The smart grid system is key to the new electrical network infrastructure. It takes into account the use of the new information and communication technologies (ITC) and the integration of the renewable energy power generation (photovoltaics PV, concentrated solar power CSP, wind, etc). The smart grid promises better management and control of energy sources. The application of the smart grid, especially, in Algeria allows the optimal control of the electricity demand since the latter keeps rising continuously. The use of smart grid allows the rationalization of the electricity consumption in smart homes through appliance automated control. Input energy savings result in the reduction of c emission. Herein, we present a new energy management strategy tested in an experimental smart home (SM). The implemented management approach was made possible by using a new electronic system that allowed the control of all appliances via the internet network. For this purpose, a dynamic monitoring web interface was developed under an open source platform in order to process the whole data delivered by the system. The final output of the system which consists of a balance between all types of energy involved, including CO2 gas emission, is displayed. It is only then that the user can take adequate decision and establish the priorities for rational use of the energy available.

Today’s, and near future, communication networks rely heavily on capacity expansion to keep pace with the massive number of mobile devices and ever-increasing mobile traffic. This expansion can be achieved through three major ingredients, namely, adding more wireless-spectrum, efficient usage of this spectrum, and adequate networks’ architectures. In this paper, a proposition for integrating these three ingredients in a cognitive-radio-inspired framework is presented. The focus is on the integration of the idle spectrum resources of different wireless networks into a single mobile heterogeneous wireless network. This framework is based on a conceptual network-architecture articulated with a generic and cooperative spectrum-harvesting scheme. The former brings the necessary agility for such heterogeneous environments, the latter keeps the network supplied with the vital spectrum resources. In our proposal, we make use of cross-correlated sequences (CCSs) for context-aware events’ signaling purposes. This choice is motivated by the particularly interesting characteristics of CCSs, namely, duration shortness, robustness to bad radio conditions, detection rather than decoding, and low probability of collision. As an illustration, we propose a reporting and detection scheme, in the context of OFDMA systems, and provide performance results from simulations to validate our proposal.

The present paper deals with a real-time implementation of a novel Fuzzy logic energy management strategy (EMS), applied to a battery–super capacitor hybrid energy system and associated with a permanent magnet synchronous motor (PMSM) which emulates the traction part of an electric vehicle (EV). On the sources side, the fuzzy logic supervisor acts in a smart way to permute smoothly between the various operations modes via an efficient power frequency splitting. In addition, it permits a quite regulation of both the DC bus and the super-capacitor (SC) voltages regardless of the speed profile variations to ensure an optimal power flow to the load and to keep the SC operation in a safe voltage range while providing or absorbing power in transients. On the traction side, a second order sliding mode control called ‘super-twisting’ (ST), associated with a space vector modulation (SVM) strategy is applied to ensure …

Multilevel thresholding is a widely used technique to perform image segmentation. It consists of dividing an input image into several distinct regions by finding the optimal thresholds according to a certain objective function. In this work, we generalize the use of the SSIM quality measure as an objective function to solve the multilevel thresholding problem using empirically tuned swarm intelligence algorithms. The experimental study we have conducted shows that our approach, producing near-exact solutions, is more effective compared to the state-of-the-art methods. Moreover, we show that the computation complexity has been significantly reduced by adopting a shared-memory parallel programming paradigm for all the algorithms we have implemented.