Autoconfiguration of a dynamic nonoverlapping camera network.

In order to monitor sufficiently large areas of interest for surveillance or any event detection, we need to look beyond stationary cameras and employ an automatically configurable network of nonoverlapping cameras. These cameras need not have an overlapping field of view and should be allowed to move freely in space. Moreover, features like zooming in/out, readily available in security cameras these days, should be exploited in order to focus on any particular area of interest if needed. In this paper, a practical framework is proposed to self-calibrate dynamically moving and zooming cameras and determine their absolute and relative orientations, assuming that their relative position is known. A global linear solution is presented for self-calibrating each zooming/focusing camera in the network. After self-calibration, it is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the dynamic network configuration. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic, as well as on real data.     

Study of Lateral Spread of Ions Emitted from 2.3 kJ Plasma Focus with Hydrogen and Nitrogen Gases

The characteristics of ion beams of hydrogen and nitrogen with different filling pressures emitted from the plasma focus device of 2.3 kJ energy are investigated. CR-39 SSNTDs are employed for the registration of tracks of ions. The exposed detectors are etched in 6N NaOH solution at 70°C and then examined with an optical microscope. The ion flux is estimated to be of the order of 10^5–6 tracks/cm². The flux with the radial position does not exhibit any regular pattern of variation.     

On uniform observation of nonuniformly observable systems

In [4,7,9,12], classes of nonlinear systems are considered for which observers can be designed. Although observability of nonlinear systems is known to be dependent on the input, the proposed observers have the property that the estimation error decays to zero irrespective of the input. In the first part of this paper, it is shown that this phenomenon follows from a common property of these systems: for all of them, the “unobservable states” with respect to some input, are in some sense “stable” (in the linear case, these systems are called detectable), and for this reason, a reduced order observer can be designed. In the second part is given a more general class of nonlinear systems for which such an observer can be designed.     

Harmonic and intermodulation performance in an R-LED series network

Analytical expressions are obtained for predicting the harmonic and intermodulation performance of R-LED series networks. These expressions are in terms of the ordinary Bessel functions with arguments depenedent on the modulation index.     

Shortcoming, problems and analytical comparison for flooding-based search techniques in unstructured P2P networks

Peer-to-Peer networks attracted a significant amount of interest because of their capacity for resource sharing and content distribution. Content distribution applications allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content. Searching in unstructured P2P networks is an important problem, which has received considerable research attention. Acceptable searching techniques must provide large coverage rate, low traffic load, and optimum latency. This paper reviews flooding-based search techniques in unstructured P2P networks. It then analytically compares their coverage rate, and traffic overloads. Our simulation experiments have validated analytical results.     

Comparing the Effects of Different Daily and Sub-Daily Downscaling Approaches on the Response of Urban Stormwater Collection Systems

Water Resources Management - Change in the spatiotemporal pattern of precipitation is one the most important effects of climate change. This may result in considerable changes in urban flooding and...     

GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R²). In other words, GA-SVR with RMSE of 0.017 and R² of 0.980 has higher performance than SVR with RMSE of 0.035 and R² of 0.912, and linear regression model with RMSE of 0.079 and R² of 0.625.

     

The Bivariate Transmuted Family of Distributions: Theory and Applications

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used.     

A review on fault detection and diagnosis techniques: basics and beyond

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.