Matlab Code for Amplitude Modulation

 Program:

clc;

clear all;

t = 0:0.001:1;

vm = input('Enter the amplitude of message signal = ');

vc = input('Enter the amplitude of carrier signal = ');

fm = input('Enter the message frequency = ');

fc = input('Enter the carrier frequency = ');

m = vm/vc;

sm = vm.*sin(2*pi*fm*t);

subplot(3,1,1);

plot(t,sm);

xlabel('Time ---->');

ylabel('Amplitude ---->');

title('Message Signal');

grid on;

sc = vc.*sin(2*pi*fc*t);

subplot(3,1,2);

plot(t,sc);

xlabel('Time ---->');

ylabel('Amplitude ---->');

title('Carrier Signal');

grid on;

y = (vm+m*vm.*sin(2*pi*fm*t)).*sin(2*pi*fc*t);

subplot(3,1,3);

plot(t,y);

xlabel('Time ---->');

ylabel('Amplitude ---->');

title('AM Signal');

grid on;

Output:

Enter the amplitude of message signal = 5

Enter the amplitude of carrier signal = 10

Enter the message frequency = 10

Enter the carrier frequency = 100

Output Waveforms:

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Matlab Code for Phase Modulation

 Program:

clc;

clear all;

t = 0:0.001:1;

vm = input('Enter the amplitude of message signal = ');

vc = input('Enter the amplitude of carrier signal = ');

fm = input('Enter the message frequency = ');

fc = input('Enter the carrier frequency = ');

m = input('Enter modulation index = ');

sm = vm*sin(2*pi*fm*t);

subplot(3,1,1);

plot(t,sm);

xlabel('Time ---->');

ylabel('Amplitude ---->');

title('Message Signal');

grid on;

sc = vc*sin(2*pi*fc*t);

subplot(3,1,2);

plot(t,sc);

xlabel('Time ---->');

ylabel('Amplitude ---->');

title('Carrier Signal');

grid on;

y = vc*sin(2*pi*fc*t+m.*sin(2*pi*fm*t));

subplot(3,1,3);

plot(t,y);

xlabel('Time ---->');

ylabel('Amplitude ---->');

title('PM Wave');

grid on;

Output:

Enter the amplitude of message signal = 5

Enter the amplitude of carrier signal = 5

Enter the message frequency = 10

Enter the carrier frequency = 100

Enter modulation index = 4

Output:

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Matlab Code for Pulse Code Modulation

 Program:

clc;

clear all;

t = 0:0.0005:20;

partition = -1:0.1:1;

codebook = -1:0.1:1.1;

x = sin(t);

[index,quants] = quantiz(x,partition,codebook);

subplot(3,1,1);

plot(t,x);

title('Message Signal');

xlabel('Time(s) ---->')

ylabel('Amplitude(V) ---->')

subplot(3,1,2);

plot(t,quants);

title('Quantized Signal');

xlabel('Time(s) ---->')

ylabel('Amplitude(V) ---->')

y = uencode(quants,3);

subplot(3,1,3);

plot(t,y);

title('PCM Signal');

xlabel('Time(s) ---->');

ylabel('Amplitude(V) ---->')

Output: 

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Matlab Project Topic List for Final Year Submission

 RECONSTRUCTION OF UNDERWATER IMAGE BY BISPECTRUM

DESIGN AND ANALYSIS OF BIT INTERLEAVED CODED SPACE-TIME MODULATION

CODING SCHEMES APPLIED TO PEAK-TO-AVERAGE POWER RATIO (PAPR) REDUCTION IN OFDM SYSTEMS

DETECTING DOMINANT MOTIONS IN DENSE CROWDS

A REAL-TIME ADAPTIVE LEARNING METHOD FOR DRIVER EYE DETECTION

DROP PARAMETER ESTIMATION FROM UNDERWATER NOISE PRODUCED BY RAINDROP IMPACT

EFFICIENT SPATIAL COVARIANCE ESTIMATION FOR ASYNCHRONOUS CO-CHANNEL INTERFERENCE SUPPRESSION IN MIMO-OFDM SYSTEMS

FAST AND EFFICIENT QOS-GUARANTEED ADAPTIVE TRANSMISSION ALGORITHM IN THE MOBILE WIMAX SYSTEM

DISCOV: A FRAMEWORK FOR DISCOVERING OBJECTS IN VIDEO

FUZZY LOGIC BASED EDGE DETECTION

FUZZY BASED PID CONTROLLER USING MATLAB FOR TRANSPORTATION APPLICATION

APPLICATION OF SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM FOR IMPROVED BLOOD CELL RECOGNITION

DESIGN OF A DISTRIBUTED TRAFFIC MONITORING SYSTEM AND ALGORITHM BASED ON WEBCAMERA

AN IMPROVING MODEL WATERMARKING WITH IRIS BIOMETRIC CODE

AN EFFICIENT HARDWARE ARCHITECTURE FOR MULTIMEDIA ENCRYPTION AND AUTHENTICATION USING THE DISCRETE WAVELET TRANSFORM

A NEW STATISTICAL DETECTOR FOR DWT-BASED ADDITIVE IMAGE WATERMARKING USING THE GAUSS–HERMITE EXPANSION

A HISTOGRAM MODIFICATION FRAMEWORK AND ITS APPLICATION FOR IMAGE CONTRAST ENHANCEMENT

A MULTICODE APPROACH FOR HIGH DATA RATE UWB SYSTEM DESIGN

ULTRA-WIDE-BAND PROPAGATION CHANNELS

PERFORMANCE OF CDMA

OBJECT RECOGNITION USING EUCLIDEAN DISTANCE WITH KNN ALGORITHM

ROBUST IMAGE SEGMENTATION ALGORITHM USING FUZZY

PERSONAL AUTHENTICATION BASED ON IRIS TEXTURE ANALYSIS

A TOKEN-BASED SCHEDULING SCHEME FOR WLANS SUPPORTING VOICEDATA TRAFFIC AND ITS PERFORMANCE ANALYSIS

ADAPTIVE RADIO RESOURCE ALLOCATION FOR DOWNLINK OFDMASDMA SYSTEMS WITH MULTIMEDIA TRAFFIC

COOPERATIVE MIMO-BEAMFORMING FOR MULTIUSER RELAY NETWORKS

DUCHA A NEW DUAL-CHANNEL MAC PROTOCOL FOR MULTIHOP AD HOC NETWORKS

EFFICIENT POWER ALLOCATION FOR CODED OFDM SYSTEMS

MCMAC A PARALLEL RENDEZVOUS MULTI-CHANNEL MAC PROTOCOL

POWER ALLOCATION FOR TWO DIFFERENT TRAFFICS IN LAYERED MIMO SYSTEMS

SEMISOFT HANDOVER GAIN ANALYSIS OVEROFDM-BASED BROADBAND SYSTEMS

SEQUENTIAL DETECTION FOR MULTIUSER MIMO CDMA SYSTEMS WITH SINGLE SPREADING CODE PER USER

A MEDIUM ACCESS CONTROL SCHEME FOR TDD-CDMA CELLULAR NETWORKS WITH TWO-HOP RELAY ARCHITECTURE

A NEW PARAMETER FOR UWB INDOOR CHANNEL PROFILE IDENTIFICATION

VARIANCE-REDUCED PARTIAL PARALLEL INTERFERENCE CANCELLATION FOR MC-CDMA UPLINK SYSTEMS

MEASUREMENT BASED CHANNEL-ADAPTIVE VIDEO STREAMING FOR MOBILE DEVICES OVER MOBILE WIMAX

IEEE 802.16/WIMAX SECURITY

BIOMETRICS SECURITY IN WIMAX

DISTRIBUTED SUPPLY CHAIN MANAGEMENT USING ANT COLONY OPTIMIZATION

A CLOSED-FORM BLIND CFO ESTIMATOR BASED ON FREQUENCY ANALYSIS FOR OFDM SYSTEMS

BANDWIDTH EXCHANGE: AN ENERGY CONSERVING INCENTIVE MECHANISM FOR COOPERATION

EFFICIENT POWER ALLOCATION FOR CODED OFDM SYSTEMS

PERFORMANCE ANALYSIS OF DISTRIBUTED DECISION FUSION USING A CENSORING SCHEME IN WIRELESS SENSOR NETWORKS

NEURAL NETWORK BASED ENERGY EFFICIENT CLUSTERING AND ROUTING IN WIRELESS SENSOR NETWORKS

PERFORMANCE ANALYSIS OF MULTI-CARRIER DS-CDMA WIRELESS COMMUNICATION SYSTEM

SPEECH ENHANCEMENT USING HARMONIC EMPHASIS AND ADAPTIVE COMB FILTERING

A FAULT TOLERANT COMMUNICATION ARCHITECTURE SUPPORTING CRITICAL MONITORING WITH WIRELESS SENSOR NETWORKS

COOPERATIVE SENSING FOR PRIMARY DETECTION IN COGNITIVE RADIO

AUDIO CODING USING A PSYCHOACOUSTIC PRE- AND POST-FILTER

TIME-DOMAIN SIGNAL DETECTION BASED ON SECOND-ORDER STATISTICS FOR MIMO-OFDM SYSTEMS

·      A Hybrid Time Divisioning Scheme for Power Allocation in DMT-Based DSL Systems

·      A New Dual-Channel Mac Protocol for Multihop Ad Hoc Networks

·      A Performance Study of Mobile Handoff Delay in IEEE 802.11-Based Wireless Mesh Networks

·      Adaptive Routing in Dynamic Ad Hoc Networks

·      Analysis of IEEE 802.11e for Delay Sensitive Traffic In Wireless Lans

·         •

·      Backup Path Set Selection in Ad Hoc Wireless Network using Link Expiration Time

·         •

·      Call Admission Control Optimization in Wimax Networks

·         •

·      Code Shift Keying Impulse Modulation for Uwb Communications

·         •

·      Contention-Based Qos Mac Mechanisms for Vbr Voip In Ieee 802.11e

·         •

·      A Medium Access Control Scheme for Tdd-Cdma Cellular Networks With Two-Hop Relay

·      Architecture

·         •

·      A New Parameter for Uwb Indoor Channel Profile Identification

·         •

·      Novel Channel Interference Reduction In Optical Synchronous Fsk-Cdma Network using a

·      Data-Free Reference

·         •

·      Performance Improvement in Wireless Networks using Cross-Layer Arq

·         •

·      Performance of Optical Burst Switched Networks for Grid Applications

·         •

·      Power Allocation and Scheduling for Ultra-Wideband Wireless

·         •

·      Networks

·         •

·      An Efficient Data Extraction Mechanism for Mining Association Rules From Wireless Sensor

·      Networks

·         •

·      An FPGA-Based Architecture for Real Time Image Feature Extraction

·         •

·      An Improving Model Watermarking with Iris Biometric Code

·         •

·      Automatic Recognition of Exudative Maculopathy using Fuzzy Cmeans Clustering and

·      Neural Networks

·         •

·      Reconstruction of Underwater Image by Bispectrum

·         •

·      Hierarchical Contour Matching for Dental X-Ray Radiographs

·         •

·      Robust Image Watermarking Based On Multiband Wavelets and Empirical Mode

·      Decomposition

·         •

·      Image Segmentation using Iterative Watersheding Plus Ridge Detection

·         •

·      Real-Time System for Monitoring Driver Vigilance

·         •

·      Robust Dwt-Svd Domain Image Watermarking:Embedding Data in all Frequencies

·         •

·      Optimized Software Implementation of A Full-Rate IEEE 802.11a Compliant Digital Base band

·      Transmitter on a Digital Signal Processor

·         •

·      Active Noise Cancellation with a Fuzzy Adaptive Filtered-X Algorithm

·         •

·      Design and Analysis of Bit Interleaved Coded Space-Time Modulation

·         •

·      Non-Symmetric Decompanding for Improved Performance of Companded ofdm Systems

·         •

·      Implementation of IEEE 802.11 a Wlan Baseband Processor

·         •

·      Signal Adaptive Subband Decomposition for Adaptive Noise Cancellation

·         •

·      A Performance Study of Mobile Handoff Delay in IEEE 802.11-Based Wireless Mesh Networks

·         •

·      Analysis of IEEE 802.11e for Delay Sensitive Traffic in Wireless Lans

·         •

·      Backup Path Set Selection in Ad Hoc Wireless Network using Link Expiration Time

·         •

·      Performance Improvement in Wireless Networks using Cross-Layer ARQ

·         •

·      Fingerprint Recognition System for FingerCode based

·         •

·      Face Recognition System

·         •

·      Speech Recognition System for isolated words

·         •

·      Recursive Gabor Filtering for 1D and 2D signals

·         •

·      Photorefractive Simulator System for calculating the vector space-charge field induced by

      the Photorefractive effect.

DCT-based Watermarking for grayscale images

•

Wavelet-based Watermarking grayscale images

•

Iris Recognition System

•

Speaker Recognition System

•

Adaptive Equalizer

•

Advance Digital Signal Processing Graphics Compression

•

Audio Signal Processing

•

Channel Tracking using using Kalman Filter

•

Deriving Intrinsic Images from Image Sequences

•

Digit Recognition

•

Effect of equalization on a QAM based modulation scheme

•

Gesture Recognition

•

High Performance Implementation of MPEG-1 Layer 3 Audio

•

Human Hearing Threshold from EEG Signals using Neural Network

•

Image and Sound Compression using Wavelet

•

JPEG, motion compensation, MPEG-1, MPEG-2

•

License plate Recognition / Number Plate recognition

•

Power Spectrum Estimation Periodogram & Modified Periodogram

•

Quadrature Amplitude Modulation (M-QAM Implementation)

•

Recognition of Brain Tissues & Coloring of Magnetic Resonance Images

•

Coding of stereoscopic images - wavelet domain techniques

•

Compression, Predictive, Transform, Quantization

•

Car Thefts Detection

•

Content-Based Image Retrieval

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C program to display the following pattern of numbers

 1

1    2

1    2    3

1    2    3    4

1    2    3    4    5

1    2    3    4    5    6

1    2    3    4    5    6    7

Program code

#include <stdio.h>

#include <conio.h>

void main()

{

int inner, outer, rows=1;

clrscr();

printf(“How many rows do you want to see? \n”);

scanf(“%d”, &rows);

printf(“The pattern is displayed below.\n”);

for (outer=1;outer<=rows;outer++)

{

for (inner=1;inner<=outer;inner++)

printf(“%d\t”,inner);

printf(“\n\n”);

}

getch();

}

Example of output

How many rows do you want to see?

7

The pattern is displayed below.

1

1    2

1    2    3

1    2    3    4

1    2    3    4    5

1    2    3    4    5    6

1    2    3    4    5    6    7

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C program to display the following pattern

 *

*    *

*    *    *

*    *    *    *

*    *    *    *    *

*    *    *    *    *    *

*    *    *    *    *    *    *

Program code

#include <stdio.h>

#include <conio.h>

void main()

{

int inner, outer, rows=1;

clrscr();

printf(“How many rows do you want to see? \n”);

scanf(“%d”, &rows);

printf(“The pattern is displayed below.\n”);

for (outer=1;outer<=rows;outer++)

{

for (inner=1;inner<=outer;inner++)

printf(“*\t”);

printf(“\n\n”);

}

getch();

}

Example of output

How many rows do you want to see?

7

The pattern is displayed below.

*

*    *

*    *    *

*    *    *    *

*    *    *    *    *

*    *    *    *    *    *

*    *    *    *    *    *    *

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C program to check whether the given number is odd or even

 Program code

#include <stdio.h>

#include <conio.h>

void main()

{

int a;

clrscr();

printf("Please input a number to find whether it is odd or even.\n");

scanf("%d",&a);

if ((a%2)==0)

printf("%d is even.\n",a);

else

printf("%d is odd.\n",a);

getch();

}

Example of output

Please input a number to find whether it is odd or even.

700

700 is even.

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C program to find whether the given number is prime or composite

 Program code

#include <stdio.h>

#include <conio.h>

void main()

{

int question=0, i=2;

clrscr();

printf(“Please input a positive integer to find out whether it is a prime number or composite number.\n”);

scanf(“%d”,&question);

if (question==1)

{

printf(“%d is neither prime nor composite.\n”, question);

goto stop;

}

if (question==2)

{

printf(“%d is a prime number.\n%d is the only even prime number.\n”,question,question);

goto stop;

}

do

{

if (question%i==0)

{

printf(“%d is a composite number.\n”,question);

goto stop;

}

i++;

}

while(i<question);

printf(“%d is a prime number.\n”,question);

stop:

getch();

}

Example of output

Please input a positive integer to find out whether it is a prime number or composite number.

3

3 is a prime number

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C program to display the reverse of the given number

 Program code

#include <stdio.h>

#include <conio.h>

void main()

{

long int temp=0, number=0, reverse=0;

clrscr();

printf(“Please input a whole number to get its reverse.\n”);

scanf(“%ld”,&number);

for (temp=number;temp>0;temp=temp/10)

reverse=(reverse*10)+(temp%10);

printf(“The reverse of %ld is %ld\n”, number, reverse);

getch();

}

Example of output

Please input a whole number to get its reverse.

6754

The reverse of 6754 is 4576

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C program to swap two numbers

 Program code

#include <stdio.h>

#include <conio.h>

void main()

{

int a=0, b=0, temp=0;

clrscr();

printf(“Please input a number to store in a.\n”);

scanf(“%d”,&a);

printf(“Please input a number to store in b.\n”);

scanf(“%d”,&b);

printf(“Before swapping.\na=%d\nb=%d\n”, a,b);

/*displays the values of a and b before swapping.*/

/* swapping starts */

temp=a;

a=b;

b=temp;

/*swapping ends*/

printf(“After swapping.\na=%d\nb=%d\n”, a,b);

/*displays the values of a and b after swapping.*/

getch();

}

Example of output

Please input a number to store in a.

98

Please input a number to store in b.

34

Before swapping.

a=98

b=34

After swapping.

a=34

b=98

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C program to print a fractional number after rounding it to two decimal places

Program code

#include <stdio.h>

#include <conio.h>

void main()

{

float a;

clrscr();

printf("Please insert a number.\n");

scanf("%f",&a);

printf("The number you have entered is %.2f",a);

getch();

}

Example of output

Please insert a number.

7.7777

The number you have entered is 7.78 

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MATLAB Code for Histogram Equalization

 The process of adjusting intensity values can be done automatically using histogram equalization. Histogram equalization involves transforming the intensity values so that the histogram of the output image approximately matches a specified histogram. By default, the histogram equalization function, histeq, tries to match a flat histogram with 64 bins, but you can specify a different histogram instead.

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Matlab Project On Types of Breast Cancer Detection Using Matlab Source Code

  ABSTRACT

            Cancer is the second cause of death in the world. 8.8 million patients died due to cancer in 2015. Breast cancer is the leading cause of death among women. Several types of research have been done on early detection of breast cancer to start treatment and increase the chance of survival. Most of the studies concentrated on mammogram images. However, mammogram images sometimes have a risk of false detection that may endanger the patient’s health. It is vital to find alternative methods which are easier to implement and work with different data sets, cheaper and safer, that can produce a more reliable prediction. This project we proposes a model of Machine Learning (ML) algorithms including Support Vector Machine (SVM). Here it also detect types of Breast Cancer in different categories like NORM=Normal, CALC=Calcification, CIRC=Circumscribed Masses, SPIC=Speculated Masses, MISC=ill-defined Masses, ARCH=Architectural Distortion, ASYM=Asymmetry.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr.Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Matlab Project for DCT Based Steganography Hide Text in Image Source Code

