Saturday, August 30, 2014


 1-Computer Aided Detection of Solid Breast Nodules: Performance Evaluation of Support Vector Machine and K- Nearest Neighbor Classifiers

Abstract—Breast Cancer is one of the major health concerns of women all over the world. Computer Aided Detection (CAD) aids radiologists for the early detection of abnormalities in the breast masses. Abnormalities in the breast may be cancerous or non cancerous. This work proposes an effective CAD system that considerably reduces the misclassification rates of these abnormalities. 60 mammogram images were taken and subjected to Segmentation and Feature Extraction techniques. K-means clustering algorithm is employed for segmentation and Fast Fourier Transform has been employed for the extraction of features. The unique set of feature vectors is given to the classification module. The classification of solid masses of breast nodule is done using Supervised Classifiers Support Vector Machine (SVM) and K- Nearest Neighbor (K- NN). The investigation reveals that SVM outperforms K- NN in terms of sensitivity, specificity and accuracy.
Index Terms—Mammogram, Segmentation, K- means clustering, Feature Extraction, Fast Fourier Transform, Support Vector Machine, K- Nearest Neighbor Classifier.

 Textural Features Based Computer Aided Diagnostic System for Mammogram Mass Classification

Abstract— Computer Aided Diagnosis (CAD) could be applied as a solution to reduce the chances of human errors and helps Medical Practioners in the correct classification of Breast Masses. This paper emphasizes an algorithm for the early detection of breast masses. Textural analysis is one of the efficient methods for the early detection of abnormalities. The paper enumerates an efficient Discrete Wavelet Transform (DWT) algorithm and a modified Grey-Level Co-Occurrence Matrix (GLCM) method for textural feature extraction from segmented mammogram images. Each tissue pattern after classification is characterized into Benign and Malignant masses. A total of 148 mammogram images were taken from Mini MIAS database and solid breast nodules were classified into benign and malignant masses using supervised classifiers. The classifier used is Radial Basis Function Neural Network (RBFNN). The proposed system has a high potential for cancer detection from digitized screening mammograms.
Index Terms—Mammogram, Pre-processing, Feature Extraction, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform, Radial Basis Function Neural Networks.

A non-extensive entropy feature and its application to texture classification
a b s t r a c t This paperproposesanewprobabilisticnon-extensiveentropyfeaturefortexturecharacterization, based onaGaussianinformationmeasure.Thehighlightsofthenewentropyarethatitisboundedby finite limitsandthatitisnon-additiveinnature.Thenon-additivepropertyoftheproposedentropy makes itusefulfortherepresentationofinformationcontentinthenon-extensivesystemscontaining some degreeofregularityorcorrelation.Theeffectivenessoftheproposedentropyinrepresentingthe correlatedrandomvariablesisdemonstratedbyapplyingitforthetextureclassification problemsince texturesfoundinnaturearerandomandatthesametimecontainsomedegreeofcorrelationor regularity atsomescale.Thegraylevelco-occurrenceprobabilities(GLCP)areusedforcomputingthe entropyfunction.Theexperimentalresultsindicatehighdegreeoftheclassification accuracy.The performance ofthenewentropyfunctionisfoundsuperiortootherformsofentropysuchasShannon, Renyi,TsallisandPalandPalentropiesoncomparison.Usingthefeaturebasedpolarinteractionmaps (FBIM) theproposedentropyisshowntobethebestmeasureamongtheentropiescomparedfor representingthecorrelatedtextures.
Content-based Image Retrieval by Information Theoretic Measure
Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases
based on the content of the images. A new entropy function that serves as a measure of information content in an
image termed as ‘an information theoretic measure’ is devised in this paper. Among the various query paradigms,
query by example (QBE) is adopted to set a query image for retrieval from a large image database. In this paper,
colour and texture features are extracted using the new entropy function and the dominant colour is considered as a
visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature
vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices
in a large database system help retrieve the images relevant to the query image without looking at every image
in the database. The entropy values of colour and texture and the dominant colour are considered for measuring
the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is
demonstrated on a benchmark dataset.
Keywords: Image retrieval, fuzzy features, descriptors, entropy, indexing
A practical design of high-volume steganography
in digital video files
Abstract In this research, we consider exploiting the large volume of audio/video
data streams in compressed video clips/files for effective steganography. By observing
that most of the distributed video files employ H.264 Advanced Video Coding
(AVC) and MPEG Advanced Audio Coding (AAC) for video/audio compression,
we examine the coding features in these data streams to determine appropriate data
for modification so that the reliable high-volume information hiding can be achieved.
Such issues as the perceptual quality, compressed bit-stream length, payload of
embedding, effectiveness of extraction and efficiency of execution will be taken into
consideration. First, the effects of using different coding features are investigated
separately and three embedding profiles, i.e. High, Medium and Low, which indicate
the amount of payload, will then be presented. The High profile is used to embed the
maximum amount of hidden information when the high payload is the only major
concern in the target application. The Medium profile is recommended since it is
designed to achieve a good balance among several requirements. The Low profile is
an efficient implementation for faster information embedding. The performances of
these three profiles are reported and the suggested Medium profile can hide more
than 10%of the compressed video file size in common Flash Video (FLV) files.
Keywords Steganography · H.264/AVC ·MPEG AAC· Information hiding

Block Matching Algorithms
For Motion Estimation
Abstract—This paper is a review of the block matching
algorithms used for motion estimation in video compression. It
implements and compares 7 different types of block matching
algorithms that range from the very basic Exhaustive Search to
the recent fast adaptive algorithms like Adaptive Rood Pattern
Search. The algorithms that are evaluated in this paper are
widely accepted by the video compressing community and have
been used in implementing various standards, ranging from
MPEG1 / H.261 to MPEG4 / H.263. The paper also presents a
very brief introduction to the entire flow of video compression.
Index Terms— Block matching, motion estimation, video
compression, MPEG, H.261, H.263

Visual Cryptography Scheme for Color Image Using Random Number
with Enveloping by Digital Watermarking
Visual Cryptography is a special type of encryption technique to
obscure image-based secret information which can be decrypted
by Human Visual System (HVS). This cryptographic system
encrypts the secret image by dividing it into n number of shares
and decryption is done by superimposing a certain number of
shares(k) or more. Simple visual cryptography is insecure
because of the decryption process done by human visual system.
The secret information can be retrieved by anyone if the person
gets at least k number of shares. Watermarking is a technique to
put a signature of the owner within the creation.
In this current work we have proposed Visual Cryptographic
Scheme for color images where the divided shares are enveloped
in other images using invisible digital watermarking. The shares
are generated using Random Number.
Keywords: Visual Cryptography, Digital Watermarking,
Random Number.

 Image Compression Using Discrete Wavelet Transform

Abstract: This Project presents an approach towards MATLAB implemention of the Discrete Wavelet Transform (DWT) for image compression. The design follows the JPEG2000 standard and can be used for both lossy and lossless compression. In order to reduce complexities of the design linear algebra view of DWT has been used in this concept.With the use of more and more digital still and moving images, huge amount of disk space is required for storage and manipulation purpose. For example, a standard 35-mmphotograph digitized at 12μm per pixel requires about 18 Mbytes of storage and one second of NTSC-quality color video requires 23 Mbytes of storage. JPEG is the most commonly used image compression standard in today’s world. But researchers have found that JPEG has many limitations. In order to overcome all those limitations and to add on new improved features, ISO and ITU-T has come up with new image compression standard, which is JPEG2000
Artificial Bee Colony Data Miner (ABC-Miner)

Abstract—Data mining aims to discover interesting, non-trivial,
and meaningful information from large datasets. One of the data
mining tasks is classification, which aims to assign the given
datasets to the most suitable classes. Classification rules are used
in many domains such as medical sciences, banking, and
meteorology. However, discovering classification rules is
challenging due to large size and noisy structure of the datasets,
and the difficulty of discovering general and meaningful rules. In
the literature, there are several classical and heuristic algorithms
proposed to mine classification rules out of large datasets. In this
paper, a new and novel heuristic classification data mining
approach based on artificial bee colony algorithm (ABC) was
proposed (ABC-Miner). The proposed approach was compared
with Particle Swarm Optimization (PSO) rule classification
algorithm and C4.5 algorithm using benchmark datasets. The
experimental results show the efficiency of the proposed method.
Keywords: Artificial bee colony, Classification, Rule learning, Data
mining, ABC-Miner.

Facial expressions play an important role in human
communication. The contours of the mouth, eyes and
eyebrows play an important role in classification. Eigen
faces are used to classify facial expression. It has been
assumed that, facial expression can be classified into
some discreet classes (like happiness, sadness, disgust,
fear, anger and surprise) whereas absence of any
expression is the “Neutral” expression. Intensity of a
particular expression can be identified by the level of its
“dissimilarity” from the Neutral expression.
Keywords- Principal component, edge detection, feature
extraction, segmentation
Tracking TetrahymenaPyriformis Cells using Decision Trees
Matching cells over time has long been the most difficult
this problem by recasting it as a classification problem.
We construct a feature set for each cell, and compute a
feature difference vector between a cell in the current
frame and a cell in a previous frame. Then we determine
whether the two cells represent the same cell over
time by training decision trees as our binary classifiers.
With the output of decision trees, we are able to formulate
an assignment problem for our cell association task
and solve it using a modified version of the Hungarian



1. Motor shield interfacing using ARDUINO-MATLAB / SIMULINK
2. DC motor control using ARDUINO-MATLAB / SIMULINK
3. Stepper- Motor control using ARDUINO-MATLAB / SIMULINK
4. Servo motor interfacing using ARDUINO-MATLAB / SIMULINK
5 .Robot designing using ARDUINO-MATLAB / SIMULINK
6. Raspberry pi interfacing with simulink
7. Camera interfacing using Raspberry pi simulink
8. Live video acquisition using Raspberry pi simulink
9. Edge detection using Raspberry pi simulink
10. Image inversion in live video using Raspberry pi simulink
11. Color detection in live video using Raspberry pi simulink
12. Motion detection in live video using Raspberry pi simulink
13. Object detection in live video using Raspberry pi simulink
14. Live signal acquisition using Raspberry pi simulink
15. Filtering in live video using Raspberry pi simulink
16. Audio processing using Raspberry pi simulink
17. Filtering in live audio using Raspberry pi simulink
18. LED interfacing using Raspberry pi simulink
19. Various LED pattern using Raspberry pi simulink
20. Seven Segment display using Raspberry pi simulink
21. Sensors interfacing using Raspberry pi simulink
22. Real time sensors data acquisition & plotting using Raspberry pi simulink
23. Accelerometer interfacing using Raspberry pi simulink
24. Real time accelerometer data acquisition & plotting using Raspberry pi simulink
25. LCD Interfacing using Raspberry pi simulink
26. Motor shield interfacing using Raspberry pi simulink
27.  DC motor control using Raspberry pi simulink
28.  Stepper- Motor control using Raspberry pi simulink
29. Servo motor interfacing using Raspberry pi Simulink

electrical and electronics specialised projects

1. Load Flow Analysis on IEEE 14, 30, 57 buses System. Using NR method.
2. Voltage Profile Analysis for IEEE 30 Bus System Incorporating with UPFC
3. A Method for Transmission Loss Allocation Using Optimal Power Flow.
4. Studyon the performance of NEWTON – RAPHSON load Flow in DISTRIBUTION SYSTEMS
5. Load Modeling in Optimal Power Flow Studies (analysis with IEEE 14 bus load flow studies.)
6. Transmission Loss Calculation from Load Flow Analysis using incremental load flow approach.
7. Transmission Loss Calculation from Load Flow Analysis using Z bus method approach.
8. Location of statcom in IEEE 14 bus system using genetic algorithm.
Simulink Based Projects
Based on Renewableand other source of Energy. (Wind,Solar,Fuel cell, micro gas turbine, Ultra capacitor)
9. PMSG Based Wind Power Generation System.
10. Modeling and control of a single phase grid connected PV.
11. Grid Connected Battery storage System.
12. Fuel cell electrical energy system Simulink model.
13. Load Flow Analysis on IEEE 14and voltage profile improvement using Statcom .
14. Photovoltaic cell electrical energy system with dc boost and inverter 3ph connected to grid Simulink model.
15. Micro gas turbine connected with Grid system.
16. DFIG based wind power power generation system.
Power Electronics.
17. Zener Diode Regulator Simulink model.
18. Three phase shunt active filter power quality improvement.
19. High Voltage direct current Transmission system with three phase AC-DC-AC PWM power converter.
20. Three Phase Fully Controlled Bridge Rectifier.
21. Three phase inverter using PWM techniques.
22. Thyrister based single phase AC controller.
23. SVPWM control based three phase inverter.
24. Current Controller based 1-Phase Inverter.
25. Z-Converter for maintaining constant voltage at load.
26. SPWM switching pulse based seven, nine, eleven, thirteen, fifteen level H-Bridge inverter.
27. SPWM switching pulse based seven, nine, eleven, thirteen, fifteen level diode clamped inverter.
28. SPWM based open loop Buck Boost Converter.
29. Load Connected by Boost Converter Based 3-Phase Inverter.
30. Load Connected by Open Loop Buck Boost Converter Based 1-Phase Inverter.
31. Half Bridge DC-DC Converter and Full Bridge DC-DC Converter.
32. Dual Bridge Dc to DC Converter.
33. PI Controller Based Closed Loop Dual Bridge Dc to DC Converter.
35. Starting and Speed control of DC motor Simulink model.
36. DC motor drive through a DC chopper using GTO thyristor and a free-wheeling diode.
37. Brushless DC Motor Drive during Speed Regulation Simulink model.
38. SVPWM based Speed Control of Induction Motor with 3-Level Inverter using V/F method.
39. IEEE 14and voltage profile improvement using Statcom.
40. Power quality improvement in IEEE 9 BUS system using SSSC(Static Synchronous Series Compensator)
41. Reactive power compensation using SVC (Static var compensator) in transmission Power system.

Tuesday, August 19, 2014

biomedical engineers , electrical engineers , electronics engineers , all circuit branches we introduce -Arduino / Raspberry Pi / BeagleBoard / MATLAB / Simulink / Matlab to C/ MAtlab to.Net - Training & Workshops . aLSO Faculty development programme (FDA) FOR ENGINEERING COLLEGES

Hi friends Please contact me regarding 

1. Workshops - 1day ,2-day ,3-day ,4-day ,5-day, 6-day,7-day , 15 days  and 15 weeks 

2. Complete training programme (at our lab) - Fundementals or tollbox based ( varying from 1 month to 2month depending on the batch requirment)

3. In house research programme (4 months)

4. Faculty development programme 

   For  details please cotact me on 

+91 9945757753