Time filter

Source Type

Sathyamangalam, India

The structure of an adaptive time varying linear filter largely depends on its tap-length and the delay units connected to it. The no of taps is one of the most important structural parameters of the liner adaptive filter. Determining the system order or length is not a trivial task. Fixing the tap-length at a fixed value sometimes results in unavoidable issues with the adaptive design like insufficient modeling and adaptation noise. On the other hand a dynamic tap-length adaptation algorithm automatically finds the optimum order of the adaptive filter to have a tradeoff between the convergence and steady state error. It is always difficult to get satisfactory performance in high noise environment employing an adaptive filter for any identification problem. High noise decreases the Signal to noise ratio and sometimes creates wandering issues. In this chapter an improved pseudo-fractional tap-length selection algorithm has been proposed and analyzed to find out the optimum tap-length which best balances the complexity and steady state performance specifically in high noise environment. A steady-state performance analysis has been presented to formulate the steady state tap-length in correspondence with the proposed algorithm. Simulations and results are provided to observe the analysis and to make a comparison with the existing tap-length learning methods. © Springer International Publishing Switzerland 2014. Source

Sarkar B.K.,BIT | Sana S.S.,Bhangar Mahavidyalaya C.U. | Chaudhuri K.,Jadavpur University
Applied Soft Computing Journal

Individual classifiers predict unknown objects. Although, these are usually domain specific, and lack the property of scaling up prediction while handling data sets with huge size and high-dimensionality or imbalance class distribution. This article introduces an accuracy-based learning system called DTGA (decision tree and genetic algorithm) that aims to improve prediction accuracy over any classification problem irrespective to domain, size, dimensionality and class distribution. More specifically, the proposed system consists of two rule inducing phases. In the first phase, a base classifier, C4.5 (a decision tree based rule inducer) is used to produce rules from training data set, whereas GA (genetic algorithm) in the next phase refines them with the aim to provide more accurate and high-performance rules for prediction. The system has been compared with competent non-GA based systems: neural network, Naïve Bayes, rule-based classifier using rough set theory and C4.5 (i.e., the base classifier of DTGA), on a number of benchmark datasets collected from UCI (University of California at Irvine) machine learning repository. Empirical results demonstrate that the proposed hybrid approach provides marked improvement in a number of cases. © 2011 Elsevier B.V. All rights reserved. Source

Pal M.,National Institute of Technology Kurukshetra | Singh N.K.,BIT | Tiwari N.K.,National Institute of Technology Kurukshetra
Engineering Applications of Artificial Intelligence

This paper investigates the potential of support vector machines based regression approach to model the local scour around bridge piers using field data. A dataset of consisting of 232 pier scour measurements taken from BSDMS were used for this analysis. Results obtained by using radial basis function and polynomial kernel based Support vector regression were compared with four empirical relation as well as with a backpropagation neural network and generalized regression neural network. A total of 154 data were used for training different algorithms whereas remaining 78 data were used to test the created model. A coefficient of determination value of 0.897 (root mean square error=0.356) was achieved by radial basis kernel based support vector regression in comparison to 0.880 and 0.835 (root mean square error=0.388 and 0.438) by backpropagation neural and generalized regression neural network. Comparisons of results with four predictive equations suggest an improved performance by support vector regression. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data with this dataset. Sensitivity analysis suggests the importance of depth of flow and pier width in predicting the scour depth when using support vector regression based modeling approach. © 2010 Elsevier Ltd. All rights reserved. Source

Prakash G.,k-Technology | Thangaraj P.,BIT
Journal of Computer Science

Problem statement: The IEEE 802.11e EDCA protocol with different access Categories (ACs) supporting for Quality-of-Service (QoS). Due to internal or external packet collision, the Contention Window (CW) of the station increases the channel idle time under high Bit Error Rate (BER). Approach: In this study, we propose an analytical model for performance evaluation of IEEE 802.11e EDCA scheme under non-saturation condition and error prone channel. The new markov chain model have decrease the channel idle time in IEEE 802.11 EDCA and considerably increases the throughput for minimum number of station. Results: We develop an expression for the nonsaturation throughput as a function of the number of stations, packet sizes and BER. Conclusion: We validate the accuracy of our analysis with simulation expression. Using this model, the contention factors can be set appropriately to attain particular Quality-of-Service (QoS) requirements. © 2011 Science Publications. Source

Kar A.,IIIT | Chandra M.,BIT
International Journal of Information and Communication Technology

An advanced low complexity, fast converging variable tap-length (VT) learning algorithm for stereophonic acoustic echo cancellation (SAEC) based on multiple sub-filters (MSF) approach is designed and analysed in this paper. The proposed algorithm retains advantage of both VT different error algorithm (DEA) and common error algorithm (CEA). A systematic procedure is presented to set key variables that affect the structure adaptation to provide the best performance when compared to the existing stereophonic echo cancellers. The convergence performance of the MSF-based parallel structure is studied, for the designed dynamic structure and improvements are addressed over the VT-single long filter, VT-CEA, VT-DEA. A VT-selective coefficient update method to reduce the computational cost of adaptive design is discussed in context with the proposed design. It leads to the closest possible performance to the full update algorithm. Simulated results are shown to make a comparison of the proposed design with existing SAEC algorithms. Copyright © 2014 Inderscience Enterprises Ltd. Source

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