Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

Adsorptive performance of penta-bismuth hepta-oxide nitrate, Bi?O?NO?, for removal of methyl orange dye

The adsorption of methyl orange dye from aqueous solution onto penta-bismuth hepta-oxide nitrate, Bi(5)O(7)NO(3), synthesized by precipitation method, was studied in a batch adsorption system. The effects of operation parameters such as adsorbent dose, initial dye concentration, pH and temperature were investigated. The adsorption equilibrium and mechanism of adsorption was evaluated by Langmuir and Freundlich isotherm and different kinetic models, respectively. The results indicate that adsorption is highly dependent on all operation parameters. At optimum conditions, the adsorption capacity was found to be 18.9 mg/g. The adsorption data fits well with the Langmuir isotherm model indicating monolayer coverage of adsorbate molecules on the surface of Bi(5)O(7)NO(3). The kinetic studies show that the adsorption process is a second-order kinetic reaction. Although intra-particle diffusion limits the rate of adsorption, the multi-linearity plot of intra-particle model shows the importance of both film and intra-particle diffusion as the rate-limiting steps of the dye removal. Thermodynamic parameters show that the adsorption process is endothermic, spontaneous and favourable at high temperature.     

Effect of wafer thinning methods towards fracture strength and topography of silicon die

This paper characterizes die damage resulting from various wafer thinning processes. Die fracture strength is measured using ball breaker test with respect to die surface finish. Further study on surface roughness and topography of each surface finish is obtained by atomic force microscopy (AFM) and scanning electron microscopy (SEM) techniques. Stress relief process with 25 μm removal is able to strengthen 100 μm wafer by 20.4% using chemical wet etch and 75 μm wafer by 36.4% with plasma etch. Relatively, plasma etching shows higher fracture strength and flexibility compared to chemical wet etch. This is due to topography of the finished surface of plasma etch is smoother and rounded, leading to a reduced stress concentration, hence improved fracture strength.     

Adherence to antiretroviral therapy and its impact on clinical outcome in HIV-infected patients.

We analyse data on patient adherence to prescribed regimens and surrogate markers of clinical outcome for 168 human immunodeficiency virus infected patients treated with antiretroviral therapy. Data on patient adherence consisted of dose-timing measurements collected for an average of 12 months per patient via electronic monitoring of bottle opening events. We first discuss how such data can be presented to highlight suboptimal adherence patterns and between-patient differences, before introducing two novel methods by which such data can be statistically modelled. Correlations between adherence and subsequent measures of viral load and CD4+T-cell counts are then evaluated. We show that summary measures of short-term adherence, which incorporate pharmacokinetic and pharmacodynamic data on the monitored regimen, predict suboptimal trends in viral load and CD4+T-cell counts better than measures based on adherence data alone.     

Concrete Contribution to the Shear Resistance of Fiber Reinforced Polymer Reinforced Concrete Members

Seven beams were tested in bending to determine the concrete contribution to their shear resistance. The beams had similar dimensions and concrete strength and were reinforced with carbon fiber reinforced polymer bars for flexure without transverse reinforcement. They were designed to fail in shear rather than flexure. The test variables were the shear span to depth ratio, varying from 1.82 to 4.5, and the flexural reinforcement ratio, varying from 1.1 to 3.88 times the balanced strain ratio. The test results are analyzed and compared with the corresponding predicted values using the American Concrete Institute, the Canadian Standard, and the Japan Society of Civil Engineers (JSCF) fiber reinforced polymer design recommendations. Based on these results and previous experimental data, it is shown that the ACI recommendations are extremely conservative whereas the Canadian and JSCE recommendations, albeit still conservative, are in closer agreement with the experimental data. Overall the Canadian Standard’s predictions are in better agreement with experimental data than the JSCE predictions.     

Electric-field induced fluctuations in laser generated plasma plume

The effect of an external electric field on laser-generated plasma has been studied. It is observed that the laser-generated plasma can be used for the ignition of a spark in the presence of a low voltage external electric field. An eight-fold emission intensity enhancement in Cu Ⅰ spectral lines are measured as compared to the signal intensity in the absence of an external electric field.The plasma parameters remain the same initially, up to a few microseconds after the generation of plasma, and this feature makes it more interesting for the quantitative analysis of any sample using laser induced breakdown spectroscopy(LIBS). In the presence of an external electric field,fluctuations(contraction and expansion) in the laser-generated plasma are observed which increase the plasma decay time and consequently result in enhanced signal intensity.     

A New Maximum EWMA Control Chart for Simultaneously Monitoring Process Mean and Dispersion Using Auxiliary Information

The maximum exponentially weighted moving average (MaxEWMA) control charts have gained considerable attention for simultaneously detecting both increases and decreases in the mean and/or dispersion of a process. In this paper, we propose a new auxiliary information‐based (AIB) MaxEWMA control chart, called the AIB‐MaxEWMA chart. The AIB‐MaxEWMA chart encompasses the existing MaxEWMA chart. Extensive Monte Carlo simulations are performed to evaluate the average run length, standard deviation of the run length, and diagnostic abilities of the AIB‐MaxEWMA chart. An extensive comparison reveals that the AIB‐MaxEWMA chart performs uniformly better than the MaxEWMA chart. An example is also used to explain the implementation and working of the AIB‐MaxEWMA chart. Copyright © 2017 John Wiley & Sons, Ltd.     

Synthesis and Physical Properties of Mn Doped ZnO Dilute Magnetic Semiconductor Nanostructures

Mn doped ZnO nanostructures have been prepared using low temperature simple, quick, and versatile synthesis approach. The structural, microstructural, and vibrational investigations reveal that as prepared nanostructures with low Mn doping concentration have single hexagonal phase and are grown along the preferred c-axis. The X-rays photoelectron spectroscopy demonstrates that the Mn ions are in mixed oxidation states for high doping concentration of Mn, while are in 2+ oxidation state for low concentration into ZnO lattice. The photoluminescence spectrum (PL) exhibits a significant red-shift of 22 nm in the optical band gap of doped ZnO and shows the improved luminescence properties, which makes it potential for its use in the photocatalyst, optoelectronics and solar cell nanodevices. Furthermore, the magnetic measurement of Mn doped ZnO nanostructures exhibits the ferromagnetism at room temperature.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

Learning multiple experiences useful visual features for active maps localization in crowded environments

Crowded urban environments are composed of different types of dynamic and static elements. Learning and classification of features is a major task in solving the localization problem in such environments. This work presents a gradual learning methodology to learn the useful features using multiple experiences. The usefulness of an observed element is evaluated by a scoring mechanism which uses two scores – reliability and distinctiveness. The visual features thus learned are used to partition the visual map into smaller regions. The robot is efficiently localized in such a partitioned environment using two-level localization. The concept of active map (AM) is proposed here, which is a map that represents one partition of the environment in which there is a high probability of the robot existing. High-level localization is used to track the mode of the AMs using discrete Bayes filter. Low-level localization uses a bag-of-words model to retrieve images and accurately localize the robot. The pose of the robot is the one retrieved from the AM that has maximum a posteriori. Experiments have been conducted on a unique highly crowded data-set collected from Indian roads. The results support the proposed method due to speed and localization accuracy.