  This paper considers a generalized version of the coin weighing problem with
a spring scale that lies at the intersection of group testing and compressed
sensing problems. Given a collection of $n\geq 2$ coins of total weight $d$
(for a known integer $d$), where the weight of each coin is an unknown integer
in the range of $\{0,1,\dots,k\}$ (for a known integer $k\geq 1$), the problem
is to determine the weight of each coin by weighing subsets of coins in a
spring scale. The goal is to minimize the average number of weighings over all
possible weight configurations. For $d=k=1$, an adaptive bisecting weighing
strategy is known to be optimal. However, even the case of $d=k=2$, which is
the simplest non-trivial case of the problem, is still open. For this case, we
propose and analyze a simple and effective adaptive weighing strategy. A
numerical evaluation of the exact recursive formulas, derived for the analysis
of the proposed strategy, shows that this strategy requires about ${1.365\log_2
n -0.5}$ weighings on average. To the best of our knowledge, this is the first
non-trivial achievable upper bound on the minimum expected required number of
weighings for the case of $d=k=2$. As $n$ grows unbounded, the proposed
strategy, when compared to an optimal strategy within the commonly-used class
of nested strategies, requires about $31.75\%$ less number of weighings on
average; and in comparison with the information-theoretic lower bound, it
requires at most about $8.16\%$ extra number of weighings on average.
