Search Results (79)

This article concerns the issue of contactless estimation of the complex electrical permittivity of masonry materials by means of a microwave technique in the frequency domain. The main aim of the study was to develop a method enabling the determination of the real part of relative permittivity and the electrical conductivity of ceramic building materials using microwave reflection measurements, as well as to assess the applicability of the proposed approach for moisture diagnostics in porous media. The research was performed using a reflection-mode measuring setup comprising a vector network analyser and a broadband horn antenna, while measurements were carried out in the frequency range from 1 to 6 GHz on samples of solid ceramic brick with six gravimetric moisture levels. A one-dimensional model of electromagnetic wave propagation in the material was developed, considering complex permittivity, impedance transformation, and a calibration procedure compensating for the influence of the antenna and free-space propagation. Based on the fitting of the magnitude and phase characteristics of the reflection coefficient, the electrical parameters of the tested samples were estimated. The results obtained showed an increase in both permittivity and conductivity with increasing moisture content and revealed very good agreement with the reference values determined using the time-domain method. It can be concluded that the frequency-domain microwave approach may be effectively applied for contactless and non-destructive diagnostics and estimation of the dielectric properties and moisture content in ceramic materials. Full article

The adulteration of wheat flour with borax poses a serious food safety risk, yet conventional rapid non-destructive screening methods remain limited. This study developed a machine learning-based microwave non-destructive semi-quantitative detection method for identifying borax adulteration in wheat flour. Using a proprietary microwave detection system, which acquires broadband frequency-domain amplitude attenuation and phase shift responses in the 2.5–11.5 GHz band, amplitude attenuation spectra and dimensional phase offset spectra were obtained from 155 samples prepared at three adulteration levels (0%, 0.1–0.9%, 1–5%). These samples simulated real-world adulteration scenarios. To address high-dimensionality and class imbalance, a hybrid Random Forest-Whale Optimization Algorithm (RF-WOA) was employed to synergistically optimize feature selection and model hyperparameters. Through hierarchical repeated validation and macro-level metric evaluation, this approach achieved an overall classification accuracy of 94.6% and a macro F1 score of 0.95 while compressing the original 1800-dimensional feature space to approximately 200 effective features. Confusion matrix analysis indicates 100% recall for undiluted samples, with misclassifications primarily occurring between adjacent adulteration levels and no false negatives introduced for adulterated samples. These results demonstrate that microwave sensing combined with the RF-WOA provides a rapid, non-destructive, and robust preliminary screening and grading evaluation strategy for borax adulteration in wheat flour, exhibiting significant potential in food safety monitoring and regulatory inspection. Full article

This work investigates the feasibility of identifying steel reinforcing bars in concrete using a fully contactless radar system operating in the X-band. High-frequency electromagnetic inspection is particularly challenging due to attenuation and strong reflections at the air–concrete interface. This study combines numerical simulations and laboratory experiments to assess the sensitivity of microwave scattering measurements to the presence of reinforcement. Ad hoc mini reinforced-concrete pillars, both reinforced and unreinforced, were designed and built as benchmark specimens. Measurements were performed in a bistatic configuration using X-band horn antennas and a vector network analyzer, and were compared with finite-difference time-domain simulations reproducing the experimental setup. The qualitative results, comprising a processing strategy to detect the bars, show a clear agreement between numerical and experimental data and confirm that the scattered field remains sensitive to the presence of reinforcing bars despite unfavorable propagation conditions. Full article

The accurate and non-invasive measurement of moisture content in wood is essential for the preservation of historical and artistic artifacts. This study presents the calibration of a planar Microwave Planar Capacitive Coupled Ring Resonator (MPCCRR) designed to indirectly and non-destructively assess the water content in wood samples. The method relies on analyzing shifts in the resonant frequencies and variations in the transmission parameter |S21| resulting from changes in the material’s dielectric permittivity. After preliminary characterization via parametric simulations (εr = 1–10) and validation with low-permittivity reference materials, the sensor was tested on three wood species (poplar, fir, beech), including measurements at two sensor positions and with different grain orientations. The results demonstrate a monotonic, repeatable response to increasing moisture content with frequency shifts up to ≈220 MHz and normalized sensitivities ranging from 3 to 9 MHz/% water content, depending on species and measurement position. Position 2 showed the greatest sensitivity due to stronger field–sample interaction, while Position 1 provided a quasi-isotropic response with excellent repeatability. Linear regression analyses revealed good correlations between the frequency shifts and the gravimetric water content (R² ≥ 0.85). The MPCCRR sensor therefore proves to be a promising tool for the non-invasive monitoring of wood moisture, which is particularly suitable for the low-moisture range encountered in cultural heritage conservation, with an estimated moisture uncertainty of 0.12–0.35% under controlled laboratory conditions. Full article

During the long-term operation of composite insulators in transmission lines, they are easily affected by harsh environments, resulting in hidden defects such as surface contamination, shed damage, and adhesive failure. A defect detection method based on microwave for composite insulators was proposed, and a corresponding numerical simulation model was established. A large-aperture horn antenna model with a wide frequency band and high gain was built, the accuracy of which was verified. In the simulation, shed crack defects were selected as representative probes to model typical defects in the sheds, sheath, and core rod of composite insulators. This study investigated defects with varying severity levels and spatial distributions while also exploring optimal placement configurations for detection antennas. An experimental platform was built for testing, and it was found that the experimental results showed a similar changing trend to the simulation results, which further verified the accuracy of the simulation model and the feasibility of simulating defects. Full article

This study aims to propose a defect diagnosis method for distribution cable intermediate joints based on microwave reflection. The research focuses on 10 kV cold-shrink-type distribution cable intermediate joints, employing both simulation analysis and experimental methods. Firstly, a microwave defect detection model for intermediate joints is derived. CST simulations are conducted to analyze the variation of the reflection coefficient (S₁₁) under different detection frequencies, defect depths, and defect types. Next, flat plate and real prototype samples of intermediate joints with defects such as insulation scratches, conductive impurities, and moisture ingress are fabricated. A microwave reflection detection platform is established to test the artificially defective samples. Reflection voltage signals corresponding to different defects are obtained. The concept of the relative value of the reflection voltage difference is then introduced, resulting in significant changes in the detection results, which effectively indicate the presence of different defects. Finally, the reflection voltage signals under different defect sizes, silicone rubber thicknesses, detection distances, and detection angles are studied. The results show that this method is capable of detecting defects as small as 2 mm in width and 0.2 mm in depth. The silicone rubber thickness, detection distance, and detection angle significantly affect the detection results. This demonstrates that microwave reflection signals can effectively identify the type and severity of defects within cable intermediate joints, and the method can be extended to detect internal defects in other layered composite insulation structures. Full article

Many industrial sectors are concerned about microbiological contamination and the associated risk of microbiologically influenced corrosion (MIC). This applies in particular to the transmission and storage of fuels in the refining industry. Exceeding a certain level of these contaminants poses a serious risk to fuel quality and can cause storage and pipeline infrastructure corrosion. This situation requires an urgent evaluation of microorganism levels in the fuel to avert such detrimental consequences. Diesel fuels containing biofuel additives are particularly susceptible to these phenomena. Traditional detection methods are limited by low sensitivity, high costs, and long turnaround times, making them unsuitable for quick, on-site, and real-time detection and monitoring. A novel approach involves the application of microwave dielectric testing to quantify microbial load in diesel fuel. Microwave dielectric spectroscopy offers a non-destructive, label-free solution, providing rapid information on microorganism presence. Combined with chemometric techniques, it effectively estimates total microorganism counts in diesel fuel. Measurement in the X-band range (8.2–12.4 GHz) takes a few seconds. Calibration with known bacterial and fungal concentrations (10³ to 10⁷ CFU/mL) and principal component analysis (PCA) of the spectroscopic data allow for clear differentiation of contamination levels, categorizing them from acceptable to hazardous. The sensitivity limit of the proposed method corresponds to a bacterial concentration of 10³ CFU/mL. Full article

This study investigates the influence of electromagnetic field polarization in the non-destructive testing of reinforced concrete structures through both theoretical analysis and experimental validation. Theoretical models predict that the orientation of reinforcement bars relative to the incident electric field significantly affects the scattered signal, influencing their detectability. Laboratory experiments on realistic reinforced concrete specimens presenting both vertical bars and horizontal brackets confirm these predictions, demonstrating that polarization can be exploited to enhance measurement accuracy. These findings provide useful insights into the development of microwave-based diagnostic techniques for structural assessment. Full article

High-sensitivity microwave sensing plays a vital role in material characterization and nondestructive testing, with its performance being largely determined by the quality factor (Q factor) of the sensing structure. In this work, a high-Q microwave metasurface sensor based on the mechanism of bound states in the continuum (BIC) is designed and realized to overcome the intrinsic Q-factor limitations of conventional microwave resonators. By introducing a controlled asymmetric perturbation into the meta-atom, a quasi-BIC mode is successfully excited, and its sensing performance is systematically investigated through frequency-domain simulations. The results indicate that the proposed metasurface achieves an exceptionally high radiation Q factor of up to 4599.7 in the microwave band, along with a refractive index sensitivity of 31.267 GHz/RIU. These findings not only demonstrate the significant potential of the BIC mechanism for achieving ultra-high-Q microwave resonators but also provide an effective and promising approach for the development of high-performance microwave sensing systems. Full article

With the widespread use of high-density polyethylene (HDPE) pipes in various industrial and municipal applications, ensuring the structural integrity of their joints is crucial. This paper presents a novel defect detection method based on a microwave resonant probe, designed to perform efficient and non-destructive evaluation of butt fusion joints in HDPE pipes. The experimental setup integrates a microwave antenna and resonant cavity to record real-time variations in resonance frequency and S₂₁ magnitude while scanning the pipe surface. This method effectively detects common defects, including cracks, holes, and inclusions, within the butt fusion joints. The results show that the microwave resonant probe exhibits high sensitivity in detecting HDPE pipe defects. It can identify different sizes of cracks and holes, and can distinguish between talc powder and sand particles. This technique offers a promising solution for pipeline health monitoring, particularly for evaluating the quality of welded joints in non-metallic materials. Full article

Accurate and non-invasive assessment of fat and muscle composition is crucial for biomedical monitoring to track health conditions in humans and pets, as well as for classifying meats in the meat industry. This study introduces a cost-effective, multifunctional ultra-wideband microwave system operating from 2.4 to 4.4 GHz, designed for rapid and non-destructive quantification of fat thickness, muscle thickness, and fat-to-muscle ratio in diverse ex vivo samples, including pork, beef, and oil–water mixtures. The compact handheld device integrates essential RF components such as a frequency synthesizer, directional coupler, logarithmic power detector, and a dual-polarized Vivaldi antenna. Bluetooth telemetry enables seamless real-time data transmission to mobile- or PC-based platforms, with each measurement completed in a few seconds. To enhance signal quality, a two-stage denoising pipeline combining low-pass filtering and Savitzky–Golay smoothing was applied, effectively suppressing noise while preserving key spectral features. Using a random forest regression model trained on resonance frequency and signal-loss features, the system demonstrates high predictive performance even under limited sample conditions. Correlation coefficients for fat thickness, muscle thickness, and fat-to-muscle ratio consistently exceeded 0.90 across all sample types, while mean absolute errors remained below 3.5 mm. The highest prediction accuracy was achieved in homogeneous oil–water samples, whereas biologically complex tissues like pork and beef introduced greater variability, particularly in muscle-related measurements. The proposed microwave system is highlighted as a highly portable and time-efficient solution, with measurements completed within seconds. Its low cost, ability to analyze multiple tissue types using a single device, and non-invasive nature without the need for sample pre-treatment or anesthesia make it well suited for applications in agri-food quality control, point-of-care diagnostics, and broader biomedical fields. Full article

We present a compact dielectric lens integrated at the aperture of a WR90 rectangular waveguide, achieved using polytetrafluoroethylene (PTFE). This innovative configuration enables, for the first time in the X- and Ku-bands, the direct generation of a subwavelength electromagnetic jet from a guided structure. The beam exhibits the hallmark features of an electromagnetic jet: strong near-field focusing, a subwavelength beam width surpassing the diffraction limit, and a quasi-planar wavefront sustained over a propagation distance of about $2\lambda$. The lens design was systematically optimized, and its performance was assessed through full-wave finite element simulations and experimentally validated on a fabricated prototype. Excellent agreement between the simulation and measurement confirms the robustness of the approach. Beyond its simplicity and low cost, this solution achieves state-of-the-art focusing performance compared to free-space and guided-wave alternatives. It offers strong potential for applications in high-resolution imaging, precision sensing, and material characterization, particularly in opaque or highly lossy environments. Full article

Three-layer polyethylene (3LPE) coated steel pipelines are currently the preferred solution for global oil and gas transmission. However, external corrosion beneath the 3LPE coating poses a serious threat to pipeline operations. The pressing concern for pipeline safety and integrity involves non-destructive evaluation techniques for the non-invasive and quantitative interrogation of such defects. This study therefore explores linear frequency-sweeping microwave near-field non-destructive testing (NDT) techniques for imaging and evaluating the pitting corrosion beneath 3LPE coating. An improved branch-cut method is proposed for the high-precision phase unwrapping of the microwave phase image sequence, and its superiority over traditional methods in terms of accuracy and robustness is validated. A background subtraction method based on kernel density estimation (KDE) is presented to suppress the lift-off effect on the pipeline geometry. In addition, the principal-component-analysis-wavelet-based principal component extraction and fusion enhance the detection signal-to-noise ratio (SNR) and image contrast, while mitigating the annular artifacts around the corrosion. The experimental results demonstrate the feasibility of the proposed approach for the detection, imaging, and characterization of external corrosion beneath the 3LPE coating of pipelines. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure. Full article