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Computer Vision Recognition and Communication Sensing System

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1376

Special Issue Editors


E-Mail Website
Guest Editor
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: Internet of Things; signal processing and data fusion in sensor systems; logistics big data application; computer vision perception; software security; OOP (object-oriented programming); image and digital signal processing
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: wireless communication; power amplifier design; power electronic circuits design of new energy systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Communication and Information System in Signal Processing and Information Theory, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: signal processing; MmWave communication; communication system; physical communication; intelligent communication; information theory; RIS; UAV

Special Issue Information

Dear Colleagues,

Visual recognition and communication perception are two fundamental components of cognitive science and human–computer interactions that play crucial roles in interpreting and understanding the world around us. Visual recognition refers to the ability to identify and process visual stimuli, encompassing everything from recognizing faces and objects to understanding complex scenes and spatial relationships. This capability is not only pivotal in everyday tasks but also forms the backbone of numerous applications in fields such as security, autonomous driving, and augmented reality. On the other hand, communication perception extends beyond the visual to include the interpretation of verbal and non-verbal cues in a communicative context. It involves the decoding of language, intonation, gestures, and facial expressions, enabling individuals to derive meaning and respond appropriately in social interactions. This perceptual ability is fundamental for effective communication and is integral to building relationships, facilitating collaboration, and navigating social complexities. Both visual recognition and communication perception are deeply intertwined with artificial intelligence research, particularly in enhancing machine learning models that aim to replicate human-like understanding and responsiveness. By advancing our knowledge in these areas, we can develop more intuitive and effective systems that better mimic human perceptual and communicative capacities, leading to improvements in technology-mediated interactions and interfaces.

This Special Issue aims to present the advantages and research trends of visual detection and wireless communication technology in multi-dimensional perception of unknown environments, especially based on AI algorithms. Topics of interest for publications include, but are not limited to, the following:

  • Visual recognition perception and tracking in industrial scenes;
  • Integrated sensing and communication;
  • Infrared visual detection and perception;
  • Visual integration navigation and environmental awareness;
  • 3D visual pose estimation;
  • Applications of deep learning in the field of vision.

Dr. Weizhong Qian
Dr. Weimin Shi
Dr. Yue Xiu
Guest Editors

Manuscript Submission Information

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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. Sensors 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 2600 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

  • visual recognition perception
  • waveform design
  • visual positioning navigation
  • deep learning
  • algorithm design
  • integrated sensing and communication

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Published Papers (2 papers)

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Research

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22 pages, 15279 KiB  
Article
Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer
by Yuhai Li, Youchen Fan, Shunhu Hou, Yufei Niu, You Fu and Hanzhe Li
Sensors 2024, 24(14), 4562; https://doi.org/10.3390/s24144562 - 14 Jul 2024
Viewed by 548
Abstract
Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods’ intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using [...] Read more.
Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods’ intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods. Full article
(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)
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Review

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35 pages, 848 KiB  
Review
Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation
by Zhuang Wang, Peiyuan Jiang, Zixuan Wang, Boyuan Han, Haijun Liang, Yi Ai and Weijun Pan
Sensors 2024, 24(14), 4715; https://doi.org/10.3390/s24144715 - 20 Jul 2024
Viewed by 451
Abstract
In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers’ workload and plays [...] Read more.
In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers’ workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology’s applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community. Full article
(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)
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