计算机类 | SCI期刊专刊信息5条

计算机科学与技术

Vehicular Communications

Special Issue on Security and Privacy in Internet of Vehicles

全文截稿: 2018-12-01

影响因子: 3.289

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 1区

  • 小类 : 电信学 - 1区

  • 小类 : 运输科技 - 1区

网址: https://www.journals.elsevier.com/vehicular-communications

Internet of Vehicles (IoV) is an important part of intelligent transportation system. It enables information sharing and the gathering of information on vehicles/autonomous vehicles, roads, and their surroundings through V2X communications. IoV faces various security and privacy challenges, for example, various types of attacks including jamming, tampering and eavesdropping.

This special issue aims to attract contributions from academic and industrial organizations addressing emerging security and privacy issues in IoV.

Topics of interest include (but are not limited to):
- Security framework of IoV
- Privacy-preserving data aggregation
- Trust and reputation in IoV
- Secure routing in IoV
- Secure and privacy-preserving V2X communications
- Secure vehicular fog computing
- Security in cloud based IoV
- Access control in IoV
- Vulnerability scanning technologies for IoV
- Intrusion detection technologies for IoV
- Blockchain and the IoV
- Accountability in IoV

计算机体系结构,并行与分布式计算

Parallel Computing

Special Issue on “Programming, Resource Management and Autotuning Tools for Heterogeneous HPC”

全文截稿: 2018-12-15

影响因子: 0.938

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：理论方法 - 3区

网址: http://www.journals.elsevier.com/parallel-computing/

Future High Performance Computing (HPC) systems face complex challenges deriving from the push towards Exascale, the limits of the power grid to support such large infrastructures, and emerging classes of applications imposing quality of service requirements other than pure throughput.

To address such challenges, heterogeneous computing architectures have emerged as a solution to achieve both higher performance and lower energy consumption. Their expression in the form of GPGPU and other many-core accelerators coupled with traditional HPC processors dominate the current Green500 and Top500 lists. Even higher degrees of heterogeneity can be achieved by introducing reconfigurable fabrics and/or application- or domain-specific accelerators.

The cost of heterogeneity lays in the complexity of management. Writing and managing HPC application is already a challenging task, requiring the cooperation of domain experts and HPC experts. The introduction of heterogeneous architectures makes the development and runtime management even more complex. Furthermore, at Exascale levels, hardware failures become sufficiently likely that computations running on such large infrastructure need to take them into account.

As a result, challenges include the management of heterogeneous resources, energy efficiency of computation, as well as the capability to meet timing constraints in face of transient or long time hardware failures. To solve such issues, manual control of computing resource will not suffice. New programming, resource management and autotuning models and tools are needed to effectively tackle such challenges.

Papers submitted to the special issue should have a strong emphasis on multi-node parallelism. Topics to be covered in this special issue include, but are not limited to, the following:

- Runtime resource management for heterogeneous HPC systems;

- Power, thermal, and performance prediction and management;

- Programming models integrating parallelism at multi-node level with other aspects, including resource management and access to heterogeneous resources, access to advanced storage (e.g., converging Big Data and HPC), fault management;

- Strategies, frameworks and methodologies for autotuning and self-management of the application and system.

计算机科学与技术

Microprocessors and Microsystems

Special Issue on Intelligent Embedded Systems Architectures and Applications

全文截稿: 2019-01-15

影响因子: 1.049

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 4区

  • 小类 : 计算机：理论方法 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: https://www.journals.elsevier.com/microprocessors-and-microsystems

The purpose of this special issue is to provide an up-to-date picture of intelligent embedded systems architectures and applications with emphasis on Smart IoT and Cyber Physical Systems, including hot topics such as accelerating deep learning. The proposal covers several aspects, from the hardware related ones to embedded software and application issues.

Topics include, but not limited to, the following:

- Special purpose hardware to support deep learning in embedded architectures

- Edge computing for smart embedded systems: hardware and software aspects

- Run-time resource management for smart IoT/Edge Computing systems

- HW/SW codesign of Cyber Physical Systems

- Programming models for IoT/Edge computing applications

- Applications and case studies of intelligent embedded systems

- Design methodologies and platforms for wearable computing

- In-memory computing for unsupervised learning

计算机科学与技术

Journal of Computational Science

Special Issue on Finite Difference Methods: Recent Developments and Applications in Computational Science

全文截稿: 2019-04-01

影响因子: 1.925

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 计算机：理论方法 - 3区

网址: https://www.journals.elsevier.com/journal-of-computational-science

Computational Science is a rapidly growing interdisciplinary field concerned with constructing mathematical models, numerical approximations of forward and inverse problems, quantitative analysis techniques, and using advanced computing capabilities to analyze, investigate and solve a wide range of complex problems in the natural and social sciences, medicine, and engineering, among others.

Finite difference approximations of differential equations are one of the oldest and simplest methods which are frequently used for computing approximate solutions of the underlying equations modeling complex phenomenon. With the availability of ever more powerful computational resources, the large but finite algebraic system of equations arising from finite difference approximations can be easily solved on present day computers, and the resulting efficient algorithms provide gold standards to beat for other approximation techniques.

Modeling and simulation tools based on Finite Difference techniques find increasing applications not only in fundamental research, but also in several real-world applications. However, the simplicity and efficiency of Finite Difference Methods comes at the cost of reduced accuracy and stability in the approximation of problems involving heterogenieties and nonsmooth interfaces.

The objective of this special issue is to present recent important developments in the construction, analysis and simulation of approximation techniques based on the Finite Difference Method (FDM) that address these and other limitations of the FDMs and provide efficient solutions to advance research in this area. High-quality original contributions to this special issue are invited from researchers working in this area.

TOPICS

Potential topics include but are not limited to the following:

· (High order) FDMs for Elliptic, Parabolic and Hyperbolic Problems

· (High order) FDMs for Initial and Boundary Value Problems

· Alternating Direction Implicit FDMs

· Compact and Symplectic FDMs

· Nonstandard FDMs, positivity-preserving, convexity preserving schemes

· High Order Operator Splitting based FDMs; Mimetic FDMs

· Adaptive Mesh Refinement in FDMs

· Multiscale Methods based on FDMs

· FDMs on unbounded domains and on domains with irregular geometry

· Irregular Grid FDMs

· Element-free and Meshless FDMs

· Parallel FD methods, FDMs on GPUs

· Applications of FDMs in Physics, Chemistry, Biology, Finance andEngineering.

计算机科学与技术

Physical Communication

Special Issue on Big Data Driven Millimeter Wave Systems (submission due: September 1, 2019)

全文截稿: 2019-09-01

影响因子: 1.522

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 工程：电子与电气 - 4区

  • 小类 : 电信学 - 4区

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

Millimeter wave (mmWave) communication systems have raised increasing attentions from both academia and industry. Compared with existing wireless communication systems, such as Wi-Fi and 4G, mmWave systems adopt much higher carrier frequencies and thus come with advantages including wider bandwidth, narrower beam, higher transmission quality, and stronger detection ability. These advantages well address the challenging situations caused by recent popular applications. For example, mmWave systems can significantly reduce the delivery time of skyrocketing video streaming.

In the meantime, more and more bandwidth intensive applications are emerging (e.g., HDTV, UHDV). These massive data traffic bring great pressure to existing wireless systems. To meet this incredible increase, mmWave communication systems, which can offer multi-gigabit data rate, hold potential to be utilized in future wireless networks. However, applying mmWave into practice is challenging. Since mmWave links are highly directional to combat severe attenuation. For example, in order to reduce the complexity and cost, mmWave communications are suggested to assist data transmission in data centers. Nevertheless, densely deployed servers will cause interference to each other and thus mmWave links are easily broken. Therefore, multiple data flows in data centers should be scheduled to optimize the system performance based on the unique features of mmWave communications. Another typical application scenario for mmWave communications is the future 5G networks, to meet the transmission requirements of the arrival of the big data era. To ensure the quality of service, mmWave links should be maintained in highly dynamic environments.

The goal of this Special Issue is to disseminate the latest research and innovations on big data driven mmWave systems, including the system modelling, design principles, architecture, performance evaluation, communication protocols and routing schemes. The topic of interests includes, but not limited to:

- Architecture design and modelling of mmWave systems used for big data services and applications

- Security, privacy and reliability in big data driven mmWave networks

- Network capacity and performance in big data driven mmWave networks

- Framework design for big data driven mmWave networks

- Traffic engineering for massive data in mmWave networks

- Green wireless communications in big data based mmWave networks

- Reliable communication protocols in big data based mmWave networks

- Routing strategies and algorithms for big data based mmWave networks

- Practical implementations of large-scale mmWave networks

- Interference management in big data driven mmWave networks

- Massive MIMO technology in big data driven mmWave networks

- Network coding in big data driven mmWave networks

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