Deep Learning Technologies for Internet of Video Things

Submission Deadline:  31 March 2021

IEEE Access invites manuscript submissions in the area of Deep Learning Technologies for Internet of Video Things.   

The past decade has witnessed tremendous advances in Internet of Things (IoT) technologies, protocols, and applications. The goal of IoT is to connect every physical device/sensor (with video, audio, texting capabilities) in a seamless network to allow communication and perform intelligent decisions. Among these, video data devices (things) in IoT are increasingly becoming important since video communications are an essential part of everyone’s experiences and everything that is happening around the world. The technologies of Internet of Video Things (IoVT) are widely expected to bring exciting services and applications for security, healthcare, education, transportation, and so on.

Due to the huge amount of video data that is being generated and consumed nowadays, there are many challenges and problems that are yet to be solved to have practical real-world applications of IoVT, from sensing/capturing to displaying the data. First, efficient video sensing technologies are required to capture high quality videos with low-power. Second, video coding and communication technologies are essential for compressing and transmitting enormous volumes of data. Third, since not all transmitted data are useful for (or targets) human consumption, there are huge challenges in the areas of data learning/understanding to filter and extract high-level information. Finally, video quality enhancement and assessment algorithms are indispensable for improving and evaluating the video quality, respectively.

Recently, data-driven algorithms such as deep neural networks have attracted a lot of attention and have become a popular area of research and development. This interest is driven by several factors, such as recent advances in processing power (cheap and powerful hardware), the availability of large data sets (big data) and several small but important algorithmic advances (e.g., convolutional layers). Nowadays, deep neural networks are the state-of-the-art for several computer vision tasks, such as the ones that require high level understanding of image semantics, e.g. image classification, object segmentation, saliency detection, but also in low level image processing tasks, such as image denoising, inpainting, super-resolution, among others. Deep learning can handle large volumes of video data by making use of its powerful non-linear mapping, and extracting high-level features with very deep networks. Incorporating deep learning into IoVT can provide radical innovations in video sensing, coding, enhancing, understanding, and evaluation areas, to better handle the enormous growth in video data compared to traditional methods. On the other hand, incorporating deep learning into IoVT also brings a lot of challenges such as the long training latency of data training and huge computational cost.

This Special Section in IEEE Access provides a perfect platform for researchers from academia and industry to discuss the prospective developments and innovative ideas in applying deep learning technologies for IoVT.

The topics of interest include, but are not limited to:

  • Deep learning technologies in video sensing/capturing systems
  • Deep learning technologies in visual communications
  • Deep learning algorithms, architectures, and databases for image and video compression/coding
  • Deep learning for enhancement and quality assessment of visual data
  • Deep learning-based visual attention and saliency detection
  • Deep learning-based real-time and low-power video coding technologies
  • Deep learning-based algorithms, architectures, and databases for video analysis and understanding
  • Deep learning-based 3D visual coding and processing (from 360º degrees to light-fields)
  • Technologies for reducing the complexity of deep learning-based IoVT
  • Technologies for reducing training latency of deep learning-based IoVT

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

  Associate Editor:  Jinjia Zhou, Hosei University, Japan

  Guest Editors:

    1. Joao Ascenso, University of Lisbon, Portugal
    2. Victor Sanchez, University of Warwick, UK
    3. Lu Zhang, INSA Rennes, France
    4. Jianquan Liu, NEC Corporation, Japan
    5. Jiu Xu, Apple, USA

 

Relevant IEEE Access Special Sections:

    1. Mobile Multimedia: Methodology and Applications
    2. Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing
    3. Deep Learning Algorithms for Internet of Medical Things

 IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

 Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

 For inquiries regarding this Special Section, please contact: jinjia.zhou.35@hosei.ac.jp.

Advanced Artificial Intelligence Technologies for Smart Manufacturing

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Advanced Artificial Intelligence Technologies for Smart Manufacturing.

As the world enters a new phase of industrialization (Industry 4.0, or the fourth industrial revolution), smart manufacturing has become crucial. Industry 4.0 refers to an industrial transformation aided by smart manufacturing and data exchange, such as high-level factory automation and Internet of Things applications. Artificial intelligence and smart machinery have also become integral research areas in manipulation. Researchers from academia and various industries are now working to develop the next generation of intelligent smart manufacturing applications. With the application of advanced artificial intelligence technologies, the revolution of the smart manufacturing industry can beadvanced more quickly.

To build a competitive advantage, keep up with the “Industry 4.0” trend, and to attract and filter high quality academic contributions, we have organized this Special Section in IEEE Access on “Advanced Artificial Intelligence Technologies for Smart Manufacturing.” High quality articles within the field are highly encouraged and considered in this Special Section.

The topics of interest include, but are not limited to:

  • Artificial Intelligence, Embedded Systems and Cloud Computing in Manufacturing
  • Smart Actuators and Adaptive Control of Machine Tools
  • Man-machine interface and integration
  • Intelligent machinery equipment
  • Intelligent Automation
  • Intelligent manufacturing
  • Advanced signal processing and machine perception of mechanical systems
  • Machine learning techniques for smart manufacturing
  • M2M technology
  • Big Data Analytics in Manufacturing

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor: Her-terng Yau, National Chin-Yi University of Technology, Taiwan

Guest Editors:

    1. Stephen D. Prior, University of Southampton, UK
    2. Yang Wang, Georgia Institute of Technology, USA
    3. Yunhua Li, BeiHang University, China

 

Relevant IEEE Access Special Sections:

  1. Artificial Intelligence Technologies for Electric Power Systems
  2. Big Data Technology and Applications in Intelligent Transportation
  3. Advances in Machine Learning and Cognitive Computing for Industry Applications

 

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: pan1012@ms52.hinet.net; htyau@ncut.edu.tw.

Multi-Energy Computed Tomography and its Applications

Submission Deadline:  01 May 2021

IEEE Access invites manuscript submissions in the area of Multi-Energy Computed Tomography and its Applications.

X-ray Computed Tomography (CT) can reconstruct the internal image of an object by passing x-rays through it and measuring the information. However, the conventional CT not only has poor performance in tissue contrast and spatial resolution, but also fails to provide quantitative analysis results and specific material components. To avoid these limitations, as a natural extension of the well-known dual-energy CT, the multi-energy CT (MECT) has emerged and is attracting increasing attention. A typical MECT system has great potential in reducing x-ray radiation doses, improving spatial resolution, enhancing material discrimination ability and providing quantitative results by collecting several projections from different energy windows (e.g. photon-counting detector technique) or spectra (e.g. fast kV-switching technique) either sequentially or simultaneously. It is a great achievement in terms of tissue characterization, lesion detection and material decomposition, etc. This can enhance the capabilities of imaging internal structures for accurate diagnosis and optimized treatments.

On the one hand, the limited photons within the narrow energy windows can result in energy response inconsistency. On the other hand, due to spectral distortions (e.g., charge sharing, K-escape, fluorescence x-ray emission and pulse pileups), the projections of MECT are tarnished by complicated noise. In this case, it is a challenge to find meaningful insights by utilizing these projections for practical applications. Therefore, there are new research opportunities to overcome this issue for higher levels of MECT imaging and applications.

This Special Section on IEEE Access aims to capture the state-of-the-art advances in imaging techniques for MECT and other related research.

The topics of interest include, but are not limited to:

  • MECT image reconstruction
  • MECT image denoising
  • MECT material decomposition
  • MECT hardware development
  • MECT system design
  • MECT image analysis
  • MECT image quality assessment
  • Applications of machine learning in MECT
  • X-ray spectrum estimation for MECT
  • Clinical diagnosis using MECT technique
  • Multi-contrast contrast agent imaging
  • K-edge imaging technique
  • Simulation software package for MECT imaging
  • Scattering correction for MECT
  • Artifacts removal of MECT image
  • Noise estimation models for MECT imaging

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor:  Hengyong Yu, University of Massachusetts Lowell, USA

Guest Editors:

    1. Yuemin Zhu, CNRS, University of Lyon, France
    2. Raja Aamir Younis, Khalifa University of Science and Technology UAE

 

Relevant IEEE Access Special Sections:

  1. Deep Learning Algorithms for Internet of Medical Things
  2. Millimeter-Wave and Terahertz Propagation, Channel Modeling and Applications
  3. Trends and Advances in Bio-Inspired Image-Based Deep Learning Methodologies and Applications

 

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: Hengyong-yu@ieee.org.

Beyond 5G Communications

Submission Deadline: 30 September 2020

IEEE Access invites manuscript submissions in the area of Beyond 5G Communications.

