermentation experiments, fermentation was carried out in a 1000 L tank over 7 days and data was recorded and analysed. In the lab experiments, a good linear relationship between the sugar content and the corresponding Brix levels was observed. In the fermentation experiment, the sensor performed as expected but some problems such as residue build up were encountered. Overall, the proposed sensing solution carries a great potential for continuous monitoring of the Brix level in liquids. Due to the usage of low-cost pressure sensors and the interface electronics, the cost of the system is considered suitable for large scale deployment at wineries or juice processing industries.
Characterisation and Quenching Correction for an Al2O3:C Optical Fibre Real Time System in Therapeutic Proton, Helium, and Carbon-Charged Beams
Authors: de Freitas Nascimento Luana , Leblans Paul , van der Heyden Brent , Akselrod Mark , Goossens Jo , Correa Rocha Luis Enrique , Vaniqui Ana , Verellen Dirk
Published: November 2022

Real time radioluminescence fibre-based detectors were investigated for application in proton, helium, and carbon therapy dosimetry. The Al2O3:C probes are made of one single crystal (1 mm) and two droplets of micro powder in two sizes (38 μm and 4 μm) mixed with a water-equivalent binder. The fibres were irradiated behind different thicknesses of solid slabs, and the Bragg curves presented a quenching effect attributed to the nonlinear response of the radioluminescence (RL) signal as a function of linear energy transfer (LET). Experimental data and Monte Carlo simulations were utilised to acquire a quenching correction method, adapted from Birks’ formulation, to restore the linear dose–response for particle therapy beams. The method for quenching correction was applied and yielded the best results for the ‘4 μm’ optical fibre probe, with an agreement at the Bragg peak of 1.4% (160 MeV), and 1.5% (230 MeV) for proton-charged particles; 2.4% (150 MeV/u) for helium-charged particles and of 4.8% (290 MeV/u) and 2.9% (400 MeV/u) for the carbon-charged particles. The most substantial deviations for the ‘4 μm’ optical fibre probe were found at the falloff regions, with ~3% (protons), ~5% (helium) and 6% (carbon).
A Proposed System for Multi-UAVs in Remote Sensing Operations
Authors: Flores Peña Pablo , Luna Marco Andrés , Ale Isaac Mohammad Sadeq , Ragab Ahmed Refaat , Elmenshawy Khaled , Martín Gómez David , Campoy Pascual , Molina Martin
Published: November 2022

This paper proposes the design of the communications, control systems, and navigation algorithms of a multi-UAV system focused on remote sensing operations. A new controller based on a compensator and a nominal controller is designed to dynamically regulate the UAVs’ attitude. The navigation system addresses the multi-region coverage trajectory planning task using a new approach to solve the TSP-CPP problem. The navigation algorithms were tested theoretically, and the combination of the proposed navigation techniques and control strategy was simulated through the Matlab SimScape platform to optimize the controller’s parameters over several iterations. The results reveal the robustness of the controller and optimal performance of the route planner.
Recent Advances in Electrochemical Sensors for Caffeine Determination
Authors: Tasić Žaklina Z. , Petrović Mihajlović Marija B. , Simonović Ana T. , Radovanović Milan B. , Antonijević Milan M.
Published: November 2022

The determination of target analytes at very low concentrations is important for various fields such as the pharmaceutical industry, environmental protection, and the food industry. Caffeine, as a natural alkaloid, is widely consumed in various beverages and medicines. Apart from the beneficial effects for which it is used, caffeine also has negative effects, and for these reasons it is very important to determine its concentration in different mediums. Among numerous analytical techniques, electrochemical methods with appropriate sensors occupy a special place since they are efficient, fast, and entail relatively easy preparation and measurements. Electrochemical sensors based on carbon materials are very common in this type of research because they are cost-effective, have a wide potential range, and possess relative electrochemical inertness and electrocatalytic activity in various redox reactions. Additionally, these types of sensors could be modified to improve their analytical performances. The data available in the literature on the development and modification of electrochemical sensors for the determination of caffeine are summarized and discussed in this review.
CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals
Authors: Botros Jad , Mourad-Chehade Farah , Laplanche David
Published: November 2022

Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart’s rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.
A New Method for Moving-Target HRRP via Double Step Frequency Verified by Simulation
Authors: Shen Xiaofeng , Zhuang Zhihong , Wang Hongbo , Shu Feng
Published: November 2022

The stepped-frequency (SF) waveform is highly sensitive to the target motion, which induces range shift and echo spread in a high-range-resolution profile (HRRP). This paper proposes a method based on a cross-transmitted double-stepped frequency (DSF) waveform and the phase-cancellation technique. The proposed method obtains the stationary HRRP of the moving targets according to the inverse discrete Fourier transform (IDFT) and complex multiplication. The results also show that the proposed method eliminates the generated false peaks from the existing methods. As a result, the obtained signal-to-noise ratio (SNR) of the HRRP using the proposed method is improved. Due to the cross-transmitted DSF waveform application, the proposed method adapts to higher speed targets. The analysis and simulation results validate the effectiveness of the proposed approach.
Very Long-Length FFT Using Multi-Resolution Piecewise-Constant Windows for Hardware-Accelerated Time–Frequency Distribution Calculations in an Ultra-Wideband Digital Receiver
Authors: Wu Chen , Elangage Janaka
Published: November 2022

The hardware-accelerated time–frequency distribution calculation is one of the commonly used methods to analyze and present the information from intercepted radio frequency signals in modern ultra-wideband digital receiver (DRX) designs. In this paper, we introduce the piecewise constant window blocking FFT (PCW-BFFT) method. The purpose of this work is to show the generation of spectrograms (formed by a number of spectrum lines) using a very large number of samples (N) in an FFT frame for each spectrum line calculation. In the PCW-BFFT, the N samples are grouped into K consecutive time slots, and each slot has M number of samples. As soon as the M samples in the current time slot are available from a high-speed analog-to-digital convertor (ADC), the frequency information will be obtained using K M-point FFTs. Since each time the FFT frame hops one time slot for the next spectrum line calculation, the frequency information obtained from a time slot will be reused in many spectrum line calculations, as long as these spectrum lines share those samples in the time slot. Although the use of the time domain PCW introduces spikes in the frequency spectrum of the window, the levels of those spikes are still much lower than the first side lobe level of a rectangular window. Using a Gaussian window as an example, the highest spike level can be lower than the main lobe level by at least 38 dB. The PCW-BFFT method allows a DRX to produce multiple spectrograms concurrently with different analysis window widths when the time domain samples become available continuously from the ADC. This paper presents the detailed derivation process of the PCW-BFFT method and demonstrates the use of the method with simulation results. The hardware implementation process will be reported in another paper. The computer simulation results show that long signals with slowly changing frequencies over time can be depicted on the spectrograms with wide analysis windows, and short pulses and signals with rapidly changing instantaneous frequencies can be captured in the narrow analysis window spectrograms.
Understanding Autonomous Shuttle Adoption Intention: Predictive Power of Pre-Trial Perceptions and Attitudes
Authors: Golbabaei Fahimeh , Yigitcanlar Tan , Paz Alexander , Bunker Jonathan
Published: November 2022

The capability of ‘demand-responsive transport’, particularly in autonomous shared form, to better facilitate road-based mobility is considered a significant advantage because improved mobility leads to enhanced quality of life and wellbeing. A central point in implementing a demand-responsive transit system in a new area is adapting the operational concept to the respective structural and socioeconomic conditions. This requires an extensive analysis of the users’ needs. There is presently limited understanding of public perceptions and attitudes toward the adoption of autonomous demand-responsive transport. To address this gap, a theory-based conceptual framework is proposed to provide detailed empirical insights into the public’s adoption intention of ‘autonomous shuttle buses’ as a form of autonomous demand-responsive transport. South East Queensland, Australia, was selected as the testbed. In this case study, relationships between perceptions, attitudes, and usage intention were examined by employing a partial least squares structural equation modeling method. The results support the basic technology acceptance model casual relationships that correspond with previous studies. Although the direct effects of perceived relative advantages and perceived service quality on usage intention are not significant, they could still affect usage intention indirectly through the attitude factor. Conversely, perceived risks are shown to have no association with perceived usefulness but can negatively impact travelers’ attitudes and usage intention toward autonomous shuttle buses. The research findings provide implications to assist policymakers, transport planners, and engineers in their policy decisions and system plans as well as achieving higher public acknowledgment and wider uptake of autonomous demand-responsive transport technology solutions.
Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
Authors: Zhang Pinchang , Liu Yangyang , He Ji
Published: November 2022

