A $70 toy robot has been successfully used in Computer Science undergraduate laboratory courses in teal-time programming and advanced operating systems to provide students with hands on experience.A custom designed interface card connects a Radio Shack Armatron toy mobile robot with an IBM PC. To provide sensory input and hence introduce feedback, the robot is shackled to a track fitted with sensors. Extra sensors in the robot's environment allow challenging experiments such as picking up an object from a moving belt.While progrznming the robot and its environment in Turbo Pascal, the students learn how to write s&ware drivers to control low level hardware mat requires real-time response. This experimental design obviates the need to use sophisticated test equipment or special software development tools, and so the robot has transformed potentially routine courses into a exciting and fultilling learning experiences.

Absfruct-This paper presents new results on the performance of a token ring network interface adapter, incorporating devices optimized for the network interface application. Previous work [1,2] has demonstrated the performance improvements possible by employing adapter bypass circuitry in the physical layer of the adapter. Here, we seek to extend the operating speed of the token ring by employing state of the art device technologies, with devices optimized for this application. We seek to determine the operating speed performance limit of the token ring, achievable with these technologies. We investigate three technologies, G a b , ECL, and high speed CMOS. We determine the operating speed limits of the adapter, using each of these three technologies. We show that the limit is much higher than presently used.

Statistical tables belong to an important subset of tables published in the web, because they represent up-to-date, vital information sources for decision makers. These tables are often carefully designed for easy reading by analysts, and then mechanically produced by an OLAP database system. The general practice of extracting attribute-value pairs from statistical tables does not ensure high accuracy when they are used as a database for an information retrieval system. In this paper, we show how a human may visualize a statistical table as an multidimensional object, defined by a suitably modified OLAP model. In this way, the keywords are classified into semantically distinct groups, i.e., dimension hierarchies, without any ontological knowledge or resorting to machine learning. A prototype system which mimics the human reasoning for table processing has been implemented. Experiments on 150 randomly chosen tables from Statistics Canada have confirmed the validity of this approach.