As the commercial deployment of the fifth generation of cellular networks (5G) is well underway in many countries of the world, academia as well as industrial research organizations turn their attention to what comes next. As it typically takes ten years to develop a new cellular communication standard, it is now the perfect time to identify promising topics and research directions for the next decade, which will lay the foundations for a possible 6G system. Moving from 4G to 5G, no disruptive changes to the physical layer were made. The main novelty was to simultaneously support a set of diverse applications with different throughput, latency, and reliability requirements, thanks to a flexible OFDM numerology and the concept of network slicing. Also, the spectral efficiency could be dramatically increased by supporting larger bandwidths and antenna arrays at the base station, i.e., massive MIMO. Although machine learning is currently one of the hottest topics in the field of communications, it did not play any role in the design of 5G and will mainly be used to implement, optimize, and operate such systems efficiently. 6G will likely be driven by a mix of past trends (e.g., more cells, larger and distributed antenna arrays, higher spectrum) as well as new technologies, services, applications, and devices.

The aim of this Special Section is to gather forward-looking contributions on radio access technologies beyond 5G. Topics of interest comprise new frequency bands, new multiple-antenna technologies (passive and/or active), new network deployments, new waveforms, and new applications of RF signals beyond mere communications, as well as the fusion of wireless and sensor information. A tool of central importance is machine learning, to either learn entirely new communication protocols or simply enhance traditional algorithms. Since the development of a new standard is largely driven by use cases, e.g., mobile broadband, mission critical applications, massive machine-type traffic, we explicitly solicit opinion and vision articles concerning the potential requirements and key enablers of 6G.

The topics of interest include, but are not limited to:

  • New wireless communication systems, network deployments, and spectrum sharing
  • Machine learning-based wireless systems and services
  • Terahertz communications and networks
  • Radar enhanced wireless systems
  • New multiple antenna technologies and deployments
  • Massive connectivity in communication systems
  • Edge intelligence for beyond 5G networks
  • Wireless big data enabled technologies
  • Photonics and wireless integration
  • Autonomous networks

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor:  Jakob Hoydis, Nokia Bell Labs, France

Guest Editors:

    1. Ulf Gustavsson, Ericsson AB, Sweden
    2. Urbashi Mitra, University of Southern California, USA
    3. Luca Sanguinetti, University of Pisa, Italy
    4. Christoph Studer, Cornell University, USA
    5. Meixia Tao, Shanghai Jiao Tong University, China

 

Relevant IEEE Access Special Sections:

  1. Antenna and Propagation for 5G and Beyond
  2. 5G and Beyond Mobile Wireless Communications Enabling Intelligent Mobility 
  3. Millimeter-wave and Terahertz Propagation, Channel Modeling and Applications

 

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: jakob.hoydis@nokia-bell-labs.com.

Emerging Deep Learning Theories and Methods for Biomedical Engineering

Submission Deadline: 31 August 2020

IEEE Access invites manuscript submissions in the area of Emerging Deep Learning Theories and Methods for Biomedical Engineering.

The accelerating power of deep learning in diagnosing disease and analyzing medical data will empower physicians and speed up decision-making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated large amounts of biomedical information in recent years. However, new AI methods and computational models for efficient data processing, analysis, and modeling with the generated data is important for clinical applications and in understanding the underlying biological process.

Deep learning has rapidly developed in recent years, in terms of both methodological development and practical applications. It provides computational models of multiple processing layers to learn and represent data with various levels of abstraction. It can implicitly capture intricate structures of large-scale data and is ideally suited to some of the hardware architectures that are currently available.

The purpose of this Special Section aims to provide a diverse, but complementary, set of contributions to demonstrate new theories, techniques, developments, and applications of Deep learning, and to solve emerging problems in biomedical engineering.

The ultimate goal of this Special Section is to promote research and development of deep learning for multimodal & multidimensional signals in biomedical engineering by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

The topics of interest include, but are not limited to:

  • Theoretical understanding of deep learning in biomedical engineering
  • Transfer learning and multi-task learning
  • Joint Semantic Segmentation, Object Detection and Scene Recognition on biomedical images
  • Improvising on the computation of a deep network; exploiting parallel computation techniques and GPU programming
  • Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.
  • Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)
  • Optimization by deep neural networks, multi-dimensional deep learning
  • New model or new structure of convolutional neural network
  • Visualization and explainable deep neural network
  • Missing data imputation for multi-source biomedical data
  • Sparse screening, feature screening, feature merging, quality assessment for biomedical data

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor: Yu-Dong Zhang, University of Leicester, United Kingdom

Guest Editors:

    1.  Zhengchao Dong, Columbia University, USA
    2. Juan Manuel Gorriz, University of Granada, Spain
    3. Yizhang Jiang, Jiangnan University, China
    4. Ming Yang, Nanjing Medical University, China
    5. Shui-Hua Wang, Loughborough University, UK

 

Relevant IEEE Access Special Sections:

  1. Deep Learning Algorithms for Internet of Medical Things
  2. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts
  3. Data-Enabled Intelligence for Digital Health

IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  yudongzhang@ieee.org.

Geometric Algebra in Signal Processing

Submission Deadline: 02 October 2020

IEEE Access invites manuscript submissions in the area of Geometric Algebra in Signal Processing.

Geometric algebra (GA) is the proper algebra to handle geometric elements and their transformations. It is a coherent body of concepts and methods that works for any dimension, and has great algebraic and algorithmic power, but is fundamentally simple. Since these features are lacking in the currently taught geometric methods at any level, there are strong reasons to see geometric algebra as the geometric language of choice for the 21st century and to strive for its wide acceptance in education and research.

This Special Section on geometric algebra in signal processing is intended to help bridge the gap between geometric algebra and signal processing research communities by providing a forum for high-quality research articles that explore challenges faced in this overlap. The Special Section is an opportunity to foster increased dialogue between these research communities and encourage new and exciting avenues of research that span across them.

The topics of interest include, but are not limited to:

  • Applications of GA to image processing
  • Applications of GA to medical images
  • Applications of GA to wireless networks
  • Applications of GA to computer vision
  • Applications of GA to artificial neural networks
  • Applications of GA to image understanding
  • Applications of GA to target tracking
  • Applications of GA to intelligent vision systems
  • Applications of GA to multimedia
  • Applications of GA to deep learning
  • Applications of GA to communication

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor: Wenming Cao, Shenzhen University, Shenzhen, China

Guest Editors:

    1. Zhihai He, Missouri University, USA
    2. Mylène C.Q. Farias, University of Brasília (UnB), Brasília – DF, Brazil
    3. Guitao Cao, East China Normal University, China
    4. Guoping Qiu, University of Nottingham, Nottingham, UK
    5. Daquan Feng, Shenzhen University, Shenzhen, China

 

Relevant IEEE Access Special Sections:

  1. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions
  2. Emerging Trends, Issues and Challenges for Array Signal Processing and Its Applications in Smart City
  3. Biologically inspired image processing challenges and future directions


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: wmcao@szu.edu.cn.

Visual Perception Modeling in Consumer and Industrial Applications

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Visual Perception Modeling in Consumer and Industrial Applications.

In recent literature, various visual perception mechanisms have been modeled to facilitate the relevant consumer and industrial applications, from low-level visual attention to higher-level quality of experience, object detection, and recognition. Specifically, visual attention can help us handle massive amounts of visual information efficiently, and visual attention modeling can help us simulate such visual attention mechanisms and focus on more salient information. Since the ultimate receiver of the processed signal is often human, the receiver’s perception of the overall quality is also very important, and quality perception modeling can help control the whole processing chain and guarantee a good perceptual quality of experience. Due to the rapid advancement of machine learning techniques, higher-level perception modeling related to semantics also becomes possible. How to utilize the most recent big data and learning techniques to interpret and model visual perception also becomes a problem. What’s more, a growing number of advanced multimedia technologies have become available over the last decade, such as High dynamic range (HDR) imaging, virtual reality (VR), augmented reality (AR), mixed reality (MR), and light field imaging. Visual perception modeling for such advanced multimedia technologies also needs further research. This Special Section solicits novel and high-quality articles to present reliable solutions and technologies of the above-mentioned problems.

The topics of interest include, but are not limited to:

  • Visual perception modeling for various consumer and industrial applications
  • Visual attention modeling, including the mechanism of visual attention, visual saliency prediction and the utilization of visual attention models in relevant applications
  • Visual quality of experience modeling, including visual quality assessment, control, and optimization for consumer electronics and industrial applications
  • Advanced learning technologies, such as deep learning, random forests (RF), multiple kernel learning (MKL), and their applications in visual perception modeling
  • Statistical analytics and modeling based on big data (cloud) for images, videos or other formats of industrial data
  • Emerging multimedia technologies, such as virtual reality (VR), augmented reality (AR), 4-dimensional (4-D) light fields, and high dynamic range (HDR), including visual perception modeling for these emerging technologies and their use in industry

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.

 

Associate Editor: Guangtao Zhai, Shanghai Jiao Tong University, China

Guest Editors:

    1. Xiongkuo Min, The University of Texas at Austin, USA
    2. Vinit Jakhetiya, Indian Institute of Technology (IIT), Jammu, India
    3. Hamed Rezazadegan Tavakoli, Aalto University, Finland
    4. Menghan Hu, East China Normal University, China
    5. Ke Gu, Beijing University of Technology, China

 

Relevant IEEE Access Special Sections:

  1. Recent Advances in Video Coding and Security
  2. Biologically inspired image processing challenges and future directions
  3. Integrative Computer Vision and Multimedia Analytics


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: zhaiguangtao@sjtu.edu.cn.

Millimeter-Wave Communications: New Research Trends and Challenges

Submission Deadline: 30 September 2019

IEEE Access invites manuscript submissions in the area of Millimeter-Wave Communications: New Research Trends and Challenges.

With various applications emerging, e.g., virtual reality, artificial intelligence, ultra-high definition video, Internet of things, and mobile Internet, there is an urgent demand to increase the bandwidth of wireless networks. To meet the bandwidth requirement of new and emerging applications, it is necessary to move from the existing microwave bands toward higher frequency, i.e., the millimeter wave (mmWave) band. In the last decade, the unlicensed spectrum around 60 GHz has been applied to wireless local area network (WLAN), exploring the indoor use of mmWave communications. Recently, mmWave communication has been proposed as one of the key technologies for 5G cellular networks to fulfill the demand of ultra-high data rates. Satellite communications and high-altitude unmanned aerial vehicle (UAV) communications also tend to exploit the mmWave band (Ka band) to increase the transmission capacity.

Both industry and academia have developed key technologies of mmWave communications, and have made significant progress, e.g., in beamforming design, channel estimation, and capacity evaluation. However,  there are new emerging areas where mmWave communications also play a crucial role, with key challenges demanding substantial research; for instance, mmWave communications with non-orthogonal multiple access (mmWave-NOMA) to accommodate the rapidly increasing number of users; full-duplex mmWave communications for relay and backhaul to double link throughput; mmWave UAV communications for both low-altitude and high-altitude UAVs; mmWave communications for 5G vehicle-to-everything (V2X), etc. The key challenges on the design of these new mmWave communication technologies include multi-user interference mitigation in mmWave-NOMA, self-interference cancellation in full-duplex mmWave communications, fast beam tracking in mmWave UAV communications, and the security and multiple access issues in mmWave communications, among others.

This Special Section in IEEE Access focuses on new trends and challenges for mmWave communications. The aim of this Special Section is to share and discuss recent advances and future trends of mmWave communications, and to bring academic researchers and industry developers together.

The topics of interest include, but are not limited to:

  • MmWave communication for 5G/B5G/6G
  • Beamforming, precoding, channel estimation, etc., for mmWave communication
  • Beamforming, power allocation, user pairing techniques and joint designs for mmWave-NOMA
  • Performance evaluation and system design for mmWave-NOMA
  • Beamforming and self-interference cancellation for mmWave full-duplex communications
  • Performance analysis and system design for mmWave full-duplex communications
  • UAV deployment, beamforming, beam tracking techniques and joint designs for mmWave UAV communications
  • Mobility management and blockage issues in mmWave UAV communications
  • Very high date rate and very long distance mmWave data links
  • Security and Privacy in mmWave communications

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Zhenyu Xiao, Beihang University, China

Guest Editors:

  1. Jinho Choi, Deakin University, Australia
  2. Ning Zhang, Texas A&M University-Corpus Christi, USA
  3. Jianhua He, Aston University, UK
  4. Lin Bai, Beihang University, China
  5. Qinyu Zhang, Harbin Institute of Technology, China

 

Relevant IEEE Access Special Sections:

  1. New Waveform Design and Air-Interface for Future Heterogeneous Network towards 5G
  2. Green Signal Processing for Wireless Communications and Networking
  3. D2D Communications: Security Issues and Resource Allocation


IEEE Access Editor-in-Chief:  Derek Abbott, Professor, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: xiaozy@buaa.edu.cn.

Digital Forensics through Multimedia Source Inference

Submission Deadline: 30 June 2019

IEEE Access invites manuscript submissions in the area of Digital Forensics through Multimedia Source Inference.

