Abstract: An assembly and method for deploying, retrieving, and storing a cable having a minimum bend radius onto a drum. The assembly may comprise a frame, a rotatable drum carried by the frame, a levelwind carried by the frame, and a powertrain. The levelwind may comprise a rail spanning at least the width of the drum, and a traversable carriage carried by the rail. The levelwind may also comprise a cable guide carried by the carriage, where the cable guide comprises an elongated chute having contoured walls. The contoured walls of the chute include no bends having a radius less than the minimum bend radius of the cable. The powertrain may be operatively connected to rotate the drum about the drum axis and traverse the carriage along the rail. In some examples, the contoured walls of the chute are coated with a supplmentary coating.

Abstract: An assembly and method for deploying, retrieving, and storing a cable having a minimum bend radius onto a drum. The assembly may comprise a frame, a rotatable drum carried by the frame, a levelwind carried by the frame, and a powertrain. The levelwind may comprise a rail spanning at least the width of the drum, and a traversable carriage carried by the rail. The levelwind may also comprise a cable guide carried by the carriage, where the cable guide comprises an elongated chute having contoured walls. The contoured walls of the chute include no bends having a radius less than the minimum bend radius of the cable. The powertrain may be operatively connected to rotate the drum about the drum axis and traverse the carriage along the rail. In some examples, the contoured walls of the chute are coated with a supplmentary coating.

Abstract: A fuzzy logic estimator for minimizing signal measuring inaccuracy effects in a signal processing system preferably includes a microprocessor-based system operable to receive a number of measured signal values and estimate a solution to an overdetermined system of equations that minimizes differences between the measured signal values and corresponding model values. The fuzzy logic solution estimate process includes assigning a probability distribution to delta values representing differences between the measured signal values and corresponding model values to form a corresponding number of probability distribution functions, associating at least some of the probability distribution functions with each equation of a system of equations defining a number of unknown parameter values, solving the system of equations for a domain of possible solutions, and determining a unique solution for the unknown parameter values from the domain of possible solutions.