IDR @ IIT Bhubaneswar
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Item Efficient design of radar waveforms using novel multiobjective optimization technique(2016) Baghel V.; Panda G.; Verma S.K.Optimal choice of the parameters of stepped frequency train of linear frequency modulated pulses (SFT-LFM) can substantially improve the detection potentiality of radar target. In this paper this challenging signal design problem has been formulated as a two objectives optimization problem. A minor variant of existing MOEA/D known as multiobjective evolutionary algorithm based on normalize Tchebycheff decomposition (MOEA/NTD) is developed and employed to achieve various best possible combinations of these parameters. Through simulation study the effects of the chosen parameters on mainlobe width, grating lobes and sidelobes of compressed SFT-LFM have been demonstrated. The comparison of the simulation results exhibit that the optimization performance of proposed MOEA/NTD is superior to those obtained by the MOEA/D based method. � 2015 IEEE.Item On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices(2014) Majhi B.; Rout M.; Baghel V.This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II) for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer) and the output mean square error (MSE) of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500) and Dow Jones Industrial Average (DJIA) stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF)-based forecasting model in terms of the mean average percentage error (MAPE), directional accuracy (DA), Thelis' U and average relative variance (ARV) values. � 2013 King Saud University.Item Development and performance evaluation of generalised Doppler compensated adaptive pulse compression algorithm(2014) Baghel V.; Panda G.The adaptive pulse compression (APC) algorithm is superior to conventional normalised matched filter (NMF) and least square mismatched filter techniques. However, its performance degrades for non-stationary targets in noisy condition as the algorithm has not taken into consideration the Doppler shift effect. On the other hand, the recently reported Doppler compensated APC (DC-APC) technique performs well only for non-stationary objects. Thus, there is a need to develop an algorithm that would work efficiently both for stationary and non-stationary conditions. Keeping this in view, a generalised APC (G-APC) algorithm is developed in which the effect of target Doppler is incorporated in the received signal model. The efficacy of the proposed algorithm has been evaluated through five different cases. The results of the simulation study demonstrate comparable and superior performance of the proposed G-APC over several pulse compression models based on NMF, APC and DC-APC in all cases. � The Institution of Engineering and Technology 2014.Item Aco and Ga based fault-tolerant scheduling of real-time tasks on multiprocessor systems-A comparative study(2014) Kumar A.; Panda S.; Pani S.K.; Baghel V.; Panda A.Fault-tolerant scheduling of real-time (RT) tasks in multiprocessor environment is essentially a NP-hard problem. This basically involves allocating a set of tasks to a set of processors so as to minimize the makespan and ensure tasks to meet their timing constraints. Many traditional heuristic approaches, such as earliest deadline first (EDF) and least laxity first (LLF) have been adopted to find optimal solution to this scheduling problem. However, conventional approach to achieve fault-tolerance (FT) in scheduling RT tasks based on traditional heuristic approach suffers from poor performance and results in inefficient processor utilization. Nature-inspired heuristic algorithms are gaining increased acceptance among researcher for solving real world NP-hard combinatorial optimization problems. This paper presents a comparative study of the novel primary-backup (PB) based fault-tolerant scheduling (PBFTS) technique for RT tasks in multiprocessor environment using two popular nature-inspired heuristic algorithms: the Ant Colony Optimization (ACO) and the Genetic Algorithm (GA). Exhaustive simulation reveals that the PBFTS algorithm based on GA and ACO both outperform the traditional PBFTS schemes in terms of performance, system utilization and efficiency. However, the comparative study also shows that the ACO based scheme surpasses the GA based scheme in terms of speed of execution whereas GA based scheme displays superior convergence with respect to ACO counterpart. � 2014 IEEE.Item Development of an efficient hybrid model for range sidelobe suppression in pulse compression radar(2013) Baghel V.; Panda G.An efficient hybrid model that substantially reduces the sidelobe of the compressed output of the binary phase coded waveforms is suggested by suitably combining a matched filter (MF) and a radial function (RF). The sidelobe suppression is achieved by modulating the MF output by the RF output. Simulation study is carried out to evaluate the performance of standard MF, multilayer artificial neural network (MLANN) and radial basis function neural network (RBFNN) based pulse compressors for binary phase coded pulse compression. The evaluation is based on comparative analysis of the peak to sidelobe ratio (PSR) of the compressed output under noisy as well as Doppler shift conditions. The experimental results demonstrate that the performance of proposed method is significantly superior compared to that of the other standard methods. Further, the hardware requirement of the proposed model is significantly less and unlike other neural networks it does not require training operation. � 2012 Elsevier Masson SAS. All rights reserved.Item Development and performance evaluation of an improved complex valued radar pulse compressor(2013) Baghel V.; Panda G.Pulse compression is an important and burning issue in radar signal processing. In the recent past, many adaptive and neural network based methods have been proposed to achieve effective pulse compression performance for real coded transmitted waveforms. Even though the radar signal is complex, it is mostly processed as real-valued in-phase and quadrature components. Hence it is desirable that for processing complex radar signal for pulse compression both the structure as well as the learning algorithm associated with it need to be complex in nature. Accordingly in this paper a novel adaptive method is proposed by employing a complex valued fully connected cascaded (CFCC) neural network. For training this network, a new complex Levenberg-Marquardt (CLM) algorithm is derived and used for imparting effective training of its weights. The new CLM based CFCC (CFCC-CLM) model offers superior convergence performance with the least residual mean squared error during training phase compared to those provided by the multilayer perceptron (MLP) trained with complex domain backpropagation (CDBP) and CLM based methods. Further the comparison of peak signal-to-sidelobe ratio (PSR) under noisy and Doppler shift conditions of the proposed method exhibits best performance compared to those offered by the MLP-CDBP, MLP-CLM and the matched filter (MF) based methods. � 2013 Elsevier Ltd. All rights reserved.Item An efficient hybrid adaptive pulse compression approach to radar detection(2013) Baghel V.; Panda A.; Panda G.The adaptive pulse compression (APC) technique has been proposed in the literature of pulse compression radar for superior performance for polyphase coded signals in comparison to conventional matched filter (MF) and mismatched filter techniques. However, its performance degrades in case of binary phase coded signals. The recently reported hybrid MF and radial function (MF-RF) model performs well only for the single target case. Thus there is a need to develop a model which would perform efficiently both for single and multi-targets conditions for binary phase coded signals. To achieve this objective a hybrid model (APC-RF) combining the APC and radial function is developed. The performance of the new scheme has been evaluated under different noise and Doppler shift conditions. The results of the simulation study demonstrate superior performance of the proposed APC-RF model over several pulse compression techniques such as MF, APC and MF-RF. � 2013 IEEE.Item Enhancement of the frequency resolution of the S-transform using the fourier transform(2011) Baghel V.; Panda G.; Mansinha L.; Tiampo K.F.; Valluri S.R.The S-transform is a method of time-frequency analysis of a time series, essentially the Fourier spectrum as a function of time. While attractive in concept, the S-transform suffers from inherently poor frequency resolution, particularly at the high frequencies. We present here a technique (named STF) to enhance the frequency resolution through the use of the global Fourier spectrum. The scaling of the localising Gaussian window in STF is made a function of the global Fourier amplitude spectrum. Tests with several data series show better frequency resolution, with no increase in computation time or resource usage. � 2011 IEEE.Item New GOPSO and its application to robust identification(2011) Baghel V.; Nanda S.J.; Panda G.Modeling of complex nonlinear systems has become a challenging task in presence of outliers. In this scenario a robust norm with an evolutionary approach does a potential job. A modified evolutionary algorithm GOPSO (global selection based orthogonal PSO) is proposed which offers a more accurate and computationally efficient training compared to OPSO (Orthogonal PSO). The potential of the proposed algorithm has been demonstrated on six benchmark multi-modal optimization problems. Further, robust identification models has been developed by combining Wilcoxon norm with a functional link artificial neural network (FLANN) structure trained by the proposed GOPSO. Exhaustive simulation studies on five complex plants show superior performance of proposed models when output of plant gets corrupted upto 50% outliers. � 2011 IEEE.Item An efficient multi-objective pulse radar compression technique using RBF and NSGA-II(2009) Baghel V.; Panda G.; Srihari P.; Rajarajeswari K.; Majhi B.The task of radar pulse compression is formulated as a multi-objective optimization problem and has been effectively solved using radial basis function (RBF) network and multiobjective genetic algorithm (NSGA-II). The pulse compression performance of three different codes in terms of signal to peak side-lobe ratio (SSR) under noisy environment