With the development of science and technology, gas sensors have gradually expanded to many vertical fields. However, each application requires different technical requirements for gas sensors, such as low cost, small size, high sensitivity, high precision, and low power consumption. To create gas sensors that meet the performance requirements, researchers worldwide are working on solutions in fields such as sensitive materials, sensor arrays, and interface circuits. However, there are bottlenecks in the research of interface circuits, which limits the volume, power consumption, and intelligent design development of gas sensors. Therefore, this article reviews various types of gas sensors and interface circuits implemented with different technologies, analyzes their research significance in various areas, and provides ideas for future gas sensor research directions.

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.

Text emotion recognition (TER) is an important natural language processing (NLP) task which is widely used in human–computer interaction, public opinion analysis, mental health analysis, and social network analysis. In this paper, a deep learning model based on XLNet with bidirectional recurrent unit and attention mechanism (XLNet-BiGRU-Att) is proposed in order to improve TER performance. XLNet is used to build bidirectional language models which can learn contextual information simultaneously, while the bidirectional gated recurrent unit (BiGRU) helps to extract more effective features which can pay attention to current and previous states using hidden layers and the attention mechanism (Att) provides different weights to enhance the ’attention’ paid to important information, thereby improving the quality of word vectors and the accuracy of sentiment analysis model judgments. The proposed model composed of XLNet, BiGRU, and Att improves performance on the whole TER task. Experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database and the Chinese Academy of Sciences Institute of Automation (CASIA) dataset were carried out to compare XLNet-BiGRU-Att, XLNet, BERT, and BERT-BiLSTM, and the results show that the model proposed in this paper has superior performance compared to the others.

The physiological information transmitted by biological signals is of great importance for the function of biomedical sensors. In the initial stage of sensor signal acquisition, the acquisition accuracy of the analog front end (AFE) directly determines the full preservation of signal quality. As the core component of AFE, the instrumentation amplifier (IA), as the first stage amplifier not only significantly amplifies the input signal, but also effectively suppresses the corresponding noise at the input of the subsequent circuit. Therefore, the low noise design of IA plays an important role in the overall design of AFE. This paper focuses on the two core technologies of DC servo loop (DSL) technology and input impedance boosting technology to improve IA accuracy. An in-depth analysis of the specific implementation methods of these technologies in IA design reveals their respective significant advantages and potential limitations in optimizing signal quality and improving system performance. Additionally, a comparative analysis of commercial versus academic instrumentation amplifiers is provided, highlighting their performance, applications, and costeffectiveness. The paper concludes with a synthesis of recent advancements in IA design and projections for future developments, informed by current trends.

Conventional methods for rotor position detection of micro-gyroscopes include common exciting electrodes (single frequency) and common sensing electrodes (frequency multiplex), but they have encountered some problems. So we present a high resolution and low noise pick-off circuit for micro-gyroscopes which utilizes the time multiplex method. The detecting circuit adopts a continuous-time current sensing circuit for capacitance measurement, and its noise analysis of the charge amplifier is introduced. The equivalent output noise power spectral density of phase-sensitive demodulation is 120 nV/Hz^1/2. Tests revealed that the whole circuitry has a relative capacitance resolution of 1 × 10⁻⁸.