  In the first part of the series papers, we set out to answer the following
question: given specific restrictions on a set of samplers, what kind of signal
can be uniquely represented by the corresponding samples attained, as the
foundation of sparse sensing. It is different from compressed sensing, which
exploits the sparse representation of a signal to reduce sample complexity
(compressed sampling or acquisition). We use sparse sensing to denote a board
concept of methods whose main focus is to improve the efficiency and cost of
sampling implementation itself. The "sparse" here is referred to sampling at a
low temporal or spatial rate (sparsity constrained sampling or acquisition),
which in practice models cheaper hardware such as lower power, less memory and
throughput.
  We take frequency and direction of arrival (DoA) estimation as concrete
examples and give the necessary and sufficient requirements of the sampling
strategy. Interestingly, we prove that these problems can be reduced to some
(multiple) remainder model. As a straightforward corollary, we supplement and
complete the theory of co-prime sampling, which receives considerable attention
over last decade.
  On the other hand, we advance the understanding of the robust multiple
remainder problem, which models the case when sampling with noise. A sharpened
tradeoff between the parameter dynamic range and the error bound is derived. We
prove that, for N-frequency estimation in either complex or real waveforms,
once the least common multiple (lcm) of the sampling rates selected is
sufficiently large, one may approach an error tolerance bound independent of N.
