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This book evolved over the past ten years from a set of lecture notes developed while teaching the undergraduate Algorithms course at Berkeley and U.C. San Diego. Our way of teaching this course evolved tremendously over these years in a number of directions, partly to address our students' ...

　　电子发烧友网核心提示：Altera公司昨日宣布，在业界率先在28 nm FPGA器件上成功测试了复数高性能浮点数字信号处理（DSP）设计。独立技术分析公司Berkeley设计技术有限公司（BDTI）验证了能够在 Altera Stratix V和Arria V 28 nm FPGA开发套件上简单方便的高效实现Altera浮点DSP设计流程，同时验证了要求较高的浮点DSP ...

USB接口控制器参考设计,xilinx提供VHDL代码 usb xilinx vhdl ;  This program is free software; you can redistribute it and/or modify ;  it under the terms of the GNU General Public License as published by ;  the Free Software Foundation; either version 2 of the License, or ;  ...

《TCP/IP详解，卷1：协议》是一本完整而详细的TCP/IP协议指南。描述了属于每一层的各个协议以及它们如何在不同操作系统中运行。作者用Lawrence Berkeley实验室的tcpdump程序来捕获不同操作系统和TCP/IP实现之间传输的不同分组。对tcpdump输出的研究可以帮助理解不同协议如何工作。 本书适合作为计算机专业学生学习网络的教 ...

最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, ...

Program: PC FEAP Title: Finite Element Analysis Program Developer: R.L. Taylor, Department of Civil Engineering, University of California, Berkeley 1997.

Hidden Markov Toolkit (HTK) 3.2.1 HTK is a toolkit for use in research into automatic speech recognition and has been developed by the Speech Vision Robotics Group at the Cambridge University Engineering Department (http://svr-www.eng.cam.ac.uk) and Entropic Ltd (http://www.entropic.com).

一本linux下进行网络编程的教程，内容包括网络协议的介绍，进程，berkeley socket，网络安全

学生信息管理系统 GNU通用公共许可证 第二版，1991年 版权所有（C）1989，1991 Free Software foundation, Inc. 675 Mass Ave, Cambridge, MA02139, USA 允许每个人复制和发布这一许可证原始文档的副本，但绝对不允许对它进行任何修改。 序言 大多数软件许可证决意剥夺你的共享和修改软件的自由。对比之下，GNU通用 ...

On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and deta ...