Volume 11, Issue 2, February 2012

Reliability Analysis Based on Jump Diffusion Model for an Open Source Software [Full Text]

Yoshinobu Tamura and Shigeru Yamada

Many open source softwares are developed in all parts of the world, e.g., Firefox, Apache HTTP server, Linux, Android, etc. Open source software is now attracting attention as the next-generation software development paradigm because of the cost reduction, quick delivery, work saving. This paper focuses on the irregular fluctuation of version upgrade and the total track of fault data for open source software. A new approach to software reliability assessment by using the jump diffusion model based on the stochastic differential equations in order to consider the change of requirements specification and the irregular fluctuation of version upgrade is presented. Also, actual software fault-count data is analyzed in order to show numerical examples of software reliability assessment for several open source softwares. In particular, several reliability assessment measures are derived from our jump diffusion model. Moreover, this paper shows that the proposed the method of reliability analysis can assist quality improvement for the open source software project.


Comparisons of Indoor Position Enhancements by Using Mean and Kalman Filtering Techniques [Full Text]

Hakan Koyuncu and Shuang Hua Yang

In this paper, a study of two filtering techniques is compared during indoor localization. Linear mean filtering and Kalman filtering techniques are employed during the pre and post position estimation phases to determine the location accuracies of unknown objects. Zigbee wireless sensor nodes (WSN) are employed together with RF Link quality indicator (LQI) values in measurements. Fingerprint based localization technique is utilized and k-NN algorithms are used to calculate the unknown positions. Linear mean filtering gives an average position accuracy of 3.5 meters in a sensing area with a grid space of 4 meters. Kalman filtering, on the other hand, gives an average position accuracy of 4.5 meters in the same sensing area.


The Application of Catastrophe Theory in the Credit Risk Assessment Of Famers’ Microfinance [Full Text]

Gang Lv, Longyi Zhu, Jing Wang and Zongfang Zhou

The famers’ microfinance is of highly risk because it doesn’t need any collateral and guarantees. So it’s important to assess and control the credit risk of the microfinance. Based on the establishment of the evaluation index, this paper used catastrophe theory to assess the credit risk of the farmers. In the end, we took ten farmer families as samples to carry the empirical study. The result shows that it’s an objective and reasonable way to assess the credit risk of famer.


Control Unit Design and simulation of an experimental RISC CPU [Full Text]

Ajay Anant Joshi, Siew Leong Lam and Yee Yong Chan

An 8-bit RISC-CPU is designed at gate level using completely custom chip approach. CPU has an 8-bit integer unit and 16-bit floating point unit. The circuits are optimized by using more efficient algorithms. The algorithm discussed in this paper was applied for an 8-bit CPU design, however there is no reason that this couldn't be used for more powerful and serious CPU development. This paper discusses the design of a Control Unit section of the said CPU with respect to algorithm and VHDL. The project is implemented using VHDL and simulated using Altera MaxPlus II sim software which can map the design into Altera CPLD.


Implementation and Enhancing the design of Signature-based Intrusion Detection Model [Full Text]

Muna M. T. Jawhar and Monica Mehrotra

Intrusion detection is an interesting approach that could be used to improve the security of network systems. Intrusion detection system detects suspected patterns of network traffic on the remaining open parts by monitoring user activities. In this paper we evolve a signature based intrusion detection system based on Neural Networks for recognizing attacks types in the network traffic packet. We use Hamming network to detect attacks in this model. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time of real word intrusion detection systems. Training and testing data we obtain from the real network traffic by using packet sniffer.