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Medical imaging has been widely used to diagnose various disorders over the past 20 years. Primary challenges in medicine include accurate disease identification and improved therapies. It is challenging for the medical experts to diagnose diseases using a single imaging modality. The fusion of two or more images obtained from different imaging modalities is known as multi modal image fusion (MMIF).The fused image contains complementary information for all the input images. The main objective of MMIF is to obtain complementary information (structural and spectral) from input images to improve the quality and clear assessment of medical related problems. The aim of fusion process is not only to reduced the amount of data but construct image having more useful and complementary information which are understandable for human and computer. This review provides a detailed overview of: (i) medical imaging modalities, (ii) multimodal medical image databases, (iii) MMIF steps/rules, (iv) MMIF methods, (v) modalities integration, (vi) performance evaluation and empirical results, (vii) current modalities strengths and limitations, and (viii) future directions. This review is expected to be useful in establishing a solid foundation for the development of more valuable medical image fusion methods for clinical diagnosis. This review presented the detailed studies on the multimodal databases, research trends in imaging modality grouping, and fusion steps which are the critical areas in MMIF. Furthermore, current challenges and future directions are thoroughly discussed.

In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R 2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Background Spidroins are well-known for their exceptional mechanical properties, which have inspired extensive research and applications across various fields. Large-scale production of spidroin continues to face major challenges due to the complexity involved in inducing host organisms to express full-length spidroins. This process requires advanced techniques and has issues such as plasmid instability and potential misfolding. Results In this study, we developed a novel expression system by combining a fusion tag with a self-cleavage intein, enabling the convenient expression of three chimeric spidroins with varying numbers of repetitive units. After optimising expression conditions, NT2RepCT, NT4RepCT and NT6RepCT spidroins were obtained in soluble form, with yields of 266, 135 and 125 mg/L, respectively. All three spidroins exhibited an increased β-sheet content with increased numbers of repetitive units during transition from soluble to dry state. In terms of nanofibril morphologies, the self-assemblies of NT4RepCT and NT6RepCT closely resemble those of native silk proteins. Conclusion This study can serve as a reference for preparation of high-performance spider silk materials and soluble expression of proteins, such as collagen, and as a foundation for preparation of other structurally complex polymer materials.

This article introduces a unique and first attempt at the fusion of visible and infrared images depending on multi-scale decomposition and salient feature map detection. The proposed technique integrates the bidimensional empirical mode decomposition (BEMD) strategy with Bayesian’s probabilistic strategy for fusion. The proposed mechanism can effectively handle the uncertainty in the challenging source pairs and retain maximum details of the sources at a multi-scale level. The BEMD level features are extracted and integrated with Bayesian’s probabilistic fusion strategy to extract several salient feature maps from the infrared and visual sensors images, which are able to preserve the common information and reduce the source images’ superfluous information at various scales. The combination of these salient feature maps generates an image that gives the target scene complete information with reduced artifacts. The performance of the proposed algorithm is estimated by testing it on the benchmark “TNO” database. The empirical results of the proposed algorithm are evaluated using both visual analysis and quantitative assessment. In this work, the efficiency of the proposed technique is corroborated against seventeen existing state-of-the-art (SOTA) techniques and found to be effective. For the quantitative assessment, we have used the four most-cited quantitative evaluation measures: mutual information for the discrete cosine features ( FMI dct ) , amount of artifacts added during the fusion process ( N abf ) , structure similarity index ( SSIM a ) , and edge preservation index ( EPI a ) . It is observed that the proposed algorithm attained the best average values: Avg. FMI dct = 0.39863, Avg. N abf = 0.00102, Avg. SSIM a = 0.77820, and Avg. EPI a = 0.78404. It is also observed that the proposed scheme outperforms the competitive SOTA techniques in terms of different considered quantitative evaluation measures with at least a gain of 3% and the highest gain of 94%.

Aiming at the complexity of network architecture design and the low computational efficiency caused by variations in the number of modalities in multimodal cloud detection tasks, this paper proposes an efficient and unified multimodal cloud detection model, M2Cloud, which can process any number of modal data. The core innovation of M2Cloud lies in its novel multimodal data fusion method. This method avoids architectural changes for new modalities, thereby significantly reducing incremental computing costs and enhancing overall efficiency. Furthermore, the designed multimodal data fusion module possesses strong generalization capabilities and can be seamlessly integrated into other network architectures in a plug-and-play manner, greatly enhancing the module’s practicality and flexibility. To address the challenge of unified multimodal feature extraction, we adopt two key strategies: (1) constructing feature extraction modules with shared but independent weights for each modality to preserve the inherent features of each modality; (2) utilizing cosine similarity to adaptively learn complementary features between different modalities, thereby reducing redundant information. Experimental results demonstrate that M2Cloud achieves or even surpasses the state-of-the-art (SOTA) performance on the public multimodal datasets WHUS2-CD and WHUS2-CD+, verifying its effectiveness in the unified multimodal cloud detection task. The research presented in this paper offers new insights and technical support for the field of multimodal data fusion and cloud detection, and holds significant theoretical and practical value.

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.

Infrared and visible image fusion methods based on feature decomposition are able to generate good fused images. However, most of them employ manually designed simple feature fusion strategies in the reconstruction stage, such as addition or concatenation fusion strategies. These strategies do not pay attention to the relative importance between different features and thus may suffer from issues such as low-contrast, blurring results or information loss. To address this problem, we designed an adaptive fusion network to synthesize decoupled common structural features and distinct modal features under an attention-based adaptive fusion (AAF) strategy. The AAF module adaptively computes different weights assigned to different features according to their relative importance. Moreover, the structural features from different sources are also synthesized under the AAF strategy before reconstruction, to provide a more entire structure information. More important features are thus paid more attention to automatically and advantageous information contained in these features manifests itself more reasonably in the final fused images. Experiments on several datasets demonstrated an obvious improvement of image fusion quality using our method.

Target detection plays a crucial role in the intelligent interpretation of remote sensing images and has a wide range of potential applications. However, in the presence of targets with high aspect ratios and significant scale variations in remotely sensed images, existing methods prefer CNN or transformer architectures but suffer from the limitations of overly fixed receptive fields or excessive computational complexity. Recently, Mamba-based methods have become hot in the field of target detection and show significant potential in capturing remote dependencies with linear complexity but lack in-depth customization for remote sensing targets. To address the above challenges, we propose a new target detection framework for complex remote sensing images, LS-MambaNet. Specifically, firstly, a group fusion strategy is combined with the introduction of large-band convolution to adaptively adjust the receptive domains of the features, which enhances the spatial context information extraction for objects with high aspect ratios. In addition, a Multi-Granularity Spatial Mamba Block is proposed, and this employs a multi-granularity scanning strategy to reduce the computational cost and feature redundancy on different scanning paths and is able to efficiently model the global contextual information of the target. Experimental results show that LS-MambaNet outperforms baselines on DOTA1.0 and HRSC2016 datasets. In particular, LS-MambaNet significantly improves the inference speed and achieves a higher FPS while maintaining state-of-the-art detection accuracy.

The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition. In this study, a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions. First, a novel stacked autoencoder (NSAE) is constructed using a denoising autoencoder, batch normalization, and the Swish activation function. Second, a series of source-domain NSAEs with multisensor vibration signals is pretrained. Third, the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs. Finally, a modified voting fusion strategy is designed to obtain a comprehensive result. The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method. The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample, thereby outperforming the existing methods.