This code is based on mpeg_play, available from: http://bmrc.berkeley.edu/frame/research/mpeg/
標(biāo)簽： available mpeg_play berkeley research
上傳時(shí)間： 2014-11-27
上傳用戶：duoshen1989
基于小生競的遺傳算法 Genetic Algorithm Based 0n Niche
標(biāo)簽： Algorithm Genetic Based Niche
上傳時(shí)間： 2015-10-11
上傳用戶：zhanditian
Physics-Based Modeling Methods Improve Control System Design Multidomain systems (mechanical, electrical, hydraulic, chemical) Successful controller development requires thorough and accurate understanding of plantControllerElectricalMechanicalDeviceActuatorsSensorsPlant
標(biāo)簽： Physics-Based Multidomain mechanical Modeling
上傳時(shí)間： 2015-10-13
上傳用戶：xaijhqx
This I develops based on the B/S structure student managementsystem management system, hoped brings a help to the novice
標(biāo)簽： managementsystem management structure develops
上傳時(shí)間： 2014-01-07
上傳用戶：釣鰲牧馬
LinCAN is a Linux kernel module that implements a CAN driver capable of working with multiple cards, even with different chips and IO methods. Each communication object can be accessed from multiple applications concurrently. It supports RT-Linux, 2.2, 2.4, and 2.6 with fully implemented select, poll, fasync, O_NONBLOCK, and O_SYNC semantics and multithreaded read/write capabilities. It works with the common Intel i82527, Philips 82c200, and Philips SJA1000 (in standard and PeliCAN mode) CAN controllers. LinCAN project is part of a set of CAN/CANopen related components developed as part of OCERA framework.
標(biāo)簽： implements multiple capable working
上傳時(shí)間： 2015-10-14
上傳用戶：磊子226
Linux Kernel 2.6.9 for OMAP1710
標(biāo)簽： Kernel Linux 1710 OMAP
上傳時(shí)間： 2014-01-03
上傳用戶：dave520l
LPC213x Driver For T6963 based Graphic LCD
標(biāo)簽： Graphic Driver T6963 based
上傳時(shí)間： 2014-10-30
上傳用戶：1109003457
A Simple Fuzzy Classifier based on Inconsistency Analysis of Labeled Data
標(biāo)簽： Inconsistency Classifier Analysis Labeled
上傳時(shí)間： 2013-12-26
上傳用戶：libenshu01
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
標(biāo)簽： Introduction Classifiers Algorithms introduces
上傳時(shí)間： 2015-10-20
上傳用戶：aeiouetla
這是CISS會(huì)議上發(fā)表的著名論文“Tensor Canonical decomposition based method for blind identification of MIMO system with 3-input 2-output case”的源程序，主要是講基于張量規(guī)范分解的多天線系統(tǒng)的忙識別問題，里邊包含了相應(yīng)的文章，可以一起對照著看。
標(biāo)簽： identification decomposition Canonical Tensor
上傳時(shí)間： 2015-10-21
上傳用戶：牛津鞋
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