Advances in Signal Processing for Non-Orthogonal Multiple Access

Submission Deadline: 31 May 2019

IEEE Access invites manuscript submissions in the area of Advances in Signal Processing for Non-Orthogonal Multiple Access.

Driven by the continuous growth in the number of mobile devices and rapid development of the Internet-of-Things (IoT), the fifth generation (5G) wireless communication networks anticipate an explosive demand for massive connectivity over limited radio resources. Towards this direction, researchers are motivated to develop new transmission technologies for maximizing the achievable throughput. Among various solutions, non-orthogonal multiple access (NOMA) has been envisioned as a prospective technology to enlarge the number of connections, increase the spectral efficiency, and balance the user fairness. Owing to its promising features, NOMA has been recently deployed in 3GPP Long Term Evolution Advanced (LTE-A) and recognized as a breakthrough technology for 5G wireless networks in both industry and academia.

From the first generation (1G) to the fourth generation (4G), cellular communications have deployed orthogonal multiple access (OMA) technologies to mitigate multiple access interference and enjoy low-complexity signal processing, in which the communication resources allocated to different users are orthogonal in at least one radio resource dimension (e.g., frequency, time, code, etc.). As a result, the number of active users allowed access to the OMA system is strictly limited by the number of available orthogonal resources, which becomes less useful for supporting massive connectivity and achieving user fairness. In contrast to OMA, NOMA simultaneously accommodates a multitude of users with the same radio resource via superposition signaling and employs various transmit or receive signal processing techniques to mitigate the interference. Specifically, by introducing a controllable interference and an acceptable signal processing complexity, NOMA is beneficial to enlarge the number of connections and support high overloading transmission. However, the success of NOMA technologies relies critically on the implementation of advanced signal processing techniques for transceivers, which may introduce large processing delays and increase computation complexity. Thanks to the recent progress of hardware and theory in signal processing and machine learning, large signal processing complexity becomes affordable and processing latency can be significantly reduced, which promote the rapid development of NOMA. Therefore, sophisticated signal processing algorithms for multi-user detection, scheduling, and interference management are indispensable for the successful implementation of NOMA in next-generation wireless systems.

As a novel multiple access technology, NOMA is a promising candidate to achieve high spectral efficiency and massive connectivity for future wireless communications. However, there are still many signal processing problems remaining to be solved to unlock the potential of NOMA technologies for later phases of 5G. This Special Section in IEEE Access aims to capture the state-of-the-art advances in NOMA particularly from the perspective of signal processing and foster new avenues for research in this area.

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

  • Novel signal detection and transceiver design for NOMA
  • Emerging applications of NOMA in 5G, IoT, V2X, and UAV
  • Cooperative signal processing for NOMA
  • Resource allocation and schedule in NOMA networks
  • Adaptive signal processing algorithms for NOMA
  • Energy efficiency optimization for NOMA systems
  • Grant-free NOMA system design
  • Advanced channel coding and modulation schemes for NOMA
  • Security provisioning in NOMA
  • Multiple antenna signal processing techniques for NOMA
  • Pilot design and channel estimation for NOMA
  • NOMA assisted wireless caching and mobile edge computing
  • Machine learning for NOMA
  • NOMA in wireless powered communications

 

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Associate Editor: Miaowen Wen, South China University of Technology, China

Guest Editors:

  1. Zhiguo Ding, University of Manchester, UK
  2. Ertugrul Basar, Koc University, Turkey
  3. Yuanwei Liu, Queen Mary University of London, UK
  4. Fuhui Zhou, Nanchang University, China
  5. Ioannis Krikidis, Cyprus University, Cyprus
  6. Mojtaba Vaezi, Villanova University, USA
  7. Vincent Poor, Princeton University, USA

 

Relevant IEEE Access Special Sections:

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


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: eemwwen@scut.edu.cn.

Neural Engineering Informatics

Submission Deadline: 31 July 2019

IEEE Access invites manuscript submissions in the area of Neural Engineering Informatics.

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, cognitive systems have been proposed as useful and effective frameworks for the modeling and understanding of brain activity patterns. They also enable direct communication pathways between the brain and external devices (brain computer/machine interfaces). However, most of the research so far has focused on lab-based applications in constrained scenarios, which cannot be extrapolated to realistic field contexts. Considering the decoding of brain activity, biomedical engineers provide excellent tools to overcome the challenge of learning from brain activity patterns that are very likely to be affected by non-stationary behaviors and high uncertainty. The application of health and neural engineering to learning and modeling has recently demonstrated its remarkable usefulness for coping with the effects of extremely noisy environments, as well as the variability and dynamicity of brain signals. Additionally, neurobiological studies have suggested that the behavior of neural cells exhibits functional patterns that resemble the properties of computational neuroscience to encode logical perception. This paves the way for developing new computational neuroscience techniques in medicine and healthcare that foster the capabilities for modeling and understanding brain function from a quantitative point of view, which is also the basis of this Special Section in IEEE Access.

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

  • Novel models and theoretical computational learning for the synthesis and analysis of neuroimaging data: EEG, MEG, fMRI /MRI, PET/SPECT, fNIRS, DOI, EROS, etc
  • Modeling and learning for the recognition of cognitive processes including medical informatics and public health informatics
  • Bioinformatics decoding of brain activity patterns and brain computer/machine interfaces (BCI/BMI)
  • Imaging Informatics that explain the structure and function of the human brain in patients
  • Neuro methods for big data neuroimaging analytics and neuro-informatics
  • Hardware architectures in health and neuro-engineering

 

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Associate Editor:  Zehong Cao, University of Technology Sydney, Australia

Guest Editors:

  1. Peng Xu, University of Electronic Science and Technology of China, China
  2. Zhiguo Zhang, Shenzhen University, China
  3. Gang Wang, Xi’an JiaoTong University, China
  4. Samu Taulu, University of Washington, USA
  5. Leandro Beltrachini, Cardiff University, UK

 

Relevant IEEE Access Special Sections:

  1. Healthcare Information Technology for the Extreme and Remote Environments
  2. Advanced Information Sensing and Learning Technologies for Data- centric Smart Health Applications
  3. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things


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: zhcaonctu@gmail.com or Zehong.Cao@uts.edu.au.

Advances in Power Line Communication and its Applications

Submission Deadline: 30 April 2019

IEEE Access invites manuscript submissions in the area of Advances in Power Line Communication and its Applications.

Power line communication (PLC) is a growing technology which utilizes the existing pre-installed power delivery network for data transmission. While it is true that the history of this technology goes back to the beginning of the last century when the first data transmission over power lines took place for low data rate control and monitoring purposes, PLC has recently regained a considerable amount of research attention due to the dawn of the internet and the increasing need for fast connectivity. PLC is also expected to serve as a reliable communication medium for many emerging applications of Internet-of-things (IoT) and Smart Grids.

PLC has a number of advantages that make it an appealing complement, as well as a competitor, to other wireless technologies. For example, PLC does not require any new wiring installations which can significantly reduce the deployment costs. Another advantage of PLC is that it can enable communication with hard-to-reach nodes where the RF wireless signal suffers from high levels of attenuation, as in underground structures, buildings with obstructions and metal walls, or simply when the wireless signal is undesirable for EMI issues such as in hospitals. Furthermore, PLC can provide a low-cost solution to complement other existing technologies such as RF wireless or visible light communication (VLC) systems. In particular, integrating PLC and VLC systems has recently received a considerable amount of research attention, enabling new generation of high-speed indoor communications with numerous applications.

With this in mind, the smart grid (SG) is one of the most important applications of PLCs. Although the realization of SG can be achieved using several communication solutions including, but not limited to, ZigBee, WiMAX, long range wireless and cellular (3-5G), PLC remains the most popular and attractive candidate to SG developers because of the widely available infrastructure and because PLC is a through-the-grid technology. This is an important feature because it reduces the reliance of utility companies on third party connectivity which of course can diminish security and privacy issues. Other applications that can also benefit from the fact that new cables are not required include smart city, telemetry, in-home automation, etc. Furthermore, PLC for in-vehicle data buses is also an interesting research area which has received a substantial attention over the past years. In this respect, many PLC networks have been explored in cars, trains, ships and aircraft systems.

