DSpace collection: 專書之單篇

Physical literacy in the era of multiple realities

Fri, 27 Mar 2026 06:18:50 GMT

title: Physical literacy in the era of multiple realities abstract: This chapter delves into the concept of physical literacy in the situation of our modern era, where multiple "realities" coexist. The introduction highlights the importance of physical literacy (L), education (E), activity (A), and fitness (F), collectively referred to as physical LEAF, within the context of these multiple realities. The guiding framework combines physical literacy, simulation theory, and ecological dynamics to provide a comprehensive understanding of the topic. The discussion section focuses on the development of 21st-century skills and inner development goals (IDGs) related to the domains of physical literacy, the roles of sense and embodiment, and the shift from perceiving two realities to acknowledging multiple realities. Furthermore, the chapter explores the philosophy of physical literacy as a means of human flourishing within the context of transhumanism. Finally, a conceptual model called Physical Literacy in Multiple Realities (PLinMRs) is presented. In the concluding remarks, this chapter provides a fundamental platform to develop future research studies, operational guides, assessment standards, and debates in the field of physical LEAF and beyond.

An Optimization Model for Visual Cryptography Schemes with Unexpanded Shares

Wed, 19 Jun 2013 08:24:25 GMT

title: An Optimization Model for Visual Cryptography Schemes with Unexpanded Shares abstract: The method of visual cryptography is to encrypt a secret image into N shares so that any qualified set of participants can recover the hidden secret by their eyes; whereas any forbidden set of participants cannot obtain any secret information. In the study of visual cryptography, pixel expansion and contrast are two important issues. Most visual cryptographic methods are based on the technique of pixel expansion, and the result is that the size of each share is larger than that of the secret image. Pixel expansion not only results in distortion of the shares, but also consumes more storage space. In this paper, we proposed a new method to cope with the problems of pixel expansion. We used the concept of probability and considered the security issue on the forbidden set and the contrast issue on the qualified set to construct an optimization model for general access structures. Finally, we analyzed the contrast and blackness of black pixels of our experimental result, and we found that our method is better than Ateniese et al.’s.

Accelerating Volkov's Hybrid Implementation of Cholesky Factorization on a Fermi GPU

Mon, 04 Mar 2013 08:32:16 GMT

title: Accelerating Volkov's Hybrid Implementation of Cholesky Factorization on a Fermi GPU abstract: In linear algebra, Cholesky factorization is useful in solving a system of equations with a symmetric positive definite coefficient matrix. Cholesky factorization is roughly twice as fast relative to LU factorization which applies to general matrices. In recent years, with advances in technology, a Fermi GPU card can accommodate hundreds of cores compared to the small number of 8 or 16 cores on CPU. Therefore a trend is seen to use the graphics card as a general purpose graphics processing unit (GPGPU) for parallel computation. In this work, Volkov's hybrid implementation of Cholesky factorization is evaluated on the new Fermi GPU with others and then some improvement strategies were proposed. After experiments, compared to the CPU version using Intel Math Kernel Library (MKL), our proposed GPU improvement strategy can achieve a speedup of 3.85x on Cholesky factorization of a square matrix of dimension 10,000.

Further GPU implementation of prediction-based lower triangular transform using a zero-order entropy coder for ultraspectral sounder data compression

Mon, 04 Mar 2013 06:27:26 GMT

title: Further GPU implementation of prediction-based lower triangular transform using a zero-order entropy coder for ultraspectral sounder data compression abstract: The ultraspectral sounder data consists of two dimensional pixels, each containing thousands of channels. In retrieval of geophysical parameters, the sounder data is sensitive to noises. Therefore lossless compression is highly desired for storing and transmitting the huge volume data. The prediction-based lower triangular transform (PLT) features the same de-correlation and coding gain properties as the Karhunen-Loeve transform (KLT), but with a lower design and implementational cost. In previous work, we have shown that PLT has the perfect reconstruction property which allows its direct use for lossless compression of sounder data. However PLT is time-consuming in doing compression. To speed up the PLT encoding scheme, we have recently exploited the parallel compute power of modern graphics processing unit (GPU) and implemented several important transform stages to compute the transform coefficients on GPU. In this work, we further incorporated a GPU-based zero-order entropy coder for the last stage of compression. The experimental result shows that our full implementation of the PLT encoding scheme on GPU shows a speedup of 88x compared to its original full implementation on CPU.

