2017 IEEE Access Best Multimedia Contest Part 1 Winners

IEEE Access would like to congratulate the winners of the 2017 IEEE Access Best Multimedia Contest Part 1 and recipients of a $500 USD Amazon gift card for their fine contributions to IEEE Access. View the award-winning video and the full article entitled, “Supporting Trembling Hand Typing Using Optical See-Through Mixed Reality” by clicking here: http://bit.ly/2rxaU7i

 

Compact Full Duplex MIMO Radios in D2D Underlaid Cellular Networks: From System Design to Prototype Results

 

This paper considers the implementation and application possibilities of a compact full duplex multiple-input multiple-output (MIMO) architecture, where direct communication exists between users, e.g., device-to-device (D2D) and cellular link coexisting on the same spectrum. For the architecture of the compact full duplex radio, we combine an analog self-interference canceler-based dual polarization with high cross-polarization discrimination and long-term evolution (LTE)-based per-subcarrier digital self-interference canceler. While we consider the compactness and power efficiency of an analog solution, we focus on the digital canceler design with robustness to a frequency-selective channel and high compatibility with a conventional LTE system. For an over-the-air wireless experiment of full duplex testbed with a two-user-pair, we implement a full duplex MIMO physical layer, supporting 20-MHz bandwidth, on an Field-Programmable Gate Array-based software-defined radio platform. Furthermore, we propose a novel timing synchronization method to construct a more viable full duplex MIMO link. By having the full duplex link prototype fully operating in real time, we present the first characterization of the proposed compact full duplex MIMO performance depending on the transmit power of the full duplex node. We also show the link quality between nodes. One of the crucial insights of this paper is that the full duplex operation of a user is capable of acquiring the throughput gain if the user has self-interference capability with guaranteed performance.

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Molecular Communication Networks

Submission Deadline: 30 November 2018

IEEE Access invites manuscript submissions in the area of Molecular Communication Networks.

With an improved ability to manipulate matter at the nano and micro scales via synthetic biological and chemical techniques, there are now opportunities to address challenges ranging from disease diagnosis and treatment to environmental protection. A key framework to develop these tools is nano networking, where networks are built from nanoscale devices that are able to operate in nano to micrometer scale environments, and perform simple tasks such as sensing and actuation. However, the effectiveness of nano networking strongly depends on the ability for devices to coordinate.

Molecular communication has been proposed as a means of coordination in nano networks, where information is exchanged between devices via molecules emitted and absorbed by each device. The basic principles of molecular communication are based on mature aspects of physics, chemistry, biology as well as other areas including pharmacology, microfluidics and medicine. However, there remain a number of challenges in developing signal processing and communication techniques to encode and decode information, as well as develop practical implementations. A particular challenge is how to reliably embed molecular communication systems within existing biochemical systems, which is important for medical applications from the perspective of toxicity and undesirable side effects.

Authors are encouraged to submit articles presenting new research related to theory or practice of all aspects of molecular communications and networks. The topics of interest include, but are not limited to:

  • Theoretical Modeling (e.g., channel modeling, transmitter and receiver device modeling)
  • Architectures, Protocols, Optimal Design (e.g., modulation design, channel parameter estimation, detection, inter-symbol interference mitigation)
  • Transmitter/Receiver Mechanisms & Components
  • Multi-scale and experimental analysis of Molecular Communication Networks
  • Simulation Tools (e.g., tools, models, and approaches for developing simulation packages for Molecular Communication Networks)
  • Interoperability between Molecular Communication Networks and other systems (e.g., Internet of Nano Things, Internet of Bio-Nano Things, Intra-body communication, Body Area Nano-networks)
  • Implementation techniques and for Molecular Communication Networks (e.g., exploiting Nanotechnology and Nanobioscience)
  • Power Sources and Energy efficiency models for Molecular Communication Networks
  • Security in Molecular Communication Networks
  • Potential Applications for Molecular Communication Networks

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

Associate Editor: Daniel Benevides da Costa, Federal University of Ceará, Brazil

Guest Editors:

 

  1. Trung Q. Duong, Queen’s University, UK
  2. Chan-Byoung Chae, Yonsei University, South Korea
  3. Andrew Eckford, York University, Canada
  4. Malcolm Egan, INRIA and INSA Lyon, France
  5. Arumugam Nallanathan, Queen Mary University of London, UK
  6. Marco Di Renzo, Paris-Saclay University, France

 

Relevant IEEE Access Special Sections:

  1. Nano-antennas, Nano-transceivers, and Nano-networks / Communications
  2. Physical and Medium Access Control Layer Advances in 5G Wireless Networks
  3. Future Networks: Architectures, Protocols, and 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: danielbcosta@ieee.org

Advances in Channel Coding for 5G and Beyond

Submission Deadline: 31 December 2018

IEEE Access invites manuscript submissions in the area of Advances in Channel Coding for 5G and Beyond.

