This paper describes a prototype video coding platform meant for the conception and testing of multimedia products such as next-generation videophones. The platform is largely based on ITU-T Recommendation H.263, with a number of additional object-oriented quality enhancement features which make it especially well suited for very low bit-rate coding of "head-and-shoulders" video material typical of real-time multimedia applications, video teleconferencing, and video telephony. These features consist of: (1) segmentation into objects of interest, (2) segmentation-based prefiltering, (3) model-assisted rate control, (4) adaptive vector quantization, and finally (5) segmentation-based postfiltering. In the spirit of Recommendation H.263, these enhancements are modular and can be selectively turned on or off, thereby enabling a wide variety of coding modes.

Asynchronous transfer mode (ATM) supports a number of different traffic classes, including constant bit rate (CBR), available bit rate (ABR) and variable bit rate (VBR). Applications wishing to use a particular class must determine the set of traffic parameters which characterize the connection. In this paper, we consider how to engineer the traffic parameters for VBR WAN voice, where multiple voice streams, utilizing compression and silence detection, are multiplexed into a single virtual circuit. We define two criteria which the VBR parameters must satisfy: 1) Achievement of a desired probability of cell marking/loss by a leaky bucket policer, and 2) Maximization of admission probabilities to the network. We compute the parameters for two vastly different systems and admission controllers. In either case, we observe a similar trend in the admission probabilities, and propose a method for choosing specific values of the VBR parameters.

Eight Artificial Intelligence programming languages (SAIL, LISP, MICROPLANNER, CONNIVER, MLISP, POP-2, AL, and QLISP) are presented and surveyed, with examples of their use in an automated shop environment. Control structures are compared, and distinctive features of each language are highlighted. A simple programming task is used to illustrate programs in SAIL, LISP, MICROPLANNER, and CONNIVER. The report assumes reader knowledge of programming concepts, but not necessarily of the languages surveyed.