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where N denotes the length of the time series, and r1 and r2 refer to the lag-one autocorrelation of each series, respectively.
In this paper, we study a new image sensing technique, called compressive image sensing (CIS), with computational complexity O m2), where m denotes the length of a measurement vector y="ϕx, which is sampled from the signal x of length n via the sampling matrix ϕ with dimensionality m × n.
where Z denotes the length of ZCZ.
| v | denotes the length of the vector v.
where the quantity L denotes the length of multipath channels.
where L denotes the length of sliding data frame.
Here, L M denotes the length of M set.
Here, ℓ denotes the length of a specific word.
Here and later, (|l|) denotes the length of l.
T denotes the length of totally investigated period.
where T ≤ δ denotes the length of integration interval.
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