CS AND IT PROJECTS 21-40

• 
  

  A Communication Perspective on Automatic Text Categorization –java/dotnet

• 
  

  Efficient Skyline Computation in Structured Peer-to-Peer Systems –java

• 
  

  Determining Attributes to Maximize Visibility of Objects –java

• 
  

  A Divide-and-Conquer Approach for Minimum Spanning Tree-Based Clustering –java

• 
  

  Evaluating the Effectiveness of Personalized Web Search –java/dotnet

• 
  

  Optimal-Location-Selection Query Processing in Spatial Databases –java

• 
  

  Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction –java/dotnet

• 
  

  Low-Complexity Iris Coding and Recognition Based on Directionlets –java

• 
  

  Watermarking Robustness Evaluation Based on Perceptual Quality via Genetic Algorithms

• 
  

  NNexus: An Automatic Linker for Collaborative Web-Based Corporation

• 
  

  Open Smart Classroom: Extensible and Scalable Learning System in Smart Space Using Web Service Technology

• 
  

  Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning

• 
  

  Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning

• 
  

  Monitoring Online Tests through Data Visualization –j2ee/dotnet

• 
  

  Clustering and Sequential Pattern Mining of Online Collaborative Learning Data –j2ee /dotnet

• 
  

  ANGEL: Enhancing the Utility of Generalization for Privacy Preserving Publication –java/dotnet

• 
  

  Rough Cluster Quality Index Based on Decision Theory-java/dotnet

• 
  

  Predictive Ensemble Pruning by Expectation Propagation –java

• 
  

  Discovery of Structural and Functional Features in RNA Pseudoknots-java/dotnet

• 
  

  Effective Collaboration with Information Sharing in Virtual Universities

CS AND IT PROJECTS LIST

PAGE # 1

1. On the Effect of Location Uncertainty in Spatial Querying Java

2. RiMOM: A Dynamic Multistrategy Ontology Alignment Framework –java /dotnet

3. Similarity-Profiled Temporal Association Mining –java/dotnet

4. Ranking and Suggesting Popular Items -java /

5. Olex: Effective Rule Learning for Text Categorization – java /dotnet

6. Multirelational k-Anonymity --- dotnet/java

7. E-card

8. Electronic Billing

9. Online E- banking

10. Digital Image Forensics via Intrinsic Fingerprints-java

11. A Fast Search Algorithm for a Large Fuzzy Database –java/dotnet

12. Unseen Visible Watermarking: A Novel Methodology for Auxiliary Information Delivery via Visual Contents –java /dotnet

13. A Game Theoretical Framework on Intrusion Detection in Heterogeneous Networks –java

14. Spread-Spectrum Watermarking Security –java./dotnet

15. A Hypothesis Testing Approach to Semifragile Watermark-Based -Authentication –java

16. Robust Blind Watermarking of Point-Sampled Geometry

17. Spatial PrObabilistic Temporal (SPOT) databases

18. Role Engineering via Prioritized -java

19. Discovery of Structural and Functional Features in RNA Pseudoknots-java/dotnet

20. Predicting Missing Items in Shopping Carts –j2ee/dotnet

Effective Collaboration with Information Sharing in Virtual Universities

Abstract
A global education system, as a key area in future IT, has fostered developers to provide various learning systems with low cost. While a variety of e-learning advantages has been recognized for a long time and many advances in e-learning systems have been implemented, the needs for effective information sharing in a secure manner have to date been largely ignored, especially for virtual university collaborative environments. Information sharing of virtual universities usually occurs in broad, highly dynamic network-based environments, and formally accessing the resources in a secure manner poses a difficult and vital challenge. This paper aims to build a new rule-based framework to identify and address issues of sharing in virtual university environments through role-based access control (RBAC) management. The framework includes a role-based group delegation granting model, group delegation revocation model, authorization granting, and authorization revocation. We analyze various revocations and the impact of revocations on role hierarchies. The implementation with XML-based tools demonstrates the feasibility of the framework and authorization methods. Finally, the current proposal is compared with other related work.

A Communication Perspective on Automatic Text Categorization –java/dotnet

Abstract
The basic concern of a Communication System is to transfer information from its source to a destination some distance away. Textual documents also deal with the transmission of information. Particularly, from a text categorization system point of view, the information encoded by a document is the topic or category it belongs to. Following this initial intuition, a theoretical framework is developed where Automatic Text Categorization(ATC) is studied under a Communication System perspective. Under this approach, the problematic indexing feature space dimensionality reduction has been tackled by a two-level supervised scheme, implemented by a noisy terms filtering and a subsequent redundant terms compression. Gaussian probabilistic categorizers have been revisited and adapted to the concomitance of sparsity in ATC. Experimental results pertaining to 20 Newsgroups and Reuters-21578 collections validate the theoretical approaches. The noise filter and redundancy compressor allows an aggressive term vocabulary reduction (reduction factor greater than 0.99) with a minimum loss (lower than 3 percent) and, in some cases, gain (greater than 4 percent) of final classification accuracy. The adapted Gaussian Naive Bayes classifier reaches classification results similar to those obtained by state-of-the-art Multinomial Naive Bayes (MNB) and Support Vector Machines (SVMs).

A Divide-and-Conquer Approach for Minimum Spanning Tree-Based Clustering –java

Abstract
Due to their ability to detect clusters with irregular boundaries, minimum spanning tree-based clustering algorithms have been widely used in practice. However, in such clustering algorithms, the search for nearest neighbor in the construction of minimum spanning trees is the main source of computation and the standard solutions take O(N2) time. In this paper, we present a fast minimum spanning tree-inspired clustering algorithm, which, by using an efficient implementation of the cut and the cycle property of the minimum spanning trees, can have much better performance than O(N2).

Ranking and Suggesting Popular Items java Project

Abstract
We consider the problem of ranking the popularity of items and suggesting popular items based on user feedback. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either from this suggestion set or from the set of all possible items. The goal is to quickly learn the true popularity ranking of items (unbiased by the made suggestions), and suggest true popular items. The difficulty is that making suggestions to users can reinforce popularity of some items and distort the resulting item ranking. The described problem of ranking and suggesting items arises in diverse applications including search query suggestions and tag suggestions for social tagging systems. We propose and study several algorithms for ranking and suggesting popular items, provide analytical results on their performance, and present numerical results obtained using the inferred popularity of tags from a month-long crawl of a popular social bookmarking service. Our results suggest that lightweight, randomized update rules that require no special configuration parameters provide good performance.

Multirelational k-Anonymity --- dotnet/java project abstract

Abstract
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous data set, any identifying information occurs in at least k tuples. Much research has been done to modify a single-table data set to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy or overly reduce the utility of the data in a multiple relation setting. We also propose two new clustering algorithms to achieve multi relational anonymity. Experiments show the effectiveness of the approach in terms of utility and efficiency

COMPUTER SCIENCE AND IT PROJECTS TOPICS ONLY

You may take these ideas and develop these
project in Java , Php , VB.Net / ASP .Net / C#

1 Business Performance Reporting
2 Case Management for Government Agencies
3 Classroom Management
4 Clinical Trial Initiation and Management
5 Competitive Analysis Web Site
6 Discussion Forum website
7 Disputed Invoice Management
8 Employee Training Scheduling and Materials
9 Equity Research Management
10 Integrated Marketing Campaign Tracking
11 Manufacturing Process Managements
12 Product and Marketing Requirements Planning
13 Request for Proposal Software
14 Sports League Management
15 Absence Request and Vacation Schedule Management
16 Budgeting and Tracking Multiple Projects
17 Bug Database Management
18 Call Center Management Software
19 Change Request Management
20 Compliance Process Support Site
21 Contacts Management Software
22 Document Library and Review
23 Event Planning and Management
24 Expense Reimbursement and Approval
25 Help Desk and Ticket Management
26 Inventory Tracking
27 I T Team Workspace
29 Job Requisition and Interview Management
28 Knowledge Base
29 Lending Library
30 Physical Asset Tracking and Management
31 Project Tracking Workspace
32. Shopping Cart .
33 Knowledge Base
34 Lending Library
35 Physical Asset Tracking and Management
36 Project Tracking Workspace
37 Room and Equipment Reservations
38 Sales Lead Pipeline
39. Yellow Pages & Business Directory
40. Time & Billing
41. Class Room Management
42. Expense Report Database
43. Sales Contact Management Database
44. Inventory Management Database
45. Issue Database
46. Event Management Database
47. Service Call Management Database
48. Accounting Ledger Database
49. Asset Tracking Database
50. Cycle Factory Works Management
51. Sales Corporation Management
52. Business Directory
53. Education Directory
54. Dental Clinic Management
55. Fund Raising Management
56. Clinic/ Health Management
57. Cable Management System
58. Survey Creation and Analytics
59. Museum Management System
60. Multi-Level Marketing System
61. Learning Management System
62. Knowledge Management System
63. Missing Person Site
64. Disaster Management Site
65. Job Management Site
66. Financial Portfolio Management
67. Market Research Management
68. Order Management System
69. Point of Sale
70. Advertisement /Banner Management and Analytics
71. Export Management System
72. Invoice Management
73. Recruitment Management System
74. Articles / Blog / Wiki Web site
75. Online Planner
76. Mock Tests and Examination Management
77. Examination System
78. Practice Test Management.
79. Asset Management System
80. Travel Agency System.
81. Placement Management System.
82. Polls Management
83. Customer Management
84. Project Management System.
85. Network Marketing System
86. Yoga Health Care Management
87. Personal Finance Management System
88. Real Estate Management System
89. Stock Mutual Funds Management
90. Careers and Employment Management System
91. Music Albums Management System
92. Classified Ads Managements
93. Property Management System
94. Sales & Retail Management
95. Dating Site
96. Hotel Management System
97. Search Engine
98. Online News Paper Site
99. Image Gallery
100. Staffing and Human Capital Management
101. Address Book
102. Inventory Management System
103. Newspaper Classifieds
104 Hostel Management
105 Music , Lyrics Website .
106 Wildlife Safari Trip Management
107 Wildlife Sanctuary Management
108 Wild life Flora and Fauna Statistics Management
109 Animal Hospital Management
110 Zoo Management System
111 Agro-Forestry Management System
112 Bus Depot Management System
113 Even t Management System
114 Clinical Research Management System
115 Food Technology Management System
116 Circus Management System
117. Resort Management System
118. Bugs/Issues Management System
119.Life /Motor Insurance Management System
120. Exam Scheduler
121. Ad Campaign Management System
123. Internet Banking Management System
124. Ad Agency Management System
125.Vechical Traffic Management System
126 Web Traffic Analytics Management System
127. Solid Waste Management System
128. Peer-To –Peer File Sharing System
129. Chat Application
130. Crisis Management System
131. Disaster Management System
132. Document Management System
133. Security Threats Evolution Software
134. Digital Rights Management System
135. Games ,Single , Multi-Player
136. Content /Document Management System
137. Archaeological Survey Management System
138. Market Research Management System
139. Crime Management System
140. Jail/Prison management System
141. Telephone Traffic Monitoring Management System
142. School Drop Out Statistics and Analytics System
143.Lost & Found Management System
144. Online Tutorials Management System
145.Bulk Sms Application
146. Criminal Records management System
147. Email Campaign Management System
148.Political Campaign Management System
149. Skill Competence and Mapping Application
150. Ontology based Web Crawler

CIRCULAR CONVOLUTION MATLAB PROGRAM

CIRCULAR CONVOLUTION

 

function f=circonv(a,b)

a=input('enter the first sequence=')

b=input('enter the second sequence=')

N1=length(a)

N2=length(b)

N=max(N1,N2)

a=[a zeros(1,N-N1)]

b=[b zeros(1,N-N2)]

for n=0:N-1

f(n+1)=0

for i=0:N-1

j=mod(n-i,N)

f(n+1)=f(n+1)+a(i+1)*b(j+1)

end

end

subplot(2,2,1)

stem(a)

xlabel('time index')

ylabel('amplitude')

subplot(2,2,2)

stem(b)

xlabel('time index')

ylabel('amplitude')

subplot(2,1,2)

stem(f)

xlabel('time index')

ylabel('amplitude')

title('circular convolution of two sequence')

 

 

 

 

OBSERVATION:

 

>> circonv(a,b)

enter the first sequence=[1,2,3]

a = 1 2 3

enter the second sequence=[1,2,3,4]

b = 1 2 3 4

N1 = 3

N2 = 4

N = 4

a = 1 2 3 0

b = 1 2 3 4

f = 0

j = 0

f = 1

j = 3

f = 9

j = 2

f = 18

j = 1

f = 18

f = 18 0

j = 1

f = 18 2

j = 0

f = 18 4

j = 3

f = 18 16

j = 2

f = 18 16

f = 18 16 0

j = 2

f = 18 16 3

j = 1

f = 18 16 7

j = 0

f = 18 16 10

j = 3

f = 18 16 10

f = 18 16 10 0

j = 3

f = 18 16 10 4

j = 2

f = 18 16 10 10

j = 1

f = 18 16 10 16

j = 0

f = 18 16 10 16

ans = 18 16 10 16

GENERATION OF FM SIGNAL DSP MATLAB PROGRAM

GENERATION OF FM SIGNAL

 

 

Fc=input('Enter the carrier frequency in Hz, Fc=');

Fm=input('Enter the modulating frequency in Hz, Fm=');

mf=input('Enter the modulation index, m=');

t=0:0.0001:1;

M=sin(2*pi*Fm*t);

Y=sin((2*pi*Fc*t)-(mf*M));

subplot(3,1,1);

plot(t,M);

axis([0 1 -1.5 1.5]);

title('Frequency modulation');

xlabel('Time');

ylabel('Modulation signal');

subplot(3,1,2);

plot(t,C);

axis([0 1 -1.5 1.5]);

xlabel('Time');

ylabel('Carrier signal');

subplot(3,1,3);

plot(t,Y);

axis([0 1 -1.5 1.5]);

xlabel('Time');

ylabel('FM signal');

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Observation:

 

Enter the carrier frequency in Hz, Fc=50

Enter the modulating frequency in Hz, Fm=10

Enter the modulation index, mf=2

 

 

FIR FILTER USING DIFFERENT WINDOW MATLAB PROGRAM

FIR FILTER USING DIFFERENT WINDOW

 

 

f=input('Samplin rate in Hz, f=');

fp=input('pass band edge frequency in Hz=');

fs=input('stop band edge frequency in Hz=');

rp=input('pass band ripple in dB=');

rs=input('minimum stop band attenuation in dB=');

wp=2*fp/f;

ws=2*fs/f;

[N,wn]=cheb1ord(wp,ws,rp,rs);

 

%Hann window

 

Hw=hann(N+1);

B=fir1(N,wn,Hw);

[H,omega]=freqz(B,1,256);

gain=20*log(abs(H));

subplot(2,2,1);

plot(omega/pi,gain);

grid;

xlabel('omega/pi');

ylabel('Gain in dB');

title('FIR LPF using HANN window');

 

%Hamming window

 

Hw=hamming(N+1);

B=fir1(N,wn,Hw);

[H,omega]=freqz(B,1,256);

gain=20*log(abs(H));

subplot(2,2,2);

plot(omega/pi,gain);

grid;

xlabel('omega/pi');

ylabel('Gain in dB');

title('FIR LPF using HAMMING window');

 

%Rectangular window

 

Hw=rectwin(N+1);

B=fir1(N,wn,Hw);

[H,omega]=freqz(B,1,256);

gain=20*log(abs(H));

subplot(2,2,3);

plot(omega/pi,gain);

grid;

xlabel('omega/pi');

ylabel('Gain in dB');

title('FIR LPF using RECTANGULAR window');

 

%Triangular window

 

Hw=triang(N+1);

B=fir1(N,wn,Hw);

[H,omega]=freqz(B,1,256);

gain=20*log(abs(H));

subplot(2,2,4);

plot(omega/pi,gain);

grid;

xlabel('omega/pi');

ylabel('Gain in dB');

title('FIR LPF using TRIANGULAR window');

 

 

 

 

 

 

 

 

Observation:

 

Samplin rate in Hz,f=2000

pass band edge frequency in Hz=200

stop band edge frequency in Hz=300

pass band ripple in dB=6

minimum stop band attenuation in dB=30

 

 

 

 

MOVING AVERAGE FILTER MATLAB PROGRAMS

MOVING AVERAGE FILTER

 

 

t=0:.01:1;

f=5;

y=sin(2*pi*f*t);

%Generation of random signal

g=0.5*randn(size(t));

z=g+y;

N=10; %order required

b=1/N*(ones(1,N));

x=filter(b,1,z); %filters noice

subplot(3,1,1);

plot(t,y);

ylabel('pure signal');

subplot(3,1,2);

plot(t,z);

ylabel('noise buried');

subplot(3,1,3);

plot(t,x);

ylabel('filtered signal');

xlabel('Time in seconds');

 

 

 

 

 

 

 

 

 

 

 

CHEBYSHEV TYPE 1 BAND PASS FILTER MATLAB PROGRAM

CHEBYSHEV TYPE 1 BAND PASS FILTER

 

 

alphap=input('pass band attenuation in dB=');

alphas=input('stop band attenuation in dB=');

fp1=input('pass band frequency fp1 in Hz=');

fp2=input('pass band frequency fp2 in Hz=');

fs1=input('stop band frequency fs1 in Hz=');

fs2=input('stop band frequency fs2 in Hz=');

f=input('Sampling frequency in Hz=');

wp1=2*fp1/f;ws1=2*fs1/f;

wp2=2*fp2/f;ws2=2*fs2/f;

wp=[wp1,wp2];

ws=[ws1,ws2];

%To find cutoff frequency and order of the filter

[n,wn]=cheb1ord(wp,ws,alphap,alphas);

%system function of the filter

[b,a]=cheby1(n,alphap,wn);

w=0:.01:pi;

[h,ph]=freqz(b,a,w);

m=20*log(abs(h));

an=angle(h);

subplot(2,1,1);

plot(ph/pi,m);

grid;

ylabel('Gain in dB');

xlabel('Normalised frequency');

subplot(2,1,2);

plot(ph/pi,an);

grid;

ylabel('Phase in radians');

xlabel('Normalised frequency');

 

 

 

 

Observation:

 

pass band attenuation in dB=2

stop band attenuation in dB=20

pass band frequency fp1 in Hz=100

pass band frequency fp2 in Hz=500

stop band frequency fs1 in Hz=200

stop band frequency fs2 in Hz=400

Sampling frequency in Hz=2000

 

 

CHEBYSHEV TYPE 1 LOW PASS FILTER MATLAB PROGRAM

CHEBYSHEV TYPE 1 LOW PASS FILTER

 

 

alphap=input('pass band attenuation in dB=');

alphas=input('stop band attenuation in dB=');

fp=input('pass band frequency in Hz=');

fs=input('stop band frequency in Hz=');

f=input('Sampling frequency in Hz=');

wp=2*fp/f;ws=2*fs/f;

%To find cutoff frequency and order of the filter

[n,wn]=cheb1ord(wp,ws,alphap,alphas);

%system function of the filter

[b,a]=cheby1(n,alphap,wn);

w=0:.01:pi;

[h,ph]=freqz(b,a,w);

m=20*log(abs(h));

an=angle(h);

subplot(2,1,1);

plot(ph/pi,m);

grid;

ylabel('Gain in dB');

xlabel('Normalised frequency');

subplot(2,1,2);

plot(ph/pi,an);

grid;

ylabel('Phase in radians');

xlabel('Normalised frequency');

 

 

 

 

 

 

 

 

 

 

Observation:

 

pass band attenuation in dB=1

stop band attenuation in dB=30

pass band frequency in Hz=200

stop band frequency in Hz=600

Sampling frequency in Hz=2000

 

 

 

 

BUTTERWORTH BAND REJECT FILTER MATLAB PROGRAM

BUTTERWORTH BAND REJECT FILTER

 

 

alphap=2;    %Passband attenuation in dB

alphas=20;    %stopband attenuation in dB

ws=[.2,.4];    %stopband frequency in radians

wp=[.1,.5];    %Passband frequency in radians

%To find cutoff frequency and order of the filter

[n,wn]=buttord(wp,ws,alphap,alphas);

%system function of filter

[b,a]=butter(n,wn,'stop');

w=0:.01:pi;

[h,ph]=freqz(b,a,w);

m=20*log(abs(h));

an=angle(h);

subplot(2,1,1);

plot(ph/pi,m);

grid;

ylabel('Gain in dB');

xlabel('Normalised frequency');

subplot(2,1,2);

plot(ph/pi,an);

grid;

ylabel('Phase in radians');

xlabel('Normalised frequency');

 

 

 

BUTTERWORTH HIGH PASS FILTER MATLAB PROGRAM

BUTTERWORTH HIGH PASS FILTER

 

 

alphap=.4;    %Passband attenuation in db

alphas=30;    %stopband attenuation in db

fs=400;    %stopband frequency in hz

fp=800;    %passband frequency in hz

F=2000;

omp=2*fp/F;oms=2*fs/F;

%To find cutoff frequency and order of the filter

[n,wn]=buttord(omp,oms,alphap,alphas);

%system function of the filter

[b,a]=butter(n,wn,'high');

w=0:.01:pi;

[h,om]=freqz(b,a,w);

m=20*log(abs(h));

an=angle(h);

subplot(2,1,1);

plot(om/pi,m);

grid;

ylabel('Gain in db');

xlabel('Normalised frequency');

subplot(2,1,2);

plot(om/pi,an);

grid;

ylabel('Phase in radians');

xlabel('Normalised frequency');

BUTTERWORTH BAND PASS FILTER MATLAB PROGRAM

BUTTERWORTH BAND PASS FILTER

 

 

alphap=2;    %Passband attenuation in dB

alphas=20;    %stopband attenuatio in dB

wp=[.2*pi,.4*pi];    %Passband frequency in radians

ws=[.1*pi,.5*pi];    %Stopband frequency in radians

%To find cutoff frequency and order of the filter

[n,wn]=buttord(wp/pi,ws/pi,alphap,alphas);

%system function of the filter

[b,a]=butter(n,wn);

w=0:.01:pi;

[h,ph]=freqz(b,a,w);

m=20*log(abs(h));

an=angle(h);

subplot(2,1,1);

plot((ph/pi),m);

grid;

ylabel('Gain in dB');

xlabel('Normalised frequency');

subplot(2,1,2);

plot((ph/pi),an);

grid;

ylabel('phase in radians');

xlabel('Normalised frequency');

 

BUTTERWORTH LOW PASS FILTER MATLAB PROGRAM

 

BUTTERWORTH LOW PASS FILTER

 

 

alphap=4;    %Passband attenuation in db

alphas=30;    %Stopband attenuation in db

fp=400;    %Passband frequency in hz

fs=800;    %Stopband frequency in hz

F=2000;    %Sampling frequency in hz

omp=2*fp/F;oms=2*fs/F;

%To find cutoff frequency and order of the filter

[n,wn]=buttord(omp,oms,alphap,alphas);

%system function of the filter

[b,a]=butter(n,wn);

w=0:.01:pi;

[h,om]=freqz(b,a,w,'whole');

m=abs(h);

an=angle(h);

subplot(2,1,1);

plot(om/pi,20*log(m));    

grid;

ylabel('Gain in db');

xlabel('Normalised frequency');

subplot(2,1,2);

plot(om/pi,an);

grid;

ylabel('phase in redians');

xlabel('Normalised frequency');

Dsp matlab program to Generate AM signal

GENERATION OF AM SIGNAL

 

 

Fc=input('Enter the carrier frequency in Hz, Fc=');

Fm=input('Enter the modulating frequency in Hz, Fm=');

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

t=0:0.0001:1;

C=sin(2*pi*Fc*t);

M=sin(2*pi*Fm*t);

Y=(1+m*M).*C;

subplot(3,1,1);

plot(t,M);

axis([0 1 -1.5 1.5]);

title('Amplitude modulation');

xlabel('Time');

ylabel('Modulation signal');

subplot(3,1,2);

plot(t,C);

axis([0 1 -1.5 1.5]);

xlabel('Time');

ylabel('Carrier signal');

subplot(3,1,3);

plot(t,Y);

axis([0 1 -1.5 1.5]);

xlabel('Time');

ylabel('AM signal');

 

 

 

Observation:

 

Enter the carrier frequency in Hz, Fc=50

Enter the modulating frequency in Hz, Fm=5

Enter the modulation index, m=0.6

 

 

 

Intelligent Automotive Ignition System

INTRODUCTION
Electronic ignition is nothing new. Many “electronic”
ignition systems still rely on mechanical properties of
the distributor for RPM sensitive modifications
(advance/retard) and for actual spark “distribution”.
The proposed system uses a PIC12C508 for total
spark control on a 4-cylinder engine. This system could
be adapted to 6 and 8-cylinder engines by using a
“double-fire” (firing on power and exhaust strokes).
In this system, each cylinder has its own high voltage
coil, allowing a “hotter” spark than is supplied through
the arcing and inaccuracies of a mechanical distributor.
The PICmicro could use either a single or dual sensor
(IR) reading from a “code-wheel”. The dual system
would indicate top dead center of cylinder #1 (or some
other relevant timing mark) and single marks for each
cylinder. The single sensor system would only require
the TDC detector.
The PICmicro would time TDC detections, thereby
determining engine RPM. This RPM would be used in
a lookup table to determine the spark timing (single
sensor) or cylinder detect (dual). Each cylinder would
fire at the appropriate time.
System Benefits:
(over a mechanical design)
Inexpensive processing power means system can be
easily tuned for performance/emissions or other criteria.
Stronger, more accurate spark can be delivered.
No parts to wear, arc, or corrode.

Free Flight Model Aircraft Dethermalizing Timer

INTRODUCTION
This application is for a Free Flight Dethermalizing
Timer for Model Aircraft. This is usually a mechanical or
fuse timer which spoils the lift of a model aircraft after 5
minutes. This is to prevent losing the aircraft in a strong
atmospheric thermal.
Using a bicolor LED and pushbuttons, a delay time of
between two and seven minutes in 10 second increments
is indicated B1 places the device in program mode and the LED
shows green. B2 then enters the number of 10 second
intervals with the LED flashing red for each interval in
groups of 5 for ease of reading. After the interval is
loaded, the timer is armed by pressing B2 again. When
armed the LED goes red. To start the timer, B2 is
pressed and held. The timer starts on release of B2.
When the unit times out, the 2N2222 is turned on, the
wire heats and contacts, and the control surface is
actuated and the 2N2222 is deactivated.
Nictol wire is a shape memory nickel titanium alloy
which contracts 3.5% of its length when heated. Nictol
wire also has a high resistance similar to nichrome
wire. Using 6-10 mil wire, 200 mA will heat the wire to
its activation temperature.

Infra Red Cordless Mouse

INTRODUCTION
Can anybody imagine that this little wonder,
PIC12C509, be used to control a cordless mouse?
Incredible! Just a handful of components, that's all! In
fact the circuit is small enough and perfectly suitable to
be fitted in the mouse housing with batteries. Current
consumption is minimized by the power reducing
SLEEP mode of the chip.
The circuit consists of two parts. A transmitter, which is
enclosed in the mouse, and the receiver, connected to
the PC via RS 232 link.
APPLICATION OPERATION
Transmitter
The PIC12C509 forms the heart of the circuit. Thanks
to the PIC12C509, it's use greatly simplifies hardware
design and the software. It senses the mouse movements,
mouse buttons and transmits the information to
the PC through infra red light emitting diodes (IR
LED's). The internal oscillator of the PIC12C509
enables one to use all of the I/O pins. The power-on
reset feature of the PIC12C509 rules out any need for
external reset circuitry, thereby saving one precious I/O
pin. Out of six I/O pins, one pin is configured to be output,
while the rest of the five pins are used as inputs.
The output pin drives two IR LED's through a MOSFET
BS170. Note that the MOSFET and one IR LED can be
saved and current consumption reduced by driving the
IR LED directly through the PIC12C509 pin at the
expense of limiting the range.
Three input pins out of the five are interfaced to the
three mouse buttons. Of course, two mouse buttons
can be used if desired. Flexibility of the design is evident.
Thanks to the PIC12C509 again! The remaining
two input pins are movement sensing inputs. Optical
sensing is used, which consists of an opto coupler with
a toothed wheel in between the LED and the phototransistor.
There are two such wheels, one for horizontal
movement and another for the vertical movement.
The wheels are mechanically coupled to the mouse ball
so that they rotate and electrical pulses are generated
with mouse movement. PIC12C509 senses the pulses
and converts the information into the appropriate format,
to be transmitted to the receiver via IR LED's. The
information, in the form of pulses, is then fed to the IR
LED through the driving MOSFET BS170. Thus the
information gets transformed into infra red light which is
transmitted to the receiver. When the microcontroller
transmits the motion information it produces exactly the
same pulses as would be produced by a regular
mouse.
Receiver
This is also a very simple circuit consisting of an IR
receiver, SFH505A, for instance and an op-amp
CA3140. The IR receiver receives the IR pulses and
transfers them into equivalent electrical pulses. The opamp
acts as an amplifier cum lavel shifter so as to make
these pulses compatible to RS 232 voltage levels. Note
that no extra power supply is needed for the receiver
circuit as it derives the power from the serial port itself.
Since this arrangement appears as a regular mouse to
the PC, there is no need to write device driver, and the
mouse can be used with the existing driver. Just plug
and play!

Infra Red Monitor

APPLICATION OPERATION
The application of IR monitor is to check an IR
emitting device such as a TV Video remote controller.
This IR monitor requires only 7 resistors, 4 LED s, 1
miniature switch, 1 IR photo transistor and 1
PIC12C671 uC.
This circuit uses an analog converter from PIC12C671

to measure the infra-red intensity from the IR photo transistor.

The intensity is displayed on the bargraph
LEDs.
OPERATION
1. Put an emitting device in front of the IR monitor
(from 1 inch to 2 feet).
2. Press the switch on the IR monitor once to
wake-up the micro-controller.
3. Press one key on the TV video controller and
watch the 4 monitor LEDs. The LEDs blink if
data is received, if all 4 LEDs stay off, no IR is
sensing. If high IR power is received, more
LEDs will be on. If IR monitor didn't receive anything
for 17 seconds, it will turn off (sleep mode).
Note: The OPTIC IR photo-transistor must be protected
from daylight source to avoid false bargraph level.

BUGSY -mobile robot with obstacle detection whiskers

Six-Legged mobile robot with obstacle detection whiskers and
Video Capture Camera

Functions: Moves around in all direction, changes paths when
whiskers are bumped with obstacles.. Can walk in tripod, wave,
and ripple gaits.. Can simulate cradle, swing motion, dizzy,
push- ups and even dance around- according to predefined program
script..

Parts used:
Electronics- 2 SSC's, Hobbico Servo motors,
Mechanical- locally fabricated materials.

Outdoor robot

Built mostly with recycling parts. Driven by 2 car wiper motors on about 17 Volts. Direction change
by a third wiper motor in the middle to change the angle of the two halves of the chassis; it works
with a steel rope that is wrapped around the motors shaft, can just be activated while driving
because of self-destruction risks.
The wheels are from a lawn mower. The rear wheels were fixed on a tilteable axe and fixed on spring
resorts.
Power supply by two 12V lead rechargeables batteries in series (7 Ah each).
A main PWM-circuit stabilizes the motor voltage two around 17 Volts.
Controlled by an 8052AH-BASIC-Evaluation board from Elektor magazine. Most other circuits self
developed and built, 2 kits assembled. Can receive commands by a TV IR-remote control, an RC5-code
receiver is used.
2 Bumpers with microswitches on the front end, two on the rear and two in the middle for the
direction motor.
2 IR-Diodes on the front an one IR-modulated receiver (38 kHz). Ultrasonic obstacle detection on the
rear.
It got some obstacle detecting and avoiding routines, but had lots of problems with his own weight.
The remote control was quite unuseable when the sun was shining.

Email: gklares@ara.lu

Wallie robot

Wallie is an attempt to make a very small and very simple robot which is still able to perform a
certain task. In this case that task is wall-following. As you can see on the picture, Wallie's body
is an old PC mouse. It uses differential steering to navigate across its world. Its two motors are
very small 5 volt gearbox motors. I have salvaged a tape pressing roll from an old cassette deck and
transformed it in a very small castor wheel. This works beatifully.

Wallie uses three infrared obstacle detection sensors to locate and follow a wall. These are mounted
on the front of the bot as can be seen on the picture. One pointing a little bit to the left, one
pointing forward and one pointing a little to the right. These sensors either see or don't see an
obstacle. There is no distance measuring capability available. When an object comes within
approximately 7 cm of the sensor, it will trigger.

The brain of wallie is an ATMEL AT90S2313 microcontroller. It is programmed with the AVR port of the
linux GCC C-compiler.

The wall following procedure is as followes: First, Wallie waits until it gets offered a wall to
follow. In other words: You have to put him so close to the wall that the sensors of the bot see it.
Wallie will then start to drive forwards a little bit in the opposite direction of the wall. When
the distance to the wall gets to great, the sensor pointing to the wall will not see it anymore.
Wallie will then start to drive towards the wall again until he sees it again. Then he starts to
move away from the wall again etc. This way he will follow the wall without touching it. When it
does not find the wall within a short period, this means the wall has moved sharply away from the
bot. Wallie will then start to turn sharpy towards the direction he expects the wall to be until it
is found again. The second special situation is when the sensor facing the wall and the sensor
facing forwards see the wall. This means the wall has made a sharp turn towards the robot. Then
wallie will react by turning away from the wall until only the sensor facing the wall sees the wall.
The third special situation is when all three sensors see the wall. This means Wallie has driven up
a dead end or a very sharp edge in the wall. He will then start to turn on the spot until the sensor
pointing to the front does not see the wall any more. He then faces in the correct direction again.

Seeker Robot -Seeker is to look around for human beings

The goal of Seeker is to look around for human beings, drive towards the first it sees and then try
to follow that person. Seeker locates humans by using a Passive Infrared (PIR) sensor. This sensor
is capable of detecting the heat signature of a human being. It is mounted inside the white cone on
the sensor unit at the front of the robot. The white cone holds a freshnell lens to focus the
infrared (heat) radiation on the sensor element. The sensor unit also holds tree SHARP GP2D02
infrared distance measurement units. These sensors take over when Seeker gets close to the person it
wants to follow. This cannot be done with the PIR sensor because it is not accurate and directional
enough at close range. The sensors of Seeker are mounted on a pan/tilt unit. This enables Seeker to
look around and point its sensors at any object of interest in its field of view.

The drive train of Seeker is the same as that of Roamer and Wallie. It consists of two propelled
front wheels and a castor wheel at the back, enabling the bot to navigate the world by using
differential steering.

The brain of seeker is an ATMEL AVR 90S8535 microcontroller. It is programmed in C using the AVR
port of the linux GCC C-compiler.

The procedure to locate and follow humans is as follows: At powerup, Seeker starts to turn on the
spot. When it detects a heat signature, it drives towards it. When the heat signature is lost during
the approach of the target, it starts turning in a circle again to relocate the heat signature. When
Seeker gets close enough to the target, the infrared distance measurement sensors take over. In
order to follow the target, the distance measured by the left sensor is compared to that of the
right sensor (the third sensor is not used right now). If the left sensor measures a greater
distance than the right sensor, it concludes the target is located on its right side. If its the
other way around, it assumes the target is on the left. It will then move its sensor head in the
direction of the target. The motor controller of Seeker is programmed to drive in the direction the
sensor head is looking, and therefore the whole robot will start following the target. If seeker
gets close to the target he will stop. If the person being followed starts to move towards Seeker he
will start to drive backwards to avoid being stepped on. If Seeker loses the target he will start
looking for a new heat signature.

Roverbot

Description



When the robot hits something on the right side of the bumper, the right push button
is depressed. This makes the robot stop, back up, turn to the left, and continue moving forward. If
the robot collides with an object on the left side of the bumper, the left push button is hit. The
robot stops, backs up, turns to the right, and continues going forward. Because of the bumper, the
robot can maneuver around obstacles and keep moving without any human interference.

Line Follower

Functions
The buggy features two main wheels positioned opposite each other, and independently driven by
stepper motors. The chassis is balanced with a simple peg that skids along the ground.
The motors and sensors plug into two circuit boards mounted in the buggy chassis, and this in turn
is linked by means of umbilical ribbon cable, to an input/output port used in conjunction with a
Sinclair ZX81.
The ZX81 provides the intelligence to make the buggy follow a black line (electrical black
insulation tape). It could be argued that a basic line follower does not really require the use of a
computer, with the buggy being made to operate properly by getting the sensors to control the motors
through more direct electronic means. However, using a computer allows easy behaviour refinement by
software changes. For example after the basic line following was implemented the buggy was
programmed to be able to negotiate branches in the line.

Specifications
The chassis is built from a combination of Meccano® and Perspex®. The Meccano enabled a chassis to
be quickly constructed, and the Perspex facilitated the non Meccano parts (stepper motors and
wheels) to be easily incorporated into the design.
The robot electronics comprised two circuit boards - the driver board and the sensor board. These
boards are stacked one over the other.
The step resolution of the stepper motors is 1.8 degrees. To turn this step size into a smaller
wheel travel, a reduction gearing comprising a small cog on the motor shaft and a much larger cog
connected on the wheel is utilised on each motor drive.

Driver board
Two SAA1027 stepper motor drive ICs are employed on the driver board, each one to control a four
phase stepper motor. The ICs simplify control of the stepper motors by requiring just a digital
direction signal (clockwise/anti clockwise) and digital clock signal (advance step) for each motor.
The SAA1027 ICs require a 12v power supply, and 12v control signals. LM324 quad operational
amplifiers are used to level shift the 5v TTL levels from the ZX81 up to the 12v control signals.

Sensor Board
To enable the buggy to follow a black line, two optical sensors (TIL81) are used. They are
positioned at the front underside of the buggy. The sensors are separated by a distance of 1cm.
Additionally an infra-red LED (TLN1 10) is placed between the sensors, so that they are less
effected by the surrounding ambient light. Depending on the surface either black or white the
infrared beam is either absorbed or reflected respectively.

The sensor board comprises two identical circuits each connected to a corresponding optical sensor.
Each circuit converts the optical sensor output from an analogue value to a 5v TTL signal that can
be read by the ZX81 via the input port.

Online E- banking (or Internet banking) project abstract For Computer science B.Tech Students

Online banking (or Internet banking) allows customers to conduct financial transactions on a secure website operated by their retail or virtual bank, credit union or building society.
Features

Online banking solutions have many features and capabilities in common, but traditionally also have some that are application specific.
The common features fall broadly into several categories
Transactional (e.g., performing a financial transaction such as an account to account transfer, paying a bill, wire transfer... and applications... apply for a loan, new account, etc.)
Electronic bill presentment and payment - EBPP
Funds transfer between a customer's own checking and savings accounts, or to another customer's account
Investment purchase or sale
Loan applications and transactions, such as repayments
Non-transactional (e.g., online statements, check links, cobrowsing, chat)
Bank statements
Financial Institution Administration - features allowing the financial institution to manage the online experience of their end users
ASP/Hosting Administration - features allowing the hosting company to administer the solution across financial institutions
Features commonly unique to business banking include
Support of multiple users having varying levels of authority
Transaction approval process
Wire transfer
Features commonly unique to Internet banking include
Personal financial management support, such as importing data into personal accounting software. Some online banking platforms support account aggregation to allow the customers to monitor all of their accounts in one place whether they are with their main bank or with other institutions.

TeMo - Telerobotics over Mobile packet data services

TeMo is a tele-operated mobile internet robot. While other internet robots mostly use WiFi (or a nearby PC with an internet connection), TeMo connects to the internet using Mobile packed data services (e.g. GPRS / EDGE / UMTS / HSDPA). The advantage is virtually unlimited mobility for the robot.

Simply put, TeMo is a robot that can
Move around and do stuff (since it has tank tracks and a robotic arm)
Can be controlled from any where in the world (since it has Internet connectivity)
Can boldly go where no robot has gone before !! (since it used mobile packet data services for Internet connectivity)

TeMo is controlled using an Ajax based webage. The webpage is served by a tiny webserver running on a mobile phone that is mounted on the robotic platform. TeMo is also capable of sending pictures in realtime to the user terminal (and possibly also video in the near future).

Lets look at TeMo in detail. TeMo is made up of the following parts:
Lego (technic) blocks for the basic mechanical structure.
5 servo motors for mobilty, torso rotation and arm control.
A microcontroller that controls the motors, listens for commands from the webserver
A standard mobile phone that runs a tiny Webserver, connects to the Internet using GPRS/EDGE/UMTS and communicates with the microcontroller over Infrared.

The following diagram shows how the overall system works.

Educational purposes Robot :Webots

Webots is a professional robot simulator widely used for educational purposes. The Webots project started in 1996, initially developed by Dr. Olivier Michel at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland.

Webots uses the ODE (Open Dynamics Engine) for detecting of collisions and simulating rigid body dynamics. The ODE library allows one to accurately simulate physical properties of objects such as velocity, inertia and friction.

A large collection of freely modifiable robot models comes in the software distribution. In addition, it is also possible to build new models from scratch. When designing a robot model, the user specifies both the graphical and the physical properties of the objects. The graphical properties include the shape, dimensions, position and orientation, colors, and texture of the object. The physical properties include the mass, friction factor, as well as the spring and damping constants.

Webots includes a set of sensors and actuators frequently used in robotic experiments, e.g. proximity sensors, light sensors, touch sensors, GPS, accelerometers, cameras, emitters and receivers, servo motors (rotational & linear), position and force sensor, LEDs, grippers, gyros and compass.

The robot controller programs can be written in C, C++, Java, Python and MATLAB. The AIBO, Nao and E-puck robot models can also be programmed with the URBI language (URBI license required).

Webots offers the possibility to take PNG screen shots and to record the simulations as MPEG (Mac/Linux) and AVI (Windows) movies. Webots worlds are stored in .wbt files which have a format very similar to VRML. It is also possible to import and export Webots worlds or objects in the VRML format. Another useful feature is that the user can interact with a running simulation at any time, i.e. it possible to move the robots and other object with the mouse.

Webots is used in several online robot programming contests. The Robotstadium[1] competition is a simulation of the RoboCup Standard Platform League. In this simulation two teams of Nao play soccer with rules similar to regular soccer. The robots use simulated cameras, ultrasound and pressure sensors. In the Rat's Life[2] competition two simulated e-puck robots compete for energy resources in a Lego maze. Matches are run on a daily basis and the results can be watched in online videos.

Light Sensor VIRTUAL FLOWER ROBOT

In this project we build a robot that has two optical light sensors and turns its head in the direction of light. The head is the only moving part of the robot and it is controlled by a gearbox manufactured by Tamiya. The light sensors are formed by two CdS photoresistors available from RadioShack. I used two smallest ones from the package of 5 photoresistors available there. The cell diameter is about 5mm, the maximum dark resistance is about 14M and the minimum light resistance is about 0.5K. The daylight resistance in my room is about 50K.Light sensor Motor and gear


The first prototype

The photoresistors are mounted on the robot head which in turn is attached to the gear axe.  I used a paper strip separating the photoresistors and its optimal length in my setting is 1in measured from the photoresistors. The separator is needed to shadow one of the photoresistors when the light source is moving. For simplicity, the head can move in a 2-dim horizontal plain only, thus making a difference with a real sunflower. The head is formed by a small breadboard ,which for now has just the photoresistors and the paper separator mounted on it.

The light sensors (L and R on the schematic) are connected to the PIC which periodically measures their resistance and controls the motor accordingly. To measure the resistance of the photocells I use a classic RC-chain and measure the time of charging a capacitor, which for a fixed C is proportional to R. The direction of the motor rotation is controlled by the classic H-bridge composed entirely from NPN Darlington transistors TIP120. These transistor structures contain the diodes protecting them from the high voltage caused by inductive load. The bases of the bridge transistors are connected to PIC. If the RB6 and RB7 outputs are both 0, the motor is not rotating. If one of them is 0 and the other one is 1, the motor is rotating in the corresponding direction. The situation when both outputs are 1 is prevented by the software, since in this case the 3V battery would be short cut.Schematic Layout


This is just the first prototype of the design and I use LCD for tuning and debugging. The LCD displays the numbers coming out of the resistance measurement. The larger numbers correspond to a darker resistance. The built-in PIC program does not allow the numbers to exceed 255. The minimum numbers corresponding to lighting the device with a desktop 60W lamp is about 30, so we have almost the full range of the light intensity measurements of 30 - 255. The motor starts to move if the absolute difference between the numbers is larger than 15, which is defined experimentally. This constant defines how much the light source can move before the robot starts following it. The larger is the constant, the less is the accuracy of following the light. The sensor resistance is measured approximately every 80msec, which is also near optimal for the gear ratio 719:1 and the motor voltage in the range 3 - 5V. Increasing the measurement time up to 250msec causes the head moving back and force several times before it finally stabilizes.
The embedded program source for the first prototype is photo1.asm
The Second prototype

The LCD is actually not needed in a real device and can be excluded. This decreases the number of interface pins down to 6. Hence, a smaller PIC can be used as it is shown on the updated schematics. This PIC 12F675 has built-in 4MHz RC-oscillator which further simplifies the circuit. Also, smaller transistors can be used to drive the motor. However, they do require the diodes protecting them from the high voltage peaks caused by the motor.

Excluding the LCD significantly simplifies the program. One needs, however, to rename th output ports and other registers according to the PIC specs. The 12F675 has built-in comparators and ADC that are not used in this design and must be turned off. Also, all I/O ports must be setup for the digital mode. Finally, the PIC configuration fuses have some extra bits.
The embedded program source for the second prototype is photo2.asm
The Final Design

The robot electronics is assembled on a small board available form RadioShack. To simplify the power supply I added 3 silicon diodes 1N4003 that drop the 5V voltage down to about 3V for the motor. This way the entire unit can be powered up from a single 5V source. The maximum current consumption is about 200ma when the motor is on and just a couple of milliamperes when it is off.


The code is practically the save as the one for the second prototype with just a few changes. Two procedures that measure the light intensity are merged into one and I set up manually all PIC control registers instead of relying on their default values after power reset.
The embedded program source for the final design is photo3.asm
Things to consider

The used way for measuring the resistance if not optimal. It takes 2 pins of PIC - one for charging/discharging the cap and one for actually measuring a voltage. This can be accomplished with just one PIC pin. For this disconnect the right (on schematic) end of the cap and attach it to +5V. Rising up the voltage on PIN GP3 (in this case it should be configured for output) will discharge the cap. Now, configure this pin for input, and measure the voltage as described above.

For more details,ckts and help Contact
report4all@gmail.com

GPS Based Vehicle Tracking and Security System Abstract

Global Positioning System, usually called GPS, is the only fully-functional satellite navigation system. A constellation of more than two dozen GPS satellites broadcasts precise timing signals by radio, allowing any GPS receiver (abbreviated to GPSr) to accurately determine its location (longitude, latitude, and altitude) in any weather, day or night, anywhere on Earth.

GPS has become a vital global utility, indispensable for modern navigation on land, sea, and air around the world, as well as an important tool for map-making and land surveying. GPS also provides an extremely precise time reference, required for telecommunications and some scientific research, including the study of earthquakes. GPS receivers can also gauge altitude and speed with a very high degree of accuracy.

The United States Department of Defense developed the system, officially named NAVSTAR GPS (Navigation Signal Timing and Ranging Global Positioning System), and launched the first experimental satellite in 1978. The satellite constellation is managed by the 50th Space Wing. Although the cost of maintaining the system is approximately US$400 million per year, including the replacement of aging satellites, GPS is available for free use in civilian applications as a public good.

A GPS reciever placed in a car can recieve signals from these satellites and will calculate the exact location of the car in terms of latitude and longitude. This data can be sent to a server that can monitor the location. A GSM modem can be integrated into this project for providing security and remote control. The current location of the car can be found out by sending an SMS. The car can also be disabled by sending an SMS.

Technologies:-

GSM/GPRS, Embedded Systems