A sensing matrix maps input vector to measurement vector through linear wighted summation of input. What makes a specefic matrix good, is application dependent. Now, both distributions more or less satisfy RIP. However hardware implementation of the Bernoulli matrix (binary or bipolar) is much much easier especially in analog domain. A Bernoulli wight is either 0 or 1 (or -1/1 in case of polar Bernoulli), but a Gaussian wight is a floating point figure. Multiplication of a flouting point number either in digital or analog, is resource consuming, while multiplication of a Bernoulli wight is feasible through implementation of a simple switch in analog domain or and instruction in digital. As an example consider RMPI analog to information devices which compressively sample the signal in analog and then reconstruct the signal in digital. Prior to quantization the sensing matrix should be applied, were through incorporating a Bernoulli matrix, the mulpliers are implemented as simples switches