BlyncSync: Enabling Multimodal Smartwatch Gestures with Synchronous Touch and Blink

Bryan Wang, Tovi Grossman. Conditionally accepted to the ACM Conference on Human Factors in Computing Systems (CHI '20). Details to be announced.

PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network

Bryan Wang, Yi-Hsuan Yang. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’19). Oral Presentation (acceptance rate: 6.4%)

We propose PerformanceNet, a deep convolutional model that learns in an end-to-end manner the score-to-audio mapping between a symbolic representation of music called the piano rolls and an audio representation of music called the spectrograms. The model consists of two subnets: the ContourNet, which uses a U-Net structure to learn the correspondence between piano rolls and spectrograms and to give an initial result; and the TextureNet, which further uses a multi-band residual network to refine the result by adding the spectral texture of overtones and timbre.

ActiveErgo: Automatic and Personalized Ergonomics using Self-actuating Furniture

Yu-Chian Wu, Te-Yen Wu, Paul Taele, Bryan Wang, Jun-You Liu, PO-EN LAI, Pin-sung Ku, Mike Y. Chen In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI ’18).

We present ActiveErgo, the first active approach to improving ergonomics by combining sensing and actuation of motorized furniture. Our prototype system uses Kinect for skeletal sensing and monitoring to determine the ideal furniture positions for each user, then uses a combination of automatic adjustment and live feedback to adjust the computer monitor, desk, and chair positions.

CircuitSense: Automatic Sensing of Physical Circuits and Generation of Virtual Circuits to Support Software Tools

Te-Yen Wu, Bryan Wang, Jiun-Yu Lee, Hao-Ping Shen, Yu-Chian Wu, Yu-An Chen, Pin-Sung Ku, Ming-Wei Hsu, Yu-Chih Lin, Mike Y. Chen. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST ’17).

We present CircuitSense, a system that automatically recognizes the wires and electronic components placed on breadboards. It uses a combination of passive sensing and active probing to detect and generate the corresponding circuit representation in software in real-time. It also dramatically simplifies the sharing of circuit designs with online communities.

CircuitStack: Supporting Rapid Prototyping and Evolution of Electronic Circuits

Chiuan Wang, Hsuan-Ming Yeh, Bryan Wang , Te-Yen Wu, Hsin-Ruey Tsai, Rong-Hao Liang, Yi-Ping Hung, Mike Y. Chen. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST ’16). Best Talk Award

We present CircuitStack, a system that combines the flexibility of breadboarding with the correctness of printed circuits, for enabling rapid and extensible circuit construction. This hybrid system enables circuit reconfigurability, component reusability, and high efficiency at the early stage of prototyping development.