Sanchita Ghosh

On hydromagnetic rotating flow of a dusty fluid near a pulsating plate

An initial value problem is solved for the motion of an incompressible viscous conducting fluid with embedded small inert spherical particles bounded by an infinite rigid non-conducting plate. Both the plate and the fluid are in a state of solid-body rotation with constant angular velocity about an axis normal to the plate. The unsteady flow is generated in the fluid-particle system due to velocity tooth pulses subjected on the plate in presence of a transverse magnetic field. It is assumed that no external electric field is imposed on the system and the magnetic Prandtl number is very small. The operational method is used to derive exact solutions for the fluid and the particle velocities and the shear stress at the wall. Some limiting cases of these solutions including the steady-state results are discussed. The general solutions for the fluid velocity and the wall shear stress are examined numerically and the simultaneous effects of rotation, the magnetic field and the particles on them are determined. Finally, the present result for the fluid velocity has been compared numerically with that generated by an impulsively moved plate in a particular case when time is large.

Call Admission Control in Mobile Cellular Networks

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Dynamic Channel Assignment Problem in Mobile Networks Using Particle Swarm Optimization

The problem of optimal channel assignment has become increasingly important because of available frequency spectrum and increasing demand for cellular communication services. This has been shown to be an Np complete optimization problem. Many heuristic approaches including neural network, simulated annealing and genetic algorithm have been used to solve it. In this paper we propose a novel and efficient channel assignment approach, particle swarm optimization, to seek a conflict free channel assignment such that demand is achieved. Simulations on six well-known benchmark problems showed that the PSO could effectively generate the low-band results.

Automatic call management in a cellular mobile network by fuzzy threshold logic

The current literature on mobile communication usually considers the channel assignment and the call admission control as two independent problems. However, in practice these two problems are not fully independent. This paper attempts to solve the complete problem uniquely by two alternative approaches. The first approach is concerned with the development of a fuzzy to binary mapping of the measurement variables to decision variables. The latter approach deals with fuzzy to fuzzy matching, and then employs a fuzzy threshold to transform the fuzzy decisions into binary values for execution. The performance of both the call management techniques are studied with the standard Philadelphia benchmark and the results outperform reported results on independent call admission and channel assignment problems.

Behaviour state analysis through brain computer interface using wearable EEG devices: a review

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG) based human computer interface in order to enhance the quality of life for medically as well as non-medical applications. Industry and community of research have been attracted by wireless EEG reading devices and they are easily available in the market. Such technology can be incorporated into psychology, anesthesiology, and for real-time patients monitoring. A brain computer interface (BCI) is a direct communication channel between the human brain and the digital computer. In this paper, we present a review on characteristics and specification of EEG-based human computer interfaces for real-time applications using wearable or wireless EEG devices.

A novel feature extraction scheme for N400 detection

The presented work proposes a simple feature extraction technique which is designed for robust detection of event related potentials (ERP). This technique was tested to detect the N400 which is an ERP generally associated with recall. The chief advantages of the proposed technique are that it is robust to different ocular artifacts and yet sensitive to event related potentials. Further each signal will correspond to only a few features as opposed to 100s and 1000s of features obtained by traditional feature extraction techniques. The proposed steps involve a) Computing the first and second order difference of the data b) measuring mean and variance respectively for first and second order differencing over 1 second windows c) repeating the steps a and b after lagging the signal by 0.5 seconds. Differencing computes the change in amplitude of EEG signals, which is considered important in ERP analysis. Step (b) is a unique way of getting rid of abrupt signal changes which are artifacts, as for abrupt changes in the signal; the computed variance of the second difference is high. Also, computing windowed average of the first difference reveals the (increasing/ decreasing) trend of the data. Step (c) ensures that potential changes are not missed if they lie across two windows during the first phase of windowing. The proposed approach of feature extraction by the above steps outperforms three established traditional feature extraction schemes in identifying N400 waveforms using support vector machines. The average classification accuracy obtained by the proposed feature set is 96.91%.

SOFM and T1FS based measurement of correctness in dance postures for e-learning applications

Dance posture recognition has emerged as one of the most enriched and pragmatic research genre because of its wide applications for facilitating learning of dance by exploiting electronic means only. Ballet dance is one of the most ancient dance forms; moreover the artistic postures and unprecedented elegance of the ballet dance form fascinate the dance enthusiasts a lot. This motivated us to design a system enabling e-learning of ballet dance that allows a user to learn the art all by himself with the help of associative devices and to correct the postures by measuring the extent of correctness, thus not requiring the presence of any instructor to identify the flaws. In this paper, the principles of Type 1 Fuzzy Inference rules have been embedded in the correctness measurement phase of Self Organizing Feature Map. This paper primarily employs Self Organizing Feature Map because it serves the purpose of dance posture recognition and correctness measurement employing Type 1 Fuzzy rules inside a single hybrid framework. This scheme deals with 20 different body joint oriented features covering entire human skeleton and thus provides significantly better results. After analyzing and comparing the experimental findings it can be easily inferred that the designed scheme surpasses the existing methodologies by a noticeable margin.