CMCL: Cross-Modal Compressive Learning for Resource-Constrained Intelligent IoT Systems-Reference-Cited by-同舟云学术

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CMCL: Cross-Modal Compressive Learning for Resource-Constrained Intelligent IoT Systems

Published:2024-08-01 Issue:15 Volume:11 Page:25534-25542
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Chen Bin¹^ORCID,Tang Dong¹,Huang Yujun²,An Baoyi³^ORCID,Wang Yaowei²^ORCID,Wang Xuan¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China

2. Research Center of Artificial Intelligence, Peng Cheng Laboratory, Shenzhen, Guangdong, China

3. Network Technology Lab, Huawei Technologies Company Ltd., Shenzhen, Guangdong, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Shenzhen Science and Technology Program

PCNL KEY Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10607832/10416268.pdf?arnumber=10416268

Reference53 articles.

1. Throughput maximization for periodic real-time systems under the maximal temperature constraint

2. Retraining Strategy-Based Domain Adaption Network for Intelligent Fault Diagnosis

3. Loop scheduling and bank type assignment for heterogeneous multi-bank memory

4. Deep Residual Learning based Enhanced JPEG Compression in the Internet of Things

5. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

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