Abstract
We present a distributed algorithm for cyber-physical systems to obtain a snapshot of sensor data. The snapshot is an approximate representation of sensor data; it is an interpolation as a function of space coordinates. The new algorithm exploits a prioritized Medium Access Controll (MAC) protocol to efficiently transmit information of the sensor data. It scales to a very large number of sensors and it is able to operate in the presence of sensor faults.

Abstract
Skeletal muscle is a fibrous tissue composed by muscle fibers and interstitial fluid. Due to this constitution, the muscle presents a non uniform refractive index profile that origins strong light scattering. One way to improve tissue transmittance is to reduce this refractive index mismatch by immersing the muscle in an optical clearing agent. As a consequence of such immersion tissue also suffers dehydration. The study of the optical clearing effect created by a simple mixture composed by ethanol, glycerol and distilled water has proven its effectiveness according to the variations observed in the parameters under study. The effect was characterized in terms of its magnitude, time duration and histological variations. The applied treatment has created a small reduction of the global sample refractive index that is justified by the long time rehydration caused by water in the immersing solution. From the reduction in sample pH we could also identify the dehydration process created in the sample. The immersion treatment has originated fiber bundle contraction and a spread distribution of the muscle fiber bundles inside. New studies with the mixture used, or with other combinations of its constituents might be interesting to perform with the objective to develop new clinical procedures.

Abstract

Abstract
The automatic extraction and analysis of visual information is becoming generalised. The first step in this processing chain is usually separating or segmenting the captured visual scene in individual objects. Obtaining a perceptually correct segmentation is however a cumber some task. Moreover, typical applications relying on object segmentation, such as visual surveillance, introduce two additional requirements: (1) it should represent only a small fraction of the total amount of processing time and (2) realtime overall processing. We propose a technique that tackles these problems using a cascade of change detection tests, including noise-induced, illumination variation and structural changes. An objective comparison of common pixelwise modelling methods is first done. A cost-based partition- distance between segmentation masks is introduced and used to evaluate the methods. Both the mixture of Gaussians and the kernel density estimation are used as a base to detect structural changes in the proposed algorithm. Experimental results show that the cascade technique consistently outperforms the base methods, without additional post-processing and without additional processing overheads. © 2007 ACADEMY PUBLISHER.

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
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.