Abstract: A mounting assembly for mounting an external cargo basket onto a helicopter and a method of using the mounting assembly. The mounting assembly comprises a plurality of connectors—corresponding male and female connectors being positioned on the helicopter and on the basket, one of said corresponding connectors being an articulating connector. The cargo basket is connected to the helicopter by first connecting the articulating connector then pivoting and rotating the cargo basket using the articulating connector so as to align and connect the remaining connections.

Abstract: Disclosed is a moving object recognition system. The system generates an optical flow vector field based on a two-dimensional brightness signal. The system employs a bi-directional optical flow neural network extended with a motion selective neural network. The motion selective neural network interacts with the optical flow neural network by providing an attentional bias for each of the optical flow neurons. The motion selective neural network is adjustable by input to focus on a certain expected motion. The motion selective neural network can be influenced by additional neural networks which receive a bottom-up input from the motion selective neural network.

Abstract: Data elements, preferably representative of video data, are logically divided into blocks. In a bit-wise fashion, each block is inspected to determine whether the data elements for that block may be represented in a highly compact format. If a given block may not be represented in this manner, it is sub-divided into blocks having smaller dimensions. This process of identifying suitable blocks and sub-dividing is recursively repeated until minimum block dimensions are reached. The same result may be achieved through the use of a plurality of ascending tables that are constructed by repetitively forming tables of reduced data elements. The plurality of ascending tables is traversed and, based on the reduced data elements, blocks of data are identified that are susceptible to the highly compact format. Wavelet transforms are preferably used to provide video data to be compressed.

Abstract: Data elements, preferably representative of video data, are logically divided into blocks. In a bit-wise fashion, each block is inspected to determine whether the data elements for that block may be represented in a highly compact format. If a given block may not be represented in this manner, it is sub-divided into blocks having smaller dimensions. This process of identifying suitable blocks and sub-dividing is recursively repeated until minimum block dimensions are reached. The same result may be achieved through the use of a plurality of ascending tables that are constructed by repetitively forming tables of reduced data elements. The plurality of ascending tables is traversed and, based on the reduced data elements, blocks of data are identified that are susceptible to the highly compact format. Wavelet transforms are preferably used to provide video data to be compressed.

Abstract: Data elements, preferably representative of video data, are logically divided into blocks. In a bit-wise fashion, each block is inspected to determine whether the data elements for that block may be represented in a highly compact format. If a given block may not be represented in this manner, it is sub-divided into blocks having smaller dimensions. This process of identifying suitable blocks and sub-dividing is recursively repeated until minimum block dimensions are reached. The same result may be achieved through the use of a plurality of ascending tables that are constructed by repetitively forming tables of reduced data elements. The plurality of ascending tables is traversed and, based on the reduced data elements, blocks of data are identified that are susceptible to the highly compact format. Wavelet transforms are preferably used to provide video data to be compressed.