Using measurement data in a TSPSM project

One of the challenges for software engineering is collecting meaningful data from industrial projects. Software process improvement depends on measurement to provide baseline status and confirming evidence of the effect of process changes. Without data, any conclusions rely on intuition and guessing. The Team Software ProcessSM (TSPSM) provides a powerful framework for data collection and analysis, in addition to its primary goal as a basis for highly effective software development. In this paper, we describe the experiences of, and benefits realized by, a team using the TSP for the first time. By reviewing how this particular team collected and used data, we show features of the TSP that make it a powerful foundation for software process improvement.

A formal object-oriented approach to defining consistency constraints for UML models

We discuss how integrity consistency constraints between different UML models can be precisely defined at a language level. In doing so, we introduce a formal object-oriented metamodeling approach. In the approach, integrity consistency constraints between UML models are defined in terms of invariants of the UML model elements used to define the models at the language-level. Adopting a formal approach, constraints are formally defined using Object-Z. We demonstrate how integrity consistency constraints for UML models can be precisely defined at the language-level and once completed, the formal description of the consistency constraints will be a precise reference of checking consistency of UML models as well as for tool development.

A fuzzy approach to texture segmentation

The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.

A lightweight encryption algorithm for mobile online multimedia devices

Online multimedia data needs to be encrypted for access control. To be capable of working on mobile devices such as pocket PC and mobile phones, lightweight video encryption algorithms should be proposed. The two major problems in these algorithms are that they are either not fast enough or unable to work on highly compressed data stream. In this paper, we proposed a new lightweight encryption algorithm based on Huffman error diffusion. It is a selective algorithm working on compressed data. By carefully choosing the most significant parts (MSP), high performance is achieved with proper security. Experimental results has proved the algorithm to be fast. secure: and compression-compatible.

A sensor-based multimedia authentication system

User requirements of multimedia authentication are various. In some cases, the user requires an authentication system to monitor a set of specific areas with respective sensitivity while neglecting other modification. Most current existing fragile watermarking schemes are mixed systems, which can not satisfy accurate user requirements. Therefore, in this paper we designed a sensor-based multimedia authentication architecture. This system consists of sensor combinations and a fuzzy response logic system. A sensor is designed to strictly respond to given area tampering of a certain type. With this scheme, any complicated authentication requirement can be satisfied, and many problems such as error tolerant tamper method detection will be easily resolved. We also provided experiments to demonstrate the implementation of the sensor-based system

Motion vector encryption in multimedia streaming

Streaming video application requires high security as well as high computational performance. In video encryption, traditional selective algorithms have been used to partially encrypt the relatively important data in order to satisfy the streaming performance requirement. Most video selective encryption algorithms are inherited from still image encryption algorithms, the encryption on motion vector data is not considered. The assumption is that motion vector data are not as important as pixel image data. Unfortunately, in some cases, motion vector itself may be sufficient enough to leak out useful video information. Normally motion vector data consume over half of the whole video stream bandwidth, neglecting their security may be unwise. In this paper, we target this security problem and illustrate attacks at two different levels that can restore useful video information using motion vectors only. Further, an information analysis is made and a motion vector information model is built. Based on this model, we describe a new motion vector encryption algorithm called MVEA. We show the experimental results of MVEA. The security strength and performance of the algorithm are also evaluated.