期刊信息

Signal Processing (SIGPRO)

请登录查看期刊网址
免费注册：查看官网链接、跟踪截稿日期，并接收邮件提醒。
注册 登录
用 API 获取这条数据
搜索与榜单列表完全无需凭证；本页的完整详情需要一把免费 API 密钥。详见开发者接入页。
影响因子:
3.6
出版商:
Elsevier
ISSN:
0165-1684
浏览:
31410
关注:
23

征稿

Signal Processing (SIGPRO) is an academic journal published by Elsevier. (ISSN 0165-1684, impact factor 3.6, CCF C).

An International Journal, A publication of the European Association for Signal Processing (EURASIP) Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work covering novel signal processing tools as well as tutorial and review articles with a focus on the signal processing issues. It is intended for a rapid dissemination of knowledge to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Statistical Signal Processing; Detection and Estimation; Spectral Analysis and Filtering; Machine Learning for Signal Processing; Optimization methods for Signal Processing; Multi-dimensional Signal Processing; Graph Signal Processing; Signal Processing over Networks; Signal Processing for Communications and networking; Biomedical Signal Processing; Image and Video Processing; Audio and Acoustic Signal Processing; Multimedia Signal Processing; Radar and Sonar Signal Processing; Remote Sensing; Data Science; Network Science; Software Developments and Open Source Initiatives; New Applications. Type of Contributions: The journal welcomes the following types of contributions. Original research articles: Research articles should not exceed 30 pages (single column, double spaced, including figures, tables and references) in length and must contain novel research within the scope of the journal. Review articles: Review articles are typically 30-60 pages (single column, double spaced, including figures tables and references) in length, and provide a comprehensive review on a scientific topic. They may be relatively broad in scope, thereby serving a tutorial function, or be quite specialized, aimed at researchers in the chosen field. Fast Communications: A Fast Communication is a short, self-contained article highlighting ongoing research, or reporting interesting possibly tentative ideas, or comments on previously published research. Such articles should not exceed 10 pages (single column, double spaced, including figures, tables and references) in length. The objective is to provide detailed, constructive feedback on submitted papers and publish high quality papers within a very short period of time.
由 Dou Sun 最后更新于

Special Issues

Special Issue on Beneficial Noise Processing in Multimodal Signal Processing 截稿日期: 2026-12-15 Beneficial noise processing is a new keyword and an emerging field in signal processing (especially times series) and artificial intelligence. In conventional signal processing, noise removal is a classical problem. However, as shown by stochastic resonance, the noise can be beneficial if the noise is at an appropriate level. In recent years, the noise-based models have attracted more and more attention, including but not limited to random forest, dropout in neural networks (a kind of structural beneficial noise), generative adversarial networks, adversarial training, noisy augmentation, diffusion models, and flow matching models. In particular, with the rise of multimodal large models, more and more researchers try applying multimodal large models to signal processing tasks. In particular, the augmentation and generation of time series is highly related to beneficial noise learning. Although most of these models don’t explicitly claim that they aim to learn noise, they actually utilize the beneficial noise implicitly. In many current studies, it is pointed out that noise can be also beneficial to multimodal models. Noise should not be regarded as a harmful component any more. The benefits of noise deserve more systematic studies. However, scientific studies of beneficial noise learning, especially in multimodal signal processing, are still lacking to some extent. Most of these noise-based models just use beneficial noise in a heuristic way. This Special Issue seeks to cover a wide range of topics related to beneficial noise learning and analysis, including but not limited to: 1. Noise-based multimodal generative models for signals; 2. Beneficial noisy and uncertain structure in multimodal models for signal processing; 3. Noisy model training multimodal signal; 4. Noisy augmentations for signal; 5. Positive-incentive noise; 6. Explainable analysis for beneficial noise in multimodal signal processing. Guest editors: Dr. Hongyuan Zhang The University of Hong Kong, Hong Kong, China [email protected] Prof. Xuelong Li Institute of Artificial Intelligence (TeleAI), China Telecom, Beijing, China [email protected] Prof. Feiping Nie Northwestern Polytechnical University, Xi’an, China [email protected] Manuscript submission information: Submission Open Date: 01/01/2026 Manuscript submission deadline: 15/12/2026 Submission Site: Editorial Manager® Article Type Name: "VSI: Benef. Noise Processing" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor Dr. Hongyuan Zhang via [email protected]. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Signal Processing - ISSN 0165-1684 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Signal Processing | Journal | ScienceDirect.com by Elsevier Keywords: Beneficial Noise Processing, Noise Learning, Multimodal Signal Processing https://www.sciencedirect.com/special-issue/329018/beneficial-noise-processing-in-multimodal-signal-processing
由 Dou Sun 最后更新于

看过这个的人也在看

CCF全称影响因子出版商ISSN
CFuture Generation Computer Systems5.9Elsevier0167-739X
BTheoretical Computer Science1.1Elsevier0304-3975
CDiscover Computing1.9Springer2948-2992
CIEEE Journal of Biomedical and Health Informatics6.7IEEE2168-2194

相关期刊

CCF全称影响因子出版商ISSN
Digital Signal Processing3.0Elsevier1051-2004
Mechanical Systems and Signal Processing8.9Elsevier0888-3270
Biomedical Signal Processing and Control4.9Elsevier1746-8094
Journal of Signal Processing Systems1.8Springer1939-8018
IEEE Transactions on Signal Processing5.8IEEE1053-587X
Circuits, Systems, and Signal Processing2.0Springer0278-081X
Journal on Advances in Signal Processing1.9Springer3091-4507
Engineering11.6Elsevier2095-8099
Journal of Materials Processing Technology7.5Elsevier0924-0136
Electronics and Signal ProcessingELSP2959-913X

相关会议

评论 0

暂无评论。

请登录后发表评论