Abstract: A first ultrasound dataset that represents an object in a first coordinate system can be acquired at a first time. Three landmarks of the object can be located in the first ultrasound dataset to define a second coordinate system. A first transform from the first to the second coordinate system can be determined for the first ultrasound dataset. A second ultrasound dataset that represents the object in the first coordinate system can be acquired at a second time. The same three landmarks in the second ultrasound dataset can be located to define a third coordinate system. A second transform from the first to the third coordinate systems can be determined for the second ultrasound dataset.

Abstract: A volumetric ultrasound system is used to determine the velocity of tissue using 3D echo data. In particular, the velocity of tissue in a 3D volume is determined by steering ultrasound beams to the tissue in the 3D volume and forming 3D echo data from receive ultrasound beams formed from reflections of the steered ultrasound beams from the tissue in the 3D volume. The velocity of the tissue associated with the 3D echo data can be determined using velocity determination techniques. The velocity of the tissue can be displayed in real-time. For example, in one embodiment, a sub-volume of the 3D volume can be scanned and the determined velocity of the tissue displayed in about 50 milliseconds (ms).

Abstract: A weld controller utilizes a neural network to compute power factor of a secondary circuit of a welding transformer supply power to a workpiece through a pair of welding electrodes. Phase controlled switches supply power to the transformer and the neural network uses the phase angle at the time of energization of the switches and the length of time that the switches conduct to compute the power factor. The computed power factor is compared with previous computations of power factor online and any changes are interpreted as changes in resistance of the secondary circuit. This provides a measure of contact wear and the weld controller can compensate for these changes by increasing weld power. The neural network is adaptable for use with other types of control systems.