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京都大学 / 新制・課程博士 / 博士(理学) / 甲第23697号 / 理博第4787号 / 新制||理||1685(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 青木 慎也, 教授 大西 明, 教授 萩野 浩一 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

<p>We present an extension to the language of Atomic Verifiable Operation (AVOp) streams to allow the expression of loops which are rewritable via an arbitrary permutation. Inspired by (and significantly extending) hardware register rotation to data buffers in multi-core programs, we hope to achieve similar performance benefits in expressing software pipelined programs across many cores. By adding loops to AVOp streams, we achieve significant stream compression, which eliminates an impediment to scalability of this abstraction. Furthermore, we present a fast extension to the previous AVOp verification process which ensures that no data hazards are present in the program’s patterns of communication. Our extension to the verification process is to verify loops without completely unrolling them. A proof of correctness for the verification process is presented.</p> / Master of Science (MSc)

This research provides the real-time computational resource for three dimensional tomographic chemical threat mapping using mobile hyperspectral sensors from sparse input data. The crucial calculation limiting real-time execution of the algorithm is the determination of the projection matrix using the algebraic reconstruction technique (ART). The computation utilizes the inherent parallel nature of ART with an implementation of the algorithm on a field programmable gate array. The MATLAB Fixed-Point Toolbox is used to determine the optimal fixed-point data types in the conversion from the original floating-point algorithm. The computation is then implemented using the Xilinx System Generator, which generates a hardware description language representation from a block diagram design. / Electrical and Computer Engineering

This thesis presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection), a novel data reduction technique that can be used in conjunction with PIGSCR (parallel IGSCR), a noise removal method based on the maximum noise fraction (MNF), and a continuous version of IGSCR (CIGSCR) that outputs soft classifications. All of the above are either classification algorithms or preprocessing algorithms necessary prior to the classification of high dimensional, noisy images. PIGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The feature reduction method introduced in this thesis is based on the singular value decomposition (SVD). This feature reduction technique demonstrated that SVD-based feature reduction can lead to more accurate IGSCR classifications than PCA-based feature reduction. This thesis describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. The adaptive filtering scheme improves image quality as shown by estimated SNRs and classification accuracy improvements greater than 10%. The continuous iterative guided spectral class rejection (CIGSCR) classification method is based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. This type of semisupervised classification method is particularly useful in remote sensing where datasets are large, training data are difficult to acquire, and clustering makes the identification of subclasses adequate for training purposes less difficult. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that influence iterations. / Ph. D.