::ApEx Of E-mArKeTiNg

Triple clicks is like more than home

2011-09-04T07:07:00.001-07:00

Justin Bieber and Selena Gomez on Vacay

2011-06-06T21:48:00.001-07:00

What do you think Justin is doing?

Do you think this cutie pie can handle that on the Hawaii beach.

See the second pic, Justin wants to move Selena's hands away.

He looks like can not control if Selena keep her hand in Justin's west.

What do you think so?

Note: these pictures were not taken by blogger. This is widely found on the internet while browsing

Fourier Analysis

2011-04-03T00:33:00.000-07:00

Commands covered:

dft
idft
fft
ifft
contfft

The dft command uses a straightforward method to compute the discrete Fourier transform. Define a vector x and compute the DFT using the command
X = dft(x)
The first element in X corresponds to the value of X(0).
The command idft uses a straightforward method to compute the inverse discrete Fourier transform. Define a vector X and compute the IDFT using the command

x = idft(X)

The first element of the resulting vector x is x[0].
For a more efficient but less obvious program, the discrete Fourier transform can be computed using the command fft which performs a Fast Fourier Transform of a sequence of numbers. To compute the FFT of a sequence x[n] which is stored in the vector x, use the command
X = fft(x)
Used in this way, the command fft is interchangeable with the command dft. For more computational efficiency, the length of the vector x should be equal to an exponent of 2, that is 64, 128, 512, 1024, 2048, etc. The vector x can be padded with zeros to make it have an appropriate length. MATLAB does this automatically by using the following command where N is defined to be an exponent of 2:

X = fft(x,N);

The longer the length of x, the finer the grid will be for the FFT. Due to a wrap around effect, only the first N/2 points of the FFT have any meaning.
The ifft command computes the inverse Fourier transform:

x = ifft(X);

The FFT can be used to approximate the Fourier transform of a continuous-time signal as shown in Section 6.6 of the textbook. A continuous-time signal x(t) is sampled with a period of T seconds, then the DFT is computed for the sampled signal. The resulting amplitude must be scaled and the corresponding frequency determined. An M-file that approximates the Fourier Transform of a sampled continuous-time signal can be downloaded from contfft.m. Let a vector x be defined as the sampled continuous-time signal x(t) and let T be the sampling time.

[X,w] = contfft(x,T);

The outputs are the Fourier transform stored in the vector X and the corresponding frequency vector w.

Source: Georgia Tech

Darwin Cyclone watch.....

2011-02-15T06:05:00.000-08:00

via: http://www.bom.gov.au/products/IDD65011.shtml

Community Threat

Past Cyclone Details

	Warning Zone - Gales within 24 hours
	Watch Zone - Gales from 24 to 48 hours

	Past Location and Intensity Number
	Past Track and Movement

Current Cyclone Details

Forecast Cyclone Details
(at 24 and 48 hours from issue)

	Current Location and Intensity Number
	Very Destructive Winds
	Destructive Winds
	Gale Force Winds

	Forecast Location and Intensity Number
	Very Destructive Wind Boundary
	Destructive Wind Boundary
	Gale Force Wind Boundary
	Most Likely Future Track
	Range of Likely Tracks of Cyclone Centre

The forecast path shown above is the Bureau's best estimate of the cyclone's future movement and intensity. There is always some uncertainty associated with tropical cyclone forecasting and the grey zone indicates the range of likely tracks of the cyclone centre.
Due to the uncertainty in the future movement, the indicated winds will almost certainly extend to regions outside the rings on this map. The extent of the warning and watch zones reflects this.
This product is designed for land-based communities; mariners should read the coastal waters and high seas warnings.

Remarks:
LOCALLY DAMAGING wind gusts up to 100 kilometres per hour are being experienced with squally showers and storms in the northwest Darwin-Daly District and the Tiwi Islands.

GALES with gusts to 110 kilometres per hour are expected to develop between Daly River Mouth and Point Stuart, including Darwin and the Tiwi Islands, during Wednesday.

DESTRUCTIVE WINDS with gusts to 130 kilometres per hour may develop over the Tiwi Islands late Thursday if the cyclone continues to intensify.

HEAVY RAIN may cause widespread flooding in the northern Darwin-Daly District.
The Territory Controller advises residents from Daly River Mouth to Point Stuart including the Tiwi Islands that now is the time to make final preparations to your home shelter or identify which public emergency shelter to use.

Residents of Darwin and the Rural Area are advised that if you DO NOT have accommodation constructed to the Building Code or are unsure of your present accommodation you should decide which public emergency shelter to use. You should now have your emergency kit complete and ready. DO NOT PROCEED TO PUBLIC EMERGENCY SHELTERS UNTIL ADVISED TO DO SO.

The Territory Controller advises communities under Cyclone Watch that now is the time to put together your emergency kit, clear your yards and balconies, and commence home shelter preparations.

Name: Tropical Low

Details:

	Time (CST)	Intensity Category	Latitude (decimal deg.)	Longitude (decimal deg.)	Estimated Position Accuracy (km)
0hr	10 pm February 15	tropical low	12.8S	130.6E	55
+6hr	4 am February 16	tropical low	12.5S	130.7E	85
+12hr	10 am February 16	tropical low	12.2S	130.7E	110
+18hr	4 pm February 16	1	12.0S	130.6E	140
+24hr	10 pm February 16	1	11.9S	130.5E	165
+36hr	10 am February 17	1	11.8S	130.2E	230
+48hr	10 pm February 17	1	11.8S	130.0E	285

The next Forecast Track Map will be issued by 2:00 am CST Wednesday

Audio processing

2010-09-29T07:35:00.000-07:00

Audio signals, much like images, can undergo filtering. It is somewhat easier

to understand the impact of signal processing on audio, since audio needs not

be translated from a spatial to a frequency domain.

To load a wave (PCM) audio file, Matlab provides the function wavread :

funky = wavread('funky.wav');

It's important to capture the sampling frequency at which the sound was

recorded, otherwise the speed of playback and results of further processing

is not guaranteed to be correct:

[funky, f] = wavread('funky.wav');

To play a wave file at sampling frequency f:

wavplay(funky, f);

To view the waveform, plot the wave. Since audio is represented with many

thousand samples per second, it may be required to plot small portions of the

waveform at a time.

subplot(2,1,1), plot(funky), title('Entire waveform');
smallRange = 100000:100000+floor(f/100);
subplot(2,1,2), plot(smallRange, funky(smallRange)),..
title('100 milliseconds');

Figure 10

Spectrogram

Figure 10.27

2-dimensional plots of audio waves can be used to easily identify magnitude;
however, combined frequency distributions and magnitudes are more easily
viewed in a spectrogram:

specgram(funky, 512, f);

where 512 is the number of samples that are used for the discrete Fourier
Transform, and thus a grouping factor of samples per column in the spectrogram image.

Figure 10.28

Plotting both the original waveform and the spectrogram, it is possible to find
correspondences between the two graphical representations:

subplot(2,1,1), plot(funky), axis('tight');
subplot(2,1,2), specgram(funky,128,f);

However, it is easier to find such similarities in smaller portions of audio.
We can also find

repeating patters:

subplot(2,1,1), plot(funky(100000:150000)), axis('tight');
subplot(2,1,2), specgram(funky(100000:150000),128,f);

Figure 10.29

Filtering

We will examine audio filtering in the sense of specific frequency suppression and

extraction. There are many different types of filters available for the construction

of filters. We will specifically use the Butterworth filter.

Matlab includes function butter for building Butterworth filters of three sorts:

'low' : Low-pass filters, which remove frequencies greater than some specified value.
'high' : High-pass filters, which remove frequencies lower than some specified value.
'stop' : Stop-band filters, which remove frequencies in a given range of values.

Frequencies values are specified in normalized terms between 0.0 and 1.0, where 1.0 corresponds

to half the sampling frequency: f/2. A given frequency is thus expressed in terms of this value,

for example, 1000Hz = 1000/(f/2).

Filters are described in terms of 2 vectors ([b, a] = [numerator, denominator]).

To apply a filter to a 1-D audio waveform, Matlab provides function filtfilt , which takes

as arguments the result [b, a] from butter, the waveform, and a value denoting the

order (number of coefficients) of the filter.

A filter's frequency response can be plotted using function freqz . Magnitude values at zero dB

are unaffected by the filter. Magnitude values below 0 dB are suppressed.

10.3.2.1 Low-pass filter

We design a 10th order low-pass filter to supress frequencies higher than 200Hz.

fNorm = 200 / (f/2);
[b,a] = butter(10, fNorm, 'low');
funkyLow = filtfilt(b, a, funky);

Add caption

The frequency response for this filter:

freqz(b,a,128,f);

wavplay(funkyLow,f);

Playing the new audio waveform clearly reveals that low (bass) frequencies are preserved, while all higher frequencies have been suppressed.

10.3.2.2 High-pass filter

We design a 10th order high-pass filter to supress frequencies below 5kHz.

fNorm = 5000 / (f/2);
[b, a] = butter(10, fNorm, 'high');
funkyHigh = filtfilt(b, a, funky);

Figure 10.31

The frequency response for this filter:

freqz(b,a,128,f);

wavplay(funkyHigh,f);

Playing the new audio waveform reveals only high-pitched tones.

Stop-band filter

Figure 10.32

fNorm = [500/(f/2), 2500/(f/2)];
[b, a] = butter(10, fNorm, 'stop');
funkyBand = filtfilt(b, a, funky);

The frequency response for this filter:

freqz(b,a,128,f);

wavplay(funkyBand,f);

MATLAB Basics

2010-09-29T07:15:00.000-07:00

MATLAB is started by clicking the mouse on the appropriate icon and is ended by typing exit or by
using the menu option. After each MATLAB command, the "return" or "enter" key must be
depressed.

A. Definition of Variables
Variables are assigned numerical values by typing the expression directly, for example, typing
a = 1+2
yields: a = 3
The answer will not be displayed when a semicolon is put at the end of an expression, for example type
a = 1+2;.
MATLAB utilizes the following arithmetic operators:
+ addition
- subtraction
* multiplication
/ division
^ power operator
' transpose
A variable can be assigned using a formula that utilizes these operators and either numbers or
previously defined variables. For example, since a was defined previously, the following expression is
valid
b = 2*a;
To determine the value of a previously defined quantity, type the quantity by itself:
b
yields: b = 6
If your expression does not fit on one line, use an ellipsis (three or more periods at the end of the line)
and continue on the next line.
c = 1+2+3+...
5+6+7;

There are several predefined variables which can be used at any time, in the same manner as userdefined
variables:
i sqrt(-1)
j sqrt(-1)
pi 3.1416...
For example,
y= 2*(1+4*j)
yields: y= 2.0000 + 8.0000i
There are also a number of predefined functions that can be used when defining a variable. Some
common functions that are used in this text are:
abs magnitude of a number (absolute value for real numbers)
angle angle of a complex number, in radians
cos cosine function, assumes argument is in radians
sin sine function, assumes argument is in radians
exp exponential function
For example, with y defined as above,
c = abs(y)
yields: c = 8.2462
c = angle(y)
yields: c = 1.3258
With a=3 as defined previously,
c = cos(a)
yields: c = -0.9900
c = exp(a)
yields: c = 20.0855
Note that exp can be used on complex numbers. For example, with y = 2+8i as defined above,

c = exp(y)
yields: c = -1.0751 + 7.3104i
which can be verified by using Euler's formula:
c = e2cos(8) + je2sin(8)

B. Definition of Matrices
MATLAB is based on matrix and vector algebra; even scalars are treated as 1x1 matrices. Therefore,
vector and matrix operations are as simple as common calculator operations.
Vectors can be defined in two ways. The first method is used for arbitrary elements:
v = [1 3 5 7];
creates a 1x4 vector with elements 1, 3, 5 and 7. Note that commas could have been used in place of
spaces to separate the elements. Additional elements can be added to the vector:
v(5) = 8;
yields the vector v = [1 3 5 7 8]. Previously defined vectors can be used to define a new
vector. For example, with v defined above
a = [9 10];
b = [v a];
creates the vector b = [1 3 5 7 8 9 10].
The second method is used for creating vectors with equally spaced elements:
t = 0:.1:10;
creates a 1x101 vector with the elements 0, .1, .2, .3,...,10. Note that the middle number defines the
increment. If only two numbers are given, then the increment is set to a default of 1:
k = 0:10;
creates a 1x11 vector with the elements 0, 1, 2, ..., 10.
Matrices are defined by entering the elements row by row:
M = [1 2 4; 3 6 8];
creates the matrix

M = é
ë ê
ù
û ú
1 2 4
3 6 8
There are a number of special matrices that can be defined:
null matrix: M = [];
nxm matrix of zeros: M = zeros(n,m);
nxm matrix of ones: M = ones(n,m);
nxn identity matrix: M = eye(n);
A particular element of a matrix can be assigned:
M(1,2) = 5;
places the number 5 in the first row, second column.
In this text, matrices are used only in Chapter 12; however, vectors are used throughout the text.
Operations and functions that were defined for scalars in the previous section can also be used on
vectors and matrices. For example,
a = [1 2 3];
b = [4 5 6];
c = a + b
yields: c = 5 7 9
Functions are applied element by element. For example,
t = 0:10;
x = cos(2*t);
creates a vector x with elements equal to cos(2t) for t = 0, 1, 2, ..., 10.
Operations that need to be performed element-by-element can be accomplished by preceding the
operation by a ".". For example, to obtain a vector x that contains the elements of x(t) = tcos(t) at
specific points in time, you cannot simply multiply the vector t with the vector cos(t). Instead you
multiply their elements together:
t = 0:10;
x = t.*cos(t);

C. General Information
Matlab is case sensitive so "a" and "A" are two different names.
Comment statements are preceded by a "%".
On-line help for MATLAB can be reached by typing help for the full menu or typing help
followed by a particular function name or M-file name. For example, help cos gives help on the
cosine function.
The number of digits displayed is not related to the accuracy. To change the format of the display, type
format short e for scientific notation with 5 decimal places, format long e for scientific
notation with 15 significant decimal places and format bank for placing two significant digits to
the right of the decimal.
The commands who and whos give the names of the variables that have been defined in the
workspace.
The command length(x) returns the length of a vector x and size(x) returns the dimension
of the matrix x.

D. M-files
M-files are macros of MATLAB commands that are stored as ordinary text files with the extension
"m", that is filename.m. An M-file can be either a function with input and output variables or a list of
commands. All of the MATLAB examples in this textbook are contained in M-files that are available
at the MathWorks ftp site.
The following describes the use of M-files on a PC version of MATLAB. MATLAB requires that the
M-file must be stored either in the working directory or in a directory that is specified in the MATLAB
path list. For example, consider using MATLAB on a PC with a user-defined M-file stored in a
directory called "\MATLAB\MFILES". Then to access that M-file, either change the working
directory by typing cd\matlab\mfiles from within the MATLAB command window or by
adding the directory to the path. Permanent addition to the path is accomplished by editing the
\MATLAB\matlabrc.m file, while temporary modification to the path is accomplished by typing
addpath c:\matlab\mfiles from within MATLAB.
The M-files associated with this textbook should be downloaded from
www.ece.gatech.edu/users/192/book/M-files.html and copied to a subdirectory named
"\MATLAB\KAMEN", and then this directory should be added to the path. The M-files that come
with MATLAB are already in appropriate directories and can be used from any working directory.
As example of an M-file that defines a function, create a file in your working directory named yplusx.m
that contains the following commands:

function z = yplusx(y,x)
z = y + x;
The following commands typed from within MATLAB demonstrate how this M-file is used:
x = 2;
y = 3;
z = yplusx(y,x)
MATLAB M-files are most efficient when written in a way that utilizes matrix or vector operations.
Loops and if statements are available, but should be used sparingly since they are computationally
inefficient. An example of the use of the command for is
for k=1:10,
x(k) = cos(k);
end
This creates a 1x10 vector x containing the cosine of the positive integers from 1 to 10. This operation
is performed more efficiently with the commands
k = 1:10;
x = cos(k);
which utilizes a function of a vector instead of a for loop. An if statement can be used to define
conditional statements. An example is
if(a <= 2),
b = 1;
elseif(a >=4)
b = 2;
else
b = 3;
end
The allowable comparisons between expressions are >=, <=, <, >, ==, and ~=.
Several of the M-files written for this textbook employ a user-defined variable which is defined with the
command input. For example, suppose that you want to run an M-file with different values of a
variable T. The following command line within the M-file defines the value:
T = input('Input the value of T: ')
Whatever comment is between the quotation marks is displayed to the screen when the M-file is
running, and the user must enter an appropriate value.

Make Money Online: Tourists face fines for going shirtless in French towns:

2010-09-29T06:57:00.000-07:00

Make Money Online: Tourists face fines for going shirtless in French towns:

Man jailed for killing wife of 50 years

2010-09-06T01:16:00.000-07:00

Peter Caruso has been sentenced.(AAP)

An elderly retired fruiterer who brutally murdered his wife of 50 years has been jailed for 18 years.

Peter Caruso, 77, is likely to die in prison for the axe murder of his wife Rosa at their Melbourne home in 2008.

Caruso claimed during his Victorian Supreme Court trial that his wife was murdered by a burglar while he was at the shops.
But a jury took less than four hours to find him guilty of murdering Mrs Caruso, who was hit hard by a hatchet or similar item 36 times.
The prosecution alleged Caruso messed up the house to look like it had been robbed in a poor attempt to cover up his crime.
The jury had heard a blood-stained hatchet and clothing matched to Mrs Caruso's DNA were found in her husband's garage.
Justice Betty King said there was no known motive for his attack, but she said the killing was particularly gruesome.
"The murder of your wife was brutal and horrific," she said.
Justice King jailed him for a minimum of 13 years, saying that while some people would believe the sentence was merciful, it was highly likely he might die in jail. Source: ninemsn, AU

Want your own Free Site Sign Up Website That Will Help You Make More Money !

2010-04-06T07:49:00.000-07:00

So ... you want to make money online?
Don't know HTML or FTP no problem with FREE Free Site Sign Up websites you get your very own website for FREE ... They give you everything you need to get started.
You don't need to know anything about marketing, html, ftp or anything like that.
If you want to get up and running fast and making money online asap! Free Site Sign Up is your answer.
Lets face it: If you want to make any money online these days you need a website to do it. The problem is not building the website it's finding the right product and the right customer so you make the most amount of money. Having a website and the right product to sell is a PROVEN way to increase sales and commissions. 100%
With Free Site Sign Up We give you a proven product that sells along with everything else you need to succeed with making money online.

The best part is you get a website that converts. Which mean you make more cash more often and will have a proven online business that didn't cost 1000's to set up.
If you want an unstoppable sales force selling for you 24/7 you need a website.
Awan Chaulagain

http://www.freesitesignup.com/a/coupon.php?id=26010

A history ends with the death of Girija Prasad Koirala

2010-03-20T20:14:00.000-07:00

A history ends with the death of Girija Prasad Koirala

2010-03-20T03:06:00.000-07:00

In the streets of Kathmandu, life was as usual, Saturday afternoon. But a large number of people and leaders had gathered outside a house in Mandikhatar in nervous mood. There, the supreme commander of Nepalese politics Girija Prasad Koirala breathed his last at 12.10 pm. His party officially confirmed his death at 1:45 pm.

NC president Girija Prasad Koirala (File photo)

Koirala died after battling with bad health for a long time while still commanding the Nepali politics.

Born in Tadi, Saharsha district in Bihar, India, in 1925 when Koirala’s family was in exile, Koirala returned to his homeland Biratnagar at the age of 5. But the Koirala family was again sent into exile for waging war against the autocratic Rana regime. He was the youngest son of Krishna Prasad Koirala and Divya Koirala. Two of his brothers B. P. Koirala and M. P. Koirala also became Prime Ministers of Nepal.

A high school dropout, Koirala was married to Sushma Koirala and has a daughter Sujata Koirala, currently serving as deputy prime minister and foreign minister. He started his political career at the age of 23 as a leader of workers union in Biratnagar Jute Mill. He was jailed for seven years along with his brother B. P. Koirala in 1960 following coup by King Mahendra dismissing the first elected government led by B. P. Koirala. He was released after 21-day strike in jail.
In 1948 Koirala founded the Nepal Mazdoor Congress, later known as the Nepal Trade Union Congress. In 1952 he became the district president of Morang of his party and held that office until he was arrested in royal coup in 1960.
Upon his release in 1967, Koirala, along with other leaders and workers of the party, was sent into exile. He returned to country in 1979 along with B. P. Koirala under national reconciliation campaign. Since then he continued his active participation in national politics.
Koirala served as prime minister of Nepal for five times -- from May 26, 1991 to November 30, 1994; from April 15, 1998 to May 31, 1999; from March 22, 2000 to July 26, 2001 and from April 25, 2006 to August 5, 2008. Besides being prime minister, he was acting head of the state between May 28, 2008 and July 23, 2008 when the Constituent Assembly formally abolished monarchy from the country.
He was the first democratically elected prime minister of Nepal after the restoration of democracy in 1990.
Koirala, who served as the general secretary of the party from 1975 to 1991, was elected as the party president during ninth convention held in Kathmandu on May 11, 1996 and has been occupying the position. During his first term, the house of representatives enacted legislation to liberalize education, media and health sectors in the country. The government also founded the Purwanchal University and the B. P. Koirala Institute of Health and Sciences (BPKIHS) in the Eastern Development region and granted licenses to private sector to run medical and engineering colleges. The government also undertook the construction of the BP Memorial Cancer hospital in Bharatpur.
Koirala took over as Prime Minister from Surya Bahadur Thapa following the collapse of the coalition government led by Thapa in his second term. Koirala first headed a Nepali Congress minority government until December 25, 1998 after which he headed a three-party coalition government with the Communist Party of Nepal (UML) and the Nepal Sadhbhawana Party.
He became prime minister for the third time following resignation by Krishna Prasad Bhattarai, under whose leadership party had won the parliamentary election.
During this term, the insurgency in the country had reached to a critical height. The Royal Palace massacre that killed the whole family of king Birandra also took during this term.
He played a crucial role in forging political consensus among parties and held secret talks with underground CPN (Maoist). The Jana Andoland II, led by Koirala forced King Gyanendra to withdraw his steps and reinstate parliament as demanded by the party.
After the reinstatement of the House of Representatives on April 24, 2006 Koirala was selected to become prime minister by the leaders of the Seven Party Alliance. Following the promulgation of the interim constitution, Koirala, as the Prime Minister, acted as the interim head of state of Nepal until election of first president on July 23, 2008.
On April 1, 2007, Koirala was re-elected as Prime Minister to head a new government composed of the SPA and the CPN (Maoist).
He played steering role in armed struggle against the Rana regime. In an interview with the Kantipur Television of Nepal, Koirala admitted to printing fake Indian currencies to run political campaigns while being in exile in India. He was also involved in hijacking the Royal Nepal Airlines aircraft to put further pressure for ending Rana rule in the country.
His latest contribution to national politics was restoration of peace by dragging armed insurgent Maoist into political mainstream. He led the coalition government that included Maoist, and successfully held the first Constituent Assembly, which has been mandated to write new constitution within May 28. nepalnews.com

Have your own Cash Pulling Affiliate Website for free!!

2010-03-19T04:03:00.000-07:00

So ... what is FreeSiteSignUp and how can it benefit you?
This concept is new ... it looks too good to be true!
FreeSiteSignUp is a service offering Cash Pulling Affiliate websites which enables you to have your own website business Live WITHOUT having to pay a single penny for the service...

Thats not the end, its made better than anything else out there, by making it possible to have the website up and running within 5 minutes along with necessary guidelines to fetch traffic for free.

This conceptual masterpiece has already been PROVEN to be the best way to make money online, and the users who manage to get their hands on it are going to be at a huge monetary benefits with an extra edge to their life.

Not only you get a free website but also the guidance to get traffic to your website to fetch cash as soon as you have your business live.

It's basically a cash machine which never sleeps and keeps working for you and building your income 24/7 with no breaks!

Not many service providers even attempt to over-deliver as much as FreeSiteSignup does!

Here is how it works:

1. Enter your name, email and phone number above.
2. FreeSiteSignUp build you your own affiliate cash-pulling website and give you an upgrade option with even more benefits.
3. FreeSiteSignUp show you how to get traffic for FREE (plus many other ways).
4. You get paid each month, on time, for the commissions earned.
5. Work 15-30 minutes or less per day and build an online income following their EASY system.

Click here

Thanks for Reading

Awan Chaulagain

Want to make your String strong

2010-03-15T08:28:00.001-07:00

Model Extortion

2010-03-10T18:30:00.000-08:00

source: Yahoo7
A man who blackmailed Cindy Crawford has been sentenced to two years in prison.

Edis Kayalar has been found guilty of extorting money from the supermodel - who is married to Rande Gerber - by threatening to release a photograph of her then-seven-year-old daughter Kaia gagged and bound to a chair.
Court documents from his appearance in court in Kirchheim unter Teck, Germany state that Kayalar - who turned himself into police in Germany last November - stole the photograph from the couple's former nanny in 2009.
Cindy reportedly gave into Kayalar's initial requests and paid him $1,000 for a copy of the image. However, Kayalar attempted to extort a further $100,000 from the couple and repeatedly threatened to sell or release the photograph to the media if they failed to pay.
According to the documents, Kaia - who is now eight - told her parents the photo, which they had been unaware of, was taken during a "cops and robbers"
game with her nanny.
Cindy and Rande married in 1998 and they also have a 10-year-old son Presley.

Edis Kayalar arriving and sitting in court

Gurshan 'knocked out by opening door'

2010-03-10T18:27:00.000-08:00

Source: Yahoo!7 March 11, 2010, 9:46 am
The father of the man accused of killing Indian toddler, Gurshan Singh, has claimed the death was an "innocent mistake".
Gursewak Dhillon, 23, has been charged with criminal negligence and manslaughter over the death of the three-year-old, who was found on a roadside in Melbourne last Thursday.
Police allege the part-time taxi driver put the unconscious child in the boot of his car and drove him around for at least three hours before dumping him.
The father of the accused, Pragat Singh Dhillon, has told the Indian media that his son detailed the death in a telephone conversation three days ago.
"Gursewak told me that as he opened the main door to his residence in Melbourne, which he shared with 12 others, including parents of Gurshan, he hit the boy. He said he could not see from outside that Gurshan was standing next to the door, and the child got hit and fell unconscious," he told the Indian Express newspaper.
Gursewak reportedly told his father he shook Gurshan to try to wake him up, but the child didn't respond.
"He then called out to Gurshan's mother, who was taking a bath, and his own wife, Simrat Kaur, who was in the kitchen at the time, but both couldn't hear his calls. He then put the child in his car to take him to hospital," he told the Indian Express.
"He then put the child in his car to take him to hospital."
When his car had run out of petrol, the accused reportedly dumped the body out of fear of being charged with murder.
The case has been adjourned till June 29, with the prosecution seeking extra time to prepare evidence against Gursewak.
He has been remanded in custody amid concerns that he posed an unacceptable flight risk and could also tamper with witnesses in the case.
Although Gursewak lived in the same house as Gurshan, he is not a relative.

Get New from Amazon and update your daily life

2010-02-27T20:29:00.000-08:00

Forty-year-old trapped in child's body

2010-02-27T20:07:00.000-08:00

by: LISA CALAUTTI, The West Australian

At first glance, Nicky Freeman looks like an average 10-year-old boy, but his small stature belies his true age.

Mr Freeman is 40. He is trapped in a body which grows one year in every four. He celebrated his 40th birthday in December by graduating to size 10 children's clothing.

His bones are the size of a 10-year-old child. At 16, he had the bone structure of a four-year-old.

Mr Freeman has defied the odds to reach middle age. He was born in Esperance, one month overdue with hydrocephalous - fluid on the brain and a large head.

In the days following his birth, doctors told Kayleen Freeman her son would not live. To this day, Ms Freeman cannot help but wonder whether his multiple disabilities were caused by an anti-histamine she took for allergies during her pregnancy.

"Five women in Esperance who took it had spontaneous abortions and another had a child with a serious disability," she said.

Mr Freeman did not walk until he was almost two and after he did not seek toys to play with, doctors realised he was blind. His pituitary gland was also found to perform very slowly.

As a toddler, to cope with the swelling in his brain, Mr Freeman would not eat or drink for days at a time.

The swelling affected his hearing and he could not stand loud noise.

Doctors attempted to drain the liquid in his head by inserting a shunt into the lining of his brain. The procedure proved fruitless after they found his brain was absorbing the fluid.

Mrs Freeman said the procedure was so traumatic for her son that for almost six months he stopped walking, eating and drinking.

He never spoke again.

Again, doctors warned Mrs Freeman her son's death was imminent. Despite his hardships, Mrs Freeman said her eldest son's life was full.

"He is a fighter and he is meant to be here," she said. "He loves horse riding and in the season goes up to three times a week. He loves travel, especially long car trips."

She believes Mr Freeman's "true age" will never be discovered because he has no language.

"Nicky is extremely efficient in body language," she said. "If he doesn't want to eat something or do something you are certainly not left in any doubt about the fact. He may only be small, but he is 40 and a man."

Mrs Freeman, who has three other healthy children, said she often pondered whether his lifespan would exceed the average life expectancy.

"My friend Anne asked me years ago, when they said he was growing one year in four, that 'the average life span is 70 years - could he live to be 280'," Mrs Freeman said.

University of WA medical genetics professor David Ravine said Mr Freeman's case was extraordinarily rare and he had never come across anything like it in his 22-year career.

"There are so many biological aspects here about what we could learn from these unusual genetic events," he said. "The thing we learn in this field particularly, is to obviously treasure the exceptional cases and do as much as we can for the family."

Low-pass FIR Filter Design

2009-01-08T21:07:00.000-08:00

Laboratory 3:

I. Introduction

In the present project, students are required to implement and simulate a low-pass FIR filter using DSP Builder in the Simulink environment. The design has to be downloaded to the FPGA device on the Stratix EP1S25 DSP development board to perform hardware simulation and verification.

II. Theory

An FIR (Finite Impulse Response) filter, oppositely to IIR filters, has a finite response to impulse signals, which is explained because it does not have feedback. This way, FIR filters define a class of filter that has only zeros in the z-transform (the poles are in the origin z=0).

In addition, FIR filters have other characteristics that become these filters very attractive to many applications, such as linearity of phase, stability in the frequency response and constant group delay.

In general, FIR filters are preferred because of its linear phase characteristic and stability. However, IIR filters can be used in applications that require sharp cut-off or narrow band filters and where linear phase is not a requirement. That is because FIR filters require much higher order implementations than IIR filters for a similar performance.

III. Background about DSP Builder and the Altera EP1S25 DSP development board

Capabilities of DSP Builder and Simulink were introduced with an example design in the Tutorial: “A/D and D/A Conversion on Altera Stratix EP1S25 Development Board using Simulink and DSP Builder” [1], which contain a design targeting the 12-bit 125MHz A/D and 14-bit 165 MHz D/A converters onboard. For further information about the Stratix EP1S25 DSP development board, the student is referred to [3] and [4].

Similarly to the previous lab design, the present project will include multirate capabilities by using the PLL blocks in the Altera FPGA. The student is referred to Laboratory 1 [2] for information about working with different sampling rates in the same circuit design.

IV. Requirements

For this laboratory, students are required to accomplish the next requirements:

Define the floating-point coefficients for a low-pass FIR filter with the next specifications:

Direct-form FIR filter

Cut-off frequency: 0.1178 x F_N

Stop-band frequency: 0.2958 x F_N

Max. attenuation pass-band: 3dB

Min. attenuation stop-band: 97dB

Use fdatool from Matlab to design the filter under the previous specifications and determine the number of coefficients. To open this tool, type ‘fdatool’ in the Matlab command window. Set the Design Method of the FIR filter to Equiripple and the Design Factor to 20. It is important to note that we are using normalized frequency F_N with a range from 0.0 to 1.0. The maximum value (1.0) corresponds to the half of the sampling rate.

The coefficients of the designed filter using fdatool are in floating-point format. You can plot very easily the frequency response of the filter by using one of the available options in fdatool (Analysis>Magnitude Response). Check if the filter requirements are fulfilled.

Export the coefficients from fdatool to the Matlab Workspace (File>Export…). By default, coefficients are stored in the variable ‘Num’. Then, convert coefficients to 16-bit fixed-point with the next command in Matlab:

Num1 = round (Num*2^16-1)

Check if the new fixed-point coefficients fulfill the filter specifications. File > Import Filter from Workspace permits to analyze the fixed-point design. First, select ‘Direct-Form FIR’ from the drop down menu in Filter Structure and then type Num1 in the ‘Numerator’ field, which contains the 16-bit coefficients. In the ‘Units’ field, ‘Normalized (0 to 1)’ has to be select from the drop down menu below Sampling Frequency. Then, click Import Filter, and now you can check the frequency response of the fixed-point FIR filter. Analyze if it adjusts to the requirements and compare it with the frequency response obtained from the floating-point design.

Implement the direct-form FIR filter in Simulink (DSP Builder) using the 16-bit fixed-point coefficients (stored in variable ‘Num1’) and the A/D and D/A converters onboard. You can use the design given in the Tutorial [1] as a template. The A/D converter must convert a sinusoidal signal to digital, filter it and finally convert it back to analog through the D/A converter. Place a SignalTap II node at the output of the filter to acquire the signal later (see Figure 1, Tutorial [1]).

NOTE: filter symmetry property should be used for efficiency.

Simulate your filter in Simulink with different sinusoidal signals from 100KHz to 5MHz, observing at which frequencies the amplitude of the signal drops significantly.

Introduce two additional sampling rates to your design: 2.5MHz and 10MHz, similarly to Laboratory 1. Make two additional copies of the FIR filter working at 80MHz and make the necessary modifications to have them working at 2.5MHz and 10MHz, respectively. A PLL and Tsamp blocks are required to modify the sampling rate in these two additional circuits. At the output, use a Multiplexer and two switches from SW3 onboard to select in real-time one out of the three FIR filter outputs (default at 80MHz, 10MHz and 2.5MHz).

NOTE: the Down Sampling block would not be required since the FIR filter is not combinatorial. Also, to simplify the complexity of the circuit, it is suggested to use HDL Subsystem blocks from the Altera DSPBuilder library. Each of these blocks would contain one of the three FIR filters working at different sampling rates.

Simulate your filter in Simulink with different sinusoidal signals from 100KHz to 5MHz, observing at which frequencies the amplitude of the signal is attenuated significantly. Observe the effects of the different sampling rates, and particularly, observe the aliasing effect when working with the filter sampled at 2.5MHz.

Download your design to the Stratix EP1S25 DSP development board and test it by using a signal generator and oscilloscope for the different sampling rate options. Observe the effect of the different sampling rates, and again, observe the aliasing effect when working with the circuit sampled at 2.5MHz.

Capture and analyze the data using SignalTap II Analysis. Show when the amplitude of the signals drops significantly. Also, using the data acquired through SignalTap II, show the frequency response using ‘fft’ command, choosing one input signal frequency in pass-band and another in stop-band.

NOTE: you can use similar commands to those given in the Tutorial [1], “Importing the data acquired from the board in Matlab workspace”, step2.

What happened if the bit-precision of the coefficients is fixed at 8 bits? Modify your design by replacing 16-bit coefficients by 8-bit coefficients (convert your floating-point coefficients to 8 bits), and observe the behavior. Acquire the data through SignalTap II and show the frequency response using ‘fft’ command, choosing one input signal frequency in pass-band and another in stop-band.

V. Submission

You must submit:

· The Simulink model (.mdl) developed using DSP Builder blocks with three FIR filters working at three different sampling rates: 2.5, 10MHz and 80MHz, respectively.

· A report describing the general procedure to accomplish the project requirements, and answering questions regarding filtering and aliasing effects as detailed in the requirements. The report must contain graphs showing the filtered signals and frequency response using ‘fft’ command for different sampling rates. Choose one input signal frequency in pass-band and another in stop-band. Also, discuss and show the effects of reducing the coefficient precision from 16 to 8 bits.

VI. References

[1] Tutorial: “A/D and D/A Conversion on Altera Stratix EP1S25 Development Board using Simulink and DSP Builder”.

[2] Laboratory 1: Real-Time Implementation for Observing Quantization Effects

[3] DSP Builder User Guide, ver. 5.1.0, Altera, 2005. Local copy at: c:\altera\61\DSPBuilder\Doc\ug_dspbuilder.pdf

[4] Stratix EP1S25 DSP Development Board Data Sheet, ver. 1.6, Altera, 2004. Available online at: http://www.altera.com/literature/ds/ds_stratix_dsp-board-starter.pdf or local copy at: c:\altera\61\kits\stratix_dsp_kit-v1.3.0\Docs\ds_stratix_dsp_bd.pdf

[5] DSP Development Kit Stratix & Stratix Professional Edition (Getting Started User Guide), ver. 1.3.0 rev. 1, Altera, 2004. Available online at: http://www.altera.com/literature/ug/ug_stratix_dsp_kit.pdf or local copy at: c:\altera\61\kits\stratix_dsp_kit-v1.3.0\Docs\ug_stratix_dsp_kit.pdf

23 Most Incredible Photoshop Tutorials

2008-12-30T22:05:00.000-08:00

Paired with yesterday’s 24 Perfect Vectors, I wanted to list off some incredible Photoshop tutorials I’ve found on the web that will help you utilize those resources. I’ve done my best to select a wide variety of tutorials from web graphics, to photo enhancement, to just plain incredible.

Remember, its not about the destination, instead its all about the journey. Don’t neglect a tutorial because you aren’t interested in the final product. Instead, follow each one, step by step. I can personally attest that the knowledge you will gain about the various tools and functions that are available for you to use in Photoshop will be much more beneficial than the final image.

Further Reading:
25 More Most Incredible Photoshop Tutorials
How To Master Photoshop In Just One Week

30 Most inspirable light Web Designs
75 Insane Hi-Res Photoshop Brushes
You Know You’re Addicted To Photoshop If…
21 Best Photoshop Text Effects

.
.

.
The Ultimate Wood Texture Tutorial

.
Hot Chick On A Muscle Car

.
How To Create A Stunning Vista Inspired Menu

.
Badass Bling Effect In Photoshop

.
Create A Slick Black iMac In Photoshop

.
Glass Ball Tutorial

.
Create A Spectacular Flaming Meteor Effect On Text

.
Creating Smoke

.
Making A Print Ready Business Card Using Only Photoshop

.
Slow Shutter Text Effect

.
Creating A Mac Type Background In Photoshop

.
Exploding Planet

.
The Pen Tool

.
Awesome Underwater Scenery

.
Draw A Macbook Air Ad From Scratch

.
3D Tentacles

.
Gritty HDR

.
Add Texture To Improve Artwork

.
Ornate Lettering Process

.
Abstract Reflective Bubbles

.
Making Grunge Brushes

.
Realistic Water

.
Create An iPod Shuffle

How To Master Photoshop In Just One Week

2008-12-27T19:31:00.000-08:00

So you want to master Photoshop huh? You’ve come to the right place. This is the first part of a three part series that will help you to master Photoshop in and out in just one week. Today’s part is all about getting to know the basics of the program and how to operate the complex software - nothing amazing, but you need to crawl before you can walk!
Part two will be coming up in a couple of days, so feel free to take your time going through these resources. To get free updates on the next two parts of the series, just subscribe today!
Part two has been posted. Click here to read!
Part three has been posted. Click here to read!

DSP Projects: Lab Project -2

2008-12-24T23:42:00.000-08:00

FIR Filter Implementation in MATLAB and in C

I. FIR Digital Filter Design and Implementation in MATLAB
The MATLAB function, or “M-file”, shown in Listing 1 below provides an example of how to design and use finite impulse response (FIR) filters using the FIR filter design function fir1(N,Wn), the digital filter response calculating function freqz(A,B), and the digital filtering function filter( B,A,X) which are provided in the “MATLAB Signal Processing Toolbox”, which is described at
(http://www.mathworks.com/).

Make sure that you completely understand this M-file. MATLAB provides the following “help” documentation for these three routines: (For example, you may obtain information on the FIR1 function when running MATLAB by typing “help FIR1 [Enter]” at the MATLAB prompt):

_____________________________________________________________________________________

FIR1 FIR filter design (using the window method) function
B = FIR1(N,Wn) designs an N'th order lowpass FIR digital filter
and returns the filter coefficients in length N+1 vector B.
The cut-off frequency Wn must be between 0 < Wn < 1.0, with 1.0
corresponding to half the sample rate. The filter B is real and
has linear phase, i.e., even symmetric coefficients obeying B(k) =
B(N+2-k), k = 1,2,...,N+1.

If Wn is a two-element vector, Wn = [W1 W2], FIR1 returns an
order N bandpass filter with passband W1 < W < W2.
B = FIR1(N,Wn,'high') designs a highpass filter.
B = FIR1(N,Wn,'stop') is a bandstop filter if Wn = [W1 W2].

If Wn is a multi-element vector,
Wn = [W1 W2 W3 W4 W5 ... WN],
FIR1 returns an order N multiband filter with bands
0 < W < W1, W1 < W < W2, ..., WN < W < 1.
B = FIR1(N,Wn,'DC-1') makes the first band a passband.
B = FIR1(N,Wn,'DC-0') makes the first band a stopband.

For filters with a passband near Fs/2, e.g., highpass
and bandstop filters, N must be even.

By default FIR1 uses a Hamming window. Other available windows,
including Boxcar, Hanning, Bartlett, Blackman, Kaiser and Chebwin
can be specified with an optional trailing argument. For example,
B = FIR1(N,Wn,kaiser(N+1,4)) uses a Kaiser window with beta=4.
B = FIR1(N,Wn,'high',chebwin(N+1,R)) uses a Chebyshev window.

By default, the filter is scaled so the center of the first pass band
has magnitude exactly one after windowing. Use a trailing 'noscale'
argument to prevent this scaling, e.g. B = FIR1(N,Wn,'noscale'),
B = FIR1(N,Wn,'high','noscale'), B = FIR1(N,Wn,wind,'noscale').

FREQZ Z-transform digital filter frequency response function
When N is an integer, [H,W] = FREQZ(B,A,N) returns the N-point frequency
vector W in radians and the N-point complex frequency response vector H
of the filter B/A:
jw -jw -jnbw
jw B(e) b(1) + b(2)e + .... + b(nb+1)e
H(e) = ---- = ----------------------------
jw -jw -jnaw
A(e) a(1) + a(2)e + .... + a(na+1)e

given numerator and denominator coefficients in vectors B and A. The
frequency response is evaluated at N points equally spaced around the
upper half of the unit circle. If N isn't specified, it defaults to 512.

FILTER One-dimensional digital filter function
Y = FILTER(B,A,X) filters the data in vector X with the
filter described by vectors A and B to create the filtered
data Y. The filter is a "Direct Form II Transposed"
implementation of the standard difference equation:

a(1)*y(n) = b(1)*x(n) + b(2)*x(n-1) + ... + b(nb+1)*x(n-nb)
- a(2)*y(n-1) - ... - a(na+1)*y(n-na)

If a(1) is not equal to 1, FILTER normalizes the filter
coefficients by a(1)

Note that in the example M-file of Listing 1, we must set the pole coefficient array, A, equal to 1, which implies that a1 = 1, and all the other coefficients, a2, a3, a4, .... = 0, since an FIR filter has no poles.
_______________________________________________________________________________________
Listing 1. Example MATLAB M-file illustrating FIR filter design and evaluation.
% Finite Impulse Response filter design example
% found in the MATLAB Signal Processing Toolbox
% using the MATLAB FIR1 function (M-file)

Fs=8e3; %Specify Sampling Frequency
Ts=1/Fs; %Sampling period.
Ns=512; %Nr of time samples to be plotted.

t=[0:Ts:Ts*(Ns-1)]; %Make time array that contains Ns elements
%t = [0, Ts, 2Ts, 3Ts,..., (Ns-1)Ts]
f1=500;
f2=1800;
f3=2000;
f4=3200;

x1=sin(2*pi*f1*t); %create sampled sinusoids at different frequencies
x2=sin(2*pi*f2*t);
x3=sin(2*pi*f3*t);
x4=sin(2*pi*f4*t);

x=x1+x2+x3+x4; %Calculate samples for a 4-tone input signal
grid on;
N=16; %FIR1 requires filter order (N) to be EVEN
%when gain = 1 at Fs/2.
W=[0.4 0.6]; %Specify Bandstop filter with stop band between
%0.4*(Fs/2) and 0.6*(Fs/2)

B=FIR1(N,W,'DC-1'); %Design FIR Filter using default (Hamming window.
B %Leaving off semi-colon causes contents of
%B (the FIR coefficients) to be displayed.
A=1; %FIR filters have no poles, only zeros.

freqz(B,A); %Plot frequency response - both amp and phase response.

pause; %User must hit any key on PC keyboard to go on.
figure; %Create a new figure window, so previous one isn't lost.
subplot(2,1,1); %Two subplots will go on this figure window.
Npts=200;
plot(t(1:Npts),x(1:Npts)) %Plot first Npts of this 4-tone input signal
title('Time Plots of Input and Output');
xlabel('time (s)');
ylabel('Input Sig');
%Now apply this filter to our 4-tone test sequence

y = filter(B,A,x);

subplot(2,1,2); %Now go to bottom subplot.
plot(t(1:Npts),y(1:Npts)); %Plot first Npts of filtered signal.
xlabel('time (s)');
ylabel('Filtered Sig');
pause;

figure; %Create a new figure window, so previous one isn't lost.
subplot(2,1,1);
xfftmag=(abs(fft(x,Ns))); %Compute spectrum of input signal.
xfftmagh=xfftmag(1:length(xfftmag)/2);
%Plot only the first half of FFT, since second half is mirror imag
%the first half represents the useful range of frequencies from
%0 to Fs/2, the Nyquist sampling limit.
f=[1:1:length(xfftmagh)]*Fs/Ns; %Make freq array that varies from
%0 Hz to Fs/2 Hz.
plot(f,xfftmagh); %Plot frequency spectrum of input signal
title('Input and Output Spectra');
xlabel('freq (Hz)');
ylabel('Input Spectrum');
subplot(2,1,2);
yfftmag=(abs(fft(y,Ns)));
yfftmagh=yfftmag(1:length(yfftmag)/2);
%Plot only the first half of FFT, since second half is mirror image
%the first half represents the useful range of frequencies from
%0 to Fs/2, the Nyquist sampling limit.
plot(f,yfftmagh); %Plot frequency spectrum of input signal
xlabel('freq (Hz)');
ylabel('Filt Sig Spectrum');

A. Download the M-file of Listing 1, called lab2.m, from the afs.rose-hulman.edu\class\ee\hoover\ece581\lab2\lab2.mAFS network “class directory”. Start MATLAB and execute this M-file. Verify that this function does what you expect.
B. Now modify this M-file to obtain the sixteen FIR filter coefficients that correspond to a
1. 16th-order band-pass FIR filter with a pass-band between 800 Hz and 2.4 kHz, and a sampling frequency of 8 kHz.
2. 16th-order high-pass FIR filter with unity-gain passband above 2.0 kHz, and a sampling frequency of 8 kHz.

Include a printout of each of these sets of FIR coefficients as Attachment A in your lab report. II. Real-Time, Floating Point FIR Digital Filter Implementation Study the digital filtering program shown in Listing 2. This FIR filtering program was written to be easily understood, and is therefore not very efficient. For example, the “for loop” in the ISR that updates the input sample storage array, x[ ], by shifting the newly converted sample into x[0], what was in x[0] goes into x[1], what was in x[1] goes into x[2], etc., is certainly not efficient, though it makes the convolution calculation very straightforward. The use of a circular input buffer to hold the last N input samples leads to much more efficient code, though the subscript variable management becomes trickier. In this case, a reference pointer (that points to the most recent input sample in the buffer) is simply rotated around the circular buffer, allowing the previous inputs in the buffer remain in their original position within the buffer.Listing 2. C-Language FIR Real-time Digital Filtering Program
_______________________________________________________________________
/* Floating Point FIR Digital Filter Implementation */
/* Digital Signal Processing Laboratory */

#include
#include
#include
#include
#include
#include
#include
#include
#include
#include

float coeff[20],x[20]; /* coeff array holds FIR filter coeffs */
int Norder; /* x array holds past input samples */
void hookint(void);
interrupt void McBSPRcvISR(void);

int main()
{
Mcbsp_dev dev;
Mcbsp_config mcbspConfig;
int sampleRate,Actual_Sampling_Rate;

/* Band-Stop Filter Coefficients Cut From MATLAB
B =

Columns 1 through 7

-0.0038 0.0000 0.0218 0.0000 -0.0821 0.0000 0.1625

Columns 8 through 14

0.0000 0.8031 0.0000 0.1625 0.0000 -0.0821 0.0000

Columns 15 through 17

0.0218 0.0000 -0.0038
*/

coeff[0] = -0.0038; /* Here are the 17 coefficients corresponding */
coeff[1] = 0.0000 ; /* to a 16th-order FIR Band Stop FIR filter */
coeff[2] = 0.0218; /* FIR filter obtained using MATLAB FIR1 */
coeff[3] = 0.0000; /* Fs = 8 kHz, stop band between 1.6 kHz and */
coeff[4] = -0.0821; /* 2.4 kHz. */
coeff[5] = 0.0000;
coeff[6] = 0.1625;
coeff[7] = 0.0000;
coeff[8] = 0.8031;
coeff[9] = 0.0000;
coeff[10] = 0.1625;
coeff[11] = 0.0000;
coeff[12] =-0.0821;
coeff[13] = 0.0000;
coeff[14] = 0.0218;
coeff[15] = 0.0000;
coeff[16] =-0.0038;

/******************************************************/
/* Initialize EVM */
/******************************************************/
printf("Initializing EVM board\n");
evm_init();
printf("Done initializing EVM\n");
/******************************************************/
/* Open MCBSP */
/******************************************************/
mcbsp_drv_init(); /* initialize McBSP driver, allocates memory for
the device handles */
dev=mcbsp_open(0); /* dev is the handle to control the McBSP */
if(dev==NULL)
{
printf("Error Opening MCBSP 0\n");
return(ERROR);
}
/******************************************************/
/* Configure McBSP */
/******************************************************/
memset(&mcbspConfig,0,sizeof(mcbspConfig));
mcbspConfig.loopback =FALSE;
mcbspConfig.tx.update =TRUE;
mcbspConfig.tx.clock_mode =CLK_MODE_EXT;
mcbspConfig.tx.frame_length1 =0;
mcbspConfig.tx.word_length1 =WORD_LENGTH_32;
mcbspConfig.rx.update =TRUE;
mcbspConfig.rx.clock_mode =CLK_MODE_EXT;
mcbspConfig.rx.frame_length1 =0;
mcbspConfig.rx.word_length1 =WORD_LENGTH_32;
mcbsp_config(dev,&mcbspConfig); /* configuration adjustments */
MCBSP_ENABLE(0,MCBSP_BOTH); /* McBSP is activated */
/******************************************************/
/* Configure CODEC */
/******************************************************/
codec_init();
/* A/D 0.0dB (min) gain, turn OFF 20dB mic gain, sel(L/R)LINE input */
/* Set codec for stereo mode of operation */
codec_adc_control(LEFT,MIN_ADC_INPUT_GAIN,FALSE,LINE_SEL);
codec_adc_control(RIGHT,MIN_ADC_INPUT_GAIN,FALSE,LINE_SEL);
/* MUTE(L/R)LINE input to “DSP Bypass Path” by setting mute switch to TRUE */
codec_line_in_control(LEFT,MIN_AUX_LINE_GAIN,TRUE);
codec_line_in_control(RIGHT,MIN_AUX_LINE_GAIN,TRUE);
/* D/A 0.0dB attenuation, do not mute DAC outputs */
codec_dac_control(LEFT,0.0,FALSE);
codec_dac_control(RIGHT,0.0,FALSE);

sampleRate=8000;
Actual_Sampling_Rate = codec_change_sample_rate(sampleRate,TRUE);
/* set to the closest allowed rate */
printf("The actual sampling rate is = %d\n",Actual_Sampling_Rate);
hookint();
codec_interrupt_enable();
/* codec generates interrupts when data are received in the DRR */
/******************************************************/
/* Main Loop, wait for Interrupt */
/******************************************************/
Norder = 16;
while(1)
{
}
}

void hookint()
{
/* an interrupt is assigned to DRR event of the serial port
then, the interrupt will branch to ISP */
intr_init(); /* initialize ISTP with the address of vec_table.
Placing the base address of the vector table in ISTP */
intr_map(CPU_INT15,ISN_RINT0); /* map CPU_INT15 to DRR interrupt */
intr_hook(McBSPRcvISR,CPU_INT15); /* connect ISR to the CPU_INT15 */
INTR_ENABLE(15);
INTR_GLOBAL_ENABLE();
return;
}

interrupt void McBSPRcvISR(void)
{
/* ISR for the DRR interrupt */
/*
This routine convolves the present and the Norder previous input
samples with the “Norder+1” FIR coefficients h(0) through h(Norder):

y(n) = h(0)*x(n) + h(1)*x(n-1) + ... + h(Norder)*x(n-Norder)

*/
int intsamp,i;
float floatsamp,sum;

intsamp=MCBSP_READ(0); /* read from CODEC’s data receive register (DRR) */
intsamp = (intsamp >>16); /* Shift right channel data down to bottom 16 bits */
if(intsamp & 0x8000)
intsamp = intsamp 0xffff0000; /* Sign extend right channel data */
floatsamp = (float) intsamp; /* Convert right channel to floating point */

for(i=Norder; i >= 0;i--) /* Update past input sample array, where
x[0] holds present sample, x[1] holds
sample from 1 sample period ago, x[N]
holds sample from N sampling periods ago*/
{ /* This time-consuming loop could be */
x[i]=x[i-1]; /* eliminated if circular buffering were*/
} /* to be employed. */
x[0] = floatsamp;
sum = 0;
for(i=0;i<=Norder;i++) /* Perform FIR filtering (convolution) */
{
sum=sum+x[i]*coeff[i];
}

intsamp = (int) sum; /* Convert result back to integer form */
intsamp = intsamp << 16; /* Shift result back into Right Channel position
while zeroing the Left Channel position */
MCBSP_WRITE(0,intsamp); /* Send to CODEC (right channel only) */
}

This real-time FIR filtering program implements the digital band-stop filter that corresponds to the MATLAB M-file of Listing 1. Recall that this was a stop-band filter with a stop band between 1.6 kHz and 2.4 kHz at a sampling frequency of 8 kHz.

A. Download this program from the previously cited AFS network class directory. It is named “lab2a.c”. Place this file in a subdirectory, along with the other relevant linker command, assembly, and library files, and follow the steps outlined in Lab 1 to build the project. To make sure that the C67x C compiler produces well-optimized code, make sure that compiler optimization is enabled compiler optimization in Code Composer Studio 2 by clicking on Project – Build Options – Basic. Make sure that “Speed Most Critical” option is selected in the “Opt Speed Vs. Size” box. Also, in the “Opt Level” box, choose “Register –o0”. The “Generate Debug Info” box should normally have “Full Symbolic Debug” selected, when you are in the process of debugging your code, though I have found that program execution is substantially speeded up by changing this to “No Debug” after you code is debugged, allowing you to go to a higher sampling rate.Run the program. Verify, using a function generator and an oscilloscope, that this filter attenuates audio signals roughly between 1.6 kHz and 2.4 kHz, as expected. Replace the oscilloscope with a loudspeaker. Note that the attenuation in the stop band is not as apparent with the loudspeaker, since your hearing responds logarithmically rather than linearly.B. Now insert the seventeen filter coefficients you obtained in Part II (B) above for the bandpass filter. Run the program. Listen to the output of this filter when you speak into the microphone. Your voice should have that characteristic band-limited “telephone sound”!
C. Use the Microsoft EXCEL spreadsheet to plot the observed filter magnitude response of this band-pass filter in decibels versus frequency, where AvdB = 20log(Voutpeak/Vinpeak) Vary the frequency using a function generator over a range of 100 Hz – 3.7 kHz. Be sure that the amplitude of the function generator is not turned up too high, since we do not want the filter output to become distorted at any frequency within the specified range. Use an oscilloscope to measure the gain at (at least) eleven evenly-spaced frequencies of 100 Hz, 500 Hz, 900 Hz, 1.3 kHz, 1.7 kHz, 2.1 kHz, 2.5 kHz, 2.9 kHz, 3.3 kHz, 3.7 kHz. You may want to take additional measurements at frequencies where the response of the filter is changing dramatically. You may have trouble estimating the amplitude of the output sinusoid at the higher frequencies, since there are fewer samples per cycle, although the CODEC data sheet claims to provide the proper reconstruction low-pass filtering, with a break frequency of Fs/2. Compare the observed frequency response with that predicted by MATLAB for this band-pass filter. Include both of these plots in your report memo as Attachment B. The experimentally measured frequency response plot of gain (in dB) vs. frequency should have the same shape as the MATLAB-predicted plot, however, it may differ by an additive constant, since the gain of the mixer box is not known (unless the mixer box is bypassed.)
D. Now change the coefficients to those of Part II (B) above (for the high-pass filter). (Isn’t it amazing that changing the coefficient values can so thoroughly change the “personality” of the filter!) Then repeat Parts C and D. Include both the observed and the MATLAB predicted frequency response curves for this high-pass filter in your report memo as Attachment C.
III. Fixed-Point FIR Filter Implementation
Modify the floating point FIR band-pass filtering program that you obtained in Part I (B) so it can be run on the (cheaper and faster) C62x DSP chip, which executes the (fixed-point) subset of the C67 instruction set. The C62x DSP has the same instructions and architecture (even the same pinout!) as the C67x DSP chip. Now the “rules of the game” have changed: you may no longer use any floating point variables or operations in your C program, since floating point operations cannot be efficiently executed by the C62.Obviously, you will have to translate your filter coefficients into integer form. In order to take advantage of the full dynamic range of the C62’s 32-bit integer representation, you should multiply these coefficients by 0x7FFF. This will convert (scale up) these original floating point coefficients into signed 16-bit integer quantities (of type “short int”), since the FIR coefficients of a unity gain filter range between (-1.0, 1.0). These (short int) coefficient values should be entered into your new program, and declared to be of the “short int” data type. In your filtering program, you should multiply by the signed 16-bit input sample in the upper half of the 32-bit integer coming in from the CODEC, corresponding to the right CODEC channel. This may be done without downshifting by 16 bits, and then sign extending, as was done in the floating point version of the program (See Listing 2). Instead, you should use a “C compiler instrinsic function” which forces the use of a specific C62x/67x machine instruction that has no direct counterpart in the C language. The intrinsic C function that is “just what the doctor ordered” in this particular case is “_mpyhl” (multiply high-low), and it may be invoked by a line of C code similar to this: temp=(_mpyhl(sample,coeff));This line of C code forces the in-line insertion of the “MPYHL” C62x/C67x DSP chip’s “16 X 16” integer multiply instruction. This instruction multiplies the 16 MSBs of the first (32-bit) integer argument (sample) by the 16 LSBs of second (32-bit) integer argument (coeff), and returns a 32-bit signed integer product to the integer variable “temp”. The upper 16 bits of this 32-bit variable “temp” holds the16-bit signed output sample. Note that taking this upper 16-bit portion of the 32-bit product in “temp” as the result, corresponds to dividing the 32-bit result by 0x10000, since it lies in the upper 16 bits of the 32-bit integer. However, we want to divide by 0x8000, which almost corrects for the multiplication of the coefficients by 0x7FFF that we had to perform earlier, in order to scale the coefficients for use in this fixed-point (integer) version of the FIR filtering program. Therefore, one final left shift must be applied to our result to make the upper 16-bits of “temp” to be equivalent to the result divided by 0x8000. temp = temp << 1;The 16-bit result is now in the proper position (upper-most 16 bits) to go out to the right channel of the CODEC. However, before sending the result (temp) out to the CODEC, the bottom 16 bits of temp should be masked out, to ensure that no residual noise will go out the CODEC on the left channel.

Demonstrate that your fixed-point FIR band-pass filter program passes frequencies with least attenuation in the range 800 Hz – 2.4 kHz . Obtain the instructor’s validating signature on the program listing. Include the listing of your modified “fixed-point” FIR filter program listing as Attachment D.IV. More Efficient FIR Filter ImplementationWrite, and then demonstrate, the proper operation of a more efficient FIR floating-point band-pass filtering program. As suggested earlier, a circular buffer should be used to replace the input sample storage array x[ ]. This modification would eliminate the need for the time-wasting “for loop” that updates x[ ] each time the interrupt service routine. (Of course, the most efficient FIR implementations would require use of C6x assembly-language coding.) Try running this more efficient filtering program at higher clock rates. Can you get this program to filter properly at 48 kHz, which is the highest sampling frequency supported by the CODEC?

Include the listing of your more efficient filter implementation as Attachment E, and on this listing, indicate the highest sampling rate you were able to attain.

By the way, please note that the band-pass filter’s break frequencies will be scaled up with increasing sampling frequency. Recall that the bandpass filter was designed to break (provide unity gain) between the “normalized frequencies” of 0.1 and 0.3. Thus the filter passband was found to lie between the break frequencies of 0.1*8 kHz = 0.8 kHz = 800 Hz and 0.3*8 kHz = 2.4 kHz, when fs was set to 8 kHz. However, if fs is changed, the break frequencies will lie between 0.1*fs and 0.3*fs.

A simple way to tell if the filter is operating properly (and not missing one or more sampling interrupts) is to gradually increase the frequency of the function generator from 0 Hz up to fs while listening in the loudspeaker. Verify that no aliasing can be hear over this range. Aliasing is heard when the audible frequency starts to go back down, even while the input frequency is being increased. The reason you would expect to hear aliasing is that the CODEC sets its switched-capacitor LPF to ½ of the current sampling rate, but if the interrupt processing is taking too long and every other sample is missed because the previous sample is still being processed, the effective sampling rate is only ½ of what you thought it was, and thus the anti-aliasing filter’s break frequency is set to twice as high as it should be, and so aliasing will be heard. (I will demonstrate this effect in the laboratory!)

5 Things To Focus on this Short Holiday Season

2008-12-16T19:15:00.002-08:00

Since Thanksgiving came a week later than last year, it's got both businesses and consumers flustered. Not only is there a week of less sellin' there's a week of less buyin'! For those of you who aren't retailers this might even be the end of your fiscal year. Couple that with the economy and everyone is freaking out!

So what do you do? Sit down for a moment, relax and let's think about things. How will you treat this short season?

1. Get your email marketing ducks in a row - Because email is so quick, there doesn't have to be a big lead time and a lot of moving parts. However, you need to devote an hour, where you sit down and calendar out your campaigns. Then create them all at once and schedule them out. If you do this now, you can get at least 4 emails out to customers before it's too late.

Click here to read 4 more ideas for this season

5 Things To Focus on this Short Holiday Season

2008-12-16T19:15:00.000-08:00

So what do you do? Sit down for a moment, relax and let's think about things. How will you treat this short season?

Click here to read 4 more ideas for this season

ICS - SMS sending

2008-11-19T18:49:00.000-08:00

Want to send message to Mero mobile Users................
SMS sending

DSP Lab-1 Signals in Matlab

2008-11-12T23:15:00.000-08:00

Introduction
This lab will describe how to use Matlab for some basic signal representation and manipulation:
• Creating and importing signals
• Sampling and resampling
• Signal visualization
• Modeling noise
• Modulation

Discrete Signals
Time base: t = [0.0 0.1 0.2 0.3]
Signal data: x = [1.0 3.2 2.0 8.5]
The central data construct in Matlab is the numeric array, an ordered collection of real
or complex numeric data with one or more dimensions. The basic data objects of signal
processing (one-dimensional signals or sequences, multichannel signals, and two-dimensional
signals) are all naturally suited to array representation.
Matlab represents ordinary one-dimensional sampled data signals, or sequences, as vectors.
Vectors are 1-by-n or n-by-1 arrays, where n is the number of samples in the sequence.
One way to introduce a sequence into Matlab is to enter it as a list of elements at the command
prompt. The statement
x = [1 2 3 4 5]
creates a simple five-element real sequence in a row vector. It can be converted to a column
vector by taking the transpose:
x = [1 2 3 4 5]’
Column vectors extend naturally to the multichannel case, where each channel is represented
by a column of an array.

c 2006GM
Another method for creating vector data is to use the colon operator. Consider a 1-second
signal sampled at 1000 Hz. An appropriate time vector would be
t = 0:1e-3:1;
where the colon operator creates a 1001-element row vector representing time from zero to
one second in steps of one millisecond.
You can also use linspace to create vector data:
t = linspace(0,1,1e3);
creates a vector of 1000 linearly spaced points between 0 and 1.
Try:
t1 = [0 .1 .2 .3];
t2 = 0:0.1:0.3;
t3 = linspace(0, 0.3, 4);
T = [t1’ t2’ t3’];
X = sin(T)
Q: What does this code show?

Sampling Signals
Analog signal sources include electromagnetic, audio, sonar, biomedical and others. Analog
signals must be sampled in order to be processed digitally.
Sampling
x(n) = xa(nTs)
x is a discrete signal sampled from the analog signal xa with a sample period of Ts and a
sample frequency of Fs = 1/Ts.
Try:
Fs = 100;
N = 1000;
stoptime = 9.99;
t1 = (0:N-1)/Fs;
t2 = 0:1/Fs:stoptime;
x1 = sin(2*pi*2*t1);
x2 = sin(2*pi*3*t2);
plot(x1)
figure, plot(x2)
An alternative to creating signals is to use a toolbox function. A variety of toolbox functions
generate waveforms . Each of them requires that you begin with a vector representing a time
base. Some of these functions will be described later in this lab.
Aliasing
Digital signals are often derived by sampling a continuous-time signal with an analog-todigital
(A/D) converter. If the continuous signal, xa(t), is bandlimited, meaning that it does
not contain any frequencies higher than a maximum frequency fM, the Shannon sampling
theorem says that it can be completely recovered from a set of samples if the sampling
frequency fs is greater than two times the maximum frequency of the signal to be sampled:
Fs > 2fM
This maximum frequency fM is known as the Nyquist frequency. If the sampling frequency is
not greater than two times the Nyquist frequency, the continuous signal cannot be uniquely
recovered and aliasing occurs. (You heard examples of aliased signals in Homework No.1).
fs > 2fM: Original signal and sampled signal have the same frequency.
fs 2fM: Sampled signal is aliased to half the original frequency.
Try:
t = 0:0.001:2;
xa = sin(2*pi*5*t);
plot(t,xa)
hold on
fs = 15;
ts = 0:1/fs:2;
xs1 = sin(2*pi*5*ts);
plot(ts,xs1,’ro-’)
fs = 7.5;
ts = 0:1/fs:2;
xs2 = sin(2*pi*5*ts);
plot(ts,xs2,’ro-’)
hold off
Q: What is the frequency of xs2?// (There is aliasing here. We need sampling theory.
However can use the fft function on the signal to determine the frequency).
Signal Visualization
• View signal amplitude vs. time index
• Functions: plot, stem, stairs, strips
• Listen to data: sound
Note: the sound and soundsc commands will not work if your computer hardware isn’t set
up. If that is the case, view the signals instead of listening to them.
Try:
t = [0.1 0.2 0.3 0.4];
x = [1.0 8.0 4.5 9.7];
plot(t,x)
figure, stem(t,x)
figure, stairs(t,x)
fs = 1000;
ts = 0:1/fs:2;
f = 250 + 240*sin(2*pi*ts);
x = sin(2*pi*f.*ts);
strips(x,0.25,fs)
sound(x,fs)
plot(ts,x)
plot(ts(1:200),x(1:200))
Q: What does the strips command do? (See ’help strips’.)
Q: What does the .* operator do?
Signal Processing Tool
The Signal Processing Toolbox application, SPTool, provides a rich graphical environment
for signal viewing, filter design, and spectral analysis.
You can use SPTool to analyze signals, design filters, analyze filters, filter signals, and analyze
signal spectra. You can accomplish these tasks using four GUIs that you access from
within SPTool:
• The Signal Browser is for analyzing signals. You can also play portions of signals using
your computer’s audio hardware.
• The Filter Designer is for designing or editing FIR and IIR digital filters. Note that the
FDATool is the preferred GUI to use for filter designs. FDATool is discussed in later labs.
• The Filter Viewer is for analyzing filter characteristics.
• The Spectrum Viewer is for spectral analysis.
Open SPTool by typing sptool at the command prompt.
Try:
sptool
Look at the train signal, FIRbp filter, and trainse spectrum. (You see 3 panes
- Signals, Filters, Spectra. Filter Designer is available through File 7! Preferences. You
can play sounds using the LS icon. When viewing spectra, note that many methods of
determining spectra, including the fft, are available.)
Importing a Signal
You can use SPTool to analyze the signals, filters, or spectra that you create at the Matlab
command line.
You can import signals, filters, or spectra from the Matlab workspace into the SPTool
workspace using the Import item under the File menu.
Try:
fs = 1000;
ts = 0:1/fs:0.5;
f = 250 + 240*sin(2*pi*ts);
x = sin(2*pi*f.*ts);
Import these signals (f and x) into the SPTool and use the tool to examine them.
Q: What are the icons to use for horizontal zoom?
Try zooming in using the mouse.
Signal Browser
The Signal Browser tool is an interactive signal exploration environment. It provides a
graphical view of the signal object(s) currently selected in teh Signals list of SPTool.
Using the Signal Browser you can
• View and compare vector/array signals
• Zoom in on a range of signal data to examine it more closely
• Measure a variety of characteristics of signal data
• Play signal data on audio hardware
To open/activate the Signal Browser for the SPTool,
• Click one or more signals (use the Shift key for multiple selections) in the Signals list of
SPTool.
• Click the View button in the Signals list of SPTool.
Changing Sample Rates
To change the sample rate of a signal in SPTool,
1. Click a signal in the Signals list in SPTool.
2. Select the Sampling frequency item in the Edit menu.
3. Enter the desired sampling frequency and cliick OK.
Try changing the sampling rate of the imported signal.
Signal Generation
Signals
• Create a time base vector
t = [0:0.1:2];
• Create a signal as a function of time
x = sin(pi*t/2);
plot(t,x)
Useful Matlab functions
• Nonperiodic functions
ones, zeros
• Periodic functions
sin, cos, square, sawtooth
Nonperiodic Signals
t = linspace(0,1,11)
• Step:
y = ones(1,11);
stem(y)
• Impulse:
y = [1 zeros(1,10)];
stem(y)
• Ramp:
y = 2*t;
plot(y)
Useful Matlab functions
step, impulse, gensig
Try:
Step function:
fs = 10;
ts = [0:1/fs:5 5:1/fs:10];
x = [zeros(1,51) ones(1,51)];
stairs(ts,x)
Impulse function with width w:
fs = 10;
w = 0.1;
ts = [-1:1/fs:-w 0 w:1/fs:1];
x = [zeros(1,10) 1 zeros(1,10)];
plot(ts,x)
Delta function:
ts = 0:0.5:5;
x = [1 zeros(1,length(ts)-1)];
stem(ts,x)
axis([-1 6 0 2])
Sinusoids
Sinusoid parameters
• Amplitude, A
• Frequency, f
• Phase shift,
• Vertical offset, B
The general form of a sine wave is
y = Asin(2 ft + ) + B
Example: generate a sine wave given the following specifications:
• A = 5
• f = 2 Hz
• = /8 radians
t = linspace(0,1,1001);
A = 5;
f = 2;
p = pi/8;
sinewave = A*sin(2*pi*f*t + p);
plot(t, sinewave)
Try:
edit sine_wave
sine_wave
edit sinfun
[A T] = sinfun(1,2,3,4)
Square Waves
Square wave generation is like sine wave generation, but you specify a duty cycle, which is
the percentage of the time over one period that the amplitude is high.
Example:
• duty cycle is 50% (the Matlab default)
• frequency is 4 Hz.
t = linspace(0,1,1001);
sqw1 = square(2*pi*4*t);
plot(t,sqw1)
axis([-0.1 1.1 -1.1 1.1])
Example:
• duty cycle is 75%
• frequency is 4 Hz.
t = linspace(0,1,1001);
sqw2 = square(2*pi*4*t,75);
plot(t,sqw2)
axis([-0.1 1.1 -1.1 1.1])
Sawtooth Waves
Sawtooth waves are like square waves except that instead of specifying a duty cycle, you
specify the location of the peak of the sawtooth.
Example:
• peak at the end of the period (the Matlab default)
• frequency is 3 Hz.
t = linspace(0,1,1001);
saw1 = sawtooth(2*pi*3*t);
plot(t,saw1)
Example:
• peak is halfway through the period
• frequency is 3 Hz.
t = linspace(0,1,1001);
saw2 = sawtooth(2*pi*3*t,1/2);
plot(t,saw2)
Complex Signals
Periodic signals can be represented by complex exponentials:
x(t) = ej2 ft = cos(2 ft) + jsin(2 ft) = cos(
t) + jsin(
t)
If t is measured in seconds, then f will have units of sec−1, and
will have units of radians/
second.
In signal processing, we associate the unit circle with one sampling cycle, so that a sampling
frequency of Fs is associated with 2 radians, and the Nyquist frequency Fs/2 is associated
with radians. Values of
in the upper half-plane, in units of Hz, then correspond to
frequencies within the sampled signal.
In Matlab, type:
x = exp(2*pi*j*f*t);
plot(x)
Matlab recognizes either j or i as the square root of -1, unless you have defined variables j
or i with different values.
Useful Matlab functions
real, imag, abs, angle
Try:
edit zsig
zsig(5)
Look at both figures and describe what you see.
Importing Data
An important component of the Matlab environment is the ability to read and write data
from/to external sources. Matlab has extensive capabilities for interfacing directly with data
from external programs and instrumentation.
In this lab, we concentrate on reading and writing data that has already been stored in
external files.
Files come in a variety of standard formats, and Matlab has specialized routines for working
with each of them. To see a list of supported file formats, type:
help fileformats
To see a list of associated I/O functions, type:
help iofun
Matlab provides a graphical user interface, the Import Wizard, to the various I/O functions.
You access the Wizard by choosing File ! Import Data or by typing:
uiimport
The Matlab command importdata is a programmatic version of the Wizard, accepting all of
the default choices without opening the graphical user interface. You can use importdata in
M-files to read in data from any of the supported file formats.
Matlab also has a large selection of low-level file I/O functions, modeled after those in the C
programming language. These allow you to work with unsupported formats by instructing
Matlab to open a file in memory, position itself within the file, read or write specific formatted
data, and then close the file.
Try:
help fileformats
help iofun
jan = textread(’all_temps.txt’,’%*u%u%*[^\n]’,’headerlines’,4);
[data text] = xlsread(’stockdata.xls’);
plot(data(:,2))
legend(text{1,3})
Explain how the colon operator works in the preceding plot command.
I = importdata(’eli.jpg’);
image(I)
which theme.wav
uiimport
Browse for:
theme.wav
soundsc(data,fs)
Save and Load
Two data I/O functions are especially useful when working with Matlab variables.
• The save command writes workspace variables to a binary Matlab data file (MAT-file)
with a .mat extension. The file is placed in the current directory.
• The load command reads variables from a MAT-file back into the Matlab workspace.
Although quite specialized, save and load can be used for day-to-day management of your
Matlab computations.
Try:
doc save
doc load
t = 0:0.1:10;
x1 = sin(t);
x2 = sin(2*t);
x3 = sin(3*t);
save myvars
clear
load myvars t x3
Note the list of variables in the workspace tab in the upper left of the Matlab window.
Modeling Noise
To model signals in space, in the atmosphere, in sea water, or in any communications channel,
it is necessary to model noise.
Matlab has two functions for generating random numbers, which can be added to signals to
model noise.
Uniform random numbers
A = rand(m,n);
generates an mxn array of random numbers from the uniform distribution on the interval
[0,1]. To generate uniformly distributed random numbers from the interval [a,b], shift and
stretch:
A = a + (b-a)*rand(m,n);
Gaussian random numbers
A = randn(m,n);
generates an mxn array of random numbers from the standard normal distribution with
mean 0 and standard deviation 1. To generate random numbers from a normal distribution
with mean mu and standard deviation sigma, shift and stretch:
A = mu + sigma*rand(m,n);
Random numbers from other distributions
Random numbers from other distributions can be generated using the uniform random number
generator and knowledge of the distribution’s inverse cumulative distribution function.
Random number generators for several dozen common distributions are available in the
Statistics Toolbox.
Adding Noise to a Signal
noisy signal = signal + noise
y1 = x + rand(size(x)) % uniform noise
y2 = x + randn(size(x)) % Gaussian noise
Example:
Add Gaussian noise to middle C.
fs = 1e4;
t = 0:1/fs:5;
sw = sin(2*pi*262.62*t); % middle C
n = 0.1*randnsize(sw);
swn = sw + n:
Try:
edit noisyC
noisyC
strips(swn, .1,1e4)
Zoom in on the strips plot. (Note: you might have to cut and paste from the noisyC script
to generate swn.)
Pseudorandomness
This number:
0.95012928514718
is the first number produced by the Matlab uniform random number generator with its
default settings. Start up Matlab, set format long, type rand, and you get the number.
If all Matlab users, all around the world, all on different computers, keep getting this same
number, is it really “random”? No, it isn’t. Computers are deterministic machines and
should not exhibit random behavior. If your computer doesn’t access some external device,
like a gamma ray counter or a clock, then it must really be computing pseudorandom
numbers.
A working definition of randomness was given in 1951 by Berkeley professor D. H. Lehmer,
a pioneer in computing and, especially, computational number theory:
A random sequence is a vague notion ... in which each term is unpredictable
to the uninitiated and whose digits pass a certain number of tests traditional with
statisticians ...
Random number generators proceed deterministically from their current state. To view the
current state of rand, type:
s = rand(’state’)
This returns a 35-element vector containing the current state.
To change the state of rand:
rand(’state’,s) Sets the state to s.
rand(’state’,0) Resets the generator to its initial state.
rand(’state’, sum(100*clock)) Sets to a new state each time.
Commands for randn are analogous.
Try:
s = rand(’state’)
format long
rand
rand(’state’,sum(100*clock))
s = rand(’state’)
format long
rand
Resampling
The Signal Processing Toolbox provides a number of functions that resample a signal at a
higher or lower rate.
y = downsample(x,n)
decreases the effective sampling rate of x by keeping every nth sample starting with the first
sample. x can be a vector or a matrix. If x is a matrix, each column is considered a separate
sequence.
y = upsample(x,n)
increases the effective sampling rate of x by inserting n−1 zeros between samples. x can be
a vector or a matrix. If x is a matrix, each column is considered a separate sequence. The
upsampled y has x n samples.
y = resample(x,p,q)
resamples the sequence in vector x at p/q times the original sampling rate, using a polyphase
filter implementation. p and q must be positive integers. The length of y is equal to
ceil(length(x) p/q). If x is a matrix, resample works down the columns of x.
y = interp(x,r)
increases the sampling rate of x by a factor of r. The interpolated vector y is r times longer
than the original input x.
y = decimate(x,r)
reduces the sampling rate of x by a factor of r. The decimated vector y is r times shorter
in length than the input vector x. By default, decimate employs an eighth-order lowpass
Chebyshev Type I filter. It filters the input sequence in both the forward and reverse
directions to remove all phase distortion, effectively doubling the filter order.
Try:
load mtlb
sound(mtlb,Fs)
mtlb4 = downsample(mtlb,4)
mtlb8 = downsample(mtlb,8)
sound(mtlb8,fs/8)
What are the sizes of mtlb, mtlb4, and mtlb8?
(If sound doesn’t work, plot the signals.)
t = 0:0.00025:1;
x = sin(2*pi*30*t) + sin(2*pi*60*t);
y = decimate(x,4);
subplot(211), stem(x(1:120))
axis([0 120 -2 2])
title(’Original Signal’)
subplot(212), stem(y(1:30))
title(’Decimated Signal’)
Modulation and Demodulation
Modulation varies the amplitude, phase, or frequency of a carrier signal with reference to a
message signal.
The Matlab modulate function modulates a message signal with a specified modulation
method. The syntax is
y = modulate(x,fc,fs,’method’)
where:
• x is the message signal.
• fc is the carrier frequency.
• fs is the sampling frequency.
• method is a flag for the desired modulation method (see table below).
Method Description
amdsb-sc or am Amplitude modulation, double side-band, suppressed carrier
amdsb-tc Amplitude modulation, double side-band, transmitted carrier
amssb Amplitude modulation, single side-band
fm Frequency modulation
pm Phase modulation
ppm Pulse position modulation
pwm Pulse width modulation
qam Quadrature amplitude modulation
The demod function performs demodulation, that is, it obtains the original message signal
from the modulated signal. The syntax is:
x = demod(y,fs,fs,’method’)
demod uses any of the methods shown for modulate. The signal x is attenuated relative to
y because demodulation uses lowpass filtering.
Exercise: High and Low
1. Create a signal equal to the sum of two sine waves with the following characteristics:
• 3-second duration
• Sampling frequency = 2 kHz
• Sinusoid 1: frequency 50 Hz (low), amplitude 10, phase = 0
• Sinusoid 2: frequency 950 Hz (high), amplitude 1, phase = 0
2. View and listen to the signal using an M-file
3. Import the signal into SPTool and view it. Listen to the signal.
HAND IN:
ANSWERS TO ALL QUESTIONS STARTING WITH THE LETTER Q IN
FRONT OF IT.
(The material in this lab handout was put together by Paul Beliveau and derives principally from
the MathWorks training document “MATLAB for Signal Processing”, 2006.)