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Purpose: Inhomogeneities of the main magnetic field cause line broadening and location-dependent frequency shifts in brain MRSI. These are often visible despite advanced B shimming. The purpose of this work is to propose an advanced B correction method that can easily be applied during postprocessing.
Methods: A target-driven overdiscrete reconstruction method previously introduced for MRSI is modified by dividing it into two steps. In a first step, an intermediate spectroscopic image with arbitrarily high resolution is generated, on which B correction is performed as an additional processing step based on an additionally acquired B map. This frequency-aligns metabolite peaks and destroys noise correlations between neighboring subvoxels. Second, the voxel is shaped by application of the spatial response target. The method was tested with simulated spectroscopic imaging data as well as in a series of MRSI data sets obtained from four healthy volunteers at 7T.
Results: A systematic gain in spectral signal-to-noise ratio is achieved, due to spatial averaging now occurring over peak aligned and noise decorrelated subvoxel spectra. At the same time, metabolite peak line widths are reduced.
Conclusion: In the presence of B inhomogeneities across the field of view, the proposed method offers the potential to improve spectral quality with only a minimal additional effort during acquisition. Magn Reson Med 77:44-56, 2017. © 2015 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/mrm.26118 | DOI Listing |
This paper presents a novel multiscale signal processing framework for power quality disturbance (PQD) and cyber intrusion detection in smart grids, combining Non-Subsampled Contourlet Transform (NSCT), Split Augmented Lagrangian Shrinkage Algorithm (SALSA), and Morphological Component Analysis (MCA). A key innovation lies in an adaptive weighting mechanism within NSCT's directional sub bands, enabling dynamic energy redistribution and enhanced representation of both low-frequency anomalies (e.g.
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Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, China.
Triboelectric sweat sensors, endowed with the technical advantages of non-invasive ex vivo and in situ detection, have catalyzed the rapid advancement of personalized medicine and precision health management systems. However, the inherently low secretion rate and rapid evaporation of sweat pose significant challenges for its efficient collection and rapid analytical screening. This study leverages laser cutting and aqueous interfacial self-assembly strategies to develop a biomimetic heterogeneous wettability triboelectric material (HWTM).
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View Article and Find Full Text PDFSensors (Basel)
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School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644005, China.
In order to solve the problems of modulation signals in low signal-to-noise ratio (SNR), such as poor feature extraction ability, strong dependence on single modal data, and insufficient recognition accuracy, this paper proposes a multi-dimensional feature MFCA-transformer recognition network that integrates phase, frequency and power information. The network uses Triple Dynamic Feature Fusion (TDFF) to fuse constellation, time-frequency, and power spectrum features through the adaptive dynamic mechanism to improve the quality of feature fusion. A Channel Prior Convolutional Attention (CPCA) module is introduced to solve the problem of insufficient information interaction between different channels in multi-dimensional feature recognition tasks, promote information transmission between various feature channels, and enhance the recognition ability of the model for complex features.
View Article and Find Full Text PDFEntropy (Basel)
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The School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.
This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity.
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