Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians. Approach. We propose a deep learning architecture, LungAttn, which incorporates augmented attention convolution into ResNet block to improve the classification accuracy of lung sound. We adopt a feature extraction method based on dual tunable Q-factor wavelet transform and triple short-time Fourier transform to obtain a multi-channel spectrogram. Mixup method is introduced to augment adventitious lung sound recordings to address the imbalance dataset problem. Main results. Based on the ICBHI 2017 challenge dataset, we implement our framework and compare with the state-of-the-art works. Experimental results show that LungAttn has achieved the Sensitivity, S e , Specificity, S p and Score of 36.36%, 71.44% and 53.90%, respectively. Of which, our work has improved the Score by 1.69% compared to the state-of-the-art models based on the official ICBHI 2017 dataset splitting method. Significance. Multi-channel spectrogram based on different oscillatory behavior of adventitious lung sound provides necessary information of lung sound recordings. Attention mechanism is introduced to lung sound classification methods and has proved to be effective. The proposed LungAttn model can potentially improve the speed and accuracy of lung sound classification in clinical practice.

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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.

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