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Description: SR-tree is an index structure for high-dimensional nearest neighbor queries，C++ sourcecode. SR-tree outperforms the R*-tree and the SS-tree especially for high-dimensional and non-uniform data which are likely to appear in the actual image / video applications.-SR-tree is an index structure for high-dimensional nearest neighbor queries,C++ sourcecode. SR-tree outperforms the R*-tree and the SS-tree especially for high-dimensional and non-uniform data which are likely to appear in the actual image/video applications.
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Size: 461186 |
Author: hu |
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Description: 基于内容的多媒体数据检索算法SR-Tree，类似于R*-tree和SS-tree的最近邻搜索
-content-based multimedia data retrieval algorithm SR-Tree, similar to the R *- tree and SS-tree search of the nearest neighbor
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Size: 21008 |
Author: 章明 |
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Description: rss readerr ss reader-rss reader rss readerr ss r eader
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Size: 7750 |
Author: shua |
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Description: 2006年由IEEE和WILEY联合出版的新书《Mobile Wireless and Sensor Networks》全面论述了无线移动网和传感器网的最新发展-2006 by the IEEE and WILEY joint publication of a new book entitled "Mobile Wirele ss and Sensor Networks "comprehensive exposition of wireless sensor networks and mobile networks on the latest developments
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Size: 3131887 |
Author: lhwbrike |
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Description: Please excuse my english... It's so bad :)
Hacker Disassembler Engine, or HDE, is small disassembler engine, which intend to
code analyse. HDE get length of command, prefixes, ModR/M and SIB bytes, opcode,
immediate, displacement, relative address, etc. For example, you can use HDE when
writing unpackers or decryptors executable files, because more others disassemblers
too big, get only disasm listing and aren't intended for code analys, but more simple
length disassemblers usually get too little info. HDE get enough info to analyse, but
it has very small size. HDE package include DLL, objects, headers files and and source.
+ support MMX, SSE, SSE2, SSE3, 3DNow! instructions
+ high-speed & small size (coded in assembler ;)
+ compatibility with most coding language
To disassemble should call hde_disasm function:
DWORD hde_disasm(
void *pCode // pointer to code
HDE_STRUCT *pHDE_STRUCT // pointer to structure HDE_STRUCT
);
Function return length of command and fill structure HDE_STRUCT:
struct HDE_STRUCT
{
BYTE len; // length of command
BYTE p_rep; // rep/repnz/.. prefix: 0xF2 or 0xF3
BYTE p_lock; // lock prefix 0xF0
BYTE p_seg; // segment prefix: 0x2E, 0x36, 0x3E, 0x26, 0x64, 0x65
BYTE p_66; // prefix 0x66
BYTE p_67; // prefix 0x67
BYTE opcode; // opcode
BYTE opcode2; // second opcode, if first opcode equal 0x0F
BYTE modrm; // ModR/M byte
BYTE modrm_mod; // - mod byte of ModR/M
BYTE modrm_reg; // - reg byte of ModR/M
BYTE modrm_rm; // - r/m byte of ModR/M
BYTE sib; // SIB byte
BYTE sib_scale; // - scale (ss) byte of SIB
BYTE sib_index; // - index byte of SIB
BYTE sib_base; // - base byte of SIB
BYTE imm8; // immediate imm8
WORD imm16; // immediate imm16
DWORD imm32; // immediate imm32
BYTE disp8; // displacement disp8
WORD disp16; // displacement disp16, if prefix 0x67 exist
DWORD disp32; // displacement disp32
BYTE rel8; // relative address rel8
WORD rel16; // relative address rel16, if prefix 0x66 exist
DWORD rel32; // relative address rel32
};
Opcode and len fields always exist, others are optional and depend of command. If
field's value equal zero, then it isn't existing.
Note: HDE work only with 32-bit commands of x86 processors !
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Size: 23447 |
Author: sys0007 |
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Description: SR-tree is an index structure for high-dimensional nearest neighbor queries，C++ sourcecode. SR-tree outperforms the R*-tree and the SS-tree especially for high-dimensional and non-uniform data which are likely to appear in the actual image / video applications.-SR-tree is an index structure for high-dimensional nearest neighbor queries,C++ sourcecode. SR-tree outperforms the R*-tree and the SS-tree especially for high-dimensional and non-uniform data which are likely to appear in the actual image/video applications.
Platform: |
Size: 460800 |
Author: hu |
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Description: 基于内容的多媒体数据检索算法SR-Tree，类似于R*-tree和SS-tree的最近邻搜索
-content-based multimedia data retrieval algorithm SR-Tree, similar to the R*- tree and SS-tree search of the nearest neighbor
Platform: |
Size: 20480 |
Author: 章明 |
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Description: rss readerr ss reader-rss reader rss readerr ss r eader
Platform: |
Size: 7168 |
Author: shua |
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Description: 2006年由IEEE和WILEY联合出版的新书《Mobile Wireless and Sensor Networks》全面论述了无线移动网和传感器网的最新发展-2006 by the IEEE and WILEY joint publication of a new book entitled "Mobile Wirele ss and Sensor Networks "comprehensive exposition of wireless sensor networks and mobile networks on the latest developments
Platform: |
Size: 3131392 |
Author: lhwbrike |
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Description: Linux中进行802.1认证mdc-ssd-01.1.2-1.i386.rpm.gz-802.1 Certification Linux in mdc-ssd-01.1.2-1.i386.rpm.gz
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Size: 116736 |
Author: wangcm |
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Description: 向当前激活的记事本中，添写指定的按键
向当前激活的记事本中，添写指定的按键 向当前激活的记事本中，添写指定的按键 向当前激活的记事本中，添写指定的按键 -this is a key Press memo
this is a key Press memo
this is a key Press memo
this is a key Press memo
this is a key Press memo
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Size: 339968 |
Author: yonglinxu |
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Description: 一个静态网站，采用了一些模板!对初学着来书看看还是很有用途的！-A static website!