In this paper, we address the problem of authenticating transmitters in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems, and propose a location verification scheme based on multi-dimensional mmWave MIMO channel features. In particular, we first examine the mmWave MIMO channel features in terms of azimuth angle of arrival (AAoA), elevation angle of arrival (EAoA), and path gain, and then extract these fine-grained channel features through the maximum-likelihood (ML) estimation method. Based on the extracted feature parameters, authentication validation is cast in the framework of hypothesis testing theory. We also derive the analytical expressions for the typical false alarm and detection rates by using the likelihood ratio test and thus the statistical performance is analytically established. Finally, extensive numerical results are provided to demonstrate the performance of the proposed authentication scheme.
SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance
Authors: Miraglio Thomas , Coops  Nicholas C.
Published: November 2022

Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.
A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
Authors: Morales-Aragones José Ignacio , Williams Matthew St. Michael , Kupolati Halleluyah , Alonso-Gómez Víctor , Gallardo-Saavedra Sara , Redondo-Plaza Alberto , Muñoz-García Miguel Ángel , Sánchez-Pacheco Francisco José , Hernández-Callejo Luis
Published: November 2022

Within this paper, a PLC system that takes advantage of the loop resonance of an entire DC-PV string configured as a circular signal path is developed and implemented. Low cost and extremely simple transceivers intended to be installed within each PV module of a string have been designed and successfully tested. In addition, an anti-saturation coil has been conceived to avoid saturation of the core when the entire DC current of the string flows through it. Bi-directional half-duplex communication was successfully executed with up to a 1 MHz carrier frequency (150 kbps bitrate), using a simple ASK modulation scheme. The transmission and reception performance are presented, along with the overall system cost in comparison to the previous literature.
Single Transmitter Direction Finding Using a Single Moving Omnidirectional Antenna
Authors: Eliyahu Guy , Maor Amnon Menashe , Meshar Roei , Mukamal Reem , Weiss Anthony J.
Published: November 2022

Traditional direction-finding systems are based on processing the outputs of multiple spatially separated antennas. The impinging signal Angle-of-Arrival (AOA) is estimated using the relative phase and amplitude of the multiple outputs that are sampled simultaneously. Here, we explore the potential of a single moving antenna to provide useful direction finding of a single transmitter. If the transmitted signal frequency is steady enough during the collection of data, a single antenna can be moved while tracking the phase changes to provide an Angle-of-Arrival measurement. The advantages of a single-antenna sensor include the sensor size, the lack of a need for multiple-receiver synchronization in time and frequency, the lack of mutual antenna coupling, and the cost of the system. However, a single-antenna sensor requires an accurate knowledge of its position during the data collection and it is challenged by transmitter phase instability, signal modulation, and transmitter movement during the measurement integration time. We analyze the performance of the proposed sensor, support the analysis with simulations and finally, present measurements performed by hardware configured to check the validity of the proposed single-antenna sensor.
3D Object Recognition Using Fast Overlapped Block Processing Technique
Authors: Mahmmod Basheera M. , Abdulhussain Sadiq H. , Naser Marwah Abdulrazzaq , Alsabah Muntadher , Hussain Abir , Al-Jumeily Dhiya
Published: November 2022

Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects. The fast overlapped block-processing algorithm reduces the computational complexity of feature extraction. This paper also exploits Charlier polynomials and their moments along with support vector machine (SVM). The evaluation of the presented method is carried out using a well-known dataset, the McGill benchmark dataset. Besides, comparisons are performed with existing 3D object recognition methods. The results show that the proposed 3D object recognition approach achieves high recognition rates under different noisy environments. Furthermore, the results show that the presented method has the potential to mitigate noise distortion and outperforms existing methods in terms of computation time under noise-free and different noisy environments.
Cybersecurity Testing for Automotive Domain: A Survey
Authors: Luo Feng , Zhang Xuan , Yang Zhenyu , Jiang Yifan , Wang Jiajia , Wu Mingzhi , Feng Wanqiang
Published: November 2022

Modern vehicles are more complex and interconnected than ever before, which also means that attack surfaces for vehicles have increased significantly. Malicious cyberattacks will not only exploit personal privacy and property, but also affect the functional safety of electrical/electronic (E/E) safety-critical systems by controlling the driving functionality, which is life-threatening. Therefore, it is necessary to conduct cybersecurity testing on vehicles to reveal and address relevant security threats and vulnerabilities. Cybersecurity standards and regulations issued in recent years, such as ISO/SAE 21434 and UNECE WP.29 regulations (R155 and R156), also emphasize the indispensability of cybersecurity verification and validation in the development lifecycle but lack specific technical details. Thus, this paper conducts a systematic and comprehensive review of the research and practice in the field of automotive cybersecurity testing, which can provide reference and advice for automotive security researchers and testers. We classify and discuss the security testing methods and testbeds in automotive engineering. Furthermore, we identify gaps and limitations in existing research and point out future challenges.
Intelligent System for Detecting Deterioration of Life Satisfaction as Tool for Remote Mental-Health Monitoring
Authors: Prokopowicz Piotr , Mikołajewski Dariusz , Mikołajewska Emilia
Published: November 2022

The research described in this article is a continuation of work on a computational model of quality of life (QoL) satisfaction. In the proposed approach, overall life satisfaction is aggregated to personal life satisfaction (PLUS). The model described in the article is based on well-known and commonly used clinimetric scales (e.g., in psychiatry, psychology and physiotherapy). The simultaneous use of multiple scales, and the complexity of describing the quality of life with them, require complex fuzzy computational solutions. The aim of the study is twofold: (1) To develop a fuzzy model that allows for the detection of changes in life satisfaction scores (data on the influence of the COVID-19 pandemic and the war in the neighboring country were used). (2) To develop more detailed guidelines than the existing ones for further similar research on more advanced intelligent systems with computational models which allow for sensing, detecting and evaluating the psychical state. We are concerned with developing practical solutions with higher scientific and clinical utility for both small datasets and big data to use in remote patient monitoring. Two exemplary groups of specialists at risk of occupational burnout were assessed three times at different intervals in terms of life satisfaction. The aforementioned assessment was made on Polish citizens because the specific data could be gathered: before and during the pandemic and during the war in Ukraine (a neighboring country). That has a higher potential for presenting a better analysis and reflection on the practical application of the model. A research group (physiotherapists, n = 20) and a reference group (IT professionals, n = 20) participated in the study. Four clinimetric scales were used for assessment: the Perceived Stress Scale (PSS10), the Maslach Burnout Scale (MBI), the Satisfaction with Life Scale (SWLS), and the Nordic Musculoskeletal Questionnaire (NMQ). The assessment was complemented by statistical analyses and fuzzy models based on a hierarchical fuzzy system. Although several models for understanding changes in life satisfaction scores have been previously investigated, the novelty of this study lies in the use of data from three consecutive time points for the same individuals and the way they are analyzed, based on fuzzy logic. In addition, the new hierarchical structure of the model used in the study provides flexibility and transparency in the process of remotely monitoring changes in people’s mental well-being and a quick response to observed changes. The aforementioned computational approach was used for the first time.
Improving the Accuracy of the Effective Atomic Number (EAN) and Relative Electron Density (RED) with Stoichiometric Calibration on PCD-CT Images
Authors: Son Kihong , Kim Daehong , Lee Sooyeul
Published: November 2022

