voice control robotic arm_march02

VOICE CONTROLLED ROBOTIC ARM

AuthorsZESHAN ANWAR…….07-ECT-02BASIT AHMAD……….07-ECT-18

JIBRAN SIDDIQI……....07-ECT-19 RAMEEZ JAVED………07-ECT-31

Project SupervisorProf.Dr.UMAR FAROOQ

DEPARTMENT OF ELECTRONIC ENGINEERINGUNIVERSITY OF ENGINEERING AND TECHNOLOGY TAXILA

MARCH, 2011

Voice Controlled Robotic Arm I

ABSTRACT

Making the machine to learn to recognize the human voice is a great need of the day.

Voice recognition is the processes of making the computer intelligent enough to

distinguish a voice spoken by a human from all other voices. In voice recognition, voice

is first recorded sampled, framed, and windowed and then different features that make it

distinguishable are extracted. These features include short time energy, zero crossing rate,

and spectral roll off spectral flux spectral centriod. Once these features are extracted than

pattern recognition is applied, whose output moves the robotic arm according to the voice

command of the user. Neural networks are used for pattern recognition with five neurons

at the input layer. This voice recognition system is 80 percent accurate when

experimented with a single user. Our project covers all the commands needed to control a

robotic arm.

Voice Controlled Robotic Arm II

UNDERTAKING

We certify that research work titled “VOICE CONTROLLED ROBOTIC ARM” is our

own work. The work has not, in whole or in part, been presented elsewhere for

assessment. Where material has been used from other sources it has been properly

acknowledged/ referred.

Signature of Student

ZESHANANWAR…….07-ECT-02

BASIT AHMAD……….07-ECT-18

JIBRAN SIDDIQI……...07-ECT-19

RAMEEZ JAVED………07-ECT-31

Voice Controlled Robotic Arm III

DEDICATION

We dedicate this project to our

Beloved, Caring, Loving and Respected

Parents

And

Family members.

Voice Controlled Robotic Arm IV

ACKNOWLEDGEMENTS

With the Grace of the Almighty ALLAH WHO bestowed us, many blessings and we

became able to complete our project, we are thankful to ALLAH who is the CREATOR

of the whole Universe.

We acknowledge the help and support of our Parents and teachers. Due to which we

reached our destination. We are thankful to our supervisor Prof.Dr.Umar Farooq and

special thanks to Engr.Ahmad Nauman for encouraging us during our project.

Voice Controlled Robotic Arm V

TABLE OF CONTENTSAbstract…………………………………………………………………………...ii

Undertaking............……………………………………………………………....iii

Dedication…………………………………………………………………….......iv

Acknowledgement……………………………………………...………….......…v

Table of Contents................................................................................................. vi

List of Figures………………………………………………………..…………...x

List of Tables……………………………………………………………..……....xi

Chapter 1: Introduction

1.1 Problem Statement ……………………………………………...............12

1.2 Objective………………………………………………….…….………..12

1.3 Organization of Project Report………………………………….............13

Chapter 2: Front End Processing

2.1 Introduction………………………………………………............….….15

2.2 First Phase……………………………………………………...............15

2.2.1 Sampling Rate…………………...............................….…..…..15

2.2.2 Recording…………………………….……...........…….……….16

2.2.3 Retrieving…………………………….……………………..........16

2.2.4 Normalizing………………………….…………………..............17

2.2.5 Framing…………………………….…………………....…….....17

2.2.6 Windowing…………………...................................................17

Voice Controlled Robotic Arm VI

Chapter 3: Feature Extraction

3.1 Introduction………………………………………….………...….20

3.2 Features……………………………………………………….......21

3.2.1 Zero Crossing Rate……………………………………….….21

3.2.2 Short Time Energy………………………………………….23

3.2.3 Spectral Roll Off……………………………………….......24

3.2.4 Spectral Flux……………………………………………......25

3.2.5 Spectral Centroid……………………………………………25

Chapter 4: Introduction To Machine Learning

4.1 Artificial Intelligence……………………………………………..26

4.2 Machine Learning…………………………………………..…….26

4.2.1 Definition ……………………………………....…….........27

4.2.2 Generalization…………………………………………....…27

4.3 Human Interaction ………………………………………..…..….27

4.4 Algorithmic Types………………………………………………..27

4.4.1 Supervised Learning…………………………..………........28

4.4.2 Statistical Classification…………………….…………........29

Chapter 5: Artificial Neural Network

5.1 Introduction…………………………………………….……..…31

5.2 ANN in Matlab……………………………………………........31

5.3 How does an ANN Work? ....................................................32

5.3.1 Transfer Function……………………………………..........34

5.3.2 Feed-Forward Back Propagation……………………..……..37

5.3.3 Epoch………………………………………………….…….39

5.3.4 Training of ANN……………………………………….......39

Voice Controlled Robotic Arm VII

Chapter 6: GUI (Graphical User Interface)

6.1 Introduction……………………………………………….….40

6.2 GUI in Matlab…………………………………………….….40

6.2.1 How does GUI work................................................41

6.2.2 Ways to built Mat lab GUI’s.....................................42

6.3 Creating GUI with guide…………………..……………......42

6.3.1 What is guide? ………………..………………….....42

6.3.2 Opening Guide …………..…………………….....43

6.3.3 Laying Out a Guide to GUI …….…………………. 43

6.3.4 Adding Component to GUI ….………………….….44

6.4 GUI of Our Project…………………..………………......….45

6.4.1 Start………………………..………………………...46

6.4.2 Trouble Shoot………………………………………..47

6.4.3 Training……………….…………………………..…48

6.4.4 Simulation Mode…………………………………….49

Chapter 7: Robotic Arm

7.1 Introduction.....................................................................50

7.2 Types of Industrial Robotics…………………………………50

7.3 Mechanical Component of Robotic Arm…………………….51

7.3.1 Power Source…………………………………………..51

7.3.2 Actuator…………………………………………..…....51

7.3.3 Plat Form……………………………………..…….....51

7.3.4 End Effecter………………………………..…………..51

7.4 Characteristics of Robotic Arm .........................................52

7.4.1 Joints………………………………..…………..……..52

7.4.2 Payload…………………………..……………...….….52

7.4.3 Reach………………………………………………..….52

7.4.4 Repeatability…………………………………...……....53

7.4.5 Axis of Rotation………………………………...…......53

Voice Controlled Robotic Arm VIII

Chapter 8: Electronic Components Of Robotic Arm

8.1 Electronic Components............................................................54

8.1.1 AC and DC Supply…………………………………………54

8.1.2 H-Bridge……………………………………………..…..…54

8.1.3 7805 Voltage Regulator………………………...……..…...55

8.1.4 Actuator…………………………………………………….56

8.1.5 7404N IC………………………………………………..….57

8.1.6 DB25 Connector……………………………………….......57

8.2 Motor Driving Circuit............................................................60

Problems and Troubleshooting................................................................61

