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Intelligent Sensing and Digital Signal Processing in Smart Data

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1632

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Editors

Dr. Hana Charvátová

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Guest Editor

Centre for Security, Information and Advanced Technologies (CEBIA–Tech), Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 05 Zlín, Czech Republic
Interests: modelling of manufacturing processes of natural and synthetic polymers; thermal process analysis in building technology; sensor systems and wireless communication; signal processing for motion monitoring; artificial intelligence; computational methods

Prof. Dr. Aleš Procházka

E-Mail Website
Guest Editor

1. Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic
2. Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic
Interests: digital signal processing; wearable sensors; artificial intelligence; machine learning; multimedia signal analysis; augmented reality; data features classification; computational methods; biomedical and engineering applications
Special Issues, Collections and Topics in MDPI journals

Dr. Oldřich Vyšata

E-Mail Website
Guest Editor

Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University in Prague, 500 05 Hradec Králové, Czech Republic
Interests: neurophysiology; wearable sensors; multichannel digital signal processing; biological systems modelling; motion features analysis; classification of neurological disorders; artificial intelligence; computational methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of sensor technologies, advanced signal processing, and artificial intelligence is driving a new wave of intelligent computational systems. Sensors provide vast streams of data from physical, biological, and environmental domains. However, to convert these raw data into actionable knowledge, robust computational models are needed that integrate digital signal analysis with AI-based learning and decision-making.

This Special Issue will bring together innovative research that bridges these domains, fostering advances in computational modelling that enable real-time sensing, adaptive signal processing, and intelligent decision support. This Special Issue will serve as a platform for sharing cutting-edge methodologies, frameworks, and applications that transform sensor data into meaningful insights.

Dr. Hana Charvátová
Prof. Dr. Aleš Procházka
Dr. Oldřich Vyšata
Guest Editors

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-anonymized 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

computational modelling
sensors
sensor fusion
digital signal processing
artificial intelligence
machine learning
autonomous systems
robotics sensors
smart applications

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Published Papers (5 papers)

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Research

25 pages, 2868 KB

Open AccessArticle

DCAF-Net: Density-Conditioned Attention Fusion Network for Single-Image Dehazing

by Nianfeng Li, Shaojie Liu, Hongjie Ding, Shenyan Gao, Zhiguo Xiao and Qian Liu

Sensors 2026, 26(14), 4656; https://doi.org/10.3390/s26144656 (registering DOI) - 22 Jul 2026

Abstract

Single-image dehazing aims to recover clear scenes from degraded images affected by atmospheric scattering, serving as a critical preprocessing technique for improving the imaging quality of visual sensors. Existing deep learning-based dehazing methods exhibit limited generalization ability in real-world scenarios, primarily due to the spatial non-uniformity of haze and its coupling with illumination and texture degradation, as well as the scarcity of real paired data. To address these issues, this paper proposes a haze-density conditional attention fusion network (DCAF-Net). The network employs an adaptive haze density perception module to fuse priors such as the dark channel, local contrast, and saturation, generating a spatial haze density guidance map. This map is then embedded as conditional information into the multi-scale feature modulation and attention fusion process, enabling adaptive restoration of regions with different degradation levels. Furthermore, a residual dense cascaded feature enhancement module is designed to leverage feature reuse, gated fusion, and residual learning to enhance the representational capacity of deep features. Training adopts a joint optimization objective combining Charbonnier reconstruction loss, perceptual contrast loss, and structural similarity loss. Experimental results demonstrate that DCAF-Net achieves competitive performance against representative methods on multiple synthetic and real-world hazy datasets, and shows promising restoration performance on representative real-world hazy scenes, and can provide high-quality image preprocessing support for visual-sensor-based intelligent perception systems. Full article

(This article belongs to the Special Issue Intelligent Sensing and Digital Signal Processing in Smart Data)

33 pages, 1862 KB

Open AccessArticle

Multisource Urban Sensing Data Fusion and Dynamic Causal Graph Modeling for Explainable Traffic State Prediction

