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Advanced Self-X Sensory Systems: Concepts, Challenges, and Applications

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

Deadline for manuscript submissions: 25 February 2026 | Viewed by 612

Special Issue Editor


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Guest Editor
Institute of Integrated Sensor Systems, TU Kaiserslautern, 67663 Kaiserslautern, Germany
Interests: integrated sensor systems related to microelectronics/MEMS and intelligent sensor systems related to computational intelligence
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Special Issue Information

Dear Colleagues,

The intriguing versatility, adaptability, robustness, resilience and restoration capabilities of living beings have always been the envy of engineers. However, technical solutions have been very far from that for a long time. Approaches based on mimicking neural networks and nervous systems are one interesting direction of study, a technically more universal and rewarding one is the creation of self-X or self-* systems, which provide in a more abstract embodiment, not confined to neurons and neural networks, numerous of the coveted capabilities of living beings. Self-X capabilities are among many more, e.g., self-monitoring, -calibration, -trimming, and -healing/repairing, which have been postulated as key functionalities for Industry 4.0 and numerous other deployment fields, e.g., IoT, CP(P)S, wearable electronics, AAL/healthcare assistance systems, point-of-care-systems, etc. The surging advance in micro/nano integration and packaging technologies, and the advent of a plethora of novel sensory concepts and technologies also opens the door to expend extra effort and system complexity required on all levels, both in the analog and/or the digital domain, for the realization of self-X functionality and related optimization and reconfiguration, and/or adaptation resources in the application systems. This explicitly extends design automation techniques from design time to run time, being able to deal with both static and dynamically occurring issues for system performance, which is denoted as extrinsic and intrinsic evolution or optimization in the related field of Evolvable Hardware Systems. Goals have to be addressed, such as the following: cost-effectiveness, long-term system reliability/dependability and accuracy, consistency of modeling, simulation and rapid design of heterogeneous systems, suitability of observer and/or non-obtrusive sensors in the optimization loop, information gain from redundant homogeneous imperfect sensors by dedicated dimensionality reducing mapping, and sensors with micro actuation or switching devices, for example.

Topics of primary interest for this Special Issue include, but are not limited to, the following: systems with multi-level and/or mixed-signal self-x- (or self-*) -capabilities, related redundancy and reconfiguration and/or adaptation concepts, architectures, and actuator/switching devices for sensors, inherently robust signal representations and processing, including time or pulse coding, homogeneous and/or heterogeneous multi-sensor approaches, design automation and CAD, yield optimization and reliability issues, and achieving high-quality and reliable references and reduced measurement uncertainty, e.g., NIST NoaC activity, artificial immune systems, multidimensional signal processing algorithms, dimensionality reduction techniques, learning and optimization methods, sensor fusion, and sensor, and data visualization.

In this Special Issue, we seek to collect review articles, original research papers, and short communications, as well as history surveys, covering aspects of multi-level self-X systems in the context of the given primary interest.

Authors are welcome to contact the Guest Editor prior to submission if their work addresses additional issues and challenges to clarify whether this falls within the general scope of this Special Issue.

Prof. Dr. Andreas König
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

  • self-x-capabilities
  • reconfiguration and adaptation
  • design automation
  • yield and dependability optimization
  • extrinsic and intrinsic evolution
  • run-time optimization
  • robust signal representations
  • non-obtrusive sensors
  • non-ideal observer compensation

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Published Papers (1 paper)

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Research

24 pages, 8345 KiB  
Article
Enhancing Reliability in Redundant Homogeneous Sensor Arrays with Self-X and Multidimensional Mapping
by Elena Gerken and Andreas König
Sensors 2025, 25(13), 3841; https://doi.org/10.3390/s25133841 - 20 Jun 2025
Viewed by 283
Abstract
Mechanical defects and sensor failures can substantially undermine the reliability of low-cost sensors, especially in applications where measurement inaccuracies or malfunctions may lead to critical outcomes, including system control disruptions, emergency scenarios, or safety hazards. To overcome these challenges, this paper presents a [...] Read more.
Mechanical defects and sensor failures can substantially undermine the reliability of low-cost sensors, especially in applications where measurement inaccuracies or malfunctions may lead to critical outcomes, including system control disruptions, emergency scenarios, or safety hazards. To overcome these challenges, this paper presents a novel Self-X architecture with sensor redundancy, which incorporates dynamic calibration based on multidimensional mapping. By extracting reliable sensor readings from imperfect or defective sensors, the system utilizes Self-X principles to dynamically adapt and optimize performance. The approach is initially validated on synthetic data from tunnel magnetoresistance (TMR) sensors to facilitate method analysis and comparison. Additionally, a physical measurement setup capable of controlled fault injection is described, highlighting practical validation scenarios and ensuring the realism of synthesized fault conditions. The study highlights a wide range of potential TMR sensor failures that compromise long-term system reliability and demonstrates how multidimensional mapping effectively mitigates both static and dynamic errors, including offset, amplitude imbalance, phase shift, mechanical misalignments, and other issues. Initially, four individual TMR sensors exhibited mean absolute error (MAE) of 4.709°, 5.632°, 2.956°, and 1.749°, respectively. To rigorously evaluate various dimensionality reduction (DR) methods, benchmark criteria were introduced, offering insights into the relative improvements in sensor array accuracy. On average, MAE was reduced by more than 80% across sensor combinations. A clear quantitative trend was observed: for instance, the MAE decreases from 4.7°–5.6° for single sensors to 0.111° when the factor analysis method was applied to four sensors. This demonstrates the concrete benefit of sensor redundancy and DR algorithms for creating robust, fault-tolerant measurement systems. Full article
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