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Much attention has been paid to anew form ofspread spectrum technique called Ultra WideBand (UWB) Impulse Radio system for future high speed wireless communication services. In this paper, we derive some expressions which quantify U W B interference to SS signal and viceversa. This theoretical analysis is then compared with computer simulations.

The aim of this research is the development of a patient robot for use in actual clinical training. Electro pneumatic regulators and electromagnetic valves incorporated in the robot is operated by manipulating air cylinders. It is possible to conduct training assuming several patients enabling trainees to learn a flexible response under a wide range of circumstances. A simple interface was used for ease of operation. Further, a built-in sensor inside the oral cavity responds to the trainee's actions leading to a vomiting reflex and pain during drilling teeth. Attaching the pain sensor to the body of test subjects, will also be useful for training social service workers during nursing care examinations.

The CNNs have achieved a state-of-the-art performance in many applications. Recent studies illustrate that CNNs recognition accuracy drops drastically if images are noise corrupted. We focus on the problem of robust recognition accuracy of noise-corrupted images. We introduce a novel network architecture called Streaming Networks. Each stream is taking a certain intensity slice of the original image as an input, and stream parameters are trained independently. We use network capacity, hard-wired and input-induced sparsity as the dimensions for experiments. The results indicate that only the presence of both hard-wired and input-induces sparsity enables robust noisy image recognition. Streaming Nets is the only architecture which has both types of sparsity and exhibits higher robustness to noise. Finally, to illustrate increase in filter diversity we illustrate that a distribution of filter weights of the first conv layer gradually approaches uniform distribution as the degree of hard-wired and domain-induced sparsity and capacities increases.

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