Efficient transmission of compressed data for remote volume visualization

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.     

Selected organ dose conversion coefficients for external photons calculated using ICRP adult voxel phantoms and Monte Carlo code FLUKA

The adult reference male and female computational voxel phantoms recommended by ICRP are adapted into the Monte Carlo transport code FLUKA. The FLUKA code is then utilised for computation of dose conversion coefficients (DCCs) expressed in absorbed dose per air kerma free-in-air for colon, lungs, stomach wall, breast, gonads, urinary bladder, oesophagus, liver and thyroid due to a broad parallel beam of mono-energetic photons impinging in anterior-posterior and posterior-anterior directions in the energy range of 15 keV-10 MeV. The computed DCCs of colon, lungs, stomach wall and breast are found to be in good agreement with the results published in ICRP publication 110. The present work thus validates the use of FLUKA code in computation of organ DCCs for photons using ICRP adult voxel phantoms. Further, the DCCs for gonads, urinary bladder, oesophagus, liver and thyroid are evaluated and compared with results published in ICRP 74 in the above-mentioned energy range and geometries. Significant differences in DCCs are observed for breast, testis and thyroid above 1 MeV, and for most of the organs at energies below 60 keV in comparison with the results published in ICRP 74. The DCCs of female voxel phantom were found to be higher in comparison with male phantom for almost all organs in both the geometries.     

Investigation of the double input power converter with N stages of voltage multiplier using PSO-based MPPT technique for the thermoelectric energy harvesting system

This research work presents a thermoelectric energy harvesting system comprises of a double input DC-DC converter with maximum power point tracking (MPPT) technique under varying temperature conditions (VTCs). The converter has two inputs with N stages of diode-capacitor to boost the output voltage. It has the advantages of higher voltage gain and flexibility of power-sharing by both the independent sources. The perturb and observe (P&O)-based MPPT algorithm is an efficient and simple method to track the maximum power. However, the power-current (P-I) characteristics of the thermoelectric modules exhibit multiple peaks at VTCs; it fails to identify the global peak point (GPP) and gets track the local peak point. To overcome the drawback of the P&O technique, a particle swarm optimization (PSO)-based MPPT technique is implemented to track the GPP. A comparison is performed between the P&O and PSO technique in terms of MPPT tracking efficiency and oscillation around the maximum power point. From the acquired results of simulation and experiment, it is recommended that the PSO-based MPPT technique has furnished better overall performance.     

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on real-time data from Phasor measurement units. Resilience assessment based on failure probability, financial impact, and time-series data in grid failure management determine the norm H₂. To demonstrate the effectiveness of the proposed framework, a time-domain simulation case study based on the IEEE-39 bus system was performed. For a one-channel attack on the test system, the resiliency index increased to 0.962, and inter-area damping ξ was reduced to 0.005. The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well. Results also offer robust management of power system resilience and timely control of the operating conditions.     

Lithium Ion-conducting Blend Polymer Electrolyte Based on PVA–PAN Doped with Lithium Nitrate

A new blend polymer electrolyte based on poly(vinyl alcohol) and polyacrylonitrile doped with lithium nitrate (LiNO₃) has been prepared and characterized. The complexation of blend polymer (92.5 PVA:7.5 PAN) with LiNO₃ has been studied using X-ray diffraction and Fourier transform infrared spectroscopy. Differential scanning calorimetry thermograms show a decrease in glass transition temperature with the addition of salt. The maximum ionic conductivity of the blend polymer electrolyte is 1.5 × 10^?3 Scm^?1 for 15 wt% LiNO₃ doped–92.5 PVA:7.5 PAN electrolyte. The conductivity values obey Arrhenius equation. Ionic transference number measurement reveals that the conducting species are predominantly ions.     

Synthesis of a nonrecurrent associative memory model based on anonlinear transformation in the spectral domain

A new nonrecurrent associative memory model is proposed. This model is composed of a nonlinear transformation in the spectral domain followed by the association. The Moore-Penrose pseudoinverse is employed to obtain the least squares optimal solution. Computer simulations are done to evaluate the performance of the model. The simulations use one-dimensional speech signals and two-dimensional head/shoulder images. Comparison of the proposed model with the classical optimal linear associative memory and an optimal nonlinear associative memory is presented.     

Stochastic DT-MRI connectivity mapping on the GPU

We present a method for stochastic fiber tract mapping from diffusion tensor MRI (DT-MRI) implemented on graphics hardware. From the simulated fibers we compute a connectivity map that gives an indication of the probability that two points in the dataset are connected by a neuronal fiber path. A Bayesian formulation of the fiber model is given and it is shown that the inversion method can be used to construct plausible connectivity. An implementation of this fiber model on the graphics processing unit (GPU) is presented. Since the fiber paths can be stochastically generated independently of one another, the algorithm is highly parallelizable. This allows us to exploit the data-parallel nature of the GPU fragment processors. We also present a framework for the connectivity computation on the GPU. Our implementation allows the user to interactively select regions of interest and observe the evolving connectivity results during computation. Results are presented from the stochastic generation of over 250,000 fiber steps per iteration at interactive frame rates on consumer-grade graphics hardware.     

Reinforcement Learning Based Controller Synthesis for Flexible Aircraft Wings

Aeroelastic study of flight vehicles has been a subject of great interest and research in the last several years. Aileron reversal and flutter related problems are due in part to the elasticity of a typical airplane. Structural dynamics of an aircraft wing due to its aeroelastic nature are characterized by partial differential equations. Controller design for these systems is very complex as compared to lumped parameter systems defined by ordinary differential equations. In this paper, a stabilizing statefeedback controller design approach is presented for the heave dynamics of a wing-fuselage model. In this study, a continuous actuator in the spatial domain is assumed. A control methodology is developed by combining the technique of "proper orthogonal decomposition" and approximate dynamic programming. The proper orthogonal decomposition technique is used to obtain a low-order nonlinear lumped parameter model of the infinite dimensional system. Then a near optimal controller is designed using the single-network-adaptive-critic technique. Furthermore, to add robustness to the nominal single-network-adaptive-critic controller against matched uncertainties, an identifier based adaptive controller is proposed. Simulation results demonstrate the effectiveness of the single-network-adaptive-critic controller augmented with adaptive controller for infinite dimensional systems.      

Lobelia trigona Roxb ‐based nanomedicine with enhanced biological applications: in vitro and in vivo approach

This is the first study to report the green synthesis of Lobelia trigona Roxb‐ mediated silver nanoparticles (LTAgNPs). The optical and structural properties of the synthesised LTAgNPs were analysed using ultraviolet–visible spectroscopy, scanning electron microscopy, Fourier transform infrared, dynamic light scattering and energy dispersive X‐ray. LTAgNps were evaluated for their anti‐bacterial and anti‐fungal properties against 18 pathogens and exhibited significant inhibition against all the strains tested. LTAgNPs had potential scavenging effects on the DPPH, ^• OH, O₂ ^•− free radical scavenging assays and reducing power assay. LTAgNps possess strong anti‐cancer activity against five human cancer cell lines (A549, MCF‐7, MDA‐MB‐231, HeLa and KB) in a dose‐dependent manner. The antiproliferative, anti‐inflammatory and genotoxicity effects of LTAgNPs were further confirmed by the lactate dehydrogenase release assay, nitric oxide inhibitory assay and comet assay. Furthermore, the incision, excision and burn wound‐healing activity of formulated LTAgNPs ointment was assessed in rats. All the wounds had significant healing in groups treated with LTAgNPs ointment compared to the groups treated with the commonly prescribed ointment (Silverex^TM). This study shows and suggests that the previously unreported LTAgNPs could be used as a nanomedicine with significant biological applications.Inspec keywords: molecular biophysics, biomedical materials, scanning electron microscopy, biochemistry, cancer, microorganisms, silver, cellular biophysics, nanofabrication, wounds, nanomedicine, ultraviolet spectra, toxicology, antibacterial activity, light scattering, nanoparticles, enzymes, visible spectra, Fourier transform infrared spectraOther keywords: Lobelia trigona Roxb‐based nanomedicine, biological applications, Lobelia trigona Roxb‐mediated silver nanoparticles, optical properties, structural properties, ultraviolet‐visible spectroscopy, dynamic light scattering, antibacterial properties, antifungal properties, scavenging effects, free radical scavenging, power assay, anticancer activity, antiinflammatory effects, genotoxicity effects, lactate dehydrogenase release assay, nitric oxide inhibitory assay, excision, burn wound‐healing activity, formulated LTAgNPs ointment, in vivo approach, in vitro approach, scanning electron microscopy, Fourier transform infrared spectroscopy, energy dispersive X‐ray analysis, pathogens, strains, A549 human cancer cell lines, MCF‐7 human cancer cell lines, MDA‐MB‐231 human cancer cell lines, HeLa human cancer cell lines, antiproliferative effects, comet assay, Ag     

Field Effect Transistors for Terahertz Detection and Emission

Resonant frequencies of the two-dimensional plasma in field effect transistors (FETs) increase with the reduction of the channel dimensions and can reach the Terahertz (THz) range. Nonlinear properties of the plasma/electron gas in the transistor channel can be used for the rectification and detection of THz radiation. The excitation of plasma waves by sub-THz and THz radiation was demonstrated for short gate transistors at cryogenic temperatures. At room temperature plasma oscillations are usually overdamped, but the FETs can still operate as efficient broadband rectifiers/detectors in the THz range. We present a few recent experimental results on THz detection by FETs showing some new ways of improvement of FETs for THz imaging at room temperature as well as the new physical phenomena like detection in quantizing magnetic fields. We also demonstrate THz emission properties of GaN based FETs.