3
Delay Models
in Data Networks
3.1 INTRODUCTION
One of the most important perfonnance measures of a data network is the average delay
required to deliver a packet from origin to destination. Furthenn
INSTRUCTORS MANUAL TO ACCOMPANY
ALPHA (incomplete) VERSION DATED: August 28, 2001
Database System Concepts
Fourth Edition
Abraham Silberschatz
Bell Laboratories
Henry F. Korth
Bell Laboratories
S. Sud
Plotting in Matlab
Page 1
Basics of Plotting in Matlab
GSF 3/22/12
Table of Contents
Basic Overview
o Syntax
o Labeling Axes
o Legends
Manipulating Axes
Subplots
Multiple Y-Axes
Statistics
3-D plots
A
EEL 703(Computer Networks) Tutorial Sheet No.1
1. We consider a buer that receives messages to be sent. The transmission is made by means of two modern
lines that operate at the same speed. We know th
Learning curves for stochastic gradient descent
in linear feedforward networks
Justin Werfel
Dept. of EECS
MIT
Cambridge, MA 02139
[email protected]
Xiaohui Xie
Dept. of Molecular Biology
Princeton Uni
The
Interpolating
Polynomial
Math 45 Linear Algebra
David Arnold
[email protected]
Abstract
A polynomial that passes through a given set of data points is called an
interpolating p
Chapter 3
Interpolation
Interpolation is the process of dening a function that takes on specied values at
specied points. This chapter concentrates on two closely related interpolants: the
piecewise c
Chapter 5
Process and Thread
Scheduling
One of the major tasks performed by an operating system is to allocate ready
processes or threads to the available processors. This task may usefully be
divided
AppendixB:AnExampleof
Backpropagationalgorithm
November 2011
1
Figure 1: An example of a multilayer feed-forward neural network. Assume
that the learning rate is 0.9 and the first training example, X
Installation Procedure for MATLAB Software
Redundant clients- LINUX/MAC systems:
Get administrator privileges for the system on which you plan to install
MATLAB.
Download:
MAC.iso (for mac) and LINU
Chapter12:IndexingandHashing
Rev.Sep17,2008
DatabaseSystemConcepts,5thEd.
Silberschatz,KorthandSudarshan
Seewww.dbbook.comforconditionsonreuse
Chapter12:IndexingandHashing
s BasicConcepts
s OrderedInd
Artificial Neural Network
Lecture Module 22
Neural Networks
Artificial neural network (ANN) is a machine learning
approach that models human brain and consists of a
number of artificial neurons.
Neur
3. The Back Propagation Algorithm
Having established the basis of neural nets in the previous chapters, lets now have a
look at some practical networks, their applications and how they are trained.
Ma
Neural Network Models of Categorical Perception
R.I. Damper and S.R. Harnad
University of Southampton
Studies of the categorical perception (CP) of sensory continua have a long and rich history in
psy
Huffman Coding
Vida Movahedi
October 2006
Contents
A simple example
Definitions
Huffman Coding Algorithm
Image Compression
A simple example
Suppose we have a message consisting of 5 symbols,
e.g. []
Operating Systems -1
EEL602: Operating Systems
http:/www.cse.iitd.ac.in/sumantra/courses/os/os.html
General Information
No one shall be permitted to audit the course. People are welcome to sit through
Feb 4: Recap of Jan 30 class
Data Models: E-R and Relational (and some others of
mostly historical interest)
We examined the E-R model
Entities, Relationships, and Attributes
Diagram-based model
ARTIFICIAL NEURAL NETWORKS
GIRISH KUMAR JHA
Indian Agricultural Research Institute
PUSA, New Delhi-110 012
[email protected]
1. Introduction
Artificial Neural Networks (ANNs) are non-linear
Chapter A: Network Model
s Basic Concepts s Data-Structure Diagrams s The DBTG CODASYL Model s DBTG Data-Retrieval Facility s DBTG Update Facility s DBTG Set-Processing Facility s Mapping of Networks
480
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. COM-30, NO. 3 , MARCH 1982
An Improved Selective-Repeat ARQ Strategy
E. J. WELDON, JR.,
M EMBER, IEEE
tocols like SDLC [13] and ADCCP [ 141 employ the gob
MAL704: Test Functions For 1-Dimension Minimization
1. f (x) = sin3 x + sin5 x + sin7 x sin 9x, x [, ].
(Ans: x = 1.27)
1
x4
+
, x [1, 1]. (Ans: x = 1)
1 + x2 1 + x8
()
0.75
1 1
0.65 tan
, x [2, 2].
Compression Algorithms
An Introduction To Probability
Coding
Compression Outline
Introduction
Information
Theory
Probability Coding
Applications Of Probability Coding
Resources
Introduction
What
Math 436
Fall 2008
November 5
Polynomials in MATLAB
1
Polynomial Evaluation
A polynomial is completely known by its coecients! For example the polynomial
p(x) = x3 3x2 + 1
has coecients, beginning wit
Huffman Coding
Huffman Coding
Huffman codes can be used to compress
information
Like WinZip although WinZip doesnt use the
Huffman algorithm
JPEGs do use Huffman as part of their compression
proces