A modified underdetermined blind source separation algorithm using competitive learning

The problem of underdetermined blind source separation is addressed. An advanced classification method based upon competitive learning is proposed for automatically determining the number of active sources over the observation. Its introduction in underdetermined blind source separation successfully overcomes the drawback of an existing method, in which the goal of separating more sources than the number of available mixtures is achieved by exploiting the sparsity of the non-stationary sources in the time-frequency domain. Simulation studies are presented to support the proposed approach

A new cross-correlation and constant modulus type algorithm for PAM-PSK signals

We address the problem of blind recovery of multiple sources from their linear convolutive mixture with the cross-correlation and constant modulus algorithm. The steady state mean-squared error of this algorithm is first derived to justify the proposal of a new cross-correlation and constant modulus type algorithm for PAM-PSK type non-constant modulus signals. Simulation studies are presented to support the improved steady-state performance of the new algorithm

Bounds for the mixing parameter within the CC-CMA algorithm applied in non ideal multiuser environments

We derive new bounds for the mixing parameter, γ, within the cross-correlation constant modulus algorithm (CC-CMA) for blind source separation and equalization in non-ideal multiuser environments. Channel undermodelling and noise are considered when the complex sources are circularly symmetric. These tighter bounds are obtained by surface topography of the error performance surface of the CC-CMA algorithm, and replace earlier work which suggested that γ>4/3. The validity of the bounds is confirmed by simulation studie

Non-negative matrix factorization for note onset detection of audio signals

A novel approach using non-negative matrix factorization (NMF) for onset detection of musical notes from audio signals is presented. Unlike most commonly used conventional approaches, the proposed method exploits a new detection function constructed from the linear temporal bases that are obtained from a non-negative matrix decomposition of musical spectra. Both first-order difference and psychoacoustically motivated relative difference functions of the temporal profile are considered. As the approach works directly on input data, no prior knowledge or statistical information is thereby required. A practical issue of the choice of the factorization rank is also examined experimentally. Numerical examples are provided to show the performance of the proposed method

PF-DMD: Physics-fusion dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics

The DMD (Dynamic Mode Decomposition) method has attracted widespread attention as a representative modal-decomposition method and can build a predictive model. However, the DMD may give predicted results that deviate from physical reality in some scenarios, such as dealing with translation problems or noisy data. Therefore, this paper proposes a physics-fusion dynamic mode decomposition (PFDMD) method to address this issue. The proposed PFDMD method first obtains a data-driven model using DMD, then calculates the residual of the physical equations, and finally corrects the predicted results using Kalman filtering and gain coefficients. In this way, the PFDMD method can integrate the physics-informed equations with the data-driven model generated by DMD. Numerical experiments are conducted using the PFDMD, including the Allen-Cahn, advection-diffusion, and Burgers' equations. The results demonstrate that the proposed PFDMD method can significantly reduce the reconstruction and prediction errors by incorporating physics-informed equations, making it usable for translation and shock problems where the standard DMD method has failed

The green GDP accounting system based on the BP neural network: an environmental pollution perspective

Introduction: The green GDP accounting system has become the focus of sustainable development, but a comprehensive accounting of environmental pollution cost and resource depletion cost has not yet been formed.Methods: This study measures environmental pollution cost and resource loss cost, and establishes the green GDP accounting system based on the SEEA-2012. To analyze the environmental effects brought by the adoption of green GDP accounting system, a BP neural network model including green GDP, traditional GDP and global climate indicators is constructed to predict the global climate changes.Results: The empirical results show that after the adoption of the green GDP accounting system, the global climate extreme weather can be reduced, the sea level will be lowered, and the climate problem is thus alleviated

SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

Accurate medical image segmentation especially for echocardiographic images with unmissable noise requires elaborate network design. Compared with manual design, Neural Architecture Search (NAS) realizes better segmentation results due to larger search space and automatic optimization, but most of the existing methods are weak in layer-wise feature aggregation and adopt a ``strong encoder, weak decoder" structure, insufficient to handle global relationships and local details. To resolve these issues, we propose a novel semi-supervised hybrid NAS network for accurate medical image segmentation termed SSHNN. In SSHNN, we creatively use convolution operation in layer-wise feature fusion instead of normalized scalars to avoid losing details, making NAS a stronger encoder. Moreover, Transformers are introduced for the compensation of global context and U-shaped decoder is designed to efficiently connect global context with local features. Specifically, we implement a semi-supervised algorithm Mean-Teacher to overcome the limited volume problem of labeled medical image dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate that SSHNN outperforms state-of-the-art approaches and realizes accurate segmentation. Code will be made publicly available.Comment: Submitted to ICASSP202

Does SYNTAX score II predict poor myocardial perfusion in ST-segmen

Background: SYNTAX score II (SS-II) has been demonstrated to predict long-term outcomes in unprotected left main or multiple vessels in patients with coronary artery disease. However, its prognostic value for patients with ST-segment elevation myocardial infarction (STEMI) remains unknown. The poor myocardial perfusion (myocardial blush grade [MBG] 0/1) after primary percutaneous coronary intervention (pPCI) has a negative prognostic value in patients with STEMI. We aimed to assess SS-II and its possible relationships with MBG 0/1 in patients with STEMI treated with pPCI. Methods: The study included 477 patients with STEMI who underwent pPCI between October 2010 and May 2014. SYNTAX Score II and MBG were determined in all patients. Myocardial blush grade were divided into MBG 0/1 (poor myocardial perfusion) and MBG 2/3 (normal myocardial perfusion). Patients were divided into three tertiles: SS-IIlow (￡ 20), SS-IIintermediate (20–26) and SS-IIhigh (≥ 26). Results: Compared with the SS-IIintermediate and SS-IIlow tertiles, the SS-IIhigh tertile had more MBG 0/1 (46.1%, 32.1% and 21.8%, p < 0.001, respectively). On multivariate logistic regression analysis, SS-II was an independent predictor of MBG 0/1 (hazard ratio 1.084, 95% confidence interval 1.050–1.119, p < 0.001). Receiver operating characteristic analysis identified SS-II > 24 as the best cut-off value predicting MBG 0/1 (sensitivity of 66%, specificity of 54%). Conclusions: High SS-II is an independent predictor of MBG 0/1 in patients with STEMI undergoing pPCI.