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The Journal of Systems Architecture: Embedded Software Design (JSA)

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インパクトファクター:
4.1
出版社:
Elsevier
ISSN:
1383-7621
閲覧:
51117
フォロー:
45

論文募集

The Journal of Systems Architecture: Embedded Software Design (JSA) is an academic journal published by Elsevier. (ISSN 1383-7621, impact factor 4.1, CCF B).

Aims & Scope The Journal of Systems Architecture: Embedded Software Design (JSA) is a journal covering all design and architectural aspects related to embedded systems and software. It ranges from the microarchitecture level via the system software level up to the application-specific architecture level. Aspects such as real-time systems, operating systems, programming languages, communications (limited to analysis and the software stack), mobile systems, parallel and distributed architectures as well as additional subjects in the computer and system architecture area will fall within the scope of this journal. Technology will not be a main focus, but its use and relevance to particular designs will be. Case studies are welcome but must contribute more than just a design for a particular piece of software. Design automation of such systems including methodologies, techniques and tools for their design as well as novel designs of software components fall within the scope of this journal. Novel applications that use embedded systems are also central in this journal. While JSA does not focus on hardware design, hardware/software co-design techniques with an emphasis on software are also relevant here. We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications (OSP) which describe the application of the software to research and the published outputs.
最終更新：Dou Sun（）

Special Issues

Special Issue on Security and Efficiency for LLM-Based Edge Intelligence 投稿締切日: 2026-06-30 With the rapid development of artificial intelligence, big data, and machine learning, Large Language Models (LLMs) have advanced rapidly in recent years, and they are now applied across a wide range of domains. In another aspect, Edge Intelligence has emerged as new hot research area. It can reduce latency and lowers bandwidth usage by processing data on local devices. The integration of LLM with Edge Intelligence (EI) has introduced a new field of research, which is called LLM-Based Edge Intelligence. Despite these tremendous advances, LLM-Based Edge Intelligence introduce considerable new security risks. LLMs are trained with massive volumes of data collected from online repositories, as well as forums and websites. This training process inevitably incorporates private information, including user credentials and secrets. Because of the powerful memorization capacity of LLMs, LLMs are prone to reproducing such sensitive information. On the other hand, there is large room to improve the efficiency and scalability of LLM-Based Edge Intelligence with new optimization techniques considering the resource-constrained edge environments. Guest editors: Prof. Meikang Qiu, Augusta University, USA Prof. Wenqi Wei, Fordham University, USA Special issue information: This special issue thus serves as a forum for gathering original research outcomes in security and privacy protection for new architectures, approaches, and applications of LLM-Based Edge Intelligence, with the ultimate goal of advancing secure, efficient, robust, and reliable LLM-based Edge Intelligent systems. Scope of the special issue Security for LLM-based Edge Intelligent systems Privacy protection for LLM-based Edge Intelligent systems Storage and memory system for LLM-based Edge Intelligent systems Energy-efficient computing for LLM-based Edge Intelligent systems Intelligent resource provision and load balance for LLM-based Edge Intelligent systems Protocols and architecture for LLM-based Edge Intelligent systems Advanced multi-cloud and hybrid cloud for LLM-based Edge Intelligent systems Environment impact and evaluation for LLM-based Edge Intelligent systems New robust algorithms design for LLM-based Edge Intelligent systems Reliable design and framework for LLM-based Edge Intelligent systems Manuscript submission information: General information for submitting papers to JSA can be found at Guide for authors - Journal of Systems Architecture. Submissions should be made online at Submission site for Journal of Systems Architecture. Please select the “VSI: LLMEI” option as article type of the paper. Final Manuscript Submission Deadline: 30th June 2026
最終更新：Dou Sun（）

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