Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Optical multicarrier sources for spectrally efficient optical networks

During the last 30 years the capacity of commercial optical systems exceeded the network traffic requirements, mainly due to the extraordinary scalability of wavelength division multiplexing technology that has been successfully used to expand capacity in optical systems and meet increasing bandwidth requirements since the early 1990’s. Nevertheless, the rapid growth of network traffic inverted this situation and current trends show faster growing network traffic than system capacity. To enable further and faster growth of optical communication network capacity, several breakthroughs occurred during the last decade. First, optical coherent communications, which were the subject of intensive research in the 1980’s, were revived. This triggered the employment of advanced modulation formats. Afterwards, with the introduction of orthogonal frequency division multiplexing (OFDM) and Nyquist WDM modulation techniques in optical communication systems, very efficient utilisation of the available spectral bandwidth was enabled. In such systems the spectral guard bands between neighbouring channels are minimised, at the expense of stricter requirements on the performance of optical sources, especially the frequency (or wavelength) stability. Attractive solutions to address the frequency stability issues are optical multicarrier sources which simultaneously generate multiple phase correlated optical carriers that ensure that the frequency difference between the carriers is fixed. In this thesis, a number of optical multicarrier sources are presented and analysed, with special focus being on semiconductor mode-locked lasers and gain-switched comb sources. High capacity and spectrally efficient optical systems for short and medium reach applications (from 3 km up to 300 km), based on optical frequency combs as optical sources, advanced modulation formats (m-QAM) and modulation techniques (OFDM and Nyquist WDM) have been proposed and presented. Also, certain optoelectronic devices (i.e. semiconductor optical amplifier) and techniques (feed-forward heterodyne linewidth reduction scheme) have been utilised to enable the desired system performance

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

On PAPR Reduction of OFDM using Partial Transmit Sequence with Intelligent Optimization Algorithms

In recent time, the demand for multimedia data services over wireless links has grown up rapidly. Orthogonal Frequency Division Multiplexing (OFDM) forms the basis for all 3G and beyond wireless communication standards due to its efficient frequency utilization permitting near ideal data rate and ubiquitous coverage with high mobility. OFDM signals are prone to high peak-to-average-power ratio (PAPR). Unfortunately, the high PAPR inherent to OFDM signal envelopes occasionally drives high power amplifiers (HPAs) to operate in the nonlinear region of their characteristic leading out-of-band radiation, reduction in efficiency of communication system etc. A plethora of research has been devoted to reducing the performance degradation due to the PAPR problem inherent to OFDM systems. Advanced techniques such as partial transmit sequences (PTS) and selected mapping (SLM) have been considered most promising for PAPR reduction. Such techniques are seen to be efficient for distortion-less signal processing but suffer from computational complexity and often requires transmission of extra information in terms of several side information (SI) bits leading to loss in effective data rate. This thesis investigates the PAPR problem using Partial Transmit Sequence (PTS) scheme, where optimization is achieved with evolutionary bio-inspired metaheuristic stochastic algorithms. The phase factor optimization in PTS is used for PAPR reduction. At first, swarm intelligence based Firefly PTS (FF-PTS) algorithm is proposed which delivers improved PAPR performance with reduced searching complexity. Following this, Cuckoo Search based PTS (CS-PTS) technique is presented, which offers good PAPR performance in terms of solution quality and convergence speed. Lastly, Improved Harmony search based PTS (IHS-PTS) is introduced, which provides improved PAPR. The algorithm has simple structure with a very few parameters for larger PTS sub-blocks. The PAPR performance of the proposed technique with different parameters is also verified through extensive computer simulations. Furthermore, complexity analysis of algorithms demonstrates that the proposed schemes offer significant complexity reduction when compared to standard PAPR reduction techniques. Findings have been validated through extensive simulation tests