IEEE SIGNAL PROCESSING MAGAZINE
IEEE SIGNAL PROCESSING MAGAZINE(英文缩写 IEEE SIGNAL PROC MAG),ISSN 1053-5888,eISSN 1558-0792 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
指标来源：jcr_cas_ifqb
期刊简介
暂无简介。
历年影响因子趋势
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IEEE SIGNAL PROCESSING MAGAZINE 最新收录文献
※ 中文译文由 AI 辅助生成,仅供学术参考,请以英文原文为准。
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Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities.
Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic long-form recordings …
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Multimodal data fusion in neuroscience: promises, challenges and future directions.
Multimodal fusion provides significant benefits over single modality analysis by leveraging both shared and complementary information across diverse data sources. In this article, we systematically re…
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The Marriage of Neurotechnologies and Artificial Intelligence: Ethical, regulatory, and technological aspects.
The dual concepts of neurotechnology and artificial intelligence (AI) form an intriguing but also potentially explosive mixture because of its many ethical and legal implications. The advent of AI and…
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Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.
Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizabilit…
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Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.
Rapid advances in generative artificial intelligence (AI) and deep representation learning have revolutionized numerous engineering applications in signal processing, computer vision, speech recogniti…
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Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey.
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoisi…
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High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions.
Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limite…
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Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging. 计算磁共振成像的物理驱动深度学习：结合物理和机器学习改进医学成像
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provide…
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Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging.
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the fie…
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Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness.