Volume 4, Issue 11, November 2012

Performance Optimization of a Monte Carlo Simulation Code for Estimating the All-Terminal Reliability of a Network

Beatriz Otero Calviño, Silvia Pascual Martínez and Claudio M. Rocco Sanseverino

All-terminal reliability (ATR), defined as the probability that every node in a network can communicate with every other node, is an important problem in research areas such as mobile ad-hoc wireless networks, grid computing systems, and telecommunications. The assessment of ATR has also been part of related problems like the reliability allocation problem. However, the exact calculation of ATR is a NP-hard problem. To obtain this probability, there are approaches based on analytic methods for small networks or estimation through Monte Carlo simulation (MCS). In this paper, first a Fortran code that estimates the ATR is improved using optimization software techniques. Secondly a parallel implementation, based on the Message Passing Interface (MPI) standard is presented. The implementation can take advantage of the existence of multiple processors thus reducing the time required for the ATR assessment. One example related to a real network illustrates the benefits. The parallel implementation can reduce by up to 74% the execution time of a serial Monte Carlo simulation.

Keywords: All-terminal reliability, Monte Carlo Simulation, MPI

Feature Extraction and Segmentation of CT Lungs Images for better Assessments

S. Yasin, M. S. Naweed and M. Rehman

Computed Tomography scan is widely being used for several medical diagnoses. CT imaging has already shown its practical impact in diagnosis of brain and lungs diseases. CT scan has brought to the seen; which was unseen before with the naked eye; and its native support as a digital imaging modality has invited the researchers to exploit the digital image processing techniques to better process the information available in the CT scan images as compared to the information processed by the naked eye. It is obvious that human vision has many constraints and limitations, and hence cannot process complete information available in the CT images; where as digital images can be manipulated by computer at pixel level. The objective of this research is to develop a segmentation technique in the field of medical imaging to help better visual inspection of certain lungs features which lead to better diagnosis of lungs diseases. In this research paper we have proposed a method by which the lungs are segmented using iterative threshold, morphological operators, and flood-fill algorithm; as well as basic lungs anatomy is labeled through eight-neighbor connectivity technique. Also, we have segmented different regions of the lungs anatomy by applying specific gray level filtering; through which the filtered anatomy of the lungs is not only separated but better examined as compared to previous implemented techniques. The overall accuracy of our results is almost 94%.

Keywords: Computed Tomography (CT), Lungs Segmentation, Lungs Anatomy, Lungs Features Extraction, Image Processing

The IoT Development Strategies for Taiwan

Hsien-Tang Ko, Chi Chang and Nan-Shiun Chu

The internet of things enables new forms of communications between people and things, and between things themselves by embedding short-range mobile transceivers into everyday items. The IoT can be applied in environmental monitoring, disaster prevention, health & safety, medical biometrics, smart transportation and smart vehicles to provide green environment and create new opportunities for ICT industry. Although several challenges remain to be overcome, Taiwan government has implemented application oriented policies and provided strategic incentives to stimulate the development of IoT. This study reviews international trends, Taiwan’s progress and strategies of six main applications, smart disaster prevention, smart logistics, smart energy, smart transportation, smart healthcare, and smart building. Also, the study underlines the establishment of IoT infrastructure and the integration of Taiwan ICT industry.

Keywords: IoT; smart disaster prevention, smart logistics, smart energy, smart transportation, smart healthcare, smart building

Iris Texture Recognition with DCT Compression for Small Scale System

Shuvra Chakraborty and Md.Haider Ali

Person identification based on iris recognition is a popular biometric for its universality, uniqueness and permanence. By far, it is a prominent, matured and well developed biometric technique that provides positive identification with a high degree of confidence. Here, we have implemented both iris based identification and verification. Iris segmentation has been proposed with conventional Hough transform with lots of improvements in speed. Eyelash detection process has been integrated with eyelid detection to make the image preprocessing step faster. An automated segmentation integrity checking has been proposed to detect the failure of proper iris segmentation. A correction to the segmentation failure also has been proposed. If the correction process fails the automated integrity checking again then improperly segmented images are not enrolled for further feature extraction.A DCT(50%) column wise feature extraction based method has been proposed for iris recognition which requires less memory due to the energy compaction property of DCT. Matching is performed using Euclidian distance between feature vectors by shifting to get the best alignment with minimum matching score. In order to evaluate the performance of the iris recognition system, the popular CASIA-I iris image database with 756 grey scale images are used and with ideal template storing , it gives a satisfactory accuracy rate of about 92% and precision rate above 98%.

Keywords: Edge and feature detection, Feature evaluation and selection, Image processing software, Texture

Denoising based Clustering Algorithm for Classification of Remote Sensing Image

B. Saichandana, M. L. Phanendra, P. Naga Srinivasu, K. Srinivas and R. Kiran Kumar

Clustering is an unsupervised classification method widely used for classification of remote sensing images. However, noises are introduced into the images during acquisition or transmission process, affecting the classification results. Noise reduction is a prerequisite step prior to many information extraction attempts from remote sensing images. In order to overcome this drawback, this paper presents a new clustering technique that can be used in classifying noisy remote sensing images without going through any filtering stage. We call this method as Denoising Fuzzy Moving K-means Clustering algorithm (DFMK). The proposed algorithm is able to minimize the effect of Salt-and-Pepper noise during the classification process without degrading the fine details of the images. The method incorporates a noise detection stage to the clustering algorithm, producing an adaptive clustering based classification technique specifically for classifying the noisy remote sensing images. The results obtained from the proposed algorithm are more quantitative and qualitative than the conventional clustering algorithms in classifying the remote sensing images.

Keywords: Remote Sensing, Salt-and-Pepper, Image Classification, Image Processing