Conclusion ………………………………………………………………...62

Future Suggestions……………………………………………………......63

Appendix A. ………………………………………………………………64

Appendix B. ………………………………………………………….......66

Appendix C. ………………………………………………………...........67

Reference……..……………………………………………………...........69

Voice Controlled Robotic Arm IX

List of Figures

Figure Number Page

Fig 2.1 Sampling Rate....................................................................................15Fig 2.2 Retrieving..........................................................................................15Fig 2.3 Hamming Window............................................................................18Fig 2.4 Result of Hamming Window.............................................................19Fig 3.1 Block diagram of Voice/Unvoiced Classification.............................21Fig 3.2 Defining of Zero Crossing Rate.........................................................22Fig 3.3 Distributio0n of Zero Crossing for Voice/Unvoiced Speech……….22Fig 3.4 Computation of Short Time Energy..................................................24Fig 5.1 Style of Neural Computation.............................................................32Fig 5.2 Network Data Manager.....................................................................33Fig 5.3 Create the Input Data.........................................................................33Fig 5.4 Create the Network............................................................................34Fig 5.5 Hard Limit Transfer Function................................................…...35Fig 5.6 Pure Line Transfer Function..............................................................35Fig 5.7 Log Sigmoid Transfer Function........................................................36Fig 5.8 View of Our Project Network............................................................37Fig 5.9 Feed Forward Back Propagation.......................................................38Fig 5.10 Result of Training............................................................................38Fig 6.1 Adding Component to GUI...............................................................44Fig 6.2 our Project GUI……………………………………………………...45Fig 6.3 Start Button………………………………………………………….46Fig 6.4 Trouble Shoot Button........................................................................47Fig 6.5 Training Button..................................................................................48Fig 6.6 Simulation Button..............................................................................49Fig 8.1 L298...................................................................................................55Fig 8.2 Regulator Circuit...............................................................................55Fig 8.3 DC Motor...........................................................................................56Fig 8.4 7404N IC..............................................................….................57Fig 8.5 DB25 Connector................................................................................58Fig 8.6 Motor Driving Circuit........................................................................60

Voice Controlled Robotic Arm 10

List of TablesTable 8.1 Pin configuration of DB25…………………………………………59


CHAPTER 1:

INTRODUCTION

1.1 PROBLEM STATEMENT:

Implementation of voice command and control for a robotic arm requires extraction of voice

feature that make a voice command distinguishable from all other voices in the language.

Feature extraction requires that voice should be clean from all noise so that feature extracted

per frame of a voice command should have a value within the range of the class the voice

command belongs [1].

Pattern recognition techniques are applied on the features so that they can be classified [2].

Pattern recognition techniques should be able to learn the pattern present in the dataset of a

feature extracted from a same voice command class

Combining the feature extraction and pattern recognition, the robotic arm can be made

intelligent to act on the voice command from a single user

1.2 OBJECTIVE:

Prime objective of our project is to develop a voice recognition system that can be used as

“command and control” for any machine.

For that, we have chosen robotic arm. Since it has an extensive set of commands, which can

be used to train our voice recognition system

Most of the voice recognition systems are developed for speech to text conversion [2]. Since

these systems are operated for a large vocabulary they are less accurate and require more

computation. our aim was to develop a voice recognition for the command and control of a


machine, while using less computational power by using non conventional approach to voice

recognition i.e. instead of using convention features of voice(e.g. MFCC and LPC) we have

chosen a set five feature that make a voice distinguishable enough to control a robotic arm.

In our project, we achieve the accuracy of 80%, for a single user.

Voice command and control has many applications from military to air force and from

telephony to people with disabilities.

Voice command and control is used in military ground vehicles where commander can rotate

and aim its gun on the target through his voice while sitting inside in the safe environment of

tank [3]. This application of voice command and control was our major source of inspiration

for making this project since a robotic arm and machine gun has the difference of only the

end effecter.

1.3 ORGANIZATION OF THE PROJECT REPORT:

Chapter 2 of this report describes the front end processing of the voice command. In this

chapter, we have described the sampling recording, retrieving and windowing of the voice

signal. Chapter 3 describes the second phase of voice recognition that is Feature extraction.

In feature extraction, we have explained the six features, which are used, in our voice

recognition system. Chapter 4 is concerned with Machine Learning and its different

techniques, which are used in our project. After Machine Learning the next chapter, i.e.

Chapter 5 describes Artificial Neural Network and its implementation in our project.

Chapter 6 of this report describes the Graphical user interface (i.e. GUI) developed for our

project. After explaining GUI Chapter 7 and Chapter 8 both are concern with design and

development of robotic arm. Chapter 7 describes the type of the robotic arm we have

developed to use in our project along with its different characteristics. Chapter 8 describes


interfacing circuitry and different electronic components used in the making of robotic arm.

At the end of report, we have mention some Problems and Troubleshooting associated with

our project. Report ends by giving the Conclusion on the project and suggesting some

Future work on the project.


CHAPTER 2:

Front End Processing

2.1 INTRODUCTION

Front end are generalized terms that refer to the initial and the end stages of a process. The

front end is responsible for collecting input in various forms from the user and processing it to

conform to a specification the back end can use. The front end is an interface between the user

and the back end. In our project front end, processing has two phases. In first phase, we have

done sampling and recording, retrieving and normalizing, and finally framing and windowing.

The second phase involves feature extraction [4].

2.2 First Phase

2.2.1 Sampling Rate

The sampling rate, sample rate, or sampling frequency defines the number

of samples per second (or per other unit) taken from a continuous signal to make a discrete

signal. For time-domain signals, the unit for sampling rate is hertz (inverse seconds, 1/s, s−1).

The inverse of the sampling frequency is the sampling period or sampling interval, which is the

time between samples.

Sample rate is usually noted in Sa/s (non-SI) and expanded as kSa/s, MSa/s, etc. The common

notation for sampling frequency is fs.


Fig 2.1: Sampling rate……………………..[4]

.The sampling rate in our project is 44100 samples per sec

2.2.2 Recording

The voice is recorded with the help of microphone and saved in the hard drive with the

sampling rate (44100 samples per sec).

