mlsanityData to 7 Oct 2026

Roadmap on Signal Processing for Next Generation Measurement Systems

arXiv 2111.02493 ↗First posted 3 Nov 2021eess.SP cs.AI cs.CVcited by 4

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Abstract (arXiv)

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 204 references that are in the map. First posted on arXiv 3 Nov 2021.

Builds on

8 of 204 references.

References · 8

  1. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 129
  2. 2019CNN-based Lidar Point Cloud De-Noising in Adverse Weathercited by 33
  3. 2019WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosiscited by 23
  4. 2020A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantagecited by 23
  5. 2019Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasoundcited by 12
  6. 2019Biometrics Recognition Using Deep Learning: A Surveycited by 11
  7. 2018Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Imagescited by 10
  8. 2020Fast and Accurate Modeling of Transient-State Gradient-Spoiled Sequences by Recurrent Neural Networkscited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.