ieee research papers on bioengineering

Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

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Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.

*Published in the IEEE Electronics Packaging Society Section within IEEE Access .

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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

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

Guest Editors:

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

Relevant IEEE Access Special Sections:

Deep Learning Algorithms for Internet of Medical Things

• Millimeter-Wave and Terahertz Propagation, Channel Modeling and Applications
• 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: [email protected] .

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.

• 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

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

Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

• Data-Enabled Intelligence for Digital Health

For inquiries regarding this Special Section, please contact:   [email protected] .

Behavioral Biometrics for eHealth and Well-Being

Submission Deadline: 28 February 2021

IEEE Access invites manuscript submissions in the area of Behavioral Biometrics for eHealth and Well-Being.

Artificial Intelligence (AI) is changing the healthcare industry from many perspectives. A very challenging issue deals with the development of non-intrusive AI technologies that could be integrated into everyday activities, thus allowing continuous health state monitoring and enabling automatic warnings when a dangerous change is predicted. Behavioral biometrics play a crucial role within this challenge. Behavioral biometrics, such as speech, handwriting, gait, etc. can be used to quantify human physiology, pathophysiological mechanisms, and actions. The final acquired signal is a mixture of at least four components:

• The physical one, which enables the user to make the action (e.g. mouth, lips, tongue, etc.);
• The cognitive one, which deals with mental abilities (learning, thinking, reasoning, remembering, problem-solving, decision-making, and attention);
• The learned one, which deals with culture, habits, personalization, etc.;
• The contingent contour one, which deals with the acquisition device, the emotional state, the specific task to be performed, etc.

It is evident that disease at its early stage, as well as during its course, could affect one or more of these components. Behavioral biometrics in eHealth seek solutions to diagnose, assess, and monitor diseases that are measurable just when the patient performs an action. This action could be walking, talking, writing or typing on a touchscreen, and many more. Behavioral biometrics also deal with the way the human being responds to natural and social events around her/him and emotions. The adoption of non-intrusive behavioral biometrics techniques in the set of daily activities would be pervasive: the user would be asked to do what she/he already does normally. The output of these systems could be provided to doctors, thus helping them in a deep disease inspection. At the same time these technologies could be directly adopted by doctors. These aspects are extremely important for the development of Computer Aided Diagnosis (CAD) tools. Nevertheless, specific behavioral biometrics tasks and activities could be planned to support rehabilitation activities.

This Special Section in IEEE Access aims to attract original research articles that advance the state of the art in behavioral biometrics for e-health and well-being. The goal is that it provides an opportunity to gain a significantly better understanding of the field’s current developments and future direction.

• Signal processing techniques
• Pattern Recognition techniques
• Computer Vision techniques
• Artificial Intelligence techniques
• Continuous learning and recognition
• Acquisition tools, procedures and protocols
• Biometrics data mining
• Wearable and non-intrusive sensors
• Brain signals analysis for disease and emotional states recognition
• Eye movement analysis for disease recognition
• Face analysis for disease and emotional state recognition
• Gait analysis for disease and emotional state recognition
• Handwriting analysis for disease and emotional state recognition
• Keystroke dynamics for disease and emotional state recognition
• Sleep analysis for disease and emotional state recognition
• Speech analysis for disease and emotional state recognition
• Biometric data and clinical data fusion
• Multiple behavioral biometrics
• Development of complete CAD systems
• Real-time health alerts and long-term health trend analytics
• Applications

Associate Editor:  Donato Impedovo, University of Bari Aldo Moro, Italy

• Thurmon Lockhart, Arizona State University, United States
• Jiri Mekyska, Brno University of Technology, Czech Republic
• Bijan Najafi, Baylor College of Medicine, United States
• Toshihisa Tanaka, Tokyo University of Agriculture and Technology, Japan
• Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams

For inquiries regarding this Special Section, please contact: [email protected] .

Energy Harvesting Technologies for Wearable and Implantable Devices

Submission Deadline: 31 December 2020

IEEE Access invites manuscript submissions in the area of Energy Harvesting Technologies for Wearable and Implantable Devices.

Implantable and wearable electronic devices can improve the quality of life as well as the life expectancy of many chronically ill patients, provided that certain biological signs can be accurately monitored. Thanks to advances in packaging and nanofabrication, it is now possible to embed various microelectronic and micromechanical sensors (such as gyroscopes, accelerometers and image sensors) into a small area on a flexible substrate and at a relatively low cost. Furthermore, these devices have been integrated with wireless communication technologies to enable the transmission of both signals and energy.  However, to ensure that these devices can truly improve a patient’s quality of life, new preventative, diagnostic and therapeutic devices that can provide hassle-free, long-term, continuous monitoring will need to be developed, which must rely on novel energy harvesting solutions that are non-obstructive to their wearer.  So far, research in the field has focussed on materials, new processing techniques and one-off devices. However, existing progress is not sufficient for future electronic devices to be useful in any new application and a great demand exists towards scaling up the research towards circuits and systems. A few interesting developments in this direction indicate that special attention should be given towards the design, simulation and modeling of energy harvesting techniques while keeping system integration and power management in mind.

• Novel piezoelectric, thermoelectric and photovoltaic energy harvesting technologies that lead to enhanced efficiency and controllability under standard or varying working conditions
• Novel control strategies for achieving maximum or optimum energy harvesting
• Power management circuits for energy harvesters
• Novel data driven techniques for optimizing and forecasting the amount of energy that can be harvested
• Low-Power circuits and sensors
• Flexible sensors, circuits and energy harvesters for wearables
• Implantable electronics
• Novel wireless power transfer and delivery techniques
• Numerical and computational modeling techniques

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Associate Editor: Hadi Heidari, University of Glasgow, UK

• Mehmet Ozturk, North Carolina State University, USA
• Rami Ghannam,University of Glasgow, UK
• Law Man Kay, University of Macau, China
• Hamideh Khanbareh, University of Bath, UK
•  Abdul Halim Miah, University of Florida, USA

Neural Engineering Informatics

• Wearable and Implantable Devices and Systems

For inquiries regarding this Special Section, please contact:  [email protected] .

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Deep Learning Algorithms for Internet of Medical Things.

Traditionally, devices used in medical industry predominantly rely on medical images and sensor data; this medical data is processed to study the patient’s health condition and information. Presently, the medical industry requires more innovative technologies to process the large volume of data and improve the quality of service in patient care, and needs an intelligent system to detect early symptoms of diseases in the beginning stage and provide appropriate treatment.

Internet of Medical Things (IoMT) and its recent advancements have included a new dimension towards enhancing the medical industry practices and realizing an intelligent system. In addition, the medical data of IoMT systems is constantly growing because of increasing peripherals introduced in patient care.

