Sensor-based Methods Research Articles

Survey of Autonomous Vehicles’ Collision Avoidance Algorithms

Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations. The survey also emphasizes how Machine Learning methods can improve the efficacy of obstacle avoidance. Combined, these techniques are necessary for enhancing the dependability and safety of autonomous driving systems, ultimately increasing public confidence in this game-changing technology.

Inkjet Printed Ti3C2Tx Sensor for External Temperature Monitoring of Batteries

The importance of various data delivered via temperature makes temperature sensing one of the most essential measurements in various fields of science [1,2]. Temperature is, in fact, an indicator of specific processes and is able to provide information about the surrounding environment. Temperature sensors must accurately and rapidly detect target signals for multiple applications, as in food logistics, healthcare, robotics and energy storage systems. A high-precision temperature measurement, however, occurs provided that the direct contact with the object is guaranteed. This aspect cannot always be guaranteed by traditional, rigid sensors, as they cannot ensure a conformal contact with uneven, flexible surfaces and because of their poor mechanical compliance [3,4]. Lithium-ion batteries (LIBs) have many desirable characteristics, but they suffer fire/explosion failure mode, which is a major safety concern. Therefore, how to improve the security and reliability of LIBs has attracted much attention. Most of the failure modes of LIBs are related to temperature variations, as temperature affects the normal operation of the battery and is one of the main factors contributing to battery safety incidents. The measurement methods are divided into two types: contact and non-contact measurements. Contact measurements include sensor-based methods such as thermistors and resistance temperature detectors. Our proposalis to develop a resistance temperature sensor based on Ti3C2Tx MXenes for the monitoring of external temperature in lithium-ion batteries (LIBs). The idea is to print a flexible electrode on the package of a commercial battery. Flexibility will be achieved through alternative deposition techniques, as printing techniques. In particular, inkjet printing results to be an optimal technique as it allows to produce a patterned geometry and achieve high resolutions. First, a preliminary characterization of the synthetized MXene will be necessary in terms of SEM, XRD and DLS, in order to assess whether the synthesis was successful and to assess the dimension of the MXene flakes, which directly affects the conductivity of the material. Then, the configuration design of the sensor will be considered in order to understand the best solution to maximize the sensitivity and accuracy of the sensor. The best configuration will be electrically tested in order to achieve parameters as the temperature coefficient of resistance, the sensitivity, the accuracy of the temperature sensor.Bibliography[1] S. Yao, P. Swetha, Y. Zhu, Nanomaterial-Enabled Wearable Sensors for Healthcare, Advanced Healthcare Materials 7 (2018) 1700889. https://doi.org/10.1002/adhm.201700889.[2] M.L. Hammock, A. Chortos, B.C.-K. Tee, J.B.-H. Tok, Z. Bao, 25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress, Advanced Materials 25 (2013) 5997–6038. https://doi.org/10.1002/adma.201302240.[3] Q. Li, L.-N. Zhang, X.-M. Tao, X. Ding, Review of Flexible Temperature Sensing Networks for Wearable Physiological Monitoring, Advanced Healthcare Materials 6 (2017) 1601371. https://doi.org/10.1002/adhm.201601371.[4] S. Bielska, M. Sibinski, A. Lukasik, Polymer temperature sensor for textronic applications, Materials Science and Engineering: B 165 (2009) 50–52. https://doi.org/10.1016/j.mseb.2009.07.014.

Enhancing Smartphone-based Pedestrian Positioning: Using Factor Graph Optimization with Indoor/Outdoor Detection for 3DMA GNSS/Visual-Inertial State Estimation

This paper explores the pervasive challenges of pedestrian positioning using smartphones in densely populated urban environments where Global Navigation Satellite System (GNSS) signals are inaccessible, for example, in indoor areas. Existing sensor-based positioning methods, such as inertial navigation systems (INS), GNSS, and visual-inertial odometry (VIO), suffer from inherent restrictions that compromise the accuracy and reliability of the positioning performance. An approach based on machine learning is proposed to address these limitations, employing the Support Vector Machine (SVM) algorithm to accurately distinguish indoor/outdoor (IO) based on the measurement of GNSS. The proposed approach in this study seamlessly incorporates 3D mapping aided (3DMA) GNSS measurements and localized estimations derived by VIO via factor graph optimization (FGO), complemented by an IO detection switch, to achieve accurate pose estimation and effectively eliminate global drift. The system's effectiveness and robustness are rigorously assessed through comprehensive extensive real-life experiments, with an average reduction of 4 meters, leading to noteworthy and statistically significant findings. Received: 1 April 2024 | Revised: 13 September 2024 | Accepted: 26 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The source code that supports the findings of this study is openly available in 3DMAGNSSVINS-IOFGO at https://github.com/queenie-ho/3DMAGNSSVINS-IOFGO. Author Contribution Statement Hiu-Yi Ho and Hoi-Fung Ng: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Weisong Wen: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition. Yanlei Gu: Resources, Writing - review & editing, Writing - review & editing. Li-Ta Hsu: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition.

Corrosion effects on flexural behaviour of EPS sandwich concrete panels: a study using acoustic emission technique

The experimental research utilized expanded polystyrene (EPS) panels sized at 1300 × 600 × 130 mm (length × width × thickness) and induced with corrosion via an impressed current method. Varied extents of corrosion were applied for 120, 240, 360, and 480 h, resulting in mass loss percentages of 6.67%, 8.41%, 10.76%, and 18.13% of reinforcement in different panels. The flexural testing along with acoustic emission (AE) monitoring were performed for evaluation of EPS panels. Results obtained defend the correlation of AE energy with the load energy in the EPS panels. The AE parameters of rise angle and average frequency assist in distinguishing between tensile and shear crack types. The energy released during tensile crack formation in EPS panels is minimal compared to that during shear crack formation. These findings are a basis for further AE investigations into EPS panels and developing AE sensor-based methods for identifying corrosion-induced degradation in EPS panels within structures.

From detection methods to risk prevention: Control of N-nitrosamines in foods and the role of natural bioactive compounds.

Food processing unavoidably introduces various risky ingredients that threaten food safety. N-Nitrosamines (NAs) constitute a class of food contaminants, which are considered carcinogenic to humans. According to the compiled information, pretreatment methods based on solid-phase extraction (SPE) were widely used before the determination of volatile NAs in foods. The innovation of adsorbents and hybridization of other methods have been confirmed as the future trends of SPE-based pretreatment methods. Moreover, technologies based on liquid chromatography and gas chromatography were popularly applied for the detection of NAs. Recently, sensor-based methods have garnered increasing attention due to their efficiency and flexibility. More portable sensor-based technologies are recommended for on-site monitoring of NAs in the future. The application of artificial intelligence can facilitate data processing during high-throughput detection of NAs. Natural bioactive compounds have been confirmed to be effective in mitigating NAs in foods through antioxidation, scavenging precursors, and regulating microbial activities. Meanwhile, they exhibit strong protective activities against hepatic damage, pancreatic cancer, and other NA injuries. Further supplementation of data on the bioavailability of bioactives can be achieved through encapsulation and clinical trials. The utilization of bioinformatics tools rooted in various omics technologies is suggested for investigating novel mechanisms and finally broadening their applications in targeted therapies.

A systematic review of non-intrusive human activity recognition in smart homes using deep learning

<p>Smart homes are a viable solution for improving the independence and privacy of elderly and dependent people thanks to IoT sensors. Reliable human activity recognition (HAR) devices are required to enable precise monitoring inside smart homes. Despite various reviews on HAR, there is a lack of comprehensive studies that include a diverse range of approaches, including sensor-based, wearable, ambient, and device-free methods. Considering this research gap, this study aims to systematically review the HAR studies that apply deep learning as their main solution and utilize a non-intrusive approach for activity monitoring. Out of the 2,171 studies in the IEEE Explore database, we carefully selected and thoroughly analyzed 37 studies for our research, following the guidelines provided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. In this paper, we explore various modalities, deep learning approaches, and datasets employed in the context of non-intrusive HAR. This study presents essential data for researchers to employ deep learning techniques for HAR in smart home environments. Additionally, it identifies and highlights the main trends, challenges, and future directions.</p>

Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation.

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

Physical constraint flow field reconstruction method for immersion scan-step flow

The lithography immersion flow field is highly sensitive to minor variations due to the precision demands of lithography. The scanning flow induces pressure fluctuations on the lens surface, subsequently impacting the quality of exposure. Previous measurement methods have encountered sparse distribution measurements on the lens surface due to limitations in sensor size and flow field dimensions. In this manuscript, the authors propose a modal fusion flow field reconstruction method, effectively integrating sensor-based experimental methods and equation-based computational methods. This method achieves high-resolution measurements of stress distribution and velocity distribution. Initially, a simulation of the flow field under operational conditions is conducted to acquire a dataset, followed by decomposing the dataset into modes to establish a modal library. Throughout the measurement process, the modal library is dynamically restructured based on sensor signal using a sparse representation method to forecast the flow field, and subsequently, the flow field is corrected in accordance with the Navier-Stokes equation.

A circadian behavioral analysis suite for real-time classification of daily rhythms in complex behaviors.

Measuring animal behavior over long timescales has been traditionally limited to behaviors that are easily measurable with real-time sensors. More complex behaviors have been measured over time, but these approaches are considerably more challenging due to the intensive manual effort required for scoring behaviors. Recent advances in machine learning have introduced automated behavior analysis methods, but these often overlook long-term behavioral patterns and struggle with classification in varying environmental conditions. To address this, we developed a pipeline that enables continuous, parallel recording and acquisition of animal behavior for an indefinite duration. As part of this pipeline, we applied a recent breakthrough self-supervised computer vision model to reduce training bias and overfitting and to ensure classification robustness. Our system automatically classifies animal behaviors with a performance approaching that of expert-level human labelers. Critically, classification occurs continuously, across multiple animals, and in real time. As a proof-of-concept, we used our system to record behavior from 97 mice over two weeks to test the hypothesis that sex and estrogen influence circadian rhythms in nine distinct home cage behaviors. We discovered novel sex- and estrogen-dependent differences in circadian properties of several behaviors including digging and nesting rhythms. We present a generalized version of our pipeline and novel classification model, the "circadian behavioral analysis suite," (CBAS) as a user-friendly, open-source software package that allows researchers to automatically acquire and analyze behavioral rhythms with a throughput that rivals sensor-based methods, allowing for the temporal and circadian analysis of behaviors that were previously difficult or impossible to observe.

Innovative Approaches for Microplastic Pollution Detection and Remediation in Aquatic Ecosystems

Microplastic pollution in aquatic ecosystems is a pressing environmental issue, posing significant threats to marine life and human health. Traditional detection and remediation methods are often inadequate, necessitating the development of innovative approaches. This research aims to explore and evaluate novel techniques for detecting and mitigating microplastics in aquatic environments. The study investigates advanced detection technologies, including spectroscopy and sensor-based methods, that offer higher accuracy and efficiency compared to conventional approaches. Additionally, it explores innovative remediation techniques such as bioremediation and the use of advanced filtration systems. Field and laboratory experiments were conducted to test the effectiveness of these methods in various aquatic settings. The results indicate that these innovative approaches significantly enhance the detection and removal of microplastics, demonstrating superior performance over traditional methods. This research provides critical insights into the potential of advanced technologies in addressing microplastic pollution, highlighting their applicability and benefits for environmental conservation. By integrating these innovative solutions, we can better safeguard aquatic ecosystems and promote sustainable environmental practices.

YOLOv8-EMSC: A lightweight fire recognition algorithm for large spaces

Stringent fire prevention requirements are imperative in expansive environments. Fire detection in diverse large-scale settings typically relies on sensor-based or AI-driven target detection methods. Traditional fire detectors often suffer from false alarms and missed detections, failing to meet the fire safety requirements of large-scale structures. Many existing target detection algorithms are characterized by substantial model sizes. Some detection terminals in large structures face challenges deploying these models due to constrained computational resources. To address this issue, we propose a lightweight model, YOLOv8-EMSC, derived from YOLOv8n. The incorporation of C2f_EMSC, replacing the C2f module, significantly reduces the model parameters in the enhanced YOLOv8-EMSC model compared to YOLOv8n, thereby enhancing model inference speed. Extensive testing and validation using a custom-built large-scale infrared fire dataset demonstrates a 9.6 % reduction in parameters compared to the baseline model for YOLOv8-EMSC, achieving an average precision of 95.6 %, surpassing both the baseline and mainstream models and significantly enhancing fire detection accuracy in expansive environments.

Evaluation of correlation of physicochemical parameters and major ions present in groundwater of Raipur using discretization

Groundwater, vital for human consumption and agriculture, ecosystem support, and industrial activities, requires sustainable management using proper quality assessment techniques. This study examines the relationship between physicochemical parameters and major ions in groundwater samples collected from 44 regions in Raipur, using sensor-based data acquisition alongside traditional methods. Employing K-means clustering for data discretization, correlations between parameters are highlighted. Results show positive associations among EC, TDS, TH, and TA. ArcGIS interpolation maps visualize spatial distribution. Addressing class imbalance, an upsampling technique is utilized. Machine learning algorithms, including Logistic Regression and Random Forest, classify water quality with accuracies of 98.8 % and 98.3 %, respectively. This research, blending traditional and sensor-based methods, emphasizes informed water management.

Retracted Article: Validity and Reliability of Sensor-based Measures of Lower Limb Range of Motion in Soccer Players: a Cross-sectional Study.

A deficit in range of motion (ROM) is considered a risk factor for lower extremity injuries in soccer. Analog goniometers are used to measure the ROM of a joint. Sensor-based methods are increasingly being developed, but their quality of testing has not been sufficiently investigated. The purpose of this study was to determine the agreement and the intra- and inter-tester reliability of sensor-based lower extremity ROM measures in soccer players. 36 symptom-free male amateur soccer players (age: 26.3±4.7 years) were included in the study. Three out of five physiotherapists were randomly selected to perform the measurements. Two testers performed the measurements (1. ROM knee from sitting; 2. extension deficit knee from sitting; 3. ROM knee from standing; 4. ROM ankle dorsiflexion [DF] during lunge; 5. ROM ankle plantar flexion [PF] while sitting on a chair) with the digital sensor (index test). The third examiner performed the measurements with the analog goniometer using the neutral-zero method with the subjects in the supine position (reference standard). Pearson's correlation coefficient r, Bland-Altman analysis (BAA), and intraclass correlation coefficient (ICC) were used for statistical analysis (p≤0.05). Only measurements 4 and 5 showed acceptable mean differences of 8.4° (DF) and -10.2° (PF) in the BAA. Measurement 1 showed a moderate correlation (r=0.582). The sensor-based measurements of knee and ankle ROM revealed excellent intra- and inter-tester reliability (ICC =0.949-0.986; ICC =0.895-0.968). However, they showed limited agreement with the established reference standard used here, which can be explained by the different starting positions between the index test and the reference standard.

Comparison of classical and sensor-based methods for determination of indoor air quality

Comparison of classical and sensor-based methods for determination of indoor air quality

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual’s body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data.

Obstacle Detection For Visually Impaired

The paper examines innovative obstacle-detection solutions designed for visually impaired people who do not rely on traditional sensors and recognize the challenges they face when navigating their environment independently. It delves into techniques leveraging alternative sensory modalities such as auditory and tactile feedback, prioritizing user experience and safety. Evaluation criteria encompass effectiveness, ease of use, and affordability, during discussions involve adapting existing infrastructure for tactile navigation and collaboration with guide animal organizations to devise comprehensive solutions. In addition to case studies showing the successful implementation of sensor-free obstacle detection in a wide range of environments, i.e., cities and indoors, challenges related to training and social acceptance of alternative methods will be addressed. In particular, the paper offers beneficial insight into novel approaches to detecting obstacles aimed at improving the mobility and independence of those with visual impairment. Our method uses advanced machine learning algorithms to interpret and predict potential obstacles in real-time. Our system can identify and classify barriers with a high degree of accuracy through analysis of user's sound feedback from their surroundings as well as the use of sophisticated learning techniques. In a wide range of real-world scenarios, we demonstrate the effectiveness of our approach through extensive testing with visually impaired participants. The results show that compared to conventional sensor-based methods, the performance of obstacle detection is significantly improved. In addition, our solution is designed to improve the flexibility, affordability, and ease of integration with existing technologies to create an environment more conducive for people with visual impairment.

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

Real-Time Digital Mapped Method for Sensorless Multitimescale Operation Condition Monitoring of Power Electronics Systems

Operation condition monitoring of power electronics systems can improve analysis visibility for device administrators and researchers. However, the current sensor-based monitoring methods have limitations in accurately monitoring variables at multi-timescales. This article presents a real-time digital mapped (RTDM) method for the sensorless multi-timescale operation condition monitoring in power electronics systems using FPGA simulators. The mathematic models of power converters are deployed in the simulator considering multi-timescale characteristics. Both the digital simulator and the physical power converter are controlled by the same control signals, allowing the simulator to map the accurate operation conditions of the power converters without additional physical sensors. The RTDM method can monitor variables on the switching period timescale to the system transient timescale due to the consideration for the ideal switch modeling of the semiconductor switch. The effectiveness of the proposed method is demonstrated using a 30kW DAB converter as an example, through the integration of an experimental prototype and a simulator to create an RTDM platform. This method offers an easy and sensorless way to monitor multi-timescale variables in power electronics systems, providing a more comprehensive and visual analysis of their operation.

Robot Indoor Navigation: Comparative Analysis of LiDAR 2D and Visual SLAM

<span lang="EN-US">Robot indoor navigation has become a significant area of research and development for applications such as autonomous robots, smart homes, and industrial automation. This article presents an in-depth comparative analysis of LiDAR 2D and visual sensor simultaneous localization and mapping (SLAM) approaches for robot indoor navigation. The increasing demand for autonomous robots in indoor environments has led to the development of various SLAM techniques for mapping and localization. LiDAR 2D and visual sensor-based SLAM methods are widely used due to their low cost and ease of implementation. The article provides an overview of LiDAR 2D and visual sensor-based SLAM techniques, including their working principles, advantages, and limitations. A comprehensive comparative analysis is conducted, assesing their capabilities in terms of robustness, accuracy, and computational requirements. The article also discusses the impact of environmental factors, such as lighting conditions and obstacles, on the performance of both approaches. The analysis’s findings highlight each approach’s strengths and weaknesses, providing valuable insights for researchers and practitioners in selecting the appropriate SLAM method for robot indoor navigation based on specific requirements and constraints</span>

Recent Progress in Detecting Enantiomers in Food.

The analysis of enantiomers in food has significant implications for food safety and human health. Conventional analytical methods employed for enantiomer analysis, such as gas chromatography and high-performance liquid chromatography, are characterized by their labor-intensive nature and lengthy analysis times. This review focuses on the development of rapid and reliable biosensors for the analysis of enantiomers in food. Electrochemical and optical biosensors are highlighted, along with their fabrication methods and materials. The determination of enantiomers in food can authenticate products and ensure their safety. Amino acids and chiral pesticides are specifically discussed as important chiral substances found in food. The use of sensors replaces expensive reagents, offers real-time analysis capabilities, and provides a low-cost screening method for enantiomers. This review contributes to the advancement of sensor-based methods in the field of food analysis and promotes food authenticity and safety.