Abstract: In order to monitor for traffic traversal, sensors can be
deployed to perform collaborative target detection. Such a sensor
network achieves a certain level of detection performance with the
associated costs of deployment and routing protocol. This paper
addresses these two points of sensor deployment and routing algorithm
in the situation where the absolute quantity of sensors or total energy
becomes insufficient. This discussion on the best deployment system
concluded that two kinds of deployments; Normal and Power law
distributions, show 6 and 3 times longer than Random distribution in
the duration of coverage, respectively. The other discussion on routing
algorithm to achieve good performance in each deployment system
was also addressed. This discussion concluded that, in place of the
traditional algorithm, a new algorithm can extend the time of coverage
duration by 4 times in a Normal distribution, and in the circumstance
where every deployed sensor operates as a binary model.
Abstract: The paper presents a simple and an accurate formula
that has been developed for the conduction angle (δ) of a single
phase half-wave or full-wave controlled rectifier with RL load. This
formula can be also used for calculating the conduction angle (δ) in
case of A.C. voltage regulator with inductive load under
discontinuous current mode. The simulation results shows that the
conduction angle calculated from the developed formula agree very
well with that obtained from the exact solution arrived from the
iterative method. Applying the developed formula can reduce the
computational time and reduce the time for manual classroom
calculation. In addition, the proposed formula is attractive for real
time implementations.
Abstract: Load forecasting has always been the essential part of
an efficient power system operation and planning. A novel approach
based on support vector machines is proposed in this paper for annual
power load forecasting. Different kernel functions are selected to
construct a combinatorial algorithm. The performance of the new
model is evaluated with a real-world dataset, and compared with two
neural networks and some traditional forecasting techniques. The
results show that the proposed method exhibits superior performance.
Abstract: The mosaicing technique has been employed in more and more application fields, from entertainment to scientific ones. In the latter case, often the final evaluation is still left to human beings, that assess visually the quality of the mosaic. Many times, a lack of objective measurements in microscopic mosaicing may prevent the mosaic from being used as a starting image for further analysis. In this work we analyze three different metrics and indexes, in the domain of signal analysis, image analysis and visual quality, to measure the quality of different aspects of the mosaicing procedure, such as registration errors and visual quality. As the case study we consider the mosaicing algorithm we developed. The experiments have been carried out by considering mosaics with very different features: histological samples, that are made of detailed and contrasted images, and live stem cells, that show a very low contrast and low detail levels.