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?
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
/* 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
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.
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.
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.
Sherwood算法消除最壞實(shí)例，以達(dá)到對任何實(shí)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(tài)鏈表的長度默認(rèn)設(shè)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(chǎn)生一個(gè)長度為1000的互不相等的隨機(jī)序列，保存在s.txt中
./list s1.txt 1000 產(chǎn)生一個(gè)長度為1000的互不相等的降序序列，保存雜s1.txt中
./sherwood s1.txt 運(yùn)行算法，比較其中的4個(gè)算法的性能差異
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.
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.
