In recent years, the application of gas sensors is becoming more and more extensive. Driven by potential applications such as the Internet of Things, its technology development direction begins with miniaturization, integration, modularization, and intelligence. However, there is a bottleneck in the research of interface circuits, which restricts the development of gas sensors in volume, power consumption, and intelligence. To solve this problem, a MEMS gas sensor interface circuit based on ADC technology is proposed in this paper. Under the condition of the Huahong 110 nm process, the working voltage is 3.3 V, the resistance change of 100 Ω~1 MΩ can be detected, the conversion error is in the range of 0.5~1%, and the maximum power consumption is 986 μW. The overall layout area is 0.49 × 0.77 mm2. Finally, the correctness of the circuit function is verified by post-layout simulation.
Speech emotion recognition (SER) technology is significant for human–computer interaction, and this paper studies the features and modeling of SER. Mel-spectrogram is introduced and utilized as the feature of speech, and the theory and extraction process of mel-spectrogram are presented in detail. A deep residual shrinkage network with bi-directional gated recurrent unit (DRSN-BiGRU) is proposed in this paper, which is composed of convolution network, residual shrinkage network, bi-directional recurrent unit, and fully-connected network. Through the self-attention mechanism, DRSN-BiGRU can automatically ignore noisy information and improve the ability to learn effective features. Network optimization, verification experiment is carried out in three emotional datasets (CASIA, IEMOCAP, and MELD), and the accuracy of DRSN-BiGRU are 86.03%, 86.07%, and 70.57%, respectively. The results are also analyzed and compared with DCNN-LSTM, CNN-BiLSTM, and DRN-BiGRU, which verified the superior performance of DRSN-BiGRU.
In this paper, a low dropout (LDO) circuit based on a curvature compensation benchmark and closed-loop stability is designed. This circuit compensates for the higher order term of VBE in a BJT through the subthreshold characteristic of MOSFET and achieves the effect of curvature compensation. The bandgap reference circuit provides a stable input voltage for the LDO circuit, while the source follower and adaptive bias circuit improve the response speed and closed-loop stability of the LDO circuit. The temperature drift coefficient of the bandgap circuit is 8.11 ppm/°C, the input voltage is 3–5 V, the output voltage is 2.8 V, and the linear adjustment rate is 0.22%.
Time interleaving has become a very common choice for increasing ADC speed. However, it is accompanied by defects such as offset, gain and time offset between the individual sub-ADCs, which can seriously degrade the performance of the overall ADC. For the elimination of gain and offset errors, the solution is relatively simple, and the calibration of the time offset is still in the exploratory stage. This paper systematically reviews several current mainstream time-interleaved ADC timing offset correction methods. At the same time, the characteristics and development trend of calibration methods are summarized.
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