Motivated by the above, this Special Section in IEEE Access aims to capture the state-of-the-art advances in power line communication and its applications, and outline the possible future research directions.

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

  • Channel measurement, modeling and interoperability (EMC)
  • Advanced PLC technologies (PHY, MAC and networking including routing, QoS, QoE, security and privacy)
  • PLC networks (heterogeneous networks including PLC-VLC; and PLC-radio, interoperability, cognitive approaches)
  • PLC for smart grids, smart metering and grid control
  • Other PLC applications (IoT, Vehicular, etc)
  • Open challenges, results and the role of PLC in new standards

 

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

 

Associate Editor:  Khaled Rabie, Manchester Met University, UK

Guest Editors:

  1. Andrea M. Tonello, University of Klagenfurt, Austria
  2. Naofal Al-Dhahir, University of Texas at Dallas, USA
  3. Jian Song, Tsinghua University, China
  4. Alberto Sendin, Iberdrola, Spain

 

Relevant IEEE Access Special Sections:

  1. Software Defined Networks for Energy Internet and Smart Grid Communications
  2. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric IoT
  3. Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations


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: k.rabie@mmu.ac.uk

Artificial Intelligence for Physical-Layer Wireless Communications

Submission Deadline: 31 December 2019

IEEE Access invites manuscript submissions in the area of Artificial Intelligence for Physical-Layer Wireless Communications.

Artificial Intelligence (AI), including Deep Learning (DL) and deep reinforcement learning (DRL) approaches, well known from computer science (CS) disciplines, are beginning to emerge in wireless communications. These AI approaches were first widely applied to the upper layers of wireless communication systems for various purposes, such as routing establishment/optimization, and deployment of cognitive radio and communication network. These system models and algorithms designed with DL technology greatly improve the performance of communication systems based on traditional methods.

New features of future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements, make traditional methods no longer suitable,  and provides many more potential applications of DL. DL technology has become a new hotspot in the research of physical-layer wireless communications and challenges conventional communication theories. Currently, DL-based ‘black-box’ methods show promising performance improvements but have certain limitations, such as the lack of solid analytical tools and the use of architectures specifically designed for communication and implementation research. With the development of DL technology, in addition to the traditional neural network-based data-driven model, the model-driven deep network model and the DRL model (i.e. DQN) which combined DL with reinforcement learning, are more suitable for dealing with future complex communication systems. As in most cases of wireless resource allocation, there are no definite samples to train the model, hence DRL, which trains the model by maximizing the reward associated with different actions, can be adopted.

This Special Section in IEEE Access focuses on the application of DL/DRL methods to physical-layer wireless communications to make future communications more intelligent. We  invite submissions of high-quality original technical and survey articles, which have not been published previously, on DL/DRL techniques and their applications for wireless communication and signal processing.

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

  • DL/DRL based 5G wireless technologies
  • DL/DRL based beamforming in mmWave massive MIMO
  • DL/DRL based hybrid precoding in massive MIMO system, mmWave system
  • DL/DRL based non-orthogonal multiple access (NOMA) techniques
  • DL/DRL based MIMO-NOMA frameworks
  • DL/DRL based sparse channel estimation
  • DL/DRL based communication frameworks
  • DL/DRL based multiuser detection
  • DL/DRL based modulation and coding
  • DL/DRL based direction-of-arrival estimation
  • DL/DRL based channel modeling
  • DL/DRL based signal classification
  • DL/DRL based unmanned aerial vehicles (UAVs) techniques
  • DL/DRL based energy-efficient network operations
  • DL/DRL based ultra-dense cell communication
  • DL/DRL based testbeds and experimental evaluations

 

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

 

Associate Editor:  Guan Gui, Nanjing University of Posts and Telecommunications, China

Guest Editors:

  1. Tomohiko Taniguchi, Fujitsu Laboratories Limited, Japan
  2. Haris Gacanin, Nokia Bell Labs, Belgium
  3. Ning Zhang, Texas A&M University at Corpus Christi, USA
  4. Yue Cao, Northumbria University, UK
  5. Kezhi Wang, Northumbria University, UK

 

Relevant IEEE Access Special Sections:

  1. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  2. Applications of Big Data in Social Sciences
  3. Artificial Intelligence and Cognitive Computing for Communications and Networks


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: guiguan@njupt.edu.cn

Recent Advances in Video Coding and Security

Submission Deadline: 30 June 2019

IEEE Access invites manuscript submissions in the area of Recent Advances in Video Coding and Security.

With the development of imaging and computer graphics technologies, high dynamic range (HDR) video, immersive 360-degree video (4K and above video resolution), 3D video, and other ultra-high definition (UHD) video have become a reality. Since the UHD video can provide a more realistic visual experience, it attracts much more  attention. Compared with  traditional video, the UHD video can efficiently enhance the visual clarity while its video data volume increases significantly. The huge data volume becomes a challenge for processing, storing, and transmitting the UHD video. Hence, efficient video coding techniques are vital for the widespread applications of UHD video.

Moreover, with the development of internet technology, the video data has been widely used in multimedia devices, such as video surveillance, webcast, and so on. For video security, the sensitive video content needs to be protected before transmission. Data encryption is an efficient way to achieve this purpose. Compared with the text and binary data, the video data has large volume, and requires real-time processing. Since the traditional encryption algorithms don’t consider the video characteristics, efficient video encryption algorithms should be designed for video data security.

This Special Section in IEEE Access focuses on the theoretical and practical design issues of video coding and security. Our aim is to bring together researchers, industry practitioners, and individuals working on the related areas to share their new ideas, latest findings, and state-of-the-art achievements with others. This will provide readers with a clear understanding of the recent achievements on video coding and security.

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

  • Low-complexity video coding algorithms
  • Rate control and bit allocation optimization algorithms for video coding
  • Transform optimization algorithms for video coding
  • Advanced filter algorithms for video coding
  • Advanced transcoding algorithms
  • Visual quality assessment metrics for video coding
  • Advanced super resolution algorithms
  • Advanced video salient object detection algorithms
  • Advanced coding algorithms for 3D/HDR/ videos
  • Advanced video transmission security algorithms
  • Advanced video information hiding algorithms
  • Advanced threat detection algorithms for video broadcasting system
  • Advanced algorithms for video authentication and encryption
  • Advanced algorithms for video copyright protection
  • Advanced algorithms for video watermarking
  • Advanced video privacy protection algorithms
  • Artificial intelligence for video processing

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

 

Associate Editor:  Zhaoqing Pan, Nanjing University of Information Science and Technology, China

Guest Editors:

  1. Jianjun Lei, Tianjin University, China
  2. Byeungwoo Jeon, Sungkyunkwan University, Korea
  3. Ching-Nung Yang, National Dong Hwa University, China
  4. Nam Ling, Santa Clara University, USA
  5. Sam Kwong, City University of Hong Kong, China
  6. Marek Domański, Poznań University of Technology, Poland
  7. Weizhi Meng, Technical University of Denmark, Denmark

 

Relevant IEEE Access Special Sections:

  1. Information Security Solutions for Telemedicine Applications
  2. Security and Trusted Computing for Industrial Internet of Things
  3. Advances in Channel Coding for 5G and Beyond


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:  zhaoqingpan@nuist.edu.cn

Advanced Optical Imaging for Extreme Environments

Submission Deadline: 1 September 2019

IEEE Access invites manuscript submissions in the area of Advanced Optical Imaging for Extreme Environments.

Modern day optical systems are capable of doing more than ever before with less size, weight, and power. With the rare exception, however, these optical systems and optical processing methods adhere to age-old architectures that drive practical solutions towards unsustainable complexity under ever-increasing performance requirements. The performances of optical imaging systems will be largely jeopardized by various challenging conditions in unconstrained and extreme environments, e.g., rainy, foggy, snowy, and low illumination environments. Researchers need to reinvent optical devices, systems, architectures, and methods for extreme optical imaging. On the other hand, artificial intelligence has become a topic of increasing interest for researchers. It is foreseeable that artificial intelligence will be the main solution of the next generation of extreme optical imaging. To this end, the goal of this Special Section in IEEE Access is to provide a platform to share up-to-date scientific achievements in this field.

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

  • Low Light Imaging and Processing
  • Underwater Image Processing
  • Imaging Using Extreme Big Data
  • Infrared Imaging and Processing
  • Extreme Physics-based Optical Imaging Modeling
  • Vignetting Processing
  • Computational Optical Imaging
  • Imaging in Harsh or Unconventional Environments
  • Imaging at Extreme Size Scales
  • Big Multimedia for Extreme Optical Imaging
  • Applications of Extreme Imaging Systems
  • Extreme Optical Imaging Sensors
  • Optical Imaging for Surgical Robotic Networks
  • Intelligence Optical Imaging and Processing
  • Mixed Reality/Augmented Reality for Extreme Imaging

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

 

Associate Editor: Huimin Lu, Kyushu Institute of Technology, Japan


Guest Editors:

  1. Cosmin Ancuti, University Politehnica Timisoara, Romania
  2. Joze Guna, University of Ljubljana, Slovenia
  3. Li He, Qualcomm Inc., USA
  4. Liao Wu, Queensland University of Technology, Australia
  5. Zhangyang Wang, Texas A&M University, USA
  6. Sandra Biedron, University of New Mexico, New Mexico

 

Relevant IEEE Access Special Sections:

  1. Key Technologies for Smart Factory of Industry 4.0
  2. Information Security Solutions for Telemedicine Applications
  3. Sequential Data Modeling and Its Emerging Applications


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: dr.huimin.lu@ieee.org

 

New Trends in Brain Signal Processing and Analysis

Submission Deadline: 31 January 2019

IEEE Access invites manuscript submissions in the area of New Trends in Brain Signal Processing and Analysis.

Novel computational techniques in the field of neuroscience have been introduced in the literature and others are still under investigation. These studies cover a wide range of technological novelties such as controlled virtual environments that include higher cognitive and executive procedures and investigations of signals generated by the ailing brain, as found in posttraumatic disorders, cerebral palsy, or traumatic brain injury, for example. New trends in brain signal processing and analysis aim not only to study the pathology but also to explore ways to promote brain function recovery. Examples can include noninvasive brain stimulation (brain modulation), more accurate and faster algorithms than the traditional ones, neurorobotics and brain-machine interfaces. A better understanding of how our brain works with new realistic computational algorithms makes it possible to simulate and model specific brain functions for the development of new machine learning techniques.

The main objective of this Special Section in IEEE Access is to bring together recent advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to brain signal processing and analysis. We invite researchers to contribute original work related to the different fields of knowledge, such as neuroengineering, rehabilitation, psychology, pattern recognition, computational intelligence, machine learning and robotics, used in the context of understanding how the brain works, reacts, and adapts.

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

  • Brain Signal Processing Techniques
  • Brain Image Processing Techniques
  • Brain Source Estimation
  • Brain Network Analysis
  • The Brain in different realities: Virtual, Augmented, and Mixed
  • Brain-Machine Interface Systems
  • Brain-to-Brain Interaction
  • Automatic Detection and Diagnosis of Neurologic Diseases
  • Neurorobotics
  • Internet of Brain Things
  • EEG Biometrics

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

 

Associate Editor: Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil


Guest Editors:

  1. Alkinoos Athanasiou, Aristotle University of Thessaloniki, Greece.
  2. Robertas Damaševicius, Kaunas University of Technology, Lithuania.
  3. Pedro P. Rebouças Filho, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Brazil.
  4. Mohsen Guizani, University of Idaho, USA.

 

Relevant IEEE Access Special Sections:

  1. Information Security Solutions for Telemedicine Applications
  2. Recent Computational Methods in Knowledge Engineering and Intelligence Computation
  3. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions


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:  victor.albuquerque@unifor.br

 

Emerging Trends, Issues and Challenges for Array Signal Processing and Its Applications in Smart City

Submission Deadline: 31 December 2019

IEEE Access invites manuscript submissions in the area of Emerging Trends, Issues and Challenges for Array Signal Processing and Its Applications in Smart City.

The array signal processing technique, including what is also known as direction-of-arrival (DOA) estimation, has been widely applied in radar, sonar, wireless communications and other traditional fields. The source detection and localization capability offered by array signal processing also makes it an invaluable tool in smart cities. As an emerging research area, smart city has brought up a unique set of challenges and opportunities for sensor array research. These include low signal-to-noise ratio (SNR), limited number of measurements, high interference from a multitude of sources, and the need for energy efficiency.

Traditionally, the challenge of sensor array signal processing arises from the fact that the observed snapshots are nonlinear functions of the directions of interest. The performance of traditional DOA estimation algorithms, such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT), may greatly degrade at a low SNR or with a small snapshot number. Recently, sparse representation (SR) based methods have been rapidly developed in order to deal with these problems. These methods have many advantages such as flexibility in incorporating different noise types and measurement schemes, high estimation accuracy and spatial resolution, as well as the ability to handle correlated sources. However, the disadvantages are obvious as well, particularly the model mismatch and the grid mismatch issues. As a solution, the gridless method based on atomic norm was developed as an alternative that addresses many of the issues. However, this approach suffers from high computational complexity.

This Special Section in IEEE Access is intended to encourage high-quality research in array signal processing and its applications in smart city. Authors are invited to submit articles presenting new research related to the theory or practice about array signal processing techniques, including algorithms, models, technology and applications. All submissions must describe original research, and not published or currently under review for another workshop, conference, or journal. The topics suggested can be discussed in terms of concepts, the state of the art, implementations, and running experiments or applications.

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

  • Compressed sensing based methods for DOA estimation
  • Low computational complexity gridless methods for DOA estimation
  • Application of machine learning for DOA estimation
  • DOA estimation for sparse arrays, super-resolution for MIMO radar and DOA and polarization estimation based on SR techniques
  • Robust DOA estimation in low SNR or with a small snapshot number
  • Array geometry optimization for high accuracy DOA estimation
  • Tensor based method for high dimensional parameter estimation
  • Performance analysis for different DOA estimation methods
  • Convex and nonconvex optimizations related to array signal processing
  • Hardware implementation for advanced array signal processing techniques
  • New technologies and research trends for array signal processing
  • Array signal processing in emergency medical system
  • Array signal processing for fault detection
  • Other emerging research field of smart city exploiting array signal processing techniques

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

 

Associate Editor:  Liangtian Wan, Dalian University of Technology, China


Guest Editors:

  1. Xianpeng Wang, Hainan University, Hainan, China
  2. Gongguo Tang, Colorado School of Mines, USA
  3. Wei Liu, University of Sheffield, UK
  4. Guoan Bi, Nanyang Technological University, Singapore


Relevant IEEE Access Special Sections:

 

  1. Green Signal Processing for Wireless Communications and Networking
  2. Underwater Wireless Communications and Networking
  3. GNSS, Localization, and Navigation Technologies


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: wan.liangtian.2015@ieee.org

 

Theory, Algorithms, and Applications of Sparse Recovery

Submission Deadline: 31 December 2018

IEEE Access invites manuscript submissions in the area of Theory, Algorithms, and Applications of Sparse Recovery.

Sparse recovery is a fundamental problem in the fields of compressed sensing, signal de-noising, statistical model selection, and more. The key idea of sparse recovery lies in that a suitably high dimensional sparse signal can be inferred from very few linear observations. Recent years have witnessed a great development of the sparse recovery theory and fruitful applications in the general field of information processing, including communications channel estimation, dictionary leaning, data compression, optical imaging, machine learning etc. Extensions to the recovery of low-rank matrices and higher order tensors from incomplete linear information have also been developed, and remarkable achievements have been achieved.

