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

Ticks in Cattle: Their Importance and Chemical and Treatment Control

Ticks transfer diseases to animals and humans. Ticks create major financial losses to livestock and have a variation of adverse special effects on cattle hosts. Tick working as possible vectors for helminth parasites and haemoprotozoa cause blood loss directly. Large numbers of tick’s drinking red blood induce anaemia and decreased living weight in cattle animals, even though their bites can damage the skins. Ticks, are answerable for important financial losses due to their potential to transmit rickettsial, viral infections and protozoan to cattles. Here are a variety of ticks-control tactics available, but then again each has its own set of problems. The importance of ticks and their control are the topic of this review. Ticks have controlled by the help of vaccine and spray or chemicals compounds etc

A Study of Ballota limbata as an Alternative Medicine for Eye Diseases

Ballota limbata BTH. (syn. Otostegia limbata (BTH.) BOISS (Lamiaceae) is a medicinal herb used in folk medicine to cure a number of ailments. An aqueous extract of the herb is locally used for the treatment of eye inflammations and infections. To explore the possible cause of this application of the herb, we studied mineral elements present in it, as well as its antimicrobial effect against Bacillus subtilis. The leaves of B. limbata contain considerable amounts of zinc (4.6 mg/100g) and copper (0.847 mg/100g) which are known for their role in eye health. The quantity of iron (52.7 mg/100g) and calcium (1972 mg/100g) is also good, and the ratio of K (393.4 mg/100g) to Na (45 mg/100g) is extraordinarily high. This peculiarity of the herb may make it a very good remedy for hypertension. The leaves extracts also showed antimicrobial activity against the tested bacterium, which may explain the use of the herb against eye infections

Paradigm shift in the surgical training: The era of innovation, simulation and beyond

This is an era of transformation of surgical education and training. Modern methods of training are being introduced at a rapid pace and are being adopted in surgical practice not only to improve the outcomes and patient satisfaction, but also to provide an opportunity to develop a new well-structured training curriculum by integrating both traditional and modern approaches to teach and learn surgical skills. Various surgical simulators are in use as training aids and are constantly undergoing further refinement and development. To achieve a smooth transition in surgical training to modern methods, a structured programme has to be developed and validated to bridge the gaps in terms of safety, efficiency and ethics during the training process

Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features

Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art

Identification of Flash floods using Soil Flux and CO2: An implementation of Neural Network with Less False Alarm Rate

Flash floods are very sudden and abrupt and are the major root cause of casualties and loss of infrastructure. Flash floods can be regarded as the topmost natural disasters in many countries. Usually floods are due to high precipitation, wind velocity, water wave current and melting of ice bergs. Diversified strategies have been designed and applied to identify the flash floods. Mainly dozen of sensors have been utilized to detect the flash floods like upstream level, rainfall intensity, run-off magnitude, run-off speed, color of the water, precipitation velocity, pressure, temperature, wind speed, wave current pattern and cloud to ground (CG flashes). Ultrasonic and passive infrared (PIR) sensors have also been utilized for this purpose. Sensors generate high amount of fake alerts due to the incompetent algorithms. In our research we have proposed a novel approach analysis of soil flux depicting atmospheric carbon dioxide level as the plants take smaller amount of water from the soil due to the heightened levels of carbon dioxide. Due to this newly discovered research the soil is saturated abruptly causes more floods and run-offs. In our research we have reduced the false alarms and reduced the false alarms by using scaled conjugate gradient back propagation. Simulation results showed that scaled conjugate gradient propagation performed better than the other previous methods

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

Anticonvulsant, Antimicrobial and Cytotoxic Activities of Berberis calliobotrys Aitch ex Koehne (Berberidaceae)

Purpose: To evaluate the anticonvulsant, antimicrobial and cytotoxic activities of Berberis calliobotrys.Methods: The powdered plant material (10 kg) was extracted thrice with methanol (3 × 12 L) by dipping for seven days. The methanol extract was concentrated to dryness under reduced pressure, and then successively fractionated with solvents of different polarity, including n-hexane, chloroform, ethyl acetate and n-butanol. The anticonvulsant effect of the extract and fractions (at oral doses 500 and 1000 mg/kg) was studied against picrotoxin-, pentylenetetrazole (PTZ)- and strychnine-induced seizures in Swiss albino mice of either sex divided into 12 groups (n = 6). Diazepam was used as standard drug. Antimicrobial activity of the extract against Bacillus subtilis, Pseudomonas aeruginosa, Staphylococcus aureus as well as against Candida albicans, Penicillium notatum was conducted by disc diffusion method and minimum inhibitory concentration (MIC). Cytotoxicity of the extract/fractions was analyzed by  haemolytic method while the phenolic compounds present in the ethyl acetate fraction of the plant were determined by high performance liquid chromatography (HPLC).Results: The extract and its ethyl acetate and n-butanol fractions showed maximum response against drug-induced convulsions and provided 100 % protection to animals at both doses. They also showed zones of inhibition of 27.00 ± 2.51, and 22.00 ± 2.51 mm against all bacterial and fungal strains, respectively, especially Staphylococcus aureus. The methanol extract and ethyl acetate fraction also showed high MIC against all bacterial and fungal strains. Cytotoxicity data from hemolytic assay indicate that the extract/fractions are safe. The highest amount of phenolic found was chlorogenic acid (84.44 ± 0.06 ppm).Conclusion: The plant is thus a potential source of new lead compounds for the development of new clinically effective anticonvulsant and antimicrobial compounds.Keywords: Berberis calliobotrys, Anticonvulsant, Antimicrobial, Haemolytic, Phenolics, Chlorogenic aci