In this work, the genetic algorithm is employed to optimize both circadian action factor (CAF) and color quality of laser-based illuminants (LBIs) with three, four, and five spectral bands to disclose its possible use in two common white lighting applications, i.e. bedroom lighting and office lighting. Comparing all LBIs at a correlated color temperature (CCT) of 3000 K and a color rendering index of 80, the CAF of four-band LBIs reaches a minimum of 0.238 and maintains at a possibly highest luminous efficacy of radiation (LER) of 422 lm/W among all cases. The performances of white LBIs are also compared with those of white light-emitting diodes (LEDs). The results demonstrate that, under the same conditions of color rendering and color temperature, both four-band LBIs and four-band LEDs exhibit the largest circadian tunability of about 4.7, while four-band LBIs possess much higher LER at the same time compared with four-band LEDs. In addition, for the display application, the investigation on the optimal circadian tunability as a function of color gamut at two CCTs (3000 K and 6500 K) is also performed. We believe that this study can serve as a useful guidance for the application of LBIs in both the healthy general lighting and display.
Patellofemoral pain syndrome (PFPS) is a common, yet misunderstood, knee pathology. Early accurate diagnosis can help avoid the deterioration of the disease. However, the existing intelligent auxiliary diagnosis methods of PFPS mainly focused on the biosignal of individuals but neglected the common biometrics of patients. In this paper, we propose a PFPS classification method based on the fused biometrics information Graph Convolution Neural Networks (FBI-GCN) which focuses on both the biosignal information of individuals and the common characteristics of patients. The method first constructs a graph which uses each subject as a node and fuses the biometrics information (demographics and gait biosignal) of different subjects as edges. Then, the graph and node information [biosignal information, including the joint kinematics and surface electromyography (sEMG)] are used as the inputs to the GCN for diagnosis and classification of PFPS. The method is tested on a public dataset which contain walking and running data from 26 PFPS patients and 15 pain-free controls. The results suggest that our method can classify PFPS and pain-free with higher accuracy (mean accuracy = 0.8531 ± 0.047) than other methods with the biosignal information of individuals as input (mean accuracy = 0.813 ± 0.048). After optimal selection of input variables, the highest classification accuracy (mean accuracy = 0.9245 ± 0.034) can be obtained, and a high accuracy can still be obtained with a 40% reduction in test variables (mean accuracy = 0.8802 ± 0.035). Accordingly, the method effectively reflects the association between subjects, provides a simple and effective aid for physicians to diagnose PFPS, and gives new ideas for studying and validating risk factors related to PFPS.
<p>Current speaker extraction models have achieved good performance in extracting target speech from highly overlapped multi-talker speech. But in real-world applications, the multi-talker speech is sparsely overlapped and the target speaker may be absent from the speech mixture, making it difficult for the model to extract desired speech in this situation. The universal speaker extraction is proposed to solve the problem by evaluating the quality of estimated speech signals and silence. However, the design of existing universal speaker extraction models does not take into account distinguishing the presence or absence of the target speaker. In this paper, we propose a gated cross-attention network for universal speaker extraction. In our model, the cross-attention mechanism learns the correlation between the target speaker and the speech to distinguish whether the target speaker presents or not. According to the correlation, the gate mechanism makes the model focus on extracting speech when the target is present, while filtering out the features when the target is absent. Meanwhile, we propose a joint loss function to optimize the network in both target present and absent scenarios. We conducted experiments on the LibriMix dataset with various scenarios and evaluated the performance in terms of speech quality and speaker extraction error rate. The experiment results show that our proposed method outperforms the baselines in all of the scenarios.</p>
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