The fusion of multi-band images including far-infrared image (FIRI), near-infrared image (NIRI), and visible image (VISI) primarily suffers from four challenges. One is the problem of simultaneous fusion for multiple images. Most existing methods are oriented towards the fusion of two objects, which is generally achieved with a sequential fusion method. This means that intermediate fusion results are repeatedly integrated with the unprocessed images until all images have been fused. However, this may amplify the blurring effect, and even engender artifacts. Second, consistent training labels for image fusion cannot currently be obtained for some types of images (e.g., medical images, and multi-band images), which may lead to the failed application of supervised learning methods. Third, the existing methods often do not directly focus on the potential mapping relationship between the original, and resulting images, which usually increases the unpredictability of the fusion results. Fourth, redundant features or singularities are often not eliminated in the general fusion process, and both may interfere with or even obscure significant features in the source images. To address the abovementioned problems, this paper proposes a latent representation learning model that can synchronously integrate multi-band images without samples. Specifically, the model can capture the clean, and distinctive features of the originals via latent low-rank, and sparse embedding. The extracted intrinsic features are projected onto the target fusion space through an assumed mapping relationship. The final results were obtained through the designed optimization algorithm. In addition, numerous experiments were implemented to prove the rationality, and feasibility of the proposed fusion model with subjective evaluation, objective indexes, and convergence analysis.

Aiming at the problem of insufficient detail retention in multimodal medical image fusion (MMIF) based on sparse representation (SR), an MMIF method based on density peak clustering and convolution sparse representation (CSR-DPC) is proposed. First, the base layer is obtained based on the registered input image by the averaging filter, and the original image minus the base layer to obtain the detail layer. Second, for retaining the details of the fused image, the detail layer image is fused by CSR to obtain the fused detail layer image, then the base layer image is segmented into several image blocks, and the blocks are clustered by using DPC to obtain some clusters, and each class cluster is trained to obtain a sub-dictionary, and all the sub-dictionaries are fused to obtain an adaptive dictionary. The sparse coefficient is fused through the learned adaptive dictionary, and the fused base layer image is obtained through reconstruction. Finally, fusing the detail layer and the base layer and reconstructing them forms the ultimate fused image. Experiments show that compared to the state-of-the-art two multi-scale transformation methods and five SR methods, the proposed method(CSR-DPC) outperforms the other methods in terms of the image details, the visual quality and the objective evaluation index, which can be helpful for clinical diagnosis and adjuvant treatment.

Huawei Fusion Insight Libr A (FI-MPPDB) is a petabyte scale enterprise analytics platform developed by the Huawei data-base group. It started as a prototype more than five years ago, and is now being used by many enterprise customers over the globe, including some of the world's largest financial institutions. Our product direction and enhancements have been mainly driven by customer requirements in the fast evolving Chinese market. This paper describes the architecture of FI-MPPDB and some of its major enhancements. In particular, we focus on top four requirements from our customers related to data analytics on the cloud: system availability, auto tuning, query over heterogeneous data models on the cloud, and the ability to utilize powerful modern hardware for good performance. We present our latest advancements in the above areas including online expansion, auto tuning in query optimizer, SQL on HDFS, and intelligent JIT compiled execution. Finally, we present some experimental results to demonstrate the effectiveness of these technologies.

The use of feedback control techniques has been gaining importance in the context of scheduling in real-time systems as a means to provide predictable performance in the face of uncertain workload. We propose a novel feedback-based scheduling approach for task scheduling in real-time systems. We focus on a system with a mobile node, where the mobility characteristics affect task parameters. The objective is to achieve low miss ratio and high CPU utilization. This objective is achieved by feeding back system performances and adapting the node' mobility parameters. We study the new approach in a selective herbicide spraying problem in the agricultural production wherein the speed of an autonomous vehicle (and hence task parameters) is adapted based on weed distribution in the given farm field. Simulations and analysis show that our approach can achieve low miss ratio and high CPU utilization.