We present an efficient physical realization method of particle filters for real-time tracking applications. The methodology is based on block-level pipelining where data transfer between processing blocks is effectiv...
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We present an efficient physical realization method of particle filters for real-time tracking applications. The methodology is based on block-level pipelining where data transfer between processing blocks is effectively controlled by autonomous distributed controllers. Block-level pipelining maintains inherent operational concurrency within the algorithm for high-throughput execution. The proposed use of controllers, via parameters reconfiguration, greatly simplifies the overall controller structure, and alleviates potential speed bottlenecks that may arise due to complexity of the controller. A Gaussian particle filter for bearings-only tracking problem is realized based on the presented methodology. For demonstration, individual coarse grain processing blocks comprising particle filters are synthesized using commercial FPGA. From the execution characteristics obtained from the implementation, the overall controller structure is derived according to the methodology and its temporal correctness verified using Verilog and SystemC.
A distributed and scalable architecture for the control of an autonomous robot is presented in this work. In our proposal a whole robotic agent is divided into sub-agents. Every sub-agent is co ded into a very simple ...
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A distributed and scalable architecture for the control of an autonomous robot is presented in this work. In our proposal a whole robotic agent is divided into sub-agents. Every sub-agent is co ded into a very simple neural network, and controls one sensor/actuator element of the robot. Sub-agents learn by evolution how to handle their sensor/actuator and how to cooperate with the rest of sub-agents. Emergence of behaviors happens when the co-evolution of several sub-agents embodied into the single robotic agent is produced. It will be demonstrated that the proposed distributed controller learns faster and better than a neuro-evolved central controller.
Traditional disk arrays have a centralized architecture, with a single controller through which all requests flow. Such a controller is a single point of failure, and its performance limits the maximum number of disks...
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Traditional disk arrays have a centralized architecture, with a single controller through which all requests flow. Such a controller is a single point of failure, and its performance limits the maximum number of disks to which the array can scale. We describe TickerTAIP, a parallel architecture for disk arrays that distributes the controller functions across several loosely coupled processors. The result is better scalability, fault tolerance, and flexibility. This article presents the TickerTAIP architecture and an evaluation of its behavior. We demonstrate the feasibility by a working example, describe a family of distributed algorithms for calculating RAID parity, discuss techniques for establishing request atomicity, sequencing, and recovery, and evaluate the performance of the TickerTAIP design in both absolute terms and by comparison to a centralized RAID implementation. We also analyze the effects of including disk-level request-scheduling algorithms inside the array. We conclude that the Ticker TAIP architectural approach is feasible, useful, and effective.
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