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15,850 results on '"Data fusion"'

201. IoT-enabled Biometric Security: Enhancing Smart Car Safety with Depth-based Head Pose Estimation.

Author

Bisogni, Carmen, Cascone, Lucia, Nappi, Michele, and Pero, Chiara

Subjects
AUTOMATIC systems in automobiles, DRIVER assistance systems, OPTIMIZATION algorithms, BIOMETRY, TRAFFIC safety
Abstract

Advanced Driver Assistance Systems (ADAS) are experiencing higher levels of automation, facilitated by the synergy among various sensors integrated within vehicles, thereby forming an Internet of Things (IoT) framework. Among these sensors, cameras have emerged as valuable tools for detecting driver fatigue and distraction. This study introduces HYDE-F, a Head Pose Estimation (HPE) system exclusively utilizing depth cameras. HYDE-F adeptly identifies critical driver head poses associated with risky conditions, thus enhancing the safety of IoT-enabled ADAS. The core of HYDE-F's innovation lies in its dual-process approach: it employs a fractal encoding technique and keypoint intensity analysis in parallel. These two processes are then fused using an optimization algorithm, enabling HYDE-F to blend the strengths of both methods for enhanced accuracy. Evaluations conducted on a specialized driving dataset, Pandora, demonstrate HYDE-F's competitive performance compared to existing methods, surpassing current techniques in terms of average Mean Absolute Error (MAE) by nearly 1∘. Moreover, case studies highlight the successful integration of HYDE-F with vehicle sensors. Additionally, HYDE-F exhibits robust generalization capabilities, as evidenced by experiments conducted on standard laboratory-based HPE datasets, i.e., Biwi and ICT-3DHP databases, achieving an average MAE of 4.9∘ and 5∘, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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202. Trafficability anticipation for quadruped robot in field operation.

Author

Wang, Chengjin, Zhang, Rui, Dong, Wenchao, Li, Tao, Jiang, Lei, Xu, Wei, Xu, Peng, Zhou, Yanmin, and Zou, Meng

Subjects
GROUND reaction forces (Biomechanics), EXPECTATION (Psychology), ROBOTS
Abstract

The capability to predict catastrophic failures and anticipate challenges relating to trafficability enables quadruped robots to adjust their planned trajectory in time to prevent any damages. In this paper, an estimation‐based method is proposed to describe the validity of the trajectory from the viewpoint of the traction performance of the terrain. The main contribution of this study lies in equipping quadruped robots with the ability to comprehend the strength characteristics of the terrain and assess the validity of trajectory planning by anticipating potential catastrophic motion failures. To this end, a novel perception method of the foot terrain interface based on proprioception is proposed to estimate terrain strength parameters. The vehicle–terrain traction model is extended to a legged locomotion pattern to estimate the traction limits of the terrain. The trafficability anticipation is driven by combining the traction limits of the terrain and the expected ground reaction force, as obtained by the model‐based predictive methods. The validity of this method is verified using a quadruped robot test platform. Results highlight the potential of the presented approach, in which the anticipation of trafficability, grounded in the understanding of the terrain strength, serves as the explicit foundation for altering trajectories to avert motion failure. [ABSTRACT FROM AUTHOR]

Published
2024
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203. Transforming Physical Crime Scene into Geospatial-based Point Cloud Data.

Author

Azmil, Rabi'atul'Adawiyah, Mohd Ariff, Mohd Farid, Razali, Ahmad Firdaus, Azmy, Suzanna Noor, Darwin, Norhadija, and Idris, Khairulnizam M.

Subjects
CRIME scenes, POINT cloud, ROOT-mean-squares, VERNIERS, LASER beams
Abstract

Terrestrial Laser Scanning (TLS) and Close-Range Photogrammetry (CRP) are advanced techniques for capturing 3D data in crime scene reconstruction, offering complementary information. Despite taking multiple scans and images from different angles to ensure a comprehensive model, limitations, such as device positioning, shadows, object distance, and laser beam angles prevent the creation of a complete crime scene model. Therefore, combining TLS and CRP data is crucial for achieving a comprehensive reconstruction. This study aims to transform a physical crime scene into a geospatial-based reconstructed model known as point clouds. The technique used was highly rich in realistic features, digitally reconstructed from TLS and CRP. The data sources were then fused via a rigid body transformation, creating a comprehensive crime scene model. The combined point cloud measurements were compared with measurements obtained from a high-precision Vernier caliper to ascertain their accuracy. The resulting Root Mean Square (RMSE) difference between the fused point cloud data and the high-precision caliper measurements was approximately ±4mm. The fusion of TLS and CRP data provides reliable and highly accurate 3D model point clouds, making it suitable for forensic applications. [ABSTRACT FROM AUTHOR]

Published
2024
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204. Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement.

Author

Zheng, Yu, Wang, Lu, Zhang, Fan, Yang, Zulei, and Hu, Yuanbiao

Subjects
KALMAN filtering, COST benefit analysis, ENGINEERING reliability theory, DRILLING fluids, RANDOM walks, HUFFMAN codes, REDUNDANCY in engineering
Abstract

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements. [ABSTRACT FROM AUTHOR]

Published
2024
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205. Integrating rather than collecting: statistical matching in the data flood era.

Author

D'Alberto, Riccardo and Raggi, Meri

Subjects
STATISTICAL matching, STATISTICS, PROPENSITY score matching, MULTISENSOR data fusion, DATA integration
Abstract

Statistical matching is progressively emerging as a straightforward approach to data integration. This method of increasing importance and interest is useful to address the unsolved challenges posed by data shortage as well as the several opportunities occurring in the present data flood era. This paper offers an exhaustive review of the methodology from its early beginnings up to the most recent developments, considering also the most relevant applications. The links that statistical matching has with other integration methods are discussed, analysing how a 50-year-old method has been only recently proposed under a consistent but (yet) incomplete framework. Strengths and weaknesses of statistical matching are compared, considering different data features and sample representativeness frameworks, also, given future research ideas, always keeping an eye on uncertainty, the key problem to which statistical matching tries to answer. [ABSTRACT FROM AUTHOR]

Published
2024
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206. Distributed Jammer-Equipped Target Tracking with Hybrid Extended Kalman and Particle Filter in Sensor Network.

Author

Maghsudlu, Iman, Danaee, Meysam Raees, and Arezumand, Hamid

Subjects
WIRELESS sensor nodes, WIRELESS sensor networks, TRACKING algorithms, MULTISENSOR data fusion, KALMAN filtering, DISTRIBUTED algorithms
Abstract

In this paper, we propose a distributed tracking algorithm for a jammer-equipped target that passes through the nodes of a wireless sensor network (WSN). The jammer that the target carries is of the deceptive type, which means that it can mimic the signal of the target and confuse the sensors. Unlike other existing methods, our proposed algorithm does not require any additional hardware installation on each WSN node. It only relies on signal processing and solving the average consensus problem to detect the presence of jamming effects in the observations of the contaminated nodes and exclude them from the distributed tracking problem. For the distributed tracking problem, we use the hybrid extended Kalman filter (EKF) and particle filter to reduce the number of parameters needed for solving the average consensus problem and to decrease the communication overhead. The simulation results show that our algorithm improves the tracking performance compared to the case where the nodes with jammed observations are used. [ABSTRACT FROM AUTHOR]

Published
2024

207. Advances in viticulture via smart phenotyping: current progress and future directions in tackling soil copper accumulation

Author

Youry Pii, Guido Orzes, Fabrizio Mazzetto, Paolo Sambo, and Stefano Cesco

Subjects
viticulture, Cu toxicity, smart phenotyping, data fusion, artificial intelligence, Plant culture, SB1-1110
Abstract

Modern viticulture faces significant challenges including climate change and increasing crop diseases, necessitating sustainable solutions to reduce fungicide use and mitigate soil health risks, particularly from copper accumulation. Advances in plant phenomics are essential for evaluating and tracking phenotypic traits under environmental stress, aiding in selecting resilient vine varieties. However, current methods are limited, hindering effective integration with genomic data for breeding purposes. Remote sensing technologies provide efficient, non-destructive methods for measuring biophysical and biochemical traits of plants, offering detailed insights into their physiological and nutritional state, surpassing traditional methods. Smart phenotyping is essential for selecting crop varieties with desired traits, such as pathogen-resilient vine varieties, tolerant to altered soil fertility including copper toxicity. Identifying plants with typical copper toxicity symptoms under high soil copper levels is straightforward, but it becomes complex with supra-optimal, already toxic, copper levels common in vineyard soils. This can induce multiple stress responses and interferes with nutrient acquisition, leading to ambiguous visual symptoms. Characterizing resilience to copper toxicity in vine plants via smart phenotyping is feasible by relating smart data with physiological assessments, supported by trained professionals who can identify primary stressors. However, complexities increase with more data sources and uncertainties in symptom interpretations. This suggests that artificial intelligence could be valuable in enhancing decision support in viticulture. While smart technologies, powered by artificial intelligence, provide significant benefits in evaluating traits and response times, the uncertainties in interpreting complex symptoms (e.g., copper toxicity) still highlight the need for human oversight in making final decisions.

Published
2024
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208. Dual-branch multi-modal convergence network for crater detection using Chang’e image

Author

Feng Lin, Xie Hu, Yiling Lin, Yao Li, Yang Liu, and Dongmei Li

Subjects
Moon, Crater detection, Multi-modal remote sensing, Deep learning, Data fusion, Attention, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

Knowledge about the impact craters on rocky planets is crucial for understanding the evolutionary history of the universe. Compared to traditional visual interpretation, deep learning approaches have improved the efficiency of crater detection. However, single-source data and divergent data quality limit the accuracy of crater detection. In this study, we focus on valuable features in multi-modal remote sensing data from Chang’e lunar exploration mission and propose an Attention-based Dual-branch Segmentation Network (ADSNet). First, we use ADSNet to extract the multi-modal features via a dual-branch encoder. Second, we introduce a novel attention for data fusion where the features from the auxiliary modality are weighted by a scoring function and then being fused with those from the primary modality. After fusion, the features are transferred to the decoder through skip connection. Lastly, high-accuracy crater detection is achieved based on the learned multi-modal data features through semantic segmentation. Our results demonstrate that ADSNet outperforms other baseline models in many metrics such as IoU and F1 score. ADSNet is an effective approach to leverage multi-modal remote sensing data in geomorphological feature detection on rocky planets in general.

Published
2024
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209. Rice recognition from Sentinel-1 SLC SAR data based on progressive feature screening and fusion

Author

Sijing Tian, Qinghong Sheng, Hao Cui, Guo Zhang, Jun Li, Bo Wang, and Zhigang Xie

Subjects
Time series, Synthetic aperture radar (SAR), Sentinel-1 SLC, Data fusion, Rice recognition, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

Rice, a crucial global food crop, necessitates accurate mapping for food security assessment. China, a major rice producer and consumer, includes Jiangsu Province as a significant rice production region. The Hongzehu (HZH) area in Jiangsu contributes substantially to rice supply, supporting food security locally and province-wide. Sentinel-1 SAR data, particularly Single Look Complex (SLC) products, holds promise for precise crop mapping with enhanced phase and polarization information, enhancing sensitivity to rice growth changes by analyzing rice surface features information. However, challenges persist, especially climate impacts and timing inconsistencies between fields for planting rice. To overcome this, our study proposes a progressive feature screening and fusion method using multi-temporal SAR images. We introduce fuzzy coarse screening based on statistical distribution characteristics and refine it with Gaussian fitting. A model incorporating time-series sample separation and polarization decomposition feature fusion based on rice growth height enhances rice growth expression. For more precise results, we advocate a multi-temporal feature fusion approach using optimized sample features in the BiLSTM network to characterize rice growth and ground features. Experimental results demonstrate the method’s efficacy in two cities with a limited number of sampling points. The progressive feature fusion (DF) method outperforms classical classification methods using single feature (SF) or combined features (CF). Our proposed strategy proves effective for rice mapping applications, providing a promising approach for leveraging Sentinel-1 SLC SAR data. In conclusion, our study enhances accuracy in identifying rice fields and characterizing rice growth, contributing to improved food security assessments despite challenges associated with rainy seasons and planting times.

Published
2024
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210. Metadata augmented deep neural networks for wild animal classification

Author

Aslak Tøn, Ammar Ahmed, Ali Shariq Imran, Mohib Ullah, and R. Muhammad Atif Azad

Subjects
Wild animal detection, Wild animal classification, deep learning, data fusion, Information technology, T58.5-58.64, Ecology, QH540-549.5
Abstract

Camera trap imagery has become an invaluable asset in contemporary wildlife surveillance, enabling researchers to observe and investigate the behaviors of wild animals. While existing methods rely solely on image data for classification, this may not suffice in cases of suboptimal animal angles, lighting, or image quality. This study introduces a novel approach that enhances wild animal classification by combining specific metadata (temperature, location, time, etc) with image data. Using a dataset focused on the Norwegian climate, our models show an accuracy increase from 98.4% to 98.9% compared to existing methods. Notably, our approach also achieves high accuracy with metadata-only classification, highlighting its potential to reduce reliance on image quality. This work paves the way for integrated systems that advance wildlife classification technology.

Published
2024
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211. LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction

Author

Sidra Khan, Beenish Ayesha Akram, Amna Zafar, Muhammad Wasim, Khaldoon S. Khurshid, and Ivan Miguel Pires

Subjects
Environmental data, Data fusion, Artificial intelligence, Desert locust, Agriculture, Data processing, Electronic computers. Computer science, QA75.5-76.95
Abstract

The desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate’s environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinates (longitude and latitude) into human-readable location names (such as countries and regions) was employed. This step enhances the clarity and interpretability of the data by providing meaningful geographic context. This study’s initial dataset focused on instances where locust attacks were recorded (positive class). To ensure a comprehensive analysis, we also incorporated negative class instances, representing periods (specific years and months) in the same countries and regions where locust attacks did not occur. This research utilizes the benefits of lazy learners by employing the K-nearest neighbor algorithm (K-NN), which provides high accuracy and the benefit of no time-consuming retraining even if real-time updated data is periodically added to the system. This research also focuses on building an eco-friendly machine learning model by evaluating carbon emissions from ML models. The results obtained from LocustLens are compared with other machine learning models, including baseline–K-NN, decision trees (DT), Logistic regression (LR), AdaBoost Classifier, BaggingClassifier, and support vector classifier (SVC). LocustLens outperformed all competitors with an accuracy of 98%, while baseline-K-NN achieved 96%, SVC gave 91%, DT gave 97%, AdaBoost has accuracy of 91%, BaggingClassifier gave 94% and LR gave 83%, respectively. Carbon emissions from RAM and CPU electricity consumption are measured in kg gCO2. They are a minimum for AdaBoost Classifier equal to 0.02 and 0.07 for DT and a maximum of 9.03 for SVC. The carbon footprint of LocustLens is 4.87 kg gCO2.

Published
2024
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212. Updating assembly parameters for spacecraft assembly process state changes: based on data fusion method

Author

Yue Liu, Lijuan Li, Xuezhu Lin, Lili Guo, Jing Sun, and Hao Wang

Subjects
thermal protection system, fiber optic sensor data, 3D point cloud data, data fusion, state change, assembly parameters, Physics, QC1-999
Abstract

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.

Published
2024
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213. Integration of LC-HRMS and 1H NMR metabolomics data fusion approaches for classification of Amarone wine based on withering time and yeast strain

Author

Pier Paolo Becchi, Veronica Lolli, Leilei Zhang, Francesco Pavanello, Augusta Caligiani, and Luigi Lucini

Subjects
Wine profiling, Multi-omics, Supervised modelling, Data fusion, 1H NMR, LC-HRMS, Nutrition. Foods and food supply, TX341-641, Food processing and manufacture, TP368-456
Abstract

Two untargeted metabolomics approaches (LC-HRMS and 1H NMR) were combined to classify Amarone wines based on grape withering time and yeast strain. The study employed a multi-omics data integration approach, combining unsupervised data exploration (MCIA) and supervised statistical analysis (sPLS-DA).The results revealed that the multi-omics pseudo-eigenvalue space highlighted a limited correlation between the datasets (RV-score = 16.4%), suggesting the complementarity of the assays. Furthermore, the sPLS-DA models correctly classified wine samples according to both withering time and yeast strains, providing a much broader characterization of wine metabolome with respect to what was obtained from the individual techniques. Significant variations were notably observed in the accumulation of amino acids, monosaccharides, and polyphenolic compounds throughout the withering process, with a lower error rate in sample classification (7.52%).In conclusion, this strategy demonstrated a high capability to integrate large omics datasets and identify key metabolites able to discriminate wine samples based on their characteristics.

