Digital Signal Processing for Wireless Communications and Multimedia Systems
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: 30 October 2025 | Viewed by 101
Special Issue Editors
Interests: signal processing; pattern recognition; deep learning
Special Issue Information
Dear Colleagues,
Digital Signal Processing (DSP) plays a crucial role in machine learning and multimedia systems due to its ability to effectively transform digital signals and prepare them for further analysis. DSP is essential for extracting meaningful features from raw data. In image processing, for instance, techniques such as edge detection or texture analysis can highlight significant structures within an image, which pattern recognition algorithms can then use to identify or classify objects, faces, or other elements. Multimedia systems, such as those handling video, audio, or images, often require large amounts of storage and bandwidth. Reducing the amount of data required while preserving essential information makes storage and transmission more efficient. Very popular data compression algorithms such as JPEG for images and MP3 for audio are based on discrete cosine transform. Moreover, DSP also allows for the transformation of signals into different domains, e.g., into the frequency domain using the Fourier transform, wavelet transform, or mel-frequency cepstral coefficients, where certain patterns or features are easier to identify. In multimedia systems and machine learning applications, data are often corrupted by noise or imperfections during acquisition. Signal filtering can enhance thier quality by removing noise, making the data clearer and more suitable for further analysis. As a result, signal processing methods are foundational in implementing various pattern recognition and machine learning algorithms, and are widely used in tasks like speech recognition, object detection, or image classification.
- Feature extraction;
- Data compression;
- Transformations and representations of signals;
- Multidimensional signal processing;
- Signal enhancement and noise reduction;
- Pattern recognition and machine learning.
Dr. Urszula Libal
Dr. Tomasz Grajek
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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind 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
- signal processing
- image processing
- audio and speech processing
- noise reduction
- feature extraction
- dimensionality reduction
- sparse representation
- data compression
- signal parametrization
- signal representations
- pattern recognition
- machine learning
- statistical learning
- deep learning
- applications in signal and image processing
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue policies can be found here.