In this paper, a novel method for designing IIR variable fractional delay (VFD) digital filters with variable and fixed denominator is presented. First of all, a peak-constrained weighted least-squares (PCWLS) method ...
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In this paper, a novel method for designing IIR variable fractional delay (VFD) digital filters with variable and fixed denominator is presented. First of all, a peak-constrained weighted least-squares (PCWLS) method is employed to design a set of FIR fixed fractional delay (FD) filters according to given specifications. The PCWLS FIR filters are implemented by the projected least-squares (PLS) algorithm. An iterative WLS model reduction technique is utilized to design denominators, which can guarantee the stability of designed IIR VFD filter if the iteration converges. The numerator of IIR fixed FD filters can be designed by two approaches: The Approach 1 solves linear equations based on the orthogonality principle; and the Approach 2 formulates the numerator design problem as a standard quadratic programming (QP) problem. The coefficients of IIR fixed FD filters are finally approximated by polynomial functions of FD. Three sets of examples are given to demonstrate the effectiveness of the proposed method.
We describe our efforts to build a caching and tiling map server that greatly improves the performance and interactivity of traditional geographic map servers. We have used this system to integrate and effectively fed...
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ISBN:
(纸本)9781424433896;0978569911
We describe our efforts to build a caching and tiling map server that greatly improves the performance and interactivity of traditional geographic map servers. We have used this system to integrate and effectively federate 15 Indiana county map servers with Google map images and state-wide ortho-photography data. Our approach is an example of the so-called Web 2.0 style of development, in which we integrate external, third party services into a higher level service. This approach also allows for lightweight client development using relatively simple JavaScript programming libraries. We demonstrate this by building a Google Map client interface to our tile server. Finally, we discuss our initial efforts to make collaborative clients using a shared event model that captures and broadcasts browser events to other, listening browsers.
Cellular simultaneous recurrent neural networks (SRN) show great promise in solving complex function approximation problems. In particular, approximate dynamic programming is an important application area where SRNs h...
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Cellular simultaneous recurrent neural networks (SRN) show great promise in solving complex function approximation problems. In particular, approximate dynamic programming is an important application area where SRNs have significant potential advantages compared to other approximation methods. Learning in SRNs, however, proved to be a notoriously difficult problem, which prevented their broader use. This paper introduces an extended Kalman filter approach to train SRNs. Using the two-dimensional maze navigation problem as a testbed, we illustrate the operation of the method and demonstrate its benefits in generalization and testing performance
This paper describes a novel algorithm called CON-MODP for computing Pareto optimal policies for deterministic multi-objective sequential decision problems. CON-MODP is a value iteration based multi-objective dynamic ...
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This paper describes a novel algorithm called CON-MODP for computing Pareto optimal policies for deterministic multi-objective sequential decision problems. CON-MODP is a value iteration based multi-objective dynamic programming algorithm that only computes stationary policies. We observe that for guaranteeing convergence to the unique Pareto optimal set of deterministic stationary policies, the algorithm needs to perform a policy evaluation step on particular policies that are inconsistent in a single state that is being expanded. We prove that the algorithm converges to the Pareto optimal set of value functions and policies for deterministic infinite horizon discounted multi-objective Markov decision processes. Experiments show that CON-MODP is much faster than previous multi-objective value iteration algorithms.
This paper describes an extension to the component-based programming model to support real-time dynamic guarantee for distributed applications. The extended model aims to include an acceptance tests to component-based...
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This paper describes an extension to the component-based programming model to support real-time dynamic guarantee for distributed applications. The extended model aims to include an acceptance tests to component-based servers at bind time. We present the mapping of our model to the CORBA Component Model, the architecture to support the dynamic guarantee in component-based middleware, the implementation of this architecture in CIAO (Component Integrated ACE ORB) and the result of experiments run to evaluate the cost of the mechanisms used
Fault tolerance is a desirable feature in distributed high-performance systems, since applications tend to run for long periods of time and faults become more likely as the number of nodes in the system increase. Howe...
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Fault tolerance is a desirable feature in distributed high-performance systems, since applications tend to run for long periods of time and faults become more likely as the number of nodes in the system increase. However, most distributed environments lack any fault tolerant features, since they tend to be hard to implement and use, and often hurt performance dramatically. In this paper we discuss how we successfully added fault-tolerance to the Anthill distributed programming environment by using an application-level checkpoint/rollback solution. The programming model offers an abstraction where the programmer can easily identify points during the execution where the communication pattern is well defined, forming a consistent cut where checkpoints may be saved consistently without requiring extra communication, avoiding any domino effect during recovery from faults. We present the new abstractions for fault tolerance, describe how the solution was implemented and present performance results that show the efficiency of the solution with both regular and irregular applications.
In this paper, we briefly describe existing principles and stages for generating code from OCL expressions pointing out the drawbacks that cause inefficiencies of the resulting code. The proposed improvement of the tr...
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In this paper, we briefly describe existing principles and stages for generating code from OCL expressions pointing out the drawbacks that cause inefficiencies of the resulting code. The proposed improvement of the transformation is based on extended abstract syntax trees (AST) with context-specific attributes. Principles for defining such attributes on AST trees and an example of transformation is presented.
In this paper we introduce a new model-based approach for a data-efficient modelling and control of reinforcement learning problems in discrete time. Our architecture is based on a recurrent neural network (RNN) with ...
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In this paper we introduce a new model-based approach for a data-efficient modelling and control of reinforcement learning problems in discrete time. Our architecture is based on a recurrent neural network (RNN) with dynamically consistent overshooting, which we extend by an additional control network. The latter has the particular task to learn the optimal policy. This approach has the advantage that by using a neural network we can easily deal with high-dimensions and consequently are able to break Bellman's curse of dimensionality. Further due to the high system-identification quality of RNN our method is highly data-efficient. Because of its properties we refer to our new model as recurrent control neural network (RCNN). The network is tested on a standard reinforcement learning problem, namely the cart-pole balancing, where it shows especially in terms of data-efficiency outstanding results
This paper proposes a , the "ambit' of an action, that allows the degree of distribution of an action in a multiagent system to be quantified without regard to its functionality. It demonstrates the use of th...
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This paper proposes a , the "ambit' of an action, that allows the degree of distribution of an action in a multiagent system to be quantified without regard to its functionality. It demonstrates the use of that notion in the design, analysis and implementation of dynamically-reconfigurable multi-agent systems. It distinguishes between the extensional (or system) view and intensional (or agent-based) view of such a system and shows how, using the notion of ambit, the step-wise derivation paradigm of formal methods can be used to derive the latter from the former. In closing it addresses the manner in which these ideas inform studies in the ethics of systems of artificial agents.
Despite its widespread use, concurrent programming is still plagued by reliability problems, such as race conditions and deadlock, not found in sequential programs. We present a concurrency framework to help developer...
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Despite its widespread use, concurrent programming is still plagued by reliability problems, such as race conditions and deadlock, not found in sequential programs. We present a concurrency framework to help developers avoid these error conditions, and make it possible to verify their absence through static analysis.
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