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NS2 Throughput calculation awk program

Throughput refers to how much data can be transferred from one location to another in a given amount of time. The following code will count all the received application packets in a network such that we can calculated the network throughput.

Throughput Vs Time . more throughput is better protocol

AWK PROGRAM

######################################################################

BEGIN {
init=0;
i=0;
}
{
action = $1;
time = $2;
from = $3;
to = $4;
type = $7;
pktsize = $6;
flow_id = $8;
src = $9;
dst = $10;
seq_no = $11;
packet_id = $6;

if(action=="r" && from==2 && to==3 && type=="cbr") {
pkt_byte_sum[i+1]=pkt_byte_sum[i]+ pktsize;

if(init==0) {
start_time = time;
init = 1;
}

end_time[i] = time;
i = i+1;
}
}
END {

printf("%.2f\t%.2f\n", end_time[0], 0);

for(j=1 ; j<i ; j++){

th = pkt_byte_sum[j] / (end_time[j] - start_time)*8/1000;
printf("%.2f\t%.2f\n", end_time[j], th);
}

printf("%.2f\t%.2f\n", end_time[i-1], 0);
}

AWK

Packet delivery ratio : the ratio of the number of delivered data packet to the destination. This illustrates the level of delivered data to the destination.

∑ Number of packet receive / ∑ Number of packet send

The greater value of packet delivery ratio means the better performance of the protocol.

###################################################################

# Initialization settings
BEGIN {

sendLine = 0;
recvLine = 0;
fowardLine = 0;
if(mseq==0)
mseq=10000;
for(i=0;i<mseq;i++){
rseq[i]=-1;
sseq[i]=-1;
}
}
# Applications received packet
$0 ~/^s.* AGT/ {
# if(sseq[$6]==-1){
sendLine ++ ;
# sseq[$6]=$6;
# }
}

# Applications to send packets
$0 ~/^r.* AGT/{
# if(rreq[$6]==-1){
recvLine ++ ;
# sseq[$6]=$6;
# }

}

# Routing procedures to forward the packet
$0 ~/^f.* RTR/ {

fowardLine ++ ;

}

# Final output
END {
printf "cbr s:%d r:%d, r/s Ratio:%.4f, f:%d \n", sendLine, recvLine, (recvLine/sendLine),fowardLine;

}

###############################################################

End-to-end Delay : the average time taken by a data packet to arrive in the destination. It also includes the delay caused by route discovery process and the queue in data packet transmission. Only the data packets that successfully delivered to destinations that counted.

∑ ( arrive time – send time ) / ∑ Number of connections

The lower value of end to end delay means the better performance of the protocol.

Here is the AWK Script for calculating the delay

################################################################################

BEGIN {
highest_packet_id = 0;
}
{
action = $1;
time = $3;
#from = $3;
#to = $4;
type = $35; #aodv relationships,if 5 no infomation
pktsize = $37;
#src = $9;
#dst = $10;
#seq_no = $11;
packet_id = $41;

if ( type != "AODV" ) {

if ( packet_id > highest_packet_id )
highest_packet_id = packet_id;

if ( start_time[packet_id] == 0 )

if ( type == "cbr" && action != "d" ) {
if ( action == "r" ) {
end_time[packet_id] = time;
}
} else {

end_time[packet_id] = -1;
}
}
}
END {

for ( packet_id = 0; packet_id <= highest_packet_id; packet_id++ ) {
start = start_time[packet_id];
end = end_time[packet_id];
packet_duration = end - start;

if ( start < end ) printf("%f %f\n", start, packet_duration);
}
}

###########################################################

Extended Kalman Filter (EKF) MATLAB Implimentation

Kalman Filter (KF)

Linear dynamical system (Linear evolution functions)

Extended Kalman Filter (EKF)

Non-linear dynamical system (Non-linear evolution functions)

Consider the following non-linear system:

Assume that we can somehow determine a reference trajectory

Then:

where

For the measurement equation, we have:

We can then apply the standard Kalman filter to the linearized model

How to choose the reference trajectory?

Idea of the extended Kalman filter is to re-linearize the model around the most recent state estimate, i.e.

The Extended Kalman Filter (EKF) has become a standard technique used in a number of

# nonlinear estimation and

# machine learning applications

#State estimation

#estimating the state of a nonlinear dynamic system

#Parameter estimation

#estimating parameters for nonlinear system identification

#e.g., learning the weights of a neural network

#dual estimation

#both states and parameters are estimated simultaneously

#e.g., the Expectation Maximization (EM) algorithm

MATLAB CODE
########################################################################
function [x_next,P_next,x_dgr,P_dgr] = ekf(f,Q,h,y,R,del_f,del_h,x_hat,P_hat);
% Extended Kalman filter
%
% -------------------------------------------------------------------------
%
% State space model is
% X_k+1 = f_k(X_k) + V_k+1 --> state update
% Y_k = h_k(X_k) + W_k --> measurement
%
% V_k+1 zero mean uncorrelated gaussian, cov(V_k) = Q_k
% W_k zero mean uncorrelated gaussian, cov(W_k) = R_k
% V_k & W_j are uncorrelated for every k,j
%
% -------------------------------------------------------------------------
%
% Inputs:
% f = f_k
% Q = Q_k+1
% h = h_k
% y = y_k
% R = R_k
% del_f = gradient of f_k
% del_h = gradient of h_k
% x_hat = current state prediction
% P_hat = current error covariance (predicted)
%
% -------------------------------------------------------------------------
%
% Outputs:
% x_next = next state prediction
% P_next = next error covariance (predicted)
% x_dgr = current state estimate
% P_dgr = current estimated error covariance
%
% -------------------------------------------------------------------------
%

if isa(f,'function_handle') & isa(h,'function_handle') & isa(del_f,'function_handle') & isa(del_h,'function_handle')
y_hat = h(x_hat);
y_tilde = y - y_hat;
t = del_h(x_hat);
tmp = P_hat*t;
M = inv(t'*tmp+R+eps);
K = tmp*M;
p = del_f(x_hat);
x_dgr = x_hat + K* y_tilde;
x_next = f(x_dgr);
P_dgr = P_hat - tmp*K';
P_next = p* P_dgr* p' + Q;
else
error('f, h, del_f, and del_h should be function handles')
return
end

##############################################################################

For more

https://drive.google.com/folderview?id=0B2l8IvcdrC4oMzU3Z2NVXzQ0Y28&usp=sharing

Network security is the one of the best feilds of the research which deals with securing the privacy . Here i have selected some papers from IEEE Transactions 2014 for implementing .may be helpful for the scholars looking for their final year projects . These papers can be implemented using ns2 or other network simulators .Internet of Things and Cloud storage is two emerging technologies .I have mentioned some topics on the security of these technologies too .

1.A Bayesian-MRF Approach for PRNU-Based Image Forgery Detection
2.Adaptively Splitted GMM With Feedback Improvement for the Task of Background Subtraction
3.A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs
4.A Methodology for Direct and Indirect Discrimination Prevention in Data Mining
5.DDoS Detection Method Based on Chaos Analysis of Network Traffic Entropy
6.Dynamic Multiservice Load Balancing in Cloud-Based Multimedia System
7.Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
8.Enabling P2P One-View Multiparty Video Conferencing
9.Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage
10.Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication
11.Mona Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud
12.Optimal Multiserver Configuration for Profit Maximization in Cloud Computing
13.Preventing Private Information Inference Attacks on Social Networks
14.Privacy-Preserving Authenticated Key-Exchange Over Internet
15.Privacy-Preserving Public Auditing for Secure Cloud Storage
16.Private Searching on Streaming Data Based on Keyword Frequency
17.Scalable Distributed Service Integrity Attestation for Software-as-a-Service Clouds
18.Secure Two-Party Differentially Private Data Release for Vertically Partitioned Data
19.SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems
20.Temporal Load Balancing with Service Delay Guarantees for Data Center Energy Cost Optimization
21.Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning
22.Toward Optimal Fusion Algorithms With Security Against Wolves and Lambs in Biometrics
23.Trustworthiness Management in the Social Internet of Things
24.User Authentication Through Mouse Dynamics

If you feel difficulty in implementing any of these just ping me

CONTACT ME : MAIL ME

PROJECT ENQIRY FORM

Modeling, Design & Simulation of an Adaptive Neuro- Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor

A novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling some of the parameters, such as speed, torque, flux, voltage, current, etc. of the induction motor is presented in this paper. Induction motors are characterized by highly non-linear, complex and time-varying dynamics and
inaccessibility of some of the states and outputs for measurements. Hence it can be considered as a challenging engineering problem in the industrial sector. Various advanced control techniques has been devised by various researchers across the world. Some of them are based on the fuzzy techniques.

Fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base, which is written on the previous experiences & these rules are fired which is random in nature. As a result of which, the outcome of the controller is also random & optimal results may not be obtained. Selection of the proper rule base depending
upon the situation can be achieved by the use of an ANFIS controller, which becomes an integrated method of approach for the control purposes & yields excellent results, which is the highlight of this paper. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is
achieved using the back propagation algorithm. This integrated approach improves the system performance, cost-effectiveness, efficiency, dynamism, reliability of the designed controller. The simulation results presented in this paper show the effectiveness of the method developed & has got faster response time or settling
times. Further, the method developed has got a wide number of advantages in the industrial sector & can be converted into a real time application using some interfacing cards.

Wireless Sensor Networks Project IEEE 2014 Papers and Titles

MANET

1. AASR: Authenticated Anonymous Secure Routing for MANETs in Adversarial Environments

2.Energy-Efficient Reliable Routing Considering Residual Energy in Wireless Ad Hoc Networks

3.Joint Topology-Transparent Scheduling and QoS Routing in Ad Hoc Networks

4.Leveraging Social Networks for P2P Content-Based File Sharing in Disconnected MANETs

5.Multicast Capacity in MANET with Infrastructure Support

6.Optimally Adaptive Power-Saving Protocols for Ad Hoc Networks Using the Hyper Quorum System

7.PSR: A Lightweight Proactive Source Routing Protocol For Mobile Ad Hoc Networks

8.PSR: A Lightweight Proactive Source Routing Protocol For Mobile Ad Hoc Networks

9.Security Enhancements for Mobile Ad Hoc Networks with Trust Management Using Uncertain Reasoning

10.Video Dissemination over Hybrid Cellular and Ad Hoc Networks

MESH

1. Autonomous Mobile Mesh Networks

2.Location-Aware Chord-Based Overlay for Wireless Mesh Networks

VANET
1.A Disturbance-Adaptive Design for VANET-Enabled Vehicle Platoon

2.Efficient Privacy-Preserving Authentication for Vehicular Ad Hoc Networks

3.Safety-Message Broadcast in Vehicular Ad Hoc Networks Based on Protocol Sequences

wireless networks

1.Approach-and-Roam (AaR): A Geographic Routing Scheme for Delay/Disruption Tolerant Networks

2.A QoS-Oriented Distributed Routing Protocol for Hybrid Wireless Networks

3.Cooperative Caching for Efficient Data Access in Disruption Tolerant Networks

4.Efficient Location Privacy-Aware Forwarding in Opportunistic Mobile Networks

5.Neighbor Table Based Shortcut Tree Routing in ZigBee Wireless Networks

6.Secure Data Retrieval for Decentralized Disruption-Tolerant Military Networks

Wireless Sensor Networks

1.An Alternative Perspective on Utility Maximization in Energy-Harvesting Wireless Sensor Networks

2.An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks

3.An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks

4.A Two-Phase Dispatch Heuristic to Schedule the Movement of Multi-Attribute Mobile Sensors in a Hybrid Wireless Sensor Network

5.Constructing Load-Balanced Data Aggregation Trees in Probabilistic Wireless Sensor Networks

6.Contiguous Link Scheduling for Data Aggregation in Wireless Sensor Networks

7.R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Networks

8.Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor Networks

9.WizSync: Exploiting Wi-Fi Infrastructure for Clock Synchronization in Wireless Sensor Networks

CONTACT ME : MAIL ME

PROJECT ENQIRY FORM

Analysis of the Effect of Vignetting on MIMO Optical Wireless Systems Using Spatial OFDM

Abstract—The performance of pixelated multiple-input multiple output optical wireless communication systems can be impaired by vignetting, which is the gradual fall-off in illumination at the edges of a received image. This paper investigates the effect of vignetting for a pixelated system using spatial orthogonal frequency division multiplexing (OFDM). Our analysis shows that vignetting causes attenuation and intercarrier interference (ICI) in the spatial frequency domain. MATLAB simulations indicate that for a given constellation size, spatial asymmetrically clipped optical OFDM (SACO-OFDM) is more robust to vignetting than spatial dc biased opticalOFDM (SDCO-OFDM).Moreover, for the case of SDCO-OFDM, the very large zeroth subcarrier causes severe ICI in its neighbourhood causing flattening of the bit error rate (BER) curves. We show that this BER floor can be eliminated by leaving some of the lower spatial frequency subcarriers unused. The BER performance can also be improved by applying a vignetting estimation and equalization scheme. Finally, it is shown that equalized SACO-OFDM with 16-QAM has the same overall data rate as equalized SDCO-OFDM using 4-QAM, but requires less optical power.

Block diagram of a spatial OFDM system

Illustration of a pixelated MIMO wireless communication system

IEEE PAPER :http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6681890&ranges%3D2014_2014_p_Publication_Year%26subscribed%3Dtrue%26queryText%3Dofdm

FULL MATLAB CODE AVAILABLE

CONTACT FOR PROJECTS

Hard Reset and Soft Reset for Nokia Lumia 930 Lumia 630

NOKIA has shipped a range of new Lumia Phones including Lumia 930 Lumia 620

If you simply want to return phone to factory status, just go to

Settings -> About, and choose Reset Your Phone.

And if your phone is stuck when using some applications like whatsapp the phone may get hang this time use the soft reset expained below

SOFT RESET for Lumia 930,(Lumia 900,Lumia 920

Press and hold the volume keys, power button and camera together for 3-5 seconds
The phone should soft-reset . some phones have 3 vibrations

This option restores the .ini files from the ROM. Does not erase data (photos, videos, documents) or applications from a third party.

Lumia 630,Lumia 625

Press and hold the volume keys and power button together for 3-5 seconds
The phone should soft-reset

In some phones press and hold the POWER BUTTON FOR 10 Seconds

HOTSPOT Wi-Fi share with Android Devices from Linux Mint 17 Laptops

This is a quick tip to make your laptop as wi-fi hotspot . some people using ap-hotspot which doesnt work for me , so i found this new method which works pretty fine for me

Follow the steps

1.Open Terminal
2. run the command $ sudo synaptic

3 in synaptic search for plasma-nm

4. right click on the plasma-nm and mark for installation

5. Afrer installation finished use Alt + f2

kde-nm-connection-editor

6. select wifi(shared) From the connection manager window

configure with custom names and wPA2 passkeys

Edge Detection Technique by Fuzzy Logic CLA and Canny Edge Detector u sing Fuzzy Image Processing

Download Full paper at IJRITCC

Edge detection in an image is an major issue in image processing.Many hidden objects can be identified using edge detection which gives major clue in identifying actual truth behind the images. In this paper, double thresholding method of edge detection along with canny edge detector is used to identify the small objects in an images.Here threshold plays a major role which extracts the clear image from unclear picture

CANNY EDGE DETECTION USING FUSSY LOGIC

Conclusion :

Because of the uncertainties that exist in many aspects of image processing , and as image are always dynamic , fuzzy processing is desirable . These uncertainties include additive and non- additive noise in low level image processing , imprecision in the assumptions underlying the algorithms , and ambiguities in interpretation during high level image processing . For the common process of edge detection usually models edges as intensity ridges . Finally by increasing the threshold value greater than 50 and contrast can be improved .

And here is the full program in matlab for the same

**************************************************

%%Start of coding, Symbols have their usual meaning

%% Input Image
clear all;
clc;
A=[];
piA=[];
%Using 16 fuzzy edge templets that show the possible direction of the edge
%s in the image and then calculating the divergence between the origin
%image and the 16 fuzzy templets.

%Take any one example and uncomment it ;
%Reading the pixel of the image using imread function of the matlab
% %for rice image
% III = rgb2gray(imread('rice.tif'));%name of the image
% II = imcrop(III,[80 30 240 200]);
%
%III = rgb2gray(imread('self_fig.tif'));%name of the image
%II = imcrop(III,[50 40 650 400]);
III = rgb2gray(imread('canny1.tif'));%name of the image
II = imcrop(III,[5 5 560 450]);
%%For Tree image;
%III = rgb2gray(imread('1.2.03.tiff'));%name of the image
% %II = imcrop(III,[35 94 430 355]);

%III = imread('1.4.09.tiff');%name of the image
% %For lena photo
% III = rgb2gray(imread('lena.tiff'));%name of the image
% II = imcrop(III,[35 94 430 355]);

I = double(II);
[r,k] = size(I);%no of row and column is I

%% Selection of the 16 fuzzy templets
a=0.3; b=0.8;
t1 = [a a a; 0 0 0; b b b];
t2 = [a a b; a b 0; b 0 0];
t3 = [b b b; 0 0 0; a a a];
t4 = [b a a; 0 b a; 0 0 b];
t5 = [b a 0;b a 0; b a 0];
t6 = [a 0 b;a 0 b; a 0 b];
t7 = [0 0 0; b b b; a a a];
t8 = [0 b a; 0 b a; 0 b a];
t9 = [a a a; b b b;0 0 0];
t10 = [a b 0; a b 0;a b 0];
t11 = [0 0 0; a a a;b b b];
t12 = [0 a b; 0 a b; 0 a b];
t13 = [b b b; a a a; 0 0 0];
t14 = [b 0 a; b 0 a; b 0 a];
t15 = [b 0 0; b 0 a; a a b];
t16 = [0 0 b; 0 b a; b a a];

%% Initization of algo
xmax = max(max(max(I)));%maximum pixel/element of the image;
%converting into the fuzzy domain from the original image;
fim = I/xmax;%fim is the image data of the input image in the fuzzy domain,all value of the fim in the interval of [0 1];
%initializing the edge image as zeros matrix i.e black box;
fedgeim = zeros(r,k);%in fuzzy domain
%Increaing the boreder line of the iamge i.e to increase the row and column
%by 2 in the first and last by taking the mirror image of the immediate
%existing rows and columns respectively;
r1 = fim(2,:);%Copy of all element in the 2nd row of fim
r2 = fim(r-1,:);
c1 = fim(:,2);
c2 = fim(:,k-1);
b1 = [0 r1 0];
b2 = [0 r2 0];
b3 = [c1 fim c2];
bfim = [b1;b3;b2];%bfim = Border fuzzy image matix
bfim(1,1) = fim(1,1);
bfim(r+2, k+2) = fim(r,k);
bfim(1,k+2) = fim(1,k);
bfim(r+2,1) = fim(r,1);

%finding Hesitation degree or intuitionstic fuzzy index
%c = input("Enter the value of pi ");
c= 0.2;
pibfim = c*(1-bfim);
pit1 = c*(1-t1);pit2 = c*(1-t2);pit3 = c*(1-t3);pit4 = c*(1-t4);pit5 = c*(1-t5);pit6 = c*(1-t6);pit7 = c*(1-t7);
pit8 = c*(1-t8);pit9 = c*(1-t9);pit10 = c*(1-t10);pit11 = c*(1-t11);pit12 = c*(1-t12);pit13 = c*(1-t13);
pit14 = c*(1-t14);pit15 = c*(1-t15);pit16 = c*(1-t16);

%Calculation of the maximum of the divergance value between the 16 templets
%and the original image of the same size let the original image denoted by
%A this A arew formed by taking the 3x3 matrix in the border matix i.e from
%bfim
%Considering the fuzzy templats as mask of size 3x3 and then we will slide
%this matix in the fuzzy matrix i.e in the fim not inj the bfim
for i = 2:r+1
    for j = 2:k+1
        A = [bfim(i-1,j-1) bfim(i,j-1) bfim(i+1,j-1) ; bfim(i-1,j) bfim(i,j) bfim(i+1,j) ; bfim(i-1,j+1) bfim(i,j+1) bfim(i+1,j+1)];
        piA = [pibfim(i-1,j-1) pibfim(i,j-1) pibfim(i+1,j-1) ; pibfim(i-1,j) pibfim(i,j) pibfim(i+1,j) ; pibfim(i-1,j+1) pibfim(i,j+1) pibfim(i+1,j+1)];

        %3x3 matrix for determining the divergence with the tempelets t1,
        %t2...15,16.
        %we calculate the divergence of 3x3 matrix at a time and then
        %taking the minimun element of the matrix for all 16 fuzzy
        %tempelets;
        %d1 is a matrix of 3x3 = divergence with original matix and
        %fuzzy templets 1
        d1 = 2 - (1-A+t1).*exp(A-t1)-(1-t1+A).*exp(t1-A)+ 2- (1-(A-t1)+pit1-piA).*exp(A-t1-(pit1-piA))-(1-(pit1-piA)+A-t1).*exp(pit1-piA-(A-t1));
        min1 =min(min(d1));
        %d2 is the matix of 3x3 = divergence matix with orinigal matrix and fuzzy tempelts 2.
        d2 = 2 - (1-A+t2).*exp(A-t2)-(1-t2+A).*exp(t2-A)+2-(1-(A-t2)+pit2-piA).*exp(A-t2-(pit2-piA))-(1-(pit2-piA)+A-t2).*exp(pit2-piA-(A-t2));
        min2 =min(min(d2));
        d3 = 2 - (1-A+t3).*exp(A-t3)-(1-t3+A).*exp(t3-A)+2-(1-(A-t3)+pit3-piA).*exp(A-t3-(pit3-piA))-(1-(pit3-piA)+A-t3).*exp(pit3-piA-(A-t3));
        min3 =min(min(d3));
        d4 = 2 - (1-A+t4).*exp(A-t4)-(1-t4+A).*exp(t4-A)+2-(1-(A-t4)+pit4-piA).*exp(A-t4-(pit4-piA))-(1-(pit4-piA)+A-t4).*exp(pit4-piA-(A-t4));
        min4 =min(min(d4));
        d5 = 2 - (1-A+t5).*exp(A-t5)-(1-t5+A).*exp(t5-A)+2-(1-(A-t5)+pit5-piA).*exp(A-t5-(pit5-piA))-(1-(pit5-piA)+A-t5).*exp(pit5-piA-(A-t5));
        min5 =min(min(d5));
        d6 = 2 - (1-A+t6).*exp(A-t6)-(1-t6+A).*exp(t6-A)+2-(1-(A-t6)+pit6-piA).*exp(A-t6-(pit6-piA))-(1-(pit6-piA)+A-t6).*exp(pit6-piA-(A-t6));
        min6 =min(min(d6));
        d7 = 2 - (1-A+t7).*exp(A-t7)-(1-t7+A).*exp(t7-A)+2-(1-(A-t7)+pit7-piA).*exp(A-t7-(pit7-piA))-(1-(pit7-piA)+A-t7).*exp(pit7-piA-(A-t7));
        min7 =min(min(d7));
        d8 = 2 - (1-A+t8).*exp(A-t8)-(1-t8+A).*exp(t8-A)+2-(1-(A-t8)+pit8-piA).*exp(A-t8-(pit8-piA))-(1-(pit8-piA)+A-t8).*exp(pit8-piA-(A-t8));
        min8 =min(min(d8));
        d9 = 2 - (1-A+t9).*exp(A-t9)-(1-t9+A).*exp(t9-A)+2-(1-(A-t9)+pit9-piA).*exp(A-t9-(pit9-piA))-(1-(pit9-piA)+A-t9).*exp(pit9-piA-(A-t9));
        min9 =min(min(d9));
        d10 = 2 - (1-A+t10).*exp(A-t10)-(1-t10+A).*exp(t10-A)+2-(1-(A-t10)+pit10-piA).*exp(A-t10-(pit10-piA))-(1-(pit10-piA)+A-t10).*exp(pit10-piA-(A-t10));
        min10 =min(min(d10));
        d11 = 2 - (1-A+t11).*exp(A-t11)-(1-t11+A).*exp(t11-A)+2-(1-(A-t11)+pit11-piA).*exp(A-t11-(pit11-piA))-(1-(pit11-piA)+A-t11).*exp(pit11-piA-(A-t11));
        min11 =min(min(d11));
        d12 = 2 - (1-A+t12).*exp(A-t12)-(1-t12+A).*exp(t12-A)+2-(1-(A-t12)+pit12-piA).*exp(A-t12-(pit12-piA))-(1-(pit12-piA)+A-t12).*exp(pit12-piA-(A-t12));
        min12 =min(min(d12));
        d13 = 2 - (1-A+t13).*exp(A-t13)-(1-t13+A).*exp(t13-A)+2-(1-(A-t13)+pit13-piA).*exp(A-t13-(pit13-piA))-(1-(pit13-piA)+A-t13).*exp(pit13-piA-(A-t13));
        min13 =min(min(d13));
        d14 = 2 - (1-A+t14).*exp(A-t14)-(1-t14+A).*exp(t14-A)+2-(1-(A-t14)+pit14-piA).*exp(A-t14-(pit14-piA))-(1-(pit14-piA)+A-t14).*exp(pit14-piA-(A-t14));
        min14 =min(min(d14));
        d15 = 2 - (1-A+t15).*exp(A-t15)-(1-t15+A).*exp(t15-A)+2-(1-(A-t15)+pit15-piA).*exp(A-t15-(pit15-piA))-(1-(pit15-piA)+A-t15).*exp(pit15-piA-(A-t15));
        min15 =min(min(d15));
        %d16 is the matix of 3x3 = divergence matix with orinigal matrix and
        %fuzzy tempelts 16.
        d16 = 2 - (1-A+t16).*exp(A-t16)-(1-t16+A).*exp(t16-A)+2-(1-(A-t16)+pit16-piA).*exp(A-t16-(pit16-piA))-(1-(pit16-piA)+A-t16).*exp(pit16-piA-(A-t16));
        min16 =min(min(d16));
        %Selecting the minimun divergence among the 16 divergence values
        %and is positioned at the center of the templets position for the
        %edge iamge i.e in edgeim.
        dd = [min1 min2 min3 min4 min5 min6 min7 min8 min9 min10 min11 min12 min13 min14 min15 min16];
        fedgeim(i-1,j-1) = max(dd);
    end
end
%We wil get the edge image in the fuzzy doamin as edgeim matrix So we have
%to tranforming back in the image pixel domain i.e in the intercal [1
% 255] domain
fedgeimmax = max(max(fedgeim));
edgeim = double((1/fedgeimmax)*(fedgeim));
% edgeimage = uint8(edgeim); %this is the matrix of edge in the 1-255
% figure, imshow(edgeimage);
% figure, imshow(uint8(I));

%% Out put
tt = 255*edgeim;
ttt = uint8(tt);
subplot(2,2,1),imshow(uint8(I))
title('original image');
%figure, imshow(ttt);
subplot(2,2,2),imshow(ttt)
title('Edge without threshold');
%Set a threshold
for i = 1:r
    for j = 1:k
        if ttt(i,j)>45
            ed(i,j) = 255;
        else
            ed(i,j) = 0;
        end
    end
end
subplot(2,2,3),imshow(ed);
title('After applying threshold 45');
%applying the morphological oprators of matlab i.e bwmorph
med = bwmorph(ed,'thin');
subplot(2,2,4), imshow(med);
title('after applying morphological thin fun');

*************************************************************

Turn your Social media in to Paychecks with right Social Media Optimization Strategy

Social media optimization is the process of increasing the awareness of product or service or brand or event by using social media .

Social media includes all the common Twitter, Facebook , Linkedin , Pinterest , Blogger etc which have large content contributions . To generate the viral publicity we need a unique user generated content base

mostly all business focused on listed in Google's first page results using SEO and keyword researchs to drive traffic in to their web portals. Social media have a faster impact over people than Google . Last month traffic to Blogger was more than 220 million . So its by attracting so of that traffic surely business can achieve more

When even before starting a business people and marketers do create a Facebook Fanpage , twitter handle and Youtube channel , not all business need all these .you can do better making your own online presence which is social media friendly to linking with your products and services

Social Meida is the online version of word of mouth advertising , so by engaging with your social media can make a positive change in the customer base

Being simple the users can communicate with you , being open and honest make you as favourite to your customers . Merge the current marketing with social media . May be some them are familiar with your existing marketing campaigns link it with social media ,

Mostly all business have an SEO strategy to make Google's Search Engine ranking priority list . Blogging is the perfect way to optimize the content to Google . Concentrate on making contents for your blog and don't stop there . Cut short the contents or make the headlines for Facebook and twitter updates .use the same for print and email marketing

You can make announcements via your social media , and other great way of using social media is by offers .. give massive offer announcements which will surely increase number of the customers

If need a perfect SMO strategy contact

Edge detection methods for finding object boundaries in images

Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision.

Prewitt Edge Detection

sobel Edge Detection

canny Edge Detection

log Edge Detection

robert Edge Detection

here is the program for the above edge detections

https://github.com/ebine/edgedetection/

Digital Steganography: Hiding Data within Data - MATLAB Program

Uses of Steganography

Steganography is a means of storing information in a way that hides that information’s existence. Paired with existing communication methods, steganography can be used to carry out hidden exchanges. Governments are interested in two types of hidden communications: those that support national security and those that do not. Digital steganography provides vast potential for both types. Businesses may have similar concerns regarding trade secrets or new product information. Avoiding communication in well-known forms greatly reduces the risk of information being leaked in transit.

Images as Carriers

Images are a good medium for hiding data (for details, see Pan, Chen, and Tseng 3 ). The more detailed an image, the fewer constraints there are on how much data it can hide before it becomes suspect. The JPHide/JPSeek package (http://linux01.gwdg.de/~alatham/stego.html) uses the coefficients in a JPEG to hide information. A newer method (http://www.know.comp.kyutech.ac.jp/BPCSe/BPCSe-principle.html) embeds data in visually insignificant parts of an image. Both of these methods alter the image; however, you can explore image degradation using different images and messages of varying length. An alternative, specific to GIF images, is to manipulate an image’s palette in order to hide data. Gifshuffle (http://www.darkside.com.au/gifshuffle/) does not alter the image itself in any visible way; rather, it permutes a GIF image’s color map, leaving the original image completely intact

Here is the matlab code for stegnography . both encode and decode

download it from github https://github.com/ebine/stegnography/

Efficient Dilation, Erosion, Opening, and Closing Algorithms in MATLAB

Abstract

We propose an efficient and deterministic algorithm for computing the one-dimensional dilation and erosion (max and min) sliding window filters. For a p-element sliding window, our algorithm computes the 1D filter using 1:5 þ oð1Þ comparisons per sample point. Our algorithm constitutes a deterministic improvement over the best previously known such algorithm, independently developed by van Herk [25] and by Gil and Werman [12] (the HGW algorithm). Also, the results presented in this paper constitute an improvement over the Gevorkian et al. [9] (GAA) variant of the HGW algorithm. The improvement over the GAA variant is also in the computation model. The GAA algorithm makes the assumption that the input is independently and identically distributed (the i.i.d. assumption), whereas our main result is deterministic. We also deal with the problem of computing the dilation and erosion filters simultaneously, as required, e.g., for computing the unbiased morphological edge. In the case of i.i.d. inputs, we show that this simultaneous computation can be done more efficiently then separately computing each. We then turn to the opening filter, defined as the application of the min filter to the max filter and give an efficient algorithm for its computation. Specifically, this algorithm is only slightly slower than the computation of just the max filter. The improved algorithms are readily generalized to two dimensions (for a rectangular window), as well as to any higher finite dimension (for a hyperbox window), with the number of comparisons per window remaining constant. For the sake of concreteness, we also make a few comments on implementation considerations in a contemporary programming language.

efficient Dilation, Erosion, Opening, and Closing Algorithms

Figure : The effect of the opening (top) and closing (bottom) filters. (Original image is shown on left frame, followed by the filtered image using rectangular windows sized 2x2, 4x4, 8x8, and 16x16.)

Paper link in IEEE

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1114852&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1114852

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1114852&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F34%2F24589%2F01114852.pdf%3Farnumber%3D1114852

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,

VOL. 24, NO. 12, DECEMBER 2002

Here is the MATLAB code for the same paper is availabe on git hub

download at https://github.com/ebine/morphologicall

Hard Resets and Soft resets for your NOKIA X, Nokia Xl , Nokia X+ ANDROID Phones

Nokia adds three models in android versions Nokia X,Nokia XL,Nokia X+

1. power off your nokia x
2.now wait for 15-20 seconds
3.now press the power button you will see nokia written on screen
4.now just press the volume up button for 5-6 seconds and poof
you are in nokia android recovery

wipe data ,cache and all from your Nokia ANDROID smartphone

How to Import Google/Gmail Contacts to Nokia X,X+ and XL Nokia Android Phones Working Trick

1. First of all login to Gmail and click on Contacts at the top left side of the page.

2. Now on the menu an option is labeled as More, just click on Export.

3. You will get a pop, here just choose your contacts, and export all your contacts in vCard format.

4. Now copy vCard file to your Nokia X SD card by connecting the phone to your PC.

5. Use ASTRO File manager (preinstalled) to locate the vCard file and tap to open.

6. Once you have done this, your device will start importing your Gmail contacts to your Nokia X.

Unroot Your Nokia X, Nokia XL , Nokia X+

First of all what you need to do for unrooting Nokia X , Nokia XL and Nokia X+ is

1. !oot your phone again with framaroot

2.if you get two supersu then remove that pre installed one with root permission.

3.if there is only one then no problem out there just unroot your phone again with framaroot.

4.BOOM now supersu will get vanished

Root your nokia X+,XL,X

To Root

1. Download the Framaroot.apk on your Nokia X, Nokia X+, or Nokia XL;

2. Use the built-in Astro file manager, and browse to the folder where you downloaded Framaroot.apk, then tap on its icon to install;
3. Upon installation, start Framaroot. There are two root exploits to choose from - "Aragorn" and “Gandalf” - tap on "Gandalf";
4. Now reboot your Nokia X, Nokia X+, or Nokia XL, and you should be rooted.

Fin Trends over the world , and New era of smart rings starts here

Its new era with smart rings , here the RHLvision Technologies Pvt.Ltd introduces new gadget called fin that you can wear on your thumb as a ring and it will make your whole palm as a gesture interface,

Its waterproof tiny flexible sized device that may be a jewellery for the next generation , and with high battery efficiency of 1 week week ,and have a micro-USB charging port .Its support cross platform over Windows, MAC, iOS, Android, Windows Phones.And it open developers platform to hack and make it use for as many possibilities , to make the hands free

“People in today’s world are becoming more busy, but still they are wasting a lot of time interacting with their smart devices,” Rohildev Nattukallingal, founder of RHL Vision Technologies, the startup behind Fin,told Mashable. “Fin removes this interaction gap by allowing users to quickly interact with those devices.”

Its there in TechCrunch Hardware battle top 15 and featured and reviewed by most of the gadget doctors and said wowww to this awesome ring ,
Fin can be get on the retail market from this year end Rohil Dev ,the youngest speaker of #MWC14 told
its now going to showcased over here in GSMA Mobile World Congress 2014 and hope they will get awesome response with the live demo of fin , here they have their two prototypes now and testing is done at their R&D labs at Indian Telecom Innovation Hub at cochin

Recently they achieved their goal in http://igg.me/at/wearfin Raised of $100,000 and solding out ,place the order for yours

Services | Embedded Support | Reviews | Virtual Technologys

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NS2 Throughput calculation awk program

NS2 , AWK script for finding the packet delivery ratio

AWK code for Measure End to End Delay - NS2

Extended Kalman Filter (EKF) MATLAB Implimentation

NETWORK SECURITY FINAL YEAR PROJECT TITLES IEEE 2014