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  • FIST可堆疊文件系統

    FIST可堆疊文件系統,linux文件系統修改相關的開源軟件,使得對linux文件系統功能的改進很簡單了 The FiST (File System Translator) system combines two methods to solve the above problems in a novel way: a set of stackable file system templates for each operating system, and a high-level language that can describe stackable file systems in a cross-platform portable fashion. Using FiST, stackable file systems need only be described once. FiST s code generation tool, fistgen, compiles a single file system description into loadable kernel modules for several operating systems (currently Solaris, Linux, and FreeBSD). The project demonstrates that with FiST, code size and development time are reduced significantly, while imposing a small performance overhead of only 1-2%. These benefits are achieved, as well as portability, without changing existing operating systems or file system.

    標簽: FIST 堆疊 文件系統

    上傳時間: 2014-01-10

    上傳用戶:xsnjzljj

  • Written by the inventors of the technology, The Java™ Language Specification, Third Edition, is

    Written by the inventors of the technology, The Java™ Language Specification, Third Edition, is the definitive technical reference for the Java™ programming language. If you want to know the precise meaning of the language s constructs, this is the source for you. The book provides complete, accurate, and detailed coverage of the Java programming language. It provides full coverage of all new features added since the previous edition, including generics, annotations, asserts, autoboxing, enums, for-each loops, variable arity methods, and static import clauses.

    標簽: Specification technology the inventors

    上傳時間: 2016-01-26

    上傳用戶:youmo81

  • This the fourth edition of the Writing Device Drivers articles. This article will introduce the idea

    This the fourth edition of the Writing Device Drivers articles. This article will introduce the idea of device stacks and how devices interact with each other. We will use the previously created example device driver to demonstrate this topic. To do this we will introduce the idea of a “filter” driver in which we will create to attach to our own driver’s device stack.

    標簽: the This introduce articles

    上傳時間: 2016-01-28

    上傳用戶:CSUSheep

  • This the third edition of the Writing Device Drivers articles. The first article helped to simply ge

    This the third edition of the Writing Device Drivers articles. The first article helped to simply get you acquainted with device drivers and a simple framework for developing a device driver for NT. The second tutorial attempted to show to use IOCTLs and display what the memory layout of Windows NT is. In this edition, we will go into the idea of contexts and pools. The driver we write today will also be a little more interesting as it will allow two user mode applications to communicate with each other in a simple manner. We will call this the “poor man’s pipes” implementation.

    標簽: the articles Drivers edition

    上傳時間: 2014-01-16

    上傳用戶:ommshaggar

  • Solving Engineering Problems Using MATLAB C++ Math Library Introduction In the previous article, we

    Solving Engineering Problems Using MATLAB C++ Math Library Introduction In the previous article, we studied how can use MATLAB C API to solve engineering problems. In this article I will show you how can use MATLAB C++ math library. The MATLAB® C++ Math Library serves two separate constituencies: MATLAB programmers seeking more speed or complete independence from interpreted MATLAB, and C++ programmers who need a fast, easy-to-use matrix math library. To each, it offers distinct advantages.

    標簽: Introduction Engineering Problems previous

    上傳時間: 2014-12-02

    上傳用戶:13160677563

  • Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form

    Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.

    標簽: meta-learning classifiers combining Boosting

    上傳時間: 2016-01-30

    上傳用戶:songnanhua

  • Visual Basic Shell Programming ventures where none have gone before by showing how to develop shell

    Visual Basic Shell Programming ventures where none have gone before by showing how to develop shell extensions that more closely integrate an application with the Windows shell, while at the same time providing an advanced tutorial-style treatment of COM programming with Visual Basic. each major type of shell extension gets attention, including customized context menu handlers, per instance icons, and customized property sheets.

    標簽: Programming ventures develop showing

    上傳時間: 2016-02-01

    上傳用戶:tb_6877751

  • Traveling Salesman Problem (TSP) has been an interesting problem for a long time in classical optim

    Traveling Salesman Problem (TSP) has been an interesting problem for a long time in classical optimization techniques which are based on linear and nonlinear programming. TSP can be described as follows: Given a number of cities to visit and their distances from all other cities know, an optimal travel route has to be found so that each city is visited one and only once with the least possible distance traveled. This is a simple problem with handful of cities but becomes complicated as the number increases.

    標簽: interesting Traveling classical Salesman

    上傳時間: 2016-02-06

    上傳用戶:rocwangdp

  • How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters S

    How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.

    標簽: the decision clusters Cluster

    上傳時間: 2013-12-21

    上傳用戶:gxmm

  • 此范例可在window的DOS上執行.有9各TASK分別秀出時間.執行方式解壓鎖COPY到C碟然后BC45SOURCE下修改TEST.C在BC45TEST下執行MAKETEST.BAT就可編繹出執行檔

    此范例可在window的DOS上執行.有9各TASK分別秀出時間.執行方式解壓鎖COPY到C碟然后BC45\SOURCE下修改TEST.C在BC45\TEST下執行MAKETEST.BAT就可編繹出執行檔 to show the starting time and completion time of each task in the first round.

    標簽: TEST MAKETEST window SOURCE

    上傳時間: 2016-02-14

    上傳用戶:zhouchang199

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