Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Advanced Sensor Technologies for Multimodal Decision-Making
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Sensor Technologies for Multimodal Decision-Making

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4934

Special Issue Editor

Dr. Junyeong Kim

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: multi-modal AI; visual-language reasoning; medical AI; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution of sensor technologies has created unprecedented opportunities for intelligent systems that can process and integrate information from multiple sensing modalities to enable sophisticated decision-making processes. Modern applications across healthcare, manufacturing, autonomous systems, smart cities, and industrial automation increasingly rely on the fusion of diverse sensor data streams to achieve robust, accurate, and context-aware decisions that surpass the capabilities of single-modal approaches.

This Special Issue explores cutting-edge developments in advanced sensor technologies specifically designed for multimodal decision-making systems. We seek contributions that address the integration of heterogeneous sensor data, real-time processing architectures, and intelligent fusion algorithms that enable autonomous and semi-autonomous systems to make informed decisions in complex environments.

We welcome original research and review articles on topics including, but not limited to, the following:

  • Multimodal sensor fusion algorithms and architectures;
  • Advanced sensing technologies for autonomous systems and robotics;
  • Advanced sensing technologies for healthcare systems;
  • Advanced sensing technologies for smart city systems;
  • AI-driven sensor data interpretation and pattern recognition;
  • Adaptive and self-calibrating sensor systems.

We invite researchers and practitioners to contribute innovative solutions that advance the field of sensor-based intelligent systems and their practical deployment in real-world applications.

Dr. Junyeong Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multimodal sensor fusion
  • multimodal decision making
  • multimodal information interpretation
  • autonomous systems
  • healthcare systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 1937 KB  
Article
Machine Learning-Based Prediction of Performance Gaps in Rowing and Identification of Key Training Monitoring Indicators
by Jianyu Li, Guochun Liu, Wenjin Wang and Chunmei Cao
Sensors 2026, 26(10), 3006; https://doi.org/10.3390/s26103006 - 10 May 2026
Viewed by 707
Abstract
Although routine biomechanical monitoring in rowing increasingly relies on sensor-based and instrumented measurement systems that can capture multidimensional performance indicators with considerable precision, systematic approaches are still needed to integrate these sensor-derived data into a unified monitoring dataset and translate them into decision [...] Read more.
Although routine biomechanical monitoring in rowing increasingly relies on sensor-based and instrumented measurement systems that can capture multidimensional performance indicators with considerable precision, systematic approaches are still needed to integrate these sensor-derived data into a unified monitoring dataset and translate them into decision support for practice. This study aimed to construct a unified rowing training monitoring dataset from real-world sensor-derived biomechanical measurements, develop predictive models for athletes’ performance gaps relative to target 2 km performance, and, for target attainment classification, identify key training monitoring indicators and evaluate their practical value in training practice. A total of 249 biomechanical testing records collected during the 2024–2025 season from the Chinese National Rowing Team were included. After standardized processing, 449 athlete-level records were generated for the primary analysis. Following exclusion of observations with missing primary regression labels, 172 modeling records were retained, corresponding to 87 test reports and 119 athletes. The primary regression outcome was the percentage time difference relative to target 2 km performance. XGBoost Regressor, Elastic Net, and LASSO were used for regression modeling, whereas Logistic Regression and XGBoost Classifier were used for the secondary classification task of target attainment. Internal validation was performed using grouped cross-validation at the athlete level, and model interpretation was supported by permutation importance, sparse linear coefficients, and robustness analyses. The results showed that all formal models outperformed their respective baseline models. In the primary regression task, XGBoost Regressor achieved the best performance in terms of MAE, whereas Elastic Net performed best in RMSE and R2. The key training-monitoring indicators mainly included mean boat velocity, minimum boat velocity, stroke rate, distance per stroke, and efficiency-related variables. After removal of grouping variables related to boat class, sex, and weight category, the performance of XGBoost Regressor remained largely stable, suggesting that the primary predictive signal was mainly derived from measured technical and biomechanical features. In the secondary classification task, XGBoost Classifier achieved an ROC AUC of 0.992. This study provides a team-specific applied framework for extending sensor-derived rowing monitoring outputs from multi-indicator measurement toward interpretable performance evaluation and decision support within an elite-team setting, while broader external validation remains necessary. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Multimodal Decision-Making)
►▼ Show Figures

