【计算机类|期刊】SCI期刊专刊截稿信息8条

计算机体系结构,并行与分布式计算

Integration, the VLSI Journal

Special Issue on PRIME and SMACD 2017

全文截稿: 2017-10-01

影响因子: 1.0

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/integration-the-vlsi-journal/

The 13th Conference on PhD Research in Microelectronics and Electronics (PRIME 2017) and the 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD 2017) will take place in Giardini Naxos - Taormina, Italy from 12th to 15th June 2017.

PRIME and SMACD bring together researchers from various areas such as Analog, Mixed- Signal, RF, Mult卜Domain integrated Circuits, Heterogeneous Systems Design, as well as from Electronic Design Automation (EDA) area. This special issue aims at publishing extended versions of top ranked papers in these two conferences, referring to state-of- the-art work in terms of IC design techniques, trends and challenges, or EDA tools applied to standard and/or emerging technologies.

The topics to be covered include, but are not limited to:
- Micro/Nanoelectronics
- Semiconductors
- Analog/Digital Signal Processing
- Computer Aided Design
- Analog/Digital/Mixed/RF IC Design
- Integrated Power ICs
- Sensors/Systems and MEMS
- Semiconductor Memories
- RF, Microwave and mm-wave Circuits
- VLSI and SoC Applications
- Visual Signal Processing
- Energy Harvesting
- Automotive
- Flexible Electronics
- Technical Trends & Challenges
- Performance, Reliability, Variability modeling
- Power and Thermal modeling and simulation
- Multi-domain (optoelectronics, biologica/, MEMs, etc.) modeling and synthesis
- Fault Modeling and Simulation
- Monitoring, Diagnosis, Prognosis and
- Identification Techniques
- Analysis of reliability effects (aging, stress, parasitics, electro-migration, etc.)
- Numerical and Symbolic Simulation Methods
- High-frequency Circuit and System Design
- Low-power Design Techniques
- Variability-aware and Reliability-aware Design
- Methodologies
- Test and Design-for-Test Methods
- Language-based Synthesis Techniques
- Optimization Methods applied to circuit and System Design

计算机网络

Journal of Network and Computer Applications

Special Issue on 5G Radios and Networks

全文截稿: 2017-10-15

影响因子: 3.5

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/journal-of-network-and-computer-applications

In Sep. 2015, International Telecommunication Unit Radiocommunication Sector (ITU-R) has identified three categories of upcoming wireless features for the fifth generation (5G) radios and networks. In the meantime, ITU-R has also identified the radio transmission requirements of 5G (also known as International Mobile Telecommunications 2020, IMT-2020). To this end, 3GPP and IEEE consequently launched the standardization activity to frame 5G radios and networks. To satisfy these unprecedented radio transmission requirements, a number of innovative technologies will be adopted. The objective of this special issue is consequently to bring together state-of-the-art innovations, research activities, and the corresponding standardization impacts of 5G, so as to understand the inspirations, requirements, and the promising technical options to boost the development of 5G radios and networks.

Topics of interest include (but are not limited to):
- New waveforms, NOMA, multi-user superposition transmission (MUST), beamforming based radio access, radio resource and interference management for eMBB, URLLC and mMTC
- IEEE 802.11ax, 802.11ad, 802.11ay, 802.11ah and next generation WiFi technologies
- Ultra-dense network, multiple TRPs, cloud radio access network, massive MIMO, sidelink, mobile backhaul, unlicensed transmissions
- SDN, NFV, network slicing, open architecture for next generation core
- Simulation platform, prototypes and field-try, standardizations of 5G radios and networks
- Impacts of innovative applications supported by 5G radios and networks

软件工程

Journal of Logical and Algebraic Methods in Programming

Special Issue on Relational and Algebraic methods in Computer Science

全文截稿: 2017-10-30

影响因子: 0.692

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/journal-of-logical-and-algebraic-methods-in-programming

Relational and algebraic methods based, for instance, on relation and Kleene algebras, semigroups, lattices or semirings belong to the core of computer science. This special issue aims to showcase the variety and relevance of recent developments in this field, from theory to applications.

We invite submissions of high-quality original research articles on topics that include, but are not limited to the following.

Theory:
* algebraic structures from semigroups, residuated lattices and semirings to Kleene algebras, relation algebras and quantales
* other algebras relevant to the theory of automata, concurrency, formal languages, games, networks, programming languages and social choice
* algorithmic, category-theoretic, coalgebraic or proof-theoretic methods for such algebras
* their formalisation with automated and interactive theorem provers


Applications:
* tools and techniques for the verification and correctness of sequential and concurrent programs
* quantitative and qualitative models and semantics for computing systems
* logics of programs, e.g., modal, dynamic, interval, temporal or resource logics; logics for games, social choice and distributed systems
* design of algorithms, network protocol analysis, optimisation and control

人工智能

Computer Speech and Language

Call for Papers Special Issue: Speech and Language Processing for Behavioral and Mental Health Applications

全文截稿: 2017-10-31

影响因子: 1.9

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/computer-speech-and-language

Speech and language are integral to human communication. They encode rich linguistic and paralinguistic information of intent and emotions, including implicit cues that are reflective of our internal mental states and traits. In fact, many existing behavioral assessments and clinical diagnoses of neurological and psychiatric disorders rely on experts evaluating the human mental states through systematic manual categorization of relevant speech and language behaviors.

With advances in speech and language processing technologies (e.g., automatic speech recognition, speaker diarization, semantic analysis, information retrieval, etc.) as well as in behavioral signal processing and affective computing alongside the converging capabilities in large-scale human-centric sensing, data collection, and computing, there exists a wide range of possibilities for automatically detecting, recognizing, analysing, and predicting human mental states and traits from speech and language cues, including well-being and aspects of dysfunction, atypicality, and other indicators of illness. Such computational advances in modelling human behavioral signals have made the development of automated speech and language-based decision analytics in a variety of behavioural and mental health domains for enabling screening, diagnostics, and treatment support increasingly desirable because of its potential in achieving high reliability, consistency, and large-scale deployment. Domains of active research range from Autism Spectrum Disorders, major depressive disorders, and suicidality to Alzheimer’s disease, addiction, and relationship issues.

However, realizing end-to-end, real-world speech and language processing-based behavioral and mental health analytics require integrative handling of a wide range of technical challenges. These include deploying robust speech and language technology in clinically-valid and real-life scenarios, handling a variety of contextual and human factor-induced variabilities, uncovering hidden behavioral patterns related to the mental states of interest, and further validating and disseminating the derived speech-language analytics in mental health contexts. Many of these technical advancements have been isolated in the past, so substantial algorithmic and empirical efforts are still required to further enhance the technical capabilities, augmenting experts’ decision support and improving our quantitative understanding of human behaviors.

The objective of this Special Issue on Speech and Language Processing for Behavioral and Mental Health Applications is to bring together and share these advances in order to shape the future of the field. It will focus on technical issues and applications of speech and language processing for behavioral and mental health applications. Original, previously unpublished submissions are encouraged within (not limited to) the following scope:
- Analysis of mental and behavioral states in spoken and written language
- Technological support for ecologically- and clinically-valid data collection and pre-processing
- Robust automatic recognition of behavioral attributes and mental states
- Cross-cultural, cross linguistic, cross-domain mathematical approaches and applications
- Subjectivity modelling (mental states perception and behavioral annotation)
- Multimodal paralinguistics (e.g., voice, face, gesture)
- Neural-mechanisms, physiological response, and interplay with expressed behaviours
- Databases and resources to support study of speech and language processing for mental health
- Applications: scientific mechanisms, clinical screening, diagnostics, & therapy/treatment support
- Example Domains: Autism spectrum disorders, addiction, family and relationship studies, major depressive disorders, suicidality, Alzheimer’s disease

软件工程

Journal of Systems and Software

Special Issue on Quality Engineering and Management of Software-Intensive Systems

全文截稿: 2017-10-31

影响因子: 2.444

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-systems-and-software/

According to IEEE standards, software-intensive systems are described as "any system where software contributes essential influences to the design, construction, deployment, and evolution of the system as a whole" [IEEE Std 1471:2000] to encompass "individual applications, systems in the traditional sense, subsystems, systems of systems, product lines, product families, whole enterprises, and other aggregations of interest". [IEEE Std 42010:2011]. Examples for software-intensive systems include embedded systems for avionics and automotive applications, large-scale heterogeneous systems, or business applications with special focus on web services. Software quality plays a pivotal role when developing and managing software-intensive systems. Hence, the goal of this special issue is to collect current contributions relating to quality-engineering and management of software-intensive systems.

The topics relevant to this special issue include, but are not restricted to, the following:
- Software engineering for embedded and cyber-physical systems
- Model-based development, components and services
- Software management
- Software process and product improvement
- Software product lines and software ecosystems
- Estimation and prediction in software and systems engineering
- Software engineering and technical debt
- Sustainable software engineering

计算机科学与技术

Microelectronics Reliability

Special Issue on Wide Bandgap Materials and Devices

全文截稿: 2017-10-31

影响因子: 1.371

期刊难度: ★★

CCF分类: 无

网址: www.journals.elsevier.com/microelectronics-reliability

Wide bandgap semiconductors like GaN, SiC and oxides have become serious alternatives for the replacement of Si in power electronics and sensors devices especially in harsh environment. The aim of this special issue is to publish recent progress and results on fabrication, design, and reliability of wide bandgap materials and devices. Potential topics include, but are not limited to:
- SiC homoepitaxy on low-offcut substrates
- III-N on Si: nucleation layer, interface control
- Thermal issues in GaN and oxide related devices
- Integrating graphene with nitrides or SiC
- Wide bandgap materials for photonic, power, or sensor applications
- Reliability issues in devices based on wide bandgap materials
- Recent development in nanostructures with new optoelectronics properties

计算机科学与技术

Physical Communication

Special Issue on Optical Wireless Communications

全文截稿: 2017-11-01

影响因子: 1.583

期刊难度: ★★

CCF分类: 无

网址: www.journals.elsevier.com/physical-communication

In the light of the spectrum bottleneck at both network access and backhaul levels, the time has come to consider the upper parts of the electromagnetic spectrum for wireless communications. By doing so, we move into the optical band which includes infrared, visible and ultraviolet sub-bands. Offering significant technical and operational advantages, optical wireless communication (OWC) can be, in some applications, a powerful alternative to and, in others, complementary to existing radio frequency wireless systems and fibreoptic systems. Despite the recent surge of interest in OWC, particularly its sub-disciplines visible light communication (VLC) andfree space optical communication (FSO), this area isrelatively less explored and extensive research efforts are further required to harness the enormous potential of the optical spectrum for wireless communication applications.

This special issue will provide a forum for the latest research and innovations in OWC technologies as well as their applications. High quality papers are solicited in the following non-exclusive listof main areasof research:
- Indoor and outdoor optical wireless channel modelling and characterization
- Information theory and capacity of optical wireless channels
- Modulation, coding, MIMO and signal processing techniques for OWC systems
- Mobility management and resource allocation for VLC networks
- Multiple access, scheduling and interference coordination for VLC systems
- Backhauling for VLC networks and integration with other wireless technologies
- Spatial mode multiplexing for high capacity FSO links
- Airborne FSO systems, e.g., UAV, aircraft, satellite
- Topology control and routing for FSO networks
- Ultraviolet communications
- Optical camera communication
- OWC-based solutions for vehicular, underwater, chip-to-chip communication
- Quantum communication over optical wireless links

人工智能

Applied Soft Computing

Special Issue on Applying Machine Learning Systems for IoT Services in Industrial Informatics

全文截稿: 2017-11-20

影响因子: 3.541

期刊难度: ★★★

CCF分类: 无

网址: http://www.journals.elsevier.com/applied-soft-computing/

Machine learning techniques are delivering a promising solution to the industry for building Internet of Things (IoT) systems and to make innovation at a rapid pace. The Open IoT cloud platform offers a framework for building large scale IoT applications relying on data gathered from a complex infrastructure of sensors and smart devices. Numerous challenges exist in implementing such a framework, one of them being to meet the IoT data and services (quality of service (QoS)) requirements on Industrial informatics based applications in terms of energy efficiency, sensing data quality, network resource consumption, and latency. The new era of convergence of machine learning techniques (supervised-unsupervised and reinforcement learning) with reference to IoT quality of data and services for Industrial applications has three main components: (a) intelligent devices, (b) intelligent system of systems, and (c) end-to-end analytics. This special issue is integrating machine learning methods, advanced data analytics optimization opportunities to bring more computer IoT data and services. Further, machine learning approaches had addressed various challenges of IoT such as anomaly detection, multivariate analysis, streaming and visualization of data.

In fact, recent literatures have addressed the inherent power of fusion between machine learning algorithms and IoT applications in industrial informatics. It can provide effective solutions for machine understanding of data (structured/semi structured), optimization problems, specifically, dealing with incomplete or inconsistent information, with limited computational capability related to Internet of Things (IoT). This special issue aims to address the machine learning techniques, recent developments in diverse IoT data, services and applications as well as theoretical studies. Besides, we can consider that machine learning re-enforcement paradigms and predictive learning algorithms are more applicable to IoT datasets, time series data from IoT devices with sensor fusion and streaming. Further, it is important to make a note that machine learning systems and optimization techniques has not been adequately investigated from the perspective of IoT data and services (Quality of Services) and its related research issues in industrial applications. Furthermore, there are many noteworthy QoS metrics (system life time, latency, quality, delay, bandwidth and throughput) that need to be addressed in the view of machine learning algorithms with relate to IoT data and services. Obviously, these challenges also create immense opportunities for researchers. For the aforementioned reasons, this special issue focuses to address comprehensive nature of machine learning and to emphasize its character in modelling, identification, optimization, prediction, forecasting, and control of future IoT systems for industrial systems. Submissions should be original, unpublished, and present in-depth fundamental research contributions either from a methodological/application perspective in understanding machine learning approaches and their capabilities in solving diverse range of problems in IoT and its real-world industrial applications.

We seek original and high quality submissions related to (but not limited to) one or more of the following topics: (Note that this special issue emphasizes "real world" applications)
- Design and Evaluation of Energy Efficient Networks and Services in IoT
- Machine-Learning and Artificial Intelligence for Traffic/Quality of Experience Management in IoT
- Hybrid Intelligent Models and Applications for IoT in Industrial applications
- Nature-Inspired Smart Hybrid Systems for IoT Context-Aware Systems
- Machine learning and Data Analytics and Decision Automation in IoT for Industry
- Knowledge-Based Discovery with Evolutionary Algorithms for QoS in IoT devices
- Fuzzy Fusion of Sensors, Data and Information
- Meta-Heuristic Algorithms for IoT and wearable Computing
- Hybrid Optimization Methods Emerging real world and theoretical applications of IoT in Industry
- Innovative Deep Learning Architectures/Algorithms for Time Series Data and IoT
- Neural network modelling, analysis and synthesis techniques in ubiquitous communications
- Multi-Objective IoT System Modelling and Analysis—Performance, Energy, Reliability, Robustness
- Modelling and simulation of large-scale IoT scenarios and IoT standardization
- Machine learning for IoT and sensor research challenges: battery of sensor, routing, prediction of nodes etc.
- Quality aspects in the IoT (e.g., runtime dependability, assurances, validation, verification, privacy, security)
- State-of-practice, experience reports, industrial experiments, and case studies in the IoT

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