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  • AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yo

    AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.

    標(biāo)簽: well-known algorithm AdaBoost Adaptive

    上傳時(shí)間: 2014-01-15

    上傳用戶(hù):qiaoyue

  • 實(shí)現(xiàn)2ASK

    實(shí)現(xiàn)2ASK,2FSK,2PSK,MSK,GRAY轉(zhuǎn)換自然碼

    標(biāo)簽: 2ASK

    上傳時(shí)間: 2013-12-26

    上傳用戶(hù):h886166

  • This small utility helps to generate seven segment display codes for different I/O connections. For

    This small utility helps to generate seven segment display codes for different I/O connections. For example, AVR/PIC Port A.0 -> D Segment Port A.1 -> B Segment .... Also it can generate Common Cathode or Common Anode table codes.. I Coded it long time ago. I Hope help someone. Thank you

    標(biāo)簽: connections different generate display

    上傳時(shí)間: 2014-01-17

    上傳用戶(hù):開(kāi)懷常笑

  • ImpulseC Codeveloper fft code. This file implements the hardware portion of a 256 sample FFT using a

    ImpulseC Codeveloper fft code. This file implements the hardware portion of a 256 sample FFT using a radix-4 algorithm. This implementation demonstrates that results similar to hand-coded HDL can be achieved using the C language, and without using a low-level style of C code.

    標(biāo)簽: Codeveloper implements ImpulseC hardware

    上傳時(shí)間: 2014-01-16

    上傳用戶(hù):hustfanenze

  • 灰色預(yù)測(cè)GM(1

    灰色預(yù)測(cè)GM(1,1)模型的matlab源代碼,包括預(yù)測(cè)模型的建立,以及模型的精度檢驗(yàn)指標(biāo)c,p的計(jì)算-Gray prediction GM (1,1) model matlab source code, including the prediction model, as well as indicators of model accuracy test c, p calculation

    標(biāo)簽: 灰色預(yù)測(cè)

    上傳時(shí)間: 2017-07-25

    上傳用戶(hù):refent

  • 多普勒頻移

    有多徑信道、多普勒頻移,瑞利、RICE(萊斯)信道等仿真,QPSK調(diào)制和解調(diào)等,交織編碼。程序經(jīng)過(guò)本人測(cè)試,絕對(duì)可用,并附上本人測(cè)試說(shuō)明和仿真圖像結(jié)果-I collected information on 2, how-path channel, Doppler frequency shift, Rayleigh, RICE (Rice) channel, such as simulation, QPSK modulation and demodulation, etc., Interleaved Coded. After I tested the procedure is absolutely available, along with my test images and simulation results indicate.

    標(biāo)簽: 移動(dòng)通信 多普勒頻移

    上傳時(shí)間: 2015-06-16

    上傳用戶(hù):whtiger

  • fft analysis

          Use the fast Fourier transform function fft to analyse following signal. Plot the original signal, and the magnitude of its spectrum linearly and logarithmically. Apply Hamming window to reduce the leakage.   .   The hamming window can be coded in Matlab as   for n=1:N hamming(n)=0.54+0.46*cos((2*n-N+1)*pi/N); end;   where N is the data length in the FFT.

    標(biāo)簽: matlab fft

    上傳時(shí)間: 2015-11-23

    上傳用戶(hù):石灰?guī)r123

  • 數(shù)據(jù)結(jié)構(gòu)實(shí)驗(yàn)

    #include <stdio.h>   #include <stdlib.h> ///鏈?zhǔn)綏?nbsp;     typedef struct node   {       int data;       struct node *next;   }Node,*Linklist;      Linklist Createlist()   {       Linklist p;       Linklist h;       int data1;       scanf("%d",&data1);       if(data1 != 0)       {           h = (Node *)malloc(sizeof(Node));           h->data = data1;           h->next = NULL;       }       else if(data1 == 0)       return NULL;       scanf("%d",&data1);       while(data1 != 0)       {           p = (Node *)malloc(sizeof(Node));           p -> data = data1;           p -> next = h;           h = p;           scanf("%d",&data1);       }       return h;   }      void Outputlist(Node *head)   {       Linklist p;       p = head;       while(p != NULL )       {           printf("%d ",p->data);           p = p->next;       }       printf("\n");   }      void Freelist(Node *head)   {       Node *p;       Node *q = NULL;       p = head;       while(p != NULL)       {           q = p;           p = p->next;           free(q);       }   }      int main()   {       Node *head;       head = Createlist();          Outputlist(head);          Freelist(head);          return 0;   }   2.順序棧 [cpp] view plain copy #include <iostream>   #include <stdio.h>   #include <stdlib.h> ///順序棧   #define MaxSize 100      using namespace std;      typedef

    標(biāo)簽: 數(shù)據(jù)結(jié)構(gòu) 實(shí)驗(yàn)

    上傳時(shí)間: 2018-05-09

    上傳用戶(hù):123456..

  • 灰色預(yù)測(cè)模型

    灰色預(yù)測(cè)模型(Gray Forecast Model)是通過(guò) 少量的、不完全的信息,建立數(shù)學(xué)模型并做出預(yù) 測(cè)的一種預(yù)測(cè)方法.當(dāng)我們應(yīng)用運(yùn)籌學(xué)的思想方法 解決實(shí)際問(wèn)題,制定發(fā)展戰(zhàn)略和政策、進(jìn)行重大 問(wèn)題的決策時(shí),都必須對(duì)未來(lái)進(jìn)行科學(xué)的預(yù)測(cè). 預(yù)測(cè)是根據(jù)客觀事物的過(guò)去和現(xiàn)在的發(fā)展規(guī)律, 借助于科學(xué)的方法對(duì)其未來(lái)的發(fā)展趨勢(shì)和狀況進(jìn) 行描述和分析,并形成科學(xué)的假設(shè)和判斷.

    標(biāo)簽: 灰色預(yù)測(cè)模型

    上傳時(shí)間: 2019-07-21

    上傳用戶(hù):Tqco702

  • STC8單片機(jī)資料

    STC單片機(jī)資料 請(qǐng)用逗號(hào)分隔標(biāo)簽 * 資料描述:

    標(biāo)簽: STC8 單片機(jī)資料

    上傳時(shí)間: 2019-12-02

    上傳用戶(hù):hjd0303

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