A low voltage 3D parallel electroosmotic flow (EOF) pump composed of two electrode layers and a fluid layer is proposed in this work. The fluid layer contains twenty parallel fluid channels and is set at the middle of the two electrode layers. The distance between fluid and electrode channels was controlled to be under 45 μm, to reduce the driving voltage. Room temperature liquid metal was directly injected into the electrode channels by syringe to form non-contact electrodes. Deionized (DI) water with fluorescent particles was used to test the pumping performance of this EOF pump. According to the experimental results, a flow rate of 5.69 nL/min was reached at a driving voltage of 2 V. The size of this pump is small, and it shows a great potential for implanted applications. This structure could be easily expanded for more parallel fluid channels and larger flow rate.
The application of data-intensive automatic speech recognition (ASR) technologies to dysarthric and elderly adult speech is confronted by their mismatch against healthy and nonaged voices, data scarcity and large speaker-level variability. To this end, this paper proposes two novel data-efficient methods to learn homogeneous dysarthric and elderly speaker-level features for rapid, on-the-fly test-time adaptation of DNN/TDNN and Conformer ASR models. These include: 1) speaker-level varianceregularized spectral basis embedding (VR-SBE) features that exploit a special regularization term to enforce homogeneity of speaker features in adaptation; and 2) feature-based learning hidden unit contributions (f-LHUC) transforms that are conditioned on VR-SBE features. Experiments are conducted on four tasks across two languages: the English UASpeech and TORGO dysarthric speech datasets, the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech corpora. The proposed on-the-fly speaker adaptation techniques consistently outperform baseline iVector and xVector adaptation by statistically significant word or character error rate reductions up to 5.32% absolute (18.57% relative) and batch-mode LHUC speaker adaptation by 2.24% absolute (9.20% relative), while operating with real-time factors speeding up to 33.6 times against xVectors during adaptation. The efficacy of the proposed adaptation techniques is demonstrated in a comparison against current ASR technologies including SSL pre-trained systems on UASpeech, where our best system produces a state-of-the-art WER of 23.33%. Analyses show VR-SBE features and f-LHUC transforms are insensitive to speaker-level data quantity in testtime adaptation. T-SNE visualization reveals they have stronger speaker-level homogeneity than baseline iVectors, xVectors and batch-mode LHUC transforms.
Recently, kesterite Cu2ZnSnS4 (CZTS) has been regarded as a promising candidate for future light absorbers due to its excellent photovoltaic potentials. To further improve the efficiency of associated solar cells,...
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