error_outline You can access the new MDPI.com website here. Explore and share your feedback with us.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (2)

Search Parameters:
Keywords = multi-threshold recurrence rate plot

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
19 pages, 2102 KB  
Article
Multi-Modal Time-Frequency Image Fusion for Weak Target Detection on Sea Surface
by Han Wu, Hongyan Xing, Mengjie Li and Chenyu Hang
J. Mar. Sci. Eng. 2025, 13(9), 1625; https://doi.org/10.3390/jmse13091625 - 26 Aug 2025
Viewed by 921
Abstract
Aiming at the problem of harrowing target feature extraction for one-dimensional radar signals in the strong sea clutter background, this paper proposes a weak target detection method based on the combination of multi-modal time-frequency map fusion and deep learning in the sea clutter [...] Read more.
Aiming at the problem of harrowing target feature extraction for one-dimensional radar signals in the strong sea clutter background, this paper proposes a weak target detection method based on the combination of multi-modal time-frequency map fusion and deep learning in the sea clutter background. The one-dimensional signal is converted into three gray-scale maps with complementary characteristics by three signal processing methods: normalized continuous wavelet transform, Normalized Smooth Pseudo Wigner-Ville Distribution, and recurrence plot; the resulting two-dimensional grayscale maps are adaptively mapped to the R, G, and B channels through an adaptive weighting matrix for feature fusion, ultimately generating a fused color image. Subsequently, an improved multi-modal EfficientNetV2s classification framework was constructed, wherein the decision threshold of the Softmax layer was optimized to achieve controllable false alarm rates for weak signal detection. Experiments are carried out on the IPIX dataset and the China Yantai dataset, and the proposed method achieves certain improvement in detection performance compared with existing detection methods. Full article
(This article belongs to the Section Ocean Engineering)
Show Figures

Figure 1

17 pages, 8093 KB  
Article
Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer’s Disease and Frontotemporal Dementia
by Huang Zheng, Xingliang Xiong and Xuejun Zhang
Brain Sci. 2024, 14(6), 565; https://doi.org/10.3390/brainsci14060565 - 1 Jun 2024
Cited by 11 | Viewed by 2667
Abstract
This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our [...] Read more.
This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research. Full article
(This article belongs to the Section Neurodegenerative Diseases)
Show Figures

Figure 1

Back to TopTop