This Special Section is devoted to both the current state-of-the-art advances and new theory, algorithms and applications of sparse recovery, with the goals to highlight new achievements and developments, and to feature outstanding open issues and promising new directions and extensions, on the theory, algorithms, and applications. Both survey papers and papers of original contributions that enhance the existing body of sparse recovery are also highly encouraged. The topics of interest include, but are not limited to:

  • Fundamental limit of sparse recovery algorithms
  • Sparse recovery with phase-less sampling matrices
  • Trade-off between sparse recovery effectiveness and efficiency
  • Greedy methods for phase-less sparse recovery
  • Design and optimization for deterministic sampling matrices
  • Theory/algorithm/applications of sparse signal recovery
  • Theory/algorithm/applications of low-rank matrix recovery
  • Theory /algorithm/applications of tensor recovery
  • Efficient hardware implementation of sparse recovery algorithms
  • Sparse recovery for machine learning problems

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

Associate Editor: Jinming Wen, University of Toronto, Canada

Guest Editors:

  1. Jian Wang, Fudan University, China
  2. Bo Li, Nuance Communication, Canada
  3. Xin Yuan, Nokia Bell Labs, USA
  4. Kezhi Li, Imperial College London, UK

 

Relevant IEEE Access Special Sections:

  1. Advances in Channel Coding for 5G and Beyond
  2. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in  Internet of Medical Things
  3. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric

 

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: jinming.wen@mail.mcgill.ca

Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things

Submission Deadline: 31 October 2018

IEEE Access invites manuscript submissions in the area of Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things.

The recent advancement in Internet of Medical Things (IoMT) paradigm aims to enrich our perception of healthcare reality, and incorporating new technologies for such applications. In the context of the IoMT, several medical devices connected to healthcare IT infrastructure can offer superior and more personalized health services. The combination of IoMT data, machine learning, streaming analytics distributed computing, and biomedical systems has become more powerful by enabling the storage and analysis of more data and many different types of data much faster. Machine learning plays a crucial role in the medical imaging field, comprising computer-aided diagnosis, registration and fusion, image segmentation, image-guided therapy, and image database retrieval for providing a better understanding of medical data applied to biomedical systems in IoMT. Moreover, the potential of big data in IoMT is a critical concern to constructing and running the kinds of big data analytics applications are obligatory for IoMT data. Thus it necessitates key focus from academia and industries.

Medical data is central to the IoMT paradigm: from acquiring critical medical sensor data or imaging data to analyzing, processing, and storing of health information, which adds new insights to our view of the world. Machine learning is essential to challenges related to the data source applied to biomedical devices using IoMT. Machine learning and data-driven methods represent a paradigm shift, and they are bound to have a transformative impact in the area of medical data and imaging processing. Many challenges arise as the IoMT permeates our world, especially for low-power resource-constrained devices for accumulating patient’s data, medical data integrity, privacy and security, and network lifetime and quality of service among others. The primary goal of this Special Section in IEEE Access is to provide an overview of the current state-of-the-art advances in machine learning of data source for understanding IoMT.

Topics of interest include, but are not limited to:

  • Computer-aided detection or diagnosis applied to biomedical systems in IoMT
  • New imaging modalities or methodologies for IoMT
  • Innovative machine-learning algorithms or applications in IoMT
  • Medical data security and privacy techniques for healthcare
  • Energy harvesting and big data analytics strategies in IoMT
  • Deep learning for optimizing medical big data in IoMT
  • Low-power resource-constrained medical devices for IoMT
  • Associative rule learning and reinforcement learning in IoMT
  • Smart medical systems based on cloud-assisted body area networks
  • Flexible and wearable sensors for prognosis and follow-up based on IoMT Paradigm
  • Healthcare Informatics to analyze patient health records, for enabling better clinical decision making and improved healthcare outcomes

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

Associate Editor: Kelvin KL Wong, Western Sydney University, Australia

Guest Editors:

  1. Dhanjoo N Ghista, University 2020 Foundation, USA
  2. Giancarlo Fortino, University of Calabria (Unical), Italy
  3. Wanqing Wu, Chinese Academy of Sciences, China

 

Relevant IEEE Access Special Sections:

  1. Mobile Multimedia for Healthcare
  2. Health Informatics for the Developing World
  3. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions

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: Kelvin.Wong@westernsydney.edu.au