GENERAL-
IZATION
8 CHAPTER 1. INTRODUCTION
Width
22 salmon . sea bass
.
Lightness
2 4 6 8 1 0
Figure 1.5: Overly complex models for the sh will lead to decision boundaries that are
complicated. While such a decision may lead to perfect classication of o
DECISION
RULE
4 CHAPTER 2. BAYESIAN DECISION THEORY
assume that any incorrect classication entails the same cost or consequence, and that
the only information we are allowed to use is the value of the prior probabilities. If a
decision must be made with s
18 CHAPTER 1. INTRODUCTION
distributions are unknown and even the category membership of training patterns is
unknown. We begin in Chap. ? (Bayes decision theory) by considering the ideal case
in which the probability structure underlying the categories i
RECEIVER
OPERATING
CHARACTER-
ISTIC
34 CHAPTER 2. BAYESIAN DECISION THEORY
d' = M (76)
a
A high d is of course desirable.
While we do not know 1, #2, (I nor a:*, we assume here that we know the state
of nature and the decision of the system. Such informat
DECISION
REGION
14 CHAPTER 2. BAYESIAN DECISION THEORY
g.<x) = P(w.-|x) = M (25)
. P(X|Wj)P(wj)
]=1
94") = P(Xlwi)P(Wi) (26)
.92: (x) = 1n P(xlwi) +111 P (01): (27)
where ln denotes natural logarithm.
Even though the discriminant functions can be written
2.11. PROBLEMS 45
4. Generalize the minimax decision rule in order to classify patterns from three
categories having triangle densities as follows:
6- w- 6,? for :6- -<6-
Marlow) = T(Mi,6) E cfw_ 81 MW othelzrwislelfl 1
where 6,- > 0 is the half-width of
44 CHAPTER 2. BAYESIAN DECISION THEORY
Problems
69 Section 2.1
1. In the two-category case, under the Bayes decision rule the conditional error
is given by Eq. 7. Even if the posterior densities are continuous, this form of the
conditional error virtually
2.10. *MISSING AND N OISY FEATURES 39
The surface g(x) = 0 from Eq. 88 is shown on the left of the gure. Indeed, as we
might have expected, the boundary places points with two or more yes answers into
category wl, since that category has a higher probabil
48 CHAPTER 2. BAYESIAN DECISION THEORY
(b) Plot these discriminant functions and the decision regions for the two-category
one-dimensional case having
0 p(x|w1) ~ N(1, 1),
0 P(-I3lw2l ~ N(1,1),
o P(w1) = P(w2) = 1/2, and
. ,\,./,\5 = 1/4.
(3) Describe qua
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UNIT -1 : INTRODUCTION TO BIOPROCESS
Historical development of bioprocess technologies,
Role of bioprocess engineer in the biotechnology
industry,
Concept of Bioprocess,
Outline of an integrated bioprocess and the various
(upstream and downstream) unit
Strain improvement
Strain Improvement
What is the Need?
With the exception of the food industry, only a
few commercial fermentation processes use
wild strains isolated directly from nature.
Mutated and recombined mos are used in
production of antibioti
SOMATIC HYBRIDIZATION
Somatic hybridization technique
1. isolation of protoplast
2. Fusion of the protoplasts of desired species/varieties
3. Identification and Selection of somatic hybrid cells
4. Culture of the hybrid cells
5. Regeneration of hybrid pla
SRM UNIVERSITY
DEPARTMENT OF BIOTECHNOLOGY
SCHOOL OF BIOENGINEERING
LESSON PLAN FOR BT204 BIOPROCESS PRINCIPLES
UNIT NO
TOPIC
HOURS
GIVEN
Historical development of bioprocess technologies,
Role of bioprocess engineer in the biotechnology industry,
concept
Kla Volumetric mass transfer coefficient
Mass transfer between a moving liquid and solid is important in biological processing in a
variety of applications.
Gas-liquid mass transfer is of paramount importance in bioprocessing because of the
requirement fo
MONOD MODEL
(UNSTRUCRURED MODEL ON SEGREGATED MODEL)
Assumptions
This model assumes that a single chemical species, S, is
growth limiting.
The Monod equation is semi-empirical ; it derives from the
premise that a single enzyme system with Michealis-Mente
UNIT-3
Metabolic Stoichiometric Energetics
Yield coefficients based on other substrates or product formation may be
defined as:
Yield coefficient Yx/s= -X/S
Apparent Yield coefficient Yx/o2 = -X/O2
Observed Yield coefficient Yp/s= -P/S
Theoretical Yield c
Chapter 2
AUTOMATIC LOAD FREQUENCY
CONTROL
1. INTRODUCTION
This chapter deals with the control mechanism needed to maintain the system
frequency. The topic of maintaining the system frequency constant is commonly
known as AUTOMATIC LOAD FREQUENCY CONTROL
Freescale Semiconductor, Inc.
Advance Information
ARCHIVED BY FREESCALE SEMICONDUCTOR, INC. 2005
MC71000TB/D
Rev. 2, 8/2002
MC71000
MC71000 Bluetooth
Baseband Controller
ARCHIVED BY FREESCALE SEMICONDUCTOR, INC. 2005
Freescale Semiconductor, Inc.
Package
UNIT-5 EMBEDDED
CONTROL
APPLICATIONS
Open-loop and Closed Loop Control Systems-Application ExamplesWashing Machine, Automotive Systems, Auto-focusing digital camera,
Air-conditioner, Elevator Control System, ATM System
WHAT IS A CONTROL SYSTEM ?
A control
ARM
ARM
ARM
ARM
ARM
ARM
versions.
programming model.
memory organization.
data operations.
flow of control.
2008 Wayne Wolf
Overheads for Computers as
Components 2nd ed.
ARM versions
ARM architecture has been extended
over several versions.
ARM 7 is sp
DSP Processors
DSP vs Microcontroller
DSPs are mainly based on Harvard architecture to perform task
faster with hard wired instructions as compared to Microcontrollers
which are mainly available with von neumann architecture.
The DSP can compute the com
Chapter 13 Embedded ARM
Applications
Introduction
The VLSI Ruby II advanced communication
processor
The VLSI ISDN subscriber Processor
The OneCTM VWS22100 GSM chip
The EricssonVLSI bluetooth baseband
controller
The ARM7500 and ARM7500FE
The ARM7100
The SA
Computer control of Power Systems
ENERGY CONTROL CENTERS
Todays power systems are very huge in terms of Installed capacity, Energy
generated, Transmission and Distribution system, Number of customers and
Total investment. Installed capacity in India excee
Pattern Recognition in Medical
Images
Dr.S.Sridhar
Anna University
Introduction
One picture is worth more than ten thousand
words
Anonymous
Contents
This lecture will cover:
Overview of Medical Imaging
Pattern Recognition Tasks
Case Studies in Pattern