Performance Enhancement of Wearable Antenna Using High Impedance Surfaces

This paper presents a novel textile wearable antenna that has been designed to operate at Wi-Fi bands of 2.4GHz & 5.8GHz. Antenna performance in free space environment showed Gain of 1.8dBi at 2.4GHz and 4.5dBi at 5.8GHz. However, performance deteriorated when antenna was operated near human body which is lossy and complex in nature. For mitigating the human body effect on antenna performance, high impedance surface (HIS) was designed and integrated with this textile antenna. Due to shielding effect of HIS, antenna Gain increased to 8dBi at 2.4GHz and 9dBi at 5.8GHz. The SAR values were also reduced to 0.682W/Kg at 2.4GHz and 0.0692W/Kg at 5.8GHz for 10g tissue. The proposed antenna was also tested under bending and crumpling conditions. It was observed that antenna performance was not significantly deteriorated. The proposed textile antenna can have exciting applications in emerging wearable technologies

Localization performance evaluation of extended kalman filter in wireless sensors network

This paper evaluates the positioning and tracking performance of Extended Kalman Filter (EKF) in wireless sensors network. The EKF is a linear approximation of statistical Kalman Filter (KF) and has the capability to work efficiently in non-linear systems. The EKF is based on an iterative process of estimating current state information from the previously estimated state. Its working is based on the linearization of observation model around the mean of current state information. The EKF has small computation complexity and requires low memory compared to other Bayesian algorithms which makes it very suitable for low powered mobile devices. This paper evaluates the localization and tracking performance of EKF for (i) Position (P) model, (ii) Position-Velocity (PV) model and (iii) Position-Velocity-Acceleration (PVA) model. The EKF processes distance measurements from cricket sensors that are acquired through time difference of arrival between ultrasound and Radio Frequency (RF) signals. Further, localization performance under varying number of beacons/sensors is also evaluated in this paper. © 2014 Published by Elsevier B.V.Peer ReviewedPostprint (published version

Formulation and evaluation of Ketoprofen cream containing natural anti-inflammatory agent curcuma longa in treatment of rheumatoid arthritis

Non-Steroidal Anti-Inflammatory drugs have their origin as the derivatives of plants which were observed to have strong analgesic and anti-inflammatory effects in various disease states. Ketoprofen is a better tolerated NSAID because of its limited numbers of adverse effects and topical formulation has excellent permeation and absorption into the skin. The present investigation was to develop novel Ketoprofen cream formulation in combination of most effective and potent anti-inflammatory agent curcuma longa, which is reported to possess strong anti-inflammatory effects in Rheumatoid Arthritis and Osteoarthritis, according to the study by university of Arizona researchers. Combination of Ketoprofen and curcuma longa is good rational, where curcuma longa produces synergistic anti-inflammatory effects with ketoprofen. Formulation containing fixed concentrations (3%) of ketoprofen with curcuma longa was prepared. To access the efficacy of formulation stability studies, spread ability, tube extrudability, viscosity, pH, skin irritation test, in vitro drug diffusion study and anti- inflammatory effects were evaluated. The results obtained were encouraging and formulation containing Ketoprofen (3%) with curcuma longa was found better than alone Ketoprofen cream formulation

Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems

The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to variation in environmental conditions such as temperature and solar radiations. In the presence of these variations, it is necessary to extract the maximum power via the maximum power point tracking (MPPT) controller. This paper presents a nonlinear generalized global sliding mode controller (GGSMC) to harvest maximum power from a PV array using a DC-DC buck-boost converter. A feed-forward neural network (FFNN) is used to provide a reference voltage. A GGSMC is designed to track the FFNN generated reference subject to varying temperature and sunlight. The proposed control strategy, along with a modified sliding mode control, eliminates the reaching phase so that the sliding mode exists throughout the time. The system response observes no chattering and harmonic distortions. Finally, the simulation results using MATLAB/Simulink environment demonstrate the effectiveness, accuracy, and rapid tracking of the proposed control strategy. The results are compared with standard results of the nonlinear backstepping controller under abrupt changes in environmental conditions for further validation

Measures to Mitigate Causative Factors of Budget Overrun in Malaysian Building Projects

Completion of a construction within planned budget is one of the main criterion of project success, however budget overrun has become a recurring phenomenon in construction industry. Budget overrun can cause multiple negative effects such as loos of economy, disputes among construction stakeholders, project abandonment, and drop in construction activities. This study aims to identify causes of budget overrun in Malaysian Building projects and then to establish mitigation measures for the identified main causes. Literature review, questionnaire survey and interviews were used in this study. Detailed literature review results in identification of 28 causes of budget overrun. The main causes of budget overrun in building projects in Malaysia were investigated through a questionnaire survey of 83 contractors, 57 clients, and 40 owners. The feedback of questionnaire survey was analyzed statistically. The significant causes of budget overrun were:  improper planning, variation in materials price, poor site management, lack of communication between parties, frequent design changes, incompetent contractors, mistakes during construction, shortage of site workers, delay in material procurement, and   low speed of decisions making.   After questionnaire survey, interviews with 16 construction exports were carried out in order to develop mitigation measures for the top ten significant causes. On the basis of interviews, a list of mitigation measures for each of the top ten significant causes were established. The findings of this study can be useful for construction practitioners in controlling budget overrun and to achieve project success

Computational and pharmacological evaluation of stevioside derivatives for antinociceptive and antiinflammatory potential

Purpose: To carry out computational and pharmacological evaluation of two stevioside derivatives in order to develop more effective candidates for analgesia and inflammation.Methods: Primarily, compounds were docked against targets of nociception and inflammation such as cyclooxygenase-1, cyclooxygenase-2, 5-lypooxygenase 12-lypooxygenase, 15-lypooxygenase, prostaglandin synthase, leukotrienes C4 synthase, mu, kappa, and delta receptors to obtain their possible binding modes. Test compounds were then screened in animal model of nociception and inflammation.Results: The results of docking show that IO possesses good affinity when compared to ID. IO showed two hydrogen bonds against COX-1 and COX-2. IO also demonstrated good binding against 5-LOX, 12- LOX and 15-LOX, exhibited four, one and two hydrogen bonds respectively. Against PG synthase and LTC4, both IO and ID produced moderate binding. IO also showed significant binding against opoid receptors (p < 0.05). IO and ID significantly decrease the number of writhes to 21.20 ± 2.1 and 27.0 ± 2.12 at 10 mg/kg in acetic acid mediated pain test respectively. In hot plate method, IO and ID increase the latency period of mice to 14.14 ± 0.40 and 10.50 ± 0.34 s, respectively. IO and ID significantly reduced the paw edema to 1.69 ± 0.14 and 1.94 ± 0.14 mL, respectively, in acute inflammation (p < 0.05). In chronic inflammatory model, IO and ID decreased paw volume to 3.26 ± 0.38 and 4.20 ± 0.38 mL, respectively.Conclusion: The results show that IO is a promising candidate for further development as analgesic and anti-inflammatory agents. However, their pharmacokinetic and pharmacodynamic profiles need to be investigated. Keywords: Computational, Stevioside, Docking, Analgesic, Anti-inflammator

ADAPTIVE TRIMMED MEAN AUTOREGRESSIVE MODEL FOR REDUCTION OF POISSON NOISE IN SCINTIGRAPHIC IMAGES

A 2-D Adaptive Trimmed Mean Autoregressive (ATMAR) model has been proposed for denoising of medical images corrupted with poisson noise. Unfiltered images are divided into smaller chunks and ATMAR model is applied on each chunk separately. In this paper, two 5x5 windows with 40% overlapping are used to predict the center pixel value of the central row. The AR coefficients are updated by sliding both windows forward with 60% shift. The same process is repeated to scan the entire image for prediction of a new denoised image. The Adaptive Trimmed Mean Filter (ATMF) eradicates the lowest and highest variations in pixel values of the ATMAR model denoised image and also average out the remaining neighborhood pixel values. Finally, power-law transformation is applied on the resultant image of the ATMAR model for contrast stretching. Image quality is judged in terms of correlation, Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) of the image with latest denoising techniques. The proposed technique showed an efficient way to scale down poisson noise in scintigraphic images on a pixel-by-pixel basis