2.2.3 Retrieving

Retrieving is done at the sample rate of 44100 samples per sec through wave read function of

MATLAB.


Fig 2.2: Retrieving

After retrieving the copies of voice command signals is sent to each feature extraction blocks.

2.2.4 Normalizing

The process of normalizing is called pre emphasis.

In this process all the samples in the voice signal is divided by the maximum value of a sample.

It is done to reduce the dynamic range of the signal and to make it spectrally flat [4].

2.2.5 Framing

Framing is called frame blocking.

In this step, the voice command signal is divided into a no of blocks [4]. Each frame (block)

contain equal no of voice samples.

2.2.6 Windowing

Windowing is done to remove the discontinuities at the start and at the end of the frame.

We have employed Hamming window for this purpose.


2.2.6.1 Hamming window

It is also known as a raised cosine window. The hamming window for N points is defined as

W (i) =0.54+0.46*cos(2i/N).….[4]

Where –N/2<=I<N/2

Fig 2.3: Hamming window…[4]

These are specific examples from a general family of curves of the form [4]

W (i) = a + (1 - a) cos (2 pi i / N)…………….[4]


Fig 2.4: Result of Hamming Window[4]


CHAPTER 3:

Feature Extraction

3.1 Introduction:

The second phase of front end processing in our project is feature extraction.

After the first phase, the voice samples are sent to each feature extraction block. The voice

features are extracted per frame of voice signal. The five features of two types are extracted

per frame. The two types are time domain and spectral feature. The five features we have

used in our project are short time energy; zero crossing rate, spectral roll off, spectral flux,

and spectral centroid. The first two features are time domain and last three are spectral

features. Statistics are involved to each feature block to generalize a feature’s value over an

entire signal. The statistics are involved to each feature extraction block because the pattern

recognition technique requires that the feature should give a single value over e entire signal.

Due to which statistics are involved to represent a general trend in voice command signal. As

we used five features, every feature should give a single value so vector of five values is

given to the input of the neural network. For example, zcr is first calculated per frame after

that a mean value of it is calculated using all of its value per frame. This value is now ready

for input of pattern recognition. Similarly the statistics of other features are calculated as

fallows:


3.2 Features:

3.2.1 Zero-Crossings Rate

Zero-crossing rate is an important parameter for voiced/unvoiced classification. It is also

often used as a part of the front-end processing in automatic speech recognition system. The

zero crossing count is an indicator of the frequency at which the energy is concentrated in the

signal spectrum [5].

The analysis for classifying the voiced/unvoiced parts of speech has been illustrated in the

block diagram

In Fig 3.1

Fig 3.1: Block diagram of voiced/unvoiced classification [5]


In the context of discrete-time signals, a zero crossing is said to occur if successive samples

have different algebraic signs. The rate at which zero crossings occur is a simple measure of

the frequency content of a signal. Zero-crossing rate is a measure of number of times in a

given time interval/frame that the amplitude of the speech signals passes through a value of

zero. Speech signals are broadband signals and interpretation of average zero-crossing rate is

therefore much less precise.

However, rough estimates of spectral properties can be obtained using a representation based

on the short- time average zero-crossing rate.

Fig 3.2: Define of zero crossing rate..[5] Fig 3.3: Distribution of zero crossing for

Voiced/unvoiced speech..[5]


…..................................................[5]

3.2.2 Short Time Energy

The short time energy measurement of a speech signal can be used to determine voiced vs.

unvoiced speech. Short time energy can also be used to detect the transition from unvoiced

to voiced speech and vice versa [5]. The energy of voiced speech is much greater than the

energy of unvoiced speech. . Short-time energy can define as:

….[5]

The choice of the window determines the nature of the short-time energy representation. In

our model, we used Hamming window. The hamming window gives much greater

attenuation outside the band pass than the comparable rectangular window.


h(n) = 0.54 − 0.46 cos(2πn /( N − 1)) , 0 ≤ n ≤ N − 1

h(n) = 0 , otherwise

Fig 3.4: Computation of short time energy………..[5]

The attenuation of this window is independent of the window duration. Increasing the length,

N, decreases the bandwidth, Fig 5. If N is too small, details of the waveform. If N is too

large, E n will change very slowly and thus will not adequately reflect the changing

properties of the speech signal.

3.2.3 Spectral Roll off

Description:

The spectral Roll Offs point R determines where 85% of the window’s energy is

achieved [6]. It is used to distinguish voiced from unvoiced speech and music.

Resolution: window.

Parameters: SOUND file, start time, end time.

Based on the Sub bands RMS.

Formula:


………………………..[5]3.2.4 Spectral Flux

Description:

It determines changes of spectral energy distribution of two successive windows

Resolution: window.

Parameters: SOUND file, start time, end time.

Based on the Sub band RMS.

3.2.5 Spectral Centroid

Description:

The spectral centroid is the balancing point of the sub band energy distribution. It

determines the frequency area around which most of the signal energy concentrates

and is thus closely related to the time-domain Zero Crossing Rate feature. It is also

frequently used as an approximation for a perceptual brightness measure.

Resolution: window.

Parameters: SOUND file, start time, end time, start sub band number, end sub band

number.

Based on the Sub band RMS.

CHAPTER 4:


INTRODUCTION TO MACHINE LEARNING

4.1 Artificial Intelligence

It is the property of machines to do reasoning for any given problem with specified logics

and perceptions after a particular process known as “Machine learning”.

4.2 Machine Learning

Machine learning, a branch of artificial intelligence, is a scientific discipline that is

concerned with the design and development of algorithms that allow computers to evolve

behaviors based on empirical data, such as from sensor data or databases. A learner can take

advantage of examples (data) to capture characteristics of interest of their unknown

underlying probability distribution. Data can be seen as examples that illustrate relations

between observed variables. A major focus of machine learning research is to automatically

learn to recognize complex patterns and make intelligent decisions based on data; the

difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too

large to be covered by the set of observed examples (training data). Hence, the learner must

generalize from the given examples, to be able to produce a useful output in new cases.

Machine learning, like all subjects in artificial intelligence, require cross-disciplinary

proficiency in several areas, such as probability theory, statistics, pattern recognition,

cognitive science, data mining, adaptive control, computational neuroscience and theoretical

computer science.


4.2.1 Definition

A computer program is said to learn from experience E with respect to some class of tasks T

and performance measure P, if its performance at tasks in T, as measured by P, improves

with experience E.

4.2.2 Generalization

The core objective of a learner is to generalize from its experience. The training examples

from its experience come from some generally unknown probability distribution and the

learner has to extract from them something more general, something about that distribution,

which allows it to produce useful answers in new cases.

4.3 Human Interaction

Some machine learning systems attempt to eliminate the need for human intuition in data

analysis, while others adopt a collaborative approach between human and machine. Human

intuition cannot, however, be eliminated, since the system's designer must specify how the

data is to be represented and what mechanisms will be used to search for a characterization of

the data.

4.4 Algorithm Types

Machine learning algorithms are organized into taxonomy, based on the desired outcome of

the algorithm.

Supervised Learning generates a function that maps inputs to desired outputs. For

example, in a classification problem, the learner approximates a function mapping a

vector into classes by looking at input-output examples of the function.


Unsupervised Learning models a set of inputs, like clustering.

Semi-Supervised Learning combines both labeled and unlabeled examples to

generate an appropriate function or classifier.

Reinforcement Learning learns how to act given an observation of the world. Every

action has some impact in the environment, and the environment provides feedback in

the form of rewards that guides the learning algorithm.

Transduction tries to predict new outputs based on training inputs, training outputs,

and test inputs.

Learning to Learn learns its own inductive bias based on previous experience.

4.4.1 Supervised Learning

Artificial neural network

o Back propagation

Bayesian statistics

o Naive Bayes classifier

o Bayesian network

o Bayesian knowledge base

Case-based reasoning

Decision trees

Inductive logic programming

Gaussian process regression

Group method of data handling (GMDH)

Learning Automata


Minimum message length (decision trees, decision graphs, etc.)

Lazy learning

Instance-based learning

o Nearest Neighbor Algorithm

Probably approximately correct learning (PAC) learning

Ripple down rules, a knowledge acquisition methodology

Symbolic machine learning algorithms

Sub symbolic machine learning algorithms

Support vector machines

Random Forests

Ensembles of classifiers

o Bootstrap aggregating (bagging)

o Boosting

Ordinal classification

Regression analysis

Information fuzzy networks (IFN)

4.2.2 Statistical Classification

Linear classifiers

o Fisher's linear discriminant

o Logistic regression

o Naive Bayes classifier

o Perceptron

o Support vector machines


Quadratic classifiers

k-nearest neighbor

Boosting

Decision trees

o C4.5

o Random forests

Bayesian networks

Hidden Markov models

CHAPTER 5:

Artificial Neural Network


5.1 Introduction

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by

the way biological nervous systems, such as the brain, process information. The key element of

this paradigm is the novel structure of the information processing system. It is composed of a

large number of highly interconnected processing elements (neuron’s) working in unison to

solve specific problems. ANN’s, like people, learn by example. An ANN is configured for a

specific application, such as pattern recognition or data classification, through a learning

process [6].

5.2 ANN in MATLAB:

Artificial neural networks (ANN) are among the newest signal-processing technologies in the

engineer's toolbox. In engineering, neural networks serve two important functions: as pattern

classifiers and as nonlinear adaptive filters. Definitions and Style of Computation an

Artificial Neural Network is an adaptive, most often nonlinear system that learns to perform

a function (an input/output map) from data. Adaptive means that the system parameters are

changed during operation, normally called the training phase. After the training phase the

Artificial Neural Network parameters are fixed and the system is deployed to solve the

problem at hand (the testing phase). The Artificial Neural Network is built with a systematic

step-by-step procedure to optimize a performance criterion or to follow some implicit

internal constraint, which is commonly referred to as the learning rule [9]. The input/output

training data are fundamental in neural network technology, because they convey the

necessary information to "discover" the optimal operating point There is a style in neural

computation that is worth describing.


Fig 5.1: Style of Neural computation…………..[8] An input is presented to the neural network and a corresponding desired or target response

set at the output. An error is composed from the difference between the desired response and

the system output. This error information is fed back to the system to adjust the system

parameters in a systematic fashion (the learning rule). The process is repeated until the

performance is acceptable [8]. It is clear from this description that the performance hinges

heavily on the data. In artificial neural networks, the designer chooses the network topology,

the performance function, the learning rule, and the criterion to stop the training phase, but

the system automatically adjusts the parameters. At present, artificial neural networks are

emerging as the technology of choice for many applications, such as pattern recognition,

prediction, system identification, and control.

5.3 How Does an ANN Work?

In the ANN described above, the user selects a tool by writing in m file “nntool”


Fig 5.2: Network/data manager in nntool

User selects data and target value from “new” or if we have data in any file than data can be

imported by “Import”. Target is value from which we match our output value

Fig 5.3: Create new data in nntool

Than user create network in which training function, transfer function, number of layer and

number of neuron in each layer is selected.


Fig 5.4:Create network in nntool

5.3.1 Transfer Functions:

Three of the most commonly used functions are shown below.

5.3.1.1 Hard-Limit Transfer Function

Fig 5.5: Hard limit transfer function………….

[9]

The hard-limit transfer function shown above limits the output of the neuron

to either 0, if the net input argument n is less than 0; or 1, if n is greater than

or equal to 0.

5.3.1.2 Linear Transfer Function


Fig 5.6: Pure line transfer function……………….[9]

The linear transfer function gives the value of positive value for input value greater than one

and negative one for input value less than one.

5.3.1.3 Sigmoid Transfer Function

The sigmoid transfer function shown below takes the input, which may have

any value between plus and minus infinity, and squashes the output into the

range 0 to 1.

Fig 5.7: Log sigmoid transfer function…………..[9]


In our project we use three layers of neuron , in first layer we select five neuron for five

features of voice and in second layer we select twenty neuron in hidden layer and in third

layer one neuron is selected for output [11].

In our project, we used the feed-forward back propagation. The view of our network is given

below

.

Fig 5.8: View of the project network in nn tool

5.3.2 Feed-Forward Back Propagation:

A feed forward neural network is an artificial neural network where connections between the

units do not form a directed cycle. This is different from recurrent neural networks.


In this network, the information moves in only one direction, forward, from the input nodes,

through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the

network [10].

In Back-propagation output values are compared with the correct answer to compute the

value of some predefined error-function. By various techniques, the error is then fed back

through the network. Using this information, the algorithm adjusts the weights of each

connection in order to reduce the value of the error function by some small amount. After

repeating this process for a sufficiently large number of training cycles, the network will

usually converge to some state where the error of the calculations is small. In this case, one

would say that the network has learned a certain target function.

Fig 5.9: Feed Farward back propagation……………..[6]

The result of training is given below.


