Multi-Sensor Image Fusion: A Survey of the State of the Art

Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

Simulations of H-Mode Plasmas in Tokamak Using a Complete Core-Edge Modeling in the BALDUR Code

A theory-based model for predicting the pedestal formation in both ion and electron temperatures, and hydrogenic and impurity density is developed and implemented in the 1.5D BALDUR codes for self-consistently simulating H-mode plasma in tokamak. In the simulation, the transports around pedestal, including the electron and ion thermal, hydrogenie and impurity particle transports are calculated using an （Mixed B/gB） model, which is modified to anomalous semi-empirical mixed Bohm/gyro-Bohm include the effects of ωE×B flow shear and magnetic shear. Because of the reduction of transport, the pedestal can be formed. For a preliminary test, this core-edge model is used to simulate the temporal evolution of plasma current, temperature, and density profiles for DIII-D discharges. It is found that the simulations successfully reproduce the experimental results. A statistical analysis, including RMSE and offset, is used to quantify the agreement between the prediction and the corresponding experimental results. The simulation results show an agreement with average RMSE of 11.87%, 14.53%, 7.59% and 12.21% for electron temperature, ion temperature, electron density, and deuterium density profiles, respectively. In addition, it is found that the suppression function developed is effective only in the edge region.

Improving iris recognition performance via multi-instance fusion at the score level

Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multiinstance fusion, which combines the left and right irises of an individual at the matching score level. When fusing, a novel fusion strategy using minimax probability machine （MPM） is applied to generate a fused score for the final decision. The experimental results on CASIA and UBIRIS databases show that the proposed method can bring obvious performance improvement compared with the single-instance method. The comparison among different fusion strategies demonstrates the superiority of the fusion strategy based on MPM.

Study on the performance of data fusion system for sonar signal detection

The mathematical model and fusion algorithm for multisensor data fusion are presented, and applied to integrate the decisions obtained by multiple sonars in a distributed detection system. Assuming that all the sonars and the fusion system operate at the same false alarm probability, the expression for the detection probability of the fusion system is obtained. Computer simulations reveals that the detection probability and detection range of the fusion system are significantly improved compared to the original distributed detection system.

Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.