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We propose a block-based sampling method (I-sampling) which randomly selects the base data blocks from the block pool of a large-scale dataset rather than directly chooses records from the original dataset. I-sampling firstly partitions the given large-scale dataset into the non-overlapping primary data blocks. Secondly, the records in each primary data block are randomly shuffled and the corresponding...
Ocean remote sensing based satellite image is useful for the Earth observation such as altimetry, Significant Wave Height, and wind speed measurement. However, The Global Navigation Satellite System (GNSS) represents the new challenge using special feature of the reflected signal to observe characteristics of the ocean call GNSS - reflectometry. The advantages of this technique are that using the...
Target detection is a hard real-time task for video and image processing. This task has recently been accomplished through the feedforward process of convolutional neural net-works (CNN), which is usually accelerated by general-purpose graphic units (GPUs). However, there is a challenge for this task. The running speed remains to be improved. In this paper, we present an efficient image combination...
Breadth-First Search (BFS) is a basis for many graph traversal and analysis algorithms. In this paper, we present a direction-optimizing BFS implementation on CPU-GPU heterogeneous platforms to fully exploit the computing power of both the multi-core CPU and GPU. For each level of the BFS algorithm, we dynamically choose the best implementation from: a sequential top-down execution on CPU, a parallel...
Bi-dimensional empirical mode decomposition (BEMD) has been one of the core activities in image processing. Unfortunately, this promising technique is sensitive to boundary effect. Here, a new technique based on multivariate grey model termed as GM(1, 3) is developed for boundary extension in BEMD. More specifically, pixel values and coordinates of the image are regarded as characteristic data series...
Nowadays, it is an important trend in the system domain to use the software-based virtualization technology to build the execution environments (e.g., Clouds) and serve high performance computing (HPC) applications. However, with the extra virtualization layer, the application performance may be negatively affected. Studies revealed that the communication performance of the MPI library, which is widely...
Based on the framework of support vector machines (SVM) using one against one (OAO) strategy, a new kernel method based on Bhattacharyya distance is proposed to raise the classification accuracy by combining the characteristics of hyperspectral data. The proposed method takes advantage of the non-uniform information distribution of hyperspectral data and makes the band with greater separability play...
Support vector machine (SVM) appears to be a robust alternative for pattern recognition with hyperspectral data. However, this kernel-based method does not take into consideration the bio-physical meaning of the spectral signatures. Observation of real-life spectral signatures from the AVIRIS hyperspectral dataset shows that the useful information for classification is not equally distributed across...
An ATCA-based computation platform for data acquisition and trigger (TDAQ) applications has been developed for multiple future projects such as PANDA, HADES, and BESIII. Each Compute Node (CN) appears as one of the fourteen Field Replaceable Units (FRU) in an ATCA shelf, which in total features a high performance of 1890 Gbps inter-FPGA onboard channels, 1456 Gbps inter-board backplane connections,...
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