Conventionally, medical image processing and machine learning are used for any medical diagnosis, subsequent treatment and therapies. However, with increasing volume of data with increased dimensions and dynamics of medical data, machine learning takes a back seat over another powerful classification mechanism named deep learning. Deep learning can solve more complicated problems, unsolvable by machine learning, and produce highly accurate diagnoses. The medical industry is one of the biggest industries which implements deep learning algorithms. Deep learning can handle the large volume of medical data, including medical reports, patients’ records, and insurance records, helping medical experts to predict the necessary treatment. The scalability of deep learning which helps to process and manipulate this huge volume of data makes an indomitable paradigm for computer-aided diagnosis in medical informatics. The significance of deep learning is compounded by the ever-improving technological aspects towards acquiring precise and multidimensional IoMT data with an eye on improving the accuracy of diagnosis. Overall, incorporating deep learning into IoMT can provide radical innovations in medical image processing, disease diagnosing, medical big data analysis and pathbreaking medical applications.

This Special Section in IEEE Access provides a perfect platform to discuss the prospective developments and innovative ideas in the healthcare domain, with the inception of deep learning-based biomedical systems in IoMT.

• Deep learning for energy management in IoMT devices
• Deep learning algorithms for medical big data analysis
• Advancements in deep learning algorithms in health informatics
• Programmers perspective view on deep learning in IoMT
• Cognitive deep learning for wearable medical devices
• Deep learning for data analytics in body sensor networks
• Monitoring wearable medical devices using deep learning
• Cyber security and malware detection in IoMT using deep learning
• Deep neural networks for diagnosing and identifying suspicious lesions in IoMT
• Evidence-based treatment using IoMT
• Fraud prevention in healthcare using IoMT based data analytics model
• Telemedicine using IoMT
• Patient-centric model using deep learning with IoMT
• Abnormal identification in patient monitoring system using deep learning with IoMT
• Advanced medical image processing using deep learning
• Disease diagnosis using deep learning in IoMT
• Prioritized medical data transmission in IoMT using deep learning
• Deep Belief Neural network and IoMT in in Future Medical Industries
• Deep learning-based privacy preserving and security methods for medical data transmission in IoMT
• Deep learning algorithm for medical decision support systems in IoMT based big data

Associate Editor:  Wei Wei, Xi’an University of Technology, China

• Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil
• Naveen Chilamkurti, La Trobe University, Australia
• Marcin Woźniak, Silesian University of Technology, Poland
• Wael Guibene, Amazon Lab126, USA
• Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things
• Data Mining for Internet of Things

Deep Learning for Computer-aided Medical Diagnosis

For inquiries regarding this Special Section, please contact: [email protected] ; [email protected] .

Submission Deadline:   31 August 2019

IEEE Access invites manuscript submissions in the area of Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts.

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, evidence-based medical decision support etc. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely-coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data, and nonlinearly convert the patterns to high level medical insights.

This Special Section in IEEE Access invites academic and industrial experts to make their contributions to smart health big data, empowered by biomedical sensing and computational intelligence technologies. Studies are expected to connect the human body, data, and applications, establish an end-to-end information flow, and convert big data to big impacts. Crucial technologies include wearable sensing, in-home sensing, personal health record establishment, biomedical signal processing, deep learning, big data mining, pattern recognition, and other related techniques. This Special Section will allow readers to identify advancements, challenges and new opportunities in cutting-edge smart health big data innovations.

• Wearable sensing for big data: bio-potential sensing, behavioral sensing, optical sensing, ultrasonic sensing, flexible sensing, emerging wearable imaging, etc.
• In-home sensing for big data: on-bed sensing, sleep quality sensing, activity sensing, mobility sensing, fall detection, rehabilitation monitoring, etc.
• Personal health big data: cardiac monitoring, cardiopulmonary monitoring, brain function monitoring, mobility monitoring, life style monitoring, etc.
• Signal quality enhancement in wearable big data sensing
• Biomedical signal processing for smart health big data
• Feature extraction and critical feature selection from smart health big data
• Automatic feature mining using deep learning from smart health big data
• Dimension reduction for effective learning from smart health big data
• Time series, image and unstructured data fusion
• Data mining from large databases for pattern and correlation finding
• Knowledge discovery in smart health big data
• Telemedicine, internet of medical things, mobile health, remote health monitoring
• Precision medicine exploration based on smart health big data
• Medical relevant insight learning from long-term health records
• Sleep quality monitoring and analytics with smart health big data
• Human-computer interaction for rehabilitation and assisted living
• Lifestyle changing empowered by digital health technologies
• Smart health big data to empower clinical trials

Associate Editor:  Qingxue Zhang, Indiana University-Purdue University, USA

• Vincenzo Piuri, University of Milan, Italy
• Edward A. Clancy, Worcester Polytechnic Institute, USA
• Dian Zhou, University of Texas at Dallas & Fudan University, USA & China
• Thomas Penzel, Charite University Hospital, Germany
• Walter Hu, University of Texas at Dallas & One-Chip Co., Ltd., USA & China
• Hui Zheng, Harvard University & Massachusetts General Hospital, USA
• Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
• Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions
• Human-Centered Smart Systems and Technologies

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

For inquiries regarding this Special Section, please contact: [email protected] .

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 .

• 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

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

• Peng Xu, University of Electronic Science and Technology of China, China
• Zhiguo Zhang, Shenzhen University, China
• Gang Wang, Xi’an JiaoTong University, China
• Samu Taulu, University of Washington, USA
• Leandro Beltrachini, Cardiff University, UK
• Healthcare Information Technology for the Extreme and Remote Environments
• Advanced Information Sensing and Learning Technologies for Data- centric Smart Health Applications

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

For inquiries regarding this Special Section, please contact: [email protected] or [email protected] .

Submission Deadline:  01 March 2019

IEEE Access invites manuscript submissions in the area of Deep Learning for Computer-aided Medical Diagnosis.

With the growing popularity of neuroimaging scanners in hospitals and institutes, the tasks of radiologists are increasing. The manual interpretation suffers from inter- and intra-radiologist variance. In addition, emotion, fatigue, and other factors will influence the manual interpretation result.

Computer-aided medical diagnosis (CAMD) are procedures in medicine to assist radiologists and doctors in the interpretation of medical images, which may come from CT, X-ray, ultrasound, thermography, MRI, PET, SPECT, etc. In practical situations, CAMD can help radiologists interpret a medical image within seconds.

Conventional CAMD tools are built on top of handcrafted features. Recent progress on deep learning opens a new era that can automatically build features from the large amount of data. On the other hand, many important medical projects were launched during the last decade (Human brain project, Blue brain project, Brain Initiative, etc.) that provides massive data. Those emerging big medical data can support the use of deep learning.

This Special Section in IEEE Access aims to provide a forum to present the latest advancements in deep learning research that directly concerns the computer-aided diagnosis community. It is especially important to develop deep networks to capture normal-appearing lesions, which may be neglected by human interpretation.

• CAMD for neurodegenerative diseases, neoplastic disease, cerebrovascular disease, and inflammatory disease.
• Deep learning and regularization techniques (Multi-task learning, autoencoder, sparse representation, dropout, batch normalization, convolutional neural network, transfer learning, etc.)
• Novel training and inference methods for deep networks
• Deep network architecture for CAMD and big medical data
• Deep learning for cancer location, cancer image segmentation, cancer tissue classification, cancer image retrieval
• Other medical signal and image processing related applications.

Associate Editor:  Yu-Dong Zhang, University of Leicester, UK

• Zhengchao Dong, Columbia University, USA
• Carlo Cattani, University of Tuscia, Italy
• Shui-Hua Wang, Nanjing Drum Tower Hospital, China

New Trends in Brain Signal Processing and Analysis

• Data Mining and Granular Computing in Big Data and Knowledge Processing

For inquiries regarding this Special Section, please contact: [email protected]

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.

• 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

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

• Alkinoos Athanasiou, Aristotle University of Thessaloniki, Greece.
• Robertas Damaševicius, Kaunas University of Technology, Lithuania.
• Pedro P. Rebouças Filho, Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Brazil.
• Mohsen Guizani, University of Idaho, USA.
• Information Security Solutions for Telemedicine Applications
• Recent Computational Methods in Knowledge Engineering and Intelligence Computation

For inquiries regarding this Special Section, please contact:   [email protected]

At a Glance

• Journal: IEEE Access
• Format: Open Access
• Frequency: Continuous
• Submission to Publication: 4-6 weeks (typical)
• Topics: All topics in IEEE
• Average Acceptance Rate: 27%
• Impact Factor: 3.4
• Model: Binary Peer Review
• Article Processing Charge: US $1,995

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An analysis of IEEE publications in biomedical engineering

A previous bibliometric analysis has been reported in [ 1 ] and dealt with the all IEEE publications, available through the INSPEC database. Our objective was to see how the scientific contents were evolving over time, what relations might exist between journals and conferences, how the engineering disciplines were identified, etc. It was emphasized that the same study could be applied to specific journals, areas or societies and divisions. In this “A Look at…” column, the Biomedical Engineering production is examined. Only papers in journals, those linked to the IEEE Engineering in Medicine and Biology Society, have been included. Conference papers have been excluded due to a lack of completeness. The textual analysis relied on the same method known as Factorial Correspondence Analysis (FCA) [ 2 , 3 ]. The frequency of words permits the selection of the most salient terms without using the keywords indicated by the authors or database indexers (which might bring some biases). These words are filtered in such a way that verbs and articles are eliminated. From there, the association of groups of words provides a link to the similarity between documents or groups of documents (journals, sub-fields, etc.) and allows us to cross-analyse them (refer to [ 1 ] for more details).

Materials and method

The IEEE BME set constitutes 5 journals and a magazine. Each elementary document includes title, authors, addresses, record-types, abstract, and source (i.e journal) with the year of publication. The total number of documents (i.e papers) is 7,901. It consists of the following Transactions:

• - IEEE Transactions in Biomedical Engineering (TBME): established in 1963, it is the founding journal in BME or the first resource for BME expression in science. It could be analysed alone due to the fact that the flow of published papers is high. However, in order to match its contents with the others, we decided to only include the 1982–2005 period. The number of published papers varies from year to year (a minimum of 107 in 1982, a maximum of 267 in 2004). The total number of documents that has been used for TBME is equal to 3,917
• - IEEE Transactions on Medical Imaging (TMI): its birth in 1982 marked a time of two major breakthroughs, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Less than 100 papers per volume have been published up to 1997. Its present situation (with around 140 papers a year) points out that medical imaging (MI) remains a very active research field (evidenced as well by major conferences such as SPIE Medical Imaging and the International Symposium on Biomedical Imaging (ISBI) and Medical Image Computing and Computer Assisted Intervention (MICCAI). The data set for TMI consists in 1,929 documents.
• - IEEE Transactions on Information Technology in Biomedicine (TITB): this journal was launched in 1997. Its objective, as sketched in the first editorial by S Laxminarayan et al [ 4 ], was to create a space for new paradigms provided by Information Technology (network, mobile telecommunication, distributed computing, etc…) and healthcare concerns (home monitoring, pervasive systems, patient records, etc.). In the present study 342 papers have been included.
• - IEEE Transactions on Nanobioscience (TNS) and IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSR): these are the most recent journals (TNS was created in 2002, TNSR having its scope changed in 2001). TNS is focused on nanotechnology in biomedical research, while TNSR is addresses the areas of sensing and stimulation for neuromuscular and central nervous diseases. These journals are publishing a limited number of papers a year. Thus, some cautions must be taken in the interpretation of our results due to the limited number of documents (184 for TNS and 262 for TNSR) submitted to our analysis.
• - IEEE Engineering in Medicine and Biology Magazine (EMB): launched in 1982, it covers all areas of BME, mainly through special issues. Its place is, therefore, different from the Transactions, whose target is the publication of original research contributions. The amount of documents used here for EMB is 1,317.

To summarize the basic ideas behind lexicon analysis, let us recall that it relies on the observation that documents that use the same words with similar association frequencies have close contents. FCA is used to highlight these relations. The first step consists of estimating the frequencies of word occurrences within the whole set of documents. Then, the frequencies of word co-occurrences per document are estimated and analysed. This analysis allows the construction of a space of words and a space of documents such that the words will be closer as they are more often associated (a notion of “neighborhood” based on cooccurrence) and that the documents will be less distant as far as they contain the same word co-occurrences (“neighborhood” of documents due to characteristic co-occurring “constellations” of close words).

The Correspondence Analysis is particularly suited for textual data with its underlying barycenter interpretation, the duality of spaces (which leads to an interpretation, within the same space, of words and documents). The possible adjunction of supplementary elements (journal reference, year of publication, for instance) is also another advantage.

Simple statistics can be extracted first regarding the origins of the authors. Keeping in mind that only the first author’s address is provided by INSPEC, it appears that 54% of the papers are from the United States; more than half come from 8 states, with the largest being (in decreasing order) California, Massachusetts, and Pennsylvania; Canada is in the second position with 6%; 66% originate from English-speaking countries; Europe (25 countries) represents 22% of the total production (with 5 countries, the United Kingdom, Italy, Germany, The Netherlands, and France, accounting for 73%); South East Asia provides 8.4% of the papers (among which 48% are from Japan).

From the data sets described above (restricted to 1982–2005 as previously mentioned), a full dictionary of words was built based on titles and abstracts. The most frequent words (3,000 ranked by decreasing order) were then selected and a table was constructed with words in rows and years in columns, each cell reporting the number of words per year. Additional columns have been used to refer to the journals: these columns represent the average over 3 years (2 years for the most recent journals, TNS and TNSR). The FCA has been applied to this matrix.

A first glance through the frequencies of words

By eliminating terms like “results” which are hopefully shared by all scientific papers, it is not surprising to see that some of the most frequent words (ranked along decreasing frequencies) are common to the journals. The top one is “system” which is shared by TBME, TITB, TNSR and EMB (with a rank between 1–3). The second one is “data” especially for TBME, TITB, TMI and EMB but which ranked very low for TNSR and TNS. “Model” appears as the most frequent term for TBME and is used very often also in TNSR and TMI. The word “time”, shared by TBME and EMB, appears also for TITB, TNSR and TMI in ranks 13 – 14. These words may have, of course, different interpretations due to the generalities they cover: their combination with other close words will clarify their meaning.

However, in addition to the previous ones, words indicating of each journal are also highlighted. TBME is for instance well identified by “signal”, “analysis”, and “frequency” while TMI is clearly signaled by “image”, “reconstruction”, “dimensional”, “tomography”. Along the same vein, TNS’s most frequent words are “cell”, “structure”, “surface”, “channels”, “protein” and TNSR’s are “control”, “stimulation”, “muscle”, and “force”. EMB has its own significant, and even more general, words with “”engineering”, “information”, and “research”.

There are however more intriguing situations. Major issues addressed in imaging for a while, like “segmentation”, and “registration”, are ranked rather high (positions 19 and 20 respectively) in TMI. For almost all journals, references to organs have low frequencies: “brain” appears in rank 11 in TNSR and rank 23 in TMI; “blood”, and “heart” are the first displayed in TBME (ranked in 26 th position and higher).

TNSR is well identified with “muscle”, “brain”, “neural”, “BCI” (i.e brain computer interface), “joint”, and “gait”. The words that appear as significant for TITB are “information”, “patient”, “clinical”, and “care”, a situation that is not so dissimilar from EMB.

The availability of TBME since 1963 allowed us to match the words used before and after 1982. Most of the top ten are identical such as “model”, “system”, “data”, and “time” (the first four in different order) and “frequency”, “analysis”, and “signal”, when “blood”, and “computer” were present before 1982. However we will show that this does not mean that TBME is not evolving at all but that its core topics remains very stable.

Do the journals have their own profile?

The above comments are not easy to interpret as a whole, and graphical descriptions may help in better envisioning the relationship the journals may have. The next issues will be addressed by means of factorial representations of journals and the most representative words as well as their evolution over time. It is worth noting that the first factorial plane of the correspondence analysis, F1–F2 ( figure 1 ), represents about 24% of the total inertia (the other factors have a slowly decreasing inertia), in other words, this plane concentrates enough relevant information and will be used as reference in the following sections.

First Factorial plane F1–F2 with the most significant words (they have been sampled in order to make the graph readable). The other points, not explicitly defined, also representing words contributing to the formation of the two first axes.

This figure displays a subset of the most significant words determining the first factorial axes. It can be seen that the words “hyperthermia”, “NMR”, “Doppler”, “Fourier”, and “ultrasonic” are highly correlated with the positive side of Fl (right). They are opposed to “healthcare”, “training”, “registration”, “virtual”, and “database” on the left. The second factor is defined by “net”, “rendering, “PET”, “Radon”, and “EMF” (up) and “recording”, “stimulation”, and “circuits” (down). When we look at the most significant words projected onto the 4 quadrants, we see: “scatter”, “ECG”, “slice” (first quadrant), “fiducial”, “microcalcifications”, “project” (second), “gene”, “tensor”, “protein”, “BCI”, “wearable”, and “ICA” (third) and “”waveforms”, “firing”, “prostheses” and “BME” (fourth). These words can be easily matched with the journals. Perhaps the most impressive result is in the third quadrant where all terms of high up-to-day significance are located.

The projection (not displayed here) of the journals onto this plane (represented by their barycentres, i.e the whole set of documents belonging to a given journal) shows that TBME occupyies the right-down quadrant, TMI the upper part of the plane, TITB stays on the left side and close to Fl, TNS and TNRS, are in the left-down side. EMB is in some way at the middle of these different locations: this confirms its role in addressing all the topics within biomedical engineering. As such, these results should lead to the conclusion that these journals have distinct contents and, therefore, their own overall profiles, with their overlaps remaining relatively coherent.

In order to refine this last point, a second study has been conducted. The initial table was built with the 7,901 documents in rows and the 903 most frequent and most occurring words in columns. The occurrence of words means that we neglect the fact that the same word appears several times in a given document: we look for the presence of this word in the maximum number of documents. Supplementary rows are included that segment the journals into periods of 2 or 3 years. The first factorial plane picture resulting from the application of FCA is depicted figure 2 . Three clusters of words are clearly distinguishable, and the reader can easily see what words determine the factorial axes, what they separate or oppose, etc. The major point highlighted by this figure is that the three clusters on the left side correspond to TMI and TITB, and the one on the right side corresponds to the group of TBME, TNS and TNSR, while EMB is located along the second axis. Therefore, this picture underlines the distinct profiles of TITB and TMI with respect to TBME, while the most recent journals remain close to this historical journal. As said before, TNS and TNSR will very likely evolve on their own, but the set of documents is still too small to anticipate on that.

A second study with most frequent and occurring words. The three clusters clearly identify the different journals TBME, TMI, and TITB (the former remaining very close to the two most recent journals, TNS and TNSR)

How BME and the journals evolve with time?

Coming back to the first data set that has been used, figure 3 depicts the global evolution of all the BME papers (keeping in mind that abstracts, not full papers, are used). The parabola-like shape from 1982 to 2005 shows a coherent path over time: from the right side of Fl (the 1980’s) to the left side (the most recent period of time). There are however several ruptures (or types of jumps) that can be observed: this is the case between 1992 – 1994, 2000 – 2001, and 2001 – 2004. In contrast, the 1980’s remain concentrated in the same area as well as the years 1995–2000 and 2003–2005. We do not have explanations for these observations; that require more in-depth analysis.

A representation of the evolution over time of all BME document contents within the same factorial plane used for figure 1 .

When looking more closely at the journal evolution ( figure 4 ), this general picture for the oldest journals, TBME and TMI, is observed again: the trajectories over F1–F2 have the same shapes with an evolution from right to left, going up and then down. The evolution of TBME and TMI have a relatively similar amplitude over Fl. TBME stays basically in the bottom-left quadrant, close to words such as “signal”, “system”, “device”, and “potential”. If it is too early to interpret the trend for TITB, TNS and TSNR, we see that they are clearly separated, TITB being closer to TMI than to TBME. The path described by EMB is more complex. Here, it has been smoothed over blocks of 3 years to facilitate viewing. Although close to TBME at the beginning of the 1990’s, it moves toward TMI (1996–2002) and then to TNS and TNSR. This complex-shaped behavior may be linked to the fact that only 6 issues are published a year and that they are topic oriented, making it more sensitive than the other journals which include both more subtopics and similar subtopics in each issue.

The evolution over time of the contents of the set of IEEE BME journals: TBME, TMI, EMB, TITB, TNS, TSNR. This evolution is captured here by smoothing over the years.

In all cases, this picture shows that the journals evolve significantly over time. It does not say if they evolve enough in order to be able to capture the emerging areas, of course. Most of the early non-BME IEEE journals published depict this parabola-like shape [ 1 ], illustrating the influence of electronics or devices at the beginning and of microprocessors and computers later. Research topics are evolving very fast, and the trajectories observed here provide no more than a picture of this global evolution.

This study has provided several highlights regarding biomedical engineering viewed through the scientific production of IEEE. The first point is that the BME journals, TBME, TMI, TITB, TNS, TNSR and EMB, have, overall, quite distinct contents. The second conclusion is that they evolve significantly over time, with a trend toward software engineering for TBME, TMI and TITB. This behavior can be compared to the entirety of IEEE publications as reported in [ 1 ].

However, some limitations must be emphasized in order to avoid inappropriate extrapolations. The newest journals (TNS and TNSR) examined in this column still have a low number of published papers (about 130 and 260 respectively) and, thus, it is too early to track their evolution. Even if most BME papers are published in the journals that we have examined here, other IEEE Transactions are also of concern, either those that are very focused on, for instance ultrasound (IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control) or those that have larger scopes with an interest in BME, like IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Computer Graphics and Applications, etc.

The decision to only deal with IEEE journals is another issue. The advantage is that it allows setting a well delimited reference corpus of documents but the drawback is that it reflects a reduced set of the worldwide BME literature. The corpus of abstracts we have used can be extended to full papers: the same methodology can be applied but the access is much more difficult to obtain. Such analysis may bring additional cues as to what is going on. However, we think that with these cautions in mind, the results reported are of significance.

Bibliometric studies like this one could also be of interest for other purposes. For example, the current European project Symbiomatics [ 5 ], aimed at examining the links between Medical Informatics and Bioinformatics, is based, at least partially, on the same methodological approach. In other words, targeted questions may be answered instead of analysing disciplines and specific journals as we did here.

Fully Open Access Topical Journals

A Growing Collection of Gold Fully Open Access (OA) Options

IEEE offers more options than ever to authors with the launch of new gold fully open access journals spanning a wide range of technologies. These journals are significant additions to IEEE’s well-known and respected portfolio of fully open access journals. In addition, many of the journals featured here target an accelerated publication time frame of 10 weeks for most accepted papers to help get your research exposed faster. Visit the publication home page of each title for details.

The fully open access journals are accepting submissions. Please see each journal’s description below for more details. All of the titles are fully compliant with funder mandates including Plan S. All IEEE Open Access titles, current and new, will be hosted on the IEEE Xplore ® platform.

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IEEE Open Journal of Antennas and Propagation

High-quality, peer reviewed research covering antennas, including analysis, design, development, measurement, standards, and testing; radiation, propagation, and the interaction of electromagnetic waves with discrete and continuous media.

This fully open access journal publishes high-quality, peer reviewed papers covering antennas, including analysis, design, development, measurement, standards, and testing; radiation, propagation, and the interaction of electromagnetic waves with discrete and continuous media; and applications and systems pertinent to antennas, propagation, and sensing, such as applied optics, millimeter-and sub-millimeter-wave techniques, antenna signal processing and control, radio astronomy, and propagation and radiation aspects of terrestrial and space-based communication, including wireless, mobile, satellite, and telecommunications at all frequencies. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Konstantina (Nantia) Nikita Professor National Technical University of Athens, Greece

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Addresses the growing field of applications in Earth observations and remote sensing and provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society.

The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. Papers should address current issues and techniques in applied remote and in situ sensing, their integration, and applied modeling and information creation for understanding the Earth. Applications are for the Earth, oceans and atmosphere. Topics can include observations, derived information such as forecast data, simulated information, data assimilation and Earth information techniques to address science and engineering issues of the Earth system. The technical content of papers must be both new and significant.

IEEE Open Journal of Circuits and Systems

Featuring high-quality peer reviewed research covering the theory, analysis, design, tools, and implementation of circuits and systems.

This fully open access journal publishes high-quality, peer-reviewed papers covering the theory, analysis, design, tools, and implementation of circuits and systems. This includes their theoretical foundations, applications, and architectures, as well as circuits and systems implementation of algorithms for signal and information processing. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Gabriele Manganaro, Ph.D., FIEEE Technology Director Analog Devices, Inc., USA

IEEE Open Journal of the Communications Society

Featuring high-quality peer reviewed research covering science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks.

As a fully open access journal publishing high-quality peer reviewed papers,  IEEE Open Journal of the Communications Society  covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks, including but not limited to: Systems and network architecture, control and management; Protocols, software and middleware; Quality of service, reliability and security; Modulation, detection, coding, and signaling; Switching and routing; Mobile and portable communications; Terminals and other end-user devices; Networks for content distribution and distributed computing; and Communications-based distributed resources control. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Octavia A. Dobre, Dipl.-Ing., Ph.D. Professor and Research Chair Memorial University, Canada

IEEE Open Journal of the Computer Society

Forum for rapid publication of open access articles describing high-impact results in all aspects of theory, design, practice, and application relating to computer and information processing science and technology.

The IEEE Open Journal of the Computer Society (OJ-CS) is a rigorously peer-reviewed forum for rapid publication of open access articles describing high-impact results in all areas of interest to the IEEE Computer Society. This new fully open access journal complements existing IEEE Computer Society publications by providing a rapid review cycle and a thorough review of technical articles. It is dedicated to publishing articles on the latest emerging topics and trends in all aspects of computing with a scope that encompasses all aspects of theory, design, practice, and application relating to computer and information processing science and technology. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Song Guo Department of Computing The Hong Kong Polytechnic University

IEEE Open Journal of Control Systems

Publication of the IEEE Control Systems Society, this journal aims to publish high-quality papers on the theory, design, optimization, and applications of dynamic systems and control.

The IEEE Open Journal of Control Systems covers the theory, design, optimization, and applications of dynamic systems and control. The field integrates elements of sensing, communication, decision and actuation components, as relevant for the analysis, design and operation of dynamic systems and control. The systems considered include: technological, physical, biological, economic, organizational and other entities, and combinations thereof. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Sonia Martínez University of California, San Diego United States

IEEE Data Descriptions

Now Accepting Submissions! This new publication is a peer-reviewed journal that publishes short articles on all aspects of data: data descriptors, data collections, and metadata.

IEEE Data Descriptions is a peer-reviewed journal that publishes short articles on all aspects of data: data descriptors, data collections, and metadata. Its overarching purpose is to promote publicly available datasets (open access or subscription-based access) in support of reproducible science while at the same time bringing insights into the associated dataset, data collection methods, and data quality. The metadata collected provides enhanced dataset discoverability and creates a foundation for future data science tools such as auto-discovery and mashups.

Datasets described in IEEE Data Descriptions must be findable, accessible, interoperable, and reusable. The dataset needs to be of a quality high enough that other researchers can use it for their research experimentation and have some permanence. Articles describing datasets must be comprehensive and follow the outlined sections listed in Author Information. The preference is for data to be stored within IEEE DataPort, however, IEEE Data Descriptions accepts submissions where data is stored at other persistent/permanent locations.

