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Information
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Deep Learning and Signal Processing
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Deep Learning and Signal Processing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 14727

Special Issue Editors

Prof. Dr. Zoran H. Perić

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Guest Editor
Faculty of Electronic Engineering, University of Nis, 18106 Nis, Serbia
Interests: digital telecommunication; quantization; compression; machine learning; coding
Special Issues, Collections and Topics in MDPI journals
Dr. Vlado Delic

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Guest Editor
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: ICT; speech technologies; HCI
Dr. Vladimir Despotovic

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Guest Editor
Faculty of Science, Technology and Communication, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
Interests: spoken language understanding; speech processing; machine learning; natural language processing; fractional calculus
Special Issues, Collections and Topics in MDPI journals
Dr. Marko Milojkovic

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Guest Editor
Faculty of Electronic Engineering, University of Nis, 18106 Nis, Serbia
Interests: ICT; neural networks; machine learning; control systems

Special Issue Information

Dear Colleagues, 

Deep learning can be viewed as the class of machine learning algorithms. Deep learning is an integral part of the most contemporary systems used in many fields, especially in signal classification, signal processing, computer vision, and speech technologies. 

Modern technology relies on research in signal processing and machine intelligence, and a number of methods have been developed with the aim of solving problems such as speech recognition, speaker identification, speech synthesis, recognition and classification of signals (image, speech, audio, and medical), recognition of emotions, signal quality enhancement, detection of signals in the presence of noise, pattern recognition in signals (speech, image, audio, ECG, and other biomedical), automatic diagnosis, methods and algorithms in wireless sensor nodes, neural networks, deep neural networks, and convolution neural networks (CNNs), and business prediction. 

This Special Issue aims not only to present the application of methods for data processing but also to promote development in the fields of deep learning and signal processing, both independently and combined. 

Potential topics include, but are not limited to, the following: 

  • Parametric estimation in signal and data density models;
  • Deep neural networks (DNNs);
  • Convolution neural networks (CNNs);
  • Quantization methods in neural networks;
  • Methods of DNN compression;
  • Speech recognition;
  • Speech synthesis;
  • Speaker identification;
  • Recognition of emotions in voice signals;
  • Face recognition;
  • Linear and non-linear prediction in signals;
  • Linear and non-linear prediction in time series;
  • Estimation of the dynamic range of amplitudes and mean variance of data and signals;
  • Methods of signal compression;
  • Methods and algorithms for the recognition of disease and disease diagnosis in medical signals. 

Papers will be published upon acceptance, regardless of the Special Issue date. 

Dr. Zoran H. Peric
Dr. Vlado Delic
Dr. Vladimir Despotovic
Dr. Marko Milojkovic
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. Information 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 1600 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.

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

Published Papers (3 papers)

