Design of Smart Door Closer System with Image Classification over WLAN

Wireless Personal Communications - A dual purpose system is presented in this paper which serves not only as a door closer, but is equally effective for surveillance purposes. The currently...     

Synthesis and characterization of biocompatible hydroxyapatite coated ferrite

Ferrite particles coated with biocompatible phases can be used for hyperthermia treatment of cancer. We have synthesized substituted calcium hexaferrite, which is not stable on its own but is stabilized with small substitution of La. Hexaferrite of chemical composition (CaO)_0.75(La₂0₃)_0.20(Fe₂O₃)₆ was prepared using citrate gel method. Hydroxyapatite was prepared by precipitating it from aqueous solution of Ca(NO₃)₂ and (NH₄)₂HPO₄ maintaining pH above 11. Four different methods were used for coating of hydroxyapatite on ferrite particles. SEM with EDX and X-ray diffraction analysis shows clear evidence of coating of hydroxyapatite on ferrite particles. These coated ferrite particles exhibited coercive field up to 2 kOe, which could be made useful for hysteresis heating in hyperthermia. Studies by culturing BHK-21 cells and WBC over the samples show evidence of biocompatibility. SEM micrographs and cell counts give clear indication of cell growth on the surface of the sample. Finally coated ferrite particle was implanted in Kasaulli mouse to test its biocompatibility. The magnetic properties and biocompatibility studies show that these hydroxyapatite coated ferrites could be useful for hyperthermia.     

Hyaluronic acid induced hyaluronic acid binding protein phosphorylation and inositol triphosphate formation in lymphocytes

In this report, the role of 34 kDa HA-binding protein in hyaluronic acid-induced cellular signalling in lymphocytes has been examined. The binding of 125I-HA to lymphocytes in vivo was found to be inhibited by pre-incubation of the cells with anti-34 kDa HA-binding protein antibodies, thus confirming 34 kDa HA-binding protein as the specific HA-receptor in lymphocytes. This observation was substantiated by anti-34 kDa HA-binding protein antibodies immunoblotting and 125I-HA ligand blotting of lymphocytes cell lysate. The HA-induced cell aggregation, tyrosine phosphorylation and cytoskeletal protein phosphorylation demonstrate the HA-induced early cellular signalling events in lymphocytes. Further, to study the involvement of 34 kDa HA-binding protein in mitogen induced lymphocyte signalling, we studied in vivo phosphorylation and secondary messenger formation. The enhanced 34 kDa HA-binding protein phosphorylation by HA and the inhibition of cellular aggregation and IP3 formation by anti-HA-binding protein antibodies revealed that 34 kDa HA-binding protein is one of the potential mediators in HA-induced signal transduction.     

A comparison between isoparametric Lagrangian elements in 2D BEM

A comparison between a family of isoparametric C°-continucus elements in the context of two-dimensional elastostatics using the direct boundary element method is presented. The elements studied use Lagrangian shape functions of orders quadratic, cubic and quartic. The relative efficiencies of these elements from the viewpoint of accuracy of solution as well as cost effectiveness are examined through certain selected problems. It emerges from the present study that the quartic element is more reliable in accuracy than the lower members of the family without being necessarily more expensive.     

Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.     

Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization

In the medical field, image segmentation is a paramount and challenging task. The head and vertebral column make up the central nervous system (CNS), which control all the paramount functions. These include thinking, speaking, and gestures. The uncontrolled growth in the CNS can affect a person’s thinking of communication or movement. The tumor is known as the uncontrolled growth of cells in brain. The tumor can be recognized by MRI image. Brain tumor detection is mostly affected with inaccurate classification. This proposed work designed a novel classification and segmentation algorithm for the brain tumor detection. The proposed system uses the Adaptive fuzzy deep neural network with frog leap optimization to detect normality and abnormality of the image. Accurate classification is achieved with error minimization strategy through our proposed method. Then, the abnormal image is segmented using adaptive flying squirrel algorithm and the size of the tumor is detected, which is used to find out the severity of the tumor. The proposed work is implemented in the MATLAB simulation platform. The proposed work Accuracy, sensitivity, specificity, false positive rate and false negative rate are 99.6%, 99.9%, 99.8%, 0.0043 and 0.543, respectively. The detection accuracy is better in our proposed system than the existing teaching and learning based algorithm, social group algorithm and deep neural network.

     

Requirement-oriented risk management for incremental software development

In incremental software development (ISD) functionalities are delivered incrementally and requirements keep on evolving across iterations. The requirements evolution involves the addition of new dependencies and conflicts among functional and non-functional requirements along with changes in priorities and dependency weights. This, in turn, demands refactoring the order of development of system components to minimize the impact of these changes. Neglecting the non-functional constraints in the software development process exposes it to risks that may accumulate across several iterations. In this research work, we propose a risk management framework for ISD processes that provides an estimate of risk exposure for the project when functional features are frozen while ignoring the associations with non-functional requirements. Our framework proposes suitable risk reduction strategies that work in tandem with the risk assessment module. We also provide a tool interface for our risk management framework.

     

Multi-Model Approach to Assess the Dynamics of Hydrologic Components in a Tropical Ecosystem

Estimation of terrestrial water budget at global and regional scales are essential for efficient agricultural water management, flood predictions, and, hydrological modeling. In hydrological modeling, it is a challenging task to quantify the major hydrological components like runoff, evapotranspiration (ET), and total water storage (TWS) due to improper and limited availability of detailed meteorological datasets. Furthermore, there has been no consensus to answer a-decade-long critical question that a less data-intensive models can be an alternate to robust data-intensive models in data scarce conditions. This study aims at multi-model approach over the single models usage for representing the hydrological behaviour in the Kangsabati River Basin (KRB), India. It is done by applying the standard model selection criteria over various hydrological models. Two hydrological models are selected, a semi- distributed model, Variable Infiltration Capacity (VIC-3 L), and a conceptually lumped model, Identification of unit Hydrograph and Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES). Both models were calibrated against the observed daily discharge at the KRB outlet for the period of 2001–2006 and validated for 2008–2010. The results show that both VIC-3 L and IHACRES produce reasonable runoff estimates at daily and monthly time scale in the KRB. The ET estimates show that VIC-3 L and IHACRES captured the seasonal variations with the percent change of 0.4% and 6.6% respectively. As IHACRES is simpler, parsimonious, fewer parameters, and better performances, it can be useful for hydrological modeling in data-scarce regions.