Ultra-Wideband Relay Communication Systems

Impulse-radio ultra-wide-band (IR-UWB) signaling is a promising technique for high-speed, short-range relay communications networks. Depending on how the relay node retransmits the signal, there are two main relay schemes: conventional one-directional (one-way) relay model, and bi-directional (two-way) relay model. In bi-directional relay communications, wireless network coding (WNC), also called physical-layer network coding (PNC), could be applied to overcome the spectral efficiency limitation of the conventional one-way relay. In the first part of this work, we propose asynchronous, differential, and bidirectional decode and forward (ADBDF) and asynchronous, differential, and bidirectional denoise and forward (ADBDNF) UWB relay methods, where the relay node (RN) does not need to be synchronized with the end nodes (ENs). The proposed schemes are attractive for networks in which stringent/complicated synchronization between the RN and the ENs may not be feasible. The second part of this work focuses on UWB channel classification. We propose a 2-dimensional (2-D) LOS/NLOS classification scheme that uses skewness of the channel impulse/pulse response. The proposed channel classification decreases the complexity of existing channel classification methods and can be used in a variety of areas such as localization, relay communications, and cooperative communications. The final part of this work deals with compressive sensing (CS) algorithms that employ sub-Nyquist sampling for UWB communications. We develop coarse graining (CG) for the proposed CS sub-Nyquist sampling technique, which leads to: (1) reduced sampling rate at the receiver, and hence reduced use of analog-to-digital converters (ADCs) resources; and (2) low-complexity channel estimation

Some properties of reaction: Bonded silicon nitride

Electron Spin Resonance, Pulse Echo Ultrasonics and electrical conductivity measurements have been used to study the effect of the unreacted silicon which is present in Reaction Bonded Silicon Nitride (RBSN) as a result of the manufacturing process. One of the techniques (ESR) has been found to be very sensitive to the unreacted silicon, and the PEG Ultrasonic measurements have suggested that weight gain should not be the sole criterion by which to judge RBSN for mechanical applications. ESR studies of unreacted silicon powder gave a signal, similar to that reported for amorphous silicon with g = 2.0055; the line is attributed to dangling bonds. ESR spectra have been found for both RBSN and Hot Pressed Silicon Nitride (HPSN) with g values closer to the free electron value. Measurements on partially reacted materials showed a complex signal whose shape changed considerably over the temperature range 4 to 300 K. The behaviour of this line, presumed to be the sum of the silicon and RBSN signals is probably attributably to differences in the relaxation rates of the two species. Determination of the elastic constants of the RLSN materials has shown that partially nitrided ceramics have lower strength than fully nitrided materials with similar densities, except in the region were the reaction is nearly complete (weight gain of 59% or more) when the effect of unreacted silicon is negligible, and the major factor governing strength is density. A.C.electrical measurements on high weight gain materials have shown dielectric constant (ɛ’) behaviour analagous to the mechanical strength in that the higher ɛ' has been found for the denser (less porous), but lower weight gain material. In contrast to this, however, the high weight gain material was found to have a lower tanδ this is consistent with the lower levels of silicon in the fully reacted ceramic. D.C. 'step response' measurements at room temperature gave results which fitted Jonscher's two stage relaxation theory. I(_D) (t) α (w(_p)t)(^n) + (w(_p)t)(^k) with n in the region 0.7 to 0.8 and k in the region 1.45 to 1.6. D.C. and A.C. results over the temperature range 100ºC to 900ºC suggested that the predominant conductivity up to 750ºC was hopping either in defect bands or localized states in the band tails

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Algorithms for propagation-aware underwater ranging and localization

Mención Internacional en el título de doctorWhile oceans occupy most of our planet, their exploration and conservation are one of the crucial research problems of modern time. Underwater localization stands among the key issues on the way to the proper inspection and monitoring of this significant part of our world. In this thesis, we investigate and tackle different challenges related to underwater ranging and localization. In particular, we focus on algorithms that consider underwater acoustic channel properties. This group of algorithms utilizes additional information about the environment and its impact on acoustic signal propagation, in order to improve the accuracy of location estimates, or to achieve a reduced complexity, or a reduced amount of resources (e.g., anchor nodes) compared to traditional algorithms. First, we tackle the problem of passive range estimation using the differences in the times of arrival of multipath replicas of a transmitted acoustic signal. This is a costand energy- effective algorithm that can be used for the localization of autonomous underwater vehicles (AUVs), and utilizes information about signal propagation. We study the accuracy of this method in the simplified case of constant sound speed profile (SSP) and compare it to a more realistic case with various non-constant SSP. We also propose an auxiliary quantity called effective sound speed. This quantity, when modeling acoustic propagation via ray models, takes into account the difference between rectilinear and non-rectilinear sound ray paths. According to our evaluation, this offers improved range estimation results with respect to standard algorithms that consider the actual value of the speed of sound. We then propose an algorithm suitable for the non-invasive tracking of AUVs or vocalizing marine animals, using only a single receiver. This algorithm evaluates the underwater acoustic channel impulse response differences induced by a diverse sea bottom profile, and proposes a computationally- and energy-efficient solution for passive localization. Finally, we propose another algorithm to solve the issue of 3D acoustic localization and tracking of marine fauna. To reach the expected degree of accuracy, more sensors are often required than are available in typical commercial off-the-shelf (COTS) phased arrays found, e.g., in ultra short baseline (USBL) systems. Direct combination of multiple COTS arrays may be constrained by array body elements, and lead to breaking the optimal array element spacing, or the desired array layout. Thus, the application of state-of-the-art direction of arrival (DoA) estimation algorithms may not be possible. We propose a solution for passive 3D localization and tracking using a wideband acoustic array of arbitrary shape, and validate the algorithm in multiple experiments, involving both active and passive targets.Part of the research in this thesis has been supported by the EU H2020 program under project SYMBIOSIS (G.A. no. 773753).This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Paul Daniel Mitchell.- Secretario: Antonio Fernández Anta.- Vocal: Santiago Zazo Bell