The photon counting detector (PCD) in computed tomography (CT) can count the number of incoming photons in order to obtain energy information for photons corresponding to user-defined thresholds. Research on the extraction of effective atomic number (EAN) and relative electron density (RED) using dual-energy CT (DECT) is currently underway. This study proposes a method for improving EAN and RED accuracy of tissue-equivalent materials by using PCD-CT-based stoichiometric calibration. After obtaining DECT images in energy bin (EB) and full spectrum (FS) modes for eight tissue-equivalent materials, the EAN was calculated with stoichiometric calibration. Using the EAN image, the RED image was acquired to evaluate the accuracy. The errors of both EAN and RED obtained with EB were within 4%. In particular, the accuracy of RED was higher than that of the FS method. Study results indicate that PCD-CT contributes to improving EAN and RED accuracy. Further studies will be aimed at reducing ring artifacts by pixel-correcting PCD images and improving stopping power ratio (SPR) measurements for dose calculation in particle therapy.
Absorption Spectra Description for T-Cell Concentrations Determination and Simultaneous Measurements of Species during Co-Cultures
Authors: Wacogne Bruno , Vaccari Naïs , Koubevi Claudia , Belinger-Podevin Marine , Robert-Nicoud Marjorie , Rouleau Alain , Frelet-Barrand Annie
Published: November 2022

Advanced Therapy Medicinal Products are promising drugs for patients in therapeutic impasses. Their complex fabrication process implies regular quality controls to monitor cell concentration. Among the different methods available, optical techniques offer several advantages. Our study aims to measure cell concentration in real time in a potential closed-loop environment using white light spectroscopy and to test the possibility of simultaneously measuring concentrations of several species. By analyzing the shapes of the absorption spectra, this system allowed the quantification of T-cells with an accuracy of about 3% during 30 h of cultivation monitoring and 26 h of doubling time, coherent with what is expected for normal cell culture. Moreover, our system permitted concentration measurements for two species in reconstructed co-cultures of T-cells and Candida albicans yeasts. This method can now be applied to any single or co-culture, it allows real-time monitoring, and can be easily integrated into a closed system.
Upper Limb Kinematics of Handwriting among Children with and without Developmental Coordination Disorder
Authors: Abu-Ata Amani , Green Dido , Sopher Ran , Portnoy Sigal , Ratzon Navah Z.
Published: November 2022

Background: Children with developmental coordination disorder (DCD) often experience difficulties with handwriting legibility and speed. This study investigates the relationship between handwriting and upper limb kinematics to characterize movement patterns of children with DCD and typically developing (TD) children. Methods: 30 children with and without DCD matched for age, gender, and parent education were compared across handwriting abilities using a standardized handwriting assessment of both copied and dictated tasks (A-A Handwriting). The 3D motion capture system (Qualysis) was used to analyze upper limb kinematics and characterize movement patterns during handwriting and contrasted with written output. Results: Children with DCD wrote fewer legible letters in both copying and dictation. Children with DCD also showed poor automatization of key writing concepts. Atypical wrist postures were associated with reduced legibility for children with DCD (F (1,27) 4.71, p = 0.04, p-η2 = 0.15); whereas for TD children, better legibility was associated with greater variations in movement speed, particularly of the wrist (rho = −0.578, p < 0.05). Conclusion: Results reflect different movement parameters influencing handwriting in children with DCD. An improved understanding of the movement characteristics during handwriting of these children may assist intervention design.
MASPC_Transform: A Plant Point Cloud Segmentation Network Based on Multi-Head Attention Separation and Position Code
Authors: Li Bin , Guo Chenhua
Published: November 2022

Plant point cloud segmentation is an important step in 3D plant phenotype research. Because the stems, leaves, flowers, and other organs of plants are often intertwined and small in size, this makes plant point cloud segmentation more challenging than other segmentation tasks. In this paper, we propose MASPC_Transform, a novel plant point cloud segmentation network base on multi-head attention separation and position code. The proposed MASPC_Transform establishes connections for similar point clouds scattered in different areas of the point cloud space through multiple attention heads. In order to avoid the aggregation of multiple attention heads, we propose a multi-head attention separation loss based on spatial similarity, so that the attention positions of different attention heads can be dispersed as much as possible. In order to reduce the impact of point cloud disorder and irregularity on feature extraction, we propose a new point cloud position coding method, and use the position coding network based on this method in the local and global feature extraction modules of MASPC_Transform. We evaluate our MASPC_Transform on the ROSE_X dataset. Compared with the state-of-the-art approaches, the proposed MASPC_Transform achieved better segmentation results.
Perception of Recycled Plastics for Improved Consumer Acceptance through Self-Reported and Physiological Measures
Authors: Abella Ainoa , Llorach-Massana Pere , Pereda-Baños Alexandre , Marco-Almagro Lluís , Barreda-Ángeles Miguel , Clèries Laura
Published: November 2022

This article aims to provide in-depth insight into how consumers perceive recycled materials in comparison with natural raw materials at both the perceptual and attitudinal levels. To this end, we combined classic self-reported measures of sensory aspects, preferences, environmental attitudes, and consumption habits together with physiological measures of cognitive–emotional processing. Three different materials—two recycled materials, M2 and M3, and one raw material, M1—were chosen for inspection through three different sensory conditions, which we refer to as channels —visual, tactile, and visuo-tactile. The assignation of materials to sensory channels was counterbalanced so that each participant evaluated only one of the materials per channel. Although participants in general were not very accurate in discriminating between the materials, self-reported sensory evaluations showed that M3 (a recycled material that is made to look non-recycled), was clearly less liked. Meanwhile, the psychophysiological analyses revealed higher levels of electrodermal activity for the tactile evaluations of both recycled materials (M2 and M3). Finally, the results from the attitudes and habits evaluations indicate that the participants had positive environmental attitudes yet poor consumption habits. Altogether, these results suggest that some sensorial properties differ between recycled materials and natural raw materials and that there is a chance to improve and implement new consumption habits. The implications of these results are further discussed both in terms of suggestions for designers and methodological recommendations for researchers.
Recent Advances in Photo−Activated Chemical Sensors
Authors: Lee Dong Hyun , Yoo Hocheon
Published: November 2022

Gas detectors have attracted considerable attention for monitoring harmful gases and air pollution because of industry development and the ongoing interest in human health. On the other hand, conventional high−temperature gas detectors are unsuitable for safely detecting harmful gases at high activation temperatures. Photo−activated gas detectors improve gas sensing performance at room temperature and enable low−power operation. This review presents a timely overview of photo−activated gas detectors that use illuminated light instead of thermal energy. Illuminated light assists in gas detection and is classified as visible or ultraviolet light. The research on photo−activated gas detectors is organized according to the type of gas that can be intensively detected. In addition, a development strategy for advancing photo−activated gas detectors is discussed.
Influence of Polymer Processing on the Double Electrical Percolation Threshold in PLA/PCL/GNP Nanocomposites
Authors: Masarra Nour-Alhoda , Quantin Jean-Christophe , Batistella Marcos , El Hage Roland , Pucci Monica Francesca , Lopez-Cuesta José-Marie
Published: November 2022

For the first time, the double electrical percolation threshold was obtained in polylactide (PLA)/polycaprolactone (PCL)/graphene nanoplatelet (GNP) composite systems, prepared by compression moulding and fused filament fabrication (FFF). Using scanning electron microscopy (SEM) and atomic force microscopy (AFM), the localisation of the GNP, as well as the morphology of PLA and PCL phases, were evaluated and correlated with the electrical conductivity results estimated by the four-point probe method electrical measurements. The solvent extraction method was used to confirm and quantify the co-continuity in these samples. At 10 wt.% of the GNP, compression-moulded samples possessed a wide co-continuity range, varying from PLA55/PCL45 to PLA70/PCL30. The best electrical conductivity results were found for compression-moulded and 3D-printed PLA65/PCL35/GNP that have the fully co-continuous structure, based on the experimental and theoretical findings. This composite owns the highest storage modulus and complex viscosity at low angular frequency range, according to the melt shear rheology. Moreover, it exhibited the highest char formation and polymers degrees of crystallinity after the thermal investigation by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), respectively. The effect of the GNP content, compression moulding time, and multiple twin-screw extrusion blending steps on the co-continuity were also evaluated. The results showed that increasing the GNP content decreased the continuity of the polymer phases. Therefore, this work concluded that polymer processing methods impact the electrical percolation threshold and that the 3D printing of polymer composites entails higher electrical resistance as compared to compression moulding.
Multi-Sensing Techniques with Ultrasound for Musculoskeletal Assessment: A Review
Authors: de Oliveira Jonathan , de Souza Mauren Abreu , Assef Amauri Amorin , Maia Joaquim Miguel
Published: November 2022

The study of muscle contractions generated by the muscle-tendon unit (MTU) plays a critical role in medical diagnoses, monitoring, rehabilitation, and functional assessments, including the potential for movement prediction modeling used for prosthetic control. Over the last decade, the use of combined traditional techniques to quantify information about the muscle condition that is correlated to neuromuscular electrical activation and the generation of muscle force and vibration has grown. The purpose of this review is to guide the reader to relevant works in different applications of ultrasound imaging in combination with other techniques for the characterization of biological signals. Several research groups have been using multi-sensing systems to carry out specific studies in the health area. We can divide these studies into two categories: human–machine interface (HMI), in which sensors are used to capture critical information to control computerized prostheses and/or robotic actuators, and physiological study, where sensors are used to investigate a hypothesis and/or a clinical diagnosis. In addition, the relevance, challenges, and expectations for future work are discussed.
A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)
Authors: Khan Irfan Ullah , Aslam Nida , AlShedayed Rana , AlFrayan Dina , AlEssa Rand , AlShuail Noura A. , Al Safwan Alhawra
Published: November 2022

The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due to cyber security attacks. Therefore, there is a need for intelligent and reliable security systems that can proactively analyze the data generated by these devices and detect cybersecurity attacks. This study proposed a proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning (HVAC) attacks. Several ML algorithms were used, such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), Light Gradient Boosting (LGBM), Extreme Gradient Boosting (XGBoost), and CatBoost (CB). Furthermore, feature selection was performed using stepwise forward feature selection (FFS) technique. To alleviate the data imbalance, SMOTE and Tomeklink were used. In addition, SMOTE achieved the best results with selected features. Empirical experiments were conducted, and the results showed that the XGBoost classifier has produced the best result with 0.9999 Area Under the Curve (AUC), 0.9998, accuracy (ACC), 0.9996 Recall, 1.000 Precision and 0.9998 F1 Score got the best result. Additionally, XAI was applied to the best performing model to add the interpretability in the black-box model. Local and global explanations were generated using LIME and SHAP. The results of the proposed study have confirmed the effectiveness of ML for predicting the cyber security attacks on IoT devices and Industry 4.0.
Multiplexed Photonic Crystal Fiber Gas-Sensing Network Based on Intracavity Absorption
Authors: Wang Guangyao , Sun Jianping , Li Ting , Wang Hongjun , Li Jiahao
Published: November 2022

A highly sensitive hollow-core photonic crystal fiber (HC-PCF) gas-sensing network based on intracavity absorption is designed and experimentally verified. The capacity of the multichannel sensing network is expanded by time division multiplexing and wavelength division multiplexing technology. The voltage gradient method is employed in the wavelength scanning process of Fabry–Perot (F-P) filter to enhance the detection efficiency up to six times. The proposed sensing network has 16 sensing points. Experimental results show that the minimum detection limit (MDL) of this sensing system is 25.91 ppm and 26.85 ppm at the acetylene gas absorption peaks of 1530.371 nm and 1531.588 nm, respectively. As far as we know, it is the first time to obtain an intracavity sensing network via the application of an optical switch and DWDM at the same time. The sensing network can be used for high-capacity, low-concentration dangerous gas detection. It has great potential in environmental monitoring, industrial manufacturing, safety inspection and similar occasions.
OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results
Authors: Abdelkader Mohamed , Mabrok Mohamed , Koubaa Anis
Published: November 2022

Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications.
Validation of Wearable Device Consisting of a Smart Shirt with Built-In Bioelectrodes and a Wireless Transmitter for Heart Rate Monitoring in Light to Moderate Physical Work
Authors: Hashimoto Yuki , Sato Rieko , Takagahara Kazuhiko , Ishihara Takako , Watanabe Kento , Togo Hiroyoshi
Published: November 2022

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes.