The Experts below are selected from a list of 1052223 Experts worldwide ranked by ideXlab platform
Jean-michel Roger - One of the best experts on this subject based on the ideXlab platform.
-
A note on spectral Data Simulation
Chemometrics and Intelligent Laboratory Systems, 2020Co-Authors: Maxime Metz, Alessandra Biancolillo, Matthieu Lesnoff, Jean-michel RogerAbstract:In chemometrics, it is common to simulate Data to test new methods. However, it is difficult to find an article that only discusses the spectral Data Simulation in a global context. Most of the time, the Simulation is performed specifically for one method. In this context, it is often difficult to understand the Simulation choices and also to carry out a Simulation adapted to the problem that one wishes to highlight. A generic Simulation framework would allow a better understanding of the Simulations carried out and also make them easier to carry out. In this article, a generic framework is proposed to simulate Databases representing the problem that one wishes to simulate and facilitating the description of the Simulation procedure. This method of Simulation is based on the basic principles of chemometrics and allows a simple and fast Simulation of Data. This will be highlighted by three examples.
Maxime Metz - One of the best experts on this subject based on the ideXlab platform.
-
A note on spectral Data Simulation
Chemometrics and Intelligent Laboratory Systems, 2020Co-Authors: Maxime Metz, Alessandra Biancolillo, Matthieu Lesnoff, Jean-michel RogerAbstract:In chemometrics, it is common to simulate Data to test new methods. However, it is difficult to find an article that only discusses the spectral Data Simulation in a global context. Most of the time, the Simulation is performed specifically for one method. In this context, it is often difficult to understand the Simulation choices and also to carry out a Simulation adapted to the problem that one wishes to highlight. A generic Simulation framework would allow a better understanding of the Simulations carried out and also make them easier to carry out. In this article, a generic framework is proposed to simulate Databases representing the problem that one wishes to simulate and facilitating the description of the Simulation procedure. This method of Simulation is based on the basic principles of chemometrics and allows a simple and fast Simulation of Data. This will be highlighted by three examples.
Alessandra Biancolillo - One of the best experts on this subject based on the ideXlab platform.
-
A note on spectral Data Simulation
Chemometrics and Intelligent Laboratory Systems, 2020Co-Authors: Maxime Metz, Alessandra Biancolillo, Matthieu Lesnoff, Jean-michel RogerAbstract:In chemometrics, it is common to simulate Data to test new methods. However, it is difficult to find an article that only discusses the spectral Data Simulation in a global context. Most of the time, the Simulation is performed specifically for one method. In this context, it is often difficult to understand the Simulation choices and also to carry out a Simulation adapted to the problem that one wishes to highlight. A generic Simulation framework would allow a better understanding of the Simulations carried out and also make them easier to carry out. In this article, a generic framework is proposed to simulate Databases representing the problem that one wishes to simulate and facilitating the description of the Simulation procedure. This method of Simulation is based on the basic principles of chemometrics and allows a simple and fast Simulation of Data. This will be highlighted by three examples.
Matthieu Lesnoff - One of the best experts on this subject based on the ideXlab platform.
-
A note on spectral Data Simulation
Chemometrics and Intelligent Laboratory Systems, 2020Co-Authors: Maxime Metz, Alessandra Biancolillo, Matthieu Lesnoff, Jean-michel RogerAbstract:In chemometrics, it is common to simulate Data to test new methods. However, it is difficult to find an article that only discusses the spectral Data Simulation in a global context. Most of the time, the Simulation is performed specifically for one method. In this context, it is often difficult to understand the Simulation choices and also to carry out a Simulation adapted to the problem that one wishes to highlight. A generic Simulation framework would allow a better understanding of the Simulations carried out and also make them easier to carry out. In this article, a generic framework is proposed to simulate Databases representing the problem that one wishes to simulate and facilitating the description of the Simulation procedure. This method of Simulation is based on the basic principles of chemometrics and allows a simple and fast Simulation of Data. This will be highlighted by three examples.
Fan Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018Co-Authors: Fan Zhang, Hanyuan Tang, Qiang Yin, Xiaojie Yao, Bin LeiAbstract:Gaofen-3 is China's first meter-level multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes for the scientific and commercial applications. In order to evaluate the imaging performance of these modes, the multiple mode SAR raw Data Simulation is highly demanded. In the paper, the multiple mode SAR Simulation framework will be briefly introduced to expose how the raw Data Simulation guarantees the development of Gaofen-3 and its ground processing system. As an engineering Simulation, the complex working modes and practical evaluation requirements of Gaofen-3 mission put forward to the higher demand for Simulation simplification and Data input/output (I/O) efficiency. To meet the requirements, two improvements have been proposed. First, the stripmap mode based multiple mode decomposition method is introduced to make a solid and simplified system Simulation structure. Second, the cloud computing and graphics processing unit (GPU) are integrated to simulate the practical huge volume raw Data, resulting in improved calculation and Data I/O efficiency. The experimental results of sliding spotlight imaging prove the effectiveness of the Gaofen-3 mission Simulation framework and the decomposition idea. The results for efficiency assessment show that the GPU cloud method greatly improves the computing power of a 16-core CPU parallel method about $40\times$ speedup and the Data throughput with the Hadoop distributed file system. These results prove that the Simulation system has the merits of coping with multiple modes and huge volume raw Data Simulation and can be extended to the future space-borne SAR Simulation.
-
IGARSS - Multiple mode SAR raw Data Simulation for GaoFen-3 mission evaluation
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Co-Authors: Fan Zhang, Hanyuan Tang, Qiang Yin, Jiayin Liu, Xiaolan QiuAbstract:GaoFen-3 is China's first meter-level multi-polarization Synthetic Aperture Radar (SAR) satellite with scientific and commercial applications, which was developed by the China Academy of Space Technology (CAST) and had been launched in August, 2016. The SAR instrument and ground Data processing system were developed by the Institute of Electronics, Chinese Academy of Sciences (IECAS). It employs a multi-polarization C-band SAR based on active phased array technology, which allows flexible beam operations in azimuth scanning, range scanning, right looking and left looking. Hence, GaoFen-3 has 12 imaging modes, covering the traditional Stripmap mode, ScanSAR mode, and the emerging Wave mode and Sliding Spotlight mode, and is a SAR satellite with the most abundant imaging modes in the world. In order to evaluate the imaging performance of these modes, the multiple mode SAR raw Data Simulation is highly demanded. In the paper, the Simulation framework, the Simulation algorithms and the evaluation strategies will be briefly introduced to expose how the raw Data Simulation guarantees the development of GaoFen-3 and its processing system.
-
A Fast Synthetic Aperture Radar Raw Data Simulation Using Cloud Computing.
Sensors, 2017Co-Authors: Haijiang Zhu, Fan ZhangAbstract:Synthetic Aperture Radar (SAR) raw Data Simulation is a fundamental problem in radar system design and imaging algorithm research. The growth of surveying swath and resolution results in a significant increase in Data volume and Simulation period, which can be considered to be a comprehensive Data intensive and computing intensive issue. Although several high performance computing (HPC) methods have demonstrated their potential for accelerating Simulation, the input/output (I/O) bottleneck of huge raw Data has not been eased. In this paper, we propose a cloud computing based SAR raw Data Simulation algorithm, which employs the MapReduce model to accelerate the raw Data computing and the Hadoop distributed file system (HDFS) for fast I/O access. The MapReduce model is designed for the irregular parallel accumulation of raw Data Simulation, which greatly reduces the parallel efficiency of graphics processing unit (GPU) based Simulation methods. In addition, three kinds of optimization strategies are put forward from the aspects of programming model, HDFS configuration and scheduling. The experimental results show that the cloud computing based algorithm achieves 4_ speedup over the baseline serial approach in an 8-node cloud environment, and each optimization strategy can improve about 20%. This work proves that the proposed cloud algorithm is capable of solving the computing intensive and Data intensive issues in SAR raw Data Simulation, and is easily extended to large scale computing to achieve higher acceleration.
-
A Deep Collaborative Computing Based SAR Raw Data Simulation on Multiple CPU/GPU Platform
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Fan Zhang, Chen Hu, Wei Li, Wei Hu, Pengbo Wang, Heng-chao LiAbstract:The outstanding computing ability of a graphics processing unit (GPU) brings new vitality to the typical computing intensive issue, so does the synthetic aperture radar (SAR) raw Data Simulation, which is a fundamental problem in SAR system design and imaging research. However, the computing power of a CPU was underestimated, and the tunings for a CPU-based method were missing in the previous works. Meanwhile, the collaborative computing of multiple CPUs/GPUs was not exploited thoroughly. In this paper, we propose a deep multiple CPU/GPU collaborative computing framework for time-domain SAR raw Data Simulation, which not only introduces the advanced vector extension (AVX) method to improve the computing efficiency of a multicore single instruction multiple Data CPU, but also achieves a satisfactory speedup in the CPU/GPU collaborative Simulation by fine-grained task partitioning and scheduling. In addition, an irregular reduction based SAR coherent accumulation approach is proposed to eliminate the memory access conflict, which is the most difficult issue in the GPU-based raw Data Simulation. Experimental results show that the multicore vector extension method greatly improves the computing power of a CPU-based method through about 70× speedup, thereby outperforming the single GPU Simulation. Correspondingly, compared with the baseline sequential CPU approach, the multiple CPU/GPU collaborative Simulation achieves up to 250× speedup. Furthermore, the irregular reduction based atomic-free optimization boosts the performance of the single GPU method by 20% acceleration. These results prove that the deep multiple CPU/GPU collaborative method is promising, especially for the case of huge volume raw Data Simulation with a wide swath and high resolution.
-
IGARSS - Atomic-free optimization on GPU based SAR raw Data Simulation
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Co-Authors: Xiaojie Yao, Fan ZhangAbstract:Synthetic Aperture Radar (SAR) has been widely used in airborne remote sensing and satellite ocean observation fields to reduce the affect of weather condition and sun illumination. As technology developed, swath and resolution requirements are increased in terrain, which result in a huge increase in echo Data and simulated time[1]. With the development of graphics processing unit (GPU), it can reduce simulated time effectively. In order to simulate the coherent integration, atomic operation is always used in GPU, which has a bad influence to simulated time. To optimize simulated time, in this article, we put forward three GPU optimistic strategies for atomic-free SAR raw Data Simulation.