Fig 5.10: Result of the trainning

5.3.3 Epoch:

An entire pass through all of the input training vectors is called an epoch. When such an

entire pass of the training set has occurred without error, training is complete [11].

One iteration through the process of providing the network with an input and updating the

network's weights. Typically, many epochs are required to train the neural network.

5.3.4 Training of ANN:

If sim and learnp are used repeatedly to present inputs to a perceptron, and to change the

perceptron weights and biases according to the error, the perceptron will eventually find


weight and bias values that solve the problem, given that the perceptron can solve it. Each

traverse through all of the training input and target vectors is called a pass [12].

The function train carries out such a loop of calculation. In each pass the function train

proceeds through the specified sequence of inputs, calculating the output, error and network

adjustment for each input vector in the sequence as the inputs are presented.

CHAPTER 6:

Graphical User Interface

6.1 INTRODUCTION

In computing a graphical user interface (GUI, sometimes pronounced gooey) is a type of user

interface that allows users to interact with electronic devices with images rather than text

commands. GUIs can be used in computers, hand-held devices such as MP3 players, portable

media players or gaming devices, household appliances and office equipment. A GUI


http://en.wikipedia.org/wiki/Computing

http://en.wikipedia.org/wiki/MP3

http://en.wikipedia.org/wiki/Mobile_device

http://en.wikipedia.org/wiki/Computer

http://en.wikipedia.org/wiki/Human-computer_interaction

http://en.wikipedia.org/wiki/User_(computing)

http://en.wikipedia.org/wiki/User_interface

http://en.wikipedia.org/wiki/User_interface

represents the information and actions available to a user through graphical icons and visual

indicators such as secondary notation, as opposed to text-based interfaces, typed command

labels or text navigation. The actions are usually performed through direct manipulation of

the graphical elements [13].

6.2 GUI in MATLAB:

A graphical user interface (GUI) is a graphical display in one or more windows containing

controls, called components that enable a user to perform interactive tasks. The user of the

GUI does not have to create a script or type commands at the command line to accomplish

the tasks. Unlike coding programs to accomplish tasks, the user of a GUI need not

understand the details of how the tasks are performed.

GUI components can include menus, toolbars, push buttons, radio buttons, list boxes, and

sliders—just to name a few. GUIs created using MATLAB tools can also perform any type

of computation, read and write data files, communicate with other GUIs, and display data as

tables or as plots.

6.2.1 How Does a GUI Work?

In the GUI described above, the user selects a data set from the pop-up menu, and then

clicks one of the plot type buttons. The mouse click invokes a function that plots the selected

data in the axes.

Most GUIs wait for their user to manipulate a control, and then respond to each action in

turn. Each control, and the GUI itself, has one or more user-written routines (executable

MATLAB code) known as callbacks, named for the fact that they "call back" to MATLAB to

ask it to do things. The execution of each callback is triggered by a particular user action


http://en.wikipedia.org/wiki/Direct_manipulation

http://en.wikipedia.org/wiki/Text-based_(computing)

http://en.wikipedia.org/wiki/Secondary_notation

http://en.wikipedia.org/wiki/Icon_(computing)

such as pressing a screen button, clicking a mouse button, selecting a menu item, typing a

string or a numeric value, or passing the cursor over a component. The GUI then responds to

these events. You, as the creator of the GUI, provide callbacks, which define what the

components do to handle events.

This kind of programming is often referred to as event-driven programming. In the example,

a button click is one such event. In event-driven programming, callback execution is

asynchronous, that is, it is triggered by events external to the software. In the case of

MATLAB GUIs, most events are user interactions with the GUI, but the GUI can respond to

other kinds of events as well, for example, the creation of a file or connecting a device to the

computer.

You can code callbacks in two distinct ways:

As MATLAB language functions stored in files

As strings containing MATLAB expressions or commands (such as 'c =

sqrt(a*a + b*b);'or 'print')

Using functions stored in code files as callbacks is preferable to using strings, as functions

have access to arguments and are more powerful and flexible. MATLAB scripts (sequences

of statements stored in code files that do not define functions) cannot be used as callbacks.

Although you can provide a callback with certain data and make it do anything you want,

you cannot control when callbacks will execute. That is, when your GUI is being used, you

have no control over the sequence of events that trigger particular callbacks or what other

callbacks might still be running at those times. This distinguishes event-driven programming

from other types of control flow, for example, processing sequential data files.


6.2.2 Ways to Build MATLAB GUIs:

A MATLAB GUI is a figure window to which you add user-operated controls. You can

select, size, and position these components as you like. Using callbacks you can make the

components do what you want when the user clicks or manipulates them with keystrokes.

You can build MATLAB GUIs in two ways:

Use GUIDE (GUI Development Environment), an interactive GUI construction kit.

Create code files that generate GUIs as functions or scripts (programmatic GUI

construction).

6.3 Creating GUIs with GUIDE:

6.3.1 What Is GUIDE?

GUIDE, the MATLAB Graphical User Interface Development Environment, provides a set

of tools for creating graphical user interfaces (GUIs). These tools greatly simplify the process

of lying out and programming GUIs.

6.3.2 Opening GUIDE:

There are several ways to open GUIDE from the MATLAB Command line. You can also

right-click a FIG-file in the Current Folder Browser and select Open in GUIDE from the

context menu.

When you right-click a FIG-file in this way, the figure opens in the GUIDE Layout Editor,

where you can work on it.

.


All the tools in palette have tool tips. Setting a GUIDE preference lets you display the palette

in GUIDE with tool names or just their icons. See GUIDE Preferences for more

information

6.3.3 Laying Out a GUIDE GUI:

The GUIDE Layout Editor enables you to populate a GUI by clicking and dragging GUI

components into the layout area. There you can resize, group and align buttons, text fields,

sliders, axes, and other components you add. Other tools accessible from the Layout Editor

enable you to:

Create menus and context menus

Create toolbars

Modify the appearance of components

Set tab order

View a hierarchical list of the component objects

Set GUI options


http://www.mathworks.com/help/techdoc/creating_guis/f9-998349.html

6.3.4 Adding Components to the GUI

The component palette at the left side of the Layout Editor contains the components that you

can add to your GUI. You can display it with or without names.

Fig 6.1: Adding component to GUI……………….[13]


6.4 Our project GUI:

Fig 6.2: Our project GUI

This is the GUI of our project.

It consists of following push button.

1. Start

2. Troubleshoot

3. Help

4. Training

5. Simulation mode


6.4.1 Start:

Fig 6.3: Start button

By pressing start button dialogue box is open which contains speak push button, which allow

user to speak with in one second time.


6.4.2 Troubleshoot:

Fig 6.4: Troubleshoot button

By pressing trouble shoot button the dialogue box open which consist of following buttons:

1. Rotary motor

2. Gripper open

3. Gripper close

4. Back to main menu


6.4.3 Training:

Fig 6.5: Trainning button

By pressing training button the dialogue box open which consist of following buttons:

1. Enter samples

2. Calculate feature vectors

3. Train ANN



6.4.4 Simulation:

Fig 6.6: Simulation button

By pressing simulation button the dialogue box open which consist of following buttons:


2. Simulate


CHAPTER 7:

Robotic Arm

7.1 INTRODUCTION

A robotic arm is a robotic manipulator, usually programmable, with similar functions to a

human arm. The links of such a manipulator are connected by joints allowing either

rotational motion (such as in an articulated robot) or translational (linear) displacement. The

links of the manipulator can be considered to form a kinematics chain. The business end of

the kinematics chain of the manipulator is called the end effecter and it is analogous to the

human hand. The end effecter can be designed to perform any desired task such as welding,

gripping, spinning etc., depending on the application [14].

7.2 Types of Industrial Robots:

Cartesian Robot /Gantry Robot: Used for pick and place work, application of sealant,

assembly operations, handling machine tools and arc welding. It's a robot whose arm has

three prismatic joints, whose axes are coincident with a Cartesian coordinator.

Cylindrical Robot: Used for assembly operations, handling at machine tools, spot

welding, and handling at die-casting machines. It's a robot whose axes form a cylindrical

coordinate system.

Spherical/Polar Robot: Used for handling at machine tools, spot welding, die-casting,

fettling machines, gas welding and arc welding. It's a robot whose axes form a polar

coordinate system.


SCARA Robot: Used for pick and place work, application of sealant, assembly

operations and handling machine tools. It's a robot which has two parallel rotary joints to

provide compliance in a plane [15].

Articulated Robot: Used for assembly operations, die-casting, fettling machines, gas

welding, arc welding and spray painting. It's a robot whose arm has at least three rotary

joints.

Parallel Robot: One of the uses of parallel Robot is mobile platform handling cockpit

flight simulators. It's a robot whose arms have concurrent prismatic or rotary joints.

Our voice controlled robotic arm is a “fixed sequence cylindrical robot”. It is a robot whose

motion along an axis is limited and the motion between these axes can not be stopped. This

robot performs operation according to preset information that cannot be easily changed [14].

7.3 Mechanical Components of Robotic Arm:

7.3.1 Power Source:

We use a step down transformer that converts 220 AC into 12v DC.

7.3.2 Actuator:

Actuators are like the "muscles" of a robot, the parts which convert stored energy into

movement. In our project actuators are 3 DC motors that spin gears.

7.3.3 Platform:

Our robotic arm is supported by an iron stand that is fixed on the surface of a wooden base.

Robotic arm is made of Aluminum to reduce the weight of arm.

7.3.4 End Effecters:

In robotics, an end effecter is the device at the end of a robotic arm, designed to interact with

the work environment.


In our project an end effecter is a mechanical gripper. It consists of just two fingers,

that can opened and closed to pick up and let go of a range of small objects. We have used a

thread and two small springs. When motor rotates in one direction, thread is made to

wrapped on its shaft, as a result springs become stretched and gripper fingers open.

When motor rotates in other direction thread becomes unwrapped. AS a result springs

compress and gripper closed.

7.4 Characteristics of Robotic Arm:

7.4.1 Joints:

Our robotic arm has two joints called rotary joint and linear joint.

Rotary joint: It can rotate the robotic arm’s end effecter 270 degrees. This joint connects

robotic arm with the iron stand through a gear.

Linear joint: It can rotate the robotic arm’s end effecter along the radius of 270 degrees. This

joint connects the end effecter with the robotic arm through a gear [15].

7.4.2 Payload:

The carrying capacity of a robotic arm is called its payload. Our robotic arm can pick an

object up to 300 grams.

7.4.3 Reach:

The end effecter (gripper) of our robotic arm can move up to 12 inches on a lead screw.


7.4.4 Repeatability:

A measurement may be said to be repeatable when this variation is smaller than some agreed

limit. The repeatability of this robotic arm is ±1.5.

7.4.5 Axis of Rotation:

Our robotic arm can move in cylindrical co-ordinates having a constant z parameter. It can

rotate 270 degrees in circular motion and its end effectors can move 12 inches linearly.


CHAPTER 8:

Electronic Components of Robotic Arm

8.1 Electronic Components

Electrical and electronics components are A.C supply, D.C supply, voltage regulator (7805),

capacitors, resistors, NPN transistors (PN2222), , H-bridge(L298) and diodes (1N4001),25

db parallel cable and 25 db female connector.

8.1.1 A.C Supply and D.C Power Supply

AC Supply of 220 volts from main supply and DC Power Supply of 5 volts and 12 volts is

used in the project. DC Power Supply consists of a transformer, voltage regulator and four

diodes or a bridge [16].

8.1.2 H-Bridge (L298) DUAL FULL-BRIDGE DRIVER

Operating supply voltage up to 46 v

Total dc current up to 4 a

Low saturation voltage

Over temperature protection

Logical”0” input voltage up to 1.5 v

( High noise immunity)


Fig 8.1: Diagram of l298……………………[17]

8.1.3 7805 Voltage Regulator

The 7805 voltage regulators employ built-in current limiting, thermal shutdown, and safe-

operating area protection which make them virtually immune to damage from output

overloads.

Fig 8.2: Regulator circuit……………..[16]


7805 Voltage Regulator Pin out

8.1.4 Actuators

Solenoid actuated valves and a D.C Motor are used as actuators. Output voltage of the first

relay is 12 volt and 5volts applied to its coil. Relays which are connected in relay board have

output voltage of 220 volts and they are energized by first group of relays having output

voltage of 12volts and .The output of second group of relays is used to actuate the Solenoid

actuated valves [16].

Fig 8.3: Dc motor……………….[16]


8.1.5 7404N IC:

Fig 8.4: Diagram of 7404NIC……………..[16]

We have used 7404 IC as in current buffer it is not gate IC which takes the input (1 or 0) and

provides its complement at output. It has inverter as shown in figure above.

8.1.6 DB25 Connector:

The DB25 (originally DE-25) connector is an analog 25-pin plug of the D-Subminiature

connector family (D-Sub or Sub-D). The D-subminiature or D-sub is a common type of

electrical connector used particularly in computers. The part containing pin contacts is called

the male connector or plug, while that containing socket contacts is called the female

connector or socket [18].

The DB25 is also used for parallel port connections, and was originally used to connect printers,

and as a result is sometimes known as a "printer port”. DB25 serial ports on computer generally

have male connectors, while parallel port connectors are


Fig 8.5: Diagram of DB25 connector………………….[18]

DB25 Pin out Assignment (parallel connection)


Table 8.1: Pin configuration of DB25…………….[18]


8.2 Motor Driving Circuit (H-Bridge):

Fig 8.6: Motor driving circuit of our project


Problems and Troubleshooting

Problems

Selection of optimal software.

Selection of mathematical techniques and codes regarding software’s toolbox.

Designing lay-out of physical model.

Design of appropriate electronic hardware.

Unavailability of components while designing hardware.

Selection an optimal designing tool for hardware component’s modeling and

simulation.

Troubleshooting

We worked and discussed on various software’s while surfing on the net and

interacting with our teachers but ended up on deciding to work on MATLAB.

Among several techniques, to convert voice to do the desired action we selected the

features of voice and apply neural network.

We consider several factors like weight and then choose suitable material for

fabrication of our physical model.

Amongst several designing software’s we adopted PROTEUS for designing of

electronic components integration.


Conclusion

This report explains the implementation of voice controlled Robotic Arm.The three phases of

voice recognition,along with some basis of machine learning which were concernd with the

project were described.GUI designed for our project was also explained.Robotic Arm

designed and developed for the project was also explained in detail in the report.

Voice recognition consists of three phases’ front-end processing, feature extraction and

pattern recognition.

Front end processing consists of sampling, recording, retrieving, framing and windowing.

The second phase of voice recognition is feature extraction, which consists of extraction of

features. The third phase is pattern recognition, which is a major concern of machine

learning. The approach of pattern recognition implemented in our project is Artificial Neural

Network. ANN used in our project is feed-forward back propagation.

After voice recognition, the report explains the design and development of Robotic Arm. The

robotic arm designed for our project is a fixed sequence robotic arm with the end effecter of

gripper.

To establish the coordination between robotic arm and computer, and between human and

computer a GUI was designed in our project.

Our implementation of voice recognition is 80% accurate when tested 100 times in some

environment. Accuracy degrades with change in environment due to noise. Accuracy also

degrades with change in microphone and training samples.


Future suggestions

We have implemented the voice-recognition on PC.It can also be implemented in a

convinient microcontroller.The most suitable microcontroller for that purpose is DS PIC33,

since it has builtin ADC and also have three pin port for voice recording and playing.

Our voice recognition system some what depends upon environment.It is because of noise

(voices other than voice commands).Though we have done the noise removal in feature

extraction, in which features like ZCR detects the major voice activity region in a given

sample.But the system can be made less dependent on noise by implementing of noise

removal algorithms at the front end processing.

Moreover it has enough processing power for front end processing and pattern recognition.

We are also intrested in implementing our voice recognition system for the voice controlled

wheel chair. It will require a battery source along with the implementation of voice

recognition on the microcontroller.


Appendix A

MATLAB CODE FOR FEATURE EXTRACTION CODE OF SHORT TIME ENERGY:%variable E contain the value of short time energyFunction E = ShortTimeEnergy(signal, windowLength,step);%normalizing the vector ‘signal’ contains the samples of voice commandsignal = signal / max(max(signal));curPos = 1; %used to start computation on voice samples from the index 1L = length(signal);numOfFrames = floor((L-windowLength)/step) + 1;H = hamming(windowLength);%calculating E per frameE = zeros(numOfFrames,1);for (i=1:numOfFrames) window = (signal(curPos:curPos+windowLength-1)); E(i) = (1/(windowLength)) * sum(abs(window.^2)); curPos = curPos + step;end

CODE OF SPECTRAL CENTRIOD%variable C contain the value of spectral centriodfunction C = SpectralCentroid(signal,windowLength, step, fs)%normalizing…. the vector ‘signal’ contains the samples of voice command signal = signal / max(abs(signal));curPos = 1; %used to start computation on voice samples from the index 1L = length(signal);numOfFrames = floor((L-windowLength)/step) + 1;H = hamming(windowLength);m = ((fs/(2*windowLength))*[1:windowLength])';%calculating C per frameC = zeros(numOfFrames,1);for (i=1:numOfFrames) window = H.*(signal(curPos:curPos+windowLength-1)); FFT = (abs(fft(window,2*windowLength))); FFT = FFT(1:windowLength); FFT = FFT / max(FFT); C(i) = sum(m.*FFT)/sum(FFT); if (sum(window.^2)<0.010) C(i) = 0.0; end curPos = curPos + step;endC = C / (fs/2);) C(i) = 0.0; end curPos = curPos + step;end


CODE OF SPECTRAL FLUX%variable F contain the value of spectral fluxfunction F = SpectralFlux(signal,windowLength, step, fs)%normalizing…. the vector ‘signal’ contains the samples of voice command signal = signal / max(abs(signal));curPos = 1; %used to start computation on voice samples from the index 1 L = length(signal);numOfFrames = floor((L-windowLength)/step) + 1;H = hamming(windowLength);m = [0:windowLength-1]';%calculating F per frameF = zeros(numOfFrames,1);for (i=1:numOfFrames) window = H.*(signal(curPos:curPos+windowLength-1)); FFT = (abs(fft(window,2*windowLength))); FFT = FFT(1:windowLength); FFT = FFT / max(FFT); if (i>1) F(i) = sum((FFT-FFTprev).^2); else F(i) = 0; end curPos = curPos + step; FFTprev = FFT;end

CODE OF SPECTRAL ENTROPY:%variable En contain the value of spectral entropyfunction En = SpectralEntropy(signal,windowLength,windowStep, fftLength, numOfBins);%normalizing…. the vector ‘signal’ contains the samples of voice command signal = signal / max(abs(signal));curPos = 1; %used to start computation on voice samples from the index 1L = length(signal);numOfFrames = floor((L-windowLength)/windowStep) + 1;H = hamming(windowLength);%calculating En per frameEn = zeros(numOfFrames,1);h_step = fftLength / numOfBins;for (i=1:numOfFrames) window = (H.*signal(curPos:curPos+windowLength-1)); fftTemp = abs(fft(window,2*fftLength)); fftTemp = fftTemp(1:fftLength); S = sum(fftTemp); for (j=1:numOfBins) x(j) = sum(fftTemp((j-1)*h_step + 1: j*h_step)) / S; end En(i) = -sum(x.*log2(x)); curPos = curPos + windowStep;end


