Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

Sensor Networks for High-Confidence Cyber-Physical Systems

International audienceTechnical advances in ubiquitous sensing, embedded computing, and wireless communication are leading to a new generation of engineered systems called cyber-physical systems (CPS). This field is attracting more and more attention from researchers, practitioners, as well as the governments. Technically, cyber-physical systems are integrations of computation, networking, and physical dynamics, in which embedded devices are massively networked to sense, monitor and control the physical world. CPS has been regarded as the next computing revolution. This revolution will be featured by the envisioned transform that CPS will make on how we interact with the physical world. To facilitate unprecedented interactions between human beings and the physical world, sensor networks will become a crucial ingredient of CPS due to the need for coupling geographically distributed computing devices with physical elements. The proliferation of affordable sensor network technologies has significantly contributed to recent progress in CPS. On the other hand, the unique features of CPS (e.g. cyber-physical coupling driven by new demands and applications) give rise to a lot of open challenges for design and deployment of sensor networks in such systems. In particular, high-confidence CPS requires the employed sensor networks to support real-time, dependable, safe, secure, and efficient operations. In this issue we are focused on latest research results in wireless sensor networks (WSNs) that address key issues related to high-confidence cyber-physical systems and applications. In response to our call-for-papers, we have received 36 submissions, out of which 15 papers are finally accepted as a result of thorough review process by international experts in respective areas. The selection provides a glimpse of the state-of-the-art research in the field. In the paper "A Game Theoretic Approach for Interuser Interference Reduction in Body Sensor Networks", Wu et al present a decentralized inter-user interference reduction scheme with non-cooperative game for body sensor networks (BSNs). A no-regret learning algorithm for reducing the effect of the inter-user interference with low power consumption is proposed. The correctness and effectiveness of the proposed scheme are theoretically proved, and experimental results demonstrate that the effect of inter-user interference can be reduced effectively with low power consumption