A Java Framework to Specify Faultloads for Fault Injection Campaigns

In an operational environment, the identification and reproduction of faults may be hard to be done, specially in complex systems. Use of fault injection accelerates this process, improving the test of fault tolerance mechanisms. However, there are a significant amount of fault injectors available, using several different approaches. This diversity of tools, each one with different methods to describe faultloads for fault injection campaigns, imposes severe obstacles to the efficient use of such fault injectors. In this context, this paper presents jFaultload, which applies Java for the specification of faultloads and translates them to specific formats that are appropriate to each available fault injector. Fault injectors for communication systems were integrated in the environment and completes the test scenario. The service under test used to demonstrate the usability and expressiveness of our solution is a video streaming session using RTP Protocol.     

The gray level aura matrices for textured image segmentation

Inspired by an intuitive analogy that exists between the gray level textures and the miscibility in the multiphase fluids, the aura concept was developed from set theory tools in order to modeling the texture image. The gray level aura matrix (GLAM) has been then proposed to generalize the gray level cooccurrence matrix (GLCM) which remains very popular in the texture analysis. The GLAM indicates how much each gray level is present in the neighborhood of each other gray level. The neighborhood is defined by a structuring element as one used in mathematical morphology. The GLAM is mainly used and studied in synthesis and classification of textures framework but very few works are devoted to the segmentation. The aim of this paper is to exploit the GLAM for the segmentation of textured images. Experiments results over synthetic and real images show the efficiency of the GLAM. The influence of the shape and the size of the structuring element on the segmentation results are also studied.     

A low-noise front-end for multiplexed ENG recording using CMOS technology

Multi-electrode cuffs have been proposed as a means for the parallel recording of naturally-occurring neural signals. Compared to a conventional tripole cuff providing a single channel signal, additional information about the velocity and direction of nerve signals can be extracted from the multi-electrode recordings. Moreover, interference suppression is improved and overall power consumption reduced, as the analysis shows. In this paper a new recording system is proposed, each channel of which consists of a low-noise preamplifier employing lateral bipolar transistors to provide an impedance match to the tissue and a path to ground for the switching currents of a single high-gain amplifier, which is multiplexed between the channels. The proposed system provides low-noise, low-power operation and practically identical channel gains and is suitable for integration in a larger CMOS-based system.     

Power-efficient M-ary PSSK Communications with Uncertain Phase Errors

M-ary phase-silence-shift-keying (PSSK), one of power-efficient modulation schemes, has enhanced symbol error rate (SER) performance by 6 dB compared with that of the conventional phase-shift-keying (PSK). Phase tracking problem of the PSSK with phase-locked-loop (PLL) is discussed in this paper. Capacity of the PSSK for the additive white Gaussian noise (AWGN) channel and Rayleigh fading channel is analyzed. Capacity loss of the PSSK due to the PLL is small, which tells us that the PSSK demodulator can be practically implemented.