GPU acceleration of predictiion-based lower triangular transform for lossless compression

Mon, 04 Mar 2013 05:49:47 GMT

title: GPU acceleration of predictiion-based lower triangular transform for lossless compression abstract: The prediction-based lower triangular transform (PLT) features the same de-correlation and coding gain properties as the Karhunen-Loeve transform (KLT), but with a lower design and implementational cost. Unlike KLT, PLT has the perfect reconstruction property which allows its direct use for lossless compression. Our previous work has shown that PLT is good for lossless compression of ultraspectral sounder data with several thousands of channels. As the computation involves many operations on large matrices, this work will exploit the parallel compute power of graphics processing unit (GPU) to speed up the PLT encoding scheme. The CUDA (Compute Unified Device Architecture) platform by NVidia will be used for comparison with a single threaded CPU core. The experimental result reveals that our GPU implementation of the PLT encoding scheme shows a speedup of 95x compared to its original Matlab implementation on CPU. Thus it is promising to apply the GPU-based PLT encoding scheme for ultraspectral sounder data compression.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

A Semantics-Based Mobile Web Content Transcoding Framework

Thu, 22 Mar 2012 04:56:30 GMT

title: A Semantics-Based Mobile Web Content Transcoding Framework

Ultraspectral sounder data compression by the prediction-based lower triangular transform

Tue, 25 Oct 2011 03:33:06 GMT

title: Ultraspectral sounder data compression by the prediction-based lower triangular transform

旅遊產業供應鏈協同運籌－雄獅旅遊

Mon, 30 Nov 2009 05:22:47 GMT

title: 旅遊產業供應鏈協同運籌－雄獅旅遊

企業間網路(Extranet)導論

Mon, 30 Nov 2009 05:22:45 GMT

title: 企業間網路(Extranet)導論

全球運籌管理個案-華碩電腦公司

Mon, 30 Nov 2009 05:22:43 GMT

title: 全球運籌管理個案-華碩電腦公司

企業內部網路

Mon, 30 Nov 2009 05:22:41 GMT

title: 企業內部網路

Handwritten Numeral Recognition Based on Simplified Feature Extraction, Structural Classification and Fuzzy Memberships

Mon, 30 Nov 2009 05:22:38 GMT

title: Handwritten Numeral Recognition Based on Simplified Feature Extraction, Structural Classification and Fuzzy Memberships abstract: Structural classification recognizes handwritten numerals by extracting geometric primitives that characterize each image. We propose a handwritten numeral recognition system based on simplified feature extraction, structural classification and fuzzy memberships, with the intention to find a small set of primitives without sacrificing the recognition rate. For each image, we first perform simplified preprocessing of smoothing and thinning to obtain a skeleton. For each skeleton, the following feature points are detected: terminal, intersection, and directional. We then extract the following primitives for each skeleton: loop, horizontal, vertical, leftward curve, and rightward curve. A fuzzy S-function is used as the membership function to estimate the likelihood of these primitives being close to the vertical boundary of the image. A tree-like classifier based on the extracted feature points, primitives and fuzzy memberships is then applied to recognize the numerals. Handwritten numerals in NIST Special Database 19 are recognized with correct rate between 87.33% and 88.72%.

社團護照網路登錄系統

Wed, 23 Sep 2009 07:08:16 GMT

title: 社團護照網路登錄系統

Parallel deduction of connection graphs

Wed, 23 Sep 2009 07:08:11 GMT

title: Parallel deduction of connection graphs