Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Feature Papers in Smart Sensing and Intelligent Sensors 2025

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 16267

Share This Special Issue

Special Issue Editor

Prof. Dr. Antonio Fernández-Caballero

E-Mail Website
Guest Editor

Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
Interests: pattern recognition; human–computer interaction; affective computing; computer vision; multi-sensor fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions and recommendations from the EBMs.

The aim of this Special Issue is to publish a set of papers that showcase the most insightful and influential original articles or reviews where our section’s EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition after the deadline and will be carefully promoted.

We would also like to take this opportunity to call on more scholars to join so that we can work together to further develop this exciting field of research. Potential topics include, but are not limited to, the following:

Sensor signal processing;
Deep learning/machine learning;
Data processing/science;
Computer vision;
Integrated circuits;
Human–robot/machine/computer interactions;
Artificial intelligence;
Intelligent instrumentation;
Intelligent control;
Intelligent portable platforms;
Intelligent computing;
Wireless sensor networks (WSNs);
Smart sensor networks;
Intelligent environmental monitoring;
Smart cities;
Smart home/home automation;
Smart manufacturing and industry;
Smart energy management/smart grids;
Smart agriculture;
Smart health monitoring;
E-health/M-health;
Intelligent emotion recognition;
Smart building/smart civil infrastructure;
Smart/precision farming;
Blockchain 5G/6G.

Prof. Dr. Antonio Fernández-Caballero
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

intelligent sensors
smart sensing
artificial intelligence
sensing systems
sensor data

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 (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

25 pages, 3535 KB

Open AccessArticle

Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections

by Amr K. Shafik and Hesham A. Rakha

Sensors 2025, 25(20), 6339; https://doi.org/10.3390/s25206339 - 14 Oct 2025

Viewed by 262

Abstract

This research enhances and evaluates the performance of a Decentralized Nash Bargaining (DNB) adaptive traffic signal controller that operates a flexible National Electrical Manufacturers Association (NEMA) phasing and timing scheme responding dynamically to fluctuating traffic demands. The DNB controller is enhanced to (1) use traffic density estimates instead of queues to optimize signal timings; (2) to consider the eight-phase two-ring NEMA controller configuration within the game-theoretic approach; and (3) to consider dynamically adaptable control time steps. The enhanced DNB controller is benchmarked against (1) a fixed-time traffic signal control using the state-of-practice Webster’s method and an emerging Laguna-Du-Rakha (LDR) method for computing the optimum cycle length; (2) a state-of-the-practice actuated traffic signal control; and (3) a state-of-the-art reinforcement learning (RL) traffic signal controller presented in the literature. The controller is tested on two isolated signalized intersections, demonstrating enhanced overall intersection performance compared to the baseline pretimed and actuated controllers at various demand levels, and offers better performance than a previously developed RL controller. Specifically, the DNB controller results in a decrease in the average vehicle delay and queue size by up to 54% and 63%, respectively, compared to Webster’s state-of-the-practice pretimed control. Unlike the RL controller, the DNB controller requires no pre-training while adapting to fluctuating traffic conditions, thereby providing a flexible framework for reducing traffic congestion at signalized intersections. As such, this research contributes to the development of smarter and more responsive urban traffic control systems. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

21 pages, 2900 KB

Open AccessArticle

Optimizing Detection Reliability in Safety-Critical Computer Vision: Transfer Learning and Hyperparameter Tuning with Multi-Task Learning

by Waun Broderick and Sabine McConnell

Sensors 2025, 25(20), 6306; https://doi.org/10.3390/s25206306 - 12 Oct 2025

Viewed by 356

Abstract

This paper presents a methodological framework for selectively optimizing computer vision models for safety-critical applications. Through systematic processes of hyperparameter tuning alongside multitask learning, we attempt to create a highly interpretable system to better assess the dangers of models intended for safety operations and intentionally select their trade-offs. Using thermographic images of a specific imitation explosive, we create a case study for the viability of humanitarian demining operations. We hope to demonstrate how this approach provides a developmental framework for creating humanitarian AI systems that optimize safety verification in real-world scenarios. By employing a comprehensive grid search across 64 model configurations to evaluate how loss function weights impact detection reliability, with particular focus on minimizing false negative rates due to their operational impact. The optimized configuration achieves a 37.5% reduction in false negatives while improving precision by 2.8%, resulting in 90% detection accuracy with 92% precision. However, to expand the generalizability of this model, we hope to call institutions to openly share their data to increase the breadth of imitation landmines and terrain data to train models from. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

