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Advances in Intelligent Hardware, Systems and Applications
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Advances in Intelligent Hardware, Systems and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 640

Special Issue Editor

Dr. Zhiqiang Que

E-Mail Website
Guest Editor
Department of Computing, Imperial College London, London SW7 2AZ, UK
Interests: domain-specific hardware; design automation; hardware architecture; reconfigurable computing

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has driven the need for innovative hardware, systems, and application designs to achieve higher efficiency, scalability, and robustness. This Special Issue, “Advances in Intelligent Hardware, Systems and Applications”, aims to provide a platform for researchers and practitioners to present state-of-the-art developments in AI/ML hardware architectures, computing systems, and their applications across various domains.

We welcome original research articles and reviews that focus on topics including, but not limited to, the following:

  • novel hardware accelerators for AI/ML;
  • reconfigurable computing;
  • neuromorphic computing;
  • edge AI/ML systems;
  • hardware-aware AI/ML model design;
  • co-design of algorithms and hardware;
  • heterogeneous computing;
  • energy-efficient systems;
  • AI-driven hardware optimization techniques;
  • automation frameworks.

Furthermore, contributions exploring real-world applications of intelligent hardware in areas such as high-energy physics, healthcare, finance, autonomous systems, quantum computing systems, and natural language processing are highly encouraged. This Special Issue aims to bridge the gap between AI/ML algorithms, hardware and systems, and practical applications, fostering interdisciplinary collaborations and promoting innovative solutions for next-generation intelligent hardware and systems.

Dr. Zhiqiang Que
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI/ML hardware
  • algorithm-hardware co-design and frameworks
  • embedded AI/ML systems
  • AI/ML-driven hardware optimization
  • hardware-accelerated AI/ML applications

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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23 pages, 1302 KB  
Article
Deep Learning-Enhanced Ocean Acoustic Tomography: A Latent Feature Fusion Framework for Hydrographic Inversion with Source Characteristic Embedding
by Jiawen Zhou, Zikang Chen, Yongxin Zhu and Xiaoying Zheng
Information 2025, 16(8), 665; https://doi.org/10.3390/info16080665 - 4 Aug 2025
Viewed by 480
Abstract
Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid [...] Read more.
Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid inversion of oceanic hydrological parameters in complex underwater environments. Based on the open-source VTUAD (Vessel Type Underwater Acoustic Data) dataset, the method first utilizes a fine-tuned Paraformer (a fast and accurate parallel transformer) model for precise classification of sound source targets. Then, using structural causal models (SCM) and potential outcome frameworks, causal embedding vectors with physical significance are constructed. Finally, a cross-modal Transformer network is employed to fuse acoustic features, sound source priors, and environmental variables, enabling inversion of temperature and salinity in the Georgia Strait of Canada. Experimental results show that the method achieves accuracies of 97.77% and 95.52% for temperature and salinity inversion tasks, respectively, significantly outperforming traditional methods. Additionally, with GPU acceleration, the inference speed is improved by over sixfold, aimed at enabling real-time Ocean Acoustic Tomography (OAT) on edge computing platforms as smart hardware, thereby validating the method’s practicality. By incorporating causal inference and cross-modal data fusion, this study not only enhances inversion accuracy and model interpretability but also provides new insights for real-time applications of OAT. Full article
(This article belongs to the Special Issue Advances in Intelligent Hardware, Systems and Applications)
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