Information technology growth brings vast time-series data. Despite richness, challenges like redundancy emphasize the need for time-series data fusion research. Rough set theory, a valuable tool for dealing with uncertainty, can identify features and reduce dimensionality, enhancing time-series data fusion. The contribution of the study lies in establishing a fusion and feature selection framework for multisource time-series data. This framework selects optimal information sources by minimizing entropy. In addition, the fusion process integrates a feature selection algorithm to eliminate redundant features, preventing a sequential increase in entropy. Crucial experiments on abundant datasets demonstrate that the proposed approach outperforms several state-of-the-art algorithms in terms of enhancing the accuracy of common classifiers. This research significantly advances the field of time-series data fusion in rough set theory, offering improved accuracy and efficiency in data processing and analysis.

The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class incremental learning (CIL) by preserving limited exemplars from previous tasks. With imbalanced sample numbers between old and new classes, the classifier learning can be biased. Existing CIL methods exploit the long-tailed (LT) recognition techniques, e.g., the adjusted losses and the data re-sampling methods, to handle the data imbalance issue within each increment task. In this work, the dynamic nature of data imbalance in CIL is shown and a novel Dynamic Residual Classifier (DRC) is proposed to handle this challenging scenario. Specifically, DRC is built upon a recent advance residual classifier with the branch layer merging to handle the model-growing problem. Moreover, DRC is compatible with different CIL pipelines and substantially improves them. Combining DRC with the model adaptation and fusion (MAF) pipeline, this method achieves state-of-the-art results on both the conventional CIL and the LT-CIL benchmarks. Extensive experiments are also conducted for a detailed analysis. The code is publicly available1.

Information fusion is capable of fusing and transforming information originated from multiple sources into an integrated representation. As an important representative of information, interval-valued ordered data aims at characterizing inaccurate and ambiguous information. However, existing methods are inappropriate when applied to the multi-source fusion for them. In addition, it is inevitable that in daily-life the sources and attributes of multi-source information systems may update at the same time. Hence there is a need to fuse data as efficiently as possible. Inspired by these deficiencies, we pay attention to the effective and efficient fusion of multi-source interval-valued ordered data in varieties of cases. First, the concepts of fuzzy dominance relation and dominance classes based on it are put forward between any two samples of interval-valued ordered information systems. Second, we define the fuzzy dominating and dominated conditional entropy. Then the fusion model is established and it is multi-source interval-valued ordered data oriented. Furthermore, it is true that there are numerous real-life applications related to concurrent change of both sources and attributes. Consequently, we design four incremental mechanisms and algorithms for fusing multi-source interval-valued data on the ground of the static condition. Eventually, a series of experiments is carried out on twelve datasets to verify that the proposed fusion approach outperforms other comparative methods on efficacy. Meanwhile, our incremental fusion algorithms are efficient compared with the static one for updating multi-source interval-valued data when sources and attributes are in the simultaneous variation.

We develop and then demonstrate a modified planar subarray processing algorithm based on the inverse scaled Fourier transform applied to very high frequency ice-sounding data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both internal reflecting horizons and bedrock of the ice sheet. It is a real-time ice-sounding imaging method. First, theory analysis has been carried on the proposed algorithm. Then, we give the particular realizing steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and real data collected during the 29th Chinese Antarctic Research Expedition to prove its validity of imaging of ice sheets. Furthermore, compared with two previous algorithms in two major aspects—the power of azimuth clutter reduction and calculating time—the proposed algorithm could considerably reduce the imaging time to meet the requirement of real-time imaging of ice-sounding data without degrading the ability of azimuth clutter reduction via C+MPI language on a parallel computer system.

Chinese Chang'e-3 (CE-3) spacecraft which included a lander and a robotic rover, landed successfully on moon at 13:11 Ground UTC on December 14, 2013. The scientific objectives of the spacecraft were to examine the texture, mineralogy, and structure of the local lunar terrain, to operate an ultraviolet astronomy telescope on the moon and images the distribution of He+in Earth's plasmasphere, to determine the distribution and composition of minerals, rocks, and soils. Eight kinds of scientific payloads were chosen for CE-3 spacecraft, the lander and the robotic rover each had four scientific payloads, the Terrain Camera (TCAM), the Moon-based ultraviolet camera (MUVT), the Extreme ultraviolet camera (EUVC) and the Landing Camera (LCAM) were equipped on the lander, the Panoramic Camera (PCAM), the Lunar Penetrating Radar (LPR), the Visible and Near-Infrared Imaging Spectrometer (VNIS) and the Active Particle-induced X-ray Spectrometer (APXS) were installed on the robotic rover. This paper mainly introduces the key information on the scientific payloads principles, designs, instrument parameters and lunar surface initial experiment result.

In this paper, we propose an approach to integrate the radio-frequency interference (RFI) mitigation technique into a real-time synthetic aperture radar (SAR) imaging algorithm applied to very high frequency (VHF) ice-sounding data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both Internal Reflecting Horizons (IRHs) and bedrock of the ice sheet. According to the proposal, the RFI suppression method-an adaptive line enhancer (ALE) controlled by the normalized least mean square (NLMS) , is incorporated into the real-time imaging algorithm named modified Planar Subarray Processing (PSAP) algorithm based on inverse scaled Fourier transform (ISFT). The approach is tested successfully on real data recorded over Chinese Kunlun Station during the 29thChinese Antarctic Research Expedition (CHINARE 29). The experimental result indicates that the narrowband RFI could be subtracted from the desired chirp signal return during the imaging processing, and the computational efficiency of the imaging processing is preserved.

In this paper, we propose a new algorithm to address the speckle noise problem in imaging of ice sheets. It is a wave-equation-based ice-sounding imaging method using curvelets as building blocks of ice-sounding data, which successfully images the topography of ice sheets. First, theory analysis has been carried on to the proposed algorithm. Then, we give the specific steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and High-Resolution Ice-Sounding Radar data to prove its validity of imaging of ice sheets. Furthermore, compared with five previous methods in two major aspects—the power of clutter reduction and the equivalent number of looks—the proposed algorithm reduces the speckle noise during the imaging processing without degrading the ability in clutter reduction.

The computation core of many data-intensive applications can be best expressed as matrix computations. The MadLINQ project addresses the following two important research problems: the need for a highly scalable, efficient and fault-tolerant matrix computation system that is also easy to program, and the seamless integration of such specialized execution engines in a general purpose data-parallel computing system.