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Non-contact face-video based human heart rate (HR) estimation has attracted a lot of attentions in recent years. Almost all the state-of-the-art webcam or smartphone based HR estimation methods comprise three main steps: firstly, a region of interest (ROI) on the human face is detected in each video frame, then, the target signal is obtained by fusing multiple raw traces, which are extracted from...
Kernel methods provide an efficient nonparametric model to produce adaptive nonlinear filtering (ANF) algorithms. However, in practical applications, standard squared error based kernel methods suffer from two main issues: (1) a constant step size is used, which degrades the algorithm performance in non-stationary environment, and (2) additive noises are assumed to follow Gaussian distribution, while...
Spectrograms provide an effective way for time-frequency representation (TFR). Among these, short-time Fourier transform (STFT) based spectrograms are extensively used for various applications. However, STFT spectrogram and its revised versions suffer from two main issues: (1) there is a trade-off between time resolution and frequency resolution, and (2) almost all existing TFR methods, including...
Quantized kernel least mean square (QKLMS) algorithm is an effective up-to-date adaptive nonlinear learning algorithm which also has good performance for kernel structure growing control. It achieves good results under Gaussian noise environment. In this paper, a new algorithm, quantized kernel least mean mixed norm (QKLMMN), is proposed for adaptive nonlinear learning with non-Gaussian additive noise...
This paper focuses on two signal processing aspects of multistatic active sonar systems, namely enhanced range-Doppler imaging and improved target parameter estimation. The main contributions of this paper are: i) a hybrid dense-sparse method is proposed to generate range-Doppler images with both low sidelobe levels and high accuracy; ii) a generalized K-Means clustering (GKC) method for target association...
Effective channel estimation plays a critical role in the overall performance of multi-input multi-output (MIMO) underwater acoustic communications (UAC). This paper compares two closely related channel estimation algorithms developed under different models for sparse and frequency modulated acoustic channels. More specifically, the recently proposed channel estimation algorithm, referred to as the...
This paper focuses on mobile multi-input multi-output (MIMO) underwater acoustic communications (UAC) over double-selective channels suffering from both inter-symbol interference and Doppler scaling effects (stretching or compression). Temporal resampling is implemented to effectively convert the Doppler scaling effects to Doppler frequency shifts. Then an extension of the sparse learning via iterative...
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