Complex dielectric and magnetic properties of micro- and nano-size hexagonal ferrites have been studied. Two different measurement techniques have been applied to characterize the samples in the broad frequency range from 1.7 to 120 GHz. It was observed that the constitutive material properties, namely permittivity and permeability, as well as the ferromagnetic resonance frequency of the samples vary with the change in particle dimensions. Based on the results of these measurements, a model for calculating the ferromagnetic resonance frequency of ferrite powders has been derived, which takes into account the size and shape of the particles in the sample. It can be concluded from the size-dependent absorption properties observed in this study that these materials are considered to be tunable millimeter-wave absorbers.

To address the insufficient representation of fault characteristics in rotating machinery vibration signals, the sensitivity of conventional Transformers to variations in input response amplitudes, and the limited ability of fixed sequence embedding to preserve continuous temporal information, a rotating machinery fault diagnosis method based on Amplitude-Calibrated Sequence Embedding (ACSE) and a Response-Normalized Transformer (RNformer) is proposed. First, ACSE is designed to construct local temporal feature representations through continuous convolutional mapping, while an amplitude response estimation and adaptive amplitude calibration mechanism is employed to dynamically recalibrate the response intensity at different temporal positions. Rather than simply rescaling the signal amplitude range, amplitude calibration adaptively strengthens the feature contribution of regions associated with fault-induced impacts according to the vibration response intensity, thereby highlighting fault-sensitive information while suppressing the influence of noncritical amplitude fluctuations. In this way, continuous temporal characteristics are preserved while fault-relevant information is enhanced. Second, RNformer is constructed by incorporating a response normalization mechanism into the Transformer encoder to mitigate the interference of abnormal amplitude responses with global feature modeling, thereby improving the stability and robustness of feature representations under complex operating conditions. Finally, a lightweight channel attention mechanism is introduced to further enhance critical fault features and perform fault classification. Experiments were conducted on the Paderborn University bearing dataset and the University of Connecticut gear dataset. The proposed method achieved average diagnostic accuracies of 99.36% and 99.28%, respectively, outperforming the best-performing baseline methods by 1.82 and 1.56 percentage points. These results demonstrated the effectiveness of the proposed method for fault diagnosis.

To address the limited extraction of local fault features, ineffective fusion of multi-scale fault information and interference from redundant noise in vibration-based fault diagnosis of rolling bearings and gears under complex operating conditions, a multi-scale gated feature refinement Vision Transformer (MGFR-ViT), is proposed. First, one-dimensional vibration signals are reconstructed as two-dimensional vibration matrices, making them compatible with patch embedding and Vision Transformer-based feature modeling. A locally enhanced Vision Transformer module is then developed by incorporating a local enhancement mechanism into the standard Vision Transformer architecture, thereby improving the extraction of locally fault-sensitive features while preserving global dependency modeling. Furthermore, a multi-scale gated feature refinement module is introduced to adaptively enhance fault-relevant information and suppress redundant features and noise through parallel multi-scale convolutions with different receptive fields, channel interaction, and gated weighting. Finally, global average pooling and a fully connected classifier are employed for fault classification. Experiments conducted on bearing and gear datasets demonstrated that MGFR-ViT achieved superior diagnostic performance and feature separability compared with several representative fault diagnosis models. Ablation studies further validated the effectiveness and complementarity of the proposed modules. These results indicate that MGFR-ViT provides an effective feature-learning framework for vibration-based fault diagnosis of rotating machinery.

Two newly developed non-contact dielectric measurement techniques were applied to characterize the complex permittivity spectra of a ceramic state polymer electrolyte. The Capacitance Bridge was employed to measure the electrolyte sample in a frequency range from 50 Hz to 20 KHz with a novel air gap method. The newly designed in-waveguide measurement by VNA (vector network analyzer) was applied to measure the electrolyte in the frequency range from 8.2 GHz to 40 GHz. Both methods are newly developed non-contact techniques and there was no physical contact on the polymer sample film surface during the measurement. The weak contact error in traditional measurement could be completely avoided in these non-contact methods. The ionic conductivity and complex electrical conductivity can be derived from the accurate complex dielectric spectra.

Thin film materials are already used in a variety of microwave and higher frequency applications such as electrically tunable microwave devices, integrated circuits like MMICs, radomes, and radar absorbing coating. The determination of the dielectric properties of these films is thus of significant importance. The measurement of complex dielectric permittivity of thin films is very difficult at microwave, millimeter, and THz frequencies because both the amplitude change and phase shift are not large enough to evaluate the real part of the dielectric permittivity. A specially designed transverse slotted cavity for X-band microwave measurement has been designed and constructed to employ with a vector network analyzer to evaluate the real part of dielectric permittivity of thin films accurately and conveniently. Commercially available polymer thin films are measured to validate the methods.