Special Issue "Multidimensional Digital Signal Processing"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 29 February 2020.

Special Issue Editor

Guest Editor
Prof. Dr. Cataldo Guaragnella

Department of Electrics and Information Engineering, Politecnico di Bari, 70125 Bari, Italy
Website 1 | Website 2 | E-Mail
Interests: signal, image, and video processing; pattern analysis and recognition

Special Issue Information

Dear Colleagues,

Signal processing, image processing, and video processing are three research and application fields sharing the same background of digital signal processing. Then, they can be considered a common playground for all the scientists and researchers working in knowledge and information extraction from multidimensional data. The advent and widespread use of multi-sensor systems has substantially increased the processing burden and claim for computational aspects in this field, so that often approaches dealing with high dimensionality problems represent a common resource to be able to deal with such a big data problem. Also, novel approaches to information extraction are appearing and becoming more important in this field every day, such as machine learning, deep and convolutional neural networks, and dynamic complex networks approaches to multidimensional signal processing.

The availability of open, remote-sensing databases for Earth observation or the advent of interesting and complex scenarios of the Internet of Things have forced the signal processing community to face new challenging problems in which several sensors with different signal characteristics have to be properly fused to gain complete knowledge in a given application field.

The aim of this Special Issue is to focus on many signal, image, and video processing techniques and possible applications of multi-sensor/multi-technology/multi-dimensional data processing to give a large and complete view of this complex scenario.

Submissions to this Special Issue on ‘’Multidimensional Digital Signal Processing’’ are solicited to represent a snapshot of the field’s development by covering a range of topics that include but are not limited to new methods, algorithms, solutions, and applications in the following areas:

  • Adaptive signal processing;
  • Biomedical signal processing;
  • Communication signal processing;
  • Multi-dimensional signal processing;
  • Multimedia signal processing;
  • Non-linear signal processing;
  • Array of sensors signal processing;
  • Audio/video complex surveillance systems;
  • Action recognition and tracking;
  • Complex vision system for SLAM and scene modeling;
  • Statistical signal processing;
  • Machine learning for signal processing;
  • Complex networks in signal processing;
  • Real-time algorithms for multidimensional signal processing;
  • Hardware that is specific to multidimensional signal processing.

Prof. Dr. Cataldo Guaragnella
Guest Editor

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 papers will be 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. Electronics 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 1400 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
  • Signal, image, and video coding
  • Pattern recognition
  • Multidimensional signal processing
  • Computer vision
  • Statistical signal processing
  • Machine learning
  • Deep learning
  • Complex networks

Published Papers (2 papers)

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Research

Open AccessArticle
FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition
Electronics 2019, 8(7), 807; https://doi.org/10.3390/electronics8070807
Received: 22 May 2019 / Revised: 4 July 2019 / Accepted: 17 July 2019 / Published: 19 July 2019
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Abstract
Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. [...] Read more.
Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN. Full article
(This article belongs to the Special Issue Multidimensional Digital Signal Processing)
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Open AccessArticle
A Robust Semi-Blind Receiver for Joint Symbol and Channel Parameter Estimation in Multiple-Antenna Systems
Electronics 2019, 8(5), 550; https://doi.org/10.3390/electronics8050550
Received: 9 April 2019 / Revised: 25 April 2019 / Accepted: 8 May 2019 / Published: 16 May 2019
PDF Full-text (963 KB) | HTML Full-text | XML Full-text
Abstract
For multiple-antenna systems, the technologies of joint symbol and channel parameter estimation have been developed in recent works. However, existing technologies have a number of problems, such as performance degradation and the large cost of prior information. In this paper, a tensor space-time [...] Read more.
For multiple-antenna systems, the technologies of joint symbol and channel parameter estimation have been developed in recent works. However, existing technologies have a number of problems, such as performance degradation and the large cost of prior information. In this paper, a tensor space-time coding scheme in multiple-antenna systems was considered. This scheme allowed spreading, multiplexing, and allocating information symbols associated with multiple transmitted data streams. We showed that the received signal was formulated as a third-order tensor satisfying a Tucker-2 model, and then a robust semi-blind receiver was developed based on the optimized Levenberg–Marquardt (LM) algorithm. Under the assumption that the instantaneous channel state information (CSI) is unknown at the receiving end, the proposed semi-blind receiver jointly estimates the information symbol and channel parameters efficiently. The proposed receiver had a better estimation performance compared with existing semi-blind receivers, and still performed well when the channel became strongly correlated. Moreover, the proposed semi-blind receiver could be extended to the multi-user massive multiple-input multiple-output (MIMO) system for joint symbol and channel estimation. Computer simulation results were shown to demonstrate the effectiveness of the proposed receiver. Full article
(This article belongs to the Special Issue Multidimensional Digital Signal Processing)
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