计算机类 | 高引SCI期刊专刊信息5条

计算机科学与技术

Digital Signal Processing

Special Issue on Optimum Sparse Arrays and Sensor Placement for Environmental Sensing

全文截稿: 2019-09-15

影响因子: 2.241

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 工程：电子与电气 - 3区

网址: https://www.journals.elsevier.com/digital-signal-processing

Sampling using a set of spatially distributed sensors finds extensive applications for environmental sensing. Environmental sensing can be either active or passive. Active sensing is achieved by transmitting probing signals and measuring target backscattering from, e.g., an airborne or ground-based vehicle, or an indoor robot. Passive sensing, on the other hand, aims at localizing emitters using signals of opportunity, including electromagnetic, acoustics, and ultrasound. Sparse arrays are under-sampled sensor arrays, in which several sensors are removed from the original configuration. Sparse arrays may create spatial aliasing, which can be avoided by optimizing sensor placements. In addition to the employed signal processing scheme, sensor placement affects the underlying inference performance. That is, non-optimal sensor placement configurations might lead to significantly low signal-to-noise or signal-to-interference ratios. Since the number of sensors typically dictates the number of costly front-end transmitters and receivers, sensing objectives are constrained by the limited number of available sensors and their permissible positions.

This Special Issue deals with optimum sparse arrays and sensor placement for environment sensing, including detection, localization, estimation, imaging and classification. It focuses on sparsity in the sensing, and not limited to sparse signal recovery. The Special Issue welcomes contributions in astronomy, structural health monitoring, radar and ultrasound imaging, wireless communications and 5G, graph signal processing, and other application areas.

Topics to be covered in this Special Issue include but are not limited to:

Sensor placement under a single or multiple objectives

Sensor placement using convex and submodular optimization methods

Knowledge-based and cognitive sparse array design

Hardware realization and design

Machine learning techniques for sensor placement

Sparse sampling for graphs and networks

Applications to sonar, radar, ultrasound imaging, MRI, radio astronomy, localization, speech enhancement, 5G and mmWave systems

图形学与多媒体

Signal Processing: Image Communication

Special Issue on SPIC Deep Image/Video Feature Engineering for Human-Computer Interaction

全文截稿: 2019-09-15

影响因子: 2.073

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 工程：电子与电气 - 3区

网址: http://www.journals.elsevier.com/signal-processing-image-communication/

As desktop PCs and mobile devices are widespread nowadays, effectively and naturally interacting between human and machines is becoming an indispensable task. In practice, users prone to naturally interact with computers face-to-face as they communicate with their family members, friends, and clients. Users want to communicate through a multimodal manner, i.e., eye contact, gesture, body language, speech, and face expressions contribute collaboratively. Human–computer interaction (HCI) focuses on designing the interfaces and technology that effectively link users and computers. HCI designers investigate the ways in which humans interact with computers, based on which they employ state-of-the-art technologies that allow humans interact with computers in a convenient and natural way. As an interdisciplinary, HCI technique is related to image/video modeling, face/expression understanding, deep learning, multimodal feature fusion, 3D realistic rendering. Due to the advancement of deep learning, in human-human communication, ideas are often represented by multiple deep features, .e.g.,, deep poselet, deep gaze behavior, and deep hand motions. Based on this observation, there has been a significant growth of multimodal HCI techniques by deep learning.

Despite of the progress of deep HCI techniques, effectively engineering the multiple deep features for HCI is still a challenge. Potential difficulties include: 1) how to seamlessly and collaboratively explore the heterogeneous deep features in multimodal HCI modeling, 2) how to design deep architectures that optimally encodes multiple visual features for HCI applications, 3) how to deeply encode emotional and cognitive features into the current HCI systems, and 4) how to intelligently alleviate the negative influences of contaminated/absent visual features in multiple HCI-based features. Apparently, when HCI meets multimodal deep feature learning, many interesting issues and challenges are generated. We expect new technologies and mathematical formulations, datasets, and evaluation benchmarks to multimodal HCI.

This special issue serves as a forum to bring together active researchers from both industry and academia to exchange their opinions and experiences in multimodal HCI techniques. We solicit original contributions in threefold: 1) presenting state-of-the-art theories, technologies and novel applications of deep feature learning for HCI; 2) surveying the recent progress in these topics; and (3) releasing benchmark text/image/video dataset for evaluating deep HCI techniques. This special issue target researchers and practitioners from both industry and academia.

