Foundations and Trends in Signal Processing

Scope & Guideline

Elevating knowledge in Signal Processing and beyond.

Introduction

Delve into the academic richness of Foundations and Trends in Signal Processing with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1932-8346
PublisherNOW PUBLISHERS INC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2007 to 2014, from 2016 to 2024
AbbreviationFOUND TRENDS SIGNAL / Found. Trends Signal Process.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPO BOX 1024, HANOVER, MA 02339, UNITED STATES

Aims and Scopes

The journal 'Foundations and Trends in Signal Processing' focuses on advancing the field of signal processing through comprehensive reviews and original research that address both theoretical and practical aspects. The following key areas represent the core aims and scopes of the journal:
  1. Signal Processing Theory and Algorithms:
    This area emphasizes the development of new algorithms and theoretical frameworks that enhance the understanding and application of signal processing methods across various domains.
  2. Machine Learning Applications in Signal Processing:
    The journal increasingly highlights the intersection of machine learning and signal processing, exploring how advanced learning techniques can be applied to improve signal analysis and processing tasks.
  3. Quantum Signal Processing:
    An emerging focus on quantum phenomena and their implications for signal processing, including quantum machine learning and quantum measurements, reflects the journal's commitment to exploring innovative and cutting-edge topics.
  4. Graph Signal Processing:
    This scope delves into the application of graph theory in signal processing, particularly in high-dimensional spaces, thereby expanding traditional signal processing methodologies.
  5. Application-Oriented Research:
    The journal aims to publish works that demonstrate practical applications of signal processing techniques in fields such as communications, image processing, and speech and language processing.
The journal has identified several trending and emerging themes that reflect current advancements and interests within the signal processing community. These themes are crucial for researchers looking to contribute to the field's evolution:
  1. Energy-Based Models in Speech and Language Processing:
    Recent publications indicate a growing interest in energy-based models, particularly their applications in speech and language processing, which suggests a trend towards exploring alternative modeling techniques.
  2. Model-Based Deep Learning:
    There is an increasing focus on model-based deep learning approaches, highlighting a trend where traditional signal processing techniques are integrated with deep learning frameworks to enhance performance and interpretability.
  3. Meta-Learning in Communication Systems:
    The exploration of meta-learning, particularly in the context of communication systems, signifies an emerging trend where learning algorithms are designed to adapt quickly to new tasks with minimal data.
  4. Quantum Machine Learning:
    Emerging interest in quantum machine learning reflects a significant shift towards exploring the implications of quantum mechanics on signal processing, positioning the journal at the forefront of this innovative research area.
  5. Graph Signal Processing in High Dimensional Spaces:
    The focus on graph signal processing, especially in high-dimensional contexts, indicates a trend towards using advanced mathematical frameworks to tackle complex signal processing challenges.

Declining or Waning

While the journal continues to explore a broad range of topics within signal processing, certain themes appear to be declining in prominence. The following bullet points highlight these waning areas:
  1. Classical Signal Processing Techniques:
    There is a noticeable decrease in publications focusing solely on traditional signal processing methods, indicating a shift towards more advanced and integrated approaches involving machine learning and quantum techniques.
  2. Basic Statistical Methods in Signal Analysis:
    Papers that center on fundamental statistical methods for signal analysis have seen reduced visibility, possibly overshadowed by more sophisticated methodologies that incorporate machine learning and artificial intelligence.
  3. Generalized Frameworks without Specific Applications:
    The journal seems to be moving away from publications that propose generalized frameworks lacking specific applications, favoring more targeted research that demonstrates practical use-cases.

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