first of all, ***thank*** ***you*** to everyone for the very interesting replies, at last i've a bit of time to try your suggestions (and ask for a filter book to my boss ;-) )

before the book is bought i would like to try at least a part of your suggestions:

- equiripple (i'm completely lost, i'll probably have to way the book...)

- (i hope) raised cosine (lost as well, wait(book)...)

- gaussian

the latter seems interesting and easier to try without the book, but i have to understand and tweak it a bit; i'm using matlab to try it

matlab has this "fspecial" constructor which requires:

H = FSPECIAL('gaussian',N,SIGMA) returns a rotationally symmetric Gaussian lowpass filter with standard deviation SIGMA (in pixels). N is a 1-by-2 vector specifying the number of rows and columns in H. (N can also be a scalar, in which case H is N-by-N.) If you do not specify the parameters, FSPECIAL uses the default values of [3 3] for N and 0.5 for SIGMA.

so it's suited for example for use with (finite) images...

well... i have a continuous monodimensional stream of samples.... how do i should interpret the "N dimensions" vector? if i fix one dimension to 1, is the other is related to the filter order?

the sigma.... i'll google about it ;-) (i suppose it relates to how "shaped" is the bell curve... i don't know about gaussian curves but iirc they relate to distributions, and sigma could be the... deviation? i'll see)

moreover i found this

formatting link

"A recursive implementation of the Gaussian filter. This implementation yields an infinite impulse response filter that has 6 MADDs per dimension independent of the value of sigma in the Gaussian kernel"

still it requires a finite number of points as input....

i understand that my "finite window vs infinite stream of samples" doubt is quite trivial but... can you give me a little hint?

thank you again