Abstract: Synthetic object detection data is generated for a modeled sensor, such as a camera. Scenario data specifying objects, such as vehicles, sensor intrinsics, such as focal length, principal point, and image size, and sensor extrinsics, such location and orientation in the scenario of the sensor, may be received. An object detector model may detect a given object in the scenario if it lies within the sensor's field of view, is large enough, and is not occluded. Two dimensional (2D) image plane position and velocity measurements may be generated. A measurement noise model may add noise to the measurements. Position, velocity, and measurement noise may be mapped into a three dimensional (3D) world coordinate system. An object detection list that includes time of detection, detected position and velocity, measurement accuracy, and an object classification for detected objects may be output.

Abstract: A technique for determining the characteristics of an oscillatory test signal includes acquiring a plurality of consecutive samples of a test signal. The samples are mathematically fit to a sinusoidal model, which specifies a plurality of equations. The equations have unknowns that represent characteristics of a sinusoid that substantially intersects the plurality of samples. Solving the equations for the unknowns reveals the test signal's short-term characteristics.

Abstract: A technique for determining the amplitudes of frequency components of a waveform sampled from an automatic test system includes assembling a list of N frequencies expected to be found in the sampled waveform. A test program running on the tester generally supplies the list of frequencies. The technique assumes that the sampled waveform conforms to an idealized waveform model that mathematically corresponds to a sum of N sinusoids. Each of the N sinusoids that make up the model has unknown amplitude and a frequency that equals one of the N frequencies in the list of frequencies. The technique attempts to solve for the unknown amplitude of each of the N frequencies by mathematically minimizing, via a linear least-squares algorithm, the difference between the model and the actual, sampled waveform.

Abstract: A technique for determining the characteristics of an oscillatory test signal includes acquiring a plurality of consecutive samples of a test signal. The samples are mathematically fit to a sinusoidal model, which specifies a plurality of equations. The equations have unknowns that represent characteristics of a sinusoid that substantially intersects the plurality of samples. Solving the equations for the unknowns reveals the test signal's short-term characteristics.

Abstract: A technique for determining the amplitudes of frequency components of a waveform sampled from an automatic test system includes assembling a list of N frequencies expected to be found in the sampled waveform. A test program running on the tester generally supplies the list of frequencies. The technique assumes that the sampled waveform conforms to an idealized waveform model that mathematically corresponds to a sum of N sinusoids. Each of the N sinusoids that make up the model has unknown amplitude and a frequency that equals one of the N frequencies in the list of frequencies. The technique attempts to solve for the unknown amplitude of each of the N frequencies by mathematically minimizing, via a linear least-squares algorithm, the difference between the model and the actual, sampled waveform.