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Size: 20617216 |
Author: 陈记超 |
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Description: 遗传算法解决背包问题，其中包括算法编码初始化，交叉，变异和惩罚策略，很好的解决了背包问题-Genetic algorithm to solve knapsack problem, including the coding algorithm initialization, crossover, mutation and punishment strategy, a good solution to the knapsack problem
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Size: 1024 |
Author: ss |
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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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Size: 342016 |
Author: |
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Description: tis notes is vry important.....and useful...these r ss lab manual....since its vry lengthy subject-tis notes is vry important.....and useful...these r ss lab manual....since its vry lengthy subject..
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Size: 25600 |
Author: vinay |
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Description: 给出了一种新的基于小波多尺度分解的分层图像融合方法. 其基本思想是先对源图像
进行小波多尺度分解 其次, 按照融合规则, 采用基于区域特性量测的选择及加权算子去构造融
合图像对应的小波系数 最后, 通过逆小波变换重构融合图像. 该方法被成功地用于图像的融合
处理. 此外, 利用熵、交叉熵、互信息、均方根误差、峰值信噪比等参量, 对该融合方法的融合性能
进行了评价与分析. 实验结果表明, 该融合方法是十分有效的- A novel h ierarch ical image fusion scheme based on wavelet mult iscale de2
compo sit ion is p resented. The basic idea is to perfo rm a wavelet mult iscale decompo si2
t ion of each source image first, then the wavelet coefficient s of the fused image are
const ructed using region2based select ion and weigh ted operato rs acco rding to different
fusion rules, finally the fused image is obtained by tak ing inverse wavelet t ransfo rm.
Th is app roach has been successfully used in image fusion. In addit ion, w ith the use of
the parameters such as ent ropy, cro ss ent ropy, mutual info rmat ion, roo t mean square
erro r, peak2to2peak signal2to2no ise rat io, the perfo rmance of the fusion scheme is e2
valuated and analyzed. The experimental result s show that the fusion scheme is effec2
t ive.
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Size: 385024 |
Author: liuzhao |
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Description: 新一代高性能无人机飞控系统的研究与设计
张小林 赵宇博 范力思-I n o r de r t o cau se t he U A V f lig ht co nt r o l sy st e m has t he f o r mida ble da t a- ha ndling ca pa cit y , t h e lo w po we r lo ss , t he st r o ng
f le x ibilit y an d a hig he r int e g r at io n r a t e, pr o po sed o ne kind of t e chn ol og y ba sed on SO P C w hic h ca n so lv e t hes e p r ob lem s o n U A V f lig ht co n
t r o l sy st e m. T r a nsf e r s m any N io s so f t pr o c ess o r . A lt e r a br in gi ng t he I P co r e a s w el l as t he per ip her y ha r dw a r e cir c uit . on e kind o f h ig h
per f o r man ce f lig ht co nt r o l sy st em h as be en de sig ned. Co mpa r e s w it h t he t r a diti on al U A V co nt r o l sy s te m, t his o ne hav e ve r y st r o ng dat a handl ing capa cit y , t h e sma ll v o lume a nd lo w po w e r lo s s. T he a ct ua l fl ig ht r esul t indic at e d : Ea ch m odu la r de sig n is r ea so nab le, t he o ve r al l
sy st em mo v e ment is st a ble. T his sy st e m ca n b e ser ve d a s t he ne x t g ene r
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Size: 620544 |
Author: 天下 |
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Description: Li near r egr essi on, acti ve learning
We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty
about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to
use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion
where the go al is to pr e dict a con tin uous resp onse y
t ∈ R to e ach example ve ctor. Here
to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e
easily extended (ne xt lecture). -Li near r egr essi on, acti ve learning
We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty
about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to
use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion
where the go al is to pr e dict a con tin uous resp onse y
t ∈ R to e ach example ve ctor. Here
to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e
easily extended (ne xt lecture).
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Size: 157696 |
Author: jacobjacobjacob17 |
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Description: 液晶显示程序，很简单的一个小程序，stm32的(t h i s i s a p r oc e ss of stm 3 2)
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Size: 2641920 |
Author: 狂野猎人
|
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Description: 设计一个表示分数的类Fraction。这个类用两个int类型的变量分别表示分子和分母。
这个类的构造函数是：
Fraction(int a, int b)
构造一个a/b的分数。
这个类要提供以下的功能：
double toDouble();
将分数转换为double
Fraction plus(Fraction r);
将自己的分数和r的分数相加，产生一个新的Fraction的对象。Fraction multiply(Fraction r);
将自己的分数和r的分数相乘，产生一个新的Fraction的对象。
void print();
将自己以“分子/分母”的形式输出到标准输出，并带有回车换行。
注意，在创建和做完运算后应该化简分数为最简形式。如2/4应该被化简为1/2。
我们需要给时钟程序加上一个表示秒的Display，然后为Clock增加以下public的成员函数：
public Clock(int hour, int minute, int second);
用hour, minute和second初始化时间。
public void tick();
“嘀嗒”一下，时间走1秒。
public String toString();
返回一个String的值，以“hh:mm:ss“的形式表示当前时间。这里每个数值都占据两位，不足两位时补0。如“00:01:22"。注意其中的冒号是西文的，不是中文的。(public Clock(int hour, int minute, int second);)
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Size: 1024 |
Author: 沉合 |
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