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Asymmetrical six-phase machine (ASPM) with six balanced phases and two isolated neutral points can be analyzed in two orthogonal two-dimensional subspaces where one of them is associated with electromechanical energy transfer. Excitation of the non-energy transferring plane causes unwanted copper loss. Therefore, linear modulation techniques (LMTs) of inverter fed ASPM synthesize the desired reference voltage vector in the energy transferring plane and zero average voltage in the non-energy transferring plane. Existing space-vector PWM (SVPWM) based LMTs show good ripple-current performances but few of them suffer from the disadvantages like, higher implementation complexity, multiple switching of a leg, and simultaneous switching of multiple legs over one carrier-cycle. A novel SVPWM based LMT is proposed in this paper where the above disadvantages are avoided and, therefore, can be implemented in a computationally efficient carrier based method. The proposed strategy achieves maximum possible voltage gain of LMTs and shows current-ripple performance better or close to carrier-comparison based best known existing techniques. The proposed technique is validated through simulation and experiments performed up to 3.5 kW with a laboratory-scale hardware prototype.

This paper proposes a novel carrier-based implementation strategy of a standard space-vector pulse-width modulation (SVPWM) technique of three-phase (3ϕ) three-level inverter (TLI). To synthesize the reference voltage vector, the selected SVPWM technique uses nearest three voltage space-vectors in order to reduce output voltage THD and partially balance the neutral point voltage. The implementation complexity of this SVPWM technique is reduced by introducing the concept of common-mode signal in duty-ratio calculation of 3ϕ-TLI. Following the modulation strategy of the selected SVPWM technique, general expression of common-mode signal has been derived in terms of maximum, middle and minimum values of input 3ϕ modulation signals. It is shown in the paper that six duty ratios of 3ϕ-TLI can be directly calculated from the input 3ϕ modulation signals and the derived common-mode signal. Thus, calculation-intensive steps of SVPWM technique, like, identification of reference voltage vector location, calculation of dwell-times of individual vectors, are avoided. The proposed algorithm is validated through simulation in MATLAB and experiment on 1.5 kW hardware prototype.

Asymmetrical six-phase induction machine (ASIM) with six balanced phases and two isolated neutral points require modulation in two orthogonal two-dimensional subspaces where one of them is associated with electromechanical energy transfer. Excitation of non-energy transferring subspace causes copper loss. Therefore, this paper proposes a novel space-vector based PWM strategy (SVPWM) of matrix converter (MC) fed ASIM where excitation in non-energy transferring plane is kept as zero and energy transferring plane is excited so as to generate ripple- free torque. Eighteen switching states of 3ϕ - 6ϕ MC are used for this purpose which have zero common-mode voltage. Theoretical analysis shows that the input power-factor for this technique is same as the load power-factor. Finally, a new PWM strategy is proposed to operate the MC at unity power-factor at input. The proposed techniques are verified through simulations in Matlab and experiments on 3 kW hardware prototype.

This paper proposes a new approach to locate software components from large component online open source repositories which encompasses the inherent features of context-aware browsing, ranking and semantic tagging. Tagging of individual components helps making search fast and efficient. We are trying to improvise the results of context aware browsing by ranking them on the basis of Hidden Markov Models. The inputs to Hidden Markov Models consists of auto generated contextual queries. These queries formulate the resource set of our Hidden Markov model. The queries are ameliorated using reformulation, specialization, generalization and general association. This automation not only reduces the search space of components for an efficient browsing but also it enables developers to use those components whose existence they do not even prognosticate.

The sense of touch is crucial for distinguishing different shapes like boxes, cylinders and spheres. This paper proposes a machine learning based model to distinguish between shapes using kinesthetic information from the joint angles of the fingers. The training data from a sensorized glove was used to train a multi-layer support vector machine with a radial basis kernel. When used on simple shapes the proposed model obtained 100% accuracy. The accuracy dropped down to 71% when it was trained with shapes held in more than one way. The joints of the fingers that were critical in holding a particular shape was also identified in this paper.