Ultrahigh-frequency radio-frequency identification (UHF RFID) sensing systems have emerged as a promising technology for wireless, passive, and cost-effective monitoring applications. However, their performance is highly sensitive to variations in measurement setups, such as tag–reader distance, orientation, and interrogation conditions, which compromise sensing accuracy and severely limit the reliability and robustness of these systems. These challenges are especially pronounced in non-stationary interrogation, such as in robotic pipeline inspections, where mobile crawlers face bends, welds, irregular surfaces, and multipath effects, and in distributed monitoring systems with multiple tag positions. Conventional approaches based on raw received signal strength indicator (RSSI) or turn-on power remain tightly coupled to setup variations, leading to unreliable sensing. To overcome this, we propose a setup-independent response map-based method that integrates frequency- and power-domain RSSI measurements into a multidimensional signature, from which corrosion-specific features are extracted using PCA and Kernel PCA. A custom UHF RFID tag was developed and validated under six varying tag–reader setups and six different corrosion conditions for undercoating corrosion detection. Compared with the state-of-the-art analog identifier (AID) method, the proposed approach achieves significantly higher stability (average CV 0.4483 vs. 1.0783), tighter feature clustering (variance 0.0750 vs. 0.1235), and a fivefold reduction in measurement time. These results demonstrate a robust, efficient, and practical solution for high-fidelity, setup-independent UHF RFID-based sensing, ensuring reliable non-stationary and distributed monitoring for advanced industrial applications.

Despite large models drive unprecedented growth in data and model parameters, many real-world problems prioritize interpretability and generality, and lack sufficient training data. For instance, in Compressed Sensing (CS) where sparse reconstruction solves underdetermined systems, traditional iterative methods remain the practical choice due to their interpretability and out-of-the-box applicability to arbitrary conditions, but suffer from poor quality and inefficiency at low sampling rates. To address this, we propose Coefficients Learning (CL), a novel training-free framework for sparse reconstruction. CL employs ultra-small neural models with only $n$ trainable parameters for a length-$n$ signal. It retains the interpretability and generality of traditional iterative methods by adopting their residual-based solving process, while enhancing efficiency and accuracy by replacing closed-form solutions with automatic differentiation and embedding prior knowledge into the model losses. We evaluate CL extensively on synthetic and real one-dimensional and two-dimensional signals. A detailed analysis is first conducted using an implemented CLOMP. To demonstrate general applicability, CL is also implemented on three types of classic iterative CS reconstruction methods. Results show that CL maintains the generality of iterative methods while significantly boosting accuracy. Although it adds minor overhead for convex optimization or message-passing methods, it achieves efficiency gains of 100 to 1000 times for greedy algorithms. On the tested nine diverse image datasets, CL improves median reconstruction accuracy by approximately 163%, 78%, and 35% at sampling rates of 0.04, 0.25, and 0.5, respectively, compared to classic iterative methods. This training-free CS reconstruction method can truly empower countless industrial or medical machines that rely on sparse solution.

Current non-destructive tomography sensing systems, particularly in the domain of eddy current sensing, lack the capability to dynamically and intelligently optimize sensing parameters in response to time-varying environments. To address this limitation, we propose a hybrid tomography sensing system that integrates physical artificial intelligence (PAI) with an electromagnetic dynamics model for intelligent sensing. This system combines a physical recurrent neural network (PRNN) entities performed by the programmable planar coil arrays with its digital counterpart in a closed-loop configuration, facilitating forward inference and the backpropagation of errors respectively. Additionally, the system leverages physics-guided methods based on electromagnetic field dynamics and employs a combination of standard neural network training techniques to optimize the parameters of PRNN, enabling real-time adaptive optimization of sensing. Theoretical modelling of the PRNN has been rigorously conducted in this study. Furthermore, high-fidelity electromagnetic tomography (EMT) results for non-destructive testing are demonstrated, showcasing the potential of physics-guided analogue AI in EMT sensing. The results shows that the electromagnetic controlling system, optimized through physical AI approach, achieves higher precision results with lower complexity compared to standard digital implementations in eddy current testing. This also provides a novel potential way for optimizing PNNs, thereby enhancing the acceleration of physical AI and projecting diversity of physical information into analogue AI solver.

Ground-penetrating radar (GPR) facilitates the detection and localization of subsurface structural anomalies in critical transport infrastructure (e.g., tunnels), better informing targeted maintenance strategies. However, conventional fixed-directional systems suffer from limited coverage—especially of less-accessible structural aspects (e.g., crowns)—alongside the unclear visual output of anomaly spatial profiles, both for physical and simulated datasets. To tackle these limitations, new hybrid-rotational GPR utilizes novel 360° orientable air-launched antennas to increase subsurface coverage, principally in tunnels. Prototype systems currently lack a versatile workflow to generate practical visual output for surveyors. This study develops a versatile visualization workflow based on entirely open-access tools, returning 3-D spatial profiles of subsurface anomalies in: 1) simulated; 2) fixed-directional; and 3) hybrid-rotational GPR datasets. Work includes the development of two unique hybrid-rotational GPR systems, for laboratory and in-field data collection, respectively. Following initial 3-D grid alignment and smoothing, data undergo 3-D Stolt migration, normalization, and proximal clustering via Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). This returns segmented point subsets associated with suspected structural anomalies. Finally, 3-D convex hulls are recovered using the QuickHull method. Detection and localization performance is first appraised through numerical simulation in open-source software gprMax. Practical laboratory experimentation follows, with both commercial fixed-directional systems and developed hybrid-rotational GPR, before in-field demonstration on a large-scale, tunnel subsurface analog. In each experiment, all targets were successfully identified within returned 3-D visualizations of hybrid-rotational GPR datasets. Moreover, the spatial profiles were consistently observed to be accurately localized within decimeter-length scales of known target locations. Overall, the advances presented in this work both facilitate and demonstrate the significant practical potential of new hybrid-rotational GPR technology as a basis for future subsurface surveying of critical transport infrastructure.

Unavoidably, rolling contact fatigue (RCF) causes natural crack formation in the railhead, leading to rupture. Eddy current testing (ECT) is commonly used to quantify RCF cracks because of its higher sensitivity to a surface flaw, though with limited feature points. This article aims to characterize inclined angular RCF crack parameters in a rail-line material via a modified topology of Wireless Power Transfer (WPT)-based ECT (WPTECT) due to its magnetically coupled resonant for excitation and sensing circuits and utilize multiple resonance responses compared to other ECT. We experimentally designed and evaluated WPTECT and extracted multiple resonances and principal components analysis features to characterize inclined angular RCF cracks. The response minima point feature quantified the crack parameters incomparably; however, the second resonance feature is better than the first resonance. The reconstructed depth, opening width, and angle of the RCF cracks have a maximum correlation, R2value of 96.4%, 93.1%, and 79.1%, respectively, and root mean square error of 0.05 mm, 0.08 mm, and 6.6°, respectively.

Eddy current testing (ET) and eddy current thermography (ECT) are both important nondestructive testing methods that have been widely used in the field of conductive materials evaluation. Conventional ECT systems have often employed to test static specimens even though they are inefficient when the specimen is large. In addition, the requirement of high-power excitation sources tends to result in bulky detection systems. To mitigate these problems, in this article, a moving detection mode of multiphysics structured ET and ECT is proposed in which a novel L-shape ferrite magnetic yoke circumambulated with array coils is designed. The theoretical derivation model of the proposed method is developed which is shown to improve the detection efficiency without compromising the excitation current by ECT. The specimens can be speedily evaluated by scanning at a speed of 50-250 mm/s while reducing the power of the excitation current due to the supplement of ET. The unique design of the excitation-receiving structure has also enhanced the detectability of omnidirectional cracks. Moreover, it does not block the normal direction visual capture of the specimens. Both numerical simulations and experimental studies on different defects have been carried out and the obtained results have shown the reliability and detection efficiency of the proposed system.

Locating and characterizing rolling contact fatigue (RCF) cracks in rails is gaining attention in the railway industry. Eddy current pulsed thermography (ECPT) can detect such cracks by combining the benefits of electromagnetic excitation and thermal diffusion. To date, most studies focus on investigating specific features based on artificial defects, and verification on real cracks is lacking. In this article, to establish the best means of characterizing cracks of different inclination angles, eight spatial and temporal ECPT features are evaluated on idealized and real RCF cracks. Results show that longer time slots and pulse durations make the relations clearer. For the evaluation of real RCF cracks, the area-based and the kurtosis-based features are the most suitable and robust measures for characterizing inclination angles.

In blast wave monitoring, a traditional travel time tomography method is encountered with local minimum travel time and low coverage density of rays. In this article, a novel B-spline fitting method with the knot-optimization artificial immune system (AIS) is proposed for 2-D overpressure reconstruction. It possesses the advantages of handling point sets of large sizes and adjusts the knot vector flexibly. Based on the overpressure value in the explosion from the travel time tomography method, the proposed method combining the advantages of B-splines and knot point optimization AIS is able to achieve the optimal sensor distribution and raise the reconstruction precision. The detailed experimental results about the comparison of linear fitting interpolation, cubic fitting interpolation, natural neighbor fitting interpolation, v4 fitting interpolation, Delaunay triangulation fitting, and B-spline method are also given. Furthermore, for the knot optimization issue in B-spline, the proposed adaptive fitting method with knot-optimization AIS has a smaller root-mean-square (RMS) error with eight knot nodes in comparison with the classic B-spline fitting method. This article is conducted to provide new insights to reconstructing 2-D Internet-of-Things-based (IoT-based) overpressure in blast wave monitoring more precisely under limited sensor deployment and further give a new approach to overpressure reconstruction scenarios.

Eddy current (EC) stimulated thermography is an emerging technique for nondestructive testing and evaluation of conductive materials. However, quantitative estimation of the depth of subsurface defects in metallic materials by thermography techniques remains challenging due to significant lateral thermal diffusion. This article presents the application of eddy current (EC) pulse-compression thermography to detect surface and subsurface defects with various depths in an aluminum (AL) sample. Kernel principal component analysis and low rank sparse modeling were used to enhance the defective area, and cross-point feature was exploited to quantitatively evaluate the defects' depth. Based on experimental results, it is shown that the crossing point feature has a monotonic relationship with surface and subsurface defects' depth, and it can also indicate whether the defect is within or beyond the EC skin depth. In addition, the comparison study between AL and composites in terms of impulse response and proposed features are also presented.

This study proposes a novel physical layer authentication scheme, which uses the channel to generate a tagged signal and determine random partial padding. The scheme utilises the random wireless channel gain between communication entities to generate tag and decide a padding sequence for each symbol. Simulations for different padding percentages using the randomness provided by the channel are carried out to compare against a traditional full padding scheme, which shows improvement in error rate under the same security performance. The second layer of referee verification is introduced to increase the difficulty of compromising the authenticity of the scheme. A mathematical analysis is carried out to find an optimal size of selected time slots for the authentication scheme is also discussed.

Traditional wired monitoring systems exhibit huge limitations in blast wave monitoring. To meet the requirements of long range, low cost, weight reduction, increased ease of installation maintenance, and big-data transmission in blast wave monitoring, a new distributed linear-spatial-array (D-LSA) sensing system based on low-power wide-area network (LPWAN) is proposed in this paper. This approach adopts a multichannel LoRa and NB-IoT air-blast gateway (M-CLNAG) and multiple FPGA-based wireless pressure LoRa nodes (FWPLNs) to construct a large-scale LPWAN for blast wave monitoring. The empirical models of dynamic parameter calculation (peak overpressure, duration of the positive phase and impulse) on the basis of D-LSA sensing system are redesigned for blast wave monitoring as well. Furthermore, we have evaluated the errors between the measured data from D-LSA sensing system and data from the redesigned empirical models. Finally, the wireless quality performance in terms of received signal strength indication (RSSI) and packet receive rate (PDR) for blast wave monitoring is also verified. This paper is conducted to provide new insights into how a sensing system integrating with LPWAN is designed in blast wave monitoring for acquiring dynamic parameters accurately and carrying out remote network communication efficiently, and further opening a door for wireless sensor network (WSN) in more blast wave monitoring scenarios.

Eddy current thermography (ECT) as one of the emerging nondestructive testing and evaluation techniques has been used for defects detection in critical components, e.g., fatigue cracks in turbine blades, bond wire lift-off in IGBT modules, lack of fusion in welded parts, etc. However, in fast inspection using the early thermal response, the thin eddy current penetration depth (skin depth) of ferromagnetic materials limits ECT's capability of detecting subsurface defects. In order to increase the detectable depth range, this paper proposes a dc-biased magnetization based ECT (DCMECT) technique. Based on the nonlinear magnetic permeability in ferromagnetic material, DCMECT can increase the thermal contrast between the defective and sound areas by the enhanced permeability distortion in the skin-depth layer. Specifically, the influences of dc-biased magnetization direction and intensity on the thermal responses (of the defective and sound areas) and their thermal contrast are investigated. Results show that the dc-biased magnetization direction has the strongest influence on the thermal response when it is parallel to the ac magnetization direction generated by the coil. Both the thermal responses of defective and sound areas decrease with the magnetization intensity increasing. Whereas, the thermal contrast between two areas increases with the magnetization intensity, which presents the enhanced defect detectability of DCMECT. The proposed technique can detect the subsurface defect with a buried depth up to 6 mm.

Impact damage, caused by low-energy impact, is inevitable during the whole life time of carbon fiber reinforced plastic (CFRP) material. However, the barely visible impact damage (BVID) is difficult to be detected by visual methods. Ultrasonic thermography (UT) is an emerging nondestructive testing technique that visualizes damage in thermal images captured by an infrared (IR) camera when the material is stimulated by ultrasound. However, noise and blurry edges around the high-temperature areas may cause confusion and lead to unreliable results in the thermal images of UT test. In this paper, an impact damage inspection method is proposed based on manifold learning for the CFRP material. Low-power ultrasonic excitation is used for this UT. The IR image sequences are processed as datasets in high-dimensional space. These datasets are reduced to lower dimensions by manifold learning to find the intrinsic structure in the two-dimensional manifold. Each dimension of the embedding manifold correlates highly with one degree of freedom underlying the original pixel: steady and random components. The steady component, which reflects the temperature rise caused by damage, is used for VID and BVID detection. The experimental system was set up, and CFRP plate specimens with different impact damage were tested. All the impact damage could be detected and shown in reconstructed static image with little noise. The proposed method using image sequences could provide a visualized, reliable, and effective impact damage inspection and localization means for CFRP material during manufacturing and in service.

A novel unsupervised sparse component extraction algorithm for diagnosing micro defects in thermography imaging system is presented. The approach is optimized under Variational Bayesian framework, which is fully automated and does not require manual selection of the parameters in the solution. An internal sub sparse grouping mechanism and adaptive fine-tuning have been built into the proposed algorithm to control the sparsity. The proposed method is used to automatically detect the micro defects on metals. Other contending defect feature extraction and sparse pattern extraction methods are employed for comparison. The algorithm has been shown to improve the detection precision of both artificial and natural cracks.

Internet of Things (IoT) is a new technology which offers enormous applications that make people’s lives more convenient and enhances cities’ development. In particular, smart healthcare applications in IoT have been receiving increasing attention for industrial and academic research. However, due to the sensitiveness of medical information, security and privacy issues in IoT healthcare systems are very important. Designing an efficient secure scheme with less computation time and energy consumption is a critical challenge in IoT healthcare systems. In this paper, a lightweight online/offline certificateless signature (L-OOCLS) is proposed, then a heterogeneous remote anonymous authentication protocol (HRAAP) is designed to enable remote wireless body area networks (WBANs) users to anonymously enjoy healthcare service based on the IoT applications. The proposed L-OOCLS scheme is proven secure in random oracle model and the proposed HRAAP can resist various types of attacks. Compared with the existing relevant schemes, the proposed HRAAP achieves less computation overhead as well as less power consumption on WBANs client. In addition, to nicely meet the application in the IoT, an application scenario is given.

Reciprocating impact load leads to plastic deformation on the surface of the kinematic chains in an aircraft brake system. As a result, this causes fatigue and various complex natural damages. Due to the complex surface conditions and the coexistence damages, it is extremely difficult to diagnose microcracks by using conventional thermography inspection methods. In this paper, the thermal pattern contrast method is proposed for weak thermal signal detection using eddy current pulsed thermography. In this process, the extraction and subsequent separation differentiate a maximum of the thermal spatial-transient pattern between defect and nondefect areas. Specifically, a successive optical flow is established to conduct a projection of the thermal diffusion. This directly gains the benefits of capturing the thermal propagation characteristics. It enables us to build the motion context connected between the local and the global thermal spatial pattern. Principal component analysis is constructed to further mine the spatial-transient patterns to enhance the detectability and sensitivity in microcrack detection. Finally, experimental studies have been conducted on an artificial crack in a steel sample and on natural fatigue cracks in aircraft brake components in order to validate the proposed method.

Eddy current pulsed thermography (ECPT) as one of the emerging nondestructive testing and evaluation (NDT&E) techniques has been used for the evaluation of the integrity of rail tracks, especially for rolling contact fatigue (RCF) detection and crack sizing. This paper proposes a probability of detection (POD) analysis framework for the ECPT system. Specifically, three different features, i.e., max thermal response, first-order differential imaging, and ratio mapping of the first-order differential imaging, were used to quantify the length of the angular slot by linear fitting. Based on the fitting relation between these features and the slot length, POD curves for linear-coil-based ECPT system of angular defect detection were calculated and compared. Results show that the max thermal response feature has the highest repeatability and detectability for shorter slot detection. First-order differential imaging and ratio mapping features are more convincing for longer slot detection.

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

This paper proposes an unsupervised method for diagnosing and monitoring defects in inductive thermography imaging system. The proposed method is fully automated and does not require manual selection from the user of the specific thermal frame images for defect diagnosis. The core of the method is a hybrid of physics-based inductive thermal mechanism with signal processing-based pattern extraction algorithm using sparse greedy-based principal component analysis (SGPCA). An internal functionality is built into the proposed algorithm to control the sparsity of SGPCA and to render better accuracy in sizing the defects. The proposed method is demonstrated on automatically diagnosing the defects on metals and the accuracy of sizing the defects. Experimental tests and comparisons with other methods have been conducted to verify the efficacy of the proposed method. Very promising results have been obtained where the performance of the proposed method is very near to human perception.

This paper proposes a new system for the unsupervised diagnostic and monitoring of defects in waveguide imaging. The proposed method is automatic and does not require manual selection of specific frequencies for defect diagnostics. The core of the method is a computational intelligent machine learning algorithm based on sparse non-negative matrix factorization. An internal functionality is built into the machine learning algorithm to adaptively learn and control the sparsity of the factorization, and to render better accuracy in detecting defects. This is achieved by using Bayesian statistics methodology. The proposed method is demonstrated on automatic detection of defect in metals. In addition, we show that the extraction of the spectrum signature corresponding to the defect is significantly more efficient with the proposed optimal sparsity, which subsequently led to better detection performance. Experimental tests and comparisons with other sparse factorization methods have been conducted to verify the efficacy of the proposed method.

Traditionally, Reed-Solomon (RS) codes have been employed in magnetic data storage devices due to their effectiveness in correcting random errors and burst errors caused by thermal asperities and inter-symbol interference (ISI). However, as storage densities increase the effect of ISI becomes more severe and much longer RS codes are needed, but this requires significantly increasing the size of the finite field. A possible replacement for RS codes are the one-point Hermitian codes, which are a class of algebraic-geometric (AG) code that have larger block sizes and minimum Hamming distances over the same finite field. In this paper, we present a novel iterative soft detection-decoding algorithm for interleaved Hermitian codes. The soft decoding employs a joint adaptive belief propagation (ABP) algorithm and Koetter-Vardy (KV) list decoding algorithm. It is combined with a maximum a posteriori (MAP) partial response (PR) equalizer and likelihoods from the output of the KV or the ABP algorithm are fed back to the equalizer. The proposed scheme's iterative detection-decoding behavior will be analyzed by utilizing the Extrinsic Information Transfer (ExIT) chart. Our simulation results demonstrate the performance gains achieved by iterations and Hermitian codes' performance advantage over RS codes.

This paper reviews the state-of-the art of modelling, simulation and visualisation and reviews the recent development of modelling, simulation and visualisation software for Nondestructive Evaluation (NDE). Simulation and visualisation can assist in the design and development of electromagnetic sensing and imaging techniques and systems for nondestructive testing, feature extraction and inverse problems for quantitative nondestructive evaluation. After reviewing the state-of-the art of electromagnetic modelling and simulation, case studies from electromagnetic NDE research and development for eddy current distribution and thermography are discussed.