Search Results (7,917)

With the development of sensor technology, the sources of remotely sensed image data for the same region are becoming increasingly diverse. Unlike single-source remote sensing image data, multisource remote sensing image data can provide complementary information for the same feature, promoting its recognition. The effective utilization of remote sensing image data from various sources can enhance the extraction of image features and improve the accuracy of feature recognition. Hyperspectral remote sensing (HSI) data and light detection and ranging (LiDAR) data can provide complementary information from different perspectives and are frequently combined in feature identification tasks. However, the process of joint use suffers from data redundancy, low classification accuracy and high time complexity. To address the aforementioned issues and improve feature recognition in classification tasks, this paper introduces a multiprobability decision fusion (PRDRMF) method for the combined classification of HSI and LiDAR data. First, the original HSI data and LiDAR data are downscaled via the principal component–relative total variation (PRTV) method to remove redundant information. In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. This is achieved by utilizing the local binary pattern (LBP) and extended multiattribute profile (EMAP) for the two types of data after dimensionality reduction. The four extracted features are subsequently input into the corresponding kernel–extreme learning machine (KELM), which has a simple structure and good classification performance, to obtain four classification probability matrices (CPMs). Finally, the four CPMs are fused via a multiprobability decision fusion method to obtain the optimal classification results. Comparison experiments on four classical HSI and LiDAR datasets demonstrate that the method proposed in this paper achieves high classification performance while reducing the overall time complexity of the method. Full article

The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring. Full article

Due to increasing traffic congestion, travel modeling has gained importance in the development of transportion mode detection (TMD) strategies over the past decade. Nowadays, recent smartphones, equipped with integrated inertial measurement units (IMUs) and embedded algorithms, can play a crucial role in such development. In particular, obtaining much more information on the transportation modes used by users through smartphones is very challenging due to the variety of the data (accelerometers, magnetometers, gyroscopes, proximity sensors, etc.), the standardization issue of datasets and the pertinence of learning methods for that purpose. Reviewing the latest progress on TMD systems is important to inform readers about recent datasets used in detection, best practices for classification issues and the remaining challenges that still impact the detection performances. Existing TMD review papers until now offer overviews of applications and algorithms without tackling the specific issues faced with real-world data collection and classification. Compared to these works, the proposed review provides some novelties such as an in-depth analysis of the current state-of-the-art techniques in TMD systems, relying on recent references and focusing particularly on the major existing problems, and an evaluation of existing methodologies for detecting travel modes using smartphone IMUs (including dataset structures, sensor data types, feature extraction, etc.). This review paper can help researchers to focus their efforts on the main problems and challenges identified. Full article

This paper proposes DigitalUpSkilling, a novel IoT- and AI-based framework for improving and personalising the training of workers who are involved in physical-labour-intensive jobs. DigitalUpSkilling uses wearable IoT sensors to observe how individuals perform work activities. Such sensor observations are continuously processed to synthesise an avatar-like kinematic model for each worker who is being trained, referred to as the worker’s digital twins. The framework incorporates novel work activity recognition using generative adversarial network (GAN) and machine learning (ML) models for recognising the types and sequences of work activities by analysing an individual’s kinematic model. Finally, the development of skill proficiency ML is proposed to evaluate each trainee’s proficiency in work activities and the overall task. To illustrate DigitalUpSkilling from wearable IoT-sensor-driven kinematic models to GAN-ML models for work activity recognition and skill proficiency assessment, the paper presents a comprehensive study on how specific meat processing activities in a real-world work environment can be recognised and assessed. In the study, DigitalUpSkilling achieved 99% accuracy in recognising specific work activities performed by meat workers. The study also presents an evaluation of the proficiency of workers by comparing kinematic data from trainees performing work activities. The proposed DigitalUpSkilling framework lays the foundation for next-generation digital personalised training. Full article

Modern, large bridges and tunnels represent important nodes in transportation arteries and have a significant impact on the development of transportation. The health and safety monitoring of these structures has always been a significant concern and is reliant on various types of sensors. Distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of sensing and communication. DAS provides an effective and low-cost approach for the detection of various resources and seismic activities. In this study, field experiments are elucidated, using DAS for the Hong Kong–Zhuhai–Macao Bridge, and for studying vehicle trajectories, earthquakes, and other activities. The basic signal-processing methods of filtering and normalization are adopted for analyzing the data obtained with DAS. With the proposed DAS technology, the activities on shore, vehicle trajectories on bridges and in tunnels during both day and night, and microseisms within 200 km were successfully detected. Enabled by DAS technology and mass fiber networks, more studies on sensing and communication systems for the monitoring of bridge and tunnel engineering are expected to provide future insights. Full article

As green energy technology develops, so too grows research interest in topics such as solar power forecasting. The output of solar power generation is uncontrollable, which makes accurate prediction of output an important task in the management of power grids. Despite a plethora of theoretical models, most frameworks encounter problems in practice because they assume that received data is error-free, which is unlikely, as this type of data is gathered by outdoor sensors. We thus designed a robust solar power forecasting model and methodology based on the concept of ensembling, with three key design elements. First, as models established using the ensembling concept typically have high computational costs, we pruned the deep learning model architecture to reduce the size of the model. Second, the mediation model often used for pruning is not suitable for solar power forecasting problems, so we designed a numerical–categorical radial basis function deep neural network (NC-RBF-DNN) to replace the mediation model. Third, existing pruning methods can only establish one model at a time, but the ensembling concept involves the establishment of multiple sub-models simultaneously. We therefore designed a factor combination search algorithm, which can identify the most suitable factor combinations for the sub-models of ensemble models using very few experiments, thereby ensuring that we can establish the target ensemble model with the smallest architecture and minimal error. Experiments using a dataset from real-world solar power plants verified that the proposed method could be used to build an ensemble model of the target within ten attempts. Furthermore, despite considerable error in the model inputs (two inputs contained 10% error), the predicted NRMSE of our model is still over 10 times better than the recent model. Full article

In this article, we present a terahertz (THz) metamaterial absorber that blends two types of coordinated materials: Dirac semimetals and vanadium dioxide. Compared to other absorbers on the market, which are currently non-adjustable or have a single adjustment method, our absorber is superior because it has two coordinated modes with maximum adjustment ranges of 80.7% and 0.288 THz. The device contains four flawless absorption peaks (M1, M2, M3, and M4) spanning the frequency range of 2.0 THz to 6.0 THz, all with absorption rates greater than 99%. After calculation, the relative impedance of the device matches with that in free space, resulting in perfect absorption. In addition, our absorber has extremely excellent polarization insensitivity but is highly sensitive to changes in the environmental refractive index, with the highest environmental refractive index sensitivity of 716 GHz/RIU (gigahertz per refractive index unit). To sum up, the terahertz metamaterial absorber we showed has four perfect absorption peaks, high sensitivity, and stable polarization. This means it could be useful in areas like changing electromagnetic waves, making new sensors, and switching. Full article

Gold nanoparticles (AuNPs) are highly attractive for applications in the field of biosensing, particularly for colorimetric nucleic acid detection. Their unique optical properties, which are highly sensitive to changes in their environment, make them ideal candidates for developing simple, rapid, and cost-effective assays. When functionalized with oligonucleotides (Au-nanoprobes), they can undergo aggregation or dispersion in the presence of complementary sequences, leading to distinct color changes that serve as a visual signal for detection. Aggregation-based assays offer significant advantages over other homogeneous assays, such as fluorescence-based methods, namely, label-free protocols, rapid interactions in homogeneous solutions, and detection by the naked eye or using low-cost instruments. Despite promising results, the application of Au-nanoprobe-based colorimetric assays in complex biological matrices faces several challenges. The most significant are related to the colloidal stability and oligonucleotide functionalization of the Au-nanoprobes but also to the mode of detection. The type of functionalization method, type of spacer, the oligo–AuNPs ratio, changes in pH, temperature, or ionic strength influence the Au-nanoprobe colloidal stability and thus the performance of the assay. This review elucidates characteristics of the Au-nanoprobes that are determined for colorimetric gold nanoparticles (AuNPs)-based nucleic acid detection, and how they influence the sensitivity and specificity of the colorimetric assay. These characteristics of the assay are fundamental to developing low-cost, robust biomedical sensors that perform effectively in biological fluids. Full article

Integrating advanced technologies such as artificial intelligence (AI) with traditional agricultural practices has changed how activities are developed in agriculture, with the aim of automating manual processes and improving the efficiency and quality of farming decisions. With the advent of deep learning models such as convolutional neural network (CNN) and You Only Look Once (YOLO), many studies have emerged given the need to develop solutions to problems and take advantage of all the potential that this technology has to offer. This systematic literature review aims to present an in-depth investigation of the application of AI in supporting the management of weeds, plant nutrition, water, pests, and diseases. This systematic review was conducted using the PRISMA methodology and guidelines. Data from different papers indicated that the main research interests comprise five groups: (a) type of agronomic problems; (b) type of sensor; (c) dataset treatment; (d) evaluation metrics and quantification; and (e) AI technique. The inclusion (I) and exclusion (E) criteria adopted in this study included: (I1) articles that obtained AI techniques for agricultural analysis; (I2) complete articles written in English; (I3) articles from specialized scientific journals; (E1) articles that did not describe the type of agrarian analysis used; (E2) articles that did not specify the AI technique used and that were incomplete or abstract; (E3) articles that did not present substantial experimental results. The articles were searched on the official pages of the main scientific bases: ACM, IEEE, ScienceDirect, MDPI, and Web of Science. The papers were categorized and grouped to show the main contributions of the literature to support agricultural decisions using AI. This study found that AI methods perform better in supporting weed detection, classification of plant diseases, and estimation of agricultural yield in crops when using images captured by Unmanned Aerial Vehicles (UAVs). Furthermore, CNN and YOLO, as well as their variations, present the best results for all groups presented. This review also points out the limitations and potential challenges when working with deep machine learning models, aiming to contribute to knowledge systematization and to benefit researchers and professionals regarding AI applications in mitigating agronomic problems. Full article

Fall risk assessment is becoming an important concern, with the realization that falls, and more importantly fainting occurrences, in most cases require immediate medical attention and can pose huge health risks, as well as financial and social burdens. The development of an accurate inertial sensor-based fall risk assessment tool combined with machine learning algorithms could significantly advance healthcare. This research aims to investigate the development of a machine learning approach for falling and fainting detection, using wearable sensors with an emphasis on forward falls. In the current paper we address the problem of the lack of inertial time-series data to differentiate the forward fall event from normal activities, which are difficult to obtain from real subjects. To solve this problem, we proposed a forward dynamics method to generate necessary training data using the OpenSim software, version 4.5. To develop a model as close to the real world as possible, anthropometric data taken from the literature was used. The raw X and Y axes acceleration data was generated using OpenSim software, and ML fall prediction methods were trained. The machine learning (ML) accuracy was validated by testing with data acquired from six unique volunteers, considering the forward fall type. Full article

Balance deficits are present in a variety of clinical populations and can negatively impact quality of life. The integration of wearable sensors and machine learning technology (ML) provides unique opportunities to quantify biomechanical characteristics related to balance outside of a laboratory setting. This article provides a general overview of recent developments in using wearable sensors and ML to estimate or predict biomechanical characteristics such as center of pressure (CoP) and center of mass (CoM) motion. This systematic review was conducted according to PRISMA guidelines. Databases including Scopus, PubMed, CINHAL, Trip PRO, Cochrane, and Otseeker databases were searched for publications on the use of wearable sensors combined with ML to predict biomechanical characteristics. Fourteen publications met the inclusion criteria and were included in this review. From each publication, information on study characteristics, testing conditions, ML models applied, estimated biomechanical characteristics, and sensor positions were extracted. Additionally, the study type, level of evidence, and Downs and Black scale score were reported to evaluate methodological quality and bias. Most studies tested subjects during walking and utilized some type of neural network (NN) ML model to estimate biomechanical characteristics. Many of the studies focused on minimizing the necessary number of sensors and placed them on areas near or below the waist. Nearly all studies reporting RMSE and correlation coefficients had values <15% and >0.85, respectively, indicating strong ML model estimation accuracy. Overall, this review can help guide the future development of ML algorithms and wearable sensor technologies to estimate postural mechanics. Full article

