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Orthogonal Waveforms and Filter Banks for Future Communication Systems provides an up-to-date account of orthogonal filter bank-based multicarrier (FBMC) systems and their applications in modern and future communications, highlighting the crucial role that advanced multicarrier waveforms play. It is an up-to-date overview of the theory, algorithms, design and applications of FBMC systems at both the link- and system levels that demonstrates the various gains offered by FBMC over existing transmission schemes via both simulation and test bed experiments. Readers will learn the requirements and challenges of advanced waveform design for future communication systems, existing FBMC approaches, application areas, and their implementation. In addition, the state-of-the-art in PHY- and MAC-layer solutions based on FBMC techniques, including theoretical, algorithmic and implementation aspects are explored.

A new GiT-BEMD algorithm is developed.Based on Green's function with tension.Much faster than canonical BEMD algorithms.Easy to implement and very robust.Superior performance tested on artificial and real images. Bidimensional Empirical Mode Decomposition(BEMD) interprets an image as a superposition of Bidimensional Intrinsic Mode Functions (BIMFs). They are extracted by a process called sifting, which encompasses two-dimensional surface interpolations connecting a set of local maxima or minima to form corresponding envelope surfaces. Existing surface interpolation schemes are computationally very demanding and often induce artifacts in the extracted modes. This paper suggests a novel method of envelope surface interpolation based on Green's functions. Including surface tension greatly improves the stability of the new method which we call Green's function in tension-based BEMD (GiT-BEMD). Simulation results, using toy images with various textures, facial images and functional neuroimages, demonstrate the superior performance of the new method when compared to its canonical BEMD counterpart. GiT-BEMD strongly speeds up computations and achieves a higher quality of the extracted BIMFs. Furthermore, GiT-BEMD can be extended simply to an ensemble-based variant (GiT-BEEMD), if needed. In summary, the study suggests the new variant GiT-BEMD as a highly competitive, fast and stable alternative to existing BEMD techniques for image analysis.

Publisher Summary This chapter presents the results of a testing and validation activity carried out to analyze the performance of signal-processing techniques related to positioning and navigation. The steps included the setup of a measurement campaign and the setup of a shared database, the usage of this database to test algorithms, and the development of a software defined radio (SDR)-based architecture for a GNSS receiver, to be used for advanced laboratory tests on innovative GNSS-related signal processing algorithms. During the measurement campaign, a total of 22 ZigBee-based devices, model CC2430 made by Texas Instruments, were used to measure RSS values. Each of them is a single chip solution which integrates microprocessor and radio interface compliant with the IEEE 802.15.4 standard at 2.4 GHz. Two UWB-based devices, model PulsOn220 made by Time Domain, were used to perform TOA ranging measurements in both static and dynamic scenarios. During the test, the receiver in light-indoor condition could autonomously track only one satellite, identified as PRN 9. This PRN was also tracked by the aiding user; therefore, it was used to estimate the clock offset among the two PCs.

This chapter presents several signal processing strategies to combine together, in a seamless estimation process, position-related measurements coming from different technologies and/or systems (e.g., TOA and TDOA measurements in terrestrial networks, TOA and RSS measurements, or even satellite and terrestrial systems, or satellite and inertial navigation systems). This approach, generally indicated as “hybridization”, promises to provide better accuracy with respect to its stand-alone counterparts, or better availability thanks to the diversity of the employed technologies. For example, hybridization between satellite and inertial systems is expected to compensate for the respective fragilities of the two systems, namely the relatively high error variance of the former and the drift of the latter. The mathematical framework where hybridization is developed is Bayesian filtering: the generic structure is reviewed and the well-known Kalman filter and its variants are inserted in the framework, with examples of applications to positioning problems. Then the particle filter approach is explained, with its most used variants. Examples of hybrid localization algorithms are then shown, starting from a hybrid terrestrial architecture, then passing to the architectures that blend GNSS and inertial measurements, using either the Kalman filter approach or the direct position estimation approach. Finally, an example of hybrid localization based on GNSS and peer-to-peer terrestrial signaling is presented.

A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that the proposed algorithms perform efficiently, even in the presence of hard distortion. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of input distribution (the so-called score function). Each algorithm consists of three adaptive blocks: one devoted to adaptive estimation of the score function, and two other blocks estimating the inverses of the linear and nonlinear parts of the channel, (quasi-)optimally adapted using the estimated score functions. This paper is mainly concerned by the nonlinear part, for which we propose two parametric models, the first based on a polynomial model and the second on a neural network, while [14, 15] proposed non-parametric approaches.