Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
4.0
Journals
Sensors
Special Issues
Soft Sensors and Sensing Techniques (2nd Edition)
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

Soft Sensors and Sensing Techniques (2nd Edition)

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 3078

Editor

Dr. Chamil Abeykoon

E-Mail Website
Guest Editor
School of Materials, Faculty of Science and Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: process monitoring; modelling and control; soft sensors and soft sensing; process instrumentation; renewable energy technologies; phase change materials; additive manufacturing; polymer/composite processing; heat transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to introduce all types of soft sensors and sensing techniques, including manufacturing methods, materials, and applications in various fields such as virtual reality interfaces, health care systems, motion capture systems, fault detection, and diagnosis. We would like to invite researchers to contribute their original research and qualified reviews related to this topic. The potential topics include, but are not limited to, the following:

  • Soft sensors;
  • Innovative sensing methodologies;
  • Soft robotics;
  • Soft actuators;
  • Soft materials/composites for sensor/sensing and detection;
  • Stretchable sensors;
  • Flexible sensors;
  • Skin patch sensor/sensors printed on the skin;
  • Novel manufacturing techniques of soft sensors;
  • Soft sensors for motion capture and analysis;
  • Soft sensors in the intelligent process industry;
  • Multi-sensor data fusion for soft sensing;
  • Applications in fault detection and diagnosis and monitoring of complex processes;
  • Applications in state estimation, control, and optimization;
  • Applications in process analytical technology (PAT), manufacturing, chemical-, bio-, pharmaceutical-, oil-, and process engineering;
  • Machine learning/AI;
  • Applications in weather mapping and environmental observations;
  • Applications in agriculture, irrigation, air quality, water treatment, etc.;
  • Self-powered sensors.

Dr. Chamil Abeykoon
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-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

  • soft sensors
  • soft robotics
  • soft actuators
  • stretchable sensors
  • flexible sensors
  • soft materials/composites for sensor/sensing and detection

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.

Related Special Issue

Published Papers (3 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

38 pages, 2692 KB  
Article
Observability- and Identifiability-Guided Sensor-Set Design for Digital-Twin-Assisted Consolidated Bioprocessing
by Mark Korang Yeboah, Nana Yaw Asiedu and Ahmad Addo
Sensors 2026, 26(12), 3948; https://doi.org/10.3390/s26123948 - 21 Jun 2026
Cited by 1 | Viewed by 498
Abstract
Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, [...] Read more.
Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a 6h sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and 24h, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and 24h, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article
(This article belongs to the Special Issue Soft Sensors and Sensing Techniques (2nd Edition))
►▼ Show Figures

Graphical abstract

26 pages, 9419 KB  
Article
Machine Learning-Based Soft Sensor for Real-Time Wire Bow Prediction in Diamond Multi-Wire Sawing
by Xiangyu Zhao, Hua Liu, Jie Yang, Liang Zhu, Heng Li, Lemiao Qiu and Shuyou Zhang
Sensors 2026, 26(6), 1875; https://doi.org/10.3390/s26061875 - 16 Mar 2026
Viewed by 563
Abstract
Real-time monitoring of wire bow is critical for ensuring wafer quality and preventing wire breakage in diamond multi-wire sawing (MWS). However, the deployment physical sensors in industrial MWS environments is hindered by severe sludge contamination, limited installation space, and high maintenance costs. To [...] Read more.
Real-time monitoring of wire bow is critical for ensuring wafer quality and preventing wire breakage in diamond multi-wire sawing (MWS). However, the deployment physical sensors in industrial MWS environments is hindered by severe sludge contamination, limited installation space, and high maintenance costs. To address these challenges, this paper proposes a novel data-driven soft sensor framework utilizing machine learning methods to predict wire bow based on readily accessible process data. A feature engineering pipeline, combining variance thresholding and correlation analysis, is established to identify key process variables. Subsequently, six representative ML algorithms are systematically evaluated, with eXtreme Gradient Boosting (XGBoost) optimized via two-stage hyperparameter optimization emerging as the superior model. Experimental results from an industrial MWS machine demonstrate that the proposed model achieves a coefficient of determination (R2) of 0.992 and a mean absolute error (MAE) of 0.116 mm. Furthermore, the prediction is also extended to spatially distributed positions (head, middle, and tail) of the wire web. Finally, SHAP (SHapley Additive exPlanations) is utilized to elucidate the mechanical dependencies. This work provides a reliable and low-cost solution for wire bow monitoring during the MWS process. Full article
(This article belongs to the Special Issue Soft Sensors and Sensing Techniques (2nd Edition))
►▼ Show Figures

Figure 1

16 pages, 2961 KB  
Article
Numerical Investigation of Halbach-Array-Based Flexible Magnetic Sensors for Wide-Range Deformation Detection
by Yina Han, Shuaiqi Zhang, Chenglin Wen, Jie Han, Wenbin Kang and Zhiqiang Zheng
Sensors 2025, 25(23), 7240; https://doi.org/10.3390/s25237240 - 27 Nov 2025
Viewed by 1494
Abstract
Flexible magnetic tactile sensors hold great promise for wearable electronics and intelligent robotics but often suffer from limited strain range and complex magnetic field variations due to rigid-soft coupling between the Hall sensor and magnetic layer. In this study, we propose a Halbach-array-based [...] Read more.
Flexible magnetic tactile sensors hold great promise for wearable electronics and intelligent robotics but often suffer from limited strain range and complex magnetic field variations due to rigid-soft coupling between the Hall sensor and magnetic layer. In this study, we propose a Halbach-array-based magnetic tactile sensor that structurally decouples the soft magnetic deformation layer from the rigid Hall sensing unit. The sensor embeds k = 2 Halbach-configured magnetic cubes within a PDMS matrix, while the Hall element is fixed at a remote, rigid location. Numerical analysis using COMSOL Multiphysics demonstrates that the Halbach configuration enhances magnetic field strength and uniformity, achieving mT-level detection even at a distance of 15 mm. Moreover, the Halbach array effectively reduces the field distribution from three-dimensional to one-dimensional, enabling stronger directionality, simplified data processing, and higher sensing frequency. This work establishes a theoretical framework for wide-range, high-precision magnetic tactile sensing through magnetic field tailoring, providing valuable guidance for the design of next-generation flexible sensors for wearable, robotic, and embodied intelligence applications. Full article
(This article belongs to the Special Issue Soft Sensors and Sensing Techniques (2nd Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop