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[12] [13] [clarification needed] After calculating the cross-correlation between the two signals, the maximum (or minimum if the signals are negatively correlated) of the cross-correlation function indicates the point in time where the signals are best aligned; i.e., the time delay between the two signals is determined by the argument of the ...
Some examples where Barker code is used are: pet and livestock tracking, bar code scanners, inventory management, vehicle, parcel, asset and equipment tracking, inventory control, cargo and supply chain logistics. [25] It is also used extensively for Intelligent Transport Systems (ITS) i.e. for vehicle guidance [26]
The highest absolute cross-correlation in this set of codes is 2 (n+2)/2 + 1 for even n and 2 (n+1)/2 + 1 for odd n. The exclusive or of two different Gold codes from the same set is another Gold code in some phase. Within a set of Gold codes about half of the codes are balanced – the number of ones and zeros differs by only one. [5]
Kasami sequences have good cross-correlation values approaching the Welch lower bound. There are two classes of Kasami sequences—the small set and the large set. There are two classes of Kasami sequences—the small set and the large set.
In other words, the cross-correlation of the received signal with the transmitted signal is computed. This is achieved by convolving the incoming signal with a conjugated and time-reversed version of the transmitted signal. This operation can be done either in software or with hardware.
To compute cross-scaled-correlation for every time shift properly, it is necessary to segment the signals anew after each time shift. In other words, signals are always shifted before the segmentation is applied. Scaled correlation has been subsequently used to investigate synchronization hubs in the visual cortex. [2]
The cross-correlation matrix of two random vectors is a matrix containing as elements the cross-correlations of all pairs of elements of the random vectors. The cross-correlation matrix is used in various digital signal processing algorithms.
Many stochastic processes can be completely characterized by their correlation functions; the most notable example is the class of Gaussian processes. Probability distributions defined on a finite number of points can always be normalized, but when these are defined over continuous spaces, then extra care is called for.