by Ran Zhu, Yingxi Wu, Xiaoya Wang, Leran Chen and Yan Zhan

Sensors 2026, 26(14), 4547; https://doi.org/10.3390/s26144547 - 17 Jul 2026

Viewed by 174

Abstract

Urban traffic congestion prediction is an important problem in smart city sensing and intelligent traffic governance. Existing methods mostly rely on single-source traffic flow sensing data or static road topology, making it difficult to sufficiently characterize the dynamic congestion propagation process driven by multisource sensing information, such as traffic flow, vehicle trajectories, road images, public transportation, meteorological conditions, and sudden events. To address this issue, a spatiotemporal causal graph learning framework based on multisource urban sensing data is proposed for urban traffic state prediction, congestion identification, and explainable early warning. In this framework, traffic flow detector data, GPS trajectories, roadside camera data, public transportation data, weather data, and event records are first fused through a multisource urban sensing data collaborative encoding module, and the influence of low-quality or missing sensing modalities is suppressed using a reliability-aware attention mechanism. Subsequently, time-varying causal propagation relationships among road segments are adaptively learned from historical traffic states, road topology, and external disturbances through a dynamic spatiotemporal causal graph learning module. Finally, spatial diffusion and temporal evolution are jointly modeled by a causality-explanation-driven congestion prediction module, and key congestion sources, propagation paths, and inducing factors are outputs. Experimental results based on multisource traffic sensing data from the main urban area of Hangzhou show that the proposed method achieves MAE values of

3.21

3.79

, and

4.48

in 15-min, 30-min, and 60-min traffic state prediction tasks, respectively, outperforming ARIMA, XGBoost, LSTM, Transformer, STGCN, Graph WaveNet, GMAN, Multimodal Transformer, and the Causal Temporal Graph Network. In the ablation study, the complete model achieves an Accuracy of

0.914

, a Precision of

0.902

, a Recall of

0.889

, an F1 of

0.895

, and an AUC of

0.956

. For congestion identification and early warning under complex scenarios, F1 values of

0.927

0.904

, and

0.893

are achieved under peak-hour, rainy-weather, and traffic-event scenarios, respectively; the corresponding AUC values reach

0.966

0.957

, and

0.948

; and the false alarm rate (FAR) values are reduced to

0.061

0.072

, and

0.081

. The results indicate that the proposed method can effectively improve traffic state prediction accuracy, congestion early warning reliability, and model interpretability under multisource urban sensing conditions, thereby providing an effective technical pathway for AI-driven intelligent traffic sensing. Full article

(This article belongs to the Special Issue Intelligent Sensing and Digital Signal Processing in Smart Data)

26 pages, 5364 KB

Open AccessArticle

Deep Multimodal Phenotyping and Sensor Fusion for Preharvest Cotton Quality Assessment and Agricultural Economic Decision Support

by Jingwen Luo, Xintong Wang, Shiguo Zhang, Jiahe Zhang, Ruobing Feng, Xiuting Shu and Shuo Yan

Sensors 2026, 26(14), 4493; https://doi.org/10.3390/s26144493 - 15 Jul 2026

Viewed by 207

Abstract

Cotton fiber quality is shaped during boll development, boll opening, fluffing, and harvesting, but current assessment still relies largely on manual field inspection and postharvest laboratory testing. This limits timely harvest scheduling and plot-level quality management. To address this problem, we propose a self-supervised multimodal sensing framework for linking preharvest cotton boll status, environmental conditions, and postharvest fiber quality. First, the Cotton Boll Visual Phenotype Self-Supervised Encoding Module learns maturity-related visual representations by reconstructing masked image patches, so that boll cracking, lint exposure, and surface texture can be captured from unlabeled field images. Second, the Agricultural Sensor Temporal Masked Modeling Module reconstructs masked sensor observations to model temporal patterns in temperature, humidity, light, soil moisture, rainfall, and other environmental variables. Third, the Vision–Environment Cross-Modal Contrastive Fusion Module aligns image features with environmental features and produces a joint representation for downstream prediction. Field experiments were conducted using cotton boll images from different maturity and abnormal states, environmental sensor records, management information, and postharvest fiber quality measurements. The framework was evaluated for maturity classification, harvest-window recognition, and fiber quality prediction. The results showed that the proposed method performed consistently better than representative machine learning, single-modal deep learning, and multimodal fusion baselines, while few-shot and ablation experiments supported the value of self-supervised pretraining and multimodal fusion. These findings indicate that the proposed approach can provide useful information for preharvest cotton maturity assessment and harvest-quality management. Full article

(This article belongs to the Special Issue Intelligent Sensing and Digital Signal Processing in Smart Data)

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32 pages, 1094 KB

Open AccessArticle

A Multisource Hardware Sensing Signal Fusion Network for Robust State Prediction and Anomaly Perception

by Yufei Li, Junxian Zhao, Yi Wei, Xichen Wang, Yaqing Yang, Yang Yang and Yan Zhan

