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Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network’s utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ±10% of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method’s average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications.
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This content will become publicly available on August 1, 2026
MultiHeart: Secure and Robust Heartbeat Pattern Recognition in Multimodal Cardiac Monitoring System
The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications.
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- Award ID(s):
- 1956110
- PAR ID:
- 10655648
- Publisher / Repository:
- Electronics
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 14
- Issue:
- 15
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 3149
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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