Published
2024
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214. Roof plane parsing towards LoD-2.2 building reconstruction based on joint learning using remote sensing images

Author

Yajin Xu, Juilson Jubanski, Ksenia Bittner, and Florian Siegert

Subjects
Building reconstruction, Deep learning, Data fusion, Remote sensing, Computer vision, Physical geography, GB3-5030, Environmental sciences, GE1-350
Abstract

Building models are important for urban studies. Remote sensing multi-spectral (MS) images are widely used for its rich semantic information. The lack of geometry features is fulfilled by introducing photogrammetry derived digital surface models (DSMs), resulting in pairs of DSMs and MS images. Utilizing such pairs and a convolutional neural network, level of detail (LoD) 2.2 building models, which contain roof planes and major roof elements (e.g. dormers), are reconstructed in this work. Leveraging both raster and vector predictions, 3-D building models with straight edges and sharp corners are obtained. The proposed two-stage method first extracts vectorized roof lines from pairs of DSMs and RGB images, followed by generation of detailed 2-D and 3-D polygonal building models. We conducted our experiments based on two datasets: a custom dataset in Landsberg am Lech in Germany, and an open dataset named Roof3D. For the custom dataset, our proposed model achieved mean average precision (mAP) for building roof vertices of 64.3% and for building roof lines of 54.5% at highest. Mean precision and recall for reconstructed 2-D building roof plane polygons are 52.2% and 54.7% respectively. For the Roof3D dataset, mAP is reported to be 25.3% and 12.4% for the extracted building roof lines and roof plane polygons respectively.

Published
2024
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216. Enhance Subsurface Details Using Non Subsample Contourlet Transform

Author

Raja Sekhar, B., Suresh, Badugu, Ghali, V. S., Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Ghose, Bikash, editor, Manoharan, Venugopal, editor, and Mulaveesala, Ravibabu, editor

Published
2024
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217. A Novel Data Fusion Method for Boiler Operation in Thermal Power Plants Based on Digital Twins

Author

Wang, Zenian, Li, Zhihua, Zhao, Bin, Tian, Xiaonan, Bian, Zenan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wen, Fushuan, editor, and Aris, Ishak Bin, editor

Published
2024
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218. Feature Fusion for Improved Classification: Combining Dempster-Shafer Theory and Multiple CNN Architectures

Author

Alzahem, Ayyub, Boulila, Wadii, Driss, Maha, Koubaa, Anis, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Ngoc Thanh, editor, Franczyk, Bogdan, editor, Ludwig, André, editor, Núñez, Manuel, editor, Treur, Jan, editor, Vossen, Gottfried, editor, and Kozierkiewicz, Adrianna, editor

Published
2024
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219. Enhanced deep learning based on Fusion data to diagnosis malignancy Thyroid tumour

Author

Ziad, Bennadji, Labib, Terrissa Sadek, Karima, Benmohammed, Noureddine, Zerhouni, Luo, Xun, Editor-in-Chief, Almohammedi, Akram A., Series Editor, Chen, Chi-Hua, Series Editor, Guan, Steven, Series Editor, Pamucar, Dragan, Series Editor, Kerrache, Chaker Abdelaziz, editor, Tahari, Abdou El Karim, editor, Kassimi, Dounya, editor, and Chakraborty, Chinmay, editor

Published
2024
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221. Fusion of Hyperspectral and Multispectral Images for Land Use Segmentation

Author

Irfan, Ayesha, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2024
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222. Research on fusion and integration method of BIM and GIS data

Author

Dong, Shutao, Wang, Xiangshang, Zhao, Feifei, Zhang, Jianguang, Li, Chunlin, Wang, Jianuo, Dou, Runliang, Editor-in-Chief, Liu, Jing, Editor-in-Chief, Khasawneh, Mohammad T., Editor-in-Chief, Balas, Valentina Emilia, Series Editor, Bhowmik, Debashish, Series Editor, Khan, Khalil, Series Editor, Masehian, Ellips, Series Editor, Mohammadi-Ivatloo, Behnam, Series Editor, Nayyar, Anand, Series Editor, Pamucar, Dragan, Series Editor, Shu, Dewu, Series Editor, Haldorai, Anandakumar, editor, Singh, Dilbag, editor, Kumar, Anil, editor, Talpur, Mir Sajjad Hussain, editor, and Djeddi, Chawki, editor

Published
2024
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223. IG-GRD: A Model Based on Disentangled Graph Representation Learning for Imaging Genetic Data Fusion

Author

Feng, Shuang, Wang, Letian, Li, Chang, Wan, Xiaohua, Zhang, Fa, Hu, Bin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Xiankun, editor, and Pan, Yijie, editor

Published
2024
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224. Times of Ships in Container Ports: AIS Data for Maritime Transport and Ports Applications

Author

Polimeni, Antonio, Belcore, Orlando M., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Garau, Chiara, editor, Taniar, David, editor, C. Rocha, Ana Maria A., editor, and Faginas Lago, Maria Noelia, editor

Published
2024
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227. Point Cloud Optimization Employing Multisensory Vision

Author

Sepulveda-Valdez, Cesar, Alaniz-Plata, Ruben, Núñez-López, José A., Alba-Corpus, Ivan Yeniseysk, Andrade-Collazo, Humberto, Flores-Fuentes, Wendy, Rodríguez-Quiñonez, Julio C., Mercorelli, Paolo, Tyrsa, Vera, Camacho-López, Santiago, Sergiyenko, Oleg, Rodríguez-Quiñonez, Julio C., editor, Flores-Fuentes, Wendy, editor, Castro-Toscano, Moises J., editor, and Sergiyenko, Oleg, editor

Published
2024
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229. External Information Aided Urban Target Tracking with UAVs

Author

Chai, Jianduo, Hou, Yue, He, Shaoming, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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230. Research on Data Reconstruction Methods Based on Multiple Models of Aero-Engine Sensor

Author

Deng, Mingyang, Li, Changzheng, Song, Zijun, Yang, Hao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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231. Review of UAV Swarm Collaborative Navigation

Author

Wang, Tinghui, Zhang, Dong, Ren, Zhi, Tang, Shuo, Du, Minglei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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232. Data Collection and Wrangling Towards Machine Learning in LoD2+ Urban Models Generation

Author

Naserentin, Vasilis, Spaias, George, Kaimakamidis, Anestis, Somanath, Sanjay, Pantusheva, Mariya, Mitkov, Radostin, Dimara, Asimina, Petrova-Antonova, Dessislava, Anagnostopoulos, Christos-Nikolaos, Logg, Anders, Krinidis, Stelios, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Karydis, Ioannis, editor, Papaleonidas, Antonios, editor, and Chochliouros, Ioannis, editor

Published
2024
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234. A Review on the Temporal-Spatial Correlation Based Multiple Horizon Wind Power Forecasting Methods

Author

Yan, Juan, Wu, Haiping, Huang, Zibo, Zhang, Pinjia, Chen, Qingsong, Zhu, Wenjie, Hu, Cungang, Cao, Wenping, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Hu, Cungang, editor, and Cao, Wenping, editor

Published
2024
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235. Multi-sensor and Multi-frequency Data Fusion for Structural Health Monitoring

Author

Ponsi, Federico, Castagnetti, Cristina, Bassoli, Elisa, Mancini, Francesco, Vincenzi, Loris, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Rainieri, Carlo, editor, Gentile, Carmelo, editor, and Aenlle López, Manuel, editor

Published
2024
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236. Integrating Bayesian Networks into Enhanced Bridge Management: A Data Fusion Approach

Author

Ierimonti, Laura, Mariani, Francesco, Ubertini, Filippo, Venanzi, Ilaria, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Rainieri, Carlo, editor, Gentile, Carmelo, editor, and Aenlle López, Manuel, editor

Published
2024
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240. Synergy of Engineering and Statistics: Multimodal Data Fusion for Quality Improvement

Author

Shi, Jianjun, Biehler, Michael, Mou, Shancong, Pardalos, Panos M., Series Editor, Thai, My T., Series Editor, Du, Ding-Zhu, Honorary Editor, Belavkin, Roman V., Advisory Editor, Birge, John R., Advisory Editor, Butenko, Sergiy, Advisory Editor, Kumar, Vipin, Advisory Editor, Nagurney, Anna, Advisory Editor, Pei, Jun, Advisory Editor, Prokopyev, Oleg, Advisory Editor, Rebennack, Steffen, Advisory Editor, Resende, Mauricio, Advisory Editor, Terlaky, Tamás, Advisory Editor, Vu, Van, Advisory Editor, Vrahatis, Michael N., Advisory Editor, Xue, Guoliang, Advisory Editor, Ye, Yinyu, Advisory Editor, Gaw, Nathan, editor, and Gahrooei, Mostafa Reisi, editor

Published
2024
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241. Introduction to Multimodal and Tensor Data Analytics

Author

Gaw, Nathan, Gahrooei, Mostafa Reisi, Pardalos, Panos M., Pardalos, Panos M., Series Editor, Thai, My T., Series Editor, Du, Ding-Zhu, Honorary Editor, Belavkin, Roman V., Advisory Editor, Birge, John R., Advisory Editor, Butenko, Sergiy, Advisory Editor, Kumar, Vipin, Advisory Editor, Nagurney, Anna, Advisory Editor, Pei, Jun, Advisory Editor, Prokopyev, Oleg, Advisory Editor, Rebennack, Steffen, Advisory Editor, Resende, Mauricio, Advisory Editor, Terlaky, Tamás, Advisory Editor, Vu, Van, Advisory Editor, Vrahatis, Michael N., Advisory Editor, Xue, Guoliang, Advisory Editor, Ye, Yinyu, Advisory Editor, Gaw, Nathan, editor, and Gahrooei, Mostafa Reisi, editor

Published
2024
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242. Convolutional Neural Network Based on Multiple Attention Mechanisms for Hyperspectral and LiDAR Classification

Author

Wang, Yingying, Wang, Kun, Ding, Zhiming, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Meng, Xiaofeng, editor, Zhang, Xueying, editor, Guo, Danhuai, editor, Hu, Di, editor, Zheng, Bolong, editor, and Zhang, Chunju, editor

Published
2024
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243. Autonomous Landing of a Low-Cost Quadrotor on a Moving Target

Author

Xiao, Zhipeng, Zhang, Wei, Zhu, Qi, Shang, Erke, Xiao, Liang, Zhao, Dawei, Nie, Yiming, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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244. A DNN-Based GLMB Algorithm for Multi-sensor Multi-target Tracking with Dense Group Clutter

Author

Zhang, Yongquan, Shang, Aomen, Li, Siwei, Su, Zhenzhen, Ji, Hongbing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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248. Multi-fidelity Data Fusion for Electromagnetic Field Prediction of Electromagnetic Railgun Field

Author

Jin, Liang, Shi, Shuo, Song, Juheng, Zhang, Chenyuan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, Li, Zewen, editor, and Luo, An, editor

Published
2024
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249. A Survey of Global Information Fusion Optimization Algorithms Based on Distributed Precise Data

Author

Zhang, Yifan, Liu, Shi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Cai, Chunwei, editor, Qu, Xiaohui, editor, Mai, Ruikun, editor, Zhang, Pengcheng, editor, Chai, Wenping, editor, and Wu, Shuai, editor

Published
2024
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250. Integrating Diagnostic Tools in the Ariosto Room

Author

Calicchia, Paola, De Simone, Sara, Camassa, Antonio, Tatì, Angelo, Ribeiro, Diogo, Series Editor, Naser, M. Z., Series Editor, Stouffs, Rudi, Series Editor, Bolpagni, Marzia, Series Editor, Ceccarelli, Sofia, editor, Missori, Mauro, editor, and Fantoni, Roberta, editor

Published
2024
Full Text
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