CODE OF SPECTRAL ROLL OFF%variable mC contain the value of spectral Roll offfunction mC = SpectralRollOff(signal,windowLength, step, c, fs)%normalizing…. the vector ‘signal’ contains the samples of voice commandsignal = signal / max(abs(signal));curPos = 1; %used to start computation on voice samples from the index 1L = length(signal);numOfFrames = (L-windowLength)/step + 1;H = hamming(windowLength);m = [0:windowLength-1]';%calculating mC per framefor (i=1:numOfFrames) window = (signal(curPos:curPos+windowLength-1)); FFT = (abs(fft(window,512))); FFT = FFT(1:255); totalEnergy = sum(FFT); curEnergy = 0.0; countFFT = 1; while ((curEnergy<=c*totalEnergy) && (countFFT<=255)) curEnergy = curEnergy + FFT(countFFT); countFFT = countFFT + 1; end mC(i) = ((countFFT-1))/(fs/2); curPos = curPos + step;end

Appendix B

MATLAB CODE FOR COMPUTING STATISTICS:%FF vector contain the statistical value of the features of the voice commandfunction FF = computeAllStatistics(fileName, win, step)%win is the window size and step is the frame size[x, fs] = wavread(fileName);E = ShortTimeEnergy(x, win*fs, step*fs);Z = zcr(x, win*fs, step*fs, fs);R = SpectralRollOff(x, win*fs, step*fs, 0.80, fs);C = SpectralCentroid(x, win*fs, step*fs, fs);F = SpectralFlux(x, win*fs, step*fs, fs); FF(1) = statistic(EE, 1, length(EE), 'std');FF(2) = statistic(R, 1, length(R), 'std');FF(3) = statistic(C, 1, length(C), 'std');FF(4) = statistic(F, 1, length(F), 'std');FF(5) = statistic(E, 1, length(E), 'stdbymean');


Appendix C

MATLAB CODE FOR NEURAL NETWORK:

CODE FOR TRAINING THE ANN:%net1 is the neural network which is used for pattern recognition%fv is the feature vector of trainings samplesnet1=newff(fv,t,20,{},'traingd');%setting different parameters of net1net.trainParam.show = 50;net.trainParam.lr = 0.01;net.trainParam.epochs = 10000;net.trainParam.goal = 0.01;%training the net1 fv is the feature vector of trainings samples[net1,tr]=train(net1,fv,t);

CODE FOR SIMULATING THE ANNclcwin=0.15;%windows sizestep=0.1;%frame size for i = 1:1 file = sprintf('%s%d.wav','c',i); input('You have 1 seconds to say your name. Press enter when ready to record--> '); y = wavrecord(44100,44100); sound(y,44100); wavwrite(y,44100,file); end load net1 %loading net1 neural network object which was trained for the pattern recognition%file is the string containinfg the name of the file in which given voice %command is saved


file = sprintf('%s%u.wav','c',1);%ff1 is the feature vector containing the feature values of voice command ff1=computeAllStatistics(file, win, step); ff1=ff1';%simulating the net1 for the given voice command and the result is save in %vector aa=sim(net1,ff1);% varable b contain the result from ANN when implemented on the given %voice commandb=a(1,1);bif(b>1)%function call for movement of robotic arm

rotaryfornechay endif(b<1)%function call for movement of robotic arm

rotaryforuper end

CODE FOR MOVEMENT OF ROBOTIC ARM:%This is the code used for the parallel interfacing%dio is the object of the parallel port dio = digitalio('parallel','LPT1');%hwline are the hardware pins of the port which are added to %the object diohwlines = addline(dio,0:7,'out');%bvdata is the logical 1 and 0 ouput to the portbvdata = logical([0 1 0 0 0 0 0 0]);putvalue(dio,bvdata);pause(4);dio = digitalio('parallel','LPT1');hwlines = addline(dio,0:7,'out');bvdata = logical([0 0 0 0 0 0 0 0]);putvalue(dio,bvdata);


References

[1] Pattern Recognition. Bishop, C “Neural Networks for Pattern Recognition”3rd

Edition,1996

[2] Prasad D Polur’, Ruobing Zhou’, Jun Yang’, Fedra Adnani’, Rosalyn S. Hobsod

“SPEECH RECOGNITION USING ARTIFICIAL NEURAL”. 2001 Proceedings of the 23rd

Annual EMBS International Conference, October 25-28, Istanbul, Turkey..

[3] Roziati Zainuddin, Othman O. Khalifa.” .Neural Networks Used for Speech Recognition”.

NINETEENTH NATIONAL RADIO SCIENCE CONFERENCE, ALEXANDRIA, March,

119-21, 2002.

[4] LAWRENCE Rabinar, Biing-Hwang Jaung “Fundamentals of speech recognition”.

[5] Harmonic Spectral Centroid. McAdams, S. 1999. Perspectives on the contribution of

timbre to musical structure. Computer Music Journal. 23(3):85-102

[6] Neural Network: Eric Davalo and Patrick Naim “Neural Networks “3rd Edition,1989

[7] Feed Forward Neural Network. http://en.wikipedia.org/wiki/ /Feed forward neural

network

[8] Neural Network :Aleksander, I. and Morton “An introduction to neural computing.” 2nd

edition, 1992.

[9] Neural Network.http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html

[10] Pattern Recognition. Bishop, C “Neural Networks for Pattern Recognition”3rd

Edition,1996


[11] Neural network: Howard Demuth, Mark Beale “Neural Network Toolbox”4th Edition,

July 2002

[12] Neural Network: Hagan,M.T. and H.B. Demuth, “Neural Networks for

Control,”

Proceedings of the 1999 American Control Conference, San Diego, CA,

1999, pp.

1642-1656.

[13] GUI: http:// en.wikipedia.org/wiki/Graphical_user_interface

[14] Saeed b Nikku. “Introduction to robotics”, 2nd Edition, 2001

[15] Robotic Arm .http://en.wikipedia.org/wiki/Robotic_arm.

[16] B.L.Theraja.”Electrical Machines” 8thEdition, 4 Volumes Set by Tony Burns, Stephen...

[17] H Bridge http://en.wikipedia.org/wiki/H_bridge

[18] DB25 connector. http://www.nullmodem.com/DB-25.htm


voice control robotic arm_march02

Documents