Metric anticipation to manage mobility in mobile mesh and ad hoc wireless networks

In mobile ad hoc networks (MANETs), node mobility management is performed by the routing protocol. It may use metrics to reflect link state/quality. But, the delay between measures of the link quality and its integration in the route computation is very detrimental to the mobility management. Consequently, routing protocols may use lossy links for a few seconds leading to a significant performance deterioration. In this paper, we propose a new routing metric technique calculation which aims at anticipating link quality. Basically, the idea is to predict metric values a few seconds in advance, in order to compensate the delay involved by the link quality measurement and their dissemination by the routing protocol. Our technique is based on measurements of signal strength and is integrated in two classical routing metrics: ETX (expected transmission count) and ETT (expected transmission time). Validations are performed through both simulations and a testbed experimentation with OLSR as routing protocol. NS-3 simulations show that our metric may lead to a perfect mobility management with a packet delivery ratio of 100%. Experiments on a testbed prove the feasibility of our approach and show that this technique reduces the packet error rate by a factor of 3 in an indoor environment compared to the classical metrics calculation.     

A Survey on IP Watermarking Techniques

Intellectual property (IP) block reuse is essential for facilitating the design process of system-on-a-chip. Sharing IP designs poses significant high security risks. Recently, digital watermarking emerged as a candidate solution for copyright protection of IP blocks. In this paper, we survey and classify different techniques used for watermarking IP designs. To this end, we defined several evaluation criteria, which can also be used as a benchmark for new IP watermarking developments. Furthermore, we established a comprehensive set of requirements for future IP watermarking techniques.     

Artificial neural network approach to distance protection oftransmission lines

A distance relay for the protection of transmission lines is usually designed on the basis of fixed settings. The reach of such relays is therefore affected by the changing network conditions. The implementation of a pattern recognizer for power system diagnosis can provide great advances in the protection field. This paper demonstrates the use of an artificial neural network as a pattern classifier for a distance relay operation. The scheme utilizes the magnitudes of three phase voltage and current phasors as inputs. An improved performance with the use of an artificial neural network approach is experienced once the relay can operate correctly, keeping the reach when faced with different fault conditions as well as network configuration changes     

Study of electron traps in the thin interfacial oxide layer of Al-, Au- and Sn-n GaAs Schottky barriers by detrapping experiments

Electron traps in MIS-type Schottky barriers on n-GaAs were investigated by measuring the change of the flat-band voltage, due to detrapping, as a function of time. The trap depth and the capture cross section for a particular trap were obtained from the temperature dependence of the time constant for detrapping. It was found that the detrapping process in some cases is a two-state thermionic emission and tunneling process. For other trapping levels the results indicated clearly that the mechanism was different from the thermionic emission and tunneling process.     

Surface copper enrichment by reduction of copper chromite catalyst employed for carbon monoxide oxidation

The effect of prereduction on CO oxidation activity of unsupported copper-chromite oxide catalysts was examined. Results were found to be in good agreement with two mechanisms for a surface copper enrichment due to CO prereduction which produced an activity increase in the copper-chromite catalyst.     

An energy efficient middleware for an ad-hoc AAL wireless sensor network

DIA (Dispositivo Inteligente de Alarma, in Spanish) is an AAL (Ambient Assisted Living) system that allows to infer a potential dangerous action of an elderly person living alone at home. This inference is obtained by a specific sensorisation with sensor nodes (portables and fixes) and a reasoning layer embedded in a PC that learns of the users behaviour patterns and advices when actual one differs significantly of the normal patterns. In AAL systems, energy is a limited resource therefore sensor devices need to be properly managed to conserve energy. In this paper, we introduce the design and implementation of innovative and specific mechanisms at the sensory layer middleware which is capable of, first to discriminate spurious motion detections assuming that these signals do not resemble the patterns of real motion detections and, second to reduce the dynamics of messages by a sensor signal processing in order to compress the whole information in one single event. The middleware achieves power saving by modifying the raw information from sensors and adapting it to the predefined semantic of the reasoning layer. It manages the important task of data processing from sensors (raw information), and transfers the pre-processed information into the top layer of reasoning in a more energy efficient way. We also address the trade-off between reducing power consumption and reducing delay for incoming data. We present results from experiments using our implementation of these mechanisms at the middleware that comprises from node firmware to the PC driver. The number of messages of the proposed method with respect to the raw data is reduced by approximately 98.5%. The resources used in the PIR signal processing is reduced by approximately 85%. The resulting delay introduced is small (10–19 s) but system dynamics is slow enough to avoid contextualisation errors or reduction of system performance. We consider these results as very satisfactory.     

A survey on nonlinear analog-to-digital converters

This paper presents a survey on Nonlinear Analog-to-Digital converters (ADC). This class of converters is extremely relevant in applications where there is a need for non-uniform quantization characteristic, for example, some specific applications in the areas of light detection, hearing aid, nuclear physics, image acquisition, communication systems, etc. This survey outlines the state-of-the-art Nonlinear ADC topologies, such as, floating point, logarithmic, piecewise linear and oversampled nonlinear converters, and discusses their performance and advantages in terms of their applications.