The topics of interest include (but not limited to):

Signal processing and human-computer interaction (HCI);

Image/video modeling and multimodal interaction;

Deep visual learning architecture for HCI and affective computing;

HCI-based Healthcare and assistive technologies;

Advances in human visual communication dynamics;

Human-robot/agent visual multimodal interaction;

Multimodal visual feature fusion for HCI and affective computing;

Deep learning and other machine learning method for multimodal HCI;

Multimodal dialogue modeling techniques;

Gaze behaviors and analysis in HCI sytems;

HCI System components and multimodal media platforms;

Visual behaviors modeling in social interactive context;

Virtual/augmented reality and multimodal interaction;

Text, image, and video information fusion and representation in HCI;

Customized contents (text, image, video) generation in HCI.

Harvesting user-related visual representation from MHCI systems;

Knowledge graph based automatic annotation, conversation, and summarization.

人工智能

Cognitive Systems Research

Special Issue for the Annual International Conference on Biologically Inspired Cognitive Architectures 2019

全文截稿: 2019-10-20

影响因子: 1.425

CCF分类: 无

中科院JCR分区:

  • 大类 : 医学 - 4区

  • 小类 : 计算机：人工智能 - 4区

  • 小类 : 神经科学 - 4区

网址: http://www.journals.elsevier.com/cognitive-systems-research/

It is our great pleasure to announce that for the first time, full papers accepted to the Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2019) will be published in a Special Issue of Cognitive Systems Research. For details, please see the conference web site (bica2019.bicasociety.org). We solicit submissions of research papers and reviews in all domains of science and technology that directly or indirectly may help us to make an advance toward the BICA Challenge, which is to implement the top essential functionality of the human mind in a machine.

Specifically, submissions in the following areas are expected.

Artificial Intelligence and Computer Science

Cognitive Science and Neuroscience

Social Emotions and Creativity

Active Learning and Cognition

Believable Robots and Characters

Creative Assistants and Cobots

Technological Breakthrough, its Metrics and Impacts

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Future Generation of Service-Oriented Computing Systems

全文截稿: 2019-12-15

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Services computing has been a subject of intensive research, development, and deployment in the last decade. Since more and more private users, research institution, businesses, hospitals, cities, and industry companies wish to benefit from interconnectivity, provided in particular by wireless networks, the interest in services study and development is growing exponentially. The areas of use of services computing are heterogeneous, subject to security attacks, and high-performance, reliability, and trustworthiness expectations of users. From the first concepts of services linked to the abstractions of infrastructure, platforms, and software, we face the services created for and exploited in data storage and management, fogs, edges, security, trust, workflow, modelling, etc. This imply a need for new knowledge of services cloud, enhancing existing knowledge of in the area of services, and generate new applications that would benefit other researchers, developers of service computing systems, and create a basis for other users to consider making their areas of interest better, faster, more secure, and trustworthy.

The aim of this Special Issue is toreport the state of research on Future Generation of Service- Oriented Computing Systems. It will be comprised of selected papers drawn from submissions from extended peer-reviewed versions of the best papers presented at the 17th International Conference on Service-Oriented Computing (ICSOC), will take place in Toulouse, France, in October 28–31, 2019 (http://www.icsoc.org). ICSOC is the premier international forum for academics, industry researchers, developers, and practitioners to report and share ground breaking work in service-oriented computing. ICSOC fosters cross-community scientific excellence by gathering experts from various disciplines, such as business-process management, distributed systems, computer networks, wireless and mobile computing, cloud computing, cyber-physical systems, networking, scientific workflows, services science, data science, management science, and software engineering.

The SI seeks outstanding, original contributions, including theoretical and empirical evaluations, as well as practical and industrial experiences, with emphasis on advanced results that solve open research problems and have significant impact on the field of service-oriented computing. Specific topics of interest include but are not limited to:

1.Service Engineering and Management
o Legacy systems migration and modernization
o Service design, specification, discovery, customization, composition, and deployment
o Service innovation, governance, and change and workload management
o Theoretical foundations of Service Engineering
o Service execution, monitoring and reconfiguration
o Quality of service, Security, privacy, and trust
o Architectures for multi-host container deployments
o Microservices deployment and management

2.Services and Data
o Services for Big Data and compute-intensive applications
o Mining and analytics
o Data-provisioning services
o Services-related linked open data
o Automated Knowledge Graph creation

3.Services in the Cloud and on the Edge
o Migration to virtual infrastructures
o XaaS (everything as a service including IaaS, PaaS, and SaaS)
o Service deployment and orchestration in the Cloud
o Cloud and Edge service and workflow management
o Cloud and Edge brokers and coordination across multiple resource managers
o Workload transformation
o Analytics and knowledge generation services
o Lightweight service deployment and management
o Services and edge gateway architectures

4.Services for the Internet of Things
o Embedded and real-time services
o RFID, sensor data, and services related to the Internet of Things
o Services for IoT platforms and applications
o Service-oriented protocols for IoT applications
o REST APIs and services for IoT platforms and applications

5.Services for Softwarized Network Functions and Software Defined Networks
o Service Network Function Management and Orchestration
o Services for novel and emerging networking protocols
o Named data networking
o Virtualized Network and transport mechanisms
o Virtualized Network Functions and Services
o Virtualized Service Function Chaining

6.Services in Organizations, Business, and Society
o Services science
o Social networks and services
o Cost and pricing of services
o Service marketplaces and ecosystems
o Service business models
o Enterprise architecture and services
o Service Chatbots

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

Special Section on Smart City Oriented Cyber-Physical Systems

全文截稿: 2019-12-30

影响因子: 1.747

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 3区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

With the popularization of information technology and the continuous progress of big data technology, it has been urgent to generate various types of network intelligence and dynamic information collection systems. The Internet of Things (IoT) and computers with powerful functions can simulate urban operation by operating under reasonable safety regulation. However, a series of practical problems should be solved to make breakthroughs and realize sustainable development of a new urban generation.

Information technology is widely used for multi-dimensional aggregation in smart cities. By using technologies, such as networking, intelligent sensor placement can be applied in cities for the creation of object-linked information integration. Moreover, the collected information can be integrated with the Internet and other networks based on intelligent analyses. Such systems can implement analyses according to the requirements for intelligent decision support and communication. Practical systems can be established through information technology for public service, public industry, and public management. Also, the service efficiency of the government and the life quality of the public can be improved. A smart city emphasizes high-efficiency information processing ability, information resource integration ability, and management ability to coordinate various activities. Interdisciplinary, multi-level, cross-sectional, and cross-regional cooperation can realize the interconnection and mutual understanding among people, objects, networks and industries. Consequently, new models and new forms of urban development can be realized, which reflect the wisdom of smart cities.

As a complex multi-dimensional system, Cyber-Physical System (CPS) is a comprehensive computing, network and physical environment. The information world can be closely integrated with the physical world through the combination of control technology, communication technology and computing technology. With close relation to human life and social progress, IoT has been widely used in various fields such as industrial control, environmental monitoring, intelligent medical, intelligent transportation, intelligence grid, military reconnaissance and aerospace. As a typical application of IoT, intelligent medical systems can provide reliable, safe and real-time medical services in a wired or wireless way. The key information in intelligent transportation system, such as traffic signals, can be monitored in real time. The system analyzes, computes, and publishes a great deal of information, so that real-time road information can be shared by vehicles. By observing and real-time monitoring the system, road managers can release information and guide vehicles to improve urban traffic conditions.

High quality and up-to-date technology of CPS and IoT are the target of this special issue. It can be taken as a forum for researchers from different countries to discuss their works and progresses. Particularly, the issue aims to show the latest progress and development in the discovery and exploration of IoT. Both state-of-the-art practical applications and theoretical studies can be submitted. The submitted papers will be peer-reviewed and selected according to their quality and relevance to the special issue.

Topics:

Topics of interest include the following:

- Integration of provenance into Cyber-Physical Systems(CPS) and Internet-of-Things

- Internet of Vehicles and Internet of Things

- Real-time meanings in different application spaces: energy, transportation, medical and aeronautics

- Pervasive and Ubiquitous Technology in IoT

- Mining of spatial data in IoT

- Multimedia Communications and Visual Signal Processing in IoT

- Physical/virtual testbeds for real-time closed-loop control

- Robot navigation in IoT

- Knowledge reasoning in physical systems

- Machine learning and other approaches for real time data analytics

- Data-driven urban transportation management