• Advanced Institute of Engineering and Technology (AVITECH)

  • Research Projects

    EEG signal processing from the computer vision perspective

    In this work, we study the processing of EEG signals in the image domain, i.e., visualizing the signal in the binary image and applying computer vision techniques for signal smoothing and classification. Some research questions are being investigated:

    • 1. How well can we smooth the signal in the image domain? Here, we plot the interested signal in binary representation at different line-width levels, then “thin” using some recent effective skeletonization algorithms. The process consists of plotting the signal at a high-level line width (upscale) and then at a unit line width (downscale); hence, the name Upscale and Downscale Representation (UDR) comes into play. The skeletonized signal is then converted back to the time domain for correlation evaluation.
    • 2. How can UDR be implemented in classification models to improve detection accuracy? Here, we investigate the feasibility of implementing UDR as a smoothing filter for data pre-processing or in a shallow layer of a CNN to enhance the signal classification accuracy further using deep learning.

    Interested research students are welcome to join. Minimum requirements: coding skills (MATLAB, Python for algorithm development and implementation), English reading and writing (to read papers and write reports, for now. Students are encouraged to strengthen their verbal skills as soon as possible).

    Selected Publications

    Q. M. Doan, T. H. Dinh, N. L. Trung, D. Nguyen, A. K. Singh, CT. Lin, Extended Upscale and Downscale Representation with Cascade Arrangement. Proceedings of the 22nd IEEE Statistical Signal Processing Workshop (SSP 2023), 2023, pp. 715-719.

    Other information

    Co-PI: Dr. Dinh Tran Hiep


    Vision-based crack detection with crack propagation modeling

    In this work, the propagation of cracks on some materials, such as concrete, is analyzed, the best-fit model of which is employed for vision-based crack detection. Some research questions are being investigated: 1. What is the correlation between simulated crack propagation and real-world crack? Here, we measure the fitting errors between best-fit regression models of […]

    Machine learning based beacon placement optimization for indoor robots localization

    Indoor localization systems usually consist of transceiver with fixed positions, which are called beacons. These beacons act as landmarks for localizing objects that need to be positioned. One can easily see that the positions of beacons have a great impact on the performance of the localization system. The problem of beacon placement optimization is to […]

    Tensor tracking

    Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however non-trivial due to several inherent problems of real-time stream processing. In recent years, many research efforts have been dedicated to […]