Selected work

Representative Papers

Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals

Apr 15, 2026

The non-stationary nature of heart sound signals renders feature extraction highly sensitive to the shape and length of the analysis window, thereby limiting the performance of automated cardiovascular disease classification. This work proposes a sliding-window-based short-time statistical feature extraction approach combined with a bidirectional long short-term memory (biLSTM) network for classification. For the first time, it systematically evaluates the impact of Gaussian, triangular, and rectangular windows across varying window lengths. Experimental results demonstrate that a 75-ms Gaussian window yields the best classification performance, significantly outperforming baseline methods; the triangular window achieves the second-best results, while the rectangular window performs worst. These findings provide clear guidance for optimal window parameter selection in heart sound signal analysis.

0 citationsRead paper

Intelligent ROI-Based Vehicle Counting Framework for Automated Traffic Monitoring

Apr 14, 2026

Accurate and efficient vehicle counting in complex multi-lane traffic surveillance remains challenging. This work proposes a two-stage adaptive vehicle counting framework: first, it dynamically estimates an optimal region of interest (ROI) by fusing detection scores, tracking scores, and vehicle density, enabling compatibility with arbitrary detection and tracking algorithms; then, it performs efficient counting within the selected ROI. The adaptive ROI selection mechanism significantly enhances the system’s generalizability and robustness. Evaluated on benchmark datasets including UA-DETRAC and GRAM, the method achieves 100% counting accuracy on most videos and attains up to a fourfold speedup over full-frame analysis, outperforming existing approaches.

0 citationsRead paper

Recent publications

Latest Papers

Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals

Apr 15, 2026

The non-stationary nature of heart sound signals renders feature extraction highly sensitive to the shape and length of the analysis window, thereby limiting the performance of automated cardiovascular disease classification. This work proposes a sliding-window-based short-time statistical feature extraction approach combined with a bidirectional long short-term memory (biLSTM) network for classification. For the first time, it systematically evaluates the impact of Gaussian, triangular, and rectangular windows across varying window lengths. Experimental results demonstrate that a 75-ms Gaussian window yields the best classification performance, significantly outperforming baseline methods; the triangular window achieves the second-best results, while the rectangular window performs worst. These findings provide clear guidance for optimal window parameter selection in heart sound signal analysis.

0 citationsRead paper

Intelligent ROI-Based Vehicle Counting Framework for Automated Traffic Monitoring

Apr 14, 2026

Accurate and efficient vehicle counting in complex multi-lane traffic surveillance remains challenging. This work proposes a two-stage adaptive vehicle counting framework: first, it dynamically estimates an optimal region of interest (ROI) by fusing detection scores, tracking scores, and vehicle density, enabling compatibility with arbitrary detection and tracking algorithms; then, it performs efficient counting within the selected ROI. The adaptive ROI selection mechanism significantly enhances the system’s generalizability and robustness. Evaluated on benchmark datasets including UA-DETRAC and GRAM, the method achieves 100% counting accuracy on most videos and attains up to a fourfold speedup over full-frame analysis, outperforming existing approaches.

0 citationsRead paper