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Cognitive Computing for Image, Signal, and Biomedical Applications
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Cognitive Computing for Image, Signal, and Biomedical Applications

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289). This special issue belongs to the section "Cognitive System".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 941

Editors

Prof. Dr. Qingli Li

E-Mail Website
Guest Editor
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: hyperspectral imaging; biomedical engineering; medical imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yan Wang

E-Mail Website
Guest Editor
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: computer vision; medical imaging; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Qing Zhang

E-Mail Website
Guest Editor
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
Interests: hyperspectral imaging; remote sensing; hyperspectral interdisciplinary applications
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Wang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Donghua University, Shanghai, China
Interests: medical image analysis; multimodal data fusion; high-dimensional imaging informatics

Special Issue Information

Dear Colleagues,

The Special Issue of "Cognitive Computing for Image, Signal, and Biomedical Applications" aims to highlight recent advancements in the intersection of cognitive computing and biomedical engineering applications. We invite contributions that present novel methodologies, innovative applications, and interdisciplinary approaches, with particular emphasis on data-driven, AI-based techniques that improve accuracy, interpretability, and practical utility in medical and biomedical contexts. This Special Issue is also organized in conjunction with CISP-BMEI 2025 (http://www.cisp-bmei.cn/), a premier international congress that brings together the communities of image and signal processing (CISP) and biomedical engineering and informatics (BMEI). Building upon the vibrant discussions and innovative work presented at CISP-BMEI 2025, the Special Issue seeks to provide a premier platform for researchers and practitioners to share novel algorithms, system designs, and applications that advance both theoretical understanding and real-world impact.

Manuscripts will undergo rigorous peer review to ensure the highest quality and relevance to the field. We cordially invite submissions to this Special Issue, encompassing all facets of AI/ML/DL for the following:

  • Image and signal processing;
  • Biomedical imaging and visualization;
  • Biomedical signal processing and analysis;
  • Bioinformatics and medical informatics.

Prof. Dr. Qingli Li
Prof. Dr. Yan Wang
Dr. Qing Zhang
Dr. Qian Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image and signal processing
  • signal modeling and identification
  • informatics
  • interdisciplinary research

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

20 pages, 914 KB  
Article
Band-Limited Proximal FISTA for Efficient Sparse Harmonic Recovery on MCU
by Seongho Cho, Minjung Kim and Daejin Park
Big Data Cogn. Comput. 2026, 10(7), 205; https://doi.org/10.3390/bdcc10070205 (registering DOI) - 25 Jun 2026
Abstract
Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing ℓ1-regularized recovery on MCU-class hardware is challenging due to limited compute resources and the cost of repeated forward and adjoint operator [...] Read more.
Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing ℓ1-regularized recovery on MCU-class hardware is challenging due to limited compute resources and the cost of repeated forward and adjoint operator evaluations. This paper presents a band-limited proximal variant of FISTA that enforces known spectral support during thresholding, restricting the effective optimization domain without changing the measurement model. We implement a complete CS reconstruction pipeline on an STM32F407 (Cortex-M4) using CMSIS-DSP FFT/IFFT kernels and evaluate it using ECG waveforms acquired through an AD8232 front end as benchmark signals. With M=340 measurements (33% of uniform sampling), the embedded implementation achieves a PRDN of 24.38%, closely matching MATLAB references (CVX: 22.64%, FISTA: 22.39%) under identical hyperparameters. Cycle-accurate profiling shows that FFT/IFFT-based forward/adjoint operators dominate the per-iteration runtime. Under a 60 Hz band-limited setting, the required iterations are reduced from 30 to 16 with an acceptable PRDN, demonstrating a practical trade-off between reconstruction accuracy and computational cost on MCU-class devices. Full article
(This article belongs to the Special Issue Cognitive Computing for Image, Signal, and Biomedical Applications)
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