Earticle

The high-power generation capability of pulsed mode Silicon Double Drift Region (DDR) IMPATT devices has been studied by using generalized simulation software developed by the authors. The software is based on drift-diffusion modeling scheme, incorporating thermal design. After optimization of the device design, it is observed that a maximum efficiency of 9% with an output power of 15W can be achieved from pulsed DDR IMPATT based on Si. It is also observed that the best power output and efficiency occur at higher frequencies in the pulsed mode than in the CW mode. Simulated results are compared with experimentally reported results and quantitative agreement is demonstrated between theory and experiment. Transient thermal resistance of the diodes under pulsed mode operation has also been estimated by using computer simulation technique. Junction temperature of the pulsed diodes has been evaluated under actual operating condition. These results are useful for experimental realization of pulsed high power IMPATTs suitable for guided missiles and seekers.

This paper presents a new classification approach for detection of microcalcification clusters in digital mammograms. The proposed microcalcification detection method is done in two stages. In the first stage, features are extracted to discriminate between textures representing clusters of microcalcifications and texture representing normal tissue. The original mammogram image is decomposed using wavelet decomposition and gabor features are extracted from the original image Region of Interest (ROI). With these features individual microcalcification clusters is detected. In the second stage, the ability of these features in detecting microcalcification is done using Backpropagation Neural Network (BPNN). The proposed classification approach is applied to a database of 322 dense mammographic images, originating from the MIAS database. Results shows that the proposed BPNN approach gives a satisfactory detection performance.

The kmeans algorithm is an unsupervised classification algorithm. It has some drawbacks, the number of classes has to be known a priori, the initialization phase and the local optimums. We present in this paper an improvement based on evolutionary strategies and on the Xie and Beni criterion in order to get around these three difficulties. We design a new evolutionist kmeans algorithm. We suggest a new mutation operator that allows the algorithm to avoid local solutions and to converge to the global solution in a small computation time. We have optimized the Xie and Beni criterion by evolutionary strategies for the optimal choice of the number of classes. The proposed method is validated on several simulation examples. The experimental results obtained show the rapid convergence and the good performances of this new approach.

These days embedded systems are making great strides with the development of hardware to satisfy user’s varied demands. As one can see many kinds of systems operated in our daily life, software becomes dependent on real-time processing functions as well as various functions (for instance, multimedia system or network). These systems have the disadvantage of increasing complexity and are prone to problems when system engineer develop the system. Especially, if problems occur in the kernel layer, a developer needs to spend a lot of time and effort to solve them. In addition, as of now there are not enough tools for solving kernel timer latency problem effectively. In this paper, we propose a system named Kernel Timer Analysis System (KTAS) that can detect timer problems in kernel. The KTAS find High Resolution Timer latency which is a serious problem for the real-time processing system in kernel layer. It can effectively detect the problem and help finding its cause. In the future, we want to generalize the system to detect other problems and their causes to support the developers.