Figure 1

32 pages, 11735 KB  
Article
GEM-YOLO: A Lightweight and Real-Time RGBT Object Detector with Gated Multimodal Fusion
by Lijuan Wang, Zuchao Bao and Dongming Lu
Sensors 2026, 26(7), 2035; https://doi.org/10.3390/s26072035 - 25 Mar 2026
Viewed by 865
Abstract
Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fusion, the loss of small-object features during downsampling, and substantial computational overhead. To address these challenges, we propose [...] Read more.
Red–Green–Blue–Thermal (RGBT) object detection is critical for robust all-weather perception. However, deploying dual-stream networks on resource-constrained edge devices is severely hindered by insufficiently adaptive multimodal fusion, the loss of small-object features during downsampling, and substantial computational overhead. To address these challenges, we propose GEM-YOLO, a real-time and lightweight RGBT detector. Specifically, an Adaptive Multimodal Gated Fusion Mechanism (GFM) is designed to dynamically calibrate modality weights and suppress noise. Furthermore, Space-to-Depth (SPD) convolutions are integrated into the backbone to achieve lossless downsampling, preventing the feature collapse of small targets. Finally, a lightweight Ghost-Neck is constructed using Ghost modules and GSConv to eliminate computational redundancy. Extensive experiments on the Forward-Looking Infrared (FLIR) and Multi-Modal Multispectral Fusion Dataset (M3FD) datasets demonstrate the effectiveness of the proposed method. With only 7.58 Giga Floating-Point Operations (GFLOPs) and 3.44 million parameters (M), GEM-YOLO reduces the computational cost by 18.6% relative to the dual-stream YOLOv11n baseline. Concurrently, it achieves competitive mean Average Precision at IoU = 0.5 (mAP@50) scores of 82.8% and 69.0% on FLIR and M3FD, respectively, with more evident gains on small-target localization. In practice, GEM-YOLO maintains competitive detection performance while keeping computational overhead low, making it promising for real-time multispectral perception on resource-constrained edge platforms. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Multimodal Decision-Making)
►▼ Show Figures

Figure 1

14 pages, 879 KB  
Article
CSF-Net: Cross-Stage Fusion Network with Dual Backbone for Small Object Detection
by Beilei Wang, Hongyu Li, Lin Wei and Yichuan Zhang
Sensors 2026, 26(4), 1387; https://doi.org/10.3390/s26041387 - 23 Feb 2026
Viewed by 550
Abstract
Small object detection remains a challenge in computer vision due to low pixel occupancy, feature scarcity, and susceptibility to background interference. Conventional single-backbone networks often struggle to balance deep semantic extraction with the preservation of shallow details. Deep down-sampling can lead to the [...] Read more.
Small object detection remains a challenge in computer vision due to low pixel occupancy, feature scarcity, and susceptibility to background interference. Conventional single-backbone networks often struggle to balance deep semantic extraction with the preservation of shallow details. Deep down-sampling can lead to the loss of edge and texture information, while late-stage fusion may fail to recover these details effectively. To address these limitations, this paper proposes a Cross-Stage Fusion Network with a Dual Backbone (CSF-Net). Our network employs an asymmetric design: a shallow backbone maintains a higher resolution to preserve fine-grained details, while a deep backbone extracts contextual semantics. These two streams interact via progressive cross-stage connections, facilitating the early fusion of small object information. Experiments on the Micro RGB UAV dataset indicate that CSF-Net improves the mAP of the YOLOV8 baseline from 62.8% to 67.0%, validating the effectiveness of the proposed architecture in enhancing detection performance for small targets. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Multimodal Decision-Making)
►▼ Show Figures

Figure 1

26 pages, 3908 KB  
Article
Physics-Aware Spatiotemporal Consistency for Transferable Defense of Autonomous Driving Perception
by Yang Liu, Zishan Nie, Tong Yu, Minghui Chen, Zhiheng Yao, Jieke Lu, Linya Peng and Fuming Fan
Sensors 2026, 26(3), 835; https://doi.org/10.3390/s26030835 - 27 Jan 2026
Viewed by 674
Abstract
Autonomous driving perception systems are vulnerable to physical adversarial attacks. Existing defenses largely adopt loosely coupled architectures where visual and kinematic cues are processed in isolation, thus failing to exploit physical spatiotemporal consistency as a structural prior and often struggling to balance adversarial [...] Read more.
Autonomous driving perception systems are vulnerable to physical adversarial attacks. Existing defenses largely adopt loosely coupled architectures where visual and kinematic cues are processed in isolation, thus failing to exploit physical spatiotemporal consistency as a structural prior and often struggling to balance adversarial robustness, transferability, accuracy, and efficiency under realistic attacks. We propose a physics-aware trajectory–appearance consistency defense that detects and corrects spatiotemporal inconsistencies by tightly coupling visual semantics with physical dynamics. The module combines a dual-stream spatiotemporal encoder with endogenous feature orchestration and a frequency-domain kinematic embedding, turning tracking artifacts that are usually discarded as noise into discriminative cues. These inconsistencies are quantified by a Trajectory–Appearance Mutual Exclusion (TAME) energy, which supports a physics-aware switching rule to override flawed visual predictions. Operating on detector backbone features, outputs, and tracking states, the defense can be attached as a plug-in module behind diverse object detectors. Experiments on nuScenes, KITTI, and BDD100K show that the proposed defense substantially improves robustness against diverse categories of attacks: on nuScenes, it improves Correction Accuracy (CA) from 86.5% to 92.1% while reducing the computational overhead from 42 ms to 19 ms. Furthermore, the proposed defense maintains over 71.0% CA when transferred to unseen detectors and sustaining 72.4% CA under adaptive attackers. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Multimodal Decision-Making)
►▼ Show Figures

Figure 1

16 pages, 1433 KB  
Article
Intelligent Algorithms for the Detection of Suspicious Transactions in Payment Data Management Systems Based on LSTM Neural Networks
by Abdinabi Mukhamadiyev, Fayzullo Nazarov, Sherzod Yarmatov and Jinsoo Cho
Sensors 2025, 25(21), 6683; https://doi.org/10.3390/s25216683 - 1 Nov 2025
Cited by 1 | Viewed by 1628
Abstract
Today, a number of works are being carried out all over the world to develop data processing and management systems, as well as to apply artificial intelligence and information technologies in the fields of production, science, education, and healthcare. The optimization of the [...] Read more.
Today, a number of works are being carried out all over the world to develop data processing and management systems, as well as to apply artificial intelligence and information technologies in the fields of production, science, education, and healthcare. The optimization of the management of socio-economic process systems, and the management and reliability of databases of the digital payment information-based information systems of enterprises and organizations are relevant. This research work investigates the issue of increasing the reliability of information in information systems working with payment information. The characteristics of ambiguous suspicious transactions in payment systems are analyzed, and based on the analysis, preliminary data preparation stages are carried out for the intelligent detection of ambiguous suspicious transactions. Traditional and neural network models of machine learning for the detection of suspicious transactions in payment information management systems are developed, and a comparative analysis is carried out. Furthermore, to enhance the performance of the core LSTM model, an Artificial Bee Colony (ABC) optimization algorithm was integrated for automated hyperparameter tuning, which improved the model’s accuracy and efficiency in identifying complex fraudulent patterns. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Multimodal Decision-Making)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop