MATLAB Projects List, Matlab IEEE Projects Pune

MATLAB Projects List:

1. Audio Forensic marking technology using DWT-SVD
2. Non-blind Watermarking scheme for color images
in RGB space using DWT-SVD
3. Efficient Compression of Encrypted Grayscale Images using Slepian-Wolf coding.
4. Removal of High Density Salt and Pepper Noise
Through Modified Decision Based Unsymmetric
Trimmed Median Filter
5. Adaptive MMSE Rake Receiver for WCDMA
6. DWDM Effects of Single Model Optical Fiber in Radio over Fiber System
7. Convolutional Codes in Two-Way Relay Networks with Physical-Layer Network Coding
8. Cooperative and Constrained MIMO Communications in Wireless Ad Hoc/Sensor Networks
9. Efficiency of the LDPC Codes in the Reduction
of PAPR in Comparison to Turbo Codes and
Concatenated Turbo-Reed Solomon Codes in a
MIMO-OFDM System
10. New low complexity DCT based video compression method
11. Enhancement of Color Images by Scaling the Coefficients DCT
12. Performance Assessment of OFDM-based and
OWDM-based Radio-over-Fiber Systems in the
Presence of Phase Noise
13. BER of Adaptive Arrays in AWGN Channel
14. Bi-2DPCA: A Fast Face Coding Method for
Recognition
15. Neural Network-Based Face Detection
16. Novel Speech Signal Processing Algorithms for
High-Accuracy Classification of Parkinson’s Disease
17. On Convolution Model for Ultrasound Echo Signal Processing (*provided samples are available)
18. Automatic Segmentation of Digital Images Applied in Cardiac Medical Images
19. A Novel Haar Wavelet-Based BPSK OFDM System
Robust to Spectral Null Channels and with Reduced
PAPR
20. Diversity Gain for MIMO Neyman–Pearson
Signal Detection

MATLAB Project guidance: AIRPORT SECURITY USING RFID AND IRIS RECOGNITION

CONTACT: 9764259156

AIRPORT SECURITY USING RFID AND IRIS RECOGNITION

INTRODUCTION:

In today’s information age it is not difficult to collect data about an individual and use that information to exercise control over the individual. Individuals generally do not want others to have personal information about them unless they decide to reveal it. With the rapid development of technology, it is more difficult to maintain the levels of privacy citizens knew in the past. In this context, data security has become an inevitable feature. Conventional methods of identification based on possession of ID cards or exclusive knowledge like Social security number or a password are not altogether reliable. ID cards can be almost lost, forged or misplaced: passwords can be forgotten. Such that an unauthorized user may be able to break into an account with little effort. So it is need to ensure denial of access to classified data by unauthorized persons. Biometric technology has now become a viable alternative to traditional identification systems because of its tremendous accuracy and speed. Biometric system automatically verifies or recognizes the identity of a living person based on physiological or behavioral characteristics. Since the persons to be identified should be physically present at the point of identification, biometric techniques gives high security for the sensitive  information stored in mainframes or to avoid fraudulent use of ATMs.

   Biometric products are used for automated recognition of individuals based on their behavioral and biological characteristics. Iris recognition biometric products recognize individuals based on their iris images more specifically the distinctive patterns in the irises created by various structures, such as crypts, furrows, frills, ridges, ligaments, freckles, coronas, and collarettes. Other common biometric products use fingerprint features, facial images, hand geometry, characteristics of handwritten signatures, and voice recordings to recognize individuals. Fingerprint recognition, known for its low error rates, typically requires an individual to place their finger on a sensor to be recognized. The error rates for facial recognition technologies are typically higher than for fingerprint technologies, but facial recognition is often preferred because its operation is non-contact. Iris recognition combines the advantages of fingerprinting (low error rates) and facial recognition (non-contact operation) and as such may prove valuable for many criminal justice and border control applications.

 This project basically aims at designing an iris matching software system. Firstly, image preprocessing is performed followed by extracting the iris portion of the eye image which is called Localization. The extracted iris part is then normalized using daugman’s rubbersheet model, and Iris Code is constructed. Finally two Iris Codes are compared to find Hamming Distance, which is fractional measure of the dissimilarity. Experimental image results show that unique codes can be generated for every eye image and Hamming Distance between any two different iris code has maximum value.

With the increasing demand of enhanced security in our daily lives, reliable personal identification through biometrics is currently an active topic in the literature of pattern recognition. Nowadays many automatic security systems based on iris recognition have been deployed worldwide for border control, restricted access Iris recognition is based on the most mathematically unique biometric - the iris of the eye. The human iris is absolutely unique, even between twins or an individual's right and left eyes. The iris itself is stable throughout a person's life (approximately from the age of one). The physical characteristics of the iris do not change with age. One key tool in this area is the use of biometrics. Humans have always identified each other by recognizing faces, voices or some other physical characteristic. Personal recognition or identification by a witness is also entrenched in our law and commercial structures. Now the use of biometric technologies is providing a means to positively identify or authenticate large numbers of people without having to primarily rely on human to human identification.

Innovations

Conventional security systems are based on face recognition, RF-ID, Finger Print detection which are not 100% foolproof. Facial features can be modified, RF-ID cards can be stolen and misused, cuts and burns on the finger will cause failure of finger print identification. Iris detection has much better accuracy and is a step ahead of the above mentioned systems. The Iris Code generated is unique for every individual even for the identical twins.

 Accuracy is the parameter which differentiates Iris recognition technology from rest of the Biometric techniques such as Face, Voice, Fingerprint, Retina, Signature etc. Iris recognition is more accurate, stable and scalable. Hence iris recognition is popularly used in many applications. 

                           

Here we are developing 2 level airport security ,In this system the 1^st technique is the RFID and the 2^nd technique is IRIS recognition.

RFID: we are using a passive RFID system .In this case the user has to show the card to the reader. The RFID reader reads the card no and sends it to the µC via wiegand protocol .The µC will then go in its database and check for the RFID of the user .If match occurs then the user is asked to go for the next verification stage of  IRIS.

SOFTWARE OVERVIEW

 PROGRAMMING USING  ‘C’

 PROGRAMMING AT BASE STATION USING MATLAB (7.0)

ADVANTAGES

EFFICIENT WAY  FOR SECURITY CHECK

LESS TIME DELAYS

QUICK RESPONSE TIME

 FULLY AUTOMATE SYSTEROBUST SYSTEM, LOW POWER REQUIREMENT

       

 CONTACT: 9764259156

MATLAB DIP IEEE Based Project Guidance in Pune for ME & BE.

CONTACT: 9764259156

Some latest IEEE 2013 , 2014 DIP MATLAB  projects for this month are listed below:

More projects will be added to this list

1. Audio Forensic marking technology using DWT-SVD

2. Non-blind Watermarking scheme for color images

in RGB space using DWT-SVD

3. Efficient Compression of Encrypted Grayscale Images using Slepian-Wolf coding.

4. Removal of High Density Salt and Pepper Noise

Through Modified Decision Based Unsymmetric

Trimmed Median Filter

5. Adaptive MMSE Rake Receiver for WCDMA

6. DWDM Effects of Single Model Optical Fiber in Radio over Fiber System

7. Convolutional Codes in Two-Way Relay Networks with Physical-Layer Network Coding

8. Cooperative and Constrained MIMO Communications in Wireless Ad Hoc/Sensor Networks

9. Efficiency of the LDPC Codes in the Reduction

of PAPR in Comparison to Turbo Codes and

Concatenated Turbo-Reed Solomon Codes in a

MIMO-OFDM System

10. New low complexity DCT based video compression method

11. Enhancement of Color Images by Scaling the Coefficients DCT 

12. Performance Assessment of OFDM-based and

OWDM-based Radio-over-Fiber Systems in the

Presence of Phase Noise

13. BER of Adaptive Arrays in AWGN Channel

14. Bi-2DPCA: A Fast Face Coding Method for

Recognition

15. Neural Network-Based Face Detection

16. Novel Speech Signal Processing Algorithms for

High-Accuracy Classification of Parkinson’s Disease

17. On Convolution Model for Ultrasound Echo Signal Processing (*provided samples are available)

18. Automatic Segmentation of Digital Images Applied in Cardiac Medical Images

19. A Novel Haar Wavelet-Based BPSK OFDM System

Robust to Spectral Null Channels and with Reduced

PAPR

20. Diversity Gain for MIMO Neyman–Pearson

Signal Detection

Detail Synopsis for MATLAB Projects are as below:

Novel Speech Signal Processing Algorithms for

High-Accuracy Classification of Parkinson’s Disease

There has been considerable recent research into theconnection between Parkinson’s disease (PD) and speech impairment.Recently, awide range of speech signal processing algorithms(dysphonia measures) aiming to predict PD symptom severity usingspeech signals have been introduced. In this paper, we test howaccurately these novel algorithms can be used to discriminate PDsubjects fromhealthy controls. In total, we compute 132 dysphoniameasures fromsustained vowels. Then,we select four parsimonioussubsets of these dysphonia measures using four feature selectionalgorithms, and map these feature subsets to a binary classification

response using two statistical classifiers: random forests andsupport vector machines.We use an existing database consisting of263 samples from 43 subjects, and demonstrate that these new dysphoniameasures can outperform state-of-the-art results, reachingalmost 99% overall classification accuracy using only ten dysphoniafeatures.We find that some of the recently proposed dysphoniameasures complement existing algorithms in maximizing the abilityof the classifiers to discriminate healthy controls from PD subjects.We see these results as an important step toward noninvasivediagnostic decision support in PD.

NEUROLOGICAL disorders affect people’s lives at an epidemicrate worldwide. Parkinson’s disease (PD) is oneof the most common neurodegenerative disorders with an incidencerate of approximately 20/100 000 and a prevalence rate exceeding 100/100 000. Moreover, these statisticsmight underestimate the problem because PD diagnosis is complicated. Given that age is the single most importantfactor for PD and the fact that the population is growingolder, these figures could further increase in the not too distant future.

Software: MATLAB 2010a