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A stabilizing control scheme based on a Lyapunov function is proposed for wireless power transfer (or WPT) systems. A state-space model of the WPT system is developed and the Lyapunov function is formulated based on an energy equation of the system involving state variables. The internal resistance of a battery varies during charge and discharge. Therefore, if a WPT system is used to charge a battery, its output load will vary. Furthermore, the coupling coefficient between the transmitter (primary) and receiver (secondary) coils decreases when they are misaligned. Comparative case studies are conducted to verify the efficacy of the proposed controller in maintaining stability of the WPT system under load variation and acute misalignment of transmitter and receiver coils.

In this paper a scalable and controllable dc microgrid architecture has been presented with a source-end to load-end one-way communication based control interface to substantiate efficacious power sharing among five power management units (PMUs) attached to batteries. The system comprises a source converter and a load converter. The source converter consists of a maximum power point tracking (MPPT) controller with a boost average model and the load converter consists of a fanout node and five PMUs. The voltage of the photo voltaic (PV) array is boosted up to 400 V for transmission purpose. Perturb and observation algorithm is used for MPPT implementation. A pulse width modulation (PWM) switched full bridge (FB) converter in the fanout node is constructed to lower the source-end voltage and to generate a fixed intermittent dc bus of 48 V. Buck converter topology is used to design the PMUs and each PMU is controlled in a way to generate an output voltage suitable for a 12 V battery charging application. Sensing the solar irradiance level a one-way communication premise is designed to control the power flow towards and sharing among the PMUs so that the more important units receive more power in a crisis situation. The proposed framework has been tested in MATLAB/Simulink platform and the performance evaluations yield to system efficiencies of more than 85% for different irradiance levels.

This paper documents a scalable dc microgrid architecture offering phase shifted full bridge (PSFB) converter based power management units (PMUs). There is a set of two PMUs connected to 36 V and 12 V batteries respectively. The energy source is a photo-voltaic (PV) array of 315 W maximum power. A source converter comprising a maximum power point tracking (MPPT) unit and a boost converter has been designed to generate a dc bus. The MPPT unit applies incremental conductance method to detect the peak power condition. A load converter comprises a set of parallely connected PMUs and batteries and PMUs are responsible for supplying power to the batteries and utility loads. PSFB converter in each PMU enables zero voltage transition by phase shifted pulse width modulation (PWM). In this paper the overall system is modeled and simulated on MATLAB/Simulink platform. A generic Lead acid battery model has been used and a voltage source converter (VSC) with an R-L load has been delivered power. The behavior of the system has been tested in both without and with load situations and the evaluated power efficiency values corroborate the reliability of the proposed design. The respective MATLAB/Simulink simulation waveforms and results are presented in this paper.

Freshwater crab Paratelphusha lamellifrons from Padma River were investigated for proximate and fatty acid composition. Proximate analyses of the claw and body meat of crab revealed the presence of moisture, crude protein, crude lipid and ash and their amount ranges between 71.72%-81.30%, 35.01%-49.06%, 13.24%-17.64% and 19.62%-22.12% respectively, on a dry weight basis. Except lipid (17.64%), claw meat content higher amount of moisture (81.30%), protein (49.06%) and ash (22.12%) compared to body meat. The fatty acid composition was analyzed by GC-FID and total 12 types of fatty acid were recorded in the fat isolated from crab. Among the recorded fatty acids MUFA were observed as the highest (42.85%), SFA were the second (25.96%) whereas, polyunsaturated (PUFA) were the lowest (15.02%). The fatty acid compositions showed that the SFA, MUFA and PUFA were dominated by palmitic acid (20.49%), oleic acid (23.99%) and linoleic acid (9.81%) respectively. The proximate and fatty acid composition of the present study demonstrated that these species (P. lamellifrons) are a promising source of essential fatty acids ω-3 and ω-6 namely, DHA and EPA.

Ultrasound elastography is a medical imaging technique to visualize the tissue stiffness using a conventional ultrasound machine. To compute the elastogram, the radiofrequency echo signals are acquired before and after a small applied deformation. Then the local tissue displacement field is estimated by searching the peak of the cross-correlation function between small segments of pre- and post- deformation signals. Finally, the corresponding strain distribution is imaged by differentiating the displacement field. However, the pre- and post- signals are not exactly the shifted versions of one another and hence the displacement estimation is inaccurate and the resulting strain image is highly noisy. In this paper, we propose a novel technique to estimate the strain image from the ratios of the peak frequencies of the pre- and post- deformation signals. Unlike other time/frequency domain reported schemes, our proposed technique does not use cross-correlation or any other similarity measures and directly estimate the strain in the power spectral density domain. Simulation experiments are conducted at various strain conditions and the results show that the proposed method can be an effective and computationally efficient algorithm for ultrasound elastography.

Neural Network (NN) is widely used in all aspects of process engineering activities, such as modeling, design, optimization and control. In this paper work, in absence of real plant data, simulated data (such as sales gas flow rate, pressure, raw gases flow rates and input heat flow associated with a heater used after dehydration) from a detailed model of Kailashtilla gas processing plant (KGP) within HYSYS is used to develop NN based model. Thereafter NN based model is trained and validated from HYSYS simulator generated data and that framework can predict the output data (sales gas flow rate and pressure) very closely with the simulated HYSYS plant data. The preliminary results show that the NN based correlation is adequately able to model and generate workable profiles for the process.