As the demand for wireless bandwidth rapidly increases, alternative methods to radio frequency-based communication are investigated to overcome the limited bandwidth problem. Visible light communication (VLC) using light emitting diodes (LEDs) is one of these alternatives. LEDs are estimated to replace the incandescent bulbs within the decade. Since, LEDs can be intensity modulated faster than the human eye can detect, illumination and communication can both be provided by the same lighting system. Indoors communication constitutes 70% of the overall traffic, and VLC is a promising technology to complement Wi-Fi and cellular wireless systems. However, proper handover mechanism should be developed for VLC to be a complete indoors solution. In this paper, we present two soft handover methods for VLC. Simulation results indicate our solutions provide higher data rate for both the overall system and individual users in the handover region.

The number of applications that use industrial, scientific and medical (ISM) radio bands increase every day, creating interference problem for the wireless sensor networks (WSN) that generally operate on these bands. Cognitive radio sensor network (CRSN) has been proposed as a promising solution to this problem. However, since sensor nodes are energy-constrained devices energy efficient spectrum sensing methods are needed for CRSN. To address this need, we propose a novel cooperative coarse sensing scheme for CRSN (CC4C). CC4C is based on sequential sensing, thus, it is simple and fast. Simulation results show that CC4C incurs less sensing delay and provides significant energy conservation compared to energy detection based coarse sensing schemes, and single stage sensing schemes where no coarse sensing is performed.

Cognitive radio sensor network (CRSN) is an emerging sensor networking paradigm that aims to incorporate opportunistic spectrum access capability to the wireless sensor networks. Since sensor nodes are energy-constrained devices, design of efficient spectrum sensing schemes is imperative for the implementation of CRSNs. In order to address this need, a cooperative spectrum sensing scheme (CSS), specifically designed for CRSNs, is presented in this paper. CSS aims to minimize power consumption and delay during spectrum sensing, while meeting the performance requirements in terms of accuracy with minimal complexity. Simulation results indicate that significant power savings can be achieved with the proposed solution.

Accurate simulations of real-life electromagnetics problems with integral equations require the solution of dense matrix equations involving millions of unknowns. Solutions of these extremely large problems cannot be achieved easily, even when using the most powerful computers with state-of-the-art technology. However, with the multilevel fast multipole algorithm (MLFMA) and parallel MLFMA, we have been able to obtain full-wave solutions of scattering problems discretized with hundreds of millions of unknowns. Some of the complicated real-life problems (such as scattering from a realistic aircraft) involve geometries that are larger than 1000 wavelengths. Accurate solutions of such problems can be used as benchmarking data for many purposes and even as reference data for high-frequency techniques. Solutions of extremely large canonical benchmark problems involving sphere and National Aeronautics and Space Administration (NASA) Almond geometries are presented, in addition to the solution of complicated objects, such as the Flamme. The parallel implementation is also extended to solve very large dielectric problems, such as dielectric lenses and photonic crystals.

Due to itsO(NlogN) complexity, the multilevel fast multipole algorithm (MLFMA) is one of the most prized algorithms of computational electromagnetics and certain other disciplines. Various implementations of this algorithm have been used for rigorous solutions of large-scale scattering, radiation, and miscellaneous other electromagnetics problems involving 3-D objects with arbitrary geometries. Parallelization of MLFMA is crucial for solving real-life problems discretized with hundreds of millions of unknowns. This paper presents the hierarchical partitioning strategy, which provides a very efficient parallelization of MLFMA on distributed-memory architectures. We discuss the advantages of the hierarchical strategy over previous approaches and demonstrate the improved efficiency on scattering problems discretized with millions of unknowns.