In 1948, Shannon introduced the concept of channel capacity and proved the existence of error-correction codes (ECCs) that can realize reliable communication at any rate lower than the capacity. In the following 45 years, many researchers have endeavored to search for capacity-approaching ECCs, but obtained slow progress. Following the remarkable success of turbo codes in 1993, low-density parity-check (LDPC) codes were rediscovered. Since then, capacity-approaching ECCs have attracted more and more attention because it can significantly improve the performance of a myriad of communication systems, such as wireless communication systems, deep-space communication systems, optical communication systems, underwater acoustic communication systems, and data storage systems.

Compared with turbo codes, LDPC codes can achieve better performance and faster decoding. As such, LDPC codes have attracted growing interests in both academia and industry. Furthermore, many meritorious variants of LDPC codes, such as protograph LDPC codes and spatially coupled LDPC codes, were developed in the past decade.

In parallel with the advances in LDPC-based codes, some other capacity-approaching coding methodologies were conceived. In particular, as the first constructive codes achieving the capacity, Polar codes outperform LDPC codes in certain cases and represent an emerging class of ECCs for future wireless communications. Meanwhile, another powerful class of ECCs, called rateless codes (e.g., Luby transform (LT) codes and Raptor codes), was also extensively investigated. In practical applications, rateless codes are very suitable for scenarios where the channel state information (CSI) is unavailable at the transmitter terminal.

Recently, LDPC codes have been selected for the Enhanced Mobile Broadband (eMBB) data channels for 5G New Radio, while Polar codes have been chosen for the corresponding control channel. Beyond any doubt, LDPC codes, Polar codes, and their variants will find more deployment in many other applications and will be included in other new standards in the future. Nevertheless, the design of such codes for the next-generation wireless communication systems is still in its infancy. There are a range of open issues waiting to be addressed.

This Special Section in IEEE Access will focus on the theoretical and practical design issues of ECCs for 5G and beyond. 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. Both comprehensive surveys and original technical contributions are welcome.

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

  • LDPC codes
  • Polar codes
  • Rateless codes and their variants
  • Development trends and challenges for turbo codes
  • LDPC convolutional codes and spatially-coupled (SC) LDPC codes
  • Protograph codes and their variants
  • Algebraic constructions of low-density graph codes
  • Codes on factor graphs
  • Density evolution (DE) and extrinsic-information-transfer (EXIT) chart techniques
  • Minimum distance or weight distribution analysis for capacity-approaching codes
  • Finite-length analytical methodologies
  • Iterative decoding and turbo-like detection algorithms
  • Low-complexity LDPC/Polar codes and their hardware implementations
  • Channel coded modulations
  • Channel coding for non-orthogonal multiple access (NOMA)
  • Low-density graph codes for source coding
  • Low-density graph codes for compressed sensing (CS)
  • Joint source-and-channel coding (JSCC)
  • Joint channel-and-physical-layer-network coding (JCPNC)
  • Coded random access
  • Applications of ECCs to physical-layer security

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

Associate Editor: Yi Fang, Guangdong University of Technology, China

Guest Editors:

 

  1. Lars Kildehøj Rasmussen, Royal Institute of Technology, Sweden
  2. Yong Liang Guan, Nanyang Technological University, Singapore
  3. Kai Niu, Beijing University of Posts and Telecommunications, China
  4. Francis C. M. Lau, Hong Kong Polytechnic University, Hong Kong
  5. Soon Xin Ng, University of Southampton, UK
  6. Pingping Chen, Fuzhou University, China

 

Relevant IEEE Access Special Sections:

  1. Index Modulation Techniques for Next-Generation Wireless Networks
  2. Non-Orthogonal Multiple Access for 5G Systems
  3. Green Signal Processing for Wireless Communications and Networking


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: fangyi@gdut.edu.cn

Advanced Sensor Technologies on Water Monitoring and Modeling

Submission Deadline: 30 July 2019

IEEE Access invites manuscript submissions in the area of Advanced Sensor Technologies on Water Monitoring and Modeling.

Developing new methods and technologies for water pollution control, water resources management, and restoration of watershed ecosystems are critical for securing water security and sustainable development. The biophysicochemical parameters in an aqueous system, such as flow, hydraulic heads, temperature, pH, conductivity, turbidity, microorganisms, solutes concentration, etc., are of great significance in water research. Traditional methods for aqueous environmental monitoring and modeling are heavily dependent on instant point-in-space measurements, laboratory analysis, and physical and computing infrastructure. However, these methods are not only of high cost, but also are unable to timely provide many of the required spatiotemporal features. Thus, there is a clear need for continuous on-line monitoring water quality and hydrologic conditions using advanced sensors technologies across spatiotemporal resolutions.

The rapid expansion of Internet of Things (IoT) technologies, cloud computing and big data promote unprecedented advances in signal processing and information system. Such advances support the development of sensing technologies, as well as software-defined networks, which allow effective monitoring and modeling for water issues. Sensing and control systems that are suitable for the effective monitoring of biophysicochemical parameters, as well as for detecting concentrations of interest in solutes, are crucial to investigate water health and chemical evolutions, and also to timely implement prevention and management strategies.

The purpose of this Special Section in IEEE Access is to solicit manuscripts on the emerging trends, issues, and challenges in smart sensing for aqueous environment monitoring and modeling. Practical studies describing techniques or information system for real-time and in-situ recording of biophysicochemical parameters in an aqueous environment are encouraged. Letters, reports, and reviews with a multidisciplinary focus are also welcome. Topics of interest include, but are not limited to:

  • Advanced sensing technologies in aqueous environments
  • Low cost, portable optical sensing technologies for water monitoring
  • Design, simulation and implementation of sensor systems for water leakage monitoring
  • Water quality sensing in Water Distribution System (WDS)
  • Wireless Sensor Networks (WSNs) for water resource management and control
  • Web-based system analysis and modeling in urban water systems
  • Smartphone-based mobile water monitoring and heterogeneous sensor network
  • Transportation and environmental pollution analysis on water quality
  • Real-time flood forecasting and warning systems
  • Novel monitoring system for municipal water pipes
  • Remote Hydrologic Sensor Networks in the context of citizen science
  • Underwater acoustic signaling and interactive visualizing technologies
  • Real-time, on-site and in-situ monitoring in water and marine environments
  • Robotic and autonomous hydraulic information monitoring infrastructures
  • Experimental network measurements and characterization for aqueous monitoring
  • Water data processing pipelines and data product generation
  • Water data life-cycle management and end-to-end systems
  • Data quality assurance and quality control for observational water data
  • Innovation in water and healthy environments research
  • Adaptive measurements collection, acquisition, management, and visualization
  • Sustainable water management and cost-benefit analysis
  • Security issues and solutions for privacy in an aqueous environment

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

Associate Editor: Xiang Huang, Zhongnan University of Economics and Law, China

Guest Editors:

  1. Jie Liu, Peking University, China
  2. Eftichios Koutroulis, Technical University of Crete, Greece
  3. Branko Kerkez, University of Michigan, USA
  4. Gilberto Pastorello, Lawrence Berkeley National Laboratory, USA
  5. Nick R. Harris, University of Southampton, UK.

 

Relevant IEEE Access Special Sections:

  1. Underwater Wireless Communications and Networking
  2. Multiphase Flow Measurement: Techniques and Applications
  3. Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of 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: huangx07@gmail.com

Mobile Augmented Reality Survey: From Where We Are to Where We Go

 

The boom in the capabilities and features of mobile devices, like smartphones, tablets, and wearables, combined with the ubiquitous and affordable Internet access and the advances in the areas of cooperative networking, computer vision, and mobile cloud computing transformed mobile augmented reality (MAR) from science fiction to a reality. Although mobile devices are more constrained computationalwise from traditional computers, they have a multitude of sensors that can be used to the development of more sophisticated MAR applications and can be assisted from remote servers for the execution of their intensive parts. In this paper, after introducing the reader to the basics of MAR, we present a categorization of the application fields together with some representative examples. Next, we introduce the reader to the user interface and experience in MAR applications and continue with the core system components of the MAR systems. After that, we discuss advances in tracking and registration, since their functionality is crucial to any MAR application and the network connectivity of the devices that run MAR applications together with its importance to the performance of the application. We continue with the importance of data management in MAR systems and the systems performance and sustainability, and before we conclude this survey, we present existing challenging problems.

