Volume 170
Published on July 2025Volume title: Proceedings of the 3rd International Conference on Mechatronics and Smart Systems
This study focuses on the task of text sentiment classification, aiming to lay the foundation for in-depth analysis of user mental health. To achieve this goal, we innovatively introduced and applied the Bidirectional Gated Recurrent Unit (BiGRU) model for modeling and experimentation. This model can effectively capture the contextual dependencies in text sequences, significantly improving the learning and recognition ability of emotional semantic features. In the rigorous model evaluation process, the analysis results based on the test set confusion matrix showed that the model achieved a high prediction accuracy of 94.8%. This outstanding performance metric fully validates the high effectiveness and reliability of the proposed BiGRU model in handling text sentiment classification problems, with accurate and robust classification performance. Therefore, this study not only confirms the enormous potential of BiGRU in this field, but more importantly, its excellent classification performance provides strong core technical support for the subsequent construction of automated and intelligent text emotion recognition and classification systems. More importantly, the successful application of this model in mental health analysis scenarios means that it can efficiently identify the emotions contained in user texts, providing objective and quantifiable analysis basis for timely insight into user psychological states, warning potential risks, and providing personalized psychological support or intervention suggestions. It has important practical significance for improving the intelligence level and response efficiency of mental health services.
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This paper presents the design of a fifth-order ring voltage-controlled oscillator (VCO) featuring low phase noise, based on the TSMC 65nm CMOS RF process. The design employs a low-noise inverter amplifier to replace the conventional inverter, thereby enhancing the performance of the ring oscillator. Simulations were carried out using Cadence Virtuoso. The results show that under a tunable control voltage ranging from 0.7V to 1.3V, the oscillator achieves a frequency tuning range of 108 MHz to 203 MHz. At a resonant frequency of 203 MHz, the phase noise is -83.6 dBc/Hz@100 kHz and -104.3 dBc/Hz@1 MHz. The typical power consumption at room temperature is 30.3 μW.
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As an essential medical monitoring device, pulse oximeters play a critical role in assessing respiratory health. This paper reviews the functions, development, and future prospects of pulse oximeters, elaborates on the working principles of pulse oximeters, including optical measurement and signal processing technologies. By analyzing the development history of pulse oximeters, it summarizes their advancements in portability, accuracy, and multifunctionality. In response to current challenges in measurement accuracy, the paper explores primary methods for improving precision. Finally, it provides an outlook on the future development directions of pulse oximeters, including high-precision measurement, AI integration, and applications in special environments. A pulse oximeter is a medical device that non-invasively monitors blood oxygen saturation. It has evolved from traditional fingertip models to smarter, more portable, and multi-functional designs, with promising future applications in telemedicine, wearable technology, and chronic disease management.
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