Sentence examples for vector of data from inspiring English sources

We assume a vector of data (mathbf{y}= y_{k})), where (y_{k}) is the number of individuals reporting k social contacts when surveyed.

Let us assume that our symbol mapper produces a vector of data symbols d from some finite alphabet A N, where N is the frame (vector) length.

The vector of data streams to be transmitted is s=[s123,s12,s13,s23,s1,s2,s3] T. After linear precoding using precoder P = [p123,p12,p13,p23,p1,p2,p3], the signals are superposed and broadcast.

Similar to the previous section, suppose yis the vector of data and y ̂ designates the reconstructed signal using IPRM with a combination of the polynomial orders { m i } i = 1 N s.

Given a column vector of data (Z=left( Z_{1},ldots,Z_{n}right) ^{prime } ), where (^{prime }) denotes transpose, let (S=left( S_{1},ldots,S_{n}right) ^{prime }) and (N=left( N_{1},ldots,N_{n}right) ^{prime } ) denote the unobserved uncorrelated components of a decomposition (Z=S+N).

For Equation (2), assume g = H f in absence of noise where g ∈ ℂM × 1is a vector of data, H∈ ℂM × Na matrix whose elements can be considered as an over-complete dictionary as its columns and f∈ ℂN × 1the corresponding linear coefficients.

(i) Generate Gaussian vectors of data length with variance of the additive noise.

For example, using winter and summer seasons obtains four vectors of data: two for week days (winter and summer) with a total of 3120 data points for each, and two vectors for weekend days (winter and summer) with a total of 1260 data points for each.

Geometrically, Eq. 1 shows that the correlation coefficient can be viewed as the cosine of the angle on n-dimensional space between the two vectors of data which have been shifted by the average to have mean zero.

Additionally, for a given OFDM symbol, the optimization is limited to a single matrix-vector multiplication where matrix is complex valued, constant for a given system configuration and vector consists of data subcarriers symbols.

Denoting ( {varvec{a}} ) as the vector of malicious data which is injected into the original measurement data ( {varvec{z}} ), therefore, the measurement vector is polluted as ( {varvec{z}}_{bad} = {varvec{z}} + {varvec{a}} ) after attack.