With the prevalence of low-cost imaging devices (smartphones, tablets, camcorders, digital cameras, scanners, wearable and IoT devices), images and videos have become the main modalities of information being exchanged in every walk of life. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of multimedia on digital social platforms. In the meantime, powerful multimedia editing tools allow even unskilled people to easily manipulate digital content for malicious or criminal purposes. In all cases where multimedia serves as critical evidence, forensic technologies that help to determine the origin, the authenticity of multimedia sources and integrity of multimedia content become essential to forensic investigators.

Imaging devices and post-acquisition processing software leave unique “fingerprints” in multimedia content. This allows many challenging problems faced by the multimedia forensics community to be addressed through source inference. Source inference is the task of linking digital content to its source device or platform (e.g. social media such as Facebook) responsible for its creation.  It can facilitate applications such as verification of source device and platform, common source inference, identification of source device and platform, content integrity verification, and source-oriented image clustering. It also allows the establishment of digital evidence or history of multimedia processing steps applied to the content, starting from the acquisition procedure up to tracking the spread.

Recent adoption of multimedia source inference techniques in the law enforcement sector (e.g., UK Sussex Police, Guildford Crown Court and INTERPOL) in real-world criminal cases and child sexual exploitation databases has manifested the significant value of multimedia source inference in the fight against crime. This Special Section in IEEE Access aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in digital forensics through multimedia source inference.

The topics of interest include, but are not limited to:

  • Multimedia processing techniques for source inference
  • Machine learning and pattern recognition techniques for source inference
  • Formulation and extraction of device and platform fingerprints
  • New state-of-the-art datasets for multimedia forensics benchmarking
  • Studies of successful cases of source inference application

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  

Irene Amerini, MICC – Media Integration and Communication Center, University of Florence, Italy

Prof. Chang-Tsun Li,  Charles Sturt University, Australia

Guest Editors:

  1. Nasir Memon, NYU Tandon, USA
  2. Jiwu Huang, Shenzhen University, China


IEEE Access Editor-in-Chief:  Derek Abbott, Professor, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: irene.amerini@unifi.it.

Biologically inspired image processing challenges and future directions

Submission Deadline: 31 August 2019

IEEE Access invites manuscript submissions in the area of Biologically inspired image processing challenges and future directions.

Human beings are exposed to large amounts of data. According to statistics, more than 80% of the information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic, but also a challenging task. The unique information processing mechanism of the human visual system makes it have fast, accurate and efficient image processing capability. At present, many advanced techniques of image analysis and processing have been widely used in image communication, geographic information system, medical image analysis and virtual reality. However, there is still a big gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it also can be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanism, computer science and mathematical science, the related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain.

The goal of this Special Section in IEEE Access is to explore biological vision mechanisms and characteristics to establish an objective image-processing model and algorithm that is closer to the human visual information-processing model. This Special Section is encouraging advanced research related to biologically inspired image system study and to promote the synergetic development of biological vision and computer vision. Original research articles seeking all biologically inspired aspects of image analysis and processing techniques, including emerging trends and applications, theoretical studies, and experimental prototypes are welcome. The manuscripts should not be submitted simultaneously for publication elsewhere. Submissions of high quality manuscripts describing future potential or ongoing work are sought.

The topics of interest include, but are not limited to:

  • Biologically inspired novel color image enhancement techniques
  • Biologically inspired image/video feature modeling and extraction
  • Research on bio inspired virtual reality and human-computer interaction
  • Biologically inspired depth learning for unsupervised & semi-supervised learning
  • Biologically inspired big data analysis of image system
  • Biologically inspired multimedia quality evaluation
  • Biologically inspired target recognition technology in Real-time dynamic system
  • Biologically inspired image restoration Research and Application
  • Biologically inspired target detection and classification
  • Research and application of biologically inspired visual characteristics
  • Biologically inspired statistical learning model for image processing
  • Biologically inspired graph optimization algorithms and application

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor: Jiachen Yang, Tianjin University, China

Guest Editors:

  1. Qinggang Meng, Loughborough University, UK
  2. Maurizio Murroni, University of Cagliari, Italy
  3. Shiqi Wang, City University of Hong Kong, China
  4. Feng Shao, Ningbo University, China

Relevant IEEE Access Special Sections:

  1. Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities
  2. Recent Advantages of Computer Vision based on Chinese Conference on Computer Vision (CCCV) 2017
  3. New Trends in Brain Signal Processing and Analysis


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: yangjiachen@tju.edu.cn.