Automatic Speech Emotion Recognition- Feature Space Dimensionality and Classification Challenges

In the last decade, research in Speech Emotion Recognition (SER) has become a major endeavour in Human Computer Interaction (HCI), and speech processing. Accurate SER is essential for many applications, like assessing customer satisfaction with quality of services, and detecting/assessing emotional state of children in care. The large number of studies published on SER reflects the demand for its use. The main concern of this thesis is the investigation of SER from a pattern recognition and machine learning points of view. In particular, we aim to identify appropriate mathematical models of SER and examine the process of designing automatic emotion recognition schemes. There are major challenges to automatic SER including ambiguity about the list/definition of emotions, the lack of agreement on a manageable set of uncorrelated speech-based emotion relevant features, and the difficulty of collected emotion-related datasets under natural circumstances. We shall initiate our work by dealing with the identification of appropriate sets of emotion related features/attributes extractible from speech signals as considered from psychological and computational points of views. We shall investigate the use of pattern-recognition approaches to remove redundancies and achieve compactification of digital representation of the extracted data with minimal loss of information. The thesis will include the design of new or complement existing SER schemes and conduct large sets of experiments to empirically test their performances on different databases, identify advantages, and shortcomings of using speech alone for emotion recognition. Existing SER studies seem to deal with the ambiguity/dis-agreement on a “limited” number of emotion-related features by expanding the list from the same speech signal source/sites and apply various feature selection procedures as a mean of reducing redundancies. Attempts are made to discover more relevant features to emotion from speech. One of our investigations focuses on proposing a newly sets of features for SER, extracted from Linear Predictive (LP)-residual speech. We shall demonstrate the usefulness of the proposed relatively small set of features by testing the performance of an SER scheme that is based on fusing our set of features with the existing set of thousands of features using common machine learning schemes of Support Vector Machine (SVM) and Artificial Neural Network (ANN). The challenge of growing dimensionality of SER feature space and its impact on increased model complexity is another major focus of our research project. By studying the pros and cons of the commonly used feature selection approaches, we argued in favour of meta-feature selection and developed various methods in this direction, not only to reduce dimension, but also to adapt and de-correlate emotional feature spaces for improved SER model recognition accuracy. We used rincipal Component Analysis (PCA) and proposed Data Independent PCA (DIPCA) by training on independent emotional and non-emotional datasets. The DIPCA projections, especially when extracted from speech data coloured with different emotions or from Neutral speech data, had comparable capability to the PCA in terms of SER performance. Another adopted approach in this thesis for dimension reduction is the Random Projection (RP) matrices, independent of training data. We have shown that some versions of RP with SVM classifier can offer an adaptation space for Speaker Independent SER that avoid over-fitting and hence improves recognition accuracy. Using PCA trained on a set of data, while testing on emotional data features, has significant implication for machine learning in general. The thesis other major contribution focuses on the classification aspects of SER. We investigate the drawbacks of the well-known SVM classifier when applied to a preprocessed data by PCA and RP. We shall demonstrate the advantages of using the Linear Discriminant Classifier (LDC) instead especially for PCA de-correlated metafeatures. We initiated a variety of LDC-based ensembles classification, to test performance of scheme using a new form of bagging different subsets of metafeature subsets extracted by PCA with encouraging results. The experiments conducted were applied on two benchmark datasets (Emo-Berlin and FAU-Aibo), and an in-house dataset in the Kurdish language. Recognition accuracy achieved by are significantly higher than the state of art results on all datasets. The results, however, revealed a difficult challenge in the form of persisting wide gap in accuracy over different datasets, which cannot be explained entirely by the differences between the natures of the datasets. We conducted various pilot studies that were based on various visualizations of the confusion matrices for the “difficult” databases to build multi-level SER schemes. These studies provide initial evidences to the presence of more than one “emotion” in the same portion of speech. A possible solution may be through presenting recognition accuracy in a score-based measurement like the spider chart. Such an approach may also reveal the presence of Doddington zoo phenomena in SER

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed

WINDERFUL Wind and INfrastructures

WINDERFUL (an acronym for Wind and INfrastructures: Dominating Eolian Risk For Utilities and Lifelines) is the title of a research project carried out by eight Italian Universities from the end of 2001 to the end of 2003. The project was centred on how "to keep a city running and ensuring quality services during and after major windstorms", avoiding "major failures" of engineering facilities and main infrastructures. The book reports the main results obtained in the project, and for each typology the tool for assessing its reliability are discussed, together with the criteria for its improvement

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas