Search

Abstract Background Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status. Results & discussion NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting. Conclusion The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health.

Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable agricultural production. Due to the highly dynamic nature of ICLS management, mapping them is a challenging task. The main objective of this research was to develop a method for mapping ICLS using deep learning algorithms applied on Satellite Image Time Series (SITS) data cubes, which consist of Sentinel-2 (S2) and PlanetScope (PS) satellite images, as well as data fused (DF) from both sensors. This study focused on two Brazilian states with varying landscapes and field sizes. Targeting ICLS, field data were combined with S2 and PS data to build land use and land cover classification models for three sequential agricultural years (2018/2019, 2019/2020, and 2020/2021). We tested three experimental settings to assess the classification performance using S2, PS, and DF data cubes. The test classification algorithms included Random Forest (RF), Temporal Convolutional Neural Network (TempCNN), Residual Network (ResNet), and a Lightweight Temporal Attention Encoder (L-TAE), with the latter incorporating an attention-based model, fusing S2 and PS within the temporal encoders. Experimental results did not show statistically significant differences between the three data sources for both study areas. Nevertheless, the TempCNN outperformed the other classifiers with an overall accuracy above 90% and an F1-Score of 86.6% for the ICLS class. By selecting the best models, we generated annual ICLS maps, including their surrounding landscapes. This study demonstrated the potential of deep learning algorithms and SITS to successfully map dynamic agricultural systems. [ABSTRACT FROM AUTHOR]

Field environmental sensing can acquire real-time environmental information, which will be applied to field operation, through the fusion of multiple sensors. Multi-sensor fusion refers to the fusion of information obtained from multiple sensors using more advanced data processing methods. The main objective of applying this technology in field environment perception is to acquire real-time environmental information, making agricultural mechanical devices operate better in complex farmland environment with stronger sensing ability and operational accuracy. In this paper, the characteristics of sensors are studied to clarify the advantages and existing problems of each type of sensors and point out that multiple sensors can be introduced to compensate for the information loss. Secondly, the mainstream information fusion types at present are outlined. The characteristics, advantages and disadvantages of different fusion methods are analyzed. The important studies and applications related to multi-sensor information fusion technology published at home and abroad are listed. Eventually, the existing problems in the field environment sensing at present are summarized and the prospect for future of sensors precise sensing, multi-dimensional fusion strategies, discrepancies in sensor fusion and agricultural information processing are proposed in hope of providing reference for the deeper development of smart agriculture. [ABSTRACT FROM AUTHOR]

Mangroves are woody halophytes thriving in muddy substratum along the coastal areas of the tropics and sub-tropics. They are often credited for their exceptional carbon sequestration capability. Estimating above-ground biomass (AGB) through field survey is tedious, particularly in a hostile environment like a mangrove ecosystem. However, the quantification of AGB is made possible with the help of continued advancements in sensor technology and computational algorithms. This research attempts to model the AGB of mangroves present in Bhitarkanika, Odisha, using a multi-sensor approach. We utilized multispectral Sentinel-2 (SM) and Landsat-8 (LO), and hyperspectral Airborne Visible Infra-Red Imaging Spectrometer—Next Generation (AN) datasets in our analysis. The mangrove biomass was calculated for 42 sample plots from a field survey using species specific and common allometric equations. After data-specific preprocessing; six feature sets namely reflectance bands, band ratios, vegetation indices (VIs), texture-based Gray Level Co-occurrence Matrix (GLCM) features of reflectance, band ratios and VIs were extracted for each dataset. The co-located set of features derived from each dataset were regressed against the AGB estimated using field methods of 42 sample plots (1) independently for each feature set, (2) in a combination of feature sets for each dataset and (3) in a combination of the feature sets of all three datasets as a multi-sensor approach. Feature selection techniques were used to get the best possible output of combined AN, SM and LO datasets. The results show that the combination of textural features gave better prediction models than independent sets of features. Also, Genetic Algorithm (GA) and Recursive Feature Elimination CV (RFECV) proved to be better feature selectors than other classical approaches. AN, SM and LO resulted in the R2 value of 0.41, 0.85 and 0.35 with RMSE of 356.81, 195.49 and 366.84 t/ha, respectively; while, the multisensory approach yielded a maximum R2 value of 0.7 and RMSE of 244.86 t/ha. The results show that the structural information of vegetation canopy obtained from textural parameters of different input bands has improved the regression model to predict the biomass. [ABSTRACT FROM AUTHOR]

Aiming at the traditional single sensor vibration signal cannot fully express the bearing running state, and in the high noise background, the traditional algorithm is insufficient for fault feature extraction. This paper proposes a fault diagnosis algorithm based on multi-sensor and hybrid multimodal feature fusion to achieve high-precision fault diagnosis by leveraging the operating state information of bearings in a high-noise environment to the fullest extent possible. First, the horizontal and vertical vibration signals from two sensors are fused using principal component analysis, aiming to provide a more comprehensive description of the bearing’s operating condition, followed by data set segmentation. Following fusion, time-frequency feature maps are generated using a continuous wavelet transform for global time-frequency feature extraction. A first diagnostic model is then developed utilizing a residual neural network. Meanwhile, the feature data is normalized, and 28 time-frequency feature indexes are extracted. Subsequently, a second diagnostic model is constructed using a support vector machine. Lastly, the two diagnosis models are integrated to derive the final model through an ensemble learning algorithm fused at the decision level and complemented by a genetic algorithm solution to improve the diagnosis accuracy. Experimental results demonstrate the effectiveness of the proposed algorithm in achieving superior diagnostic performance with a 97.54% accuracy rate. [ABSTRACT FROM AUTHOR]

Drought can have significant impacts on forests, with long periods of water scarcity leading to water stress in trees and possible damages to their eco-physiological functions. Satellite-based remote sensing offers a valuable tool for monitoring and assessing drought conditions over large and remote forested regions. The objective of this study is to evaluate the hydrological consistency in the context of drought of precipitation, soil moisture, evapotranspiration, and land surface temperature observations against in situ measurements in a number of well-monitored sites in New Zealand. Results showed that drought indicators were better captured from soil moisture observations compared to precipitation satellite observations. Nevertheless, we found statistically significant causality relationships between the multi-sensor satellite observations (median p-values ranging from 0.001 to 0.019), with spatial resolution appearing to be an important aspect for the adequate estimation of drought characteristics. Understanding the limitations and capabilities of satellite observations is crucial for improving the accuracy of forest drought monitoring, which, in turn, will aid in sustainable forest management and the development of mitigation and adaptation strategies in the face of changing climate conditions. [ABSTRACT FROM AUTHOR]

This paper investigates how global navigation satellite systems (GNSSs) and inertial navigation systems (INSs), when appropriately augmented by ranging from local landmarks, can safely navigate vehicles through a real-world urban environment. We begin by considering safety requirements for driverless vehicles under fault-free assumptions and developing measurement models for multi-sensor integrated navigation systems using an extended Kalman filter. The critical elements of urban navigation are then discussed, including individual INS noise parameter specifications, vehicle speed, and the effect of velocity updates. Covariance analyses performed along a 9-km-long urban transect in downtown Chicago show that velocity updates measured by wheel speed sensors, vehicle kinematic constraints, and zero-velocity updates can extend navigation continuity by bridging intermittent GNSS signal availability. However, position reference updates at intervals between 15 and 35 m, based on light detection and ranging data from local landmarks in our case, are needed to achieve full navigation availability through the transect. [ABSTRACT FROM AUTHOR]

The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation. [ABSTRACT FROM AUTHOR]

The scarcity and low spatial resolution of hyperspectral images (HSIs) have become a major problem limiting the application of the images. In recent years, spectral reconstruction (SR) has been applied to convert multispectral images (MSIs) with abundant quantities and high spatial resolution into HSIs. With the launch of several new multispectral (MS) satellites with a short repeat period, the simultaneous acquisition of images from multiple MS sensors in the same area is gradually becoming feasible. Unfortunately, existing SR methods only consider the reconstruction of the MSIs of a single sensor without considering using MSIs from different MS sensors to obtain a better construction effect through their complementary bands. However, multi-sensor SR is characterized by two problems: inconsistency in the amplitude information of real multisensor imaging and difficulty in the extraction of the complex correlations of bands from different sensors. To solve these problems, this paper proposes a multi-sensor SR framework based on a two-step approach in which the problems of amplitude inconsistency and band information extraction are solved using an ideal projection network and an ideal multi-sensor SR network, respectively. The effectiveness of the proposed method is verified by experiments on three datasets. [ABSTRACT FROM AUTHOR]

Predictive maintenance is a modern Industry 4.0 strategy in which machines are continuously monitored to detect flaws and prevent breakdowns before they occur. A single sensor focuses on a single variable while neglecting larger information components, resulting in poor data quality and an increased risk of issues in critical rotating equipment. Multi-sensory configuration technology has been developed to collect huge amounts of data from a machine in order to enhance monitoring capabilities in terms of precision, resolution, efficiency, resilience, and trustworthiness of the overall system. The goal of this work is to provide an integrated perspective on machine monitoring using the Multi Sensor Data Fusion (MSDF) technique. On four fault bearings, a case study contrasts the results of single and multiple sensors. A feature-level data fusion method is used, in which computations using time-domain vibration signature data are utilised to build a fusional vector, which is then classified using SVM and analysed with Gaussian kernels. The experimental results suggest that the proposed Gaussian kernel with SVM technique outperforms single sensor data interpretation in terms of classification accuracy and generalisation capability. It is an efficient way for finding defects in rotating machinery in excessively noisy environments. [ABSTRACT FROM AUTHOR]

Introduction: Optical and SAR image matching is one of the fields within multisensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice. [ABSTRACT FROM AUTHOR]

The research on state estimation for quadruped robots is critical. Its result passed to motion controller makes the robot navigate autonomously and adjust the gait to a more stable motion. The current research depends on a multi-sensor fusion of cameras, lidars or other proprioceptive sensors, such as Inertial Measurement Unit (IMU) and encoders. The high-frequency data are generally derived from body sensors, which is to be fused with data from external sensors directly, or preprocessed with EKF first. Due to its unguaranteed convergence and robustness of tracking state mutations, EKF is insufficient. Therefore, we study state estimation for hydraulic quadruped robot based on the fusion of IMU measurement and leg odometry in this paper, and Invariant Extended Kalman Filter (IEKF) is successfully applied to quadruped robots by using this method. Besides, neural networks are utilized to train the weight functions of foot force and the state of leg odometry, and our trained functions improve the accuracy of observation compared with common weight average methods. Finally, our experiments of accuracy show that the root mean square error of our method is significantly reduced and the absolute trajectory error is reduced by 30% compared to traditional IEKF. The algorithm achieves the drift per distance travelled below 4 cm/m. Moreover, it has good robustness. [ABSTRACT FROM AUTHOR]

Background: Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or 'free-living' measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status. Results & discussion: NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting. Conclusion: The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual's daily health-related behavior. NiMBaLWear's focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health. [ABSTRACT FROM AUTHOR]

To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD). We introduce the multi-sensor GSTM-TCPHD (MS-GSTM-TCPHD) filter to enhance tracking performance. Conventional cardinality probability hypothesis density (CPHD) filters typically assume Gaussian noise and struggle to accurately establish target trajectories when faced with heavy-tailed non-Gaussian distributions. Heavy-tailed noise leads to significant estimation errors and filter dispersion. Moreover, the exact trajectory of the target is crucial for tracking and prediction. Our proposed GSTM-TCPHD filter utilizes the GSTM distribution to model heavy-tailed noise, reducing modeling errors and generating a set of potential target trajectories. Since single sensors have a limited field of view and limited measurement information, we extend the filter to a multi-sensor scenario. To tackle the issue of data explosion from multiple sensors, we employed a greedy approximation method to assess measurements and introduced the MS-GSTM-TCPHD filter. The simulation results demonstrate that our proposed filter outperforms the CPHD/TCPHD filter and Student's t-based TCPHD filter in terms of accurately estimating the trajectories of multiple targets during tracking while also achieving improved accuracy and shorter processing time. [ABSTRACT FROM AUTHOR]

The assessment of environmental sustainability is of utmost importance for the forests and plantations in Borneo, given the critical need for environmental protection through the identification and mitigation of potential risks. This study was conducted to assess the environmental sustainability of tropical forest and plantations landscape, a case study in northern Sabah, Malaysian Borneo. Applications of the latest high-resolution multi-sensor remote sensing and geospatial MCDA are cost-effective and useful for large-scale environmental sustainability assessment. The land use land cover (LULC) of the study area was mapped with synergistic use of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical and high-resolution PlanetScope satellite imageries, resulting in overall accuracy of 87.24%. Five sustainability indicator layers: slope erosion protection, river buffer, landscape connectivity and quality, high conservation value (HCV), and water turbidity were developed from the LULC map, ancillary datasets of SRTM, and forest operation basemap with reference to standards from the Environment Protection Department (EPD), Roundtable on Sustainable Palm Oil (RSPO), and Forest Management Plan (FMP) for the analysis using multi-criteria decision analysis (MCDA) model. The results revealed that overall, the study areas are in the high sustainability category at 61%, medium at 31%, and low at only 8%. We analyzed the environmental sustainability of five land use boundaries, and the results showed that Industrial Tree Plantations (ITP) and Village Reserve are mostly in the high category. Meanwhile, oil palm plantations, rubber plantations, and forest reserve (FR) are the majority in the medium category. Both oil palm and rubber plantations are a majority in the medium class due to monocropping land use type having low landscape connectivity and quality individual sustainability indicator layer. The study presented the concept of use of multi-sensor remote sensing for LULC mapping with geospatial MCDA for environmental sustainability assessment useful to stakeholders for improving the management plan also contributing toward the progress of achieving UNSDGs and addressing REDD+.

This paper presents an advanced Surveillance Warning System (SWS) designed for next-generation video surveillance applications. While contemporary alert systems have improved analytics engines, their binary threat detection using only optical imaging sensors has limitations in low-light conditions, system security, and multilevel threat detection (i.e., low, moderate, and critical). Our approach integrates a novel alert module with an enhanced video analytics engine, contributing to a comprehensive framework integrating surveillance, access control, multilevel threat detection, and multi-tiered alert routing. We develop a Video Surveillance and Motion Detection (VSMD) application utilizing optical imaging, Passive Infrared (PIR), and fingerprint sensors for multilevel threat assessment, access control, and security. Unlike existing systems, we develop an intelligent alert module generating threat-specific alerts transmitted securely through cellular phones, the Internet, and encrypted radio frequency (RF) signals, significantly enhancing real-time threat awareness and response capabilities. Validation against existing solutions highlights the system's adaptability, rapid response, and overall security advancements in video surveillance.

ObjectiveFor mechanical equipment in actual production, it is difficult or impossible to obtain a large amount of labeled data, resulting in low accuracy of traditional fault diagnosis methods. To address this problem, a cross-device fault diagnosis method based on graph convolution and multi-sensor fusion, convolutional domain graph convolution network (CDGCN) , was proposed. This method can model class labels, domain labels, and data feature structures.MethodsFirstly, a convolutional neural network was used to extract features from the input signal. Then, the feature structure relationship of the sample was mined through the graph generation layer to construct an instance graph. The instance graph was modeled using a graph convolutional neural network, and a multi-sensor high-level feature fusion method was proposed to perform multi-sensor information fusion. Finally, domain adaptation was achieved by using distribution difference metrics, classifiers, and domain discriminators.ResultsThe proposed method can capture domain-invariant features and discriminant features, and ultimately achieve cross-device fault diagnosis. Migration experiments on two datasets show that the proposed CDGCN not only achieves the best performance among the compared methods, but also extracts transferable features for cross-device domain adaptation.

Electronic Nose (E-Nose) systems, widely applied across diverse fields, have revolutionized quality control, disease diagnostics, and environmental management through their odor detection and analysis capabilities. The decision and analysis of E-Nose systems often enabled by Machine Learning (ML) models that are trained offline using existing datasets. However, despite their potential, offline training efforts often prove intensive and may still fall short in achieving high generalization ability and specialization for considered application. To address these challenges, this paper introduces the e-rTPNN decision system, which leverages the Recurrent Trend Predictive Neural Network (rTPNN) combined with online transfer learning. The recurrent architecture of the e-rTPNN system effectively captures temporal dependencies and hidden sequential patterns within E-Nose sensor data, enabling accurate estimation of trends and levels. Notably, the system demonstrates the ability to adapt quickly to new data during online operation, requiring only a small offline dataset for initial learning. We evaluate the performance of the e-rTPNN decision system in two domains: beverage quality assessment and medical diagnosis, using publicly available wine quality and Chronic Obstructive Pulmonary Disease (COPD) datasets, respectively. Our evaluation indicates that the proposed e-rTPNN achieves decision accuracy exceeding $97~\%$ while maintaining low execution times. Furthermore, comparative analysis against established Machine Learning (ML) models reveals that the e-rTPNN decision system consistently outperforms these models by a significant margin in terms of accuracy.

Icing on overhead transmission lines can be more accurately and effectively monitored in real time through a method that integrates information from multiple sensors for data fusion. SHT75 temperature and humidity sensor and ice thickness sensor are used to collect three kinds of sensing information of environmental temperature, humidity and ice thickness of transmission lines, and then input them into the ice fusion monitoring structure based on BP neural network. The weights and thresholds of a BP neural network can be optimized using a genetic algorithm, and fusion diagnosis is performed to determine if there is icing on transmission lines. For transmission lines with icing, by employing a fuzzy control-based evaluation method for line icing levels, the proposed approach enables the real-time assessment of icing levels on overhead transmission lines. Experimental findings demonstrate the method’s efficacy in swiftly and accurately detecting icing occurrences, thereby enhancing the precision and immediacy of icing monitoring on overhead transmission lines.

The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security.

Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors.

In order to improve the operational effectiveness of multi-sensor cooperative detection of carrier-borne fighter, based on the scene of air interception, according to the combat airspace from far to near, the scene model of the extended range search of carrier-borne fighter, the radar infrared fusion detection, the cooperative guidance of formation and radar multi-beam detection are established. In the over-the-horizon, medium-range and short-range search phase, the extended range detection, interference and multi-beam detection in the multi-sensor cooperative detection mode are simulated and analyzed. According to the different combat distance, the design of multi-sensor cooperative detection mode under the combat flow is innovatively carried out.

Abstract We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao–Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments.

To meet the complex and diverse demands for low-stress mechanical measurements of fabrics and other flexible materials, two integrated multidimensional force sensors with the same structure but different ranges were explored. They can support both rapid and precise low-noise, high-precision, low-cost, easy-to-use, reliable, and intelligent solutions for the complex measurement of fabric mechanics. Having analysed the mechanical relationship of the parallel beam theory, and considering the specific requirements of fabric measurement, a novel multi-dimensional force sensor is designed, capable of measuring tensile, shear, and buckling properties. Finite element analysis is used to simulate the mechanical performance of this sensor for fabric-loading/unloading measurement, and the sensitivity of the mechanical quantity transfer, the amount of sensor deformation, the stress distribution, and the degree of inter-dimensional coupling have been investigated and verified. The basis for subsequent digital processing is achieved by a low-offset, low-temperature-drift, low-power-consumption analogue front end, 24-bit ADC circuit, and signal conditioning electronics, suitable for the measurement of fabric mechanics under low stress, which is like the end-user requirements. The sensor information channel is supported by a host microcontroller with a DSP and a floating-point processing instruction set. Information processing is performed in time-sharing with the support of a multitasking real-time operating system. The purpose of designing this sensor is to facilitate the development of a new testing instrument, which will adopt the advances of current instruments whilst eliminating their shortcomings. [ABSTRACT FROM AUTHOR]

The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission. The tower's tilt and severe deformation will cause the building to collapse. Many small changes caused the tower's collapse, but the early staff often could not intuitively notice the changes in the tower's state. In the current tower online monitoring system, terminal equipment often needs to replace batteries frequently due to premature exhaustion of power. According to the need for real-time measurement of power line tower, this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor, the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing, and then through the complementary filtering algorithm for comprehensive calculation of tilt angle, the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy, with low cost and power consumption. The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%, which has specific promotion and application value since the system has the advantages of unattended and efficient calculation. [ABSTRACT FROM AUTHOR]

In order to attain precise and robust transformation estimation in simultaneous localization and mapping (SLAM) tasks, the integration of multiple sensors has demonstrated effectiveness and significant potential in robotics applications. Our work emerges as a rapid tightly coupled LIDAR-inertial-visual SLAM system, comprising three tightly coupled components: the LIO module, the VIO module, and the loop closure detection module. The LIO module directly constructs raw scanning point increments into a point cloud map for matching. The VIO component performs image alignment by aligning the observed points and the loop closure detection module imparts real-time cumulative error correction through factor graph optimization using the iSAM2 optimizer. The three components are integrated via an error state iterative Kalman filter (ESIKF). To alleviate computational efforts in loop closure detection, a coarse-to-fine point cloud matching approach is employed, leverging Quatro for deriving a priori state for keyframe point clouds and NanoGICP for detailed transformation computation. Experimental evaluations conducted on both open and private datasets substantiate the superior performance of the proposed method compared to similar approaches. The results indicate the adaptability of this method to various challenging situations. [ABSTRACT FROM AUTHOR]

Aiming at the problems of negative pressure wave propagation attenuation, large noise interference and low data fusion rate in pipeline leak detection and location methods, a pipeline leak detection and location method based on whale optimization algorithm (WOA) with variational modal decomposition (VMD) and improved adaptive weighted fusion ( IAWF) was proposed. A three sensor leak detection and localization model was proposed, and the WOA-VMD algorithm with strong anti-interference ability was used to denoise the original signal. Then, wavelet analysis was used to find the singular point of the de-noising signal, and the time difference of the negative pressure wave signal detected by the pressure transmitter was further calculated. On this basis, the improved adaptive weighted fusion algorithm was used to fuse the multi-sensor data and the actual location of the leak point was finally obtained. The experimental results show that the method can effectively filter out the noise components and obtain more accurate fusion results, with high localization accuracy and relative localization error within 1% . It provides a new method for pipeline leakage detection and location. [ABSTRACT FROM AUTHOR]

The automation industry faces the challenge of avoiding interference with obstacles, estimating the next move of a robot, and optimizing its path in various environments. Although researchers have predicted the next move of a robot in linear and non-linear environments, there is a lack of precise estimation of sectorial error probability while moving a robot on a curvy path. Additionally, existing approaches use visual sensors, incur high costs for robot design, and ineffective in achieving motion stability on various surfaces. To address these issues, the authors in this manuscript propose a low-cost and multisensory robot capable of moving on an optimized path in diverse environments with eight degrees of freedom. The authors use the extended Kalman filter and unscented Kalman filter for localization and position estimation of the robot. They also compare the sectorial path prediction error at different angles from 0° to 180° and demonstrate the mathematical modeling of various operations involved in navigating the robot. The minimum deviation of 1.125 cm between the actual and predicted path proves the effectiveness of the robot in a real-life environment. [ABSTRACT FROM AUTHOR]

Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor's type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time–frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson's disease severity, surpassing DCLSTM's 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases. [ABSTRACT FROM AUTHOR]

Crabs have a high nutritional and economic value and there is an increased demand for live crabs. However, live crabs have limited shelf life and they are susceptibility to death and spoilage during transportation. Monitoring live crab vitality during transportation in the supply chain to meet consumer acceptability is vital. In this study, an information fusion enabled live crab viability monitoring system was developed. Hazard analysis and critical control point (HACCP) analysis was used to identify potential hazards and critical control points in the transportation supply chain that affect live crab vitality. Multi-source information during live crab transportation was collected by integrating temperature, relative humidity, oxygen, alcohol, aldehyde, and impedance sensors. The predictive modelling of live crab vitality based on information fusion effectively improved, the utilisation of information and the accuracy of vitality prediction. An ensemble learning based soft-voting classifier outperformed the individual performances of other models (i.e. support vector machine, random forest, k-nearest neighbour) and it achieved accuracy above 99% and 86% at 4 °C and 25 °C. The system evaluation indicated that the developed information fusion-based vitality monitoring offers the possibility of ubiquitous monitoring of the vitality of aquatic products in the transportation supply chain and improves the economic efficiency of the supply chain. • Development of a vitality monitoring system for live crab vitality control. • HACCP based critical hazard control points in live crab transportation supply chain. • Processing multi-sensor data based on information fusion model. • System application improved quality of live crab transportation supply chain. [ABSTRACT FROM AUTHOR]

Vehicle detection using data fusion techniques from overhead platforms (RGB/MSI imagery and LiDAR point clouds) with vector and shape data can be a powerful tool in a variety of fields, including, but not limited to, national security, disaster relief efforts, and traffic monitoring. Knowing the location and number of vehicles in a given area can provide insight into the surrounding activities and patterns of life, as well as support decision-making processes. While researchers have developed many approaches to tackling this problem, few have exploited the multi-data approach with a classical technique. In this paper, a primarily LiDAR-based method supported by RGB/MSI imagery and road network shapefiles has been developed to detect stationary vehicles. The addition of imagery and road networks, when available, offers an improved classification of points from LiDAR data and helps to reduce false positives. Furthermore, detected vehicles can be assigned various 3D, relational, and spectral attributes, as well as height profiles. This method was evaluated on the Houston, TX dataset provided by the IEEE 2018 GRSS Data Fusion Contest, which includes 1476 ground truth vehicles from LiDAR data. On this dataset, the algorithm achieved a 92% precision and 92% recall. It was also evaluated on the Vaihingen, Germany dataset provided by ISPRS, as well as data simulated using an image generation model called DIRSIG. Some known limitations of the algorithm include false positives caused by low vegetation and the inability to detect vehicles (1) in extremely close proximity with high precision and (2) from low-density point clouds. [ABSTRACT FROM AUTHOR]

Welding technology plays a vital role in the manufacturing process of ships, automobiles, and aerospace vehicles because it directly impacts their operational safety and reliability. Hence, the development of an accurate system for identifying welding defects in arc welding is crucial to enhancing the quality of welding production. In this study, a defect recognition method combining the Neighborhood Rough Set (NRS) with the Dingo Optimization Algorithm Support Vector Machine (DOA-SVM) in a multisensory framework is proposed. The 195-dimensional decision-making system mentioned above was constructed to integrate multi-source information from molten pool images, welding current, and vibration signals. To optimize the system, it was further refined to a 12-dimensional decision-making setup through outlier processing and feature selection based on the Neighborhood Rough Set. Subsequently, the DOA-SVM is employed for detecting welding defects. Experimental results demonstrate a 98.98% accuracy rate in identifying welding defects using our model. Importantly, this method outperforms comparative techniques in terms of quickly and accurately identifying five common welding defects, thereby affirming its suitability for arc welding. The proposed method not only achieves high accuracy but also simplifies the model structure, enhances detection efficiency, and streamlines network training.

Super-precision bearing lubrication condition is essential for equipment’s overall performance. This paper investigates a monitoring method of bearing lubrication using multi-sensors based on graph data. An experiment was designed and carried out, establishing a dataset including vibration, temperature, and acoustic emission signals. Graph data were constructed based on a priori knowledge and a graph attention network was employed to conduct a study on monitoring bearing lubrication abnormalities and discuss the influence of a missing sensor on the monitoring. The results show that the designed experiments can effectively respond to the degradation process of bearing lubrication, and the graph data constructed based on a priori knowledge show a good effect in the anomaly monitoring process. In addition, the multi-sensor plays a significant role in monitoring bearing lubrication. This work will be highly beneficial for future monitoring methods of bearing lubrication status.

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references.