Abstract
The advent of H.264/AVC is going to change the way Digital Television programs are broadcast. Each program can be independently encoded or jointly encoded resulting thus in a more efficient way to distribute the available channel bandwidth. This paper presents a combined coding scheme for multi-program video transmission in which the channel capacity is distributed among the programs according to the program complexities. A complexity bit rate control algorithm based on the Structural Similarity Index (SSIM) is proposed. SSIM metric is presented under the hypothesis that the Human Visual System (HSV) is very specialized in extracting structural information from a video sequence but not in extracting the errors. Thus, a measurement on structural distortion should give a better correlation to the subjective impression. Current simulations have demonstrated very promising results showing that the algorithm can effectively control the complexity of the multi-program encoding process whilst improving overall subjective.

Abstract

Abstract
Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate. The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education. This research monograph delivers to researchers and graduate students the state of the art in data stream processing in sensor networks. The huge bibliography offers an excellent starting point for further reading and future research. © Springer-Verlag Berlin Heidelberg 2007. All rights are reserved.

Abstract
Automatic spatial video segmentation is a problem without a general solution at the current state-of-the-art. Most of the difficulties arise from the process of capturing images, which remain a very limited sample of the scene they represent. The capture of additional information, in the form of depth data, is a step forward to address this problem. We start by investigating the use of depth data for better image segmentation; a novel segmentation framework is proposed, with depth being mainly used to guide a segmentation algorithm on the colour information. Then, we extend the method to also incorporate motion information in the segmentation process. The effectiveness and simplicity of the proposed method is documented with results on a selected set of images sequences. The achieved quality raises the expectation for a significant improvement on operations relying on spatial video segmentation as a pre-process. ©2007 IEEE.

Abstract
This paper presents the evaluation of computational mind model based on temperamental decision algorithms with emotional behaviors. Our computational model of emotion is inspired on appraisal theory and on superior nervous system characteristics. We define the model for temperamental agent with emotions. In this paper we prove that teams of the agents with different temperaments have different performances in the same simulation scenario. The result shows that strategies based on temperamental decision mechanism strongly influence system performance and there are evident dependencies between emotional states of agents and their temperamental type, as well as dependencies between the team performance and team configuration, and this enables us to conclude that modular approach to emotional programming based on temperamental theory is a good choice to develop computational mind models for emotional behavioral Multi-Agent systems.

Abstract
The increasing use of battery-powered embedded systems has motivated the development of power consumption models in order to help designers to build low-power systems. Due to the configurability features of FPGAs, the adoption of systems containing one or more soft-core processors on a single chip is becoming more and more attractive. This paper presents an adaptation of the instruction-level power estimation model to soft-core processors implemented in FPGAs. This model allowed to estimate the power dissipated in eleven test applications with a maximum error of 4.78%. The Ongoing work includes efforts towards a software power estimation model for multi-core systems embedded in a single FPGA device.