Data aggregation has emerged as a basic concept in wireless sensor networks. By combining data and eliminating redundancy, this concept showed its effectiveness in terms of reducing resources consumption. The data aggregation problem is not new and has been widely studied in wireless sensor networks. However, most of the proposed solutions are based on a static data aggregation scheme and do not consider the network constraints evolution such as energy bandwidth, overhead and transmission delay. To dynamically optimize the tradeoff between the data aggregation process and the network constraints, a new paradigm has been introduced recently named the feedback control system. The main idea is to adapt the data aggregation degree to the environment changes and the sensor network applications. The feedback control for data aggregation remains relatively a new research area and only few works have introduced this concept. Therefore this work points out this research field by surveying the different existing protocols.

Wavelets are known particularly to be an effective tool for extracting discriminative features in the scattered RF signals of both solid and gaseous emboli. However, the selection of an appropriate mother wavelet for the signal being analyzed is an important criterion. This offers the possibility to perform an optimization procedure to obtain the best wavelet. The purpose of the study is to propose a new approach to classify microembolic echoes using a discrete wavelet transform (DWT) based on genetic algorithm optimization and support vector machine (SVM) classifier. The experimental setup consists of a flow phantom (ATSLaB) containing a tube of 6 mm in diameter. In order to mimic the ultrasonic behavior of gaseous emboli, contrast agents consisting of microbubbles are used in our experimental setup. However, to mimic the behavior of the solid emboli we have used the Doppler fluid which contains particles with scatter characteristics comparable to red blood cells. The acquisitions are carried out at 2 MHz and 3.5 MHz transmit frequency. Ultrasound waves are transmitted at different intensities corresponding to mechanical indices (MI) of 0.21 and 0.42 for the transmit frequency of 2 MHz, and 0.31 and 0.62 for the transmit frequency of 3.5 MHz. Two concentrations of the contrast agent (100 μl and 200 μl) are diluted into a 100 ml volume of water. The polyphase representation of the discrete wavelet transform (DWT) is exploited in this study. Such representation allows generating a wavelet filter bank from a set of angular parameters, in order to minimize the fitness function based on genetic algorithm optimization and the SVM classifier. The best accuracy classifications of microemboli obtained in this study are equal to 99.90% for 2MHz and to 99.60% for 3.5MHz. These results illustrate that wavelet optimization approach works well for microemboli classification using RF signals.

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

Currently, the context management solutions for the Internet of Things are the subject of numerous studies which are achieved through the expansion of the context manager for ambient environments. In literature, there are different approaches enabling the context management for internet of things (IoT). This paper studies these approaches, which are well known according to our defined criteria such as heterogeneity, mobility, the influence of the physical world, scalability, security, privacy, quality of context, autonomous deployment of entities, characterization multi scales, and interoperability.

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

Phase diagrams are representations of the sate of a material as a function of temperature of thee constituent components. Multi-components phase diagrams are useful for alloy development processing and understanding. They are considered as roadmaps in design of new materials used in technology. To establish phase diagrams we usually use a thermodynamic approach called CALPHAD (CALculation PHase Diagrams) based on the knowledge and minimization of the Gibbs energy of all phases taking part in the system. We can obtain the Gibbs energy of each phase by using experimental methods (calorimetry). We can also use theoretical method (ab-initio) or a semiempirical model of Miedema. We reviewed and used three calorimetric methods: direct reaction, progressive precipitation and indirect mixing calorimetry. We obtained at high temperatures the enthalpies of formations of eight compounds: Ga3Mo, Ga5Mo, GaNb3, Ga5Nb4, Ga3Nb,  -GaCr3,  -GaCr3 and Ga4Cr. Their values were compared to those estimated theoretically, reviewed and commented. In addition to their exploitation to establish phase diagrams, these data permits to improve thermodynamic models and understand the behaviours of gallium towards the three transition metals molybdenum, niobium and chromium. This procedure can be applied do ternary compound if needed.