Editor-in-Chief: Stephen Makonin Simon Fraser University Vancouver, Canada

IEEE Journal of the Electron Devices Society

Featuring high quality research in the field of electron and ion devices ranging from fundamentals to applied research.

Featuring high-quality research in the field of electron and ion devices ranging from fundamentals to applied research, this journal provides authors an affordable outlet for rapid publishing and universal access, coupled with superior technical quality.

IEEE Open Journal of Engineering in Medicine and Biology

High-quality research covering the development and application of engineering concepts and methods to biology, medicine and health sciences.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Engineering in Medicine and Biology covers the development and application of engineering concepts and methods to biology, medicine and health sciences to provide effective solutions to biological, medical and healthcare problems. It encompasses the development of mathematical theories, physical, biological and chemical principles, computational models and algorithms, devices and systems for clinical, industrial and educational applications. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Paolo Bonato Associate Professor Harvard University, USA

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits

Multi-disciplinary research in solid-state circuits using exploratory materials and devices for novel energy efficient computation beyond standard CMOS (Complementary Metal Oxide Semiconductor) technology.

Multi-disciplinary research in solid-state circuits using exploratory materials and devices for novel energy efficient computation beyond standard CMOS (Complementary Metal Oxide Semiconductor) technology. Focus is on the exploration of materials, devices and computation circuits to enable Moore’s Law to continue for computation beyond a 10 to 15 year horizon (beyond end of the roadmap for CMOS technologies) with the associated density scaling and improvement in energy efficiency.

IEEE Open Journal on Immersive Displays

Now Accepting Submissions! New publication will be home to publications in display science and applications.

The IEEE Open Journal on Immersive Displays (OJID) will be home to publications in display science and applications. The field of displays is diverse, ranging from the science and engineering of materials and devices to their application in high definition, form-factor-independent displays featuring interactivity, virtual and augmented reality, and 3D content. Submissions on advanced fabrication processing, thin film active and passive devices, and lifetime and reliability evaluation are welcome when display is the focus or where there is a direct relationship to the nature of the display system. Tutorial and review papers extending the frontiers of immersive display technologies and novel applications are also published.

Editor-in-Chief: Arokia Nathan University of Cambridge Hertfordshire, U.K.

IEEE Journal of Indoor and Seamless Positioning and Navigation

Publishes original research in the fields of localization and tracking of people, robots, and objects.

IEEE Journal of Indoor and Seamless Positioning and Navigation (J-ISPIN) publishes original research in the fields of localization and tracking of people, robots, and objects. It covers all aspects of localization systems, including sensing, communications, location-based services, mapping, protocols, human interfaces and standards. The scope includes methods and systems addressing indoor environments as well as those enabling seamless transition between heterogeneous indoor contexts or between indoor and outdoor environments, for example where Global Navigation Satellites Systems are underperforming or unavailable.

Editor-in-Chief: Valérie Renaudin Senior Researcher University Gustave Eiffel, France

IEEE Open Journal of the Industrial Electronics Society

Featuring high quality research covering the theory and applications of electronics, controls, communications, instrumentation and computational intelligence to industrial and manufacturing systems and processes.

This fully open access journal publishes high-quality, peer-reviewed papers covering the theory and applications of electronics, controls, communications, instrumentation and computational intelligence to industrial and manufacturing systems and processes. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Leopoldo Garcia Franquelo Professor, Electronics Engineering Universidad de Sevilla, Spain

IEEE Open Journal of Industry Applications

Covering the advancement of the theory and practice of electrical and electronic engineering in the development, design, manufacture and application of electrical systems, apparatus, devices, and controls to the processes and equipment of industry and commerce.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Industry Applications covers the advancement of the theory and practice of electrical and electronic engineering in the development, design, manufacture and application of electrical systems, apparatus, devices, and controls to the processes and equipment of industry and commerce; the promotion of safe, reliable, and economic installations; industry leadership in energy conservation and environmental, health, and safety issues; the creation of voluntary engineering standards and recommended practices; and the professional development of its readers. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Professor Pericle Zanchetta Fellow IEEE Faculty of Engineering University of Nottingham, UK

IEEE Open Journal of Instrumentation and Measurement

Publication of the Instrumentation and Measurement Society, this journal publishes papers on the science, technology, and application of instrumentation and measurement.

The IEEE Open Journal of Instrumentation and Measurement publishes papers on the science, technology, and application of instrumentation and measurement. Instrumentation and measurement, in the current context of the IEEE IMS community, consists of methods, instruments, systems, and applications for measurement, detection, tracking, monitoring, characterization, identification, sensing, estimation, recognition, or diagnosis of a physical phenomenon; or metrology and measurement theory including measurement uncertainty, instrument precision, calibration, etc.

Editor-in-Chief: Shervin Shirmohammadi University of Ottawa Canada

IEEE Open Journal of Intelligent Transportation Systems

Featuring high-quality research covering the theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS).

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Intelligent Transportation Systems covers theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS), defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Bart van Arem Full Professor of Transport Modelling Delft University of Technology, The Netherlands

IEEE Transactions on Machine Learning in Communications and Networking

Featuring high-quality manuscripts on advances in machine learning methods for and applications to communications and networking.

The IEEE Transactions on Machine Learning in Communications and Networking publishes high-quality manuscripts on advances in machine learning methods for and applications to communications and networking. Furthermore, articles developing novel communication and networking techniques for distributed machine learning algorithms are of interest. Both theoretical contributions (including new theories, techniques, concepts, algorithms, and analyses) and practical contributions (including system experiments, prototypes, and new applications) are encouraged.

Editor-in-Chief: Walid Saad Professor Virginia Tech Research Center – Arlington, USA

IEEE Journal of Microwaves

Covering articles on the theory, techniques and applications of guided wave and wireless technologies and spanning the electromagnetic spectrum from RF/microwave through millimeter-waves and terahertz.

The IEEE Journal of Microwaves is a fully open access publication covering the complete scope of the Microwave Theory and Techniques Society which includes articles on the theory, techniques and applications of guided wave and wireless technologies and spanning the electromagnetic spectrum from RF/microwave through millimeter-waves and terahertz, covering the aspects of materials, components, devices, circuits, modules, and systems which involve the generation, modulation, demodulation, control, transmission, sensing and effects of electromagnetic signals.

Editor-in-Chief: Peter H. Siege THz Global, NASA Jet Propulsion Laboratory, California Institute of Technology Pasadena, California

IEEE Open Journal of Nanotechnology

Featuring high-quality, peer reviewed research covering the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of Nanotechnology covers the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Co-Editors-in-Chief: Professor Wen J. Li Chair Professor of Biomedical Engineering Associate Provost City University of Hong Kong, Hong Kong

Professor Jin-Woo Kim Professor of Biological Engineering and Nanoscience & Engineering University of Arkansas, USA

Professor Seiji Samukawa Director of Innovative Energy Research Center, Institute of Fluid Science (IFS) Principal Investigator of Advance Institute for Materials Research (AIMR) Tohoku University, Japan

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Covering the rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement, and more.

Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.

IEEE Photonics Journal

Dedicated to the rapid disclosure of research at the forefront of all areas of photonics and addressing issues ranging from fundamental understanding to emerging technologies.

Breakthroughs in the generation of light and its control and utilization have given rise to the field of Photonics: a rapidly expanding area of science and technology with major technological and economic impact. IEEE Photonics Journal is an online-only journal dedicated to the rapid disclosure of top-quality peer-reviewed research at the forefront of all areas of photonics. Contributions addressing issues ranging from fundamental understanding to emerging technologies and applications are within the scope of the Journal.

IEEE Open Access Journal of Power and Energy

High-quality, peer reviewed research covering the development, planning, design, construction, maintenance, installation, and operation of equipment, structures, power systems and usage of electric energy, including its measurement and control.

As a fully open access journal publishing high-quality peer reviewed papers, the IEEE Open Access Journal of Power and Energy publishes articles focused on the development, planning, design, construction, maintenance, installation, and operation of equipment, structures, and power systems for the safe, sustainable, economic, and reliable conversion, generation, transmission, distribution, storage, and usage of electric energy, including its measurement and control. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Fangxing “Fran” Li The University of Tennessee Knoxville, TN 37996 USA [email protected]

IEEE Open Journal of Power Electronics

Covering the development and application of power electronic systems and technologies, which encompass the effective use of electronic components, the application of circuit theory and design techniques and the development of analytical methods and tools.

The IEEE Open Journal of Power Electronics covers the development and application of power electronic systems and technologies, which encompass the effective use of electronic components, the application of circuit theory and design techniques and the development of analytical methods and tools toward efficient electronic conversion, control and conditioning of electric power to enable the sustainable use of energy. As a fully open access journal publishing high-quality peer reviewed papers, the Society’s aim is to publish novel developments as well as tutorial and survey articles including those of value to both the R&D and practicing professionals in the field. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Alan Mantooth, Ph.D., P.E., FIEEE Distinguished Professor of Electrical Engineering University of Arkansas, USA

IEEE Transactions on Privacy

Now Accepting Submissions! New publication will provide a multidisciplinary forum for theoretical, methodological, engineering, and applications aspects of privacy and data protection, including specification, design, implementation, testing, and validation.

The IEEE Transactions on Privacy provides a multidisciplinary forum for theoretical, methodological, engineering, and applications aspects of privacy and data protection, including specification, design, implementation, testing, and validation. Privacy, in this context, is defined as the freedom from unauthorized intrusion in its broadest sense, arising from any activity in information collection, information processing, information dissemination or invasion. The transactions publishes articles reporting significant advances in theoretical models and formalization as well as engineering tools supporting the above activities, design frameworks and languages, architectures, infrastructures, model-based approaches, study cases, and standards.

IEEE Transactions on Quantum Engineering

Publishing regular, review, and tutorial articles based on the engineering applications of quantum phenomena, including quantum computation, information, communication, software, hardware, devices, and metrology.

Publishes regular, review, and tutorial articles based on the engineering applications of quantum phenomena, including quantum computation, information, communication, software, hardware, devices, and metrology. Articles also address quantum-engineering aspects of superconductivity, magnetics, microwave techniques, photonics, and signal processing.

IEEE Journal of Selected Areas in Sensors

Now Accepting Submissions! New publication of the IEEE Sensors Council, this journal publishes papers in all areas of the field of interest of the IEEE Sensors Council.

The IEEE Journal of Selected Areas in Sensors publishes papers in all areas of the field of interest of the IEEE Sensors Council, i.e., the theory, design, simulation, fabrication, manufacturing and application of devices for sensing and transducing physical, chemical, and biological phenomena, with emphasis on the electronics, physics and reliability aspects of sensors and integrated sensor-actuators. The Journal is built exclusively from papers on selected topics of current interest to the Sensors community.

Editor-in-Chief: Chonggang Wang InterDigital, Inc. USA

IEEE Open Journal of Signal Processing

High-quality, peer reviewed research covering the enabling technology for the generation, transformation, extraction, and interpretation of information.

This fully open access journal publishes high-quality, peer-reviewed papers covering the enabling technology for the generation, transformation, extraction, and interpretation of information. It comprises the theory, algorithms with associated architectures and implementations, and applications related to processing information contained in many different formats broadly designated as signals. Signal processing uses mathematical, statistical, computational, heuristic, and/or linguistic representations, formalisms, modeling techniques and algorithms for generating, transforming, transmitting, and learning from signals. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Brendt Wohlberg Los Alamos National Laboratory, USA

IEEE Open Journal of the Solid-State Circuits Society

High-quality, peer reviewed research covering the design, implementation, and application of solid-state integrated circuits.

As a fully open access journal publishing high-quality peer reviewed papers, IEEE Open Journal of the Solid-State Circuits Society covers design, implementation and application of solid-state integrated circuits. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Jan Craninckx Distinguished Member of Technical Staff IMEC, Belgium

IEEE Open Journal of Systems Engineering

Provides a forum for practitioners, scientists, academics, and researchers engaged in the discipline of Systems Engineering.

The IEEE Open Journal of Systems Engineering (OJSE) is an open access journal that is sponsored by the consortium of IEEE Aerospace and Electronic Systems Society, IEEE Systems, Man, and Cybernetics Society, and the IEEE Systems Council. OJSE provides a forum for practitioners, scientists, academics, and researchers engaged in the discipline of Systems Engineering. Multidisciplinary aspects of systems engineering is a focus of this journal. Methodologies, tools, principles, and applied engineering aspects of the process of systems engineering for complex systems are of interest. The methodologies, tools, and principles include such elements as model-based systems engineering; digital thread; requirements generation, flowdown, tracking, needs analysis, validation/verification; integration and test; and full life cycle of the target system. OJSE deals primarily with the science, methodology, and tools of systems engineering, rather than the results of the application of systems engineering that is the focus of other IEEE journals.

Editor-in-Chief: W. Dale Blair Principal Research Engineer Georgia Tech Research Institute, USA

IEEE Systems, Man, and Cybernetics Letters

Now Accepting Submissions! New publication of the IEEE Systems, Man, and Cybernetics Society (SMC), this journal covers the ultimate aims and key strategies of the SMC society towards the next generation of symbiotic human and machine intelligence systems.

IEEE Systems, Man, and Cybernetics Letter (SMC-L) will cover the ultimate aims and key strategies of the SMC society towards the next generation of symbiotic human and machine intelligence systems. The feature of SMC-L is highlighted by its rapid publication of peer-reviewed short articles within 5 pages, which provide a timely and concise account of innovative research ideas, novel application results, and significant theoretical findings, as well as analyses of emerging trends and groundbreakingly work in SMC fields. SMC-L will provide a new means for members and readers to complement established SMC transactions.

Editor-in-Chief: Prof. Yingxu Wang Editor-In-Chief Univ. of Calgary, Canada

IEEE Journal of Translational Engineering in Health and Medicine

Bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance.

This journal bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. Its aim is to provide a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. The journal provides an active forum for clinical research and relevant state-of-the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies.

IEEE Open Journal of Ultrasonics, Ferroelectrics, and Frequency Control

Covering high-quality, peer reviewed research theory, technology, materials, and applications relating to the generation, transmission, and detection of ultrasonic waves and related phenomena.

OJ-UFFC covers theory, technology, materials, and applications relating to: the generation, transmission, and detection of ultrasonic waves and related phenomena; medical ultrasound, and associated technologies; ferroelectric, piezoelectric, and piezomagnetic materials; frequency generation and control, timing, and time coordination and distribution. This interest ranges from fundamental studies to the design and/or applications of devices, sensors, systems and manufacturing technologies within the general scope defined above. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Steven Freear University of Leeds, School of Electronic and Electrical Engineering Leeds, United Kingdom

IEEE Open Journal of Vehicular Technology

Featuring high-quality, peer reviewed research on the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation.

This fully open access journal publishes high-quality, peer-reviewed papers covering the theoretical, experimental and operational aspects of electrical and electronics engineering in mobile radio, motor vehicles and land transportation. (a) Mobile radio shall include all terrestrial mobile services. (b) Motor vehicles shall include the components and systems and motive power for propulsion and auxiliary functions. (c) Land transportation shall include the components and systems used in both automated and non-automated facets of ground transport technology. The journal peer-review process targets a publication period of 10 weeks from submission to online publication.

Editor-in-Chief: Dr. Sumei Sun Fellow of the IEEE Principal Scientist, Institute for Infocomm Research Adjunct Professor, National University of Singapore, Singapore

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• Citable Docs. (3years)
• Total Cites (3years)

-->

	Title	Type
1		journal	18.117 Q1	511	485	1066	13393	17008	461	13.24	27.61	35.19
2		journal	14.577 Q1	419	262	637	10044	17562	466	27.42	38.34	28.93
3		journal	7.951 Q1	116	201	441	11275	8533	347	20.56	56.09	31.96
4		journal	5.361 Q1	42	71	116	3972	1722	86	16.95	55.94	28.92
5		journal	3.483 Q1	175	275	635	24164	9420	605	13.75	87.87	39.78
6		journal	3.466 Q1	90	432	867	38448	16381	863	18.50	89.00	38.34
7		journal	3.099 Q1	70	43	66	6288	1188	63	21.23	146.23	30.41
8		journal	3.034 Q1	22	22	56	2883	411	56	7.73	131.05	44.55
9		journal	3.003 Q1	61	347	643	20026	6718	634	9.90	57.71	32.49
10		journal	2.971 Q1	30	41	77	3537	1389	76	16.11	86.27	43.46
11		journal	2.949 Q1	154	17	50	2550	678	50	14.47	150.00	40.82
12		journal	2.837 Q1	140	560	2014	32228	24873	2012	11.22	57.55	22.95
13		journal	2.529 Q1	110	31	66	6709	972	66	13.00	216.42	30.43
14		journal	2.337 Q1	140	971	1402	69877	15256	1380	10.43	71.96	40.57
15		journal	2.119 Q1	104	1093	3600	34552	21816	3595	5.46	31.61	21.36
16		journal	2.113 Q1	187	188	530	8029	3503	464	6.48	42.71	42.67
17		journal	2.052 Q1	239	780	2387	44861	27387	2378	11.19	57.51	40.28
18		journal	1.925 Q1	245	684	1871	48868	19146	1860	9.50	71.44	38.82
19		journal	1.840 Q1	109	478	1132	37071	12176	1131	10.46	77.55	41.81
20		journal	1.813 Q1	235	129	599	7696	4772	571	7.42	59.66	31.57
21		journal	1.769 Q1	116	128	381	8871	3711	380	8.33	69.30	38.26
22		journal	1.670 Q1	23	48	83	4945	763	82	7.79	103.02	40.55
23		journal	1.655 Q1	28	258	178	19950	1422	167	7.79	77.33	38.98
24		journal	1.575 Q1	37	66	178	3887	1093	175	5.95	58.89	44.10
25		journal	1.571 Q1	97	77	220	11752	2017	219	8.30	152.62	34.55
26		journal	1.518 Q1	37	368	431	35270	3809	431	8.68	95.84	40.52
27		journal	1.508 Q1	17	50	56	2934	517	54	9.23	58.68	29.57
28		journal	1.508 Q1	25	54	151	4570	1173	149	7.61	84.63	47.59
29		journal	1.490 Q1	16	24	110	1720	719	105	7.15	71.67	23.97
30		journal	1.462 Q1	94	130	356	5125	1873	343	4.37	39.42	24.43
31		journal	1.459 Q1	122	608	1505	24789	6199	1496	4.02	40.77	27.97
32		journal	1.439 Q1	44	133	155	11813	1521	155	8.11	88.82	38.23
33		journal	1.397 Q1	59	45	313	2956	2715	311	6.87	65.69	35.05
34		journal	1.366 Q1	114	59	150	7201	1173	150	6.11	122.05	43.90
35		journal	1.330 Q1	33	48	151	3261	1211	138	8.36	67.94	40.26
36		journal	1.315 Q1	164	477	893	22947	5340	890	5.44	48.11	34.57
37		journal	1.289 Q1	94	373	1058	19103	4928	1053	4.34	51.21	37.19
38		journal	1.284 Q1	108	1180	2030	56356	14141	2025	6.77	47.76	33.63
39		journal	1.262 Q1	48	47	157	5038	1107	157	6.72	107.19	42.77
40		journal	1.261 Q1	36	57	149	4905	943	145	6.28	86.05	40.78
41		journal	1.255 Q1	23	25	62	952	248	8	4.62	38.08	17.65
42		journal	1.246 Q1	246	394	1204	26666	7657	1193	6.12	67.68	34.87
43		journal	1.243 Q1	36	79	242	3225	897	241	2.98	40.82	36.03
44		journal	1.239 Q1	233	387	1059	15452	5349	1059	4.87	39.93	26.98
45		journal	1.235 Q1	179	392	2636	27823	21211	2635	7.62	70.98	45.91
46		journal	1.216 Q1	141	848	2611	60093	17337	2599	6.16	70.86	42.44
47		journal	1.206 Q1	97	496	1761	37279	11086	1752	5.77	75.16	42.25
48		journal	1.161 Q1	38	13	106	712	563	106	4.12	54.77	30.68
49		journal	1.150 Q1	73	54	175	3093	758	170	3.45	57.28	24.87
50		journal	1.141 Q1	15	19	59	3563	363	58	5.38	187.53	41.76

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