An efficient recursive technique for computing the Elmore delay in series-parallel resistance-capacitance (RC) networks is presented. The time complexity of the algorithm is of the order of the number of resistors tim...
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An efficient recursive technique for computing the Elmore delay in series-parallel resistance-capacitance (RC) networks is presented. The time complexity of the algorithm is of the order of the number of resistors times the number of nodes to which the delay has to be computed. In this respect it is superior to other known methods, in particular that of Chan and Karplus. Although their algorithm is more general, the present method should be attractive, given the fact that many VLSI MOS circuits are based on design styles which are restricted to series-parallel transitor networks which, in particular, exclude bridges. A special type of series-parallel RC circuit occurs in interconnection networks driven by multiple sources. A variation on the first algorithm, which is especially useful in a hierarchical simulator, is presented for computing the Elmore delay in such networks.
This paper considers the kinematics of hyper-redundant (or ''serpentine'') robot locomotion over uneven solid terrain, and presents algorithms to implement a variety of ''gaits.'' The a...
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This paper considers the kinematics of hyper-redundant (or ''serpentine'') robot locomotion over uneven solid terrain, and presents algorithms to implement a variety of ''gaits.'' The analysis and algorithms are based on a continuous backbone curve model which captures the robot's macroscopic geometry. Two classes of gaits, based on stationary waves and traveling waves of mechanism deformation, are introduced for hyper-redundant robots of both constant and variable length, We also illustrate how the locomotion algorithms can be used to plan the manipulation of objects which are grasped in a tentacle-like manner. Several of these gaits and the manipulation algorithm have been implemented on a 30 degree-of-freedom hyper-redundant robot, Experimental results are presented to demonstrate and validate these concepts and our modeling assumptions.
A description is given of the algorithms designed for use in residential load control systems. The authors present the functions to be fulfilled by such a price-responsive device and describe the end-user devices avai...
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A description is given of the algorithms designed for use in residential load control systems. The authors present the functions to be fulfilled by such a price-responsive device and describe the end-user devices available in residences and the control logics applicable to each. It is concluded that there is a need to understand customer attitudes and acceptance in the design of the response strategies and in the design of the man-machine interface.< >
In this note, the statistical behavior of the complex scalar LMS adaptation algorithm is investigated when the input data stream consists of statistically dependent inputs. The asymptotic time constant of the mean beh...
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In this note, the statistical behavior of the complex scalar LMS adaptation algorithm is investigated when the input data stream consists of statistically dependent inputs. The asymptotic time constant of the mean behavior of the algorithm is evaluated, taking into account the time correlation properties of the input data sequence. It is shown that the behavior of the correlated data case does not degrade significantly from the uncorrelated case until the input sequence is very highly correlated (ρ > 0.99).
We develop a representational model for the knowledge base (KB) of fuzzy production systems with rule chaining based on the Petri net formalism. The model presents the execution of a KB following a data driven strateg...
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We develop a representational model for the knowledge base (KB) of fuzzy production systems with rule chaining based on the Petri net formalism. The model presents the execution of a KB following a data driven strategy based on the sup-min compositional rule of Inference. In this connection, algorithms characterizing different situations have been described, including the case where the KB is characterized by complete information about all the input variables and the case where it is characterized by ignorance of some of these variables. For this last situation we develop a process of ''incremental reasoning'';this process allows the KB to take information about previously unknown values into consideration as soon as such information becomes available. Furthermore, as compared to other solutions, the rule chaining mechanism we introduce is more flexible, and the description of the rules more generic. The computational complexity of these algorithms is O((C/2 + M + N)R(2)) for the ''complete information'' case and O((M + N)R(2)) and O(2(M + N)R(2)) for the other cases, where R is the number of fuzzy conditional statements of the KB, M and N the maximum number of antecedents and consequents in the rules and C the number of chaining transitions in the KB representation.
We show that Levinson's basic principle for the solution of normal equations which are of Toeplitz form may be extended to the case where these equations do not possess this specific symmetry. The use of Levinson&...
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We show that Levinson's basic principle for the solution of normal equations which are of Toeplitz form may be extended to the case where these equations do not possess this specific symmetry. The use of Levinson's principle allows us to obtain a compact (2 x 2) form to express a system of equations of arbitrary order. This compact form is the key expression in the development of recursive algorithms and allows a compact representation of the most important Levinson-type algorithms which are used in the analysis of seismic and time series data in general. In the case when the coefficient matrix does not possess any type of special structure, the number of multiplications and divisions required in the inversion is n3 - 2n2 + 4n. We illustrate the described method by application to various examples which we have chosen so that the coefficient matrix possess various symmetries. Specifically, we first consider the solution of the normal equations when the associated matrix is the doubly symmetric non-Toeplitz covariance matrix. Second, we obtain the solution of extended Yule-Walker equations where the coefficient matrix is Toeplitz but nonsymmetric. Finally, we briefly illustrate the approach by considering the determination of the prediction error operator when the NE are in fact of symmetric Toeplitz form.
We apply the partition algorithm to the problem of time-series classification, We assume that the source that generates the time series belongs to a finite set of candidate sources. Classification is based on the comp...
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We apply the partition algorithm to the problem of time-series classification, We assume that the source that generates the time series belongs to a finite set of candidate sources. Classification is based on the computation of posterior probabilities. Prediction error is used to adaptively update the posterior probability of each source. The algorithm is implemented by a hierarchical, modular, recurrent network. The bottom (partition) level of the network consists of neural modules, each one trained to predict the output of one candidate source. The top (decision) level consists of a decision module, which computes posterior probabilities and classifies the time series tb the source of maximum posterior probability. The classifier network is formed from the composition of the partition and decision levels. This method applies to deterministic as well. as probabilistic time series. Source switching can also be accommodated. We give some examples of application to problems of signal detection, phoneme, and enzyme classification. Zn conclusion, the algorithm presented here gives a systematic method for the design of modular classification networks. The method can be extended by various choices of the partition and decision components.
The dynamic frame sizing algorithm is a throughput-optimal algorithm that can achieve maximum network throughput without the knowledge of arrival rates. Motivated by the need for energy-efficient communication in wire...
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The dynamic frame sizing algorithm is a throughput-optimal algorithm that can achieve maximum network throughput without the knowledge of arrival rates. Motivated by the need for energy-efficient communication in wireless networks, in this paper, we propose a new dynamic frame sizing algorithm, called the Greenput algorithm, that takes power allocation into account. In our Greenput algorithm, time is partitioned into frames, and the frame size of each frame is determined based on the backlogs presented at the beginning of a frame. To obtain a good delay-energy efficiency tradeoff, the key insight of our Greenput algorithm is to reduce transmit power to save energy when the backlogs are low so as not to incur too much packet delay. For this, we define a threshold parameter T-max (for the minimum time to empty the backlogs with maximum power allocation), and the Greenput algorithm enters the (mixed) power-saving mode when the backlogs are below the threshold. Using a large deviation bound, we prove that our Greenput algorithm is still throughput optimal. In addition to the stability result, we also perform a fluid approximation analysis for energy efficiency and average packet delay when T-max is very large. To show the delay-energy efficiency tradeoff, we conduct extensive computer simulations by using the Shannon formula as the channel model in a wireless network. Our simulation results show that both energy efficiency and average packet delay are quite close to their fluid approximations even when T-max is moderately large.
Signal decomposition has shown to be a useful tool for the design of robust and efficient estimators. Stack and microstatistic filters are among the filters that use the threshold decomposition architecture. The micro...
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Signal decomposition has shown to be a useful tool for the design of robust and efficient estimators. Stack and microstatistic filters are among the filters that use the threshold decomposition architecture. The microstatistic filter, in particular, uses linear combinations on the set of decomposed (thresholded) signals to produce its estimate. Thus, stack and microstatistic filters can be regarded as Boolean and linear threshold filters, respectively. The design of the optimal microstatistic filter requires the statistical characterization of the decomposed signal set (microstatistics). In this paper, we develop adaptive microstatistic filters for applications where the second-order statistics of the thresholded signals are not known, or when they may be nonstationary. A multilevel threshold decomposition is used such that real valued stochastic processes can be filtered and such that the computation complexity of the algorithm can be arbitrarily specified by the designer. The adaptation uses the least mean squares error approach of the least mean square (LMS) algorithm. The convergence of the adaptive algorithm is proved. Due to the nonhomogeneous statistical characteristics of the thresholded signals, we can assign a different step-size adaptation parameter to each threshold level. We develop simple design guidelines for the set of nonhomogeneous step sizes which in practice yield better convergence characteristics.
The principal sources of estimation error in sensor array signal processing applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics. While t...
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The principal sources of estimation error in sensor array signal processing applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics. While the effects of these errors have been studied individually, their combined effect has not yet been rigorously analyzed. In this paper, we undertake such an analysis for the class of so-called subspace fitting algorithms. In addition to deriving;first-order asymptotic expressions for the estimation error, we show that an overall optimal weighting exists for a particular array and noise covariance error model. In a companion paper, the optimally weighted subspace fitting method is shown to be asymptotically equivalent with the more complicated maximum a posteriori estimator. Thus, for the model in question, no other method can yield more accurate estimates for large samples and small model errors. Numerical examples and computer simulations are included to-illustrate the obtained results and to verify the asymptotic analysis for realistic scenarios.
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