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The introduction of thermal relaxation makes non-Fourier heat transfer more effective than Pennes equation to reflect rapid bio-heat transferring during HIFU, however, the contribution of thermal relaxation in specific tissues needs to be further clarified. In this study, we first measured the thermal relaxation times of porcine muscle and porcine fat. Combining with experimental measurements, the effects of thermal relaxation on temperature elevation in the tissues were investigated by using Pennes equation, thermal wave model of bio-heat transfer (TWMBT) and dual phase-lag (DPL) bio-heat transfer. Results showed that: a) The thermal relaxation times of porcine muscle and porcine fat are experimentally determined as 5.71± 0.11 s and 5.02± 0.06 s, respectively. b) In the absence of cavitation, DPL bio-heat transfer is more accurate to predict the temperature elevation at the focus and 2 mm from the focus than Pennes equation and TWMBT. Particularly, comparing with the reported of bologna (16 s) used in most of the theoretical analysis, the utilization of the measured thermal relaxation time for a specific tissue in DPL bio-heat transfer is more effective in predicting the temperature elevation during HIFU. c) Acoustic cavitation and nonlinear propagation is easier to happen in fat, under which all bio-heat transfer models are failed to forecast the temperature elevation induced by HIFU. The results demonstrate that different tissues have different thermal relaxation, DPL bio-heat transfer with the measured thermal relaxation times of specific tissues can accurately predict the temperature elevation during HIFU without cavitation.

Age-invariant face recognition is very challenging. To solve the problem that it is difficult to collect cross-age face data of the same person, this paper proposes to use generative adversarial networks to generate the same person's young and elderly age data for sample enhancement, and use these data to train age-invariant face recognition model. The proposed method is verified on public face-aging datasets: FGNET and CADA-VS. Rank-1 recognition rates reaches 85.04% on FGNET data sets and verification accuracies reaches 96.99% on CADA-VS data sets.

Recent studies have debated over the effect of canopy structure on the remote sensing of foliar nitrogen content. Although previous work reported on the radiative transfer modeling of canopy structural influence, the modeling implementation is still of limited use in the hyperspectral community. This study proposes to use a multi-scale tool, continuous wavelet analysis, to decompose the spectral responses to variations in canopy structure and foliar nitrogen content. Our results demonstrated that the leaf area index (LAI) and leaf nitrogen content (LNC) were best correlated to the wavelet features (732 nm, scale 6) and (735 nm, scale 4), respectively. These two wavelet features could be used to separate the spectral contributions of LAI and LNC at different scales. The findings provide a new perspective for understanding the effect of canopy structure on hyperspectral remote sensing of foliar nitrogen.