This monograph is motivated by a number of recent developments that appear to define a possible new role for researchers with an engineering profile. First, there are now several software libraries - such as IBM's Qiskit, Google's Cirq, and Xanadu's PennyLane - that make programming quantum algorithms more accessible, while also providing cloud-based access to actual quantum computers. Second, a new framework is emerging for programming quantum algorithms to be run on current quantum hardware: quantum machine learning. In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parametrized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression).This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.

Arduino 101 houses an Intel Curie module which offers a better performance at a lower power footprint. The module has two 32-bit MCUs - an x86 Intel Quark processor and an ARC EM4 processor along with 384kB flash memory and 80kB SRAM. These onboard MCUs combine a variety of new technologies including wireless communication via Bluetooth Low Energy, 6 axis motion sensor with an accelerometer, and a gyroscope. With this book, you will: Explore neural net pattern matching Have the Arduino learn gesture recognition Perfect for students, teachers, and hobbyists who need just enough information to get started with the Arduino 101.

This monograph covers the topic of Wireless for Machine Learning (ML). Although the general intersection of ML and wireless communications is currently a prolific field of research that has already generated multiple publications, there is little review work on Wireless for ML. As data generation increasingly takes place on devices without a wired connection, ML related traffic will be ubiquitous in wireless networks. Research has shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. A comprehensive introduction to these methods is presented, reviews are made of the most important works, open problems are highlighted and application scenarios are discussed.

Binary decisions guide our everyday lives in situations both critical and trivial. The choices made by politicians and physicians may have consequential implications on a global or individual scale. Perhaps less consequential is whether or not we choose to carry an umbrella on a cloudy day. Any choice made inherently involves a conscious, subconscious, or formal tradeoff between benefits and detriments.This monograph develops and presents a framework for binary hypothesis testing as it applies to both the classical and quantum mechanical environments. The authors set the scene by first describing separately the operating characteristics associated with classical binary hypothesis testing and those within quantum mechanics. They proceed to describe in detail in subsequent chapters how quantum measurements that employ redundant, or overcomplete, representations of the state of the system being measured can be used.Written in a tutorial style, readers from both classical and quantum backgrounds will find this an enlightening treatise on the topic. Examples and problems are used throughout to enable the reader to readily grasp the new concepts and to further their own understanding. This monograph is a comprehensive and accessible overview of a complex problem for students and researchers in signal processing.

In view of the extensive development of CCS 7 and fast-paced growth of ISDN in telecommunication networks throughout the world, this valuable resource serves as a timely reference and guide. Practical and up-to-date, ENGINEERING NETWORKS FOR SYNCHRONIZATION, CCS 7, AND ISDN provides in-depth instruction on three important and closely related elements of the modern digital network: network synchronization, CCITT Common Channel Signaling System No. 7 (CCS 7), and Narrowband ISDN. Sponsored by: IEEE Communications Society.

Modern day cellular mobile networks use Massive MIMO technology to extend range and service multiple devices within a cell. This has brought tremendous improvements in the high peak data rates that can be handled. Nevertheless, one of the characteristics of this technology is large variations in the quality of service dependent on where the end user is located in any given cell. This becomes increasingly problematic when we are creating a society where wireless access is supposed to be ubiquitous. When payments, navigation, entertainment, and control of autonomous vehicles are all relying on wireless connectivity the primary goal for future mobile networks should not be to increase the peak rates, but the rates that can be guaranteed to the vast majority of the locations in the geographical coverage area. The cellular network architecture was not designed for high-rate data services but for low-rate voice services, thus it is time to look beyond the cellular paradigm and make a clean-slate network design that can reach the performance requirements of the future. This monograph considers the cell-free network architecture that is designed to reach the aforementioned goal of uniformly high data rates everywhere. The authors introduce the concept of a cell-free network before laying out the foundations of what is required to design and build such a network. They cover the foundations of channel estimation, signal processing, pilot assignment, dynamic cooperation cluster formation, power optimization, front-haul signalling, and spectral efficiency evaluation in uplink and downlink under different degrees of cooperation among the access points and arbitrary linear combining and precoding. This monograph provides the reader with all the fundamental information required to design and build the next generation mobile networks without being hindered by the inherent restrictions of modern cellular-based technology.

Acoustic source localization is an essential component in many modern day audio applications. For example, smart speakers require localization capabilities in order to determine the speakers in the scene and their role. Based on the location information, they can enhance a speaker or carry out location specific tasks, such as switching the lights on and off, steering a camera, etc. Localization has often been based on creating physical models which become extremely intricate in real-world applications. Recently, researchers have started using learning techniques to address localization problems.This monograph introduces the reader to the research and practical aspects behind the approach of learning the characteristics of the acoustic environment directly from the data rather than using a predefined physical model. Written by the experts in the field who have developed many of these techniques, it provides a comprehensive overview and insights into this burgeoning area of acoustic developments. The reader is introduced to the underlying mathematics before being introduced to the localization problem in depth. The core paradigm of using manifolds for diffusion mapping and distance is then described. Building on these concepts, the authors address both single and multiple manifold localization. Finally, manifold-based tracking is covered. Data-Driven Multi-Microphone Speaker Localization on Manifolds is an illuminating introduction to designing and building acoustic systems where localization of multi-microphone and speakers forms an essential part of the system.

The book addresses the current demand for a scientific approach to advanced wireless technology and its future developments, including the current move from 4G to 5G wireless systems (2020), and the future to 6G wireless systems (2030). It gives a clear and in-depth presentation of both antennas and the adaptive signal processing that makes antennas powerful, maneuverable, and necessary for advanced wireless technology. Moving towards the increasing demand for a scientific approach to smart antennas, the book presents electromagnetic signal processing techniques to both control the antenna beam and to track the moving station, which is required for effective, fast, dynamic beamforming. In addition to presenting new, memory efficient and fast algorithms for smart antennas, another helpful feature of the book is the inclusion of complete listings of MATLABTM codes for powerful techniques such as Artificial Intelligence (AI) beamforming, Analytical Phase Shift technique and the traditional Least Mean Square method, The student, researcher or engineer may readily use these codes to gain confidence in understanding, as well as to develop and deploy powerful, new smart antenna techniques. The first part of the book presents a comprehensive description and analysis of basic antenna theory, starting from short dipole antennas to array antennas. This section also includes important concepts related to antenna parameters, electromagnetic wave propagation, the Friis equation, the radar equation and wave reflection and transmission through media. The second part of the book focuses on smart antennas, commencing from a look at traditional approach to beam forming before getting into the details of smart antennas. Complete derivation and description of the techniques for electromagnetic field signal processing techniques for adaptive beam forming are presented. Many new and research ideas are included in this section. A novel method for fast, low memory and accurate, maneuverable single beam generation is presented, as well as other methods for beamforming with fewer elements with a simple method for tracking the mobile antenna and station. In this section, for completeness, the use of antenna signal processing for synthetic aperture techniques for imaging are also presented, specifically the Inverse Synthetic Aperture Imaging technique. Some computer codes are given for the student and researcher to get started with new areas to explore. The third part of the book presents technological aspects of advanced wireless technology, including Artificial Intelligence driven steerable single beams, the 5G wireless system and the various devices needed to construct the system. While the books' main emphasis is theoretical understanding and design with the basic tools needed to develop powerful computer code for the smart antennas, it also provides the algorithms or codes in a number of important cases to show how the smart antenna computer codes may be developed using electromagnetic signal processing. Artificial Intelligence (AI) driven beam forming is presented using computationally fast and low-memory demanding technique for AI beam forming is presented with the different excitation functions available. The final chapter outlines certain techniques to develop smart antenna algorithms and computer codes for beginners, researchers, and engineers, and furthermore, to implement a part of what was learnt, including AI techniques.

