Healthcare Education and Labeling for Cardiopulmonary Sounds, HEALSound, is an interactive platform designed to enhance the identification and labeling of adventitious cardiopulmonary sounds through gamification. HEALSound enables users, particularly medical professionals, to engage in exercises that improve their knowledge of abnormal heart and lung sounds while simultaneously contributing to the labeling of raw audio data. By incorporating real-time feedback and progress tracking, the app promotes continuous learning and provides a valuable crowdsourced resource for building high-quality labeled datasets over time. This dual-purpose platform not only aids in medical education but also contributes to the advancement of machine learning models for sound classification in healthcare. The system demonstrates a new potential for significant impact in educational and clinical environments by seamlessly integrating learning with data collection, ensuring scalability and the continuous improvement of cardiopulmonary sound databases.

Advances in digital stethoscopes and wearable health sensors now support real-time cardiopulmonary monitoring and AI-driven diagnostic tools. However, the extensive manual labeling required for large datasets of respiratory sounds presents a significant barrier, traditionally dependent on expert input. To address this, we introduce HEALSound, a mobile application designed to blend educational training with crowdsourced data labeling. By engaging users in interactive auscultation exercises and providing immediate feedback, HEALSound promotes skill development while generating high-quality labeled data through weighted consensus methods. Experimental results highlight the app’s dual effectiveness: enhancing diagnostic learning for users and accelerating the development of machine-learning models through enriched datasets, thus addressing critical challenges in AI-based health diagnostics.

Modeling lithography using machine learning is extremely data-intensive. Due to intellectual property privacy concerns and potential malicious attacks, design houses and foundries are unwilling to share their designs directly. To address the aforementioned concerns, we have proposed a personalized federated learning-based framework (pFed-Litho) to perform end-to-end lithography simulation. This framework incorporates a cross-level local training algorithm along with an integrated optimization method to generate personalized and local models, which overcome the generalization problem and slow convergence and oscillatory behavior in its loss function, respectively. The experimental results show that our pFed-Litho framework achieves up to 14.07% higher accuracy with reduced oscillatory behavior in the loss curve compared to the state-of-the-art works. Even with a dataset reduced by$100\times $, our framework maintains a stable accuracy of over 91%, representing a 50% increase compared to the U-Net model.

This work introduces an innovative wearable health-care system that offers personalized cardiopulmonary monitoring by continuously capturing a variety of physiological signals. Users can engage with the system to view their own data in real-time, thereby gaining a nuanced understanding of its multi-modal sensing capabilities and the potential for remote health monitoring.

Continuous physiological sound monitoring is crucial for the prevention, diagnosis, and treatment of various diseases like cardiopulmonary and gastrointestinal conditions. Wearable healthcare sensors have emerged as a potent solution, streamlining the capture, storage, transmission, and analysis of individualized physiological sounds. However, challenges exist including large data volumes, limited hardware computational capabilities, and constrained transmission bit rates. To address these issues, we propose a physiological sound compression system using compressive sensing with self-adaptive compression ratio across sound types to implement physiological sound compression and Optimized Discrete Cosine Transform (ODCT) reconstruction to reduce loss in effective bands. Evaluated on SPRSound and PhysioNet 2016, our approach attains correlation coefficients of 0.863 and 0.883 for respiratory and cardiac sounds, with -3.14 dB and -1.84 dB signal-to-noise ratio loss at 3.5 and 3.0 compression ratios. Implemented on a custom healthcare sensor, our approach optimizes bit rate to 1.73× and power consumption to 0.82× compared to the uncompressed system.

Performing chemical mechanical polishing (CMP) modeling for physical verification on an integrated circuit (IC) chip is vital to minimize its manufacturing yield loss. Traditional CMP models calculate post-CMP topography height of the IC’s layout based on physical principles and empirical experiments, which is computationally costly and time-consuming. In this work, we propose a CmpCNN framework based on convolutional neural networks (CNNs) with a transfer learning method to accelerate the CMP modeling process. It utilizes a multi-input strategy by feeding the binary image of layout and its density into our CNN-based model to extract features more efficiently. The transfer learning method is adopted to different CMP process parameters and different categories of circuits to further improve its prediction accuracy and convergence speed. Experimental results show that our CmpCNN framework achieves a competitive root mean square error ( RMSE ) of 2.7733Å with 1.89× reduction compared to the prior work, and a 57× speedup compared to the commercial CMP simulation tool.