Apply geometric duality to energy-efficient non-local phenomenon awareness using sensor networks

A powerful concept to cope with resource limitations and information redundancy in wireless sensor networks is the use of collaboration groups to distill information within the network and suppress unnecessary activities. When the phenomena to be monitored have large geographical extents, it is not obvious how to define these collaboration groups. This article presents the application of geometric duality to form such groups for sensor selection and non-local phenomena tracking. Using a dual-space transformation, which maps a non-local phenomenon (e.g., the edge of a half-plane shadow) to a single point in the dual space and maps locations of distributed sensor nodes to a set of lines that partitions the dual space, one can turn off the majority of the sensors to achieve resource preservation without losing detection and tracking accuracy. Since the group so defined may consist of nodes that are far away in physical space, we propose a hierarchical architecture that uses a small number of computationally powerful nodes and a massive number of power constrained motes. By taking advantage of the continuity of physical phenomena and the duality principle, we can greatly reduce the power consumption in non-local phenomena tracking and extend the lifetime of the network.     

FSK heterodyne transmission experiments at 560 Mbit/s and 1 Gbit/s

Optical frequency-shift-keying (FSK) signals are obtained from directly modulated distributed feedback (DFB) semiconductor lasers. Experimental studies of the direct frequency modulation (FM) characteristics of the DFB lasers show a nonuniform FM response due to the competing effects of thermal modulation of the laser active region and carrier density modulation. Equalization of the signal current to the laser is employed to produce a flat FM response from 30 kHz to 1 GHz. Optical FSK transmission and heterodyne detection experiments at 560-Mbit/s and 1-Gbit/s are conducted at a wavelength of 1497 nm. Receiver sensitivities of -39 dBm at 560 Mbit/s and -37 dBm at 1 Gbit/s are obtained. Transmission through 100 km of single-mode fiber at 1 Gbit/s is achieved with no degradation in receiver sensitivity.     

Spectral predictions for pulse interval and width modulation

An expression for the frequency spectrum of pulse interval and width modulation (PIWM) is presented. Experimental results obtained have shown close agreement with spectral predictions from the formula. Performance figures are also included from a practical PIWM optical-fibre transmission system intended for use with TV and video signals.<>     

Signal classification through multifractal analysis and complex domain neural networks

This paper describes a system capable of classifying stochastic self-affine nonstationary signals produced by nonlinear systems. The classification and the analysis of these signals are important because these are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals without knowing the number of classes.     

Cross-Border Migration of Hong Kong Residents Under the 'One Country, Two Systems' Policy

Hong Kong is known as a migrant city because its population was sourced from mainland China, and because there has always been outflows of people to overseas countries, especially from the mid-1980s to mid-1990s. Emigration to mainland China has become a significant recent trend. This article discusses this phenomenon in the theoretical contexts of migration and trans-border residential development. It contends that the trend will intensify more quickly than other cross-border movements because the two systems are separated by a political border within one country, and because there are common cultural ties and rapid economic integration.     

Class Based Contextual Logic for DOOD

Cntextual logic provides a mechanism to reason about modules.In this paper,this theory of modules if modules is extended to a context theory of classes where class is in the true spirit of object-oriented databases.The logic,referred to as CLOG,is class-based.CLOG supports class,object identity,multiple role of object, monotonic and non-monotonic inheritance of data and method,method factoring,views,derived and query classes.Views and derived classes are queries in themselves.Objects are pure data terms representing the ground instances of facts in the class.Object identity is a first class term in the logic.Inheritance is handled through delegation.     

Application of the spline finite strip to the analysis of shear-deformable plates

A spline finite strip is proposed to analyse thick isotropic or laminated composite plates. The formulation is based upon the principle of virtual work and the third-order plate theory developed by Reddy. The variational functional requires the satisfaction of C₁,-continuity of the assumed vertical deflection variable which can be easily fulfilled by the present method. The proposed spline finite strip is a conforming element with a smaller number of unknowns at each node compared to other existing elements based on the third-order theory. For the analysis of thin isotropic or laminated plates, the present element shows no sign of shear locking. A number of computational examples are given to demonstrate the efficiency and the accuracy of the present method.     

Rival-Model Penalized Self-Organizing Map

As a typical data visualization technique, self-organizing map (SOM) has been extensively applied to data clustering, image analysis, dimension reduction, and so forth. In a conventional adaptive SOM, it needs to choose an appropriate learning rate whose value is monotonically reduced over time to ensure the convergence of the map, meanwhile being kept large enough so that the map is able to gradually learn the data topology. Otherwise, the SOM's performance may seriously deteriorate. In general, it is nontrivial to choose an appropriate monotonically decreasing function for such a learning rate. In this letter, we therefore propose a novel rival-model penalized self-organizing map (RPSOM) learning algorithm that, for each input, adaptively chooses several rivals of the best-matching unit (BMU) and penalizes their associated models, i.e., those parametric real vectors with the same dimension as the input vectors, a little far away from the input. Compared to the existing methods, this RPSOM utilizes a constant learning rate to circumvent the awkward selection of a monotonically decreased function for the learning rate, but still reaches a robust result. The numerical experiments have shown the efficacy of our algorithm     

Optical appearance of alpha particle tracks in CR-39 SSNTD

A ray tracing method based on geometrical optics was used to study the tracks from alpha particles with different energies and with an incident angle of 50°. The transmission operation mode of the microscope is simulated. Considering the distribution of light intensities from the tracks, the mean and the 80% percentile gray levels from real experiments are proposed as quantitative variables to differentiate among tracks. The gray level properties for the same track for different exposures can vary to large extents. We introduce three variables, κ, λ and ε, to make empirical corrections. It is interesting to see that these coefficients are very consistent for the same alpha particle track despite the very different gray level properties. Gray level results have been obtained for tracks from alpha particles with 50° incident angle and different incident energies. However, the track depths cannot be predicted by any one of the coefficients κ, λ and ε. Multivariate analyses can help separate the tracks corresponding to different alpha energies. By using discriminant analysis with κ, λ and ε as independents, effectively all alpha energies can be determined with an accuracy of ±0.5 MeV.