【今日新增】10条SCI期刊专刊截稿信息

全文截稿: 2017-06-30

影响因子: 2.569

期刊难度: ★★★★

CCF分类: 无

网址: http://www.signalprocessingsociety.org/publications/periodicals/jstsp/

While machine learning is achieving ground breaking success in speech recognition, computer vision, natural language processing and business analytics, its impact on radio communications, and on the associated problem area in signal processing, has been less pronounced mainly due to the lack of 'big data' and big applications. However, in the era of the Fifth Generation (5G) cellular systems and Internet-of-Things (IoT), some significant changes are under way. For example, as 5G cellular systems demand huge capacity, massive connectivity, high reliability and low latency, acquiring adequate resources to operate such systems is difficult and novel models and algorithms are needed to help improve spectrum utilization by leveraging large-scale databases, full of context and information. These databases can be sourced from handheld devices, network infrastructure, and the environment, such as typical user trajectories provided by vehicular traffic management systems. In addition, government agencies are now willing to share their spectrum with commercial users. The 3550-3650 MHz band is identified for spectrum sharing between military radars and communication systems. This requires cognition both in communication systems and radars. There is also a general trend toward cognitive radars as the next generation of environment-adaptive radars with unprecedented spectral and behavioral agility. A natural approach to handling all this is the development of efficient machine learning algorithms, which, combined with traditional signal processing methods, will allow for the automation of cognitive functionality both in radars and radio communication networks. There are nontrivial challenges and open questions in the application of machine learning to RF environments starting with the fact that, as opposed to speech recognition and computer vision where the output of machine cognition can be readily compared and verified against human auditory and visual perception, no such option is available for radio signals. The main goal of this Special Issue is to raise awareness of this emerging interdisciplinary research area, and to showcase the existing state-of-the-art and its current and future challenges. Topics of interest include (but are not limited to):  


- Machine learning for blind channel and signal characterization
- Joint optimization and learning of spectrum usage dynamics and spectrum access control
- Machine learning for source separation - Privacy-preserving machine learning for cognitive radio
- Deep learning for RF signal classification - Machine learning for cognitive technologies in 5G cellular networks
- Machine learning for channel decoding - Non-parametric Bayesian machine learning for temporal clustering of spectral activity
- Machine learning for RF-based geolocation and signal association
- Machine learning for activity recognition of partially observable wireless network nodes
- Distributed multi-agent learning in collaborative radio networks
- Machine learning in cognitive radars for spectrum sharing with communication devices
- Machine learning-based antenna selection - Machine learning for passive radars
- Reinforcement Learning in wireless networks - Machine learning for Bayesian target characterization
- Machine learning of the topology and structural properties of radio networks
- Machine learning for cognitive radar characterization and for radar waveform design

全文截稿: 2017-07-31

影响因子: 1.245

期刊难度: ★★★★

CCF分类: B类

网址: http://tvlsi.egr.duke.edu/

Today's and emerging applications are extremely demanding in terms of storage and computing power; data-intensive/big-data applications and internet-of-things (IoT) are couple of examples. They will not only impact all aspects of our life, but also change a lot the IC and computer world. Emerging applications require computing power, but with constraints on size, power consumption and guaranteed response time that are typical of the embedded applications. On the other hand, today's computer architectures and device technologies are facing major challenges making them incapable to deliver the required functionalities and features. In order for computing systems to continue deliver sustainable benefits for the foreseeable future society, alternative computing architectures and notions have to be explored in the light of emerging new device technologies, such as memristive devices. This special issue aims to present novel solutions for any aspect related to memristive based computing, for example logic and circuit design, architecture, design automation, applications, etc.

Suitable topics (but not limited to) include:

1.Novel logic and circuit design concepts using resistive devices: memristive-based logic, memristive-based circuits, multi-level based logics, resistive memories, etc.

2.System architectures and new computing paradigms: resistive computing, in-memorycomputing, neuro-inspired computing, novel architectures and CMOS integration, cellular automata and array computing, etc.

3.Applications exploiting memristive devices: signal processing, chaos and complex networks, sensory applications.

4.Automation and CAD tools for memristive circuits: mapping tools, compilers, logic synthesis tools, design space exploration tools, etc.

全文截稿: 2017-09-01

影响因子: 2.569

期刊难度: ★★★★

CCF分类: 无

网址: http://www.signalprocessingsociety.org/publications/periodicals/jstsp/

5G and beyond systems necessitate the exploitation of high-gain MIMO beamforming/precoding by using large antenna arrays at both the base stations and the mobile units to deliver the high data rates promised. The high cost and power consumption of radio frequency (RF) components such as high-resolution analog-to-digital converters (ADCs) makes dedicating a separate RF chain for each antenna prohibitive, and thus the conventional, fully digital baseband (BB) processing becomes infeasible. This is further pronounced in emerging applications such as the internet of things (IoT) involving massive connectivity. Hybrid analog-digital (AD) processing provides a key solution for allowing a reduced number of RF chains and low-specification RF components, where the transceiver processing is divided into the analog and digital domains. This special issue seeks to bring together contributions from researchers and practitioners in the area of signal processing for wireless communications with an emphasis on new methods for hybrid AD signal processing architectures and transmission.

We solicit high-quality original research papers on topics including, but not limited to:
- Fundamental limits of communication by hybrid AD architectures;
- Hybrid AD signal processing techniques for large scale MIMO systems;
- Signal processing techniques robust to low-specification RF components and hardware imperfections;
- Reduced RF chain implementations through parasitic arrays and load modulated MIMO;
- Adaptive transmission / reception techniques for parasitic, reflect, phased, load modulated and other hybrid massive antenna array structures
- Channel modelling for hybrid AD large scale antenna systems;
- Studies and optimization of antenna topologies for massive MIMO deployment with hybrid AD transmission;
- Efficient channel state information (CSI) acquisition techniques for hybrid AD transmission;
- Beamspace MIMO transmission;
- Distributed multi-cell hybrid AD transmission;
- Novel applications of hybrid AD signal processing, including security, energy harvesting, IoT among others;
- Hybrid RF antenna arrays for K, V, W and mmWave frequency bands, including wideband designs;

全文截稿: 2017-09-01

影响因子: 1.609

期刊难度: ★★★

CCF分类: C类

网址: http://www.computer.org/portal/web/tcbb/home

"Prediction" is the main task in machine learning research, and is becoming more and more popular in biomedicine and bioinformatics, especially after Obama proposed the precision medicine project. We have witnessed the great development of advanced machine learning techniques boomed in the computer science community and related conferences such as ICML, AAAI, NIPS, etc. Among them, deep learning and other deep-based representative learning algorithms have been applied successfully in imagine understanding, speech recognition, and text classification, etc. Besides, Semi-supervised Learning, Learning from Positive and Unlabeled Example (PU learning), Multi-view Learning, Transfer Learning, Probabilistic Graphical Model, etc. are also rapidly developed. Nevertheless, the latest prediction techniques were not well applied in the bioinformatics or biomedicine community. Therefore, the aim of this special issue for TCBB is to bridge the advanced machine learning approaches and the biological applications. We hope that methodology implementation and data from real-world applications could both be covered in this issue. The issue will hopefully provide novel guidance for the machine learning researchers and broaden the perspectives of medicine and bioinformatics researchers.

Topics of Interest (not limited):
- Deep learning techniques with the application in bioinformatics
- PU learning problems and solutions in bioinformatics
- Multi-view learning with the application in bioinformatics
- Transfer learning with the application in bioinformatics
- Semi-supervised learning problems in bioinformatics
- Probabilistic graphical model with the application in bioinformatics
- Other novel and efficient data integration techniques in bioinformatics
- Advanced parallel computing for DNA/protein sequences analysis, etc.

全文截稿: 2017-09-30

影响因子: 1.211

期刊难度: ★★★

CCF分类: 无

网址: http://digital-library.theiet.org/content/journals/iet-epa

Activity is increasing in the area of hybrid electric propulsion for aircraft. Some of these efforts extend to complete electric propulsion systems, while others seek to leverage electrical systems to support high power applications that can enhance the fuel consumption and emission reductions that come with operating scenarios that reach beyond what efficient engine thrust can provide. Work by governmental agencies, aircraft manufacturers, and component suppliers alike are actively pursuing electrical technologies that support the aircraft of the future that will have reduced carbon footprint. These technologies extend well into multi-MW power levels across a wide range of speeds.

Topics of interest for this Special Issue include, but are not limited to:

1.Electric machine design for aircraft electrification:
- Novel electric machine designs
- Multiphase and fault tolerant designs
- High power density electric machines
- Cooling methods for electric machines
- Insulation systems suitable for operation at high altitude
- Integration of electric machines with turbomachinery

2.Impact of electric power architectures on machine design for aircraft electrification:
- Voltage selection for multi-MW electric propulsion
- Use of active rectifier versus passive rectifiers
- Aircraft power systems with multiple voltage levels
- Aircraft power system reconfigurability for fault mitigation

3.Impact of electronic power converters for aircraft electrification:
- Effective use of wide bandgap semiconductors in aircraft converters for electric machines
- Converter topologies for aircraft electric machines
- Design for high power density
- Design for fault tolerance
- Design for electromagnetic compatibility

4.Electric machine control algorithms suitable for aircraft electrification:
- Management of multiple power sources
- Sensorless commutation algorithms
- Algorithms for fault management
- Algorithms for maximizing fuel efficiency subject to route constraints

全文截稿: 2017-10-01

影响因子: 2.569

期刊难度: ★★★★

CCF分类: 无

网址: http://www.signalprocessingsociety.org/publications/periodicals/jstsp/

Critical infrastructures such as the smart electric power grid, gas and water utility networks, transportation networks, and communication networks are crucially supporting the quality of life and economic growth. Future critical infrastructures are envisioned to integrate sensory data acquisition, communication and computation technologies, and signal processing to offer improved services to their end-users. Such an integration promises to have profound effects in improving societal welfare by enabling more efficient, open, consumer-centric, environmentally-friendly and resilient modern critical infrastructures. Thus, the design mantra for the evolution of critical infrastructures can be described, in part, as knowledge is power. Hence, at the heart of many technological challenges underlying the vision of evolved critical infrastructures is the need for signal and information processing. For instance, new problems in smart power grids require the use of distributed signal processing and big data analytics to process continuous streams of information from a variety of sources including phasor measurement units, smart meters, smart building sensors, and electric vehicle systems. Moreover, the increased penetration of renewable sources, distributed storage, controllable loads such as plug-in electric vehicles, calls for novel optimal resource management methods that provide security and privacy while yielding system-wide benefits. The relationship of smart grid systems to related critical infrastructures such as transportation and water must be addressed from both cyber and physical perspectives. Classical signal and information processing problems are thus adapting to support dynamic system requirements and complex infrastructure dependencies with evolving characteristics.

This special issue of IEEE J-STSP will showcase the research from the signal and information processing community that is providing leadership in advancing the design, analysis, optimization, and operation of critical infrastructures. Particularly of interest to this special issue will be novel multidisciplinary signal processing approaches at the nexus of big data analytics, control, game theory, and machine learning.

Topics of interest in the special issue include (but are not limited to):
- Optimal network flow problems and extensions
- Information processing for security and resilience
- Online optimization for resource management in critical infrastructures
- Robust and stochastic optimization methods for uncertain supply and demand
- Stochastic optimal control for dynamic pricing
- Topology identification
- Component placement & optimal infrastructure expansion planning
- Information processing for optimizing coupled infrastructures
- Demand forecasting and demand response
- State estimation in critical infrastructures
- Unit commitment and generator scheduling
- Infrastructure system dynamics and transient analysis
- Cyber-security of sensors and smart meters, including Phasor Measurement Units
- Energy theft detection and mitigation
- Energy management for efficient and carbonneutral data centers
- Measurement-based infrastructure system analysis

全文截稿: 2017-10-15

影响因子: 2.163

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/information-and-management/

The notion of big data and its application in driving organizational decision making has attracted enormous attention over the past few years. As the label itself indicates, big data refers to large volumes of data generated and made available online and in digital media ecosystems. Companies are realizing that the data they own and the way they use them can differentiate them from competition, provide them with a competitive edge, and even increase their impact on society. Thus, todays companies try to collect and process as much data as possible. Big data and analytics are also challenging existing modes of business and well-established companies. Yet, there is limited understanding of how organizations need to change to embrace these technological innovations, and the business shifts they entail. Even more, the business value and strategic relevance of big data and analytics technologies still remain largely underexplored. As big data tools and applications spread, they will inevitably transform long-standing ideas about decision making, management practices, and most importantly competitive strategy formulation.


The purpose of this special issue is to identify the role of big data and business analytics on digital ecosystems, shed some light on how they are reshaping contemporary companies, and assess the value of big data analytics. The focus is on how companies should optimally deploy and exploit big data as part of their competitive strategies, as well as how the analytic methods, tools, and techniques are best utilized for supporting business operations and creating solutions
that benefit society. Papers that address topics on how information sources, technological infrastructure, human skills and knowledge, organizational/team structures, and management practices coalesce to achieve desired ends, are of increased interest. Furthermore, outcomes that demonstrate the organizational impact of big data and business analytics in terms of competitive performance, innovativeness, increased agility, market capitalizing competence, and social value are highly encouraged.


Emphasis will be placed on interdisciplinary papers that bridge the domains of organizational science, information systems strategic management, information science, marketing, and computer science. Despite the hype surrounding big data, the aforementioned predicaments still remain largely unexplored, severely hampering the business and societal benefits of big data analytics. This special issue aims to add in this direction and therefore welcomes quantitative, qualitative, and mixed methods papers, as well as reviews, conceptual papers, and theory development papers. Topics of interest include but are not limited to the following:

- Big data and management
- Data-driven competitive advantage
- Big data enabled organizational capabilities
- Big data strategic alignment
- Organizational learning and innovation from big data analytics
- Big data and its impact on business strategy-formulation
- Leveraging big data for social innovation and entrepreneurship
- Human resource management in the data-driven enterprise
- How big data shapes strategy and decision making
- Big data digital business models
- Big data and the dynamics of social change
- Proactive strategy formulation from big data analytics
- Data and text mining for business analytics
- Behavioural and Recommender Systems Analytics
- Big data analytics for strategic value
- Social media analytics for business
- Data quality improvement for business analytics
- Application of big data to address societal challenges

全文截稿: 2017-11-30

影响因子: 4.353

期刊难度: ★★★★

CCF分类: 无

网址: http://www.journals.elsevier.com/information-fusion/

In the last couple of years, deep learning algorithms have pushed the boundaries for numerous problems in areas such as computer vision, natural language processing, and audio processing. The performance of advanced machine (deep) learning algorithms has attained the numbers which were unexpected a decade ago. For a given problem, information can be obtained from multiple sources and such multimodal datasets represent information at varying abstraction levels. Combining information from multiple sources can further boost the performance. Recent research has also focused on multimodal deep learning, i.e. representation learning paradigm which learns joint/combined feature from multiple sources. In this relatively new area, information from multiple sources are combined in a deep learning framework. For example, combining audio and video data to obtain joint feature representation.

This special issue focuses on sharing recent advances in algorithms and applications that involve combining multiple sources of information using deep learning. Topics appropriate for this special issue include novel supervised, unsupervised, semi-supervised and reinforcement algorithms, new formulations, and applications related to deep learning and information fusion.

Topics appropriate for this special issue include (but are not necessarily limited to):
- New models for multimodal deep learning
- Deep learning models for multimodal sensing and processing
- Multi-sensor fusion with deep learning
- Feature fusion using deep learning models
- Shared representation learning
- Combining multiple sources in deep learning
- Combining multiple deep learning models
- Joint deep feature learning
- Cross modality learning
- Hierarchical deep learning models for information fusion
- Transfer learning in multimodal deep learning
- Multimodal deep metric learning
- Applications of multimodal deep learning in image and computer vision related areas such as object recognition, biometrics, forensics and medical data analysis
- Applications of multimodal deep learning in text related areas such as natural language processing, and image to text generation.

全文截稿: 2017-12-01

影响因子: 2.569

期刊难度: ★★★★

CCF分类: 无

网址: http://www.signalprocessingsociety.org/publications/periodicals/jstsp/

The field of information theory addresses fundamental questions in various areas including statistical decision theory, data communications, data compression, security, and networking. In particular, information-theoretic methods can be used to illuminate fundamental limits and gauge the effectiveness of algorithms for various problems associated with these fields.

Recent years have witnessed a renaissance in the use of information-theoretic methods to address various problems in the general field of information processing beyond communications and networking, including signal acquisition, signal analysis and processing, compressive sensing, dictionary learning, supervised and unsupervised learning, reinforcement learning, graph mining, and more.

With a world-wide drive in both academia and industry for new approaches to data science, it is generally believed that information-theoretic methods have the potential to illuminate theory and algorithms that will underpin this emerging field.

This special issue covers emerging topics at the interface of information theory and data acquisition, analysis, and processing, with applications to the general area of data science. Its overarching aim is to map out this emerging research landscape as well as current and future research directions.

Topics of interest include (but are not limited to):
- New information measures to capture limits in modern data acquisition, analysis, and processing problems
- Information-theoretic limits on and algorithms for data acquisition and processing
- Limits on and algorithms for feature extraction, data sketching, and information embedding
- Limits on and algorithms for community detection, graph selection, and ranking
- Limits in active learning, supervised and unsupervised learning, reinforcement learning, and deep learning
- Limits on and algorithms for data acquisition, analysis, and processing problems in the presence of communication and / or computation constraints
- New approaches from the fields of approximation theory and harmonic analysis to unveil limits on and algorithms for data acquisition, analysis, and processing
- Application of new techniques to problems in signal processing, imaging, decision theory, machine learning, data analysis, security, and privacy.

全文截稿: 2018-01-31

影响因子: 0.624

期刊难度: ★★★

CCF分类: C类

网址: http://digital-library.theiet.org/content/journals/iet-com

The past decade has seen a massive growth in the number of telecommunication devices and connections. The number of devices connected and controlled by the telecommunications networks is expected to grow to 50 billion by 2020. Each device needs technologies which can offer a high throughput to support high-speed data applications such as 360'3D video streaming, movies, remote education, and online games. There is also a growing concern about green communication, which focuses on the effects of the radiation emitted from telecommunication devices on the human body. In addition, in future 5G systems, machine-type communications such as cloud computing, the Internet of Things, Internet of Everything, Web 3.0, and Smart X are expected to play an important role. Since machine-type communications are very different with human-type communications, they bring significant challenges regarding a unified radio solution for the current telecommunication systems. The research community of telecommunication networks have put in enormous efforts to meet the above demands. This Special Issue aims at bringing together academic and industrial researchers to discuss and share their work on the technical challenges and recent advances related to 5G telecommunications networks.

Topics of interest for this Special Issue include, but are not limited to:
- Mm-wave communications
- Full-duplex communications
- Energy harvesting communications
- Non-orthogonal multiple access (NOMA)
- Massive MIMO and cell-free massive MIMO
- Ultra-dense cellular networks
- Device-to-device communications
- Distributed caching in wireless communications
- Principles, algorithms, and test-bed for telecommunications networks

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