  We introduce a general framework to deterministically construct binary
measurement matrices for compressed sensing. The proposed matrices are composed
of (circulant) permutation submatrix blocks and zero submatrix blocks, thus
making their hardware realization convenient and easy. Firstly, using the
famous Johnson bound for binary constant weight codes, we derive a new lower
bound for the coherence of binary matrices with uniform column weights.
Afterwards, a large class of binary base matrices with coherence asymptotically
achieving this new bound are presented. Finally, by choosing proper rows and
columns from these base matrices, we construct the desired measurement matrices
with various sizes and they show empirically comparable performance to that of
the corresponding Gaussian matrices