21 pages, 4623 KB

Open AccessArticle

Combining Neural Architecture Search and Weight Reshaping for Optimized Embedded Classifiers in Multisensory Glove

by Hiba Al Youssef, Sara Awada, Mohamad Raad, Maurizio Valle and Ali Ibrahim

Sensors 2025, 25(19), 6142; https://doi.org/10.3390/s25196142 - 4 Oct 2025

Viewed by 335

Abstract

Intelligent sensing systems are increasingly used in wearable devices, enabling advanced tasks across various application domains including robotics and human–machine interaction. Ensuring these systems are energy autonomous is highly demanded, despite strict constraints on power, memory and processing resources. To meet these requirements, embedded neural networks must be optimized to achieve a balance between accuracy and efficiency. This paper presents an integrated approach that combines Hardware-Aware Neural Architecture Search (HW-NAS) with optimization techniques—weight reshaping, quantization, and their combination—to develop efficient classifiers for a multisensory glove. HW-NAS automatically derives 1D-CNN models tailored to the NUCLEO-F401RE board, while the additional optimization further reduces model size, memory usage, and latency. Across three datasets, the optimized models not only improve classification accuracy but also deliver an average reduction of 75% in inference time, 69% in flash memory, and more than 45% in RAM compared to NAS-only baselines. These results highlight the effectiveness of integrating NAS with optimization techniques, paving the way towards energy-autonomous wearable systems. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

19 pages, 3619 KB

Open AccessArticle

Influence of Na Additives on the Characteristics of Titania-Based Humidity Sensing Elements, Prepared via a Sol–Gel Method

by Zvezditza Nenova, Stephan Kozhukharov, Nedyu Nedev and Toshko Nenov

Sensors 2025, 25(19), 6075; https://doi.org/10.3390/s25196075 - 2 Oct 2025

Viewed by 406

Abstract

Humidity sensing elements based on sodium-doped titanium dioxide (Na-doped TiO₂) were prepared using a sol–gel method in the presence of cerium ions and sintered at 400 °C and 800 °C. Titanium (IV) n-butoxide and a saturated solution of diammonium hexanitratocerate in isobutanol served as starting materials. Sodium hydroxide and sodium tert-butoxide were used as inorganic and organometallic sodium sources, respectively. The influence of sodium additives on the properties of the humidity sensing elements was systematically investigated. The surface morphologies of the obtained layers were examined by scanning electron microscopy (SEM). Elemental mapping was conducted by energy-dispersive X-ray (EDX) spectroscopy, and structural characterization was performed using X-ray diffractometry (XRD). Electrical properties were studied for samples sintered at different temperatures over a relative humidity range of 15% to 95% at 20 Hz and 25 °C. Experimental results indicate that sodium doping enhances humidity sensitivity compared to undoped reference samples. Incorporation of sodium additives increases the resistance variation range of the sensing elements, reaching over five orders of magnitude for samples sintered at 400 °C and four orders of magnitude for those sintered at 800 °C. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

19 pages, 1186 KB

Open AccessArticle

Synthetic Patient–Physician Conversations Simulated by Large Language Models: A Multi-Dimensional Evaluation

by Syed Ali Haider, Srinivasagam Prabha, Cesar Abraham Gomez-Cabello, Sahar Borna, Ariana Genovese, Maissa Trabilsy, Bernardo G. Collaco, Nadia G. Wood, Sanjay Bagaria, Cui Tao and Antonio Jorge Forte

Sensors 2025, 25(14), 4305; https://doi.org/10.3390/s25144305 - 10 Jul 2025

Cited by 1 | Viewed by 1755

Abstract

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

23 pages, 7485 KB

Open AccessArticle

Key Vital Signs Monitor Based on MIMO Radar

by Michael Gottinger, Nicola Notari, Samuel Dutler, Samuel Kranz, Robin Vetsch, Tindaro Pittorino, Christoph Würsch and Guido Piai

Sensors 2025, 25(13), 4081; https://doi.org/10.3390/s25134081 - 30 Jun 2025

Viewed by 3130

Abstract

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

34 pages, 3932 KB

Open AccessFeature PaperArticle

Augmenting Orbital Debris Identification with Neo4j-Enabled Graph-Based Retrieval-Augmented Generation for Multimodal Large Language Models

by Daniel S. Roll, Zeyneb Kurt, Yulei Li and Wai Lok Woo

Sensors 2025, 25(11), 3352; https://doi.org/10.3390/s25113352 - 26 May 2025

Cited by 2 | Viewed by 2321

Abstract

This preliminary study covers the construction and application of a Graph-based Retrieval-Augmented Generation (GraphRAG) system integrating a multimodal LLM, Large Language and Vision Assistant (LLaVA) with graph database software (Neo4j) to enhance LLM output quality through structured knowledge retrieval. This is aimed at the field of orbital debris detection, proposed to support the current intelligent methods for such detection by introducing the beneficial properties of both LLMs and a corpus of external information. By constructing a dynamic knowledge graph from relevant research papers, context-aware retrieval is enabled, improving factual accuracy and minimizing hallucinations. The system extracts, summarizes, and embeds research papers into a Neo4j graph database, with API-powered LLM-generated relationships enriching interconnections. Querying this graph allows for contextual ranking of relevant documents, which are then provided as context to the LLM through prompt engineering during the inference process. A case study applying the technology to a synthetic image of orbital debris is discussed. Qualitative results indicate that the inclusion of GraphRAG and external information result in successful retrieval of information and reduced hallucinations. Further work to refine the system is necessary, as well as establishing benchmark tests to assess performance quantitatively. This approach offers a scalable and interpretable method for enhanced domain-specific knowledge retrieval, improving the qualitative quality of the LLM’s output when tasked with description-based activities. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

18 pages, 737 KB

Open AccessArticle

Mobile Game Evaluation Method Based on Data Mining of Affective Time Series

by Jeremi K. Ochab, Paweł Węgrzyn, Przemek Witaszczyk, Dominika Drążyk and Grzegorz J. Nalepa

Sensors 2025, 25(9), 2756; https://doi.org/10.3390/s25092756 - 26 Apr 2025

Cited by 2 | Viewed by 916

Abstract

Our work is positioned at the intersection of game data science, affective gaming, and the implementation of multimodal body sensors analysis. We propose an original method of evaluating the quality of a class of video games based on the emotional reactions of players. Game developers ask why some games are more profitable (MP games) than others (LP games). An intuitively convincing hypothesis is often put forward: MP games evoke more positive emotions and hence are sustainably engaging. Our main hypothesis is that test players who can clearly distinguish between MP game and LP game in relatively short test sessions are more reliable in scoring games and valuable to keep track of their emotions. From a random group of test players, we selected players with such abilities. We analyzed their affective spectra and obtained a fairly clear confirmation that the selected players showed more positive and less negative emotions in MP games than in LP ones. We can reasonably expect these players to be focused on playing in the test session, and their emotions may really indicate the strengths of MP games over LP games. We present the results of the experimental evaluation of our method conducted with with a leading game company in Poland. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Figure 1

Review

Jump to: Research

38 pages, 2189 KB

Open AccessReview

Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review

by Rim Missaoui, Wided Hechkel, Wajdi Saadaoui, Abdelhamid Helali and Marco Leo

Sensors 2025, 25(9), 2746; https://doi.org/10.3390/s25092746 - 26 Apr 2025

Cited by 5 | Viewed by 5557

Abstract

A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018–2024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies. Full article

(This article belongs to the Special Issue Feature Papers in Smart Sensing and Intelligent Sensors 2025)

► Show Figures

Journal Menu

Journal Browser

Feature Papers in Smart Sensing and Intelligent Sensors 2025

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI