Performance of real-time adaptive optics compensation in a turbulent channel with high-dimensional spatial-mode encoding

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

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Linear phase retrieval for real-time adaptive optics

Journal of the European Optical Society Rapid Publications ◽

10.2971/jeos.2013.13070 ◽

2013 ◽

Vol 8 ◽

Cited By ~ 4

Author(s):

A. Polo ◽

A. Haber ◽

S. F. Pereira ◽

M. Verhaegen ◽

H. P. Urbach

Keyword(s):

Real Time ◽

Adaptive Optics ◽

Phase Retrieval ◽

Linear Phase

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Improved Fuzzy FCM-LI Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.670 ◽

2013 ◽

Vol 765-767 ◽

pp. 670-673

Author(s):

Li Bo Hou

Keyword(s):

Real Time ◽

Clustering Algorithm ◽

Feature Analysis ◽

Cluster Center ◽

High Dimensional ◽

Fuzzy C Means ◽

Sample Data ◽

Fuzzy C Means Clustering ◽

Fcm Clustering ◽

Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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Tracking and Visualizing Signs of Degradation for Early Failure Prediction of Rolling Bearings

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2021.p0629 ◽

2021 ◽

Vol 33 (3) ◽

pp. 629-642

Author(s):

Sana Talmoudi ◽

Tetsuya Kanada ◽

Yasuhisa Hirata ◽

◽

Keyword(s):

Real Time ◽

Training Data ◽

High Dimensional ◽

Time Data ◽

Early Failure ◽

Rolling Bearings ◽

Full Spectrum ◽

Time Frequency ◽

Vibration Data ◽

Visualization Technology

Predictive maintenance, which means detection of failure ahead of time, is one of the pillars of Industry 4.0. An effective method for this technique is to track early signs of degradation before failure occurs. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of the full spectrum of vibration data from the machines and a data visualization technology. This scheme requires no training data and can be started quickly after installation. First, we proposed to use the full spectrum (as high-dimensional data vectors) with no cropping and no complex feature extraction and to visualize the data behavior by mapping the high-dimensional vectors into a two-dimensional (2D) map. This ensures simplicity of the process and less possibility of overlooking important information as well as provide a human-friendly and human-understandable output. Second, we developed a real-time data tracker that can predict failure at an appropriate time with sufficient allowance for maintenance by plotting real-time frequency spectrum data of the target machine on a 2D map created from normal data. Finally, we verified our proposal using vibration data of bearings from real-world test-to-failure measurements obtained from the IMS dataset.

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High-Dimensional Pixel Entanglement: Efficient Generation and Certification

Quantum ◽

10.22331/q-2020-12-24-376 ◽

2020 ◽

Vol 4 ◽

pp. 376

Author(s):

Natalia Herrera Valencia ◽

Vatshal Srivastav ◽

Matej Pivoluska ◽

Marcus Huber ◽

Nicolai Friis ◽

...

Keyword(s):

Carrying Capacity ◽

Communication Systems ◽

Degrees Of Freedom ◽

State Of The Art ◽

Quality Measurement ◽

High Dimensional ◽

Single Photons ◽

Spatial Mode ◽

Classical Communication ◽

Entanglement Of Formation

Photons offer the potential to carry large amounts of information in their spectral, spatial, and polarisation degrees of freedom. While state-of-the-art classical communication systems routinely aim to maximize this information-carrying capacity via wavelength and spatial-mode division multiplexing, quantum systems based on multi-mode entanglement usually suffer from low state quality, long measurement times, and limited encoding capacity. At the same time, entanglement certification methods often rely on assumptions that compromise security. Here we show the certification of photonic high-dimensional entanglement in the transverse position-momentum degree-of-freedom with a record quality, measurement speed, and entanglement dimensionality, without making any assumptions about the state or channels. Using a tailored macro-pixel basis, precise spatial-mode measurements, and a modified entanglement witness, we demonstrate state fidelities of up to 94.4% in a 19-dimensional state-space, entanglement in up to 55 local dimensions, and an entanglement-of-formation of up to 4 ebits. Furthermore, our measurement times show an improvement of more than two orders of magnitude over previous state-of-the-art demonstrations. Our results pave the way for noise-robust quantum networks that saturate the information-carrying capacity of single photons.

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