Submitted by Name Allam Levi Ratnakar B Suresh Roll Number 08EEB025 08EEB026

Problem:
Design a model for the plant h (z) =0.2600+0.9300z¯¹+0.2600z¯² using direct modeling (Adaptive Algorithm LMS/RMS). The channel is associated with the following functions.

where p(k) is the output of each of linear part of the channels

Theory:
The aim of this experiment is to create a model for a plant with given parameters using direct modeling for the given different functions.

Given
H (z) =0.2600+0.9300z¯¹+0.2600z¯²

Matlab code for the given problem:
Main program is given below whereas subroutines aregiven after this program ends. inp = randn(1,502); p = [0.26 0.93 0.26]; snr = input('enter snr \n'); x=rand(10,30); for i=1:10 for j=1:30 if x(i,j)>0.5 x(i,j)=1; else x(i,j)=0; end end

end for i=1:10 a(i,:)=x(i,1:10); b(i,:)=x(i,11:20); c(i,:)=x(i,21:30); end for i=1:10 wt(i,:)=[bin2deci(a(i,:)) bin2deci(b(i,:)) bin2deci(c(i,:))]; end for n=1:20 for i=1:10 for j=1:500 X(1:3) = inp(j:j+2); Y1(i,j) = X*p'; sp(j) = rms(X); noise(i,j) = sqrt(sp(j)*(10^(-snr/10))); Y2(i,j)=tanh(Y1(i,j)+noise(i,j)); Y3(i,j) = X*wt(i,:)'; er1(i,j) = Y2(i,j)-Y3(i,j); ers1(i,j)=er1(i,j)*er1(i,j); end ermax(i,:) = max(ers1(i,:)); MSE(i,:) = 0.5*db(sum(ers1(i,:))/ermax(i,:)); end T=[wt a b c MSE]; T1=sortrows(T,34);

...July 17, 2013
Ms. Chikie R. Marshall
School Director
AMA Computer Learning Center
Cainta Branch
AMA Bldg.Mr Center Marcos Highway
Cor Tuazon Ave. 1801 San Roque, Marikina City
Telefax 647-8490/682-9882
Dear Ms .Marshall,
Good Day!
We, the selected students of AMA Computer Learning Center, would like to ask permission in your good office to conduct research and study regarding your company ACLC - Cainta IdentificationSystem. This is in partial fulfilment of our requirements this semester in our subject Development and Maintains Enterprise-level Web Applications using Microsoft.Net.
The aim of this research/study is to provide knowledge for students and entrepreneurs a good inventory and sales management system in bringing an in-depth idea and design of the present sales and inventory strategy. The project consists of some questions and surveys that may help us obtain productive and worthwhile learning.
Your approval to conduct this research/study will be greatly appreciated. We will follow up this after 3 days and would be happy to answer any questions or concerns that you may have at that time. You may contact me at my email address: mhelaicharm_08@yahoo.com or on my mobile number: 09194418385
If you agree with this proposal, kindly sign below and return the signed form in the enclosed envelope acknowledging your consent and permission for us to conduct this...

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Fundamentals of GeneticAlgorithms : AI Course Lecture 39 – 40, notes, slides
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www.myreaders.info
Return to Website
Fundamentals of GeneticAlgorithms
Artificial Intelligence
Geneticalgorithms,
topics
:
Introduction,
search
optimization
algorithm; Evolutionary algorithm (EAs); GeneticAlgorithms (GAs) :
biological background, search space, working principles, basic geneticalgorithm, flow chart for Genetic programming; Encoding : binary
encoding,
value
encoding,
permutation
encoding,
and
tree
encoding; Operators of geneticalgorithm : reproduction or selection
- roulette wheel selection, Boltzmann selection; fitness function;
Crossover – one point crossover, two Point crossover, uniform
crossover, arithmetic, heuristic; Mutation - flip bit, boundary, nonuniform, uniform, Gaussian;
Basic geneticalgorithm -
solved
examples : maximize function f(x) = x2 and two bar pendulum.
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Fundamentals of GeneticAlgorithms
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(Lectures 39, 40
2 hours)
1. Introduction
Slides
03-15...

...Journal of Information Hiding and Multimedia Signal Processing Ubiquitous International Volume 1, Number 1, January 2010
A Secure Steganography Method based on GeneticAlgorithm
Shen Wang, Bian Yang and Xiamu Niu
School of Computer Science and Technology Harbin Institute of Technology 150080, Harbin, China shen.wang@ict.hit.edu.cn; bian.yang@ict.hit.edu.cn; xiamu.niu@hit.edu.cn
Received April 2009; revised August 2009
Abstract. With the extensive application of steganography, it is challenged by steganalysis. The most notable steganalysis algorithm is the RS attack which detects the steg-message by the statistic analysis of pixel values. To ensure the security against the RS analysis, we presents a new steganography based on geneticalgorithm in this paper. After embedding the secret message in LSB (least signiﬁcant bit) of the cover image, the pixel values of the steg-image are modiﬁed by the geneticalgorithm to keep their statistic characters. Thus, the existence of the secret message is hard to be detected by the RS analysis. Meanwhile, better visual quality can be achieved by the proposed algorithm. The experimental results demonstrate the proposed algorithm’s eﬀectiveness in resistance to steganalysis with better visual quality. Keywords: steganography; steganalysis; geneticalgorithm; RS algorithm
1....

...Legal SystemIdentification
Adebimpe Koyi
Cassandra Chambers
Greg Martinez
Kenya McDonald
University of Phoenix
Interoffice Memorandum
To: Supervisor
From: Legal Team (Chambers A, Koyi A, Marinez G, and McDonald K)
Subject: Mr. Al Jones’ utility easement legal situation
Date: August 18, 2008
Statement of Facts
Our client Mr. Al Jones, a land developer has been threatened to be sued for fraud against a municipality by the city he was building his newest and largest subdivision. Mr. Jones’ adjacent property owner, a citizen of Switzerland, has also threatened to sue him for damages and trespassing to his property. Our client is facing this legal dilemma because the City discovered an easement for a city utility on the subdivision that did not belong to him. Moreover, the owner of the property where the utility easement is located also discovered the error.
Questions Presented
• Describe the state and/or federal court(s) that can review and resolve the situation.
• The court(s) that may have jurisdiction over the situation. Explain.
• Identify the stages and processes for reviewing the situation in civil court.
• Compare the proposed resolution of the civil aspects of the situation with the criminal acts resolution.
• Analyze the probable success of a court proceeding and any alternative means of resolving the civil matter including the role of dispute resolution.
Analysis...

...GeneticAlgorithms and Rule Induction Analysis
Data mining is a data analyzing process that analyzes the data from different aspects and summarizes it into useful information that can be used to increase revenue and cost cuts (Data Mining: What is Data Mining? 2012). Data mining has different levels of analyzing. Geneticalgorithms and rule inductions are two of the six different levels of analysis. Geneticalgorithms are techniques that use genetic mutation, combination, and natural selection to analysis data. Rule induction is a way of analyzing data by means of extraction. Some of the attributes that will be discussed concerning geneticalgorithms and rule induction are the benefits, limitations, risks of each of the selected techniques, and practical examples of when each technique would be most effectively utilized by a health care organization.
Benefits
Data mining would not be a great tool without the parts that it is composed of. Geneticalgorithms ad rule inductions are parts of data mining and just happen to have great benefits. The advantages of geneticalgorithms are that they can be easily transferred, easy to understand and practical, and different types of problems can be solved (Advantages and disadvantages of geneticalgorithms, 2012)....

...Algorithms Homework – Fall 2000
8.1-1 Using Figure 8.1 as a model, illustrate the operation of PARTITION on the array A =
13 19 9 5 12 8 7 4 11 2 6 21
i j j
6 19 9 5 12 8 7 4 11 2 13 21
i i j j
6 2 9 5 12 8 7 4 11 19 13 21
i ………………………… j
return 11, SPLIT = and
8.1-2 What value of q does PARTITION return when all elements in the array A[p…r] have the same value?
q = (p+r)/2, where p = index 0, and r = highest index
8.1-3 Give a brief argument that the running time of PARTITION on a subarray of size n is (n).
In the worst case, PARTITION must move the j pointer by one element (to the 2nd to last element), and the i pointer all the way to j, making a comparison at each element along the way. Since there are n comparisons made, the running time is (n)
In the average (and best) case, PARTITION must move the j pointer to an element at or near the half-way point in the array and the i pointer all the way to j, making a comparison at each element along the way. Once again there are n comparisons made and the running time is (n)
8.2-1 Show that the running time of QUICKSORT is (n lg n) when all elements of array A have the same value.
T(n) =...

...A FAST ELITIST MULTIOBJECTIVE GENETICALGORITHM: NSGA-II
ARAVIND SESHADRI
1. Multi-Objective Optimization Using NSGA-II NSGA ( [5]) is a popular non-domination based geneticalgorithm for multiobjective optimization. It is a very eﬀective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare . A modiﬁed version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism and no sharing parameter needs to be chosen a priori. NSGA-II is discussed in detail in this. 2. General Description of NSGA-II The population is initialized as usual. Once the population in initialized the population is sorted based on non-domination into each front. The ﬁrst front being completely non-dominant set in the current population and the second front being dominated by the individuals in the ﬁrst front only and the front goes so on. Each individual in the each front are assigned rank (ﬁtness) values or based on front in which they belong to. Individuals in ﬁrst front are given a ﬁtness value of 1 and individuals in second are assigned ﬁtness value as 2 and so on. In addition to ﬁtness value a new parameter called crowding distance is calculated for each individual. The crowding distance is a measure of how close an individual is to its neighbors. Large average...

...ASSIGNMENT
ON ALGORITHM
Done by
Densil Hamilton
INTRODUCTION
This Assignment was done to show the methods of algorithm. It outlines the meaning of algorithm and steps to be carried out to complete a give problem. Examples were also shown for the methods of representing algorithm.
What is an Algorithm?
An algorithm consists of a set of explicit and unambiguous finite steps which, when carried out for a given set of initial conditions, produce the corresponding output and terminate in finite time. (How to Solve it by Computer, RG Dromey, Prentice Hall UK, 1982)
This is done by a series of steps:
1. Input: there are zero or more quantities which are externally supplied;
2. Output: at least one quantity is produced;
3. Definiteness: each instruction must be clear and unambiguous;
4. Finiteness: if we trace out the instructions of an algorithm, then for all cases the algorithm will terminate after a finite number of steps;
5. Effectiveness: every instruction must be sufficiently basic that a person using only pencil and paper can in principle carry it out. It is not enough that each operation is definite, but it must also be feasible.
WAYS OF REPRESENTING ALGORITHMS
Two ways of represent an algorithm are:
Flowcharts
Pseudo Code
FLOWCHARTS
This is a diagrammatic representation of...

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