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
VOICE RECOGNITION SECURITY SYSTEM
When we think of programmable speech recognition, we think of calling FedEx customer service call center with automated voice recognition response systems. We also think of PC-based speech recognition Dragon NaturallySpeaking. Now we took that a step further. We are talking about speech recognition in a tiny Mega32 microcontroller. We are talking about real-time speech processing which means there is no need to store the samples in an external memory at all. This was made possible by implementing bandpass filters in assembly language with fixed-point format onto the microcontroller. In this filter design, not only the output of the filter is calculated, but its square and accumulation also obtained. Thus much time is saved so that each speech sample can be processed to get its frequency spectrum before next new sample comes. In addition, the analysis of the voice is made using correlation and regression method to compare the voiceprint of different words. These techniques provide stronger ability to recognize the same word. Training procedure is also used to reduce the random changes due to one word is spoken different times. The training procedure can get the more accurate frequency spectrum for one word. The experimental results demonstrate high accuracy for this real-time speech recognition system
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');
DISCRETE FORIER TRANSFORM & INVERSE FORIER TRANSFORM MATLAB PROGRAM
DISCRETE FORIER TRANSFORM & INVERSE FORIER TRANSFORM
%Discrete forier transform
x1=input('Enter the sequence x1=');
N=input('Enter the value of N=');
xk=fft(x1)/N;
subplot(2,2,1);
n=0:1:length(xk)-1;
stem(n,abs(xk));
title('Absolute value of Forier transform');
subplot(2,2,2);
stem(n,angle(xk));
title('Angle of Forier transform');
pause;
%Inverse forier transform
xk1=ifft(xk)*N
subplot(2,2,3);
stem(n,xk1);
title('Input sequence');
Observation:
Enter the sequence x1=[1,2,3,4,5]
Enter the value of N=2
xk1 = 1 2 3 4 5
CHEBYSHEV TYPE 2 BAND PASS FILTER MATLAB PROGRAMS
CHEBYSHEV TYPE 2 BAND STOP 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]=cheb2ord(wp,ws,alphap,alphas);
%system function of the filter
[b,a]=cheby2(n,alphas,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=700
stop band frequency fs1 in Hz=200
stop band frequency fs2 in Hz=500
Sampling frequency in Hz=2000
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 2 LOW PASS FILTER MATLAB PROGRAM
CHEBYSHEV TYPE 2 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]=cheb2ord(wp,ws,alphap,alphas);
%system function of the filter
[b,a]=cheby2(n,alphas,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
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 find the Convolution of two sequence
CONVOLUTION
a=input('enter the first sequence=')
b=input('enter the second sequence=')
f=conv(a,b)
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('linear convolution of two sequence')
Observation:
enter the first sequence=[1,2,3,4]
a = 1 2 3 4
enter the second sequence=[1,4,3,5]
b = 1 4 3 5
f = 1 6 14 27 35 27 20
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
Dsp matlab program to generate Discrete and Continuous
DISCRETE & CONTINUOUS WAVES
f=input('Enter the frequency =');
a=input('Enter the amplitude =');
t=0:.01:1;
y=a*sin(2*pi*f*t);
subplot(2,2,1)
plot(t,y)
title('continuous sine')
xlabel('time')
ylabel('amplitude')
subplot(2,2,2)
stem(t,y)
title('discrete sine')
xlabel('time')
ylabel('amplitude')
z=a*cos(2*pi*f*t);
subplot(2,2,3)
plot(t,z)
title('continuous cosine')
xlabel('time')
ylabel('amplitude')
subplot(2,2,4)
stem(t,z)
title('discrete cosine')
xlabel('time')
ylabel('amplitude')
Observation:
Enter the frequency =5
Enter the amplitude =5