Marine spatial planning (MSP) has been proposed to facilitate the sustainable use of oceans. To implement MSP in coastal zones, we need to understand the current status of aquaculture, in order to plan for its future. In Japan, coastal aquaculture is based on fishery rights, but it is hard to establish the existing situation as there are so many aquaculture facilities in place. In this study, we used full polarimetric, L-band PALSAR-2 data to identify aquaculture areas. We calculated HH and HV backscattering coefficients, and applied polarimetric decomposition analysis to three-component scattering models. The HV backscattering coefficient successfully discriminated aquaculture areas from the sea surface, while the HH backscattering coefficient was mostly able to discriminate between raft and longline aquaculture areas. The three-component scattering model was then able to discriminate between raft and longline aquaculture types in areas where the HH backscattering coefficient could not. Weather conditions affected the sea surface and therefore influenced the outcomes of our analyses.

Network control systems have been attracting attention to manage sophisticated task at a remote place where people cannot enter. However, jitter which is a fluctuation in time delay for an information to be transmitted, is a serious problem in network control systems because it deteriorates the performance of the system. Therefore, designing control system considering jitter is extremely important. Thus, this paper proposes a novel method to reduce the effect of jitter to the system under time delayed conditions. This is done by utilizing two filters that have inverse relationships and an error corrector when information is transmitted via network. The validity of the proposed system is confirmed experimentally by implementing the proposed filter to a one way information transmission and a network control system. Also, the experimental results of the proposed method is compared with conventional methods to show the effectiveness of the proposed method.

Scale-out programs run on multiple processes in a cluster. In scale-out systems, processes can fail. Computations using traditional libraries such as MPI fail when any component process fails. The advent of Map Reduce, Resilient Data Sets and MillWheel has shown dramatic improvements in productivity are possible when a high-level programming framework handles scale-out and resilience automatically.

Deploying an application onto a target platform for high performance oftentimes demands manual tuning by experts. As machine architecture gets increasingly complex, tuning becomes even more challenging and calls for systematic approaches. In our earlier work we presented a prototype that combines efficiently expert knowledge, static analysis, and runtime observation for bottleneck detection, and employs refactoring and compiler feedback for mitigation. In this study, we develop a software tool that facilitates \emph{fast} searching of bottlenecks and effective mitigation of problems from major dimensions of computing (e.g., computation, communication, and I/O). The impact of our approach is demonstrated by the tuning of the LBMHD code and a Poisson solver code, representing traditional scientific codes, and a graph analysis code in UPC, representing emerging programming paradigms. In the experiments, our framework detects with a single run of the application intricate bottlenecks of memory access, I/O, and communication. Moreover, the automated solution implementation yields significant overall performance improvement on the target platforms. The improvement for LBMHD is up to 45\%, and the speedup for the UPC code is up to 5. These results suggest that our approach is a concrete step towards systematic tuning of high performance computing applications.

High productivity is critical in harnessing the power of high-performance computing systems to solve science and engineering problems. It is a challenge to bridge the gap between the hardware complexity and the software limitations. Despite significant progress in programming language, compiler, and performance tools, tuning an application remains largely a manual task, and is done mostly by experts. In this paper, we propose a systematic approach toward automated performance analysis and tuning that we expect to improve the productivity of performance debugging significantly. Our approach seeks to build a framework that facilitates the combination of expert knowledge, compiler techniques, and performance research for performance diagnosis and solution discovery. With our framework, once a diagnosis and tuning strategy has been developed, it can be stored in an open and extensible database and thus be reused in the future. We demonstrate the effectiveness of our approach through the automated performance analysis and tuning of two scientific applications. We show that the tuning process is highly automated, and the performance improvement is significant.