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Description: This program demonstrates how to draw on the client area, it also demonstrates how to use the mouse messages. I think the best way to learn programming is programming, so, let s begin our program now.-This program demonstrates how to draw on th e client area, it also demonstrates how to use the mouse messag es. I think the best way to learn programming is p rogramming, so, let s begin our program now.
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Size: 13330 |
Author: dcx |
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Description: SQL Server developers and administrators are no strangers to Data Transformation Services (DTS) packages. They likely develop DTS packages to perform everything from simple database operations to data aggregation. As such, when they upgrade some or all of their servers to SQL Server 2005, they must decide what to do with all of their DTS packages-SQL Server developers and administrators are no strangers to Data Transformation Servic es (DTS) packages. They likely develop DTS pack ages to perform everything from simple databas e operations to data aggregation. As such, when they upgrade some or all of their servers to SQL Server 2005, they must decide what to do with all of their DTS p ackages
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Size: 124285 |
Author: 罗 |
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Description: 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.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples.
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Size: 2537081 |
Author: google2000 |
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Description: This program demonstrates how to draw on the client area, it also demonstrates how to use the mouse messages. I think the best way to learn programming is programming, so, let s begin our program now.-This program demonstrates how to draw on th e client area, it also demonstrates how to use the mouse messag es. I think the best way to learn programming is p rogramming, so, let s begin our program now.
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Size: 13312 |
Author: dcx |
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Description: 在PocketPC上显示3D图像，使用VS2005开发，应用OPENGL ES-in PocketPC show 3D images, the use VS2005 development, OpenGL ES applications
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Size: 191488 |
Author: 哈哈哈 |
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Description: 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.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples.
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Author: |
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Description: 从TS文件中提取出音视频PES,ES,为多媒体开发者提供一个基础功能-TS files from the extracted audio and video PES, ES, for multimedia developers to provide a basis for functional
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Size: 97280 |
Author: zhenglusha |
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Description: Archive.pax.gz es lo que necesitas
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Size: 64149504 |
Author: Guiye |
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Description: opengl es 2.0 spec 详细说明了有关opengl es 2.0的情况 和1.1版本相比 差距很大-opengl es 2.0 spec details on the opengl es 2.0 and 1.1 versions of a large gap compared to
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Size: 795648 |
Author: zhangx |
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Description: A good document telling the difference between opengl es 1.1 and opengl es 2.0
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Size: 329728 |
Author: jayanth |
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Description: ACM線上題庫第750題"八后問題"的解法。http://acm.uva.es/p/v7/750.html-The solution to#750 in ACM online problem set. http://acm.uva.es/p/v7/750.html
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Size: 1024 |
Author: Tachiana |
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Description: ：介绍了采用 T MS 3 2 0 L F 2 4 0 7 A芯片实现对永磁 同步 电机 ( P MS M)的设 计与控制 ，讨论 了空间矢量脉宽调制
( S V P WM)控制方法 ，并给出了控制系统的硬件设计和 软件实现。试验结果表 明该系统具有较好的跟踪性能 ，稳态精
度较高 ；T MS 3 2 0 L F 2 4 0 7 A作为 D S P控制器 2 4 x系列 的新成员 ，是电机数字化控制的升级产品。-The d e s i g n a n d c on t r o l o f PMS M a t e r e a l i z e d u t i l i z i ng TMS 3 2 02 4 07A，a n d s pa c e v e c t o r p ul s e w d t h mo d u l a t on s d】
c u s s e d．Th e e x De r i me n t r e s uI t i n di c a t es t h a t t h e s y s t e m h a s be t t e r f ol l o wi n g p e r f o r ma n c e a n d h i g h e r s t e a d y p r e c i s o n， a s a n e wme Ⅱ l be o t
t h e 24 x s e r i e s o f DSP c o n t r o l l e r s，TMS 3 2 02 4 07A i s a up g r a d e pr o d u c t i o n f o r mo t o r d i g i t a l c o nt r o l ·
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Size: 133120 |
Author: 张国辉 |
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Description: name : Simulation Tools for Wireless Sensor Networks
E. Egea-López, J. Vales-Alonso, A. S. Martínez-Sala, P. Pavón-Mariñ o, J. García-Haro
Department of Information Technologies and Communications
Polytechnic University of Cartagena, Spain.
Email: {esteban.egea, javier.vales, alejandros.martinez, pablo.pavon, joang.haro}@upct.es
Corresponding author. Address: Campus Muralla del Mar, 30202, Cartagena, Spain.
phone: +34 968 325314, fax: +34 968 325973, e-mail: joang.haro@upct.es
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Size: 125952 |
Author: morteza |
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Description: Assumptions
There are p processors sorting n numbers.
Each processor begins with n=p numbers stored in the array x
All numbers are in the range 0 : : : M 1
When the sorting algorithm ends, each processors has a sorted list of numbers and for i < j every
number in processor i is less than every number in processor j
Each processor can compare two numbers and swap them (if necessary) in time .
Each machine can send data to one other machine at a time (but cannot send and receive at the same
time)
It takes + k= time to send k numbers to another machine
id speci es the identi er of the current machine
Local sort of k elements takes time approximately C k log2 k
Programming assignment (Due 4/20) Write a short program to estimate on the Linux Lab machines.
