Abstract: A method comprises accessing a plurality of images of a substantially same geographical area, each image of the plurality of images captured at a separate time from each other using a separate imaging sensor, each imaging sensor capturing information corresponding to a different spectral band. The method further comprises identifying, for the images, a set of blobs, each blob comprising a plurality of adjacent pixels, wherein each image of the plurality of images includes a blob of the set of blobs, and wherein each blob in the set of blobs has a location in each image that differs from a location of other blobs in the set of blobs. The method further comprises generating a score indicating a likelihood that the set of blobs correspond to a moving object, storing an indication that the set of blobs correspond to the moving object responsive to the generated score exceeding a threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. A moving vehicle analysis system receives images from an aerial imaging device. The system may perform edge analysis in the images to identify a pairs of edges corresponding to a road. The system may identify pixel blobs in the images including adjacent pixels matching each other based on a pixel attribute. The system uses a machine learning model for generating an output identifying moving vehicles in the images. The system determines a count of the moving vehicles captured by the images, where each moving vehicle is associated with corresponding pixel blobs.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. A moving vehicle analysis system receives images from an aerial imaging device. The system may perform edge analysis in the images to identify a pairs of edges corresponding to a road. The system may identify pixel blobs in the images including adjacent pixels matching each other based on a pixel attribute. The system uses a machine learning model for generating an output identifying moving vehicles in the images. The system determines a count of the moving vehicles captured by the images, where each moving vehicle is associated with corresponding pixel blobs.

Abstract: A structure for a parallel cyclic redundancy check (CRC) structure in which the number of cycles in the loopback can be arbitrarily extended is provided. The parallel CRC structure includes a reweighting module in the feedback loop that is pipelined into multiple stages. The parallel CRC structure also includes multiple feed forward reweighting modules that correspond to the multiple pipeline stages in the feedback loop. The reweighting module in the feedback loop accumulates and reweights the contribution of all symbols in the message, while the N reweighting modules in the N parallel feed-forward paths provide the contributions of the symbols that are “in-flight” within the feedback loop to the final CRC checksum.

Abstract: A minimal instruction set computer architecture (hyperscalar computer architecture) comprises a central memory, an instruction buffer, a control unit, an I/O control unit, a plurality of functional units, a plurality of register files, and a data router. In the hyperscalar computer architecture, the central memory transfers a plurality of instructions to the instruction buffer. The control unit receives multiple instructions from the instruction buffer, and automatically determines and issues the largest subset of instructions from those received that can be simultaneously issued to the plurality of functional units. Each functional unit receives data from and returns computational results to a corresponding register file. The data router serves to transfer data between each register file and any other register file, the central memory, the control unit, or the I/O control unit.