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 110
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
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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
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