About: Systems architecture is a(n) research topic. Over the lifetime, 17612 publication(s) have been published within this topic receiving 283719 citation(s). The topic is also known as: system architecture.
Papers published on a yearly basis
28 Sep 2002
TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Abstract: We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring in general, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island off the coast of Maine streaming useful live data onto the web. The application-driven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and health monitoring.
12 Nov 2000
TL;DR: Key requirements are identified, a small device is developed that is representative of the class, a tiny event-driven operating system is designed, and it is shown that it provides support for efficient modularity and concurrency-intensive operation.
Abstract: Technological progress in integrated, low-power, CMOS communication devices and sensors makes a rich design space of networked sensors viable. They can be deeply embedded in the physical world and spread throughout our environment like smart dust. The missing elements are an overall system architecture and a methodology for systematic advance. To this end, we identify key requirements, develop a small device that is representative of the class, design a tiny event-driven operating system, and show that it provides support for efficient modularity and concurrency-intensive operation. Our operating system fits in 178 bytes of memory, propagates events in the time it takes to copy 1.25 bytes of memory, context switches in the time it takes to copy 6 bytes of memory and supports two level scheduling. The analysis lays a groundwork for future architectural advances.
TL;DR: The invention relates to a circuit for use in a receiver which can receive two-tone/stereo signals which is intended to make a choice between mono or stereo reproduction of signal A or of signal B and vice versa.
Abstract: The invention relates to a circuit for use in a receiver which can receive two-tone/stereo signals. This circuit is intended to make a choice between mono or stereo reproduction of signal A or of signal B and vice versa. The circuit comprises two bistable multivibrator circuits which are controlled by a common, user-operable switch and by characteristic signals which are derived from the characteristic frequencies relevant to the different types of programs. The control is such that when the switch is operated only one bistable multivibrator circuit can be changed over (namely the bistable multivibrator circuit associated with the relevant characteristic frequency received.) A logic circuit which is controlled by the bistable multivibrator circuit as well as by the characteristic signals operates a change-over switch for switching to the desired reproduction.
01 Mar 1994-Neural Computation
TL;DR: An Expectation-Maximization (EM) algorithm for adjusting the parameters of the tree-structured architecture for supervised learning and an on-line learning algorithm in which the parameters are updated incrementally.
Abstract: We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.
01 Sep 1987-Artificial Intelligence
TL;DR: SOAR as discussed by the authors is an implemented proposal for such an architecture, which is described in detail in the paper "SOAR: An Implementation of Cognitive Architecture for Artificial Intelligence" and demonstrated in the SOAR project.
Abstract: The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities.
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