The motivation for the research that is described in these volumes is the wish to explain things in terms of their underlying causes, rather than merely being satisfied with phenomenological descriptions. When this reductionist approach is applied to information processing it allows the internal structure of information to be analysed, so information processing algorithms can then be derived from first principles. One of the simplest examples of this approach is the diagonalisation of a data covariance matrix – there are many variants of this basic approach, such as singular value decomposition – in which the assumed independent components of high-dimensional data are identified and extracted. The main limitation of this type of information analysis approach is that it is based on linear algebra applied globally to the data space, so it is unable to preserve information about any local data structure in the data space. For instance, if the data lives on a low-dimensional curved manifold embedded in the data space, then only the global properties of this manifold would be preserved by global linear algebra methods. In practice, data whose high-dimensional structure is non-trivial typically lives on a noisy version of a curved manifold, so techniques for analysing such data must automatically handle this type of structure. For instance, a blurred image of a point source is described by its underlying degrees of freedom – i.e. the position of the source – and as the source moves about it generates a curved manifold that lives in the high-dimensional space of pixel values of the sampled image. The basic problem is then to deduce the internal properties of this manifold by analysing examples of such images. A more challenging problem would be to extend this analysis to images that contain several overlapping blurred images of point sources, and so on. There is no limit to the complexity of the types of high-dimensional data that one might want to analyse. These methods then need to be automated so that they do not rely on human intervention, which would then allow them to be inserted as “components” into information processing networks. The purpose of the research that is described in these volumes is to develop principled information processing methods that can be used for such analysis. Self-organising information processing networks arise naturally in this context, in which ways of cutting up the original manifold into simpler pieces emerge automatically.

The motivation for the research that is described in these volumes is the wish to explain things in terms of their underlying causes, rather than merely being satisfied with phenomenological descriptions. When this reductionist approach is applied to information processing it allows the internal structure of information to be analysed, so information processing algorithms can then be derived from first principles. One of the simplest examples of this approach is the diagonalisation of a data covariance matrix – there are many variants of this basic approach, such as singular value decomposition – in which the assumed independent components of high-dimensional data are identified and extracted. The main limitation of this type of information analysis approach is that it is based on linear algebra applied globally to the data space, so it is unable to preserve information about any local data structure in the data space. For instance, if the data lives on a low-dimensional curved manifold embedded in the data space, then only the global properties of this manifold would be preserved by global linear algebra methods. In practice, data whose high-dimensional structure is non-trivial typically lives on a noisy version of a curved manifold, so techniques for analysing such data must automatically handle this type of structure. For instance, a blurred image of a point source is described by its underlying degrees of freedom – i.e. the position of the source – and as the source moves about it generates a curved manifold that lives in the high-dimensional space of pixel values of the sampled image. The basic problem is then to deduce the internal properties of this manifold by analysing examples of such images. A more challenging problem would be to extend this analysis to images that contain several overlapping blurred images of point sources, and so on. There is no limit to the complexity of the types of high-dimensional data that one might want to analyse. These methods then need to be automated so that they do not rely on human intervention, which would then allow them to be inserted as “components” into information processing networks. The purpose of the research that is described in these volumes is to develop principled information processing methods that can be used for such analysis. Self-organising information processing networks arise naturally in this context, in which ways of cutting up the original manifold into simpler pieces emerge automatically.

The Afro-European Conference for Industrial Advancement (AECIA) brought together the foremost experts and excellent young researchers from Africa, Europe and the rest of the world to disseminate the latest results from various fields of engineering, information and communication technologies. This volume gathers the carefully selected papers from the third installment of the AECIA, which was held in Marrakech, Morocco from November 21 to 23, 2016. The papers address important topics like Automation Systems, Intelligent Techniques and Algorithms, Information and Communication Technology (ICT) Applications in Engineering, Control, Optimization and Processing, as well as manufacturing-related topics. As such, it offers a valuable reference guide for researchers, students and practitioners in the fields of computer science and engineering.

With the growth of high-speed telecommunications and wireless technology, it is becoming increasingly important for engineers to understand radio frequency (RF) applications and their sensitivity to electrostatic discharge (ESD) phenomena. This enables the development of ESD design methods for RF technology, leading to increased protection against electrical overstress (EOS) and ESD. ESD: RF Technology and Circuits: Presents methods for co-synthesizisng ESD networks for RF applications to achieve improved performance and ESD protection of semiconductor chips; discusses RF ESD design methods of capacitance load transformation, matching network co-synthesis, capacitance shunts, inductive shunts, impedance isolation, load cancellation methods, distributed loads, emitter degeneration, buffering and ballasting; examines ESD protection and design of active and passive elements in RF complementary metal-oxide-semiconductor (CMOS), RF laterally-diffused metal oxide semiconductor (LDMOS), RF BiCMOS Silicon Germanium (SiGe), RF BiCMOS Silicon Germanium Carbon (SiGeC), and Gallim Arsenide technology; gives information on RF ESD testing methodologies, RF degradation effects, and failure mechanisms for devices, circuits and systems; highlights RF ESD mixed-signal design integration of digital, analog and RF circuitry; sets out examples of RF ESD design computer aided design methodologies; covers state-of-the-art RF ESD input circuits, as well as voltage-triggered to RC-triggered ESD power clamps networks in RF technologies, as well as off-chip protection concepts. Following the authors series of books on ESD, this book will be a thorough overview of ESD in RF technology for RF semiconductor chip and ESD engineers. Device and circuit engineers working in the RF domain, and quality, reliability and failure analysis engineers will also find it a valuable reference in the rapidly growing are of RF ESD design. In addition, it will appeal to graduate students in RF microwave technology and RF circuit design.

Selected papers from the annual meeting of the Conference.

A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.