ACM試題Problem K:Ones Description Given any integer 0 <= n <= 10000 not divisible by 2 or 5, some multiple of n is a number which in decimal notation is a Sequence of 1 s. How many digits are in the smallest such a multiple of n?
標簽： Description divisible Problem integer
上傳時間： 2015-08-23
上傳用戶：zhenyushaw
This section contains a brief introduction to the C language. It is intended as a tutorial on the language, and aims at getting a reader new to C started as quickly as possible. It is certainly not intended as a substitute for any of the numerous textbooks on C. 2. write a recursive function FIB (n) to find out the nth element in theFibanocci Sequence number which is 1,1,2,3,5,8,13,21,34,55,…3. write the prefix and postfix form of the following infix expressiona + b – c / d + e * f – g * h / i ^ j4. write a function to count the number of nodes in a binary tr
標簽： introduction the contains intended
上傳時間： 2013-12-23
上傳用戶：liansi
/* Check_SST_39VF400A Check manufacturer and device ID /* CFI_Query CFI Query Entry/Exit command Sequence /* Erase_One_Sector Erase a sector of 2048 words /* Erase_One_Block Erase a block of 32K words /* Erase_Entire_Chip Erase the contents of the entire chip /* Program_One_Word Alter data in one word /* Program_One_Sector Alter data in 2048 word sector /* Program_One_Block Alter data in 32K word block
標簽： manufacturer Check_SST CFI_Query command
上傳時間： 2013-12-15
上傳用戶：jjj0202
Each arc of a binary-state network has good/bad states. The system reliability, the probability that source s communicates with sink t, can be computed in terms of minimal paths (MPs). An MP is an ordered Sequence of arcs from s to t that has no cycle. Note that a minimal path is different from the so-called minimum path. The latter is a path with minimum cost.
標簽： binary-state reliability probability network
上傳時間： 2015-12-04
上傳用戶：xcy122677
SVMhmm: Learns a hidden Markov model from examples. Training examples (e.g. for part-of-speech tagging) specify the Sequence of words along with the correct assignment of tags (i.e. states). The goal is to predict the tag Sequences for new sentences.
標簽： examples e.g. part-of-speech Training
上傳時間： 2015-12-05
上傳用戶：gyq
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video Sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標簽： approach combines particle tracking
上傳時間： 2016-01-02
上傳用戶：yy541071797
Sherwood算法消除最壞實例，以達到對任何實例都能有好的性能的效果 文件： rd_list.c --> create a random Sequence of n integers not equal to each other list.c --> create a descending Sequence of n integers not equal to each other sherwood.c --> 就是該算法，靜態鏈表的長度默認設為1000，可在宏定義處修改 用法： gcc -o sherwood sherwood.c gcc -o rd_list rd_list.c gcc -o list list.c ./rd_list s.txt 1000 產生一個長度為1000的互不相等的隨機序列，保存在s.txt中 ./list s1.txt 1000 產生一個長度為1000的互不相等的降序序列，保存雜s1.txt中 ./sherwood s1.txt 運行算法，比較其中的4個算法的性能差異
上傳時間： 2016-01-20
上傳用戶：ainimao
FSK信號鑒頻的程序.This program implements the function of finding out the largest and the second largest values of the Sequence of "in_buffer[10]"
標簽： largest the implements function
上傳時間： 2016-02-05
上傳用戶：youth25
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in Sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood Sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
標簽： performance equalizers Adaptive several
上傳時間： 2016-02-16
上傳用戶：yan2267246
