This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieves a faster convergence rate with higher fidelity. The proposed compression algorithm is applied to the electrocardiogram signal. To assess the performance of the proposed compression algorithm, in addition to the generic assessment criteria, we consider the less discussed criteria related to the clinical needs - for the heart rate variability analysis purpose, how accurate the R peak information is preserved is evaluated. The experiments are conducted on the MIT-BIH arrhythmia benchmark database. The results show that the proposed algorithm performs better than other state-of-theart approaches. Meanwhile, it also well preserves the R peak information.

Deep learning-based hyperspectral image (HSI) classification methods have recently shown excellent performance, however, there are two shortcomings that need to be addressed. One is that deep network training requires a large number of labeled images, and the other is that deep network needs to learn a large number of parameters. They are also general problems of deep networks, especially in applications that require professional techniques to acquire and label images, such as HSI and medical images. In this paper, we propose a deep network architecture (SAFDNet) based on the stochastic adaptive Fourier decomposition (SAFD) theory. SAFD has powerful unsupervised feature extraction capabilities, so the entire deep network only requires a small number of annotated images to train the classifier. In addition, we use fewer convolution kernels in the entire deep network, which greatly reduces the number of deep network parameters. SAFD is a newly developed signal processing tool with solid mathematical foundation, which is used to construct the unsupervised deep feature extraction mechanism of SAFDNet. Experimental results on three popular HSI classification datasets show that our proposed SAFDNet outperforms other compared state-of-the-art deep learning methods in HSI classification.

Hyperspectral imageclassification is one of the most important steps in HSI analysis and challenging task for hyperspectral data processing, hyperspectral image contains rich spatial and spectral information. The abundance of spectral and spatial information is helpful to improve the classification accuracy. In this article, we propose a spectral-spatial random patches network (SSRPNet), which directly regards the random patches taken from the image as the convolution kernels without any training. The spectral-spatial feature extracted by SSRPNet stacked into a high dimensional vector, which combined with shallow, deep, spectral, spatial feature. Then, the high dimensional vector is fed into graph-based learning methods for classification, which can achieve excellent classification performance by randomly selecting a subset of features from a small sample point to create a graph. Experimental results on three datasets show that the proposed method can achieve satisfactory classification results compared with closely related methods.

This paper aims at proposing a novel 2D non-linear phase decomposition of images, which performs the image processing tasks better than the traditional Fourier transformation (linear phase decomposition), but further, it has additional mathematical properties allowing more effective image analysis, including adaptive decomposition components and positive instantaneous phase derivatives. 1D unwinding Blaschke decomposition has recently been proposed and studied. Through factorization it expresses arbitrary 1D signal into an infinite linear combination of Blaschke products. It offers fast converging positive frequency decomposition in the form of rational approximation. However, in the multi-dimensional cases the usual factorization mechanism does not work. As a consequence, there is no genuine unwinding decomposition for multi-dimensions. In this study a 2D partial unwinding decomposition based on algebraic transforms reducing multi-dimensions to the 1D case is proposed and analyzed. The result shows that the fast convergence offers efficient image reconstruction. The tensor type decomposing terms are mutually orthogonal, giving rise to 2D positive frequency decomposition. The comparison results show that the proposed method outperforms the standard greedy algorithm and the most commonly used methods in the Fourier category. An application in watermarking is presented to demonstrate its potential in applications.

Running pose in the crowd can serve as an early warning of most abnormal events (e.g., chasing, fleeing, and robbing), which can be achieved by human behavior analysis based on human pose measurement. Although deep convolutional neural networks (CNNs) have achieved impressive progress on human pose estimation (HPE), how to further improve the trade-off between estimation accuracy and speed remains an open issue. In this work, we first propose an efficient local–global estimator for HPE (called LGPose). Then based on the keypoints estimated by our LGPose, a simple regression model is defined using the geometry of the joints to achieve fast and accurate running pose measurement. To model the relationships between the human keypoints, a visual transformer (ViT) encoder is adopted to learn the long-range interdependencies between them at the pixel level. However, the operation of the transformer encoder is based on sequence processing that linearly projects the 2-D image patches to 1-D tokens. It loses the important local information. Yet, locality is crucial since it has relevance to lines, edges, and shapes. To learn the locality, we design effective CNN modules, rather than the original fully-connected network (FCN), into the feedforward module of ViT. Experiments on the MPII and COCO Keypoint val2017 datasets show that the proposed LGPose achieves the best trade-off among the compared state-of-the-art methods. Moreover, we build a lightweight running movement dataset to verify the effectiveness of our LGPose. Based on the human pose estimated by our LGPose, we propose a regression model to measure running pose with an accuracy of 86.4% without training any other classifier. Our source codes and running dataset will be made publicly available.