Abstract The Lucene Server project is an attempt to extend the Jakarta Lucene tool with server capabilities. Lucene is a robust Java API that enables you creating indexes from text sources and perform powerful searches on these indexes. With Lucene, creating an index must be done programmatically and there are almost no possibilities of integrating index management in a distributed environment. In other words, out of the box, Lucene is suitable for integrating indexing and searching possibilities in a single application but not for providing index/search services for multiple applications. The Lucene Server project comes with a Java API that propose the following make it easy to create indexes in a declarative way by simply providing an XML configuration document. make it easy to personalize the way Lucene must handle different kind of data sources. provide services for index management and searching that can be accessed from several applications. enable batch tasks scheduling.
標(biāo)簽： Lucene Abstract Jakarta project
上傳時(shí)間： 2016-01-10
上傳用戶(hù)：songrui
Perlbox Voice is an voice enabled application to bring your desktop under your command. With a single word, you can start your web browser, your favorite editor or whatever you want. This is the Linux and Unix voice recognition solution
標(biāo)簽： your application Perlbox enabled
上傳時(shí)間： 2014-11-27
上傳用戶(hù)：hgy9473
The Linux Enterprise Cluster explains how to take a number of inexpensive computers with limited resources, place them on a normal computer network, and install free software so that the computers act together like one powerful server. This makes it possible to build a very inexpensive and reliable business system for a small business or a large corporation. The book includes information on how to build a high-availability server pair using the Heartbeat package, how to use the Linux Virtual Server load balancing software, how to configure a reliable printing system in a Linux cluster environment, and how to build a job scheduling system in Linux with no single point of failure. The book also includes information on high availability techniques that can be used with or without a cluster, making it helpful for System Administrators even if they are not building a cluster. Anyone interested in deploying Linux in an environment where low cost computer reliability is important will find this book useful.
標(biāo)簽： inexpensive Enterprise computers explains
上傳時(shí)間： 2014-11-29
上傳用戶(hù)：zhangliming420
FIST可堆疊文件系統(tǒng)，linux文件系統(tǒng)修改相關(guān)的開(kāi)源軟件，使得對(duì)linux文件系統(tǒng)功能的改進(jìn)很簡(jiǎn)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(biāo)簽： FIST 堆疊 文件系統(tǒng)
上傳時(shí)間： 2014-01-10
上傳用戶(hù)：xsnjzljj
Developing internationalized products is a continuous balancing act. Developers and their managers often grossly underestimate the level of effort and attention to detail required to create either a world-ready, single-binary application ready for use in many different markets, or high-quality, foreign-language editions of a product. If you are a developer, make sure your management understands what is involved.
標(biāo)簽： internationalized Developing Developers continuous
上傳時(shí)間： 2014-12-05
上傳用戶(hù)：TRIFCT
A new cable fault location method based on wavelet reconstruction is proposed. In this method the difference between the currents of faulty phase and sound phase under the high voltage pulse excitation is used as the measured signal and is decomposed in multi-scale by wavelet transform, then reconstructed in single scale. Comparing with traditional fault location method by travelling wave, the presented method will not be interfered by the reflected wave from the branch joint of cables or from other positions where the impedances are not matched and not be influenced by fault types, otherwise, the reflected waves can be recognized even the faulty position is near to the measuring terminal, at the same time, the influence of the wave speed uncertainty can be reduced. The correctness of the proposed method is proved by simulation results.
標(biāo)簽： method reconstruction location proposed
上傳時(shí)間： 2016-02-04
上傳用戶(hù)：maizezhen
Easy-to-Use, Ultra-Tiny, Differential, 16-Bit Delta Sigma ADC With I2C Interface The LTC2453 is an ultra-tiny, fully differential, 16-bit, analog-to-digital converter. The LTC2453 uses a single 2.7V to 5.5V supply and communicates through an I2C interface. The ADC is available in an 8-pin, 3mm x 2mm DFN package. It includes an integrated oscillator that does not require any external components. It uses a delta-sigma modulator as a converter core and has no latency for multiplexed applications. The LTC2453 includes a proprietary input sampling scheme that reduces the average input sampling current several orders of magnitude lower than conventional delta-sigma converters. Additionally, due to its architecture, there is negligible current leakage between the input pins.
標(biāo)簽： Differential Easy-to-Use Ultra-Tiny Interface
上傳時(shí)間： 2014-01-08
上傳用戶(hù)：鳳臨西北
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(biāo)簽： the decision clusters Cluster
上傳時(shí)間： 2013-12-21
上傳用戶(hù)：gxmm
This document contains a description of the CAN Reference Model. This document is part of a set of documents that standardize the CAN Application Layer for Industrial Applications.
標(biāo)簽： document This description Reference
上傳時(shí)間： 2016-02-08
上傳用戶(hù)：yoleeson
The algorm of viterbi. You talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:
標(biāo)簽： discover viterbi algorm friend
上傳時(shí)間： 2016-02-16
上傳用戶(hù)：xc216
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