Articles written in Sadhana

    • Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements


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      Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter(PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFsis applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the aforementioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario

    • Implementation of modified chaotic invasive weed optimization algorithm for optimizing the PID controller of the biped robot


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      The present research work involves the implementation of Modified Chaotic Invasive Weed Optimization (MCIWO) algorithm for optimizing the gains of torque based proportional integral and derivative (PID) controller used to control the motors of the biped robot while walking on flat surface. While designing thecontroller, the dynamics of the biped robot has been derived using the well-known Lagrange-Euler (L-E) formulation. Subsequently, manual tuning procedure is employed to find the ranges of the gains of PID controller used in the developed algorithm. Once it is optimized, the effectiveness of the proposed algorithm is thencompared with the Differential Evolution (DE) algorithm, in terms of variation of error, torque required, zero moment point (ZMP) and dynamic balance margin (DBM) of the biped robot. It has been observed that the MCIWO algorithm tuned PID controller is found to perform better than DE tuned controller. Further, theoptimal gait obtained through the developed algorithm is validated by executing it on the real robot. It has been observed that the robot has successfully negotiated the flat terrain with the gaits obtained by the optimal PIDcontroller.

    • Mean centred clustering: improving melody classification using time- and frequency-domain supervised clustering


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      This paper reports a new approach for clustering melodies in audio music collections of both western as well as Indian background and its application to genre classification. A simple yet effective new classification technique called mean centred clustering (MCC) is discussed. The proposed technique maximizesthe distance between different clusters and reduces the spread of data in individual clusters. The use of MCC as a preprocessing technique for conventional classifiers like artificial neural network (ANN) and support vector machine (SVM) is also demonstrated. It is observed that the MCC-based classifier outperforms the classifiers based on conventional techniques such as Principal Component Analysis (PCA) and discrete cosine transform (DCT). Extensive simulation results obtained on different data sets of western genre (ISMIR) and classicalIndian ragas are used to validate the efficiency of proposed MCC-based clustering algorithm and ANN/SVM classifiers based on MCC. As an additional endeavour, the performance of MCC on preprocessed data from PCA and DCT is studied. Based on simulation results, it is concluded that the application of MCC on DCT coefficients resulted in the highest overall classification success rate over different architectures of the classifiers.

    • Investigations on microstructural and microhardness developments in sintered iron–coal fly ash composites


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      The present work is aimed to explore the microstructural and mechanical characteristics of coal-fly ash reinforced iron metal-matrix composites (IMMCs), synthesized through powder metallurgy technique. Coalfly ash wt%, compacting load and sintering temperature were considered as the input variables, whereas sintered density and microhardness of the composites were taken as the output responses. Flowability and compressibility of the starting materials were demonstrated using Hausner ratio and Carr’s index. Decorous morphological,crystallographic and elemental characteristics of the starting materials and IMMCs were deliberated using Scanning electron microscopy, X-ray diffraction and Energy-dispersiveX-ray spectroscopy investigations respectively. A significant improvement in the microhardness of IMMCs by 50% and drop in density by 35% were found at 15 wt% as compared to 0 wt% reinforcement. The substantial increase in the microhardness eventually resulted in an increase in their specific microhardness by a factor of two. Significant improvements inthe microhardness of IMMCs at 15 wt % of reinforcement, compacted at 10 ton and sintered at 1150°C were found to be prompted by the strengthening mechanisms like load transfer, Hall–Petch effect and Taylor strengthening. The analytically calculated microhardness in the light of strengthening mechanisms was found smaller than the corresponding experimental values as a function of wt % of reinforcement. Further, statistical analysis of the obtained results was carried out using response surface methodology

    • Production, characterisation and utilisation of grinding swarf/ feedstock for synthesis of metal matrix composite through powder metallurgy process: A short communication


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      This article is intended to demonstrate an innovative idea wherein trash of grinding swarf could be converted into treasure of metal matrix composite (MMC). The grinding swarf produced during the slitting operation of structural-mild steel using a high-speed cut-off saw was characterised for morphology, mineralogy and rheology. Scanning electron microscopy (SEM), Energy Dispersive X-Ray Spectroscopy (EDS) and X-ray diffraction (XRD) characterisation of the grinding swarf and MMC were carried out to deliberate their morphological, elemental and microstructural attributes. The rheological attributes of the swarf were quantified through the Hausner ratio, Carr’s index and angle of repose. The rheological, microstructural, morphological andelemental analysis of the grinding swarf exhibited their suitability to be used as feedstock for the powder metallurgy (P/M) process. Since the feedstock contained a mixture of soft steel chips and harder iron oxide layered spherical particles, the feedstock was processed through the P/M process to synthesise the MMC. The feedstock was compacted in a hardened steel die using a uniaxial hydraulic press at 200 MPa pressure. Subsequently, the sintering of the green compact was carried out at 1150°C in an inert environment. The green andsintered densities of the MMC were evaluated through the conventional and Archimedes principle respectively. The green and sintered densities were found as 4.78±0.01 g/cm3 and 5.12±0.01 g/cm3, respectively. Further, the average microhardness of the MMC evaluated by Vicker’s hardness tester was 336 Hv0.05. The overall physio-mechanical properties of the composite were revealed by a specific microhardness as 65.62, which is even better than some of the aluminium-MMCs.

    • Machine learning solution of a coalitional game for optimal power allocation in multi-relay cooperative scenario


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      This paper reports a novel Machine Learning (ML) solution to power allocation problem modelled as a Stackelberg game. We consider a multi-relay cooperative environment where the performance of the relays is dependent upon the resources allocated to them. As a first step, a game theoretic framework has been used for modelling cooperation and competition among the relays. This Stackelberg game-based framework considers benefits of source and relays jointly utilizing a strategy for optimal power allocation based on incentivesprovided to the cooperating relays. Subsequently, an optimal set of relays is identified through machine leaning based bilevel optimization of objective functions which define the aforementioned Stackelberg game. The proposed ML optimization helps the source increase its utility by allocating optimal power to the participating relays at an optimal price. Results from simulation experiments confirm that the ML based solution of Stackelberg game optimization problem provides consistently better performance in terms of system throughput ascompared to the centralized scheme and an earlier reported heuristic scheme.

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