Clock synchronization is a mechanism for providing a standard reference time to various devices across a distributed network. It is critical in modern computer networks because every aspect of managing, securing, planning, and debugging a network involves determining when particular events happen. Global Positioning Systems (GPS) are a popular mechanism for achieving synchronization, but these are not always practical in network systems. This monograph concentrates on a technique called Network Time Distribution which is often more cost-effective than GPS-based timing, as it does not require any dedicated hardware and can often make use of the existing network resources for synchronizing devices across the network. The technique uses a master/slave construction to synchronize the time throughout devices on a network. To do this, two-way message exchange is required which can be subject to network delays. The authors present recent developments to combat the degrading effects of stochastic delays for clock synchronization protocols based on two-way message exchange. While the techniques presented in the monograph apply to many applications and any clock synchronization protocol based on two-way message exchanges, the authors mainly discuss the applications in the context of IEEE 1588 PTP standard applied to telecommunication networks. Recent Advances in Clock Synchronization for Packet-Switched Networks is of interest to telecommunication engineers designing and building a broad range of telecommunication systems. It provides an introduction to the theory as well as practical results for implementation in real-world systems.

Biomedical imaging is a vast and diverse field. There are a plethora of imaging devices using light, X-rays, sound waves, magnetic fields, electrons, or protons, to measure structures ranging from nano to macroscale. In many cases, computer software is needed to turn the signals collected by the hardware into a meaningful image. These computer algorithms are similarly diverse and numerous.This survey presents a wide swath of biomedical image reconstruction algorithms under a single framework. It is a coherent, yet brief survey of some six decades of research. The underpinning theory of the techniques are described and practical considerations for designing reconstruction algorithms for use in biomedical systems form the central theme of each chapter. The unifying framework deployed throughout the monograph models imaging modalities as combinations of a small set of building blocks, which identify connections between modalities. Thus, the user can quickly port ideas and computer code from one to the next. Furthermore, reconstruction algorithms can treat the imaging model as a black. box, meaning that one algorithm can work for many modalities. This provides a pragmatic approach to designing effective reconstruction algorithms.This monograph is written in a tutorial style that concisely introduces students, researchers and practitioners to the development and design of effective biomedical image reconstruction algorithms.

Many natural signals possess only a few degrees of freedom. For instance, the occupied radio spectrum may be intermittently concentrated to only a few frequency bands of the system bandwidth. This special structural feature - signal sparsity - is conducive in designing efficient signal processing techniques for wireless networks. In particular, the signal sparsity can be leveraged by the recently emerged joint sampling and compression paradigm, compressed sensing (CS). This monograph reviews several recent CS advancements in wireless networks with an aim to improve the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources. The monograph covers a diversity of compressive data reconstruction, gathering, and detection frameworks in cellular, cognitive, and wireless sensor networking systems. The monograph first gives an overview of the principles of CS for the readers unfamiliar with the topic. For the researchers knowledgeable in CS, the monograph provides in-depth reviews of several interesting CS advancements in designing tailored CS reconstruction techniques for wireless applications. The monograph can serve as a basis for the researchers intended to start working in the field, and altogether, lays a foundation for further research in the covered areas.

Electromagnetic imaging has been a powerful technique in various civil and military applications across medical imaging, geophysics, and space exploration. The Nyquist-Shannon theory has formed the basis for processing the signals in such systems. The advent of Compressive Sensing techniques has enabled low-dimension-model-based techniques to be used to break many of the bottlenecks of the earlier technologies. Low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. In particular, this is the era of big data with booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This monograph gives an overview of the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. It further reviews the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. It is a comprehensive introduction for researchers and engineers wishing to understand the state-of-the-art of electromagnetic imaging.

Modern day networking and computing systems rely increasingly on knowing the location of the user. These include state-of-the art technologies such as navigation of vehicles and robots, traffic planning, or light control in smart home environments. In the future, even more services will appear. The predominant localization technology currently uses satellite signals but this only works outdoors, is expensive, and consumes considerable power. Alternative localization technologies have been recently developed using optical, ultra-sound, or radar techniques. All of these require additional hardware components to work effectively. Radio-frequency (RF) localization is a technique that uses communication signals to perform the task without the need for any extra hardware. This monograph addresses the role of synchronization in radio localization and provides a comprehensive overview of recent developments suitable for current and future practical implementations. The material is intended for both theoreticians and practitioners and is written to be accessible to novices while covering state-of-the-art topics of interest to advanced researchers of localization and synchronization systems.