Md. Kamrul Hasan

TL;DR: In this paper, a waveform-based hierarchical Artificial Neural Network (ANN-LSTM) model was proposed for continuous BP estimation, which consists of two hierarchy levels, where the lower hierarchy level uses ANNs to extract necessary morphological features from ECG and PPG waveforms and the upper hierarchy layer uses LSTM layers to account for the time domain variation of the features extracted by the lower level.

TL;DR: A novel signal processing framework which utilizes two channel PPG signals and estimates HR in two stages and increases the algorithm's robustness against offtrack errors by using recursive least squares filters complemented with an additional novel technique, namely time-domain extraction.

TL;DR: Improved results are obtained in terms of speech quality measures for various types of noise and at different SNR levels when the proposed time-frequency varying averaging factor is adapted in the conventional subtraction rules.

TL;DR: A new time domain algorithm, called threshold crossing sample count (TCSC), which is an improved version of the threshold crossing interval (TCI) algorithm for VF detection, based on an important feature of the VF signal which relies on the random behavior of the electrical heart vector.

TL;DR: This paper proposes a new network, the SegU-Net, which is formed by merging the state-of-the-art segmentation architectures–SegNet and U-Net to detect traffic signs from video sequences and proves the generalizability of the proposed architecture and its suitability for robust traffic sign detection in autonomous vehicles.