SIGNAL PROCESSING

Scope & Guideline

Catalyzing Discoveries in Signal Processing Research

Introduction

Explore the comprehensive scope of SIGNAL PROCESSING through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore SIGNAL PROCESSING in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN0165-1684
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1979 to 2025
AbbreviationSIGNAL PROCESS / Signal Process.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal SIGNAL PROCESSING focuses on the innovative and evolving field of signal processing, encompassing a broad range of applications and methodologies. By integrating theoretical and practical approaches, the journal aims to present significant advancements in both foundational and applied signal processing techniques.
  1. Signal Processing Algorithms and Methods:
    The journal emphasizes the development of novel algorithms and methodologies that enhance signal processing capabilities, including adaptive filtering, estimation techniques, and machine learning applications.
  2. Image and Video Processing:
    A core area of focus involves advanced techniques for image and video analysis, including denoising, enhancement, segmentation, and compression methods, often leveraging deep learning frameworks.
  3. Wireless Communication and Networking:
    Research on signal processing techniques for wireless communications is prominent, addressing challenges such as channel estimation, beamforming, and interference mitigation in various communication scenarios.
  4. Multimodal and Cross-Domain Signal Processing:
    The journal explores the integration of different signal modalities (e.g., audio, visual, and sensor data) and the development of cross-domain techniques for applications such as remote sensing and surveillance.
  5. Sensor Networks and Distributed Systems:
    There is a significant emphasis on algorithms and frameworks for distributed sensor networks, focusing on data fusion, target tracking, and cooperative communication strategies.
  6. Machine Learning and AI in Signal Processing:
    The application of artificial intelligence and machine learning techniques to signal processing problems is a growing trend, with a focus on improving classification, detection, and prediction accuracy.
The landscape of signal processing is constantly evolving, with new themes gaining traction in recent publications. This section outlines the emerging scopes that are reflecting the current trends in the field.
  1. Deep Learning Applications:
    There is a marked increase in research applying deep learning techniques to various signal processing tasks, including image enhancement, speech recognition, and classification problems.
  2. Multimodal Data Fusion:
    The integration of data from multiple modalities is increasingly being explored, facilitating improved performance in applications such as surveillance, autonomous driving, and environmental monitoring.
  3. Adaptive and Smart Signal Processing:
    Emerging techniques that adapt to changing conditions in real-time, such as adaptive filtering and intelligent signal processing systems, are gaining prominence as they offer enhanced performance in dynamic environments.
  4. Privacy and Security in Signal Processing:
    Research addressing privacy concerns and security measures in signal processing applications, particularly in communications and data transmission, is becoming increasingly relevant.
  5. Robustness Against Noise and Interference:
    There is a growing trend towards developing algorithms that maintain performance robustness in the presence of noise and interference, especially in real-world applications.

Declining or Waning

While SIGNAL PROCESSING continues to evolve, certain areas of research are becoming less prominent compared to emerging trends. This section highlights themes that appear to be waning in frequency or significance.
  1. Traditional Statistical Methods:
    There is a noticeable decline in the publication of papers focusing solely on traditional statistical methods for signal processing, as the field shifts towards more computationally intensive approaches, particularly those involving machine learning.
  2. Low-Complexity Linear Techniques:
    Research centered on simplistic linear techniques for signal processing appears to be decreasing, as more complex and adaptive methodologies gain prominence.
  3. Basic Image Processing Techniques:
    The focus on fundamental image processing techniques, such as basic filtering and simple transformations, is diminishing in favor of more sophisticated methods that utilize deep learning and advanced algorithms.
  4. Conventional Time-Frequency Analysis:
    Interest in classical time-frequency analysis methods is declining, possibly due to the rise of more effective adaptive and learning-based approaches that provide better performance in dynamic environments.

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