Robust palm and knuckle ROI extraction in unconstrained environment

Pattern Analysis and Applications - Palm and knuckle prints can be extracted from a hand using a low-cost camera in a contactless manner. This makes the process of palm and knuckle recognition fast...     

Importance of initial nucleation step on low temperature photoassisted MOVPE growth of ZnSe

In this work, low temperature photoassisted metal-organic vapor phase epitaxy (MOVPE) growth of ZnSe on GaAs was carried out using dimethylzinc (DMZn) as the Zn precursor and dimethylselnium (DMSe) or diethylselnium (DESe) as the Se precursor at atmospheric pressure under H₂ carrier gas, using the 458 nm wavelength from an Ar⁺ laser. In situ laser interferometry showed that the growth rate for thin films is non-linear and was explained in terms of the efficiency of coupling of the photo-irradiation with the growing film. The growth rate (GR) can be described by GR = P F (1 – exp (– d)) where is the photo-chemical quantum efficiency, d is the film thickness, F is a structural factor, P is the laser intensity and is the absorption coefficient. In situ laser interferometry has been essential in making these measurements to observe the initial non-linearity and ensure that characteristic growth rates are taken from steady state growth. Growth using DMSe as the Se precursor was very photosensitive due to its higher photochemical quantum efficiency arising from the additional source of CH ₃ ^. radicals. These layers were mostly rough due to the slight variation in laser intensity across a region giving different degree of enhancement which is magnified by the growth rate non-linearity leading to non-uniform, and roughened growth. In the case of growth using DESe, it was possible to grow an initial nucleation layer of ZnSe pyrolyticaly prior to photo-assisted growth which ensured that there was sufficient thickness, d, of ZnSe such that the exponential term in the above equation became negligible and the growth rate was dependent only on the linear terms. The slight variation in laser intensity across the surface does not then cause a very significant variation in growth rate, thus maintaining smooth morphology and uniform growth rate.     

Effects of interface style on user perceptions and behavioral intention to use computer systems

This study examines the influence of two interface styles (menu- and command-based) on the perceived ease of use, perceived usefulness, and behavioral intention of the user to use the system. We have treated the system interface style as an external factor in the technology acceptance model (TAM) to examine its direct and indirect effects on behavioral intention to accept and use a system. The results showed that the interface style had direct effects on perceived ease of use and perceived usefulness which, in turn, demonstrated significant effects on behavioral intention to use the system. Further, the results showed that perceptions of the menu-based interface were more favorable than perceptions of the command-based interface. These results provide several theoretical and practical implications for designing an effective system.     

Interpolating an Unlimited Number of Curves Meeting at Extraordinary Points on Subdivision Surfaces*

Interpolating curves by subdivision surfaces is one of the major constraints that is partially addressed in the literature. So far, no more than two intersecting curves can be interpolated by a subdivision surface such as Doo‐Sabin or Catmull‐Clark surfaces. One approach that has been used in both of theses surfaces is the polygonal complex approach where a curve can be defined by a control mesh rather than a control polygon. Such a definition allows a curve to carry with it cross derivative information which can be naturally embodied in the mesh of a subdivision surface. This paper extends the use of this approach to interpolate an unlimited number of curves meeting at an extraordinary point on a subdivision surface. At that point, the curves can all meet with either C ⁰ or C ¹ continuity, yet still have common tangent plane. A straight forward application is the generation of subdivision surfaces through 3‐regular meshes of curves for which an easy interface can be used.     

Evaluation of integrated software development environments: Challenges and results from three empirical studies

Evidence shows that integrated development environments (IDEs) are too often functionality-oriented and difficult to use, learn, and master. This article describes challenges in the design of usable IDEs and in the evaluation of the usability of such tools. It also presents the results of three different empirical studies of IDE usability. Different methods are sequentially applied across the empirical studies in order to identify increasingly specific kinds of usability problems that developers face in their use of IDEs. The results of these studies suggest several problems in IDE user interfaces with the representation of functionalities and artifacts, such as reusable program components. We conclude by making recommendations for the design of IDE user interfaces with better affordances, which may ameliorate some of most serious usability problems and help to create more human-centric software development environments.     

THE EFFICIENCY CHALLENGES OF RESOURCE DISCOVERY IN GRID ENVIRONMENTS

Resource discovery is one of the most important services that significantly affects the efficiency of grid computing systems. The inherent dynamic and large-scale characteristics of grid environments make their resource discovery a challenging task. In recent years, different approaches have been proposed for resource discovery, attempting to tackle the challenges of grid environments and improve the efficiency. Being aware of these challenges and approaches is worthwhile in order to choose an appropriate approach according to the application in different organizations. This study reviews the most important factors that should be considered and challenges to be tackled in order to develop an efficient grid resource discovery system.     

Collaborative learning in the clouds

Cloud computing has emerged as one of the most highly discussed topics both in the academic community and in the computing industry. While most of the work that has been conducted to explore this field focuses either on establishing the basis for cloud computing or almost exclusively on the issues surrounding security and data privacy, this paper takes the first exploratory step into exploring the actual internal working of cloud computing and demonstrates its viability for organizations, more specifically educational establishments . The paper starts by introducing the most important key clouds computing concepts, including virtualization technologies, Web services and Service Oriented Architectures (SOA), and distributed computing. Light will be then shed on the impact and potential benefits of cloud computing on teaching and learning in educational institutions. The paper closes by describing building a private cloud inside educational institution and highlights its offerings for students, staff and lecturers.     

Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach

Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.     

Marine pollution assessment near Alexandria,Egypt by Thematic Principal Components (TPC)

This work deals with the detection, identification and mapping of different types of pollutants in the marine environment through the application of a refined image processing technique. The technique is based on the use of already available information on each theme. Classification routines, based on selection of training areas and application of thematic principal components (TPC) analysis, are carried out. The procedure, results, and accuracy testing are presented and discussed. Application to the problem of identification of pollution in Lake Maryout, Egypt, is presented using SPOT images. Results indicate better separability and higher accuracy than traditional techniques.     

An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles

In general, neural network training is a nonlinear multivariate optimisation problem. Unlike previous studies, in the present study, particle swarm optimisation (PSO) and back-propagation (BP) algorithms were coupled to develop a robust hybrid training algorithm with both local and global search capabilities. To demonstrate the capacity of the proposed model, we applied the model to the predictions of the load–deformation behaviour of axially loaded piles. This is a soil–structure interaction problem, involving a complex mechanism of load transfer from the pile to the supporting geologic medium. A database of full scale pile loading tests is used to train and validate the product-unit network. The results show that the proposed hybrid learning algorithm simulates the load–deformation curve of axially loaded piles more accurately than other BP, PSO, and existing PSO–BP hybrid methods. The network developed using the proposed algorithm also turns out to be more accurate than hyperbolic and $t?z$ models.