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Research

15 pages, 2810 KiB  
Article
Multilingual Offline Signature Verification Based on Improved Inverse Discriminator Network
by Nurbiya Xamxidin, Mahpirat, Zhixi Yao, Alimjan Aysa and Kurban Ubul
Information 2022, 13(6), 293; https://doi.org/10.3390/info13060293 - 9 Jun 2022
Cited by 6 | Viewed by 2692
Abstract
To further improve the accuracy of multilingual off-line handwritten signature verification, this paper studies the off-line handwritten signature verification of monolingual and multilingual mixture and proposes an improved verification network (IDN), which adopts user-independent (WI) handwritten signature verification, to determine the true signature [...] Read more.
To further improve the accuracy of multilingual off-line handwritten signature verification, this paper studies the off-line handwritten signature verification of monolingual and multilingual mixture and proposes an improved verification network (IDN), which adopts user-independent (WI) handwritten signature verification, to determine the true signature or false signature. The IDN model contains four neural network streams with shared weights, of which two receiving the original signature images are the discriminative streams, and the other two streams are the reverse stream of the gray inversion image. The enhanced spatial attention models connect the discriminative streams and reverse flow to realize message propagation. The IDN model uses the channel attention mechanism (SE) and the improved spatial attention module (ESA) to propose the effective feature information of signature verification. Since there is no suitable multilingual signature data set, this paper collects two language data sets (Chinese and Uyghur), including 100,000 signatures of 200 people. Our method is tested on the self-built data set and the public data sets of Bengali (BHsig-B) and Hindi (BHsig-H). The method proposed in this paper has the highest discrimination rate of FRR of 10.5%, FAR of 2.06%, and ACC of 96.33% for the mixture of two languages. Full article
(This article belongs to the Special Issue Deep Learning and Signal Processing)
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11 pages, 21614 KiB  
Article
Enhanced Feature Pyramid Vision Transformer for Semantic Segmentation on Thailand Landsat-8 Corpus
by Kritchayan Intarat, Preesan Rakwatin and Teerapong Panboonyuen
Information 2022, 13(5), 259; https://doi.org/10.3390/info13050259 - 19 May 2022
Cited by 1 | Viewed by 3328
Abstract
Semantic segmentation on Landsat-8 data is crucial in the integration of diverse data, allowing researchers to achieve more productivity and lower expenses. This research aimed to improve the versatile backbone for dense prediction without convolutions—namely, using the pyramid vision transformer (PRM-VS-TM) to incorporate [...] Read more.
Semantic segmentation on Landsat-8 data is crucial in the integration of diverse data, allowing researchers to achieve more productivity and lower expenses. This research aimed to improve the versatile backbone for dense prediction without convolutions—namely, using the pyramid vision transformer (PRM-VS-TM) to incorporate attention mechanisms across various feature maps. Furthermore, the PRM-VS-TM constructs an end-to-end object detection system without convolutions and uses handcrafted components, such as dense anchors and non-maximum suspension (NMS). The present study was conducted on a private dataset, i.e., the Thailand Landsat-8 challenge. There are three baselines: DeepLab, Swin Transformer (Swin TF), and PRM-VS-TM. Results indicate that the proposed model significantly outperforms all current baselines on the Thailand Landsat-8 corpus, providing F1-scores greater than 80% in almost all categories. Finally, we demonstrate that our model, without utilizing pre-trained settings or any further post-processing, can outperform current state-of-the-art (SOTA) methods for both agriculture and forest classes. Full article
(This article belongs to the Special Issue Deep Learning and Signal Processing)
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12 pages, 11386 KiB  
Article
Object Detection of Road Assets Using Transformer-Based YOLOX with Feature Pyramid Decoder on Thai Highway Panorama
by Teerapong Panboonyuen, Sittinun Thongbai, Weerachai Wongweeranimit, Phisan Santitamnont, Kittiwan Suphan and Chaiyut Charoenphon
Information 2022, 13(1), 5; https://doi.org/10.3390/info13010005 - 25 Dec 2021
Cited by 19 | Viewed by 7556
Abstract
Due to the various sizes of each object, such as kilometer stones, detection is still a challenge, and it directly impacts the accuracy of these object counts. Transformers have demonstrated impressive results in various natural language processing (NLP) and image processing tasks due [...] Read more.
Due to the various sizes of each object, such as kilometer stones, detection is still a challenge, and it directly impacts the accuracy of these object counts. Transformers have demonstrated impressive results in various natural language processing (NLP) and image processing tasks due to long-range modeling dependencies. This paper aims to propose an exceeding you only look once (YOLO) series with two contributions: (i) We propose to employ a pre-training objective to gain the original visual tokens based on the image patches on road asset images. By utilizing pre-training Vision Transformer (ViT) as a backbone, we immediately fine-tune the model weights on downstream tasks by joining task layers upon the pre-trained encoder. (ii) We apply Feature Pyramid Network (FPN) decoder designs to our deep learning network to learn the importance of different input features instead of simply summing up or concatenating, which may cause feature mismatch and performance degradation. Conclusively, our proposed method (Transformer-Based YOLOX with FPN) learns very general representations of objects. It significantly outperforms other state-of-the-art (SOTA) detectors, including YOLOv5S, YOLOv5M, and YOLOv5L. We boosted it to 61.5% AP on the Thailand highway corpus, surpassing the current best practice (YOLOv5L) by 2.56% AP for the test-dev data set. Full article
(This article belongs to the Special Issue Deep Learning and Signal Processing)
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