 ABSTRACT

            Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding. Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this project the secret message is encrypted first then DCT technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain.  DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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DES Encryption Decryption Using Matlab Project Source Code - Cryptography Using DES Algorithm

  ABSTRACT

            The data encryption standard is also known as DES. DES has been the most extensively used encryption algorithm standard in recent times. Encryption and decryption comprise of cryptography. Cryptography terminology is used in the data encryption standard along with standard algorithm to hide the original text. DES applies the cipher algorithm to each data block. Data encryption is being used to hide the true meaning of data so that it is very hard to attack or crack. This project deals with the simulation and synthesis results of implemented DES algorithm. Analysis of implementation is shown in step by step process. A test case is analyzed step by step to check the results at each step of the algorithm.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Brain Tumor Detection using Convolutional Neural Network (CNN)

ABSTRACT

           Brain tumor identification is really challenging task in early stages of life. But now it became advanced with various machine learning algorithms. Now a day’s issue of brain tumor automatic identification is of great interest. In Order to detect the brain tumor of a patient we consider the data of patients like MRI images of a patient’s brain. Here our problem is to identify whether tumor is present in patients brain or not. It is very important to detect the tumors at starting level for a healthy life of a patient. There are many literatures on detecting these kinds of brain tumors and improving the detection accuracies. In this project, we Estimate the brain tumor severity using Convolutional Neural Network algorithm which gives us accurate results.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Matlab Code for Prostate Cancer Detection using Image Processing

ABSTRACT

          This project gives an overview of the method of detecting prostate cancer by associating Region of interest segmentation method with Support Vector Machine. Prostate cancer is commonly prevalent carcinoma detected in most of the male population. A diagnosis of prostate cancer was complicated due to unclear symptoms and involves many procedures. One of these procedures involves the study of prostate tissue biopsy to find cancer affected region. However, no boundary specified region was considered for further studies. Recent developmental techniques in the medical imaging field, especially in SVM, have paved the way for prostate carcinoma detection. The MRI image of the prostate gland is pre-processed to reduce noise effects and Region of interest is obtained with the svm and segmentation is done. The core idea of this project is to assume that every region of prostate tissue could be related to malignant or unnatural tissues.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Grape Leaf Disease Detection using Image Processing Matlab Source code

ABSTRACT

             Identification of the grape leaf disease is the main goal to prevent the losses and quality of agricultural product. In India grape fruit crop is widely grown. So disease detection and classification of grape leaf is very critical for sustainable agriculture. It’s not possible to farmer, to monitor continuously the grape disease manually. It requires the excessive processing time, tremendous amount of work, and some expertise in the grape leaf diseases. To detect and classify the grape disease we need fast automatic process so we use SVM classifier technique. This project presents mainly five stages, viz image acquisition, pre-processing, segmentation GLCM feature extraction and SVM classification. This project is proposed to benefit in the detection and classification of grape leaf disease using support vector machine (SVM) classifier.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Early Stage Brain Tumor Detection using Image Processing Matlab Project Code

ABSTRACT

            A tumor is a mass of tissues that is formed by an accumulation of abnormal cells. Normally, the cells in our body grow, age, die, and are replaced by new cells but the cancer and other tumors damage this cycle. The tumor cells do grow, even if the body does not want them and unlike old cells, these cells do not die easily causing tumor or cancer. The brain is the interior most part of the central nervous system and is an intracranial solid neoplasm. Tumors are created by an abnormal and uncontrollable cell division in the brain. The axial view of the brain image scan has been used. The study of brain tumor is important as it is occurring in many people. In this project, an image segmentation method was proposed for the identification or detection of tumor from the brain. The methodology consists of the following steps: pre-processing by using grey-level, sharpening and median filters; segmentation of the image was performed by thresholding and also by applying the watershed segmentation. Finally the tumor region was obtained with its area and stage of cancer.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Haze Removal using Matlab Project with Source Code

ABSTRACT

           Haze causes problems in various computer vision and image processing based applications as it diminishes the scene's visibility. The air light and attenuation are two main phenomena responsible for haze formation .The air light enhance the whiteness in the scene and contrast get reduced by attenuation. Haze removal techniques helps in recovering the contrast and color of the scene. These techniques have found many applications in the area of image processing such as consumer electronics, object detection, outdoor surveillance etc. 

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Matlab code for Image Fusion using Wavelet Transform

ABSTRACT

            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from multiple images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This paper mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Image Fusion with Wavelet Transform on preserving the feature information for the test images.

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Bone Fracture Detection using Neural Network Matlab Project with source Code

ABSTRACT

            Analysis of medical images plays a very important role in clinical decision making. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatize this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this project, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from Gray-level occurrence matrix. In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones.

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Lung Cancer Detection using Neural Network Matlab Project Source Code

ABSTRACT

             Lung cancer prevalence is one of the highest of cancers. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified. Neural Network method is implemented here to detection of lung cancer of lung samples.

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Signature Recognition using neural network Matlab Project Code

ABSTRACT

              The fact that the signature is widely used as a means of personal identification tool for humans require that the need for an automatic verification system. Verification can be performed either Offline or Online based on the application. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However human signatures can be handled as an image and recognised using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this project, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on parameters extracted from the signature using various image processing techniques. This technique is suitable for various applications such as bank transactions, passports with good authentication results etc.

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Steganography using AES Algorithm Matlab Project Source Code

ABSTRACT

            In today’s world, confidential information is growing due to various areas of works. Internet is the main area of transmission of digital data, so security must be more considered. Two common ways of providing security is cryptography and steganography. Employing a hybrid of cryptography and steganography enhances the security of data. This project employs LSB (Least significant Bit) as the steganography algorithm and AES algorithms as cryptographic algorithms to encrypt a message that should be hidden in a cover image. The results are represented in the form of execution time, PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error). The experimental results reveal that the algorithms achieve appropriate quality of stego image. They can be used as cryptographic algorithms to encrypt a message before applying steganography algorithms.

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Lung Cancer Detection using Neural Network Matlab Project With Source Code

ABSTRACT

             Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified. Neural Network method is implemented here to detection of lung cancer of lung samples.

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Video Steganography Hiding Image in Video Matlab Project Source Code

ABSTRACT

          Steganography has become great area of interest for researchers as need for secure transaction of information is increasing day by day. Information may be text, image, audio or video. Steganography is a technique in which required information is hided in any other information such that the second information does not change significantly and it appears the same as original. This paper presents a novel approach of hiding image in a video. The proposed algorithm is replacing one LSB of each pixel in video frames. It becomes very difficult for intruder to guess that an image is hidden in the video as individual frames are very difficult to analyze in a video running at 30 frames per second. The process of analysis has been made more difficult by hiding each row of image pixels in multiple frames of the video, so intruder cannot even try to unhide image until he get full video.

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Rice Leaf Disease Detection using Image Processing Matlab Project with Source Code

ABSTRACT

            Agriculture is the main backbone for most of the developing/developed countries; agriculture production itself is the main feed for ever growing populations and it is the major source of income for the rural people/farmers especially in India. In India farmers are called “the backbone of India”. The main aim of the proposed system is to detect and classify the diseases in Rice leafs. Rice Diseases Classification comprises of two steps: first one is Detection, Extraction and Segmentation of diseases. Secondly, Feature extraction, Classification level of disease by using Support Vector Machine (SVM) classifiers respectively. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging.

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Diabetic Retinopathy Detection using Convolutional Neural Network CNN Matlab Project Source Code

ABSTRACT

            Diabetic Retinopathy (DR) is one of the major causes of blindness in the western world. Increasing life expectancy, indulgent lifestyles and other contributing factors mean the number of people with diabetes is projected to continue rising. Regular screening of diabetic patients for DR has been shown to be a cost-effective and important aspect of their care. The accuracy and timing of this care is of significant importance to both the cost and effectiveness of treatment. If detected early enough, effective treatment of DR is available; making this a vital process. The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this project , we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify Diabetic Retinopathy.

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Matlab Project on Lung Cancer Detection using Neural Network Full Source Code

ABSTRACT

             Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified. Neural Network method is implemented here to detection of lung cancer of lung samples.

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Mr. Roshan P. Helonde

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Matlab Code for Signature Verification on Bank Cheque using Image processing

ABSTRACT

            The area of Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine produced by the claimed individual, or a forgery produced by an impostor. This has demonstrated to be a challenging task, in particular in the offline static scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application is to learn feature representations from signature images. In this project, we present how the problem has been handled in the past few decades, analyse the recent advancements in the field, and the potential directions for future research.

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Matlab Code for Fake Biometric Recognition using Image Processing

ABSTRACT

              Achieving high security in varied areas, biometric system has become common analysis space over past decades. Biometric system provides machine-controlled personal identification supported distinctive features of an individual. Biometric system depends on distinguishing every individual on the premise of their physiological options Face, Finger Print, Palm Print. Security will primarily be achieved by three factors: password or pin, sensible token or access card, biometric technology. Out of those three ways, biometric system is best as a result of user ought not to remember (password or pin) or keep something (smart token or access card) for identification or verification. In this project present a novel approach Biometric Recognition Using Face, Palm, Retina and Signature.

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Text Image Watermarking using Image Processing Matlab Project code

ABSTRACT

            Multimedia security is a major issue. Images, video, audio, text files are losing their credibility day by day as they can be distorted or manipulated by using several tools. Ensuring the authenticity and integrity of digital media is a major issue. The manipulation made by forgery tools are so smoothly done that we don’t even suspect that forgery may be involved in digital content. Multimedia data is facing several issues related to illegal distribution, duplication and manipulation of information conveyed by them. The digital watermarking technique plays an important role in protecting digital content. In this project, On the basis of their operating principles different watermarking techniques are categorised. Attacks, applications and requirements related to watermarking techniques are also discussed. Different watermarking techniques proposed by researchers for protecting copyrights of digital media are presented which are based on spatial and frequency domain. Frequency domain are getting much more attention due to use of wavelets which

have high degree of resemblance to human visual system. In digital watermarking, secret information is embedded with original data for maintaining ownership rights of the digital content. Spatial domain watermarking techniques work over pixel characteristics and frequency domain watermarks concerned about different transformations that can be used with digital content. Imperceptibility, robustness, security, complexity and capacity are some requirements of the digital watermarking which completely depends on the algorithm used for watermarking.

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Matlab Code for Marathi Character Recognition using neural Network

ABSTRACT

          Marathi character can be converted in to the digital information using Marathi character Recognition, which is the ability of a computer to receive and interpret handwritten input from documents. Marathi Characters are more complex for recognition due to presence of header line, conjunct characters and similarity in shapes of multiple characters. For Marathi Character recognition using neural network various approaches has been proposed. In general the process involves phases as: Scanning, Pre-processing, Feature Extraction and Recognition. Preprocessing includes noise reduction and normalisation Feature extraction includes extracting some useful information out of the pre-processed image in the form of a feature vector. Artificial neural network is used for classification. 

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Matlab Code for Maize Plant Disease Detection using Image Processing

ABSTRACT

            Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the Support Vector Machine (SVM) in order to model a network for image recognition and classification of these diseases. SVM network that recognised and classified images of the maize leaf diseases that were collected by acquisition process. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise three different types of maize leaf diseases out of healthy leaves.

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