Sensors 2026, 26(13), 4234; https://doi.org/10.3390/s26134234 - 3 Jul 2026

Viewed by 284

Abstract

With the rapid development of intelligent manufacturing, edge computing, and industrial and financial–industrial digital systems, large volumes of multisource hardware sensing signals are continuously generated in complex production environments, including environmental, electrical, vibration, network communication, and device operational signals. Owing to the heterogeneity, asynchrony, noise interference, and disturbance sensitivity of these signals, conventional state prediction methods often fail to sufficiently characterize the dynamic response relationships among different sensing sources and cannot maintain stable prediction performance under non-stationary scenarios such as load surges, network congestion, and device anomalies. To address these challenges, a multisource hardware sensing signal fusion network is proposed for the edge-computing and digital production test scenario of an intelligent equipment manufacturing enterprise in Hebei Province, China, with the aim of achieving robust state prediction and anomaly perception in complex digital systems. In the proposed method, environmental sensing, device power, edge-node operation, vibration monitoring, network communication, and system output states are uniformly modeled as multisource engineering sensing signals, and an end-to-end prediction framework is constructed with cross-source sensing signal alignment to facilitate temporal coherence, disturbance-aware residual correction to substantially mitigate disturbance contamination, and context-adaptive fusion. Experimental results show that the proposed method achieves the best performance in the overall state prediction task, with MAE, RMSE, MAPE, and

R^{2}

reaching

0.0968

0.1457

8.12

%, and

0.9416

, respectively, outperforming baseline methods including ARIMA, XGBoost, LightGBM, LSTM, TCN, Transformer, Attention Fusion, and Multimodal Transformer. In the disturbance robustness experiment, the Event-MAE and Event-RMSE of the proposed method are reduced to

0.1126

and

0.1694

, respectively, with an Avg. Drop of only

28.98

%, indicating that more stable responses can be achieved under non-stationary disturbance scenarios. In the abnormal-state recognition task, Accuracy, Precision, Recall, and F1-score values of

94.32

93.76

92.85

%, and

93.30

% are achieved, respectively. The results demonstrate that the proposed method can effectively improve the state prediction accuracy, disturbance robustness, and anomaly warning capability of multisource hardware sensing data in complex industrial and financial–industrial digital systems, thereby providing an effective modeling scheme for intelligent monitoring and engineering decision-making in AI-driven industrial and financial sensing scenarios. Full article

(This article belongs to the Special Issue Intelligent Sensing and Digital Signal Processing in Smart Data)

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36 pages, 842 KB

Open AccessArticle

Privacy-Preserving Federated Deep Learning for Robust Anomaly Detection in Distributed Security Sensing Systems

by Di Xu, Hongli Chen, Yansen Zeng, Yifan Yang, Jinghan Huang, Jiarui Song and Yan Zhan

Sensors 2026, 26(12), 3901; https://doi.org/10.3390/s26123901 - 19 Jun 2026

Viewed by 526

Abstract

With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Traditional centralized anomaly detection methods are no longer able to simultaneously satisfy the requirements of cross-institutional or cross-node collaborative modeling, client data privacy protection, and robust monitoring of transaction and system anomalies. To address this challenge, a data-local federated deep anomaly detection framework has been proposed for distributed financial security sensing systems. Initially, a local deep financial security sensing representation module is constructed to perform temporal encoding and attention-based modeling on multisource financial signals, including terminal operation status, network transaction communication, backend server operation, identity authentication, and anomaly alerts, thereby extracting representations relevant to anomalous behaviors. Subsequently, a data-local federated optimization and personalized aggregation mechanism is developed to enable cross-node knowledge sharing without transmitting raw transaction or client data, while local personalized detection heads are employed to adapt to non-independent and identically distributed (non-IID) financial institution data. Furthermore, an adversarially robust security detection and trust-aware aggregation strategy is introduced to enhance model stability under input noise, feature masking, anomaly camouflage, and potential malicious client updates. Experimental results demonstrate that the proposed method achieves an Accuracy of

92.37 %

, a Precision of

89.41 %

, a Recall of

88.26 %

, an F1-score of

88.83 %

, an AUC of

93.06 %

, and a PR-AUC of

89.15 %

in the primary financial anomaly detection task, significantly outperforming baseline methods such as Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON. In robustness experiments, the method attains F1-scores of

87.95 %

86.42 %

86.88 %

84.57 %

86.73 %

, and

83.91 %

under Gaussian noise, feature masking, temporal shift, adversarial perturbation, and

20 %

and

30 %

malicious client scenarios, respectively. Ablation studies further confirm the effectiveness of local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation mechanisms. Overall, the proposed approach provides an efficient intelligent anomaly detection solution for financial AI security monitoring scenarios characterized by data localization requirements, node heterogeneity, and attack perturbations. Full article

(This article belongs to the Special Issue Intelligent Sensing and Digital Signal Processing in Smart Data)

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