A Research and Strategy of Remote Sensing Image Denoising Algorithms

Most raw data download from satellites are useless, resulting in transmission waste, one solution is to process data directly on satellites, then only transmit the processed results to the ground. Image processing is the main data processing on satellites, in this paper, we focus on image denoising which is the basic image processing. There are many high-performance denoising approaches at present, however, most of them rely on advanced computing resources or rich images on the ground. Considering the limited computing resources of satellites and the characteristics of remote sensing images, we do some research on these high-performance ground image denoising approaches and compare them in simulation experiments to analyze whether they are suitable for satellites. According to the analysis results, we propose two feasible image denoising strategies for satellites based on satellite TianZhi-1.Comment: 9 pages, 4 figures, ICNC-FSKD 201

Megabits secure key rate quantum key distribution

Quantum cryptography (QC) can provide unconditional secure communication between two authorized parties based on the basic principles of quantum mechanics. However, imperfect practical conditions limit its transmission distance and communication speed. Here we implemented the differential phase shift (DPS) quantum key distribution (QKD) with up-conversion assisted hybrid photon detector (HPD) and achieved 1.3 M bits per second secure key rate over a 10-km fiber, which is tolerant against the photon number splitting (PNS) attack, general collective attacks on individual photons, and any other known sequential unambiguous state discrimination (USD) attacks.Comment: 14 pages, 4 figure

Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction

© 2015 IEEE. Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the main problem of multi-view canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained. In addition, a non-linear extension of TCCA is presented. Experiments on various challenge tasks, including large scale biometric structure prediction, internet advertisement classification, and web image annotation, demonstrate the effectiveness of the proposed method

TO USE A TREE OR A FOREST IN BEHAVIORAL INTENTION

Cloud computing is a new technology that has been applied to education and has e nabled the development of cloud computing classrooms; however, student behavioral intentions toward cloud computing remain unclear. Most researchers have evaluated, integrated, or compared few (1 to 3) theories to examine user behavioral intentions and few have addressed additional theories or models. In this study, we test, compare, and unify six well -known theories, namely, service quality (SQ), self - efficacy (SE), the motivational model (MM), technology acceptance model (TAM), theory of reason action (TRA)/theory of planned behavior (TPB), and innovation diffusion theory (IDT) in the context of cloud computing classrooms. This empirical study was conducted using an online survey. The data collected from the samples (n=478) were analyzed using structural equation modeling. We independently analyzed each of the six theories, formulating a united model. The analysis yielded three valuable findings. First, comparing the explained variance and degree of freedom (df) difference, yielded the following ranking in explained variance: MM=TAM\u3eIDT\u3eTPB\u3eSE=SQ (equal =; superior to\u3e). Second, comparing the explained variance yielded the following ranking in explained variance: MM\u3eTAM\u3eIDT\u3eTPB\u3eSE=SQ. Third, based on the united model of six theories, some factors significantly affect behavioral intention and others do not. The implications of this study are critical for both researchers and practitioners

A hybrid reconstruction algorithm for 3-D ionospheric tomography

In this paper, a hybrid reconstruction algorithm (HRA) is presented to solve the ill-posed inverse problem associated with 3-D ionospheric stochastic tomography. In this new method, the ionospheric electron density (IED) can be inverted by using two steps. First, a truncated singular value decomposition (TSVD) method, whose value is independent on any initial estimation, is used to resolve the ill-posed problem of the tomography system. Second, taking into account the "approximation" of its solution, an iterative improvement process of the solution is then implemented by utilizing the conventional algebraic reconstruction algorithm (ART). The HRA, therefore, offers a more reasonable approach to choose an initial approximate for the ART and to improve the quality of the final reconstructed image. A simulated experiment demonstrates that the HRA method is superior to the TSVD or the ART alone for the tomographic inversion of IED. Finally, the HRA is used to perform GPS-based tomographic reconstruction of the IED at mid- and low-latitude regions