to transfer large amounts of data within themselves. Internally, a DRAM chip consists of rows of bits and a row-buffer. To access data from any portion of a particular row, the DRAM chip transfers the entire row (e.g., 4 Kbits) into the equally-sized rowbuffer, and vice versa. While this internal data

In this work, algorithms are developed and evaluated to detect physical activities from data acquired using five small biaxial accelerometers worn simultaneously on different parts of the body. Acceleration data was collected from 20 subjects without researcher supervision or observation

mathematically by defining an objective function, a measure of performance for any possi-ble movement. The unique trajectory which yields the best performance is determined using dynamic optimization the-ory. In the work presented here, the objective function is the square of the magnitude of jerk (rate

Abstract not found

Abstract not found

of the scene may be replaced with images. Second, the image synthesis time is independent of the scene complexity. The correspondence between images, required for the morphing method, can be predetermined automatically using the range data associated with the images. The method is further accelerated by a

The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements

(movements). The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: First, a term describing the acceleration towards the desired velocity of motion. Second

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at

, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted