This paper gives a state-of-the-art review of adaptive channel equalization in digital line-of-sight radio systems employing bandwidth-efficient modulation techniques. A particular emphasis is placed on decision-feedb...
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This paper gives a state-of-the-art review of adaptive channel equalization in digital line-of-sight radio systems employing bandwidth-efficient modulation techniques. A particular emphasis is placed on decision-feedback equalizer (DFE) implementation and performance in the presence of nonminimum-phase fading. Stability problems of fractionally-spaced equalizers (FSE's) are discussed, and a newly developed adaptation technique is outlined. We also discuss the blind adaptation algorithms that are used to reduce outage by preventing the equalizer coefficients to diverge during severe fade events and carrier synchronism loss. Finally, we discuss further issues related to asymmetric equalizers and to the interaction of the equalizer adaptation algorithm with the carrier recovery loop.
A new approach based on pattern recognition techniques and dedicated to the monitoring of non stationary systems is presented in this paper. More precisely, it consists of a recursive subspace identification algorithm...
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A new approach based on pattern recognition techniques and dedicated to the monitoring of non stationary systems is presented in this paper. More precisely, it consists of a recursive subspace identification algorithm combined with an adaptive classifier set for non stationary environment. The system identification method which provides a recursive estimation of a linear state space model is firstly described. Then, a feature vector representing the system functioning state is extracted from this estimated model. Next, the dynamical clustering algorithm which online learns the functioning modes and continuously determines the current mode of the system is introduced. Its auto adaptive and unsupervised abilities to take into account system modes evolutions are finally emphasized on simulation examples.
Wavelet based data compression is superior to interpolative compression techniques like boxcar as wavelets provide an efficient joint time-scale representation of the signal in terms of wavelet coefficients. Threshold...
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Wavelet based data compression is superior to interpolative compression techniques like boxcar as wavelets provide an efficient joint time-scale representation of the signal in terms of wavelet coefficients. Thresholding over these coefficients is used to achieve compression. A recursive on-line wavelet based compression algorithm, which combines the high quality of compression of wavelets and on-line implementability of interpolative methods has already been developed. In this paper, an error based thresholding framework is introduced, which adaptively computes these thresholds, given the bounds on root mean square error and local point error. This framework is applied to the recursive on-line wavelet based compression using Haar wavelets. Experiments show that the resulting algorithm gives superior compression as compared to interpolative methods. Moreover, it can be used on-line and provides an effective way of controlling the local point error (LPE) and the root mean square error (RMSE).
Accurate simulations of the motion of cable-driven parallel robots (CDPRs) are helpful in its modal analysis, testing robust control strategies, validating designs and estimating workspaces. On that account, a modular...
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Accurate simulations of the motion of cable-driven parallel robots (CDPRs) are helpful in its modal analysis, testing robust control strategies, validating designs and estimating workspaces. On that account, a modular and computationally efficient framework to analyse the dynamics of CDPRs is developed in the present work. In contrast to the prior studies, the inertia, stiffness and damping properties of the cables, along with their temporal variations caused by feeding and retrieving, are included in the dynamic model. Further, the forward dynamics algorithm designed for this purpose is recursive in nature and has linear time complexity. Finally, the efficacy of the proposed framework is established with the help of the FAST manipulator, the largest existing CDPR. Also, its extensive application potential is established via the study of the CDPR CoGiRo, which, apart from being actuated redundantly, differs drastically from the former in terms of its mass and footprint.
The use of wireless sensor networks and the Internet of Things has increased dramatically in the last decade. The sensors measure the required parameters and send them to the data processing centers using one of the v...
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The use of wireless sensor networks and the Internet of Things has increased dramatically in the last decade. The sensors measure the required parameters and send them to the data processing centers using one of the various wireless transmission technologies (often using cellular infrastructure) to make the appropriate decision. Files containing measurement information can arrive in bursts simultaneously, which is a critical issue to be aware of. The purpose of this work is to develop and analyze a model to evaluate the effectiveness of an LTE (Long-Term Evolution) cell in serving requests from NB-IoT (Narrowband Internet of things) devices when these requests are received in bursts rather than individually. In the article, the common uses of the Internet of Things in our modern era were discussed, the NB-IoT technology was paid attention to, and a mathematical model to evaluate the performance of an LTE cell in the case of impulsive arrivals of NB-IoT requests was built. Finally, the computational algorithm and numerical evaluation were introduced.
Abstract In this paper, we consider the problem of recursively constructing smoothing spline surfaces with equality and/or inequality constraints each time when a new set of data is observed. The splines are constitut...
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Abstract In this paper, we consider the problem of recursively constructing smoothing spline surfaces with equality and/or inequality constraints each time when a new set of data is observed. The splines are constituted by using normalized uniform B-splines as the basis functions. Then various types of constraints are formulated as linear function of the so-called control points, and the problem is reduced to quadratic programming problem. Based on the results, we develop the recursive design method for constructing such constrained smoothing spline surfaces. The performance is examined by some numerical examples.
The enumeration of minimal siphons in ordinary Petri nets is fundamental in the assessment of behavioral properties and a crucial step in the development of deadlock prevention algorithms. A novel recursive algorithm ...
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The enumeration of minimal siphons in ordinary Petri nets is fundamental in the assessment of behavioral properties and a crucial step in the development of deadlock prevention algorithms. A novel recursive algorithm is proposed in the paper for this purpose, based on theoretical results that extend well known properties described in the literature. The algorithm uses already found solutions to progressively decompose the search problem into smaller sub-problems. This recursive decomposition approach is combined with the LTUR procedure, which efficiently computes a siphon, based on a logical clause description approach. A prototypical version of the search algorithm has been developed and an experiment has been carried out on a large set of random test instances to evaluate the efficiency of the method.
The new version of the recursive Modified Gram- Schmidt algorithm with directional parameter tracking technique is presented for the input-output regression models. The directional forgetting improves significantly th...
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The new version of the recursive Modified Gram- Schmidt algorithm with directional parameter tracking technique is presented for the input-output regression models. The directional forgetting improves significantly the numerical behavior of algorithm when data are not persistently exciting. The algorithm allows a highly modular and numerically safe implementation even if an extremely fast parameter tracking speed is desired. The algorithm works with a modular triangular structure of reflection coefficients. A comparative study demonstrates the improved numerical performance over the standard square root algorithms with the exponential parameter tracking.
The representation of non-linear process dynamics can cause problems for feedforward networks, and recurrent networks hold promise in this area. Two partially recurrent neural network structures with local output feed...
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The representation of non-linear process dynamics can cause problems for feedforward networks, and recurrent networks hold promise in this area. Two partially recurrent neural network structures with local output feedback are investigated, along with four training algorithms, for their ability to model non-linear processes. The training algorithms studied included two well-known backpropagation techniques and are all shown to be derivatives of the more general recursive prediction error (RPE) method. Networks were trained and tested on data from a simulated, non-linear chemical process. The performance of the partially recurrent networks is compared to a standard time delay neural network model. The results show that partially recurrent networks can model as well as time delay networks. Issues relating to the implementation of partially recurrent networks for non-linear modelling are discussed.
作者:
Henrik KureDina
Danish Informatics Network In the Agricultural Sciences Royal Veterinary and Agricultural University Bulowsvej 13 DK-1870 Frb. Copenhagen.
An alternative approach towards dynamic programming (DP) is presented: Recursions. A basic deterministic model and solution function are defined and the model is generalized to include stochastic processes, with the t...
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An alternative approach towards dynamic programming (DP) is presented: Recursions. A basic deterministic model and solution function are defined and the model is generalized to include stochastic processes, with the traditional stochastic DP model as a special case. Heuristic rules are included in the models simply as restrictions on decision spaces and a small slaughter pig marketing planning example illustrates the potential state space reductions induces by such rules.
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