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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

Cloud-based Robotic Systems for Intelligent Services

Submission Deadline: 1 July 2018

IEEE Access invites manuscript submissions in the area of Cloud-based Robotic Systems for Intelligent Services.

Recent advances in sensor/actuator as well as artificial intelligence (AI) technologies have made it possible for mobile robots such as autonomous automobiles and autonomous unmanned aerial vehicles to go about performing their tasks in varied environments. With wireless communications, these mobile robots can be connected to each other to exchange information, coordinate their movements, and cooperate to perform more extensive tasks, forming robotic systems. Using wireless communications, such robotic systems can further be connected to cloud computing services via the mobile Internet, which offers the potential to significantly enhance the capabilities of such robotic systems. Thus cloud-based robotic systems offer great promises for intelligent services beyond the capabilities of current robots or robotic systems.

First, robot systems employing advanced AI techniques that leverage multiple layer artificial neural networks for deep learning can enable intelligent services that learn from past experience to plan a course of actions that optimizes some task objectives, e.g., minimizing energy consumption, for the current environmental conditions. However, these machine learning techniques are computation intensive and may not be well supported by individual robotic systems. In contrast, cloud computing services offer virtually unlimited computation resources on-demand in a scalable manner, greatly facilitating the use of advanced AI techniques in robotic systems. Second, widespread deployment of robotic systems employing a large number of sensors results in a massive amount of data being generated over short periods of time. Cloud-based big data analytics can be employed to derive useful information to enhance the utility of cloud-based robotic systems. For example, applying big data analytics to data collected from a large number of cloud-based robotic systems, a manufacturer may be able to determine that a batch of sensors manufactured by this company is defective. Third, it is conceivable that in the future distributed general purpose robotic units connected to the cloud can be dynamically configured and programmed to form logical robotic systems under software control to perform specific services in a virtualized manner, i.e., cloud-based robotic systems can provide software-defined robotic system as a service.

Cloud computing platforms would be crucial to enable a programming environment capable of fast service creation, as well as an operational and management environment to ensure that these intelligent robotic services can operate reliably and be properly managed.

Based on the above observations, we can see that cloud-based robotic systems offer great potential for intelligent services in both the short and longer term, but there are many technical challenges that need to be addressed.

Some of the technical challenges and potential applications of cloud-based robotic systems include but are not limited to:

  1. Cloud-based big data analytics mechanisms;
  2. Cooperative mechanisms to coordinate the information of robotic systems and share updates on detected changes in the environment;
  3. Architectures, programming framework, management and control mechanisms to enable robotic function virtualization;
  4. Robotic edge computing to complement the cloud in satisfying hard real time interaction needs;
  5. Robot-assisted healthcare, especially for shut-in and elderly patients, with monitoring, diagnostic and simple treatment capabilities; by sampling data from sensors for body to the cloud system, using data mining and machine learning techniques;
  6. Smart homes, offices and factories equipped with cloud-based robotic systems for enhanced security, energy efficiency, work throughput, occupant comfort, etc.

The main objective of this Special Section in IEEE Access is to collect multidisciplinary research contributions on technological breakthrough and advancement towards cloud-based robotic systems for intelligent services. Topics explored in this Special Section include, but are not limited, to the following aspects of intelligent services involving cloud-based robotic systems:

  • Cloud computing technologies
  • Cooperative robotic systems
  • Multi-modal robotic cognition
  • Cooperative communications among robots
  • Real-time big data analytics of customers
  • Data mining techniques
  • Cloud architecture and cloud storage
  • Mobile social networks
  • Instance detection and recognition in robotic system
  • Image and scene classification in robotic system
  • Semantic interpretation in robotic system
  • Robot function virtualization
  • Robotic edge computing

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

Associate Editor: Prof. Xiping Hu, Chinese Academy of Sciences, China

Guest Editors:

  1. Victor C.M. Leung, University of British Columbia, Canada
  2. Adnan Al-Anbuky, Auckland University of Technology, New Zealand
  3. Ken Goldberg, University of California, Berkeley, USA
  4. Hesheng Wang, Shanghai Jiao Tong University, China
  5. Fei Wang, Cornell University, USA
  6. Jianwei Zhang, University of Hamburg, German

 

Relevant IEEE Access Special Sections:

  1. Trends and Advances for Ambient Intelligence with Internet of Things Systems
  2. Big Data Analytics in Internet-of-Things and Cyber-Physical System
  3. Industry 4.0


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: xp.hu@siat.ac.cn

Novel Learning Applications and Services for Smart Campus

Submission Deadline: 1 July 2018

IEEE Access invites manuscript submissions in the area of Novel Learning Applications and Services for Smart Campus.

In the past few years, the explosive growth in knowledge makes the role of education which spreads knowledge become increasingly important. Meanwhile, the education model is going through a transformation in which the learning of various students (e.g., students without disabilities, students with disabilities, students who are physically in classrooms, students who are geographically remote, students who learn quickly, students who learn slowly) needs to be performed in different ways. All these induce the smart campus, which implements education by incorporating various information and communication technologies in order to actively learn from and adapt to the needs of various students. Specifically, with smart campus, there are a lot of novel learning applications and services. One classic example is that the learning quality of students can be improved, via continuously monitoring and analyzing the status and activities of various students with information sensing devices (e.g., sensors) and information processing platforms (e.g., cloud computing platforms) to offer real-time learning feedback to various students.

Nevertheless, there are many challenges to be addressed for enabling such novel learning applications and services for smart campus. For instance, the accuracy in terms of sensing the status and activities of various students should be high. The data transmission between the information sensing devices and the information processing platforms should be secure. The learning feedback should be sent to various students quickly. The overall smart campus system should be energy efficient. Therefore, this Special Section in IEEE Access aims to advance the novel learning applications and services for smart campus, by soliciting the technical articles with solid contributions which face the challenges to achieve the novel learning applications and services for smart campus. Survey articles are also considered.

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

  • Architectures and systems for smart campus
  • Standardization, policy, and regulation for smart campus
  • Integration of various sensor technologies for smart campus
  • Sensing for smart campus
  • Reliable and robust communication for smart campus
  • Computing for smart campus
  • Data processing and analysis for smart campus
  • Energy consumption for smart campus
  • Security for smart campus
  • Quality of experience and quality of service for smart campus
  • Outlook on applications and services for smart campus

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

Associate Editor: Lei Shu, Nanjing Agricultural University, China & University of Lincoln, UK

Guest Editors:

  1. Han-Chieh Chao, National Dong Hwa University, Taiwan
  2. Jason Yi-Bing Lin, National Chiao Tung University, Taiwan
  3. Odej Kao, Technical University of Berlin, Germany
  4. Chunsheng Zhu, The University of British Columbia, Canada
  5. Zumin Wang, Dalian University, China

 

Relevant IEEE Access Special Sections:

  1. The New Era of Smart Cities: Sensors, Communication Technologies and Applications
  2. Intelligent Systems for the Internet of Things
  3. Key Technologies for Smart Factory of Industry 4.0

 

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: lei.shu@ieee.org

Rethinking Machine Vision Time of Flight With GHz Heterodyning

 

Time-of-flight (ToF) 3-D cameras like the Microsoft Kinect, are prevalent in computer vision and computer graphics. In such devices, the power of an integrated laser is amplitude modulated at megahertz frequencies and demodulated using a specialized imaging sensor to obtain subcentimeter range precision. To use a similar architecture and obtain micrometer range precision, this paper incorporates beat notes. To bring telecommunications ideas to correlation ToF imaging, we study a form of “cascaded Time of Flight” which uses a hertz-scale intermediate frequency to encode high-frequency pathlength information. We show synthetically and experimentally that a bulk implementation of opto-electronic mixers offers: 1) robustness to environmental vibrations; 2) programmability; and 3) stability in frequency tones. A fiberoptic prototype is constructed, which demonstrates 3- μm range precision over a range of 2 m. A key contribution of this paper is to study and evaluate the proposed architecture for use in machine vision.

View this article on IEEE Xplore