In this work, a miniaturized Y-sphere coupled photoacoustic (YSCPA) sensor is proposed for trace C₂H₂ gas detection. The cavity volume of the designed YSCPA sensor is about 0.7 mL. The finite element method (FEM) has been performed to analyze the comparative performance of the YSCPA sensor and T-type PA sensor, indicating that the first-order resonance frequency (FORF) of the newly proposed YSCPA sensor has been reduced by half while the PA signal has been improved by a factor of 3 compared to the T-type PA sensor. C₂H₂ is employed as a target gas to test the performance of the YSCPA sensor. The experimental test results show that the response time of the gas is 26 s. The minimum detection limit (MDL) reaches 189 ppb at a lock-in integration time of 1 s. By extending the lock-in integration time to 100 s, the MDL of the designed PA sensor is reduced to 18.1 ppb. The designed YSCPA sensor has the advantages of small size, low gas consumption, simple structure, and high sensitivity, which is expected to be an effective solution for rapid and real-time monitoring of dissolved C₂H₂ gas in transformer oil. Full article

A hierarchical surface that includes objects with different sizes, as a result of creating local fields, initiates a large number of effects. Micropores in the composition of macropores, as well as nanoclusters of the substance, were detected by scanning electron and atomic force microscopies on the surface of ZnO/Por-Si samples. An identical fractal dimension for all levels of the hierarchy was determined for these structures, which is associated with the same response to external excitation. Photoluminescence studies have shown the presence of localized levels in the band gap, with the probability of capturing both electrons and holes, which ensures charge transitions between energy bands. Decomposition of the electron paramagnetic resonance (EPR) signal into components made it possible to determine the manifestations of various types of interaction between paramagnetic particles, including the hyperfine structure of the spectrum. The ordering of the structure of the substance as a result of sequential annealing in the range from 300 to 500 °C was revealed in the EPR spectrum. This fact, as well as photo- and gas sensitivity for all types of samples studied, confirms the prospects of using these structures as sensors. Full article

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems. Full article

The paper deals with selected thermal properties of a small building that was built during the international student competition Solar Decathlon 2021/2022 and is now part of the Living Lab in Wuppertal. It summarizes the essential information about the overall design of this wooden building with construction and technologies corresponding to the passive building standard. Built-in sensors and other equipment enable long-term monitoring of thermal parameters. Part of the information comes from the building operation control system. The thermal transmittance value for the perimeter wall matches calculated expectation well, even from a short period of time and not at an achievable perfectly steady state boundary condition. The (positive) difference between the calculated values and the measured ones did not exceed 0.015 W/(m²K). It was proven that even for such a small building with a very small heat demand, the heat transfer coefficient can be estimated alternatively from a co-heating test (measured electricity power for a fan heater) and from energy delivered to underfloor heating (calorimeter in heating system). Differences among both measurement types and calculation matched in the range ± 10%. In the last section, the dynamic response test is briefly described. The measured indoor air temperature curves under periodic dynamic loads (use of fan heater) are compared with the simulation results. The simulation model working with lumped parameters for each element of the building envelope was able to replicate the measured situation well, while its use does not require special knowledge of the user. In the studied case, the differences between measured and simulated air temperatures were less than 1 Kelvin if the first two to three days of the test period are ignored due to large thermal inertia. Finally, the measurement campaign program for the next period is outlined. Full article