(This requires no parallelism)
Programming assignment (Due 4/20) Write a short program using MPI to estimate and in the
Linux Lab.
-Assumptions
There are p processors sorting n numbers.
Each processor begins with n=p numbers stored in the array x
All numbers are in the range 0 : : : M 1
When the sorting algorithm ends, each processors has a sorted list of numbers and for i < j every
number in processor i is less than every number in processor j
Each processor can compare two numbers and swap them (if necessary) in time .
Each machine can send data to one other machine at a time (but cannot send and receive at the same
time)
It takes + k= time to send k numbers to another machine
id speci es the identi er of the current machine
Local sort of k elements takes time approximately C k log2 k
Programming assignment (Due 4/20) Write a short program to estimate on the Linux Lab machines.
(This requires no parallelism)
Programming assignment (Due 4/20) Write a short program using MPI to estimate and in the
Linux Lab.
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Size: 63488 |
Author: ghn |
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Description: 根据摄像机透视投影模型, 通过提取特征点进行摄像机的运动估计, 提出一种基于FMA的补偿方法Z该方法针对图像连续帧间晃动幅度较大的情况, 通过均值滤波合成当前帧及其前几帧的运
动进行补偿。-A mo t ion model is p resen ted to est im ate the mo t ion param eters fo r im age stab ilizat ion
by t rack ing featu re po in t s f rom con secu t ive f ram es. A f ram e2to2mo saic algo rithm (FMA ) fo rmo2
t ion compen sat ion is described. The p ropo sed algo rithm is ab le to compen sate the mo t ion by fu s2
ing the mo t ion of the cu rren t f ram e w ith that of the p reviou s f ram es. Experim en tal resu lt s show
it s effect ive and po ten t ial app licat ion s
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Size: 301056 |
Author: 尹钊 |
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Description: B3log-Solo是一个基于GAE(Google App Engine)Java版的博客程序。
B3log-Solo 0.2.6 主要是修复了Bugs、加强稳定性、性能优化。在5.1国际劳动节即将到来之际，B3log开发团队祝所有劳动者节日快乐！
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Ping Google Blog Search Engine
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Size: 6906880 |
Author: bswx |
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Description: 提 出了基于修正空间 时频分布( S TF D) 矩阵 的 ES P RI T算法 以实现 对宽 带线性调 频信号 的到达
角估计-Th e a l g or i t h m f o r di r e c t i o n- o f- a r r i va l o f t he wi d e ba n d c hi r p s i gna l s ba s e d 0 1 3 ．ESPRI T u s i n g
t he mo di f i e d s p a t i a l t i me- f r e q ue n c y ma t r i x i S pr e s e nt e d．The mod i f i e d STFD ma t r i x whi c h h as t he s i mi l ar
ma t he ma t i c a l c on st r uc t i o n wi t h t he c o va r i a nc e ma t r i x c a n be ob t a i ne d wi t h t h e c r o s s W i gn e r- Vi l l e
di s t r i but i o ns o f t he o ut pu t s of t he ar r a y．Unde r t he c o nd i t i on of un i f o r m l i n e a r r a y，t he mo di f i e d STFD
ma t r i x c a n b e t r a n s f or me d i nt o t he ma t r i x wh i c h h a s t he pr op e r t y of r ot a t i on a l i nva r i a nc e ． The n t he
ESPRI T c a n be a p pl i e d t o D O A e s t i ma t i on
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Size: 145408 |
Author: fjp119 |
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Description: This function implememts Soft Output Viterbi Algorithm in trace back mode
Input:
rec_s: scaled received bits. rec_s(k) = 0.5 * L_c(k) * y(k)
L_c = 4 * a * Es/No, reliability value of the channel
y: received bits
g: encoder generator matrix in binary form, g(1,:) for feedback, g(2,:) for feedforward
L_a: a priori information about the info. bits. Extrinsic info. from the previous
component decoder
ind_dec: index of the component decoder.
=1: component decoder 1 The trellis is terminated to all zero state
=2: component decoder 2 The trellis is not perfectly terminated.
Output:
L_all: log ( P(x=1|y) ) / ( P(x=-1|y) )
Frame size, info. + tail bits- This function implememts Soft Output Viterbi Algorithm in trace back mode
Input:
rec_s: scaled received bits. rec_s(k) = 0.5 * L_c(k) * y(k)
L_c = 4 * a * Es/No, reliability value of the channel
y: received bits
g: encoder generator matrix in binary form, g(1,:) for feedback, g(2,:) for feedforward
L_a: a priori information about the info. bits. Extrinsic info. from the previous
component decoder
ind_dec: index of the component decoder.
=1: component decoder 1 The trellis is terminated to all zero state
=2: component decoder 2 The trellis is not perfectly terminated.
Output:
L_all: log ( P(x=1|y) ) / ( P(x=-1|y) )
Frame size, info. + tail bits
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Size: 1024 |
Author: yk |
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Description: 嵌入维数自适应最小二乘支持向量机
状态时间序列预测方法
Condition Time Series Prediction Using Least Squares Support Vector Machine
with Adaptive Embedding Dimension
针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应
最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参
数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维
数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发
动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测
精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition
time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em
bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af
fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap
plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida
tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to
the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an
aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em
bedding dimension selection. In compar ison w ith co nv
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Author: |
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Description: simulion for network p 2 p
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Size: 16384 |
Author: mero2285 |
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