New Advances in Digital Signal Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 98
Editors
Interests: time–frequency analysis; biomedical signal processing; applied statistical signal processing; digital signal processing; IoT
Special Issues, Collections and Topics in MDPI journals
Interests: signal and video processing; ANN; materials in electrical engineering; additive manufacturing; video surveillance; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical signal processing; vision-based robot control; active learning; IoT
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Digital Signal Processing (DSP) is evolving rapidly, driven by the upgrading of classical signal processing theory with artificial intelligence, edge computing, high-performance hardware, next-generation communication systems, and, above all, rapid technological advances. Although the fundamental principles of DSP remain unchanged, current research focuses on intelligent, adaptive, and computationally optimized algorithms to address today's real-world challenges.
This Special Issue aims to present advances in DSP methodologies, algorithms, architectures, and applications. Topics of interest include, but are not limited to, AI-driven signal processing, hybrid model- and data-based approaches, adaptive filtering, sparse signal processing, compressed sensing, foundation models for signal processing, multi-modal processing, and hardware-accelerated DSP using GPUs, FPGAs, TPUs, and embedded platforms.
The Special Issue welcomes research addressing new DSP applications in radar and remote sensing, biomedical engineering, medical imaging, speech and audio processing, multimedia forensics, deepfake detection, intelligent sensing, the Internet of Things (IoT), autonomous systems, and other emerging domains. Both theoretical developments and practical implementations demonstrating improved performance, efficiency, robustness, or scalability are encouraged, along with an overview of the latest innovations shaping the future of digital signal processing.
Prof. Dr. Joško Šoda
Prof. Dr. Igor Vujović
Prof. Dr. Mirjana Bonkovic
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
- digital signal processing
- artificial intelligence for signal processing
- adaptive signal processing
- sparse and compressed signal processing
- edge AI
- hardware acceleration
- intelligent sensing
- wireless signal processing
- biomedical signal processing
- multimedia signal processing
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