The 'Signal Processing Theory' section of Frontiers in Signal Processing publishes high-quality fundamental/basic/applied research across the field of signal and information processing, covering novel theory, algorithms, performance analyses for the processing, understanding, learning, recognition, and extraction of information from signals.Read More
The ‘Signal Processing Theory’ section of Frontiers in Signal Processing publishes high-quality fundamental/basic/applied research across the field of signal and information processing, covering novel theory, algorithms, performance analyses for the processing, understanding, learning, recognition, and extraction of information from signals.
Areas covered by this section include, but are not limited to:
• Multirate and digital signal processing, such as sampling theory and methods, compressed and non-uniform sampling, multiresolution analysis, filter banks, wavelets, signal filtering (enhancement, restoration, and reconstruction), sparsity-aware algorithms.
• Statistical signal processing, such as detection theory and methods (detection, estimation and tracking), estimation theory and methods (system identification and parameter estimation), spectral analysis, classification methods, performance analysis and bounds, linear and nonlinear system, deconvolution, filtering, signal separation, non-stationary signal processing, non-Gaussian signal processing, Bayesian signal processing, high-order statistical methods, independent component analysis, non-parametric methods, structured low-dimensional models, hierarchical models, tracking algorithms, PHD filtering, random finite set models.
• Signal and information processing over graphs, such as statistical analysis over graphs, information-theoretic studies over graphs, network dynamic modelling, network evolution modelling, stochastic processes over graphs, filtering over graphs, distributed processing over graphs, sampling over graphs, graph representation and analysis, spectral graph theory, topology algorithm, adaptation and learning over graphs.
• Optimization methods for signal processing, such as convex optimization, nonconvex optimization, convex relaxation, distributed optimization, and sparse optimization.
• Adaptive signal processing, such as adaptive filter algorithms and performance analysis, frequency domain adaptive filters, sub-band adaptive filter, and fast adaptive algorithms.
• Signal processing over networks, such as distributed processing over networks, ad-hoc wireless networks, sensor networks, energy-efficient network processing, resource management issues, data fusion over sensors, optimization over networks, distributed adaptation and fusion over networks, estimation, detection and learning over networks, source localization in sensor networks, distributed detection, estimation and learning over networks, uncertainty modelling with outliers and measurement noise, intelligent sensor management
• Sparsity-aware processing, such as dictionary learning, sparse coding, structured matrix factorization, non-negative matrix factorization, subspace and manifold learning, matrix completion, low-rank models, sparse signal recovery, latent variable models, deep models in matrix factorization, convolutional sparse coding, convolutional dictionary learning.
• Quantum signal processing.
We particularly welcome papers on theory, algorithms, methods and performance analysis, with applications in any areas such as audio, speech, image, video, biomedical data, finance, medical data, sonar, radar, etc.
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Signal Processing Theory welcomes submissions of the following article types: Brief Research Report, Code, Correction, Data Report, Editorial, Hypothesis and Theory, Methods, Mini Review, Original Research, Perspective, Review and Technology Report.
All manuscripts must be submitted directly to the section Signal Processing Theory, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
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