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Complex Valued Nonlinear Adaptive Filters
This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.
Recurrent Neural Networks for Prediction
Neural networks consist of interconnected groups of neurons which function as processing units. Through the application of neural networks, the capabilities of conventional digital signal processing techniques can be significantly enhanced.
Recurrent Neural Networks for Prediction
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.? Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectur.
Gangsters and Other Statesmen
How global organized crime shapes the politics of borders in modern conflicts Separatism has been on the rise across the world since the end of the Cold War, dividing countries through political strife, ethnic conflict, and civil war, and redrawing the political map. Gangsters and Other Statesmen examines the role transnational mafias play in the success and failure of separatist movements, challenging conventional wisdom about the interrelation of organized crime with peacebuilding, nationalism, and state making. Danilo Mandić conducted fieldwork in the disputed territories of Kosovo and South Ossetia, talking to mobsters, separatists, and policymakers in war zones and along major smugglin...
Changing Youth Values in Southeast Europe
What shapes the cultural, political and ideological values of young people living in Southeastern Europe? Which identities matter to them? How are their values changing, and how can they be changed? Who is changing them? Europe’s periphery is the testing ground for the success of European values and identities. The future stability and political coherence of the Union will be determined in large measure by identity issues in this region. This book examines the ways in which ethnic and national values and identities have been surpassed as the overriding focus in the lives of the region’s youth. Employing bottom-up, ethnographic, and interview-based approaches, it explores when and where e...
Signal Processing Techniques for Knowledge Extraction and Information Fusion
This book brings together the latest research achievements from signal processing and related disciplines, consolidating existing and proposed directions in DSP-based knowledge extraction and information fusion. The book includes contributions presenting both novel algorithms and existing applications, emphasizing on-line processing of real-world data. Readers discover applications that solve biomedical, industrial, and environmental problems.
Advanced Data Analytics for Power Systems
Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.
Tensor Networks for Dimensionality Reduction and Large-scale Optimization
Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of large-scale, multi-modal and multi-relational datasets. Given that such data are often efficiently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review low-rank tensor decompositions and tensor networks as emerging...
Financial Signal Processing and Machine Learning
The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processi...
Tensor Networks for Dimensionality Reduction and Large-Scale Optimization
This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dim...
