Abstract: Methods, systems, and apparatus for matching a driver associated with a driver timetable and a rider associated with a ride request including a detected rider location and a rider destination. The system includes a synchronizing server configured to transmit the ride request to a driver device. The driver device is configured to determine a driver value associated with incorporating the ride request into the driver timetable. The driver device is also configured to communicate, to the synchronizing server, the driver value. The synchronizing server is also configured to receive one or more other values from one or more other driver devices. The synchronizing server is also configured to determine a prime value from the one or more values, the prime value being the driver value. The synchronizing server is also configured to communicate, to the driver device, an indication to incorporate the ride request into the driver timetable.

Abstract: Object and shape detection in digital images utilizing edge detection is described. In a first edge detection approach, phase transformation is utilized in the frequency domain, such as in response to Fourier transform, followed by use of a frequency-domain phase kernel and inverse-Fourier transform. Edge detection is also provided using a phase transform in the spatial domain utilizing a convolution approach. In a second edge detection approach, phase stretching is utilized, such as in combination with phase histogramming along with thresholding and morphological operations. Numerous example images are provided illustrating benefits of the disclosed technology with different applications and under different conditions.

Abstract: Methods and systems for reconstructing data are disclosed. One method includes receiving a selection of one or more input data streams at a data processing framework, and receiving a definition of one or more analytics components at the data processing framework. The method further includes applying a dynamic principal component analysis to the one or more input data streams, and detecting a fault in the one or more input data streams based at least in part on a prediction error and a variation in principal component subspace generated based on the dynamic principal component analysis. The method also includes reconstructing data at the fault within the one or more input data streams based on data collected prior to occurrence of the fault.