ELEMENTARY,
MY DEAR WATSON
PART II
AKA: SDRI seventh meetup
SEPTEMBER 2016
Software Defined Radio Israel
WHAT IS
THE CONNECTION BETWEEN
ALICE
AND
OPTIMUS PRIME?
THE FANTASTIC STORY
OF
COMPLEX NUMBERS
NEXT TIME...
FOURIER
AND
OTHER TRANSFORMATIONS
IT ALL BEGINS WITH SERIES'
\sum _{ n=0 }^{ \infty }{ { a }_{ n }={ a }_{ 1 }+{ a }_{ 2 }+{ a }_{ 3 }+{ a }_{ 4 }... }
∑n=0∞an=a1+a2+a3+a4...
\sum _{ n=0 }^{ \infty }{ { f }_{ n }\left( x \right) ={ f }_{ 1 }\left( x \right) +{ f }_{ 2 }\left( x \right) +{ f }_{ 3 }\left( x \right) +{ f }_{ 4 }\left( x \right) +... }
∑n=0∞fn(x)=f1(x)+f2(x)+f3(x)+f4(x)+...
OUR MOTIVATION:
DESCRIBE A FUNCTION WITH...OTHER FUNCTIONS
AND
BECOME...
(AND THE OTHER WAY AROUND)
FOR
EXAMPLE:
all kinds of series'
- Geometric series
- Taylor series
- Laurent series
And more...
\sum_{n=0}^\infty \frac{ \, x^n}{n!}
∑n=0∞n!xn
\sum _{ n=-\infty }^{ \infty }{ { { a }_{ n }x }^{ n } }
∑n=−∞∞anxn
FOURIER SERIES
SERIES THAT IS COMBINED FROM SINES AND COSINES
{ s }_{ N }\left( x \right) =\frac { { a }_{ 0 } }{ 2 } +\sum _{ n=1 }^{ N }{ \left[ { a }_{ n }\cos { \left( \frac { 2\pi nx }{ P } \right) + } { b }_{ n }\sin { \left( \frac { 2\pi nx }{ P } \right) } \right] }
sN(x)=2a0+∑n=1N[ancos(P2πnx)+bnsin(P2πnx)]
A BASIC EXAMPLE:
{ s }_{ }\left( x \right) =1+3\cos { \left( x \right) }
s(x)=1+3cos(x)
{ a }_{ 0 }=2,\quad { a }_{ 1 }=3,\quad { a }_{ n>1 },b_{ n>0 }=0
a0=2,a1=3,an>1,bn>0=0
P(period)=2\pi
P(period)=2π
HOW DOES A SQUARE WAVE LOOKS LIKE?
FOURIER TRANSFORM
- FOURIER SERIES COEFFICIENTS REPRESENT THE "ENERGY" OF THEIR CORRESPONDING "FREQUENCY"
- THE COMBINATION OF ALL THE COEFFICIENTS IS THE "SPECTRAL CONTENT" OF THE ORIGINAL FUNCTION (OR SIGNAL)
- THE PROCESS OF FINDING THE COEFFICIENTS IS CALLED "FOURIER TRANSFORM"
HOW TO PERFORM A FOURIER TRANSFORM?
SIGNALS IN NATURE ARE CONTINUOUS IN TIME AND VALUE, AND THEREFORE WE DEFINE THE CONTINUOUS FOURIER TRANSFORM - CFT:
\Im \left\{ h\left( x \right) \right\} \left( f \right) =\int _{ -\infty }^{ \infty }{ h\left( x \right) { e }^{ -2\pi jxf }dx }
ℑ{h(x)}(f)=∫−∞∞h(x)e−2πjxfdx
j=\sqrt { -1 }
j=√−1
BUT IN THE REAL WORLD...
- SIGNALS ARE NOT REALLY CONTINUOUS, BUT RATHER A VECTOR OF VALUES ( = DISCRETE TIME) WHICH ARE FROM A FINITE SET OF VALUES (SAY, A BYTE SIZE)
- THEREFORE, THE FOURIER TRANSFORM SHOULD BE ABLE TO HANDLE THOSE SIGNALS TOO. HENCE: DISCRETE FOURIER TRANSFORM - DFT
X_{ k }=\sum _{ n=0 }^{ N-1 }{ x_{ n }\left[ \cos { \left( -\frac { 2\pi nk }{ N } \right) + } j\sin { \left( -\frac { 2\pi nk }{ N } \right) } \right] }
Xk=∑n=0N−1xn[cos(−N2πnk)+jsin(−N2πnk)]
DFT vs. FFT
- DFT is very easy to understand, however it takes time to evaluate
- FFT (Fast Fourier Transform) is an algorithm that can evaluate DFT very fast - given that the vector size is a power of 2
dspguide.com
common transforms
things we didn't talk about
but are very important
REAL LIFE ARE COMPLEX
THERE (SOME) MORE MATH INTO IT
Elementary, My dear Watson. Part 2
By raziele
Elementary, My dear Watson. Part 2
SDRI sixth meetup
- 1,151