A three-dimensional finite-difference time-domain (FDTD) breast cancer detection model with antenna consideration is presented in this paper. The antenna structure is completely modeled using a resistive voltage source (RVS) as the excitation. The derivation of the updating equations for RVS is described in detail. A UWB antenna is simulated and the scattering parameters show the antenna performance. The simulation detection is carried out using a 3×3 antenna array. Double constrained robust Capon beamforming algorithm is employed to reconstruct the breast image and the correct information of the tumor is shown in the imaging result.
Designing artificial microstructural metamaterials that fulfill the single‐phase vibration dampening and lightweight twin goals is challenging and critical. 2D curved and chiral hybrid star‐shaped metamaterials (CCHSM) are proposed to perform vibration reduction filtering in the midfrequency region by combining the properties of chiral structure and concave structure. The generating process of the bandgap of in‐plane elastic waves, the properties of directional attenuation, and the flow direction of energy in the dispersion curve are examined using the modal shape, dispersion surface, and group velocity. In a specific frequency range, the equivalent medium parameters satisfy the conditions of negative refraction transverse and longitudinal waves, so the structure also has double‐negative characteristics. The filtering performance is validated using the CCHSM periodic plate structure and a finite‐element simulation of transmission loss. In addition, the correlation between wave attenuation characteristics and structural parameters is explored. The radius of the circular arc and the deflection angle of the connecting rod have a significant impact on the frequency range of the structural bandgap. The results show that the created simple‐configured, single‐phase lightweight acoustic metamaterials exhibit strong local resonance properties and double‐negative features.
The objective of this study was to reveal the secrets of the unique meat characteristics of Beijing-you chicken (BJY) and to compare the difference of quality and flavor with Luhua chicken (LH) and Arbor Acres broiler (AA) at their typical market ages. The results showed the meat of BJY was richer in essential amino acids, arachidonic acid contents, inosine monophosphate (IMP), and guanosine monophosphate (GMP). The total fatty acid and unsaturated fatty acid contents of BJY chicken and LH chicken were lower than that of AA broilers, whereas the ratios of unsaturated fatty acids/saturated fatty acids (2.31) and polyunsaturated fatty acids/monounsaturated fatty acids (1.52) of BJY chicken were the highest. The electronic nose and SPME-GC/MS analysis confirmed the significant differences among these three chickens, and the variety and relative content of aldehydes might contribute to a richer flavor of BJY chicken. The meat characteristics of BJY were fully investigated and showed that BJY chicken might be favored among these three chicken breeds with the best flavor properties and the highest nutritional value. This study also provides an alternative way to identify BJY chicken from other chickens.
Flexible sensors are the main components of wearable devices. Compared with flexible substrate-based skin-like sensors, flexible textile sensors have the intrinsic advantages of excellent breathability, compliance with the human body and comfort to human skin. In this article, a flexible optical fiber-based smart textile (FOFT) sensor has been proposed for the application of intelligent human–machine interaction (HMI) and healthcare. The FOFT sensor integrated fiber Bragg gratings (FBGs) with yarns in the way of the plain weave. A double-layer flexible optical fiber crossover structure is proposed to improve the measurement accuracy of the sensor for human body signals. Such a structure leads to a highly flexible textile and wearing comfort. The weak characteristic information of the human body can be extracted by the sensor based on the wavelength demodulation of double FBGs. Combined with the backpropagation (BP) neural network, an FOFT sensor-based gesture recognition application with an off-line accuracy of 97.02% has been developed. Furthermore, the FOFT sensor is used to monitor the human respiratory signals under different postures and obtain the respiratory rate. A series of experiments demonstrate that the FOFT sensor has a huge potential in HMI and healthcare.
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