A new approach to extracting the RF parameters of asymmetric DG MOSFETs with the NQS effect

In analog circuit design an important parameter,from the perspective of superior device performance,is linearity.The DG MOSFET in asymmetric mode operation has been found to present a better linearity.In addition to that it provides,at the discretion of analog circuit designer,an additional degree of freedom,by providing independent bias control for the front and the back gates.Here a non-quasi-static(NQS)small signal model for DGMOSFET with asymmetric gate bias is proposed for extracting the parameters of the device using TCAD simulations.The parameters extracted here for analysis are the intrinsic front and back gate to drain capacitance,Cgd1and Cgd2,the intrinsic front and back distributed channel resistance,Rgd1and Rgd2respectively,the transport delay,m,and the inductance,Lsd.The parameter extraction model for an asymmetric DG MOSFET is validated with pre-established extracted parameter data,for symmetric DG MOSFET devices,from the available literature.The device simulation is performed with respect to frequency up to 100 GHz.     

Microprocessor in controlled transdermal drug delivery of anti-cancer drugs

Microprocessor controlled transdermal delivery of anticancer drugs 5-Fluorouracil (5-FU) and 6-Mercaptopurine (6-MP) was developed and in vitro evaluation was done. Drugs were loaded based on the pharmacokinetics parameters. In vitro diffusion studies were carried at different current density (0.0, 0.1, 0.22, 0.50 mA/cm2). The patches were evaluated for the drug content, thickness, weight, folding endurance, flatness, thumb tack test and adhesive properties all were well with in the specification of transdermal patches with elegant and transparent in appearance. In vitro permeation studies through human cadaver skin showed, passive delivery (0.0 mA/cm2) of 6-MP was low. As the current density was progressively increased, the flux also increased. the flux also increased with 0.1 mA/cm2 for 15-20 min, but it was less than desired flux, 0.2 mA/cm2 for 30 min showed better flux than 0.1 mA/cm2 current, but lag time was more than 4 h, 0.5 mA/cm2 current for more than 1 h, flux was >159 microg/cm2 h which was desired flux for 6-MP. 5-FU flux reached the minimum effective concentration (MEC) of 54 microg/cm2 h with 0.5 mA/cm2 current for 30-45 min, drug concentration were within the therapeutic window in post-current phase. We concluded from Ohm's Law that as the resistance decreases, current increases. Skin resistance decrease with increase in time and current, increase in the drug permeation. Interestingly, for all investigated current densities, as soon as the current was switched off, 5-FU and 6-MP flux decreased fairly, but the controlled drug delivery can be achieved by switching the current for required period of time.     

Antiidiotypic antibody to gonadotropin releasing hormone as probe for cell surface receptors

Monoclonal antibodies were raised to the idiotype of a gonadotropin releasing hormone (GnRH) specific antibody, one of which was found to bind specifically to GnRH receptors present on pituitary gonadotrophs, placental syncytiotrophoblasts and testicular Leydig cells. These observations were confirmed by Western and ligand blotting as well as by the ability of the antibody to induce an increase in intracellular Ca++ in a mouse gonadotroph cell-line viz., alpha T3-1.     

A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender & Age Assessment

Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process. Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image. A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female. The experimentations are conducted on the datasets of Radiological Society of North America (RSNA) of about 12442 images. Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%. Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.     

Aegle marmelos (L.) Correa: An Underutilized Fruit with High Nutraceutical Values: A Review

Aegle marmelos (L.) Correa (Bael) fruit, a member of the Rutaceae family, is a major cultivated fruit plant in tropical and subtropical regions in countries of southeast Asia. Bael fruit has been a major topic for studies in recent years mainly due to its high nutritional (carbohydrates, proteins, minerals, and vitamins) value and presence of various phytochemicals, which attributed to its high medicinal value. These phytochemicals include various compounds, e.g., alkaloids, flavonoids, and phenolic acids (protocatechuic acid, gallic, and ellagic acid). The fruit extract of bael has been also an important study area for its pharmacological activities, including antidiarrheal, antioxidant, antidiabetic, hepatoprotective, radioprotective, anticancer, antiulcer properties. The current review mainly highlighted the nutritional and pharmacological activities of bael fruit. The nutritional profile and phytochemical profile were discussed in the review, along with their concentration in the fruit. Moreover, the experiments carried out in vivo and in vitro of bael fruit extracts with respect to their pharmacological activities were also discussed in the article. The recent literature based on nutritional and pharmacological values of bael fruit showed its high potential as a food and pharmaceutical product. Despite having high nutritional and pharmacological value, research related to molecular mechanisms of bael fruit is still limited, and clinical trials are needed to ensure its safety as a product in the food and pharma industries.     

RADS: a real-time anomaly detection model for software-defined networks using machine learning

International Journal of Information Security - Software-defined networks (SDN) are no more a new technology as many industries are adopting it in a hybrid or full stack mode. SDN has already...     

Performance Enhancement and Comparison of Graphene Field Effect Transistor Devices Coated with HMDS Layer

Silicon - Graphene-based devices show good transfer characteristics, which depend upon the surface morphology of the material and substrate. During fabrication of the device, the substrate...     

Intramuscular EMG classifier for detecting myopathy and neuropathy

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ($1$-D) signals. In this approach, the $1$-D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated $10$-fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of $92.80$%, $94.25$%, and $93.71$% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.     

Adaptive dewarping of severely warped camera-captured document images based on document map generation

International Journal